From 4cef1e4013806998f6d3571dfa84c0ba76c3153e Mon Sep 17 00:00:00 2001 From: Soan Kim <39689481+SoanKim@users.noreply.github.com> Date: Sun, 6 Aug 2023 18:44:55 +0200 Subject: [PATCH 01/25] # Solved problems with the video # Students can easily modify the reward function and run it directly with Custom_LunarLander class --- .../ReinforcementLearning/lunar_lander.ipynb | 249 ++++++++++++++---- 1 file changed, 198 insertions(+), 51 deletions(-) diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 5f58f38f8..8106a6c89 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -60,7 +60,9 @@ "# @title Update/Upgrade the system and install libs\n", "!apt-get update > /dev/null 2>&1\n", "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", - "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1" + "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", + "!apt-get install x11-utils\n", + "!apt-get install xvfb" ] }, { @@ -75,23 +77,23 @@ "name": "stdout", "output_type": "stream", "text": [ - 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"!pip install minigrid --quiet" + "!pip install minigrid --quiet\n", + "!pip install -q swig --quiet\n", + "!pip install -q gymnasium[box2d] --quiet\n", + "!pip install 'minigrid<=2.1.1' --quiet\n", + "!pip3 install box2d-py --quiet" ] }, { @@ -154,6 +160,7 @@ "\n", "import gymnasium as gym\n", "from gym import spaces\n", + "from gym.envs.box2d.lunar_lander import *\n", "from gym.wrappers.monitoring.video_recorder import VideoRecorder" ] }, @@ -391,7 +398,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001B[33mWARN: is marked as deprecated and will be removed in the future.\u001B[0m\n", " logger.deprecation(\n" ] }, @@ -551,7 +558,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", + "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001B[33mWARN: is marked as deprecated and will be removed in the future.\u001B[0m\n", " logger.deprecation(\n" ] }, @@ -771,36 +778,176 @@ }, "outputs": [], "source": [ - "def step(self, actions):\n", - " ...\n", - " ...\n", - " ...\n", - " reward = 0\n", - " shaping = (\n", - " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", - " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", - " - 100 * abs(state[4])\n", - " + 10 * state[6]\n", - " + 10 * state[7]\n", - " ) # And ten points for legs contact, the idea is if you\n", - " # lose contact again after landing, you get negative reward\n", - " if self.prev_shaping is not None:\n", - " reward = shaping - self.prev_shaping\n", - " self.prev_shaping = shaping\n", - "\n", - " reward -= (\n", - " m_power * 0.30\n", - " ) # less fuel spent is better, about -30 for heuristic landing. You should modify these values.\n", - " reward -= s_power * 0.03\n", - "\n", - " done = False\n", - " if self.game_over or abs(state[0]) >= 1.0:\n", - " done = True\n", - " reward = -100\n", - " if not self.lander.awake:\n", - " done = True\n", - " reward = +100\n", - " return np.array(state, dtype=np.float32), reward, done, {}" + "class Custom_LunarLander(LunarLander):\n", + "\n", + " def step(self, action):\n", + " assert self.lander is not None\n", + "\n", + " # Update wind\n", + " assert self.lander is not None, \"You forgot to call reset()\"\n", + " if self.enable_wind and not (\n", + " self.legs[0].ground_contact or self.legs[1].ground_contact\n", + " ):\n", + " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " wind_mag = (\n", + " math.tanh(\n", + " math.sin(0.02 * self.wind_idx)\n", + " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", + " )\n", + " * self.wind_power\n", + " )\n", + " self.wind_idx += 1\n", + " self.lander.ApplyForceToCenter(\n", + " (wind_mag, 0.0),\n", + " True,\n", + " )\n", + "\n", + " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " torque_mag = math.tanh(\n", + " math.sin(0.02 * self.torque_idx)\n", + " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", + " ) * (self.turbulence_power)\n", + " self.torque_idx += 1\n", + " self.lander.ApplyTorque(\n", + " (torque_mag),\n", + " True,\n", + " )\n", + "\n", + " if self.continuous:\n", + " action = np.clip(action, -1, +1).astype(np.float32)\n", + " else:\n", + " assert self.action_space.contains(\n", + " action\n", + " ), f\"{action!r} ({type(action)}) invalid \"\n", + "\n", + " # Engines\n", + " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", + " side = (-tip[1], tip[0])\n", + " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", + "\n", + " m_power = 0.0\n", + " if (self.continuous and action[0] > 0.0) or (\n", + " not self.continuous and action == 2\n", + " ):\n", + " # Main engine\n", + " if self.continuous:\n", + " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", + " assert m_power >= 0.5 and m_power <= 1.0\n", + " else:\n", + " m_power = 1.0\n", + " # 4 is move a bit downwards, +-2 for randomness\n", + " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", + " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", + " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", + " p = self._create_particle(\n", + " 3.5, # 3.5 is here to make particle speed adequate\n", + " impulse_pos[0],\n", + " impulse_pos[1],\n", + " m_power,\n", + " ) # particles are just a decoration\n", + " p.ApplyLinearImpulse(\n", + " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " s_power = 0.0\n", + " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", + " not self.continuous and action in [1, 3]\n", + " ):\n", + " # Orientation engines\n", + " if self.continuous:\n", + " direction = np.sign(action[1])\n", + " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", + " assert s_power >= 0.5 and s_power <= 1.0\n", + " else:\n", + " direction = action - 2\n", + " s_power = 1.0\n", + " ox = tip[0] * dispersion[0] + side[0] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " oy = -tip[1] * dispersion[0] - side[1] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " impulse_pos = (\n", + " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", + " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", + " )\n", + " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", + " p.ApplyLinearImpulse(\n", + " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", + "\n", + " pos = self.lander.position\n", + " vel = self.lander.linearVelocity\n", + " state = [\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]\n", + " assert len(state) == 8\n", + "\n", + " # Compare with / without shaping, referring the state description below\n", + " '''\n", + " state[0]: the horizontal coordinate\n", + " state[1]: the vertical coordinate\n", + " state[2]: the horizontal speed\n", + " state[3]: the vertical speed\n", + " state[4]: the angle\n", + " state[5]: the angular speed\n", + " state[6]: first leg contact\n", + " state[7]: second leg contact\n", + " '''\n", + " reward = 0\n", + " shaping = (\n", + " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", + " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", + " - 100 * abs(state[4])\n", + " + 10 * state[6]\n", + " + 10 * state[7]\n", + " ) # And ten points for legs contact, the idea is if you\n", + " # lose contact again after landing, you get negative reward\n", + " if self.prev_shaping is not None:\n", + " reward = shaping - self.prev_shaping\n", + " self.prev_shaping = shaping\n", + "\n", + " reward -= (\n", + " m_power * 0.30\n", + " ) # less fuel spent is better, about -30 for heuristic landing\n", + " reward -= s_power * 0.03\n", + "\n", + " terminated = False\n", + " if self.game_over or abs(state[0]) >= 1.0:\n", + " terminated = True\n", + " reward = -100\n", + " if not self.lander.awake:\n", + " terminated = True\n", + " reward = +100\n", + "\n", + " if self.render_mode == \"human\":\n", + " self.render()\n", + " return np.array(state, dtype=np.float32), reward, terminated, False, {}" ] }, { From 4f22506213ac428d4db1024e8cf60cacc0b41964 Mon Sep 17 00:00:00 2001 From: Soan Kim <39689481+SoanKim@users.noreply.github.com> Date: Wed, 3 Jul 2024 18:41:04 +0900 Subject: [PATCH 02/25] # swig should be installed before gym[box2d] to avoid "error: subprocess-exited-with-error; ERROR: Failed building wheel for box2d-py ERROR: Could not build wheels for box2d-py, which is required to install pyproject.toml-based projects." --- projects/ReinforcementLearning/lunar_lander.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 8106a6c89..7622f5b5a 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -102,12 +102,12 @@ "!pip install rarfile --quiet\n", "!pip install stable-baselines3[extra] --quiet\n", "!pip install ale-py --quiet\n", + "!pip install -q swig --quiet\n", "!pip install gym[box2d] --quiet\n", "!pip install pyvirtualdisplay --quiet\n", "!pip install pyglet --quiet\n", "!pip install pygame --quiet\n", "!pip install minigrid --quiet\n", - "!pip install -q swig --quiet\n", "!pip install -q gymnasium[box2d] --quiet\n", "!pip install 'minigrid<=2.1.1' --quiet\n", "!pip3 install box2d-py --quiet" From 7c4989e40cc9f105dd4e201b7b079845ce2e7570 Mon Sep 17 00:00:00 2001 From: dalia-nasr Date: Thu, 4 Jul 2024 00:50:24 +0300 Subject: [PATCH 03/25] updated dataset source for Twitter sentiment analysis template --- .../sentiment_analysis.ipynb | 1677 +---------------- 1 file changed, 1 insertion(+), 1676 deletions(-) diff --git a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb index 7cc1ac416..a6f073666 100644 --- a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb +++ b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb @@ -1,1676 +1 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "execution": {}, - "id": "view-in-github" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "# Twitter Sentiment Analysis\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n", - "\n", - "__Production editors:__ Amita Kapoor, Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Welcome to the NLP project template\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Step 1: Questions and goals\n", - "\n", - "* Can we infer emotion from a tweet text?\n", - "* How words are distributed accross the dataset?\n", - "* Are words related to one kind of emotion?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Step 2: Literature review\n", - "\n", - "[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n", - "\n", - "[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Step 3: Load and explore the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @title Install dependencies\n", - "!pip install pandas --quiet\n", - "!pip install torchtext --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# We import some libraries to load the dataset\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from collections import Counter\n", - "from tqdm.notebook import tqdm\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "\n", - "import torchtext\n", - "from torchtext.data import get_tokenizer\n", - "\n", - "from sklearn.utils import shuffle\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.feature_extraction.text import CountVectorizer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "You can find the dataset we are going to use in [this website](http://help.sentiment140.com/for-students/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "import requests, zipfile, io\n", - "url = 'http://cs.stanford.edu/people/alecmgo/trainingandtestdata.zip'\n", - "r = requests.get(url)\n", - "z = zipfile.ZipFile(io.BytesIO(r.content))\n", - "z.extractall()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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polarityiddatequeryusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
\n", - "
" - ], - "text/plain": [ - " polarity ... text\n", - "0 0 ... @switchfoot http://twitpic.com/2y1zl - Awww, t...\n", - "1 0 ... is upset that he can't update his Facebook by ...\n", - "2 0 ... @Kenichan I dived many times for the ball. Man...\n", - "3 0 ... my whole body feels itchy and like its on fire \n", - "4 0 ... @nationwideclass no, it's not behaving at all....\n", - "\n", - "[5 rows x 6 columns]" - ] - }, - "execution_count": 4, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# We load the dataset\n", - "header_list = [\"polarity\", \"id\", \"date\", \"query\", \"user\", \"text\"]\n", - "df = pd.read_csv('training.1600000.processed.noemoticon.csv',\n", - " encoding = \"ISO-8859-1\", names=header_list)\n", - "\n", - "# Let's have a look at it\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "For this project we will use only the text and the polarity of the tweet. Notice that polarity is 0 for negative tweets and 4 for positive tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "X = df.text.values\n", - "\n", - "# Changes values from [0,4] to [0,1]\n", - "y = (df.polarity.values > 1).astype(int)\n", - "\n", - "\n", - "# Split the data into train and test\n", - "x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n", - "0: worst headache ever \n", - "0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n", - "1: doesn't know how to spell conked \n", - "0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n" - ] - } - ], - "source": [ - "for s, l in zip(x_train_text[:5], y_train[:5]):\n", - " print('{}: {}'.format(l, s))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before Tokenize: worst headache ever \n", - "After Tokenize: ['worst', 'headache', 'ever']\n" - ] - } - ], - "source": [ - "tokenizer = get_tokenizer(\"basic_english\")\n", - "\n", - "print('Before Tokenize: ', x_train_text[1])\n", - "print('After Tokenize: ', tokenizer(x_train_text[1]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "499e7fb54aa048afb3cba78dd8d6bb0e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=1280000.0), HTML(value='')))" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fff9bd0ae74e46b0ad97ad980a834a58", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=320000.0), HTML(value='')))" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n" - ] - } - ], - "source": [ - "x_train_token = [tokenizer(s) for s in tqdm(x_train_text)]\n", - "x_test_token = [tokenizer(s) for s in tqdm(x_test_text)]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We can count the words occurences and see how many different words are present in our dataset." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of different Tokens in our Dataset: 669284\n", - "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it', 'and', 'you', '?', 'is', 'for', 'in', 's', 'of', 't', 'on', 'that', 'me', 'so', 'have', 'm', 'but', 'just', 'with', 'be', 'at', 'not', 'was', 'this', 'now', 'can', 'good', 'up', 'day', 'all', 'get', 'out', 'like', 'are', 'no', 'go', 'http', '-', 'today', 'do', 'too', 'your', 'work', 'going', 'love', 'we', 'got', 'what', 'lol', 'time', 'back', 'from', 'u', 'one', 'will', 'know', 'about', 'im', 'really', 'don', 'am', 'had', ')', 'see', 'some', 'there', 'its', '&', 'how', 'if', 'still', 'they', '"', 'night', '(', 'well', 'want', 'new', 'think', '2', 'home', 'thanks', 'll', 'oh', 'when', 'as', 'he', 'more', 'here', 'much', 'off']\n" - ] - } - ], - "source": [ - "words = Counter()\n", - "for s in x_train_token:\n", - " for w in s:\n", - " words[w] += 1\n", - "\n", - "sorted_words = list(words.keys())\n", - "sorted_words.sort(key=lambda w: words[w], reverse=True)\n", - "print(f\"Number of different Tokens in our Dataset: {len(sorted_words)}\")\n", - "print(sorted_words[:100])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now we can plot their distribution." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The 0.13970153178620734% most common words account for the 80.00532743602652% of the occurrences\n" - ] - } - ], - "source": [ - "count_occurences = sum(words.values())\n", - "\n", - "accumulated = 0\n", - "counter = 0\n", - "\n", - "while accumulated < count_occurences * 0.8:\n", - " accumulated += words[sorted_words[counter]]\n", - " counter += 1\n", - "\n", - "print(f\"The {counter * 100 / len(words)}% most common words \"\n", - " f\"account for the {accumulated * 100 / count_occurences}% of the occurrences\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Usually the number of words in the dictionary will be very large. \n", - "\n", - "Here are some thing we can do to reduce that number:\n", - "\n", - "* Remove puntuation.\n", - "* Remove stop-words.\n", - "* Steaming.\n", - "* Remove very uncommon words (the words that appears in fewer than N occations).\n", - "* Nothing: we can use a pretrain model that handles this kind of situations.\n", - "\n", - "\n", - "We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n", - "\n", - "* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n", - "* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n", - "* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Step 4: choose toolkit\n", - "\n", - "Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n", - "\n", - "* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n", - "* A simple Embedding + RNN." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## Logistic regression\n", - "\n", - "We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "vectorizer = CountVectorizer(binary=True)\n", - "x_train_cv = vectorizer.fit_transform(x_train_text)\n", - "x_test_cv = vectorizer.transform(x_test_text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before Vectorize: doesn't know how to spell conked \n" - ] - } - ], - "source": [ - "print('Before Vectorize: ', x_train_text[3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "After Vectorize: \n", - " (0, 528584)\t1\n", - " (0, 165468)\t1\n", - " (0, 300381)\t1\n", - " (0, 242211)\t1\n", - " (0, 489893)\t1\n", - " (0, 134160)\t1\n" - ] - } - ], - "source": [ - "# Notice that the matriz is sparse\n", - "print('After Vectorize: ')\n", - "print(x_train_cv[3])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", - " multi_class='auto', n_jobs=None, penalty='l2',\n", - " random_state=None, solver='saga', tol=0.0001, verbose=0,\n", - " warm_start=False)" - ] - }, - "execution_count": 15, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "model = LogisticRegression(solver='saga')\n", - "model.fit(x_train_cv, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.81 0.79 0.80 160000\n", - " 1 0.79 0.81 0.80 160000\n", - "\n", - " accuracy 0.80 320000\n", - " macro avg 0.80 0.80 0.80 320000\n", - "weighted avg 0.80 0.80 0.80 320000\n", - "\n" - ] - } - ], - "source": [ - "y_pred = model.predict(x_test_cv)\n", - "\n", - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## Explainable AI\n", - "The best thing about logistic regresion is that it is simple, and we can get some explanations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 589260)\n", - "589260\n" - ] - } - ], - "source": [ - "print(model.coef_.shape)\n", - "print(len(vectorizer.vocabulary_))\n", - "\n", - "words_sk = list(vectorizer.vocabulary_.keys())\n", - "words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "roni: -3.862597673594883\n", - "inaperfectworld: -3.5734362290886375\n", - "dontyouhate: -3.500197620227523\n", - "xbllygbsn: -3.412645372640648\n", - "anqju: -3.336405291553548\n", - "sad: -3.200522312464158\n", - "pakcricket: -3.1949158120163412\n", - "condolences: -3.132498019366488\n", - "heartbreaking: -3.066508733796654\n", - "saddest: -3.041999809733714\n", - "sadd: -3.029070563580306\n", - "heartbroken: -3.0287688233900174\n", - "boohoo: -3.022608649696793\n", - "sadface: -2.9918411285807234\n", - "rachelle_lefevr: -2.925057253107806\n", - "disappointing: -2.902524113779547\n", - "lvbu: -2.894705935001672\n", - "saddens: -2.8855127179984654\n", - "bummed: -2.83650014970307\n", - "neda: -2.792944556837498\n" - ] - } - ], - "source": [ - "for w in words_sk[:20]:\n", - " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "iamsoannoyed: 2.8494314732277672\n", - "myfax: 2.797451563471618\n", - "jennamadison: 2.5667257393706113\n", - "yeyy: 2.478028598852801\n", - "tryout: 2.4383315790116677\n", - "goldymom: 2.4374026022205535\n", - "wooohooo: 2.40297322137544\n", - "thesupergirl: 2.3565118467330004\n", - "iammaxathotspot: 2.311648368632618\n", - "londicreations: 2.3074490293400993\n", - "smilin: 2.2991891636718216\n", - "worries: 2.2899429774914717\n", - "sinfulsignorita: 2.2798963640981817\n", - "finchensnail: 2.264302079155878\n", - "smackthis: 2.2376679263761083\n", - "kv: 2.2158393907798413\n", - "tojosan: 2.211784259253832\n", - "russmarshalek: 2.2095374025599384\n", - "traciknoppe: 2.1768297770350835\n", - "congratulations: 2.171590496227557\n" - ] - } - ], - "source": [ - "for w in reversed(words_sk[-20:]):\n", - " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "What does this mean?\n", - "\n", - "Remember the `model.coef_` is the $W$ in:\n", - "\n", - "$$h(x)=\\sigma(WX + b)$$\n", - "\n", - "where the label 1 is a positive tweet and the label 0 is a negative tweet." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## Recurrent Neural Network with Pytorch" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n", - "\n", - "In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n", - "\n", - "Let's start by importing the relevant libraries.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "def set_device():\n", - " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - " if device != \"cuda\":\n", - " print(\"WARNING: For this notebook to perform best, \"\n", - " \"if possible, in the menu under `Runtime` -> \"\n", - " \"`Change runtime type.` select `GPU` \")\n", - " else:\n", - " print(\"GPU is enabled in this notebook.\")\n", - "\n", - " return device" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "GPU is enabled in this notebook.\n" - ] - } - ], - "source": [ - "# Set the device (check if gpu is available)\n", - "device = set_device()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']" - ] - }, - "execution_count": 22, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# From previous section, we have a list with the most used tokens\n", - "sorted_words[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Let's select only the most used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "num_words_dict = 30000\n", - "# We reserve two numbers for special tokens.\n", - "most_used_words = sorted_words[:num_words_dict-2]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# dictionary to go from words to idx\n", - "word_to_idx = {}\n", - "# dictionary to go from idx to words (just in case)\n", - "idx_to_word = {}\n", - "\n", - "\n", - "# We include the special tokens first\n", - "PAD_token = 0\n", - "UNK_token = 1\n", - "\n", - "word_to_idx['PAD'] = PAD_token\n", - "word_to_idx['UNK'] = UNK_token\n", - "\n", - "idx_to_word[PAD_token] = 'PAD'\n", - "idx_to_word[UNK_token] = 'UNK'\n", - "\n", - "# We popullate our dictionaries with the most used words\n", - "for num,word in enumerate(most_used_words):\n", - " word_to_idx[word] = num + 2\n", - " idx_to_word[num+2] = word" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# A function to convert list of tokens to list of indexes\n", - "def tokens_to_idx(sentences_tokens,word_to_idx):\n", - " sentences_idx = []\n", - " for sent in sentences_tokens:\n", - " sent_idx = []\n", - " for word in sent:\n", - " if word in word_to_idx:\n", - " sent_idx.append(word_to_idx[word])\n", - " else:\n", - " sent_idx.append(word_to_idx['UNK'])\n", - " sentences_idx.append(sent_idx)\n", - " return sentences_idx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", - "x_test_idx = tokens_to_idx(x_test_token,word_to_idx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before converting: ['worst', 'headache', 'ever']\n", - "After converting: [721, 458, 237]\n" - ] - } - ], - "source": [ - "some_number = 1\n", - "print('Before converting: ', x_train_token[some_number])\n", - "print('After converting: ', x_train_idx[some_number])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max tweet word length: 229\n", - "Mean tweet word length: 15.0\n", - "99% percent under: 37.0\n" - ] - } - ], - "source": [ - "tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n", - "print('Max tweet word length: ',tweet_lens.max())\n", - "print('Mean tweet word length: ',np.median(tweet_lens))\n", - "print('99% percent under: ',np.quantile(tweet_lens,0.99))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - " # We choose the max length\n", - " max_length = 40\n", - "\n", - "# A function to make all the sequence have the same lenght\n", - "# Note that the output is a Numpy matrix\n", - " def padding(sentences, seq_len):\n", - " features = np.zeros((len(sentences), seq_len),dtype=int)\n", - " for ii, tweet in enumerate(sentences):\n", - " len_tweet = len(tweet)\n", - " if len_tweet != 0:\n", - " if len_tweet <= seq_len:\n", - " # If its shorter, we fill with zeros (the padding Token index)\n", - " features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n", - " if len_tweet > seq_len:\n", - " # If its larger, we take the last 'seq_len' indexes\n", - " features[ii, :] = np.array(tweet)[-seq_len:]\n", - " return features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# We convert our list of tokens into a numpy matrix\n", - "# where all instances have the same lenght\n", - "x_train_pad = padding(x_train_idx,max_length)\n", - "x_test_pad = padding(x_test_idx,max_length)\n", - "\n", - "# We convert our target list a numpy matrix\n", - "y_train_np = np.asarray(y_train)\n", - "y_test_np = np.asarray(y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n", - "After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n", - " 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n", - " 150 4 22 5 18 667 3 138 61 7 3296 4]\n" - ] - } - ], - "source": [ - "some_number = 2\n", - "print('Before padding: ', x_train_idx[some_number])\n", - "print('After padding: ', x_train_pad[some_number])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now, let's convert the data to pytorch format.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# create Tensor datasets\n", - "train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n", - "valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n", - "\n", - "# Batch size (this is an important hyperparameter)\n", - "batch_size = 100\n", - "\n", - "# dataloaders\n", - "# make sure to SHUFFLE your data\n", - "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n", - "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Each batch of data in our traning proccess will have the folllowing format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sample input size: torch.Size([100, 40])\n", - "Sample input: \n", - " tensor([[ 0, 0, 0, ..., 4, 4, 4],\n", - " [ 0, 0, 0, ..., 7447, 14027, 2],\n", - " [ 0, 0, 0, ..., 100, 22241, 4],\n", - " ...,\n", - " [ 0, 0, 0, ..., 2702, 4409, 2],\n", - " [ 0, 0, 0, ..., 162, 17, 1],\n", - " [ 0, 0, 0, ..., 67, 12904, 49]])\n", - "Sample input: \n", - " tensor([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n", - " 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n", - " 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n", - " 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n", - " 0, 0, 1, 0])\n" - ] - } - ], - "source": [ - "# Obtain one batch of training data\n", - "dataiter = iter(train_loader)\n", - "sample_x, sample_y = dataiter.next()\n", - "\n", - "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n", - "print('Sample input: \\n', sample_x)\n", - "print('Sample input: \\n', sample_y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n", - "\n", - "* Embedding Layer\n", - "> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n", - "\n", - "\n", - "* LSTM layer\n", - "> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "class SentimentRNN(nn.Module):\n", - " def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n", - " super(SentimentRNN,self).__init__()\n", - "\n", - " self.output_dim = output_dim\n", - " self.hidden_dim = hidden_dim\n", - " self.no_layers = no_layers\n", - " self.vocab_size = vocab_size\n", - " self.drop_prob = drop_prob\n", - "\n", - " # Embedding Layer\n", - " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n", - "\n", - " # LSTM Layers\n", - " self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n", - " num_layers=no_layers, batch_first=True,\n", - " dropout=self.drop_prob)\n", - "\n", - " # Dropout layer\n", - " self.dropout = nn.Dropout(drop_prob)\n", - "\n", - " # Linear and Sigmoid layer\n", - " self.fc = nn.Linear(self.hidden_dim, output_dim)\n", - " self.sig = nn.Sigmoid()\n", - "\n", - " def forward(self,x,hidden):\n", - " batch_size = x.size(0)\n", - "\n", - " # Embedding out\n", - " embeds = self.embedding(x)\n", - " #Shape: [batch_size x max_length x embedding_dim]\n", - "\n", - " # LSTM out\n", - " lstm_out, hidden = self.lstm(embeds, hidden)\n", - " # Shape: [batch_size x max_length x hidden_dim]\n", - "\n", - " # Select the activation of the last Hidden Layer\n", - " lstm_out = lstm_out[:,-1,:].contiguous()\n", - " # Shape: [batch_size x hidden_dim]\n", - "\n", - " ## You can instead average the activations across all the times\n", - " # lstm_out = torch.mean(lstm_out, 1).contiguous()\n", - "\n", - " # Dropout and Fully connected layer\n", - " out = self.dropout(lstm_out)\n", - " out = self.fc(out)\n", - "\n", - " # Sigmoid function\n", - " sig_out = self.sig(out)\n", - "\n", - " # return last sigmoid output and hidden state\n", - " return sig_out, hidden\n", - "\n", - " def init_hidden(self, batch_size):\n", - " ''' Initializes hidden state '''\n", - " # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n", - " # initialized to zero, for hidden state and cell state of LSTM\n", - " h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", - " c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", - " hidden = (h0,c0)\n", - " return hidden" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We choose the parameters of the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# Parameters of our network\n", - "\n", - "# Size of our vocabulary\n", - "vocab_size = num_words_dict\n", - "\n", - "# Embedding dimension\n", - "embedding_dim = 32\n", - "\n", - "# Number of stacked LSTM layers\n", - "no_layers = 2\n", - "\n", - "# Dimension of the hidden layer in LSTMs\n", - "hidden_dim = 64\n", - "\n", - "# Dropout parameter for regularization\n", - "output_dim = 1\n", - "\n", - "# Dropout parameter for regularization\n", - "drop_prob = 0.25" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SentimentRNN(\n", - " (embedding): Embedding(30000, 32)\n", - " (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n", - " (dropout): Dropout(p=0.25, inplace=False)\n", - " (fc): Linear(in_features=64, out_features=1, bias=True)\n", - " (sig): Sigmoid()\n", - ")\n" - ] - } - ], - "source": [ - "# Let's define our model\n", - "model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n", - " embedding_dim, drop_prob=drop_prob)\n", - "# Moving to gpu\n", - "model.to(device)\n", - "print(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Total Number of parameters: 1018433\n" - ] - } - ], - "source": [ - "# How many trainable parameters does our model have?\n", - "model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n", - "params = sum([np.prod(p.size()) for p in model_parameters])\n", - "print('Total Number of parameters: ',params)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We choose the losses and the optimizer for the training procces." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# loss and optimization functions\n", - "lr = 0.001\n", - "\n", - "# Binary crossentropy is a good loss function for a binary classification problem\n", - "criterion = nn.BCELoss()\n", - "\n", - "# We choose an Adam optimizer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", - "\n", - "# function to predict accuracy\n", - "def acc(pred,label):\n", - " pred = torch.round(pred.squeeze())\n", - " return torch.sum(pred == label.squeeze()).item()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "We are ready to train our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 1\n", - "train_loss : 0.4367361353733577 val_loss : 0.39174133955966683\n", - "train_accuracy : 79.530625 val_accuracy : 82.3628125\n", - "Validation loss decreased (inf --> 0.391741). Saving model ...\n", - "==================================================\n", - "Epoch 2\n", - "train_loss : 0.3765802335098851 val_loss : 0.3724124691961333\n", - "train_accuracy : 83.19140625 val_accuracy : 83.42031250000001\n", - "Validation loss decreased (0.391741 --> 0.372412). Saving model ...\n", - "==================================================\n", - "Epoch 3\n", - "train_loss : 0.35746844720793886 val_loss : 0.365050206175074\n", - "train_accuracy : 84.16882812499999 val_accuracy : 83.7440625\n", - "Validation loss decreased (0.372412 --> 0.365050). Saving model ...\n", - "==================================================\n", - "Epoch 4\n", - "train_loss : 0.34491546426317654 val_loss : 0.36467386982403693\n", - "train_accuracy : 84.879140625 val_accuracy : 83.77\n", - "Validation loss decreased (0.365050 --> 0.364674). Saving model ...\n", - "==================================================\n", - "Epoch 5\n", - "train_loss : 0.33429012800217606 val_loss : 0.36189084346871825\n", - "train_accuracy : 85.44296875 val_accuracy : 84.0221875\n", - "Validation loss decreased (0.364674 --> 0.361891). Saving model ...\n", - "==================================================\n" - ] - } - ], - "source": [ - "# Number of training Epochs\n", - "epochs = 5\n", - "\n", - "# Maximum absolute value accepted for the gradeint\n", - "clip = 5\n", - "\n", - "# Initial Loss value (assumed big)\n", - "valid_loss_min = np.Inf\n", - "\n", - "# Lists to follow the evolution of the loss and accuracy\n", - "epoch_tr_loss,epoch_vl_loss = [],[]\n", - "epoch_tr_acc,epoch_vl_acc = [],[]\n", - "\n", - "# Train for a number of Epochs\n", - "for epoch in range(epochs):\n", - " train_losses = []\n", - " train_acc = 0.0\n", - " model.train()\n", - "\n", - " for inputs, labels in train_loader:\n", - "\n", - " # Initialize hidden state\n", - " h = model.init_hidden(batch_size)\n", - " # Creating new variables for the hidden state\n", - " h = tuple([each.data.to(device) for each in h])\n", - "\n", - " # Move batch inputs and labels to gpu\n", - " inputs, labels = inputs.to(device), labels.to(device)\n", - "\n", - " # Set gradient to zero\n", - " model.zero_grad()\n", - "\n", - " # Compute model output\n", - " output,h = model(inputs,h)\n", - "\n", - " # Calculate the loss and perform backprop\n", - " loss = criterion(output.squeeze(), labels.float())\n", - " loss.backward()\n", - " train_losses.append(loss.item())\n", - "\n", - " # calculating accuracy\n", - " accuracy = acc(output,labels)\n", - " train_acc += accuracy\n", - "\n", - " #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n", - " nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " optimizer.step()\n", - "\n", - "\n", - " # Evaluate on the validation set for this epoch\n", - " val_losses = []\n", - " val_acc = 0.0\n", - " model.eval()\n", - " for inputs, labels in valid_loader:\n", - "\n", - " # Initialize hidden state\n", - " val_h = model.init_hidden(batch_size)\n", - " val_h = tuple([each.data.to(device) for each in val_h])\n", - "\n", - " # Move batch inputs and labels to gpu\n", - " inputs, labels = inputs.to(device), labels.to(device)\n", - "\n", - " # Compute model output\n", - " output, val_h = model(inputs, val_h)\n", - "\n", - " # Compute Loss\n", - " val_loss = criterion(output.squeeze(), labels.float())\n", - "\n", - " val_losses.append(val_loss.item())\n", - "\n", - " accuracy = acc(output,labels)\n", - " val_acc += accuracy\n", - "\n", - " epoch_train_loss = np.mean(train_losses)\n", - " epoch_val_loss = np.mean(val_losses)\n", - " epoch_train_acc = train_acc/len(train_loader.dataset)\n", - " epoch_val_acc = val_acc/len(valid_loader.dataset)\n", - " epoch_tr_loss.append(epoch_train_loss)\n", - " epoch_vl_loss.append(epoch_val_loss)\n", - " epoch_tr_acc.append(epoch_train_acc)\n", - " epoch_vl_acc.append(epoch_val_acc)\n", - " print(f'Epoch {epoch+1}')\n", - " print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n", - " print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n", - " if epoch_val_loss <= valid_loss_min:\n", - " print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n", - " # torch.save(model.state_dict(), '../working/state_dict.pt')\n", - " valid_loss_min = epoch_val_loss\n", - " print(25*'==')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize = (20, 6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(epoch_tr_acc, label='Train Acc')\n", - "plt.plot(epoch_vl_acc, label='Validation Acc')\n", - "plt.title(\"Accuracy\")\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(epoch_tr_loss, label='Train loss')\n", - "plt.plot(epoch_vl_loss, label='Validation loss')\n", - "plt.title(\"Loss\")\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# What's Next?\n", - "\n", - "You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n", - "\n", - "* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n", - "* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n", - "* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n", - "* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. Do you belive that a sentiment analysis model trained on some corpus (Twitter dataset) will perform well on another type of data (for example, youtube comments)?\n", - "\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "name": "sentiment_analysis", - "provenance": [], - "toc_visible": true - }, - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +{"cells":[{"cell_type":"markdown","metadata":{"execution":{},"id":"view-in-github"},"source":["\"Open   \"Open"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"D_fgc45VfjDz"},"source":["# Twitter Sentiment Analysis\n","\n","**By Neuromatch Academy**\n","\n","__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n","\n","__Production editors:__ Amita Kapoor, Spiros Chavlis"]},{"cell_type":"markdown","metadata":{"execution":{}},"source":["---\n","# Welcome to the NLP project template\n","\n",""]},{"cell_type":"markdown","metadata":{"execution":{}},"source":["---\n","# Step 1: Questions and goals\n","\n","* Can we infer emotion from a tweet text?\n","* How words are distributed accross the dataset?\n","* Are words related to one kind of emotion?"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"Vd1qdNW9fjD1"},"source":["---\n","# Step 2: Literature review\n","\n","[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n","\n","[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"oOYDQElpfjD2"},"source":["---\n","# Step 3: Load and explore the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":103706,"status":"ok","timestamp":1720042135196,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"EZpxSExUfjD2","outputId":"19b01445-9b83-4a93-9cc2-7830ab0dcf5b"},"outputs":[],"source":["# @title Install dependencies\n","!pip install pandas --quiet\n","!pip install torchtext --quiet\n","!pip install datasets --quiet"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":9008,"status":"ok","timestamp":1720042144200,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"DxqD3Tk5fjD3","outputId":"451d68c5-7894-4f93-9f54-bf0b7f482e20"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/torchtext/data/__init__.py:4: UserWarning: \n","/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n","Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n"," warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"]}],"source":["# We import some libraries to load the dataset\n","import os\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","from datasets import load_dataset\n","\n","from collections import Counter\n","from tqdm.notebook import tqdm\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import TensorDataset, DataLoader\n","\n","import torchtext\n","from torchtext.data import get_tokenizer\n","\n","from sklearn.utils import shuffle\n","from sklearn.metrics import classification_report\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import CountVectorizer"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"63Eg1SLbfjD4"},"source":["You can find the dataset we are going to use in [this website](http://help.sentiment140.com/for-students/)."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":567,"referenced_widgets":["fbb4191426bd485e8e965b6d432eecae","df7eba182d1b4c21bc21d157eac6b996","6d64402d9da74516ab4e1d46ae9f1ee3","d9ca809f7b1c49e595a05458251f3ab2","90908b6f69524a72860214ef8bd2d946","db432a2cd6244a7592fc9732f0ca4738","84485541f3a14c65a67d10a97b72bbad","5fa7ab2ab2004e5cb692199e2bd27d6b","ab71bd2b452146829e973d6cf99f31ed","55ba92cfe0724286ac1c2bbe6577e5c8","67a4fa49ca5349d58512a16a3742d401","afd671543846468abfe37669a72845c3","057e918ace004506aedc4e4b9942c3a8","325387f6b62d47b0b21bea61676cea72","ea1e3eb0e6ec4f8d82cf9b12cfe6e700","96c2d7ee644a438982e1792b7ec0453c","9baa1a735c0646b89953bf4a7c7fc92c","0ac9711c8ece4c5397a8cd810713adfb","a8d69769921241b8b1081e84f7770858","d189f24b0e964d1a9fc86379bad38cca","db9bf44dec914db793cc4f73751c272c","1cf3ba0f756f4aa5ad1dcb675a791cfa","c432c4efcb794ce781fcb6f176f1b60d","510eeffb32694e7798f23e3931d7a943","a8b3dfaa2831416582d8eeef01451386","db1cdafaf36f4c339476f3221abc17b3","ffd3778a96e046718828bbc5aa73f173","49c5a3fbe87b491cb3f0f450a0af0659","252949e8784c4878a62eb2e30b1e3466","7bcef602e7f441308472bc145b12dcd3","97fb30a5a31742efa1d188b9361f9938","9b34daddb9cc48bba109e547177ec654","fd2b5a6533794a2794579956c25247fb","f3a9667c8c994324a2409f227bd0a1e9","6e6c5372ffe045c0b72587989567429e","2ead0216695e4227aef44552f4ec3cc9","53843f49adda4bce8450fd91fa9fd587","40262cb3eefa45fcbe37aaafccb69f5f","b54b826314ea4b3a92eebd218c093fc1","8cd7be688b8c4818be48915db14a0792","a9a0f6ce71ed415c8c8513f68e34e162","7f638a6deacd42e88c031fa47797516b","849e39cc86f64e558ff94bf542a5121a","67b0b03c391c414bad5ea9fb3c947a2f","1cef38981af6457dbaeb393f9936a389","b0b5cfae51214c60bbca9a09b196c217","5ee2a4b33be04c6db8ee4d7995c2376d","403fffb635c2409ebeabc90063750ed3","6279343019064572adedf34cfbd437fa","2715d00db77545f9aa5eace8a0eb2839","942ce490d87347c789e229589b1b9c9f","f04df4daeb6049ab85d3d75b472ccf6e","fd0b3c53b66543cea0c396d8047445a8","2c42e2fef6314c9e842a7e9641af3cab","913d95e58aa94e4a8009768a23fbf304"]},"execution":{},"executionInfo":{"elapsed":189390,"status":"ok","timestamp":1720042333586,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"3HLOsd3rfjD4","outputId":"7653fee1-a871-472b-a978-d8ec0250dc84"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fbb4191426bd485e8e965b6d432eecae","version_major":2,"version_minor":0},"text/plain":["Downloading builder script: 0%| | 0.00/4.03k [00:00\n","
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polarityuserdatequeryusertext
00_TheSpecialOne_Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
10scotthamiltonMon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
20mattycusMon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
30ElleCTFMon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
40KaroliMon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n"," \n"],"text/plain":[" polarity user date query \\\n","0 0 _TheSpecialOne_ Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n","1 0 scotthamilton Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n","2 0 mattycus Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n","3 0 ElleCTF Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","4 0 Karoli Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","\n"," user text \n","0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n","1 scotthamilton is upset that he can't update his Facebook by ... \n","2 mattycus @Kenichan I dived many times for the ball. Man... \n","3 ElleCTF my whole body feels itchy and like its on fire \n","4 Karoli @nationwideclass no, it's not behaving at all.... "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# We load the dataset\n","\n","dataset = load_dataset(\"stanfordnlp/sentiment140\", trust_remote_code= True)\n","\n","train_data = dataset[\"train\"]\n","df = pd.DataFrame(train_data)\n","df = df.rename(columns={'sentiment': 'polarity'})\n","df = df[['polarity', 'user', 'date', 'query', 'user', 'text']]\n","df.head()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"fuKShcfjfjD4"},"source":["For this project we will use only the text and the polarity of the tweet. Notice that polarity is 0 for negative tweets and 4 for positive tweet."]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{},"executionInfo":{"elapsed":1059,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"GXHQOn6gfjD5"},"outputs":[],"source":["X = df.text.values\n","\n","# Changes values from [0,4] to [0,1]\n","y = (df.polarity.values > 1).astype(int)\n","\n","\n","# Split the data into train and test\n","x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"7kr3TO_LfjD5"},"source":["The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"FsL-xY03fjD5","outputId":"655f0ef8-c177-4f42-c024-1d628241401a"},"outputs":[{"name":"stdout","output_type":"stream","text":["1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n","0: worst headache ever \n","0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n","1: doesn't know how to spell conked \n","0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n"]}],"source":["for s, l in zip(x_train_text[:5], y_train[:5]):\n"," print('{}: {}'.format(l, s))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"4cPGXSc-fjD5"},"source":["An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"U1OugpZ0fjD5","outputId":"9e6cb4e3-8d8c-4db0-c113-bdd4fe87db5f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before Tokenize: worst headache ever \n","After Tokenize: ['worst', 'headache', 'ever']\n"]}],"source":["tokenizer = get_tokenizer(\"basic_english\")\n","\n","print('Before Tokenize: ', x_train_text[1])\n","print('After Tokenize: ', tokenizer(x_train_text[1]))"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["e1348a02ceeb4af19fbd63d52b7d843b","fbf51b14e6b34d0485ddf59c43d22c49","c29e06a72ac9401b8c41f4195021071e","48b812211db04284bfbbf02823fb879a","5455119809c74916acc50e1905903ded","2475bd62a3224bacb38a6334d07d6a8c","3d29947b5d2d4e2abc1355d900096642","3f7a8f56f15c434da70029366a37167a","3610a2db297f4686bf9043f2b7ee55b5","a1bd0616199e44538977ee2ea6049690","835fb9a91b34471fa6d61adf37616f52","d8de1a85076b453a92295e79110ba8fd","78d48ee2fb9f42089f475fcf5fc368c8","b0ca3012d0b84c5a9d7c1fc176251af7","39fa73efcbf54d8dad225d8380061dbf","6b6cc35257fe433e93736d02e898b6b8","e0fc900d8b5940a6bd6a97e58adb4651","6b7286d74e0f4a0199dbfcaf3dd0d622","a4bbd3df99cd4acab5e1b3ba5cd7c114","9a7140a6197945d5bac5c48b820dfb04","0bdc146792a64853ae06a9d185aa2b15","768da964ffcd44fea1af09e81f5621f3"]},"execution":{},"executionInfo":{"elapsed":29122,"status":"ok","timestamp":1720042363757,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"7ZggzGCXfjD6","outputId":"ae19f8d6-224d-4224-d3a0-d00c659ec9b2"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e1348a02ceeb4af19fbd63d52b7d843b","version_major":2,"version_minor":0},"text/plain":[" 0%| | 0/1280000 [00:00"]},"metadata":{},"output_type":"display_data"}],"source":["plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"o9IYA0cZfjD7"},"source":["It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"5FQIOqoRfjD7"},"source":["Usually the number of words in the dictionary will be very large.\n","\n","Here are some thing we can do to reduce that number:\n","\n","* Remove puntuation.\n","* Remove stop-words.\n","* Steaming.\n","* Remove very uncommon words (the words that appears in fewer than N occations).\n","* Nothing: we can use a pretrain model that handles this kind of situations.\n","\n","\n","We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n","\n","* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n","* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n","* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"_ul5MgYcfjD7"},"source":["---\n","# Step 4: choose toolkit\n","\n","Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n","\n","* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n","* A simple Embedding + RNN."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"GteI1PxTfjD7"},"source":["## Logistic regression\n","\n","We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)."]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{},"executionInfo":{"elapsed":22699,"status":"ok","timestamp":1720042396408,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"S_ei2qu8fjD7"},"outputs":[],"source":["vectorizer = CountVectorizer(binary=True)\n","x_train_cv = vectorizer.fit_transform(x_train_text)\n","x_test_cv = vectorizer.transform(x_test_text)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1720042396409,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"iK_zfqnLfjD7","outputId":"9b3f6db3-01bf-4246-b943-359620c717a2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before Vectorize: doesn't know how to spell conked \n"]}],"source":["print('Before Vectorize: ', x_train_text[3])"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042396409,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"wKxY8e38fjD8","outputId":"19530135-070d-4259-d6a9-7ba06b519763"},"outputs":[{"name":"stdout","output_type":"stream","text":["After Vectorize: \n"," (0, 528584)\t1\n"," (0, 165468)\t1\n"," (0, 300381)\t1\n"," (0, 242211)\t1\n"," (0, 489893)\t1\n"," (0, 134160)\t1\n"]}],"source":["# Notice that the matriz is sparse\n","print('After Vectorize: ')\n","print(x_train_cv[3])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"QTPPEMd9fjD8"},"source":["Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)."]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"execution":{},"executionInfo":{"elapsed":127277,"status":"ok","timestamp":1720042523682,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"2vEPOQS6fjD8","outputId":"3be77fc0-76e6-40b8-8847-5f6e7c6c0ce0"},"outputs":[{"data":{"text/html":["
LogisticRegression(solver='saga')
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"],"text/plain":["LogisticRegression(solver='saga')"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["model = LogisticRegression(solver='saga')\n","model.fit(x_train_cv, y_train)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1720042523683,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"37bUbqB6fjD8","outputId":"7eb9178d-6130-47d0-bdf4-ce4be164bc97"},"outputs":[{"name":"stdout","output_type":"stream","text":[" precision recall f1-score support\n","\n"," 0 0.81 0.79 0.80 160000\n"," 1 0.79 0.81 0.80 160000\n","\n"," accuracy 0.80 320000\n"," macro avg 0.80 0.80 0.80 320000\n","weighted avg 0.80 0.80 0.80 320000\n","\n"]}],"source":["y_pred = model.predict(x_test_cv)\n","\n","print(classification_report(y_test, y_pred))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"161kDLhofjD8"},"source":["## Explainable AI\n","The best thing about logistic regresion is that it is simple, and we can get some explanations."]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":1105,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"EILTmxzifjD9","outputId":"b7ce6853-7385-4a24-d4eb-e6d0843ca5d5"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1, 589260)\n","589260\n"]}],"source":["print(model.coef_.shape)\n","print(len(vectorizer.vocabulary_))\n","\n","words_sk = list(vectorizer.vocabulary_.keys())\n","words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"NGjVPON6fjD9","outputId":"d40443bc-476d-4f5a-ce90-4b5b17e47933"},"outputs":[{"name":"stdout","output_type":"stream","text":["roni: -3.8625952420933984\n","inaperfectworld: -3.5734321547933936\n","dontyouhate: -3.5002133484207576\n","xbllygbsn: -3.4126303898325787\n","anqju: -3.3363997631497493\n","sad: -3.200516823534637\n","pakcricket: -3.1949062976331675\n","condolences: -3.132503698316079\n","heartbreaking: -3.0665219866881297\n","saddest: -3.042020604188048\n","sadd: -3.029036146667248\n","heartbroken: -3.0287524416643463\n","boohoo: -3.0226033087262802\n","sadface: -2.991829110065316\n","rachelle_lefevr: -2.925076661509848\n","disappointing: -2.902522686643491\n","lvbu: -2.8947109582208865\n","saddens: -2.8855187276040715\n","bummed: -2.836500453805889\n","neda: -2.792917726280752\n"]}],"source":["for w in words_sk[:20]:\n"," print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"oxQ_jlNRfjD9","outputId":"363de58b-817a-4205-f019-2379d0d64e0d"},"outputs":[{"name":"stdout","output_type":"stream","text":["iamsoannoyed: 2.8493838469077013\n","myfax: 2.7974330510971424\n","jennamadison: 2.5667217237933104\n","yeyy: 2.4780234846131646\n","tryout: 2.438315611477797\n","goldymom: 2.4374072779309204\n","wooohooo: 2.402957513257194\n","thesupergirl: 2.356525094856456\n","iammaxathotspot: 2.3116551216589682\n","londicreations: 2.3074264075299316\n","smilin: 2.2991796213822497\n","worries: 2.2899555142510084\n","sinfulsignorita: 2.27989578448778\n","finchensnail: 2.2642827277181063\n","smackthis: 2.237672991997692\n","kv: 2.2157591386122775\n","tojosan: 2.2117938132889696\n","russmarshalek: 2.20953890861265\n","traciknoppe: 2.1768232307222153\n","congratulations: 2.1715901103136876\n"]}],"source":["for w in reversed(words_sk[-20:]):\n"," print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"9KSSAC3qfjD9"},"source":["What does this mean?\n","\n","Remember the `model.coef_` is the $W$ in:\n","\n","$$h(x)=\\sigma(WX + b)$$\n","\n","where the label 1 is a positive tweet and the label 0 is a negative tweet."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"oDHjTP2_fjD9"},"source":["## Recurrent Neural Network with Pytorch"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"TbgpKy95fjD9"},"source":["In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n","\n","In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n","\n","Let's start by importing the relevant libraries.\n"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"7nmUJV99fjEB"},"outputs":[],"source":["def set_device():\n"," device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n"," if device != \"cuda\":\n"," print(\"WARNING: For this notebook to perform best, \"\n"," \"if possible, in the menu under `Runtime` -> \"\n"," \"`Change runtime type.` select `GPU` \")\n"," else:\n"," print(\"GPU is enabled in this notebook.\")\n","\n"," return device"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"chI-18LcfjEB","outputId":"7f633079-6548-48f3-802e-94bc9cfada93"},"outputs":[{"name":"stdout","output_type":"stream","text":["GPU is enabled in this notebook.\n"]}],"source":["# Set the device (check if gpu is available)\n","device = set_device()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"01UtIN7ofjEC"},"source":["First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences."]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"afus9SyUfjEC","outputId":"bb4eb869-e2f0-4ccd-f64c-e55908272345"},"outputs":[{"data":{"text/plain":["['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["# From previous section, we have a list with the most used tokens\n","sorted_words[:10]"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"6vfQFjaufjEC"},"source":["Let's select only the most used."]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"tGLkxaGcfjEC"},"outputs":[],"source":["num_words_dict = 30000\n","# We reserve two numbers for special tokens.\n","most_used_words = sorted_words[:num_words_dict-2]"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"AzhQvekCfjEC"},"source":["We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)."]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"73Wrb-lEfjEC"},"outputs":[],"source":["# dictionary to go from words to idx\n","word_to_idx = {}\n","# dictionary to go from idx to words (just in case)\n","idx_to_word = {}\n","\n","\n","# We include the special tokens first\n","PAD_token = 0\n","UNK_token = 1\n","\n","word_to_idx['PAD'] = PAD_token\n","word_to_idx['UNK'] = UNK_token\n","\n","idx_to_word[PAD_token] = 'PAD'\n","idx_to_word[UNK_token] = 'UNK'\n","\n","# We popullate our dictionaries with the most used words\n","for num,word in enumerate(most_used_words):\n"," word_to_idx[word] = num + 2\n"," idx_to_word[num+2] = word"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"kMHVkEisfjEC"},"source":["Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model."]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"tkCIu3PKfjED"},"outputs":[],"source":["# A function to convert list of tokens to list of indexes\n","def tokens_to_idx(sentences_tokens,word_to_idx):\n"," sentences_idx = []\n"," for sent in sentences_tokens:\n"," sent_idx = []\n"," for word in sent:\n"," if word in word_to_idx:\n"," sent_idx.append(word_to_idx[word])\n"," else:\n"," sent_idx.append(word_to_idx['UNK'])\n"," sentences_idx.append(sent_idx)\n"," return sentences_idx"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{},"executionInfo":{"elapsed":9346,"status":"ok","timestamp":1720042534127,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"aHru4vpzfjED"},"outputs":[],"source":["x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n","x_test_idx = tokens_to_idx(x_test_token,word_to_idx)"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1720042534127,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"Ofj3OD7zfjED","outputId":"b2788d03-dbfa-41d7-8231-5011206baa59"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before converting: ['worst', 'headache', 'ever']\n","After converting: [721, 458, 237]\n"]}],"source":["some_number = 1\n","print('Before converting: ', x_train_token[some_number])\n","print('After converting: ', x_train_idx[some_number])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"NcCicvb-fjED"},"source":["We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1720042534128,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"BSjhdyYUfjED","outputId":"82e49be9-7868-44ac-b496-c7a48da1efee"},"outputs":[{"name":"stdout","output_type":"stream","text":["Max tweet word length: 229\n","Mean tweet word length: 15.0\n","99% percent under: 37.0\n"]}],"source":["tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n","print('Max tweet word length: ',tweet_lens.max())\n","print('Mean tweet word length: ',np.median(tweet_lens))\n","print('99% percent under: ',np.quantile(tweet_lens,0.99))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"t311WY6ZfjEE"},"source":["We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter."]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042534128,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"r4S8KTWLfjEE"},"outputs":[],"source":[" # We choose the max length\n"," max_length = 40\n","\n","# A function to make all the sequence have the same lenght\n","# Note that the output is a Numpy matrix\n"," def padding(sentences, seq_len):\n"," features = np.zeros((len(sentences), seq_len),dtype=int)\n"," for ii, tweet in enumerate(sentences):\n"," len_tweet = len(tweet)\n"," if len_tweet != 0:\n"," if len_tweet <= seq_len:\n"," # If its shorter, we fill with zeros (the padding Token index)\n"," features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n"," if len_tweet > seq_len:\n"," # If its larger, we take the last 'seq_len' indexes\n"," features[ii, :] = np.array(tweet)[-seq_len:]\n"," return features"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{},"executionInfo":{"elapsed":4762,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"Z-Cw-bBxfjEE"},"outputs":[],"source":["# We convert our list of tokens into a numpy matrix\n","# where all instances have the same lenght\n","x_train_pad = padding(x_train_idx,max_length)\n","x_test_pad = padding(x_test_idx,max_length)\n","\n","# We convert our target list a numpy matrix\n","y_train_np = np.asarray(y_train)\n","y_test_np = np.asarray(y_test)"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"8eC3YswlfjEE","outputId":"3bb0ea7f-518f-4545-9241-feb783f48122"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n","After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n"," 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n"," 150 4 22 5 18 667 3 138 61 7 3296 4]\n"]}],"source":["some_number = 2\n","print('Before padding: ', x_train_idx[some_number])\n","print('After padding: ', x_train_pad[some_number])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"SzDhnauUfjEE"},"source":["Now, let's convert the data to pytorch format.\n"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"--Yd22YWfjEF"},"outputs":[],"source":["# create Tensor datasets\n","train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n","valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n","\n","# Batch size (this is an important hyperparameter)\n","batch_size = 100\n","\n","# dataloaders\n","# make sure to SHUFFLE your data\n","train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n","valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"jQ5qPOWTfjEF"},"source":["Each batch of data in our traning proccess will have the folllowing format:"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":598,"status":"ok","timestamp":1720042563992,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"S1mqhk1hfjEF","outputId":"c97e7edd-695f-4336-a2e6-f6bed4852a63"},"outputs":[{"name":"stdout","output_type":"stream","text":["Sample input size: torch.Size([100, 40])\n","Sample input: \n"," tensor([[ 0, 0, 0, ..., 32, 203, 86],\n"," [ 0, 0, 0, ..., 1, 1, 4661],\n"," [ 0, 0, 0, ..., 169, 43, 34],\n"," ...,\n"," [ 0, 0, 0, ..., 2, 2961, 4076],\n"," [ 0, 0, 0, ..., 2319, 1325, 2],\n"," [ 0, 0, 0, ..., 7, 253, 1]])\n","Sample input: \n"," tensor([0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n"," 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n"," 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n"," 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n"," 0, 1, 0, 1])\n"]}],"source":["# Obtain one batch of training data\n","dataiter = iter(train_loader)\n","sample_x, sample_y = dataiter.__next__()\n","\n","print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n","print('Sample input: \\n', sample_x)\n","print('Sample input: \\n', sample_y)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"jn0PzZdGfjEF"},"source":["Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n","\n","* Embedding Layer\n","> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n","\n","\n","* LSTM layer\n","> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{},"executionInfo":{"elapsed":433,"status":"ok","timestamp":1720042567199,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"vfzcowAxfjEF"},"outputs":[],"source":["class SentimentRNN(nn.Module):\n"," def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n"," super(SentimentRNN,self).__init__()\n","\n"," self.output_dim = output_dim\n"," self.hidden_dim = hidden_dim\n"," self.no_layers = no_layers\n"," self.vocab_size = vocab_size\n"," self.drop_prob = drop_prob\n","\n"," # Embedding Layer\n"," self.embedding = nn.Embedding(vocab_size, embedding_dim)\n","\n"," # LSTM Layers\n"," self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n"," num_layers=no_layers, batch_first=True,\n"," dropout=self.drop_prob)\n","\n"," # Dropout layer\n"," self.dropout = nn.Dropout(drop_prob)\n","\n"," # Linear and Sigmoid layer\n"," self.fc = nn.Linear(self.hidden_dim, output_dim)\n"," self.sig = nn.Sigmoid()\n","\n"," def forward(self,x,hidden):\n"," batch_size = x.size(0)\n","\n"," # Embedding out\n"," embeds = self.embedding(x)\n"," #Shape: [batch_size x max_length x embedding_dim]\n","\n"," # LSTM out\n"," lstm_out, hidden = self.lstm(embeds, hidden)\n"," # Shape: [batch_size x max_length x hidden_dim]\n","\n"," # Select the activation of the last Hidden Layer\n"," lstm_out = lstm_out[:,-1,:].contiguous()\n"," # Shape: [batch_size x hidden_dim]\n","\n"," ## You can instead average the activations across all the times\n"," # lstm_out = torch.mean(lstm_out, 1).contiguous()\n","\n"," # Dropout and Fully connected layer\n"," out = self.dropout(lstm_out)\n"," out = self.fc(out)\n","\n"," # Sigmoid function\n"," sig_out = self.sig(out)\n","\n"," # return last sigmoid output and hidden state\n"," return sig_out, hidden\n","\n"," def init_hidden(self, batch_size):\n"," ''' Initializes hidden state '''\n"," # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n"," # initialized to zero, for hidden state and cell state of LSTM\n"," h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n"," c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n"," hidden = (h0,c0)\n"," return hidden"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"YfrLPa9mfjEF"},"source":["We choose the parameters of the model."]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{},"executionInfo":{"elapsed":471,"status":"ok","timestamp":1720042569608,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"rOm-xoFkfjEG"},"outputs":[],"source":["# Parameters of our network\n","\n","# Size of our vocabulary\n","vocab_size = num_words_dict\n","\n","# Embedding dimension\n","embedding_dim = 32\n","\n","# Number of stacked LSTM layers\n","no_layers = 2\n","\n","# Dimension of the hidden layer in LSTMs\n","hidden_dim = 64\n","\n","# Dropout parameter for regularization\n","output_dim = 1\n","\n","# Dropout parameter for regularization\n","drop_prob = 0.25"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":465,"status":"ok","timestamp":1720042571776,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"xapxpe7ufjEG","outputId":"51c90159-7d2b-4fc4-f34a-98e9901d40e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["SentimentRNN(\n"," (embedding): Embedding(30000, 32)\n"," (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n"," (dropout): Dropout(p=0.25, inplace=False)\n"," (fc): Linear(in_features=64, out_features=1, bias=True)\n"," (sig): Sigmoid()\n",")\n"]}],"source":["# Let's define our model\n","model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n"," embedding_dim, drop_prob=drop_prob)\n","# Moving to gpu\n","model.to(device)\n","print(model)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720042571776,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"GEvTs3uwfjEG","outputId":"2e15f6df-2aa8-4665-b2da-7363d2bfa09e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Total Number of parameters: 1018433\n"]}],"source":["# How many trainable parameters does our model have?\n","model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n","params = sum([np.prod(p.size()) for p in model_parameters])\n","print('Total Number of parameters: ',params)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"Pc2OC5GDfjEG"},"source":["We choose the losses and the optimizer for the training procces."]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{},"executionInfo":{"elapsed":1740,"status":"ok","timestamp":1720042574210,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"iBWjPADUfjEG"},"outputs":[],"source":["# loss and optimization functions\n","lr = 0.001\n","\n","# Binary crossentropy is a good loss function for a binary classification problem\n","criterion = nn.BCELoss()\n","\n","# We choose an Adam optimizer\n","optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n","\n","# function to predict accuracy\n","def acc(pred,label):\n"," pred = torch.round(pred.squeeze())\n"," return torch.sum(pred == label.squeeze()).item()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"OZgMwOe2fjEG"},"source":["We are ready to train our model."]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":304614,"status":"ok","timestamp":1720042880244,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"3B6YhEocfjEH","outputId":"76276a1f-7775-4b98-aab0-0e199aa133e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1\n","train_loss : 0.4366412344621494 val_loss : 0.3881208170717582\n","train_accuracy : 79.485546875 val_accuracy : 82.475\n","Validation loss decreased (inf --> 0.388121). Saving model ...\n","==================================================\n","Epoch 2\n","train_loss : 0.3760281792609021 val_loss : 0.3713956154882908\n","train_accuracy : 83.186328125 val_accuracy : 83.4575\n","Validation loss decreased (0.388121 --> 0.371396). Saving model ...\n","==================================================\n","Epoch 3\n","train_loss : 0.3574051411205437 val_loss : 0.36425656544510276\n","train_accuracy : 84.19953125 val_accuracy : 83.80375\n","Validation loss decreased (0.371396 --> 0.364257). Saving model ...\n","==================================================\n","Epoch 4\n","train_loss : 0.344456663565943 val_loss : 0.3613302929420024\n","train_accuracy : 84.89265625 val_accuracy : 84.00874999999999\n","Validation loss decreased (0.364257 --> 0.361330). Saving model ...\n","==================================================\n","Epoch 5\n","train_loss : 0.33407817618339325 val_loss : 0.3601334386831149\n","train_accuracy : 85.444921875 val_accuracy : 84.03625\n","Validation loss decreased (0.361330 --> 0.360133). Saving model ...\n","==================================================\n"]}],"source":["# Number of training Epochs\n","epochs = 5\n","\n","# Maximum absolute value accepted for the gradeint\n","clip = 5\n","\n","# Initial Loss value (assumed big)\n","valid_loss_min = np.Inf\n","\n","# Lists to follow the evolution of the loss and accuracy\n","epoch_tr_loss,epoch_vl_loss = [],[]\n","epoch_tr_acc,epoch_vl_acc = [],[]\n","\n","# Train for a number of Epochs\n","for epoch in range(epochs):\n"," train_losses = []\n"," train_acc = 0.0\n"," model.train()\n","\n"," for inputs, labels in train_loader:\n","\n"," # Initialize hidden state\n"," h = model.init_hidden(batch_size)\n"," # Creating new variables for the hidden state\n"," h = tuple([each.data.to(device) for each in h])\n","\n"," # Move batch inputs and labels to gpu\n"," inputs, labels = inputs.to(device), labels.to(device)\n","\n"," # Set gradient to zero\n"," model.zero_grad()\n","\n"," # Compute model output\n"," output,h = model(inputs,h)\n","\n"," # Calculate the loss and perform backprop\n"," loss = criterion(output.squeeze(), labels.float())\n"," loss.backward()\n"," train_losses.append(loss.item())\n","\n"," # calculating accuracy\n"," accuracy = acc(output,labels)\n"," train_acc += accuracy\n","\n"," #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n"," nn.utils.clip_grad_norm_(model.parameters(), clip)\n"," optimizer.step()\n","\n","\n"," # Evaluate on the validation set for this epoch\n"," val_losses = []\n"," val_acc = 0.0\n"," model.eval()\n"," for inputs, labels in valid_loader:\n","\n"," # Initialize hidden state\n"," val_h = model.init_hidden(batch_size)\n"," val_h = tuple([each.data.to(device) for each in val_h])\n","\n"," # Move batch inputs and labels to gpu\n"," inputs, labels = inputs.to(device), labels.to(device)\n","\n"," # Compute model output\n"," output, val_h = model(inputs, val_h)\n","\n"," # Compute Loss\n"," val_loss = criterion(output.squeeze(), labels.float())\n","\n"," val_losses.append(val_loss.item())\n","\n"," accuracy = acc(output,labels)\n"," val_acc += accuracy\n","\n"," epoch_train_loss = np.mean(train_losses)\n"," epoch_val_loss = np.mean(val_losses)\n"," epoch_train_acc = train_acc/len(train_loader.dataset)\n"," epoch_val_acc = val_acc/len(valid_loader.dataset)\n"," epoch_tr_loss.append(epoch_train_loss)\n"," epoch_vl_loss.append(epoch_val_loss)\n"," epoch_tr_acc.append(epoch_train_acc)\n"," epoch_vl_acc.append(epoch_val_acc)\n"," print(f'Epoch {epoch+1}')\n"," print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n"," print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n"," if epoch_val_loss <= valid_loss_min:\n"," print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n"," # torch.save(model.state_dict(), '../working/state_dict.pt')\n"," valid_loss_min = epoch_val_loss\n"," print(25*'==')"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"execution":{},"executionInfo":{"elapsed":894,"status":"ok","timestamp":1720042881135,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"ttJazP-nfjEH","outputId":"992bed02-611e-4614-c60f-77223d5b801a"},"outputs":[{"data":{"image/png":"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"]},"metadata":{},"output_type":"display_data"}],"source":["fig = plt.figure(figsize = (20, 6))\n","plt.subplot(1, 2, 1)\n","plt.plot(epoch_tr_acc, label='Train Acc')\n","plt.plot(epoch_vl_acc, label='Validation Acc')\n","plt.title(\"Accuracy\")\n","plt.legend()\n","plt.grid()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(epoch_tr_loss, label='Train loss')\n","plt.plot(epoch_vl_loss, label='Validation loss')\n","plt.title(\"Loss\")\n","plt.legend()\n","plt.grid()\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"iUyaF-EbfjEH"},"source":["---\n","# What's Next?\n","\n","You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n","\n","* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n","* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n","* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n","* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. 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From 59ddbe21c47770805c438f1b345c18b2965776bc Mon Sep 17 00:00:00 2001 From: Mobin Nesari Date: Thu, 4 Jul 2024 22:56:05 +0330 Subject: [PATCH 04/25] Fixing memory limit exceeding --- projects/ComputerVision/em_synapses.ipynb | 100 ++++++++++------------ 1 file changed, 47 insertions(+), 53 deletions(-) diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb index fd209caa1..58d92b82f 100644 --- a/projects/ComputerVision/em_synapses.ipynb +++ b/projects/ComputerVision/em_synapses.ipynb @@ -178,53 +178,47 @@ "url = 'https://www.dropbox.com/sh/ucpjfd3omjieu80/AAAvZynLtzvhyFx7_jwVhUK2a?dl=1'\n", "\n", "if not os.path.exists('data/'):\n", - " print('Data downlading...')\n", - " r = requests.get(url, allow_redirects=True, stream=True)\n", - " with open(fname, 'wb') as fh:\n", - " fh.write(r.content)\n", - " print('Download is cmpleted.')\n", - "\n", - " # @markdown Unzip the file\n", - " fname = 'resources.zip'\n", - "\n", - " # specifying the zip file name\n", - " fnames = ['data.zip', 'checkpoints.zip']\n", - "\n", - " # opening the zip file in READ mode\n", - " with ZipFile(fname, 'r') as zf:\n", - " # extracting all the files\n", - " print('Extracting all the files now...')\n", - " zf.extractall(path='.')\n", + " print('Data downloading...')\n", + " with requests.get(url, stream=True) as r:\n", + " r.raise_for_status()\n", + " with open(fname, 'wb') as fh:\n", + " for chunk in r.iter_content(chunk_size=16384):\n", + " fh.write(chunk)\n", + " print('Download is completed.')\n", + "\n", + " # @markdown Unzip the file\n", + " with ZipFile(fname, 'r') as zf:\n", + " # extracting all the files\n", + " print('Extracting all the files now...')\n", + " zf.extractall(path='.')\n", + " print('Done!')\n", + "\n", + " # @markdown Extract the data\n", + " fnames = ['data.zip', 'checkpoints.zip']\n", + "\n", + " for fname in fnames:\n", + " with ZipFile(fname, 'r') as zh:\n", + " # extracting all the files\n", + " print(f\"\\nArchive: {fname}\")\n", + " print(f\"\\tExtracting data...\")\n", + " zh.extractall(path='.')\n", " print('Done!')\n", "\n", + " # @markdown Make sure the order of classes matches the pretrained model\n", + " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", + " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", + " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", + " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", + " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", + " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", "\n", - " # @markdown Extract the data\n", - " fnames = ['data.zip', 'checkpoints.zip']\n", - "\n", - " for fname in fnames:\n", - " # opening the zip file in READ mode\n", - " with ZipFile(fname, 'r') as zh:\n", - " # extracting all the files\n", - " print(f\"\\nArchive: {fname}\")\n", - " print(f\"\\tExtracting data...\")\n", - " zh.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # @markdown Make sure the order of classes matches the pretrained model\n", - " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", - " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", - " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", - " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", - " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", - " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", + " # @markdown Remove the archives\n", + " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", + " if os.path.exists(i):\n", + " os.remove(i)\n", "\n", "else:\n", - " print('Data are downloaded.')\n", - "\n", - "# @markdown Remove the archives\n", - "for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", - " if os.path.exists(i):\n", - " os.remove(i)" + " print('Data are already downloaded.')" ] }, { @@ -338,7 +332,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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l2IdV1UO6+PdfVtXjW2vPrvmB1EOq6ieWbTv1f/vSLy1WDGFf5IFQCDP/qrV22/KrxFfX8rWCZRF6RS2yrWma3lzzlw2evPwy9b41vxWyw7Gag5+/q6obW2tfU5f+EvHg1tqnLgHT42sOKP57zaZ403JstfkLWfSpeWjNrzzvyjRNHzdtft2g//u4PY55eVX9YVV97fJK8afUvDj+9G77t/kT7Mdaa3dvrf1vNf/a8+3L5j+oqvdu86fnW2vtPWv+utVOYPA+NS/iD1r+quYvQfzM8v9Pq6r/o82f2DxZs9/Qjo7+N2o2/ntia+3G5QHMR9byGnBr7T1ba7e22bvo42o2HfyGy7nG1tpH1fy686dN07TxoGfZ/gHLcYdba19RVe9Yl8qzdjx8+AvVoTZ7SN1tTrabdl63Xl6zflbNv6Q9dnljrOdnqupka+2xy3U9subA+re7/G9b6mGvN5MeU7P54iv32B5CCCGMcLViJvKHVfWpy77vVVWfh+1/U/PDgh2O1bx+nl7K8rV15Vj89jc1+wEdqqqapuk1NX9g4ttaa8eXtf4926bP30GP2aq19uAlvrh3zYbTz+lkX8+oqg9trT28zW9dPb5m+flLl+3fV/NDr0dM07TzlvcOFrN9fmvtHZb/f/+ajcp/ZTn25fM/t89c6vW+NT9A2Yklf6aqHtBa+7Qlvvqamn2BXrbk98Sq+syqevg0Ta9DuZ5XVW+uqi9prd2jtbbzVtOvLsd+Umvt5BLDfnDNb58/u6uvuy/nbFV1t6UNDi3bPq7NUsKdN+af1B37pJpj4J349zk1/wj5OfUPsrqdbTsPvh5cVb93GbFiCPtjOgDO1vnL3/X8q80vZpyu+ca6/0rWv6qq7+vS9675rZedL1R8U1X9yrLthpo/MX625tdvv3LJ/+HL9ifX5lfG/qDmz27u5P3va5b+7Milfr02v7jxx9V9lewq1sH9al4k76rZM+fh3bZHV9WLu/Tjaw6YLtRsFv0Q5PXpNb/ZdK7mXzK+qaoO7XFefmXsbjW/Nnu6ZnnWd9fy9ZJl+/1rNoS8UPgK23Lev6r5yxR/WFUfM3CNv1ZzIHi++/v5bvu31Pwq/Pmag7v3Qt43LWV+2C7X+BHLdfZ/z1u2PXRJ34lz91+m+/Cl3c/X/GnVD0f+T6yVr4DUrL3f9ctt+ctf/vKXv/yN/NVVjJmW7f1Xxu5V80OWHe+bJ9fmF8m+qObY6vSy5r/Tsq6fr/khwhdW93WmuvSrZd9QVT/UpR9eVa9Y/t/it1trjnlOVdULl387UfNDkdtrvqn/g6p6VJf/20LM9ltLfd9Rs+TtCLZ/as0P+c4u57z/8u/vttT16xG/PLo7di1me1rND9kuLPX8LbUZ733U0l/O1BwP/mf0s4fXHN/ctZTrfuhTf49yfVW3/QNr/orcXTV/7fcDu20/VlWvW455WVV9CerjeXVpTPcRy7Zv7a7pVTVLxu62Rxv+UOErY2jfvh9rrJi//O3nr01TLCXCdtNa+/OaA4Zf3mP7PWpe5B82zb8Icfs3VdV9p2m6oi9nDJTzEVX1mGmaPv1anieEEEIIYTfeVmKm601ithDC2wo3+i4hbDfTNP19Vb3/Tnp5DfTuNf/y80E1v9L8+bsffVXL8dyazeRCCCGEEA4cByVmut4kZgshvK2QB0IhjHOs5ldK36nmV0O/rTp9cQghhBBCqKrETCGEcKCJZCyEEEIIIYQQQghhy7hmXxlrrX1sa+1PWmuvaK094VqdJ4QQQgghhBBCCCGMcU3eEFo+TfjyqvrnNTvvP7+qPmOappdc9ZOFEEIIIYQQQgghhCGulYfQB9f8GcdXVVW11n68qj6p5k8OXsI973nP6cSJE5edOR9itdaG9h/Nz/Ln9kOHDq2m+/1H87b9jWt9POtyv3U/mt9oed7ylrcM7W/nH2lPnvtqc7Wv5WrnZ3XF+uE4Gp0H9juWrnbfvpp57Xdc7Hd/tg2x8lrbrM2hl3O+/c7pZD/z1rWWaY/mz7LfcMMN+zp+rTynTp2qCxcu7G8RCledw4cPTzfffPNl77/fufBqryUWc7FP99u5jen9zl37He9cB/cbI1nMQ/YbB1j6zW9+80b6TW9601B5bG1Ym8/s2lhXFrPtN14djUlG+9p+2/Jq9/X93o9YfvsZe9c7JttvW1q8bNg4s/2JHU9sLHLeGKmv0Tl9v2vCWlkuB5vjrO37ujt9+vSeMdi1eiD0zlX16i59e1V9yF47nzhxoh7zmMdcTK9dTNWlHcEW/ze84Q2r+dmN593vfve9il5VVfe4xz1W04cPH95I3+1ud7v4/zfeuNkElraOYZOAXcvoAsC0Le7M/+///u830n3dVF3aNpYfy/PGN75xI82+cNdddw3tb8GKBaZ9mudmv7XAzcYJy85r47Ww7tj32LbMj2mWl/lxnJA777xzI33TTTetlof1wfOxb1l9j/Ztprl/Xx7Lm7Buebz1S7YF8yOsSysf25J1y7ZgeW3OZdtzu93UsXzsC8TmUVszWL99/XHO2+/DKrYN09yfZWddHD9+fCM9erPNuurb+nu+53sqHDxuvvnm+oIv+IKLabs5sbnOgnWuTa9//es30jbfsc8eOXJkI33Pe95zI80+3aePHj26ui/PxbLzXBZ/jj5sZt1wLrUYhefjusq6tvFtcQTTnO+YPnPmzEb6jjvu2EhzvuLcz/iacz3bt68/1iWvjTEa687iWbYN4flHH3aN3iuxL1tcwDT7AvPj+SymGr3RtR8rrD3Xxp7dF9q1WYwz+iDUxrHFaIyZ9nvfy7TF22wrlofH25rCsXf27NmNtLV1Xx5rK5adL6yw3/N4u48fjfE4h1ndcT3s57GnPOUpe573mnkIGa21x7XWXtBaewEbOoQQQgghXBsSg4UQQgih6tq9IfSXVfUuXfq25d8uMk3TU6vqqVVV973vfSdsW8187Zf3qkufytuTXXsTg0/f+KTU3ggaeRVw9I0clsWe+toTdnsl1n6Jt/1ZHj5ptdcKWT57E4H7X7hwYSN9/vz5jbT9QmJvwdiv6X1fGv31Z/QVTpaVv16N/HpSdek4sLepmB/HiY0Le4NoVOpib7mMviJsr5Gupe2NE2JvdbDfE7t2225zJMeVvcEzOsfbr2usD/5qz77JtP06Zm3N4+38Pbae8Zc1wnE9+tYk1yueb/R16bVf+vJV04MDY7D+oRDbnH2UfW70rTS+9cL8iL3Rx/xYHo7HNeytCI6PUTmtxSg2Xnl+jjeOZ+bPurC3RTl32drDNNvm9OnTq+Xj9fB4e/P42LFjG2m+8dW3p8VYLAsfnPINInvjfFSiZW+p2Fsk9maevW1q66St0xbv71e2z/zszY2+L5kaweYBS9tbxnafSuyNI44DvnlHrC55PpsH7C1se3vOYjzOA8TufddgWS0GsnsVe5OOWAxo9718g4j79/PS2r3GtXpD6PlV9d6ttXdvrd29qh5VVc+5RucKIYQQQgghhBBCCANckzeEpml6U2vtX1fVL1bVDVX1g9M0vfhanCuEEEIIIYQQQgghjHGtJGM1TdN/q6r/dq3yDyGEEEIIIYQQQghXxjV7IDTKmq7NNNrmzj76Gc7RL/ZQP0gdsX3dqMe090ybdnG/nw4c9WkxHa3p2ff7OT/WPffn+cwbxb5kZfuT3hmfPiXWVqOeNWs60t3Spge3tjXfCfMqMJ2tfbHDnP7tC3v2JYDRTx2vfY3FNMGmKba2sf05b4x+XWS/nxa2cbdfX7fRsWFfXzF9vJWvx+YQ8xAx/bh9NciuxeZ8+8LF6JwYrj/TNG20k31JyvwfiHmhGDyefhQcAxZX9OUf9eixL57Zl11H/dFsvrB1k1+cYf4s3+haQdg25s/GtmL5eH5u5/XRQ4meQmtfGSO8VpaFXx8ynxTbbj6N5ttoMYn1PUuPfp3Uxr3FLbYW2Zev2F59fqOePPbVWru3Gv0ql33ld/SLdjbn2te4WX7zLiS2pox6CNm9HftGf37zZbR+t9avqrwfs+/w2lh263tk7Su3a1y3r4yFEEIIIYQQQgghhOtDHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwID6Fpmjb0iqY7Na+O3fJfS9v+1DpSv2f+GNQfrun/eC7zFhnF6mKkrLuVZ9RjyHxxRr0JTHdL3xzz76DWdNTjiNv7vmvXZn5JZFTDbPpyG4ejengy6kvB+mB5zKuL18u2Zdq8Faw+17zNzBvMfNFG/Z5GPX243drWvAbMl8LOb14Kpk83rwFi/lh2PPtiX579zuGjXmI2p5m3lvlC2Dju97c5L1wfpmm6ZD7vYZ+1ud3iCJs7R/0srHxr6zKPtZiG281vYs0fs8rjWfP44vEWs1n8Suz67rzzztXjeT3Mj/MP5yebe3k95pHWb7e6HfWXoo8Jx5SlGZ8ynrV1x9rS4mOLSywms/jX4nWL0cwbzDyE+va0cW91bWUh5p/K89u4unDhwkba/KVGPYdYXvrcWNsSi0EtviecN5hm2/deYzYHsm2sn1p8budjP2f+LI898yD9+df6ad4QCiGEEEIIIYQQQtgy8kAohBBCCCGEEEIIYcvIA6EQQgghhBBCCCGELePAeAj1mjjq7cxbxPwyTOtI/R+3m7bRPIa4vb9WahPJqBbfdJym+SWm82T5re5HfXG4v2mciWmgra3ZdqYltfo6evToxf+nJpd5j/oVWT+lppaYd5Z5AZgXgpWP+vlRnaz5RlBjzLHUa4x32872MT+Btb45Oiexr5j2385nac65pje3vsPjTe8+qu8f9a/iWLA53foWy2Pz4sixLCv7MdcIbmfZTa9ubWleBGzbfv94CB1MWmsb8521k8Uhdvx+9+cYsbl5bW21GGrUA4jnsuMtv1HvOvNpJHa9HO+c+yyOYVuYRxOPt75mHkLcvy+/ndtiMtYN5761c+9Wdp6PPjLWl2wdsxjNvLosBrW+aX2D9XH8+PHV89m9H7f3Xiw2LkbjcfP2s3iYbXXixInV7YcPH17dzr5z7ty51e1cx0fvvUZjVBvHrK9Rv6m1eN48fsyHzfyQeDw9gKyvjHrsmYdR39Zrc0jeEAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC3jwHgI9VpceomYltO8SUzLyO2mZzctpaXX/C1MW8i6YNnpH2FeJeY/Yfp6225+SsR0umTUD8p0w9Stsn6JaazX9Pzm9cG8uL/5TxEeb+OGZTddq7WdeZOcP39+NT/zezLPoGPHjm2kqTFmfdrxVv9r9WNz0KjHjtWNaZ55PM9v44rlM98Jg+XlmmDeDTze9OejPjo2T7N8a7pt85UwP5RRXzOW3foavQHMd4LeBJerXw/Xl7W2sbXAvPO4/4ULF1aPt3WbY4TbeTz9NvoxY+uqnYtzBceHxUC2jlrds/yjHpijawnn4tG1jHOj9R3z/qNnkPny9Fhb2DzPc/FazDOHdUHfGIuvee02d5vHkN0LWRxhnky8fnqr9B6bVX797BtkrS9xTrC2tn5OmN/ovYS1BedQtjW3s+7PnDmzur+N09G+RNg37HjWP+N39iW2b3+8zek2zsmo36itV4QxFedgth37St+W8RAKIYQQQgghhBBCCBfJA6EQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAPjIdTr2qjfowbZ/BpGda9Mm5aTGrxRr5Q+f/MhMU0usboyvTWvnZjW0TyLeD5rG/MQ4nbT/Zom2XSxrF/zYBrRHa/5De2WNp8Y8y6g5tY0vFaeNd3qlWDeCuYzQY0x9em8fup8qUE2XwzzeugZ9fJiW472c2KeQExTn87tbGumWX5qoOl9YH5XLA/7HvNj2trONOOj/lZr89iob4Udb3P+qKce+wrbjm1BL7Bz587tmVc4OPT9ZtQrhH2Q4808uLiujs5fnKs593NM9Ptz3ud4YVlsbrAYaNQviZjvjXlzjPrBcXzzePO3M0Z9M7k27Mfr0NrG/JxYNms7bjcPIMYsPB/nWvPeY/52L2Pj1uqabcWYi2m7v7G4gOdfG8vcl3XNujCfwdF7ldGYjX2T18aYy2JIKy/z5zww6mtpXmt2L2f+rLGOycgAACAASURBVBYj99utLc1jiFhdm8+jPcOwmIxptlXvQbQ2P+cNoRBCCCGEEEIIIYQtIw+EQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DIOjIdQr0c0LaD5QZjezrSco/4c1EJST7imVxz1izDdqumvTTtp/krm8WNaTPM4srYho94dLD/LY14ozI/1ax5DvS7X/JtMH800+znLdvLkyY009efWN3gt5r3F8nN/1q215ZomuMp9JEy/bnp206ePap57rF+an5GNQ6ZNi8851jw/bB4zLwZ6HXB/+nyYh8ioV4Lp6c07wubt/vw2R5qPwn7b2nwyCNu616NXXapXX9Ov23werg/TNG2MKVs3zR/O1iqL4UbjAqbZZ0f6vK1DzNvGL6+V2LWP+JHthnl98HoIz8fxTWyutfqwuGHUD470x1u8TCx+tXXOfBeJrTMchxbTsS7ZlhbfW34sr42N0biBMH+udUz3cQLLbp6b1s/Mb2n03mY0/ifmTWbeaBcuXNhI27xmafO5YVsxvraxZH5gfX6jfkeEdTXqGcS25bUzxrJ5x3we+7ZY9bPcc0sIIYQQQgghhBBCeLskD4RCCCGEEEIIIYQQtow8EAohhBBCCCGEEELYMg6Eh1BrbUNzN+pbY1pKy4/Hm+eCaRWpnVzTE5pmlueiVpBlNe0iGfWjGPXsMQ8eu/5RPT11qawv07FSN0stJjGvE6b78prmmG3Jure2Ni09PXaoUzUfllEvAvYF0xibHp8aY3oAWVvY2DH9vs1DTK9pxlmWUb20jZP9aqZZPmK+OCy/+ayN9mVqrm0eszWBfdM037am9GmrG9P2E/NZ4LiyNYXj2OYF2x7eNljrRza/jPrQjPrlEZtbLd330f36mJjXCNlvzGPXZjGWrePmx8TrY8zEudHiHM7dXHeZH69vNO7o97d11TxqRuvefAUZwzA+tfxYPrbN6Fpg6+7oOj8aR4yOTfPl7PMzvyhut35n/ZRY3Y76UTEeZl2wb3G7xUA2z1l61I/V+raNRV5PPxZ4LvOYM88gw7x0ea2sa4u5yNo4WlvP8oZQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW0YeCIUQQgghhBBCCCFsGQfCQ4iYZ5DtT0yfbvp26vVMc03WNNnUVZp/hOkuTb8+WlZeKz11rLysO2ojTXNtOltqK1k+5ke9OjXW1Gxb+QmPX/OtYV2Z5w0x/bthdU/Y1qMa63Pnzm2k2fdNo2weS6Yxpsaa+dk8wPOZvn3N82nUg8Ou3TyDbA4l1JuzrcxDh32X5TO/KKtr1pf5U436OzF/nv+OO+7YSPP6jx07tpE+fPjwnnnZuDMvMfOxsPVrdI4m5tNm1xeuP9M0bfQDjg/zMLA2Ni9C9nHzaLAYzjwY1jyERj29uK6snWu3/IjN5eZXZuORMD/GSOb9QXi9Nt/Y9bA83M76t7m7L795h4x61FgMZr4vjF/ZdhbfEvMI4nbr++bDaPVhY8nmAYsTRvynuM38mezco/dW5mFkdcm+y7ajR6jdO3KcnT17dnV/9iXz5TG/LIvxbJ7rY6zd8uuPtznS1j+Lv0efIZi3rfk8sm+s+bjFQyiEEEIIIYQQQgghXCQPhEIIIYQQQgghhBC2jDwQCiGEEEIIIYQQQtgyDoyHUK+BM42y6cdNn2deIdTvmX8H9XvUG675AJmW0cpqdWF1adp+lsfqlnB/05fb9bAtqKmmT41dD8vDtjfvBJaPulvqWnvdLLcR87Ax/bu1releR/savQZOnTq1Wl6mzZOI5aVOlvp26vMNXt+oXn3Ep8fGtbUlMf065yDrC+ZjwXFkWn/rq2xLlo/jfNR7zLwKbKxQX28eKmteCeYhZF5U5hlk48zyGz2f9aX+euMndDChh5DNF+bvYNtt/HF+Mb88GzNr6/ioNxyvjXMXj+e6ZF4gtjbwfLw2mxtHPcbMo3O/MSTjBltrWL8Wg655q7CsjP/MQ8fGCeF2u3auO1a3tk6yvKP+b+bfxLSNDe7P8pmHKPO3eWXNP8riO/NfIuZLYx47Fm9b+SymZN3R04ceRDyefZeYP5XFLQbnLfZ9iwF7bE62OczGlcVgdl85Wj6r673IG0IhhBBCCCGEEEIIW0YeCIUQQgghhBBCCCFsGXkgFEIIIYQQQgghhLBlHAgPoWmaNjRxps0kpnMl1NdRN2zaUmKab2ob++ujTpO6TpbN9OyjviajvjOmgzXt5Gh61M+C18PysD6pg2V+1Ixzf6ZN99uXh21nadN7s++QUd8F07lS307/JPNxIDaOTK9OHS+3m3+J6d2Jja013wu7Vttu48D04+Z9wHHIumT+pq+3ecF8MLi/aa7NM4jY2LOxwfpY8yYzTzqbs0ybb21vbW3j/MKFC6vl5fn6vhMPoYNJa22j31h/tzZnHzIfHRvv5mdnfnFraxmPHV0HOT5HsbnL/JXMZ9HiBltbzB+KmE8kGfW1sfpgX+P+/fzFuZcxgK0D+42vbTvLw7mYdT26zq553VW5Hxbrj9fD/Xk9vP/h+cyP1bwLR9ab0bbkuUc9Ms1X0Y63+N9iQubPtjh27NhqfuZFxnmV98G8dxqNU2xeWdt/1AeR2Bw6Wlej3rXmY7fmO7f2fCRvCIUQQgghhBBCCCFsGXkgFEIIIYQQQgghhLBl5IFQCCGEEEIIIYQQwpZxYDyEej2heSKYPo+YrtQ01NQimreI5d9rQalDpTbw8OHDG+lR3aqVhdfC/c17w9rG9OTmHTDqe0NMbz6qQ6XOltpP06Sv1adp59k37nnPe26kzTvL+rnVpXkCWV/k+Vl35t/EurN5wrxUzNeCmK7Yzt+nr3Y/Z12s+Rftdn5iniCmB7e+YP5Ypqe3OZ+MeqKwfFYezsusn1Xdtvg4mEeI9RWbo80jiNfCtK05/RoWD6GDyTRNG/3GPMJGfRrN88t8tWxuZf70vyBr84fNLeYXtt/xzGtn/rZW2HxgMaGV13x0LKa1460+R+MWxmD9fLUWm1d5vE1GYzDzizPPS9tu67bFRPSB4f0I9zefHcbPVv/mB0VYn2vtafc61u84bs3/yfyjiHkA8fwWb9s8Yn3f0rwfYRxx/vz51fJajGX1ZTFuP1bMl8z8njjOLP5eK0uVe2ia35LdC60du3HcnltCCCGEEEIIIYQQwtsleSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnEgPISqxjRvppE2fw7TWprngun3qD9c06Zebe+QUb8luzZq8alBJqb/3m/b8Xi7HtYPNdGmS2XbmceTXe+aj4z5G1ldjOrbzZeF56OOlRph6mKpGebxpmGmBpv686NHj26k7fqsfng+aqKJ6dnZV/r8uY2MjmPzjSE2jkY9P1h35nNjfe/s2bMbafrcmOabmO+EzenmdTbij2U+C0wTbje9u62fpncf9TJjfr0PhPXLcH2YpmmjXUfnD+uD5sllfh3EvEfMp6Yvn3l1WFltbiQcvxaD2Hg1v7j9ehhZftbWo2sL4fVzPhr1q+rnK2tLrmtWl7YOmU+keZUwviaMubhu2tzO62OMZfEu42umzXOIsP6J3b+wPfr9bZzbvGA+NBYDEbsW8wAajVl4L8d4nljMZ+OOfcviCh7P8q61bdWl9dWX19YH1j2v1foOYVl47WR0TbA5um+7eAiFEEIIIYQQQgghhIvkgVAIIYQQQgghhBDClpEHQiGEEEIIIYQQQghbxoHwEHrLW96yoXWlPs98ci4n/x7zVKBekHo/O9707b2+zzSyphUcLQu1haM6WmqC6ZVhmmLWpfnK2PVbX+B2aphZ/6YbZv2NapxHNMzmecO6MC8PanRNv07N7rlz51a3U4NM/TrPz77BurTt5hnE8jE/02QT9h3TbHPc9/vzWkb7tbXdqC+GaarNZ8LmSNPfcx5g2rwPTM9vPhVrc/Ru+VtfWdt/dE7mtbPv0NfB6t7akuOE+9s8w+399Zk2PhwMzL/O/BpG/S5GPcnMm4T7c4z1x/NaGdOY34P5EtrcYz41zM/8mIj54thcb/MFz0//t1EsBhz1WCN9fmxbW5dtjTf/NluX2LYWs9EziDEPx6Hd64z63lj8a35Uo33RvMZG7oes7Nb2FmNZfrw2i2FGYxLOY+xL5mvD+J19i9uZ5v0Ajx/19mX5mB+3r8U11k9sziXW9nafzL5o5SHWN/trXytr3hAKIYQQQgghhBBC2DLyQCiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQt48B4CPUaO2olTXtpngvUEpqHw6gHA7fb/r1ekPpz01War4zpu5nm/kxT+zjq8WOaaisvMd8WazumR4+3/c0Tqa8P69ej/lKmQbZ+SU0xNbqjGmArr3mX0BfCNMXWFubVwPIQK7/5C/TlsXFnmmC2nZXV9PA2ri1/08NTg82+wrYyHw+bF+18x44d20ibbw718exLdv6+/BxHo15iNqfaOLe2s35uc/BaW5mfSrh+9O1knmQ2vqyPjfpCmncft1uf7tM23kbiucs5t627Nrdb3MDxaj405tHD8zE/zt22dlicwPwZB3C79U22X19e+q+xLq1fm58T82NdcV3husf9R+uebce+wbq1cWReezZWbF6x+wHzNOX2tbFssTyxeNtiCGLzhPkmWkxnnpr0+KEf1ZkzZzbS7Kvse8yPaRu3Nq/afT7Lw/rq51l7BkCsn9p9/Gia49KeYYzEx2sxWN4QCiGEEEIIIYQQQtgy8kAohBBCCCGEEEIIYcs4MJKx/nU2vv50NSVaVZe+Hmavhdqnyvm62Min3e3TgnZtxF7Jt9eT7XPOLI/JCezT4UzztUV75ZaY/M9eqSXW10alOP1rk/Y6r0ma7DOL9kqqSb7slVD7tLC9fmwyAHtl1F67HP20pLXd6NhY+9Tk6CdKTVZk84KV3epuvxIz9pXR141Hy8++wzmcUgF+Kp2wftm2TK+9vm1z8Mhnk3c7F4+3cUhszWF+vB57dT0cTPp+xT5AuQFf/7d10GImk9CaHMNeuV+Lq3gs5wKL/1hXrBuTIrOuOJ5GJaXE6spiIJOsci4mo2vFqKSO9ce1ZW1/9ivWtUk3bF0krDtbR86dO7eRPn369EbaZDhW16Pxr83to/cvJoWxvm2SubV7Ryu7SUdHP1tv667J5awuGDNR4nX27NmNNO+1LM38iNkEELse1p/dS9q971pft7Yeua/b7Xi7zzZ5n90rmjSzn0MjGQshhBBCCCGEEEIIF8kDoRBCCCGEEEIIIYQtIw+EQgghhBBCCCGEELaMA+EhVLWpqTNtp32OmpgWc/TzgNQyjnoI9VBLaHp106OPeoFQg8uyj37WcfRz2bweentY29vnqe3T6/apcmJ6fPP56a+H5zbfE/NdsM8/W9uZnp3lNe+QUc8g8wawecE0x2TU28DKw/RaecwjyNrWfBxs3BPzbTP/qdG+Ro00+5p5IxDWF31ATp48uZE+evTo6v48Pz9Tz77PT7SufXLVPo/KY837ytaQUR+J0c/YkzV/GfOsCAeDtU/2Vrk/hHnvjfrBjXoUjXxC2vq7zcWcy2xdGvV55PltnRv1GBv14jNPT8ufmP8b50vzHjEPpr5vm9/okSNHVvNm27CsTJuXHrdbTGE+kOZjYzEd0+bzaHECt5v3Chn13VzzVLJY3uaQUe+u0ZiMsHycdzhnv/a1r91I00PIPkNvcz7T5qNovnCMyex+wuKctbFh942j/qyj/qN2H2rrq/nOkf7a1+bjvCEUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClpEHQiGEEEIIIYQQQghbxoHwEJqmaUMTZ5pr81QwXS+1n/SPGPXJMY+FNf0hNa88l+lYie3Pspje2nSwljavAPNbYn7mQ8P6M0206dvNt4b1Q63oGuZNxX5p5zb9NXWp5gHE/Mx7i33LfGCs/OZrM+pZZFjfM88gYt4PPTbnEGtrjotz586tno/5cTs11zZP2bijfp352fXZPEDPnxMnTmyk6Q3BsWYa8PPnz2+keX1r84DVFceNeZSwrpgfx8WoV5eNc/NtGPVKCG99pmna6PPmpcc+xz7LPsM+xf3pB8E+c+edd66en/4TIx5tXHdt3SPm92Djh/mzLoj5xY3GBaPHm8eSzf0WT5sfHfsC97f26uuXdc1+Y55BbHu7Ns7ldi0WQ3F/u3ey8rHtzSeGfYHrKLHrs7Fo9xt2vf3+Vhcsm3mJmc+M+THZ+Vn3jOlOnTq1kaav4ete97rV/Nk2XAPMu9DuX4jd35g/lcVsTPfXy3OxLm3c2bxgXl5Wl2ves7ttt5irT6/Nj3lDKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SHUWtvQ4I3qYM3vgcdzf/MeMd0qMQ16rz/kNuomzcOH28mo39GIBvdKsLoe9Z8YbXtupxbUPJBGtJpV633VvEBMkzzqDTKqPx/1g2J5uT+vl2keT00y06N1v18vk9GxsaY7tra2fm0+D3btVlbqx80rgeW7cOHCRpr6d3opWF8i3E5PoOPHj2+k6TFifdHG/ehY6/c3HwbzsWDdsi3MT8XGvc0DHIfWl/q+s9/1I1wbDh06tOHJYH5zFkOZj4vNX+xD5rU46m3SbzcPSm7n+DOfk9HxZTGM1ZV58Y3GMMT8Lmy+MS8+W2fNE3TEx5FtSU8h63fErp2eQeYfN+praF525nlpcYiNFWLzgs0jFidZfmueT+afyhjF/EyJeRBZ2Tmn3XHHHRtpegadPn16I00PIV4P295iSF4v44DDhw+v5sc5mGON+bEvjM6jrO++PukZZOOcbWkxDzHvLHvGwGvlHGhzfr99rax5QyiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQt44ofCLXW3qW19muttZe01l7cWvvS5d9vaa39UmvtT5f/nrx6xQ0hhBBCCCGEEEII+2U/HkJvqqovn6bpha21Y1X1+621X6qqz66qX5mm6T+21p5QVU+oqn+3lpF5CJmnEPV0hFpF0+eN6vlGdaw95u1hfhKWH7WF9CjidvOzsP2t7kY9ekb7gmmOqRW1tjKtJq+H2tA1LxHTX5tHjdWF+U+ZVn+/PjSjbU/MO2DUT2vUL4qYTpfnX/PhsHE56vc0cu7dYP6sax6/5o9Udame/ezZsxtparhZftaH6cOpP+ecT/06jze/rtG+T9a8c6yuiXkIWT+mNwGvlXVnWn7W3ZrfTDyEDi59Hx7tU+ZFsnauKo+pbC20tWrNM8y8QJi3edmZ3wPHE703bK5heTnX0ytk1NvPfHLMt8ba0tYOO79dz8hczHOdP39+z32rLl2XbF02fyPz3jNPIF47+wIZjZmYn3kiEV6/+XON+j6y75onU58/z22+hsR8YVg2u0+1mI0xEz2E2HcsTrB7G5b/2LFjG2m2JWMsHk9vQ+5vbW99fcTrl9fOso76rpkflcHjbU0idu39mrVWL1f8htA0Ta+ZpumFy/+fq6qXVtU7V9UnVdXTl92eXlWffKXnCCGEEEIIIYQQQghXn6viIdRau19VfWBV/V5V3Weaptcsm/66qu5zNc4RQgghhBBCCCGEEK4O+34g1Fo7WlU/XVWPn6ZpQxcwze9V7frOX2vtca21F7TWXmCvOYYQQgghhKtDH4NRRhhCCCGE7WE/HkLVWrtbzQ+DnjlN039Z/vlvWmvvOE3Ta1pr71hVf7vbsdM0PbWqnlpVdfz48anXspp2kFpDBjPmN2H69lGfHNO5rp2P25iXedJYWYn5qBDzVyJW96a3N18X0/Xa+U2babpa04wzzf378lG/bZpj02MTq2vTs5venGnTBJvO1XxzbH9qwK0vWF8yHxm214i/lM1pxOrGfBRsf3oBUH/Ovsg052Dmx7T5W7E+WNfmEWSeQuZDYf5cPP7IkSO1Rp+feQ+Q0TmUczzr2vyrWHc2Z7L8a3UXD6GDQx+D3XLLLRPHfA/7iMUR5k3CPmV+dszPPBs4Htd8s9h/OVcwTTg+bS7l+OK1sOzM38Yb82fd2brK421utPwIfXrs+izuIMxvrS/xXGy7UV9Eptnv7MGrxf92P2AemWwrjmtiPjbMz/xU6Zdl3i3WF2wsrMXrbCvz7jN4bvZzi3m4P9NnzpzZSDPetb5mvpPEPILoCUSPIYu5bB60eyu7/1k73uYUm0PMc5N1RWy9M183K+9a+pp4CLU51x+oqpdO0/Tt3abnVNVjl/9/bFU9+0rPEUIIIYQQQgghhBCuPvt5Q+jDquoxVfXHrbU/XP7tq6rqP1bVT7bWPq+q/qKqPn1/RQwhhBBCCCGEEEIIV5MrfiA0TdNvVdVe7x497ErzDSGEEEIIIYQQQgjXln15CF0tDh06tKGbNk8EaiO5nfo52059nnkCEWryqP9b83CghtfObRpZ8xAyrw7WjeleCcvP6zMt5qgvzqjn0KhXiOlYeT0jafODsu2jHjxWd3Y+092aB5Lpzc3zx3x2eD4bl8zPvFfMj8r8pvr8Rj0xzM+I12p1zbqiXp1eCiyf5We+btSPmwcJNdnUq5t+3bwZWB4bC4R9g14JfXuwrliWUV8Gps2ngtp/m9PNt8H8lfq01WO4PkzTtNGPbK2xudHmszV/tSr3uzD/D+ujvf+GzQW2pnO8WnzKsvNaua7wePN9GfUeZH7m42LrtPUNiwl5/Rbjjfr69OUzH0G7N7Dt5idF7NpH1zGLry0eHk2zr9o6avG53R+Nxpj9+UZjeZ6bafPkoecP247bz57d+EbTJf5Nlv+oDyLTJ0+e3EjfeuutG+kTJ06sHs8YyMa99SXmP+pn2/ct6yd2L2bnZr9k37A539qO49zGtd07XTzusvYKIYQQQgghhBBCCG835IFQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW8aB8BBqrW1o4kyvbn4W1CpSq0l9H/V25t9BvZ/pite0kqaNt7xZF3Zt9OKg9nDUZ8Y0wqa3J+bhY/pwO559Yb/+UZY/NeR9+9DnhFhdmf7aNMKmzee1EGvrUf8lO7/56Fhfs77DcU2d7ujYYfv25Rn1azJvAV47fSyYpj6d3gbmBWDjZHQ7r49p1j3TrD/q123OJqMeTuZ50vcF+h3ZemZzls1h5js36oXA83F/rr99XzXtfbg+tNZW24ZtzD5gfhE2F5v/m8WAPB/X3TWPBR7LuYLXxnXBPMCYpteHze3m0cO5yuZSMurlx+u3tcLiFPMmtLma5x+Jq0bXVfNAs3sNi6ksZiIWg62tQ1Vet7ZWcDvzt/mefnYs7377Gunrfz/3bVXumzbqr8TtnMOsvOa/RDivMaa65ZZbNtKMWyzGYtta/ZqPIzEPz7V5ZTSGItyfHpzEPPLMQ8h8Im097Ot27dryhlAIIYQQQgghhBDClpEHQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZB8JD6NChQxv6RepuqQWkNpHaQ2ovqS007SYxPbvpZI8cObJneVgW6lKJaR9ZNvORITzefGx47dQusu7ZVqbDZXnMh8b8N8yrwK6X5bP86RfQb2dZibWt+ZawLdjXTN/OtrN+zroZ9Yvi/nZ9pvu181t+PJ71ZRpt7t/3fRtnNi+wLVj3TJtnEOfM0b7HurB5xjx/OGdSv378+PHV84/6UphXmV2/eSz17cuy8lpZ99SX25xj/ZhtT8znwtYU6xvh4NFa2+hnNreaT435OLJP0r+C66b57rAPcj5bG88cf+fOnas1bO6w8ce0rZusS56PMZV5BnH7qAcZ69biZ/Ma4dzK+mffMd8f9qU17xebt0e98yyGsRhl1NfRYjTzlWR5zK+O2+kZZB6mHMfsu+Z3Z21v5+vTFuMQGyesW5vzuH3U19DulaytOU54X815kWm737C+b2sE5w1bg8iaDw/rhv3YfNXYr8zjcu0+cLf82VfMO9ji3TXvrJ68IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfAQaq1taN5ME019HPV61ANS87ymKd4pTw/1fzyemC64vx7qJnlt1I2O6ljJ61//+qH9eT6Wh21jdWM6WzuflY9txes1fTzLw+3UfppnEM/fl9d0quwLzMt8T0zvzX7KujZ9unkK2fHm/2S6WqbN58XKb+WhbpftQU205ddjXlvsZ+zHnOPOnz+/kWa/NK+DUcyrgPME+yL16dSzm28cz8+24PnNa4BtbX5fI/4AbDteG7eznxGbs8yzxPyouJ11b94BPeaTEK4P9BDiXMo+YeOb2Nx85syZ1fxsbWCaY2bN88jmec6lnIvJ6Lpm8abVrXl4WXlGPY7MR5Fpi2tYfosbLP7n/LQ2d9u1mEcN5zOLKdiW9Ekxb5NTp05tpK0vmtcX+8pavLpb2nxiLP7m2sG2ZPtYnGG+PX378FiOO8KyWV9Y87Cpcu8wuzex+1aOE8ZY7Hu33HLLapp90XxrzEvQYipi84T1xb5+rK3Mf9TKYrAu2RcYz/N85n9qXmN7kTeEQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DLyQCiEEEIIIYQQQghhyzgwHkK9ttX046YbJczPNNN2PM9vWlBqO3stpenLzftiVEfJslODbDrVUY8jYhpfno/bR+vLfHeo1dyvvp71uabn5zaWnWVj3ZNRTTMxfw+2DTXVNi5ZV3Y+0+cT07ezfMzf5hHzSlnzjWHa2ubChQur29kXzp07t5E2Lyv2Y/P4sTnQfN6oVz927NhGmn2J+3O7+XBYW4/2DfNSsHmjx/TfrEuOGx5Pzwxic6Z5qNj+Nq+szXnhYHDo0KGNMce5zMY/50b2UZu7mT/XUZu/bG1ci8k41/Lc5uti5zIvO5s77fwc/6OeYuZ1wvzMU9O8UthXGLfw+s1DyeY/nq9n1BPH1gWW3WIG877j+czD0zw47XrY8GYOIQAAIABJREFU10bXDh7Pccv6Me++UW8y7s++0dc/28L6kfVzXquNE7t3sLZnW3BeYPrmm2/eSJ84cWI1PerZQ2wNYEw36i9o96p2b9lj447Xwnsz1jXb0vyTrKx2n233kv32tXrOG0IhhBBCCCGEEEIIW0YeCIUQQgghhBBCCCFsGXkgFEIIIYQQQgghhLBlHAgPoUOHDtWRI0cupk3DbNpBpke9U5g/9YDmO0O9n+kJ146lNtH014TXzuOtLo8ePbqaH3Wwpjk2TTX3t+tj3ZrfhmnEiXmPjObX1/eoh82aBrbK9eSsG+ZnPizUWJv2f1QHS0yfzvrj9ZHRvkXsem2e6jXm5oFjHjenT5/eSHNO4rk57jivmK8FPX/YF2xe4vGcV3g+8ySxec08RYitIda2Nnb78o76wNm4ZNsb5u1l43JU699fXzyEDiaMwcyHxtZ1W6t4PMcr+xjnO6775r/BdD9mbOwTeg7ZeLa51dY55mc+jDb38Xyj66jFx+ZvYX2Lfcnm9lHvl758tq6Y76GNC3rh8dpZdqZZl6wL8+Yyzx/zqbEYcdQ/yjxIT506tZFm3+7nqKpxz6X+/CPed5eTpmfQqD8px5HFm+wr9OQxzyBuZ352H21+qaMxmMWQbFvOozYv9WmL3c3by+ZgK6v5DjM+t3HF/DlO+/KszRl5QyiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQtIw+EQgghhBBCCCGEELaMA+EhdMMNN2zoG6m1NL0404R6PtM2Uh9IfR71fqajJb3WlGUxTXPvQ7Ib1ImOaAurLtWhUstoulFqG813xvwqWB/Mn+UZ8XHZLX873trW6re/Xu7LfkUNL7czbZpb87oy3at5+DBtdWGaZF6fYWNnVD9vsH6svtfa3saF6aWJeQGYNxm9D+j5w3HNumd5qf3nPGN1aV4NxPqi+XQYtv/a2GXb2fpkdW3nZvr8+fOr+XHcjXoprHma2Hwfrg+HDh3aGPNcZzn+bO2w+YeYxwLnD+bP48+cObNavn7/Ue85eoWYH9KaZ2SV1yXnXsK2GfUUsu2MmVg+80Ih5p9nMTH7kvnq0Mulb2/z2DFvEfNTY/7md2QeRBwHr33tazfS5mUy4rFzOfvzfLYuc+zYPMH65/GsH6Z5PX35LP7mtZuXrN2XWt/idrt3Ygx38uTJ1TQ9gxiDse5ZHvNEMm80G/d2v0Ds3m0txrb1zOI7K7t5W/H8bAu7l7D8R+fkHfKGUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBkHwkPoxhtvrFtuueVimvo66kYvXLiwkTatIfNb05XuhulozVOBGvVe38e8TNd59uzZ1e3Mj5raEZ1l1aV1T20idazmazPisbPbdtNKsq2pwzXNuGmqTU9P1s7HfmH90Bj1N+L+5hFkOltra2t7O5/Vl7WF6f9tbLB+zDtszfPJ2mq/vhDmI0MNMn0q6EXG/TnubZ6hvtzGpflpma+bedWY7wSxvkDW1giWnW1tc5yx3/XNvL3YtuaZ0m+3fh+uD4cOHdrwJ2EcYuss+4jFJewzNjfbfMbxTq+VtbjC5hLzAOL+jBG4bjGmGvVTM98Vm6u4neWzddH2N+8+83+yvmF9xfpqvz/7Le8tzEPIrsViIKsb7s91l74wLK/5Mlp5rO9Y37T7A/Ylppkfxxav18ZuH3eYJ6eNk3Pnzq0eb/Gq+dgwRmJMxjmO29k3GKNZPMu+wHFpXr/Wd6yvW3xtfW2tr5rfqHnJ2vpk8ejotdm8YuO8T6/dJ+UNoRBCCCGEEEIIIYQtIw+EQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DIOhIfQDTfcsOEhRP0bNdbmkUCdKfV11OuZLtb8OkZ9cvrtpiWk9pC60lHPHfONMZ0sNbncnzpO05uz7sxPwzTco3p68xyi7pZ96c4779xIs77YPr0W1LTu1o/WvKmqLr1WjiPCtjUfFpbPxsmobwSvZ20cVV1afvNCML8r1h/nFfNcWtMN27g33zPr57b/aJrjmn2J44b6duvLpqG2vsPzjY6tUZ8dYvXfl9f8kiyvtTml6tI5yuZYmyPZtrb+rvnH7NcnLVwbbrjhhg0PCvO/4FzIPmGeZ+bhwPOxj7N83M4xsuYBYR5YzMvmMhvP5vfAmIMxBr1CRseU+VmYZ5DFkKw/8xyzud389EY9Pvv6Mt8S6wvmMcT8zTvP/JS4DrPu6U9ldUHY1oT52TprXl92v2Ixm61FHGt9mtvMz4nntnFifeP48eMbafaNY8eObaTNQ6i/h666tK9YfGpp88XZb4xnfcHippH7dNa13VvZHMOyWN/idpuHeDzXBPPi6vOLh1AIIYQQQgghhBBCuEgeCIUQQgghhBBCCCFsGXkgFEIIIYQQQgghhLBlHAgPoUOHDm3opk2DTV0v9YDm88I0z0dGvV3Mi6TX99m17tdnxvwmmN+FCxc20tTB8njqWM3bxHS63J/bCctjXgK23TTP1ramh+/zG/X2IKNaehtHpsc2fycr7369S4h5IZgXmI0tG5vmvbDWl3msYXpsXpt5Eew3bb4RTFtfN28D8/Chd4KNQ9PDW3nIiG/c6LGm5TcvMfNdI9xOvyj2c9PDx0Po4NNa25gzzNvE1kHGEba/zdXnz59f3T66Tvd92OZimwtYFvPqG/UiYby65ouyW9r8mmxtGfXq4/Vxf1s3LT/C66V3ytrcz7LYujka7x09enQjPepVwrqzcWh9ld5fXDfNS9D8oCxGsphuNKaytZH118M5xeYgls2OZ1nNh5HeYfQMsuOZZl+xcWU+lqN+WOYDyb5vcYblb/6wfV9g3tbvrZ9Zv+c44/rI/Ozezu7jrzTOyhtCIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwID6GqTY0c9XDHjh3bSFOPd/LkyY00NcXmb2GeQ+brQz0iWdMZmw8Ky2YaXuoyTWNs+nFqjqmJpq6V5TO/Cp7ftJyjvjqEx1t+5nXA+qSulrrg/vg1b6kq91kxbxDmbxpj8/QZ1ala+U1jbWPDfCSsr5v+f7T85tnUp9e07VXuLWDeXtY2pnc3/belmb95ANm4HPXoGdV8E5sXbd4iaz4l1rbsC7Ym2LXb/tbvzafCxmk4eBw6dGhjfbC50Maz9WFi3n5cRy3OsbWsL7/5NZBRzx0yGjMxzf0ZozFetvFsXh02tzM/W5dHPdKYtrXHPM0sRlzbl/2U8Z7FTObrQszHxuJl66uEbcFxZjGQea9YXGBrEccC65/Hsy/07Wn3bSP9pMp9EzmHMc1rYX6896J/66hvjM0z5r/K89m44/Fr90a7HW9jxdaoNf9W6wusC865PJ7PEOgZxOPt3o/Y+mqeQ3uRN4RCCCGEEEIIIYQQtow8EAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHLOBAeQocOHdrQT5qWkF4o3J/aS+pauZ16PGo5z58/v5GmPpCYJrvPn1pB6lbNj8H8IKhVNG2i1ZXpOunvZNp+Xo9pos2rg9fL8o96iVh5TRNO3W+PtS3Lat4d9Ca4+eabN9LmW8NrYdvx2kzrP+qBxOs3Hxnb3/Tz7Es2D3C7jaW1vsKyjXpt2bga9eyx/Nl3TJNsdWP7j16f+YCM6s/Z1rYmcd5bG2t2LmrxLc05j2WhvpzrF/e3eYfbre/21zfaL8Jbh0OHDm2sVezv7HOcG4nFNeyTtq4eP358I80+az43a+s8y8LxaPEc1xGLaXg+89og5gFk6xTnJpsrLQ4YPT8x3xmL90e8+6o2+yL7NeveYgzzfzI/JfM3tXE26r/E/M2D1Pom247nMw9Q1j8xjyGbR9bKY54z5j1l6x7j8VF/VNa1xefmX8W2N78l7s84w+ZBu1fjHG7+V+ZxxPzWvBa5rx1rPsM8np5B1m+JzYHmq8a+crl+WHlDKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SE0TdOGXpC62VFN86gXiXm5mNaTWkvTpPfnMx3okSNHVstKXadpFU0na9dOLeXhw4dX82P5TAc76l/B/Gx/072aLti8VSz/vnymA2XbmT6bOlPrt9bWrFv2PRtX5k3A9GhftfKYN8GoBpv5Mz+bh/o08+a1W90zzf3ZF0z7bz4YrCvTc1vbmTeA9d3RvkKNt/mCEPMUMp+Qfn+7ds6Z1KtTn3727NmNNOdo8xAy7y9bH82brN/f5tdwfWitbYwB830x3xbzUjHPMPNyGfWN4fjtMW8+xqPmjWfedaNefuYZxPF96tSpjTTLb14dxK531L/N/C7MD8q8AC1G6+dH1iWvxTw6bV20GMLmwzNnzmykR31VzKuEfWPUu8/qetRHcnRdt7G3tp3bRvulef6YX5PdR9ocam3H8pgnknkq2b0aMa8xlp9xi90fmS8d8+u9gNn2nDPNl9F8H60uyeh9qXnjjnqUXtzvsvYKIYQQQgghhBBCCG835IFQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW8aB8BAipgc3bxBi3irUA5p2k/o96gupZaTnw7lz5y7+v+lYTQtvZWH+5gdhenUez/ON+jvRI4meRHa8ta1pkI3R/U3/32+/6aabNraZfpvXZt4APDfPZz4tphEmpl9n21lfHfWhIOZFYPXL8ps/gNHXJ/Pu9c27wX7Ftjd/pTUPjd32t75o84qlR30rzDuNfdX05eYJZOXh9dtYXDsXy0o9OsdJv37stp11w7T1a2L6dfOF6693dD4Nbz36trG50Xxc2Ce4rpO18bLb9jV/tqpL+yjn1378m8+gzfvmdcG5gmXjtZkPCz3AOJ8w3rTxyxiKac4XvB4b05wb2TfM387WIosz1vw0zJfE1gkez7nWvOasb1j8bZ45ox5G5kUyerylbZ0fjQGtvtb6qnnemOePeQaZR5B5AJnXoN172bzF481fy+ZN69ucp0b9b3n9jJuYXz9vrq0Hu223ecHOPXrvYfe55uO2Nmcy743j9twSQgghhBBCCCGEEN4uyQOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SHUWtvQvJmOlJjvDvV2ptcj3J9eLNRG8nzUz/fnO3369MY283cwTbP5Lxmm0zRt/qgfhelUWXfmF3X06NHV89nxo14iLC91v2zP/njTR494c1xO2nSw7CtWF+atwPxN90ovlNG+bJpp05Nb/qb5tvpd25fYuLa2NZ8IjpMTJ05spNn37Pz71fKP9lXOS2fPnl3dbppuK/+ov9daXzJPDvO1oIcI8+N28/ywecg8SLh9zcctHkIHl5EYzHxerM8QG4/m1WJxB8vbz4fskzZ+CK+N+Zmnjfknmbce/Ss4X9jxtjYQu17zVqGnkPlBWV9jfuw79Crp53ZbV9bityrvt8T8k8wTyOJZ8wAy7xNrG7s/sLjG1tHROMB8bdh3+rStRbZO2rpp/q92X8njra+wLUY9Qs0/1u49Ld42z1Drq9bXzHuxTzMv+jLy3BaT2bgd9ZNiX7B43ObQvi3W+n3eEAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC3jQHgIkVFt5369RcxTyLSkPJ5aRnLzzTfvWTbTNlK7aDpT8+wxTDdqulirK5bftJzUu7MtTQNtOlfT85uu1vT8vSbd9M/mI2PeBla3pnM1zbT5NpiulrDtTC9vfdk8kFg/5nNjY4vHm//ASF6maWa/pUbZ2tbq0spnenDrC9b36evB9KlTpzbSpjc3rwfrq6bptvpaO3bUN4HjenT9NF8LXottZ1/s97c5I1w/+j5q43fNm6Pq0rmcc6Hlz/HL7eyDxLxeemy8mFeF+TlwrrIYyPzXbK62GMf25/WaRw/h/uaXweunfwaxOISeQex7/XZei8VvxGI4mxttXeI6ZD4yNo6sL5mnkPVVnt98YwjLS0a9/tbu/Y4dO7aat63pxGIy1iXbknVz5syZjTSv1WI88zlk3bDt2RY275jnD7H7GZsHWd61cV61eb02LszLyuYFMuoPa/c+1s/XvG3jIRRCCCGEEEIIIYQQLpIHQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZB8ZDqNe1mfcHMR2taUGpt6PWkvo98zAyTXmf5r5/93d/t5E+evRorWF67rVzV7lW3zTC5i3A83H/US0kOXz48Eaa18P8TedrHk2sb+ZHXSs11L2u1fq5ta3puU2/bW1BjbL5M41qkplm/qMeS+aFwO30KmD9jY5z05j3GmfztjJGfV1Mw0xvLpsnrC8RG9fMz/T0d9xxx0aafY1tSX058x/1EjO/gbX6MJ+1/ZbdPILYtjbObb3kOOL1HTlyZM9zh4PBNE0b/Yh9ynxgzDvFPIU4fs1nx2IyshYHWQzCsjIv80PjtZNRHxmbe20tsHWM+dMDyXwb2Za2NrBvmc8MYf0x7mG6L595h1hbWN1aPE3Ydja3j/YNy4993WIui/mYH9uCcP9RPzzzmel9g3gu8/ixdY79mMdzO/OzvmjxvN3LjPpZ8Xw2Z58/f34jPRojmVfimh/rbvmtrRHmr2RltfXHYqzR89m9icV4No52yBtCIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwYD6FeM2c+LeYRtJZ3levfzefgcvV4e7HmF0JtInWc1J3a/qb/Nj8L00yb34W1ncH9mZ/p/3l95g1CqP00XSy9Vqiz7duDmlvWpfnAjPrOmOeN+T6M6tM5rlg3pqdneSx/839i/XGs2P5sS9Pvr/UVXrv5Moxqkpmmxpj5c3+bM01/znmA12s+ERwb3J99ybwPrL5HfXi4/4iXhPVT84cyzxDrx1ZWYnMw16Q1n7b9rp3h2tBa22gb86GxPjHqc8P5xManrfN2fL8/fQiZF2Mo8wyyucdiqNFrJ6PehLa/eYfQf45taz6QNh+OxhnmY9O3n/VrW9PNY9M8bVhX5hVi66p56tg6bH1vtL7MF4b1MTJuq3xtXItb2G/JqKcm10GL4Sy+Nb+m0XnHxpXF62wrps+ePbuRNo8jnp/zLBn1jSP9/hbPmq+Z+bDtd862ujZvYPP824u8IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWse8HQq21G1prf9Ba+9kl/e6ttd9rrb2itfYTrbW7Wx4hhBBCCCGEEEII4a3H1fAQ+tKqemlVHV/S31RV3zFN04+31v5TVX1eVX3fWgbTNG1oY0d9XswTwXSw5pViXivU71Hny/P3WlPzM6JGllAneuHChdWymTeHaaCZNg2y6VzteCuf6WCppTQfHuZHfwHT+1N3S5+avn2oCzUfllFvAPZr8xYx7b+Vl3Acm2cP6850r6N9xXxrWB7Tv1vfJX1ftH7EtjD9um1nfjavWF/jtZu3AeuOfYF96/Tp06v783yGeQLxelmf9Afg9dia0NcXr5XwXMT8XUb7pc1htp5yO/veSFnC9WGapo22sRhs1FPIxofFCebraF6Fa54L5rVhZeVcZD6KzJ9eJvTS4PHmC2nzizHq/0TMG5DXz/Jy/rF1m+3B+ljzgOJcNeohxP3Nb8nifct/v/Erz2fXa+uceRRZffB83M55yMYm8+PY6svPsh45cmQjbT6NzNt8HG1OtWsd9cqyGM7iWebP+jLPH8IYjlhMZ97C5OjRo3sez3681k92g3PUqKcm87c1xPyheP61Zxhr/WJfbwi11m6rqk+oqv9nSbeq+qiqetayy9Or6pP3c44QQgghhBBCCCGEcHXZr2TsO6vqK6tq59HbrVV1epqmncdZt1fVO+92YGvtca21F7TWXnDmzJl9FiOEEEIIIVwOfQzGL8SEEEIIYXu44gdCrbVPrKq/nabp96/k+GmanjpN00OmaXrIiRMnrrQYIYQQQghhgD4GO378uB8QQgghhLdL9uMh9GFV9S9aax9fVTfV7CH0XVV1c2vtxuUtoduq6i8to2maNnRtpos1bST1eabNZH7U53G7aUHNW6XPj2U13Sr3p86TukrzGuHbWWt6690wfyXzEjHPIdM423bTbo4eb+WlTnbNU8j8mczLg23NvmI+NeaNwLpg3zE/EPN3srYgvH6Wj9ttnJsul21v9W/0+fNcHKfMm74y1DybR5DNI4bNkTbO2Zc5z/ANBe5vGmuWj9dn9WP+Aax/Xi/7onkx9LDs7Au8VpbNtP+2HrFfE/PRMD+ovvy2noTrR9+ubGOLeSxG4ngxLxTzBmH+5gHGdN9HOT5GYy7z9rC5gfD85hnE8Wd+TMTawjCPMV4vy8u2Gc3P+spazGleHYb5rFjbmx8b9+e6NRrz8Hhbt60+Rn1oDItpR3142Lf66zdvL/ov8aG5+ZPaujo6x7KvWj+3vmj3G8TOZ+Uf9b0ZjWmZZvv18zj3tXNZvMy2t3mBdUfvX1vP7N6EddnHr2v98orfEJqm6YnTNN02TdP9qupRVfWr0zQ9uqp+raoeuez22Kp69pWeI4QQQgghhBBCCCFcffb92fld+HdV9W9aa6+o2VPoB67BOUIIIYQQQgghhBDCFXI1Pjtf0zQ9r6qet/z/q6rqg69GviGEEEIIIYQQQgjh6nNVHgjtl9bahkbPvDq4nXo87k/9H4+ndpLaSjvedMIsT38+0/haWZjmuZg2HxU7H7WRpjcf9S5h+cwrxPwrTHdq+nT2Das/6v3XdLzMy/yfrK+M1u2Iz8lumAbY+ob5RRk83vT0hG3LtPlW0MeH6bXjrazmYcPt1vb7HYesa/P0YZoaaaZN/07Mv4nXy7F09OjRjTT15tzO/Djv2JrQX4/5Loz6r5iPHI+3vmfeY6OeJP2cOOpPEt46tNY2xoj5No764HC+srWP2Npg3iprfnUWMxHz+OJcYjEGj7fxzbmJdcu51bw9iMWAbAvub14hXGcthjXY1pybrbxr28yvzdrOYiQeT0b9TS0uYNrGMevDPDXZFhyX1vYW8xLmb3FL7xVo8TbjOfMZNB81a2vzmbH7TMt/tK9ZfG73lrY/05w3WX+MgS0eX7uX5jaLr7k/txMbpxwH9PJleUbuK3crX7//2pi6FpKxEEIIIYQQQgghhHCAyQOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SFUtaknHPUSMR3rmofPbttNZzvqm0P6/Mz/yPwjTN/NsnP/Y8eObaTNv4J6dqbNk8j8K1he6kqZv+lsqZu1+iKj+TPN/fvz8dpNc2t68P16CrGtmOb+9Crg/qP6bsL6sfKYHt78odh21j4cK0yvtSfLQs0w9eymeTZ9tWmgzT+JdUNNM30b2DeYP+cN66vEPJF4vZxHqDc/ceLERprzIvOnjwexvtpjenDr56xb9oVRDyHmx7Yk5tXV18Xo2h7eevRtYzEM1yaLM9hHOJ9wvDI/8+0Z9aHp87N4z7w0ON4sBmDZzPOHcyXhdvNJJHZ9VvfmLzfqgzO67pqv5FqcYzGG1Y31FRtH7HvWdtaW5sFp+7PuRtvS1j3zWBr1heTY4bq+FkNzjeeazjQ9hBhTjK5tdu8weu9lbW993dZx87lkWzJGZNvw/JxnWN8cK+azueZjZ2VnXlyfWNbRew/WDfvS+fPnV/dnv+f2K/VqzBtCIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwID6HW2obWc9TLxLSQ5gFkOt9R3bBppNc0zMS8Msx/wuqOuk7zEKLu1vwmTE9v3gPUAJtOl8ebvt3y5/Uzf9Noc/+1tjcNMHWro3p189Yy3wXzfTCPIPNCINaXR3xadisP+y7zY32YD4bp4/vj2ZaW5ji1/Y8fP76RtroyTbR5ClHzfObMmY20zZE8/6gXATXh1P9z3mJ9UsNt3g7m52V6/R7WLa/FfBrYj81nzfJj+uzZsxtp+kxwXK/lb+tduD601jb63aj/nPV/80pkfuYpZN4jI351PBfThNdm/mU2d9KvgtBLg+PdymPxLudem9vMM4kxoMXDox5BNl+NeChZ3qN1bTEU4f48v61D5o9KOHfzePMANe8ui2m5Vti4tvuRUW/Fvq9zG2MAxhCMqTguid170PdltK5sXiHmc2N+TdYXuT9jLpuTzceH8xTPZ76RPez3bEue2+6VrC5Y99y+5ndUdem12Ry+5gW2trbnDaEQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jDwQCiGEEEIIIYQQQtgyDoSHUNWmvtB0qEyb9pG6U6ZHyla1rkmuulTvR011D/V85qljZTHdqml+zYeG2knTNpq/BNOjmm3rK+Y1Ynp0y5/5Wf317Wv92vTg7CvmJ2UeQtaviWl62fbm4WNeAywv87e+YX1x1KuAsD5I31dMv079NfXs9MAxjTPrxjTH1LdbW7FuOe/YdvPcMa8G1qfp/1nfnMes/qyvrbWH9SPTt7MtzDNjdM3gvMHzs2/QY4ht2/eVUY+N8NaBPo7m8WMeQjZ32/i3tPlp2HzSj3fzijNPG5sbLEbgeDZvu9GYh1hMxfKaL6N5f1g8bmuX+dqYLxn7Wn+8zfO2DnEdtZjK+jHbnm1hbWM+NKx7Kw+xtuTx5hsz6q1iafO5WfMQMj8ixgzcbvdu7CuM8ei7yLbm9tF5YTTGMp8ai3msbWweMR8dG/drHkPmwWllN2x/O5/5mVpdr3lvra0PeUMohBBCCCGEEEIIYcvIA6EQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAPhITRN04ae0HSxux3fY/py81QwLaZ5t6xpF7n/qFaeukrzAhn1V6K+0OrSdKfcTh0udaQsP8/HtjINMqGe3Bj1YDIfmTX/KtaFaWaZ16g+e9QnxfyYCPsCr8f06tRpHc+MAAAgAElEQVS/s2/w+td0s1Xug0HMQ8jqj9fb+/7Ql4V6cqa5v/k0mDeZzVlMcxzQF+38+fMbafrMsG+O6s1ZHnoo0TOI8wzTPN76OjGvg7X6tLzN08R8Jayf2/nZV9iWdv413wrzWAvXj75fjPpP2NpEbLyQNV+qKo9T1taaUe+80RiJ+ZufmnnmjHrzMSaxdY+YXxvPb2vR6Lo8GofYut3nN+q3xmtn3VpbEV6b+drYusjj9+u/OnqvxL7PvmHeKhbj3XLLLRtp8zBdqz+7T2PMZTGL1TXzM68+1h37mvmtEpunzDPIPISs7lkf5uNo95ZM2/1Ev7+NM/PQY9vYuOJ280var9cW8+v7ytoYzBtCIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwIQT89hEwLSQ2c+cqY7na38qylTSNNqO/r9YnUj5u+m/ubttD8J5jfqN7d6obno3cHtZvmM2OaZvN74vlNV2u6Vupqzbul7wssOzW1Vrdnz57dSI96E1g/Np2qeepY3zRvA5bfNN/7LS8xfT/7NjXg9LXp+x7bmvuaZ5Bp5wnr1ry6DPO5MD8ltr355pi+3zyEzHPJ+o758Ix4oti4tn5rWv5RvyhbT1meEydObKTZFpyHev26jblw/ViLwcxnZzSmYp+0GMrWeSvvmh8F9zVfReZl6yzTHB/0Xzt9+vRG+syZM6vlo5cIy8PtLD/Hr8Vg5iHGuZf1x7SVn+1jMRn7BmO+vv7YFrbu8FotprG51bywRj1+WLfmjWL5ma/lml/cbvnb2mK+PeaDaWtb337mmWkxlvnIrJ276tJxwrbnOsvynDt3bvV4toXNuebRY/Hw2n3ubmm7V7SYze5nyNoas59jLwerC1vPLOazcdWXPx5CIYQQQgghhBBCCOEieSAUQgghhBBCCCGEsGUcCMlY1ebrbvaKrr2aZ6/yjX5CzmRc9lnNtf1HP7lpkil7NY2vHzM/voZon/6214nt887c3165tbS9kmuSMntdmftbfa+97myvPNonPu2VTab5arrJD+21e3u13l7vJfbao0lrTP7H+ra+YZ/bPXny5EaafZ2fju+3m+yG42BUwmSvwI6+qm6SDfvkKLG+xbqmJIz1x/oy7FX80VfRrW+vrSGjryNbWU36afIf+6zz6P5921m/CNcHyvZtHbP0qITM+uyoPMDWjj6/0U9xc+5kjMT0nXfeuZG+cOHC6v6MwSip4v4Wz1qMxrjAJFc83uSzNtfb8bwei3Osb/ZSe4s5LP5jDGVzuUmz7V5mdK0YXadsf4u3zfaAWF8bjRNsHum3m/TRxpFZk4zIeKourTv2LbYF97dxMirrt+2se5Mr2r2d5ce+wnl1JOa0fklsHIzeO1kMZtfC87Fu1+oykrEQQgghhBBCCCGEcJE8EAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHLODAeQj2mASb2uWvT+Y5+0nTUr2PNy2T0s5Kjn1k3LT717NSrmw6WmE8Lz2e+MNRGmn6dmI8M0+arY8eTtc9+8lrsXKwr6q3ZF0wTbNdOr4JRPT0xDbX1NfvMPNP2SVSrX/MqoK8N25MeQv32UQ8M5m2eHaO+a4R1xbYd1WCbN4F5BtmnjFlfpke3+jX9uqVtHl/D+rXlbZ9ztXHLumNf47ixujJPvXD9maZpo1+YJ4LFQPb5aR5va8foeLP5dS0vizdHYzDWBX0YmbZPcTPN/OlBZDGNefHZ9Y1+6tw8kCxmHfUs4vn6+c3WSfMXJaMeoLzW0Rhwv2mLqfbrO2OfOrdPn9u926jvY5+/xaMWH1vZWZd2POvSPI1sXbWYkcdbjMXyWczF+xP7LD3zt7F2/PjxjTTrl/eyfXvw2q2uOC5ZVqtb8/wZ8Z6t8rpf62tr98x5QyiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQtIw+EQgghhBBCCCGEELaMA+Eh9Ja3vGVD72faTkJ93pp2cLf8TTNNfSH1fNR2mqa5Lx/1fKYlNK8Q08Gaxtn8I6iNND29ee7w/OblYfp08w6gttK8DawvjWqa++Ot35iG1tqKdWXeA/R3Mg8e7s+6Nf34mr67atz3xjTZ5q9lPjc2bxCWv9cN25xi84K1vWHj2trePD1sDjevMOrPeb3cn/VHPbzNk1b/5qNh9d+3N4+1su3XV87yszmMmG/bmhfYiLdLeOvSt5v5OJqnkPXx0T5k8wvh/pzP1tYS83GxdYjroHnj2TponjrmJWIxGMtnnmI2X5k3oMV0lh/XzlHfm37tsHXP2pLYOmH+ajZ3s+1sHeJ21t2o5xHbipjXiY0dYjGY+e5w/zXPz1HvO8YgFs+PjmOLscx3zby+rC0sRhtNW0w1uiaM+lP1+Y/OUaNz5v9k711Ctt3yvLx1F02o+k57V3VD02ogDroDZhRpciAQQjoQ40QHImbUiNCTHCBOWjNxakggJAQMTTS2EDQiCToIASMJGUXoHDDGGGwMaktrq71rf6ddhKafDGq7uZ/re/e69n8/u+p7q5/rgqL2+u7TOvzXWv/75vn9XlujDPN9nHqQnvt6d22/EIqIiIiIiIiIuDP6IBQRERERERERcWf0QSgiIiIiIiIi4s54NB5Cn3zyyWdl06HyuGk9H3reGdPjmZ7drjcd7O5e5kc0rRvvR68O0yib/tt0rqaL5fOo6Wb9TadLrL5Tf4+pd8IZ03vbs60vpzpTegWwPm/fvr0qc2ynenbTt1ODbDpam2e839Q7xbxYGFs77xfzPeCaZj4y9mzrO/PNsFgjNi/NQ8m8AcxzyGLnVg+T6bqx6y+LBWJ9b14IU08+u/+t+2O8f+jjSCwmON845uafwfluc8C8AM1H5jz/bT4R3tvWSvOl4drGtYx1t32W93v16tVVmeN8zr3Xcu+PqW+j7dOGeQhZrOxyVttXmPPs5shDsO6MBVtLp1hsGuwPu/80B7N5ave3PIh7Hdt/znE5tuY3ZN6xrLvlm1N/KdbXchC2h7Fr/k7TnIwepby/zXvz7WF7+b7C9nEdPY+PzQsbK3sXmHjWfZHnTdc482v63Hp8obMiIiIiIiIiIuI3DH0QioiIiIiIiIi4M/ogFBERERERERFxZzwKD6G19ho7aq5Np2paRGozqa8zrSPrQ60i2Wm8zX/BPGumbTGvEdPwUjfKvp1q+81vwrSRpmdn/aw91NOb55B5Lu000jzXvLCmemvzN2Jb2HbzsZn6RvB+jBX25c5r64tg6wQ1yKZZNg8kXk/N93m8bJ6b7wv71vymeD+ONfXXEy+stVyjbH5Ltoabj5zp9Yl5N0z8oB6q385zxXzhDJvnvB/HeurTYF5gNva7uI/HweVyuVqPbV+z+cF9lTHKtdE8t6a+Wpxju7XcYtL818xLxNb66T7H66fPs+czn+VY29pKbH0h071uulfu1mbGKXME62vzsLGx5v2ZI9nzuQ/y+fYuZH1p89T2SWKxbPm7vRvy+C7H5ppE6JFDpu+R03cv61tbhwiPTz1+bF3gcWJzx55POFd3/rPcnyw/JTyfz2bOxb558uTJ9v63xhI5P393br8QioiIiIiIiIi4M/ogFBERERERERFxZ/RBKCIiIiIiIiLizngUHkLHcVzp+8wjwfwmjKmu1XSu1Cqa1vJ8/VRTTHg9y9TQmv57qu9m26jPtrGilpMaaPMisP4x/biNtenVzU9jp+Geeg+YrpRl03vb83mcPjPmO2PeI3b89evXV2XGkmmsqdNlLBHzAGL9dhrltd6NFZ6/g/OAZfOfsrg0Hwn2JcfafDSmXl+2xlt5qse3eT716eHY7jxYph4ctmaaFxjHzrT/5t9ivg5k4tcS7wd6CJmfA2Nwuk/Tp4YxZHmMeZfYfD6XzTfFvORsvlhdbB80rzoe575lPo8cK+41ls8SWy8sDzA/KN7Pnr/Lo2xs7VnWNvNhnL5rsC841tyHrD2WH9veY9ebB9PU62/aHu6F5/aY35CtafYuMc1Zpvm+eYra9da3Nla2Tk7rb2u6eS+y/lzHdvfe+Yo99Gxi+a3ly1Y/83syz7xzLOchFBERERERERERn9EHoYiIiIiIiIiIO6MPQhERERERERERd8aj8BAi1ChTHzfVMpr+z3wNTAtJTDt6vp6aX2oF2RemOzXfF9NlEupqpx5BrD+P0+eFus+pb4xptqf69mmZz995jZgXgelErW+JedqYD421baqJtnlN+HzTHN/qc2GacfME2vltTXxXHrqXtc3mCbHj7Bt6F0y9CKZ6cdNocx7bummxZt5kxObquX62XxHzlWDbrW95/XReEfMyONcvD6HHyXEcV2uIefuZtwnnw9S7hM+zvcP8L8g55i3HMdg2y/+I9QXbQl8Urg+2T3FttL3k2bNnD1X7c+vH/ph66fF+9KEhlvPtnmdeeeZTsvPqeKhu5pticcux5/WMZVu7zVvQ6s9YspyU/WP7tvlccjxsPM/3t5xk5/+51rs+aOZRyXnE86d+ppYXmHeg1ddyJFsneZzPZ39bLFneZHnOrj225k594WxNttgz7N3M+vrz6BdCERERERERERF3Rh+EIiIiIiIiIiLujD4IRURERERERETcGY/CQ+g4jivNnWkrp7pe8zoxPd/UT4LnU9d7vr/5nphu1Opu2nvqTFlXYnpt089TD86y+UeZd4DV1zTJu7F6qGzsNNimUzXNr2lq2VZq6alHZ13NW8GeN/UcmnqdmOfPVK/P9rG/2V/mVWCxv8O8sei9xbJp+dlWjh1hW+jxMZ2HUy8u1s88hWzemibcrp96Ep3ra54hU38jW5NsrEw/b3uOMd0/4/vP1772tStPDMYoY4jz0zyEzKvEfHZ2nlwPPc/2+XP7LMearpWsi5WZg7GvbN8z/wiOpeVc3EvolcK8wDzMrD3mnTjdW9ifHM9djmtxamuZreW3ehJZ3zBHYVttnll92NeE6wRjw/rDckpbZ8xf63w/i2set3ydcF5NvcksVqZeXeybp0+fXpXNh5L34zph/rLWfvMMIvbuafU5c2t+bB5BFgv2Xm1r5GTN3OWL/UIoIiIiIiIiIuLO6INQRERERERERMSd0QehiIiIiIiIiIg749F4CJ31fVNN9FRryesNag9Nc05Yn/Pzqbk1HaRp46kDpS6Wuk9rC+tj+m/zLqF+3dpvulLTPJsPDjHNtHmbsPz27dur8nnsTeO60z8/VFfGAp9tflHmLWI+K3Z8qr83GFscm5cvX16VOe+fP39+VTavFdbf9OqM3Qls23RdMK8uGzubZzzOvjXNNfuOen2uU4zdad+a/n/qizP1YtjtEeZHZHpy813jvLB5yvvZGmdjP91v4/vPcRxXuQPng+UF05ji3mM5nsWQeQpxPu7Wj12+tta7a8fU/4jXc21m31jf8nqrL71Dpj6Ntpaaj415lNl6Ms0r3rx587nXm9/R1FuOsO30YbFYsLGwHNJ8IQmvt742D05ie5ntVcxpic3zcywxp3jx4sW2bsxRiM1Dezea+hSal5h5iNoazef/yI/8yFWZ89zWOcYG31WJ+UfZuruLLfNxtLGw/HbqY2zzwtZ049z3eQhFRERERERERMRn9EEoIiIiIiIiIuLO6INQRERERERERMSd8Sg8hH7913/9Ss9p2kJ6fVATN9X3TfXsxDTQu/On2kDTkZpOk+dPtYqmoTYtpPmssL7U2Zr3h/ldENPhsmz9YT445+tNZ0qNs409NcLU+FpfTD1wzGuBz7O+sXljsWax8vHHH2/vTw24eR2wfZx7FjtnWPfXr19flTkW0zid6tGn896un/rOTO9Hff/Ux8baY2u89edZ727zZuqfYj4W1Nqb3woxrwPzlTtfn5/Q4+U8bhxz88Mw/wrbJ+3+dr9bPBnMO27qYWN1s7Wafc99hWud+c2ZHxT72ua3eXbyeczp2B/mWzONJfPtOWPrvJ1vOQf3cfYlrzfPTOuLqR+U5fuWYxG2j/B627enc8n2l/Nx80mcznvWxcaCPon0R2Lf0AuL65a9+7D+9r7A+rJ+9CKb+lLaHmO+dVPfuF1s2Jo2Pd/6gnWxsbT68PguJ8tDKCIiIiIiIiIiPqMPQhERERERERERd0YfhCIiIiIiIiIi7oxH4SF0uVyuNHPmx2A6UfM8MB2w6f1Mu2jax13dTAtPbaDpwXncNMdT7SKZ+tTcqlm2/mP9+Xz2J/XnU/06z+fzzvc3XxZrG2Fb6EFEnSqxvrJYsVg1rxGWzcPIYoNjyfOpiTZvA5Z3PjFrvduf5+fxWfQvsrrxfMK+Y9n01uYLYWvgzkdmLdfvU99u65p5hhA7Pt0DJnuM+cTZcfN9sHlh83rqKzfR01vb4nFg82Pq5ceYo68M12pbf6Y+W7v6mWfl1FvP1jbLObhP00vEPIuI+Uiabw7h2PL+bC+9SaaxMl3beT7rd66P5YuW45hfEdtuccrnmZ+p5fcWu1Mvr+k8nO51lhMSts9y3vP4WFzZvjb1vGFf8v7mm8a2vHz5cls/8+LiumIeQh9++OFV2caOsWJeZ3Y/zi1bZ3f5//S9duovNfGwW2v/jeCh822sd/XNQygiIiIiIiIiIj6jD0IREREREREREXdGH4QiIiIiIiIiIu6MR+MhdNa8UeNGzbV5d1jZdK5TbxPzbDCt6e5c0wRPfWaI6VxNq0hd66tXr7b1mbbP9OSEY8H6mcfRrR5GNj47HeskTtZyLwKOrfmyUGtv84R6bdaH9+fzpxpktofHzaOI8PzXr19flamxNh+cidcE78W+tDXL2sb7sy7mQUR4vdXX/KjYHt6PY2Fzg+219tkeYXsQzzdvt513gfk5kekaPz2fcM20ecz65xv0g8EkL5nuXeYVaHuN+UdMvVF25049tbjW7HwDHyqz7uYHYTmZ+TFZ35mnj+VENtaWT/N68ygi3EvIOSe0nGHqB8U45XGbR8S88yznYl/a+ZZzTd99GIvmUcR93/rTfHh264aNJeOOccX8kPB+9Px5/vz5Vdl8ZNg2y/dZX47Vmzdvrsrf/OY3r8rsH96P11vsmX+trQuMHcYK78f+OY8nj019hqeYxybrY/mkvcfucrZdPtYvhCIiIiIiIiIi7ow+CEVERERERERE3Bk3fRA6juPD4zj+3HEcf/04jv/7OI5/8TiObx3H8ReP4/gbn/7/N/1OERERERERERHx/eJWD6H/ZK31318ul99zHMc/sdZ6stb699daf+lyufzR4zj+0FrrD621fnZ3k+M4thpv05tT6znVZJu/xdTbxa4/l007b35J1B5O62I+KObbwr7lWPA4tY2moTYvFdOZ0kOIulNeT+2m1ddiZ6d5Nl2qaZxZt6n3lY2teRFYbPF51Fzz/uZVYGXztzKdsOmKnz59elXeeQQ9dP9z/5n3AOtunje8H/XeLE/X1OnYW19PvQDYXtP3M5YsFmzszRfH/KXO97N5OrnXQ5hnj61pFkumX9/5L1nd4/1wHMdVnFnMmU+MeZ2Yn4T5bHG+2vzf5QnTfXPqycW1h23h2m/14Xw1TyKeP/XCm+aQt/pxTOvP5z158uSqzNg7e59YW81DyMaKTP2TPvnkk219LBZvjQVeb7E89Wdl7HJvYXn6Lra73jwouabQK2wXV2u9G4eWU7EvuC4wFgz2lfW1vWu+ffv2qsz2me8j83/zZrN1kZgPz7m/zfeQZY69rXGGeW6a15i9O7Ht59jZ9eOX/oXQcRwfrLX+5bXWH//0If/f5XL59lrrd621fv7T035+rfW7v+wzIiIiIiIiIiLiq+cWydhvXWv9g7XWf3kcx/9+HMd/cRzH07XWj14ul1/+9Jy/t9b60YcuPo7jZ47j+IXjOH6BXxYjIiIi4nvDOQf7+OOP33d1IiIi4j1xywehH1pr/fa11h+7XC7/7FrrzfquPOwzLt/9bdKDv0+6XC4/d7lcfvJyufzks2fPbqhGRERERHxRzjnYBx988L6rExEREe+JWzyEfmmt9UuXy+Uvf1r+c+u7H4T+/nEcP3a5XH75OI4fW2v9it3oOI4rrarpYqmXM98d07US6u+om6W2cephxPufMU2vtc00vaZBnvpbmO7UNNfTsbLjfB7Hgokvz+fYUhdMHS1jw/T0O/2maXqtbGMz9R6xeWhja9ebLpfY/cyHYurNwP7g+DC22P87jyfTi/NarjHmqWN1s3lrfk2Me84L61u2j/enft+801g2n4mpX5VpzG0dnvizWN+Yp4f5NVmc06vA/Fms/uc11Dzj4v3w67/+61dr0DTGOD9tLzM/DYtZ2yssbzrX13Ica5v5Tdh8oxcHz3/58uVVmWv71GPM5ivLHAvzCrS13rz+2B7ujeb3ZnvFeXxv9U20nMI8bti3U/9T2ysstol5CbJvLedje1hf5g3sD5bp42jrEmPnXLa+53HGhnkE8VeX03yd+zDbwrEgrL/5Q3Gft7yDY2fz3N6bWT96DvFdzHK0nZ+r+Q3ZGmT5nvWd7W+2ppunEMfyi+ZgX/oXQpfL5e+ttf7OcRz/9Kf/9FNrrb+21voLa62f/vTffnqt9ee/7DMiIiIiIiIiIuKr59a/MvbvrLX+q0//wtjfXGv9/vXdj0x/9jiOP7DW+ltrrd974zMiIiIiIiIiIuIr5KYPQpfL5f9Ya/3kA4d+6pb7RkRERERERETE945bfyH0lXAcxzu66zOmg536tpivDpnqA+1+Z62o6TpNW0j9tfmsmL7czjdfFYP1n3qX8Pm8H3W9Ux8e9h/rx/42bwPqZM/tMS8C6ytifkq83u5n88h8JOx+5tVFbh1Lzi3T9drzOX6mKz6337wIqFkmPE69NevG803Pbmsqx9q0++xbm/fUz3PseJzzjEznAttv89x8Mc7XT/1ZzPfBxs708qY/53FisXv+K6J5CD1OjuO4iiNbW2/1YzMfR8vZbJ8mXA93OZp5Bk33ZfMfI2y75TSWj1peYfWzfZVl1pfPM78O83eb+k9ZXnGGbbE93vyYpnHMtk09giy2eL35UdlYWB5ge4flaLZPWx7B+51j03Iki1N62kzXAWI5jPkm2ruRvauxfvQIsjyEOah5NPGdn+03rzDLacm5faybvZuYjyLbYusIY8XqY/czb65zvrz7BnDLXxmLiIiIiIiIiIgfQPogFBERERERERFxZ/RBKCIiIiIiIiLizngUHkJf+9rXrvSDptM1fZ1pnqlVNN8dYp5Bk/qaRw6hztN0nVOtvh037aI9jzpSYn1rOlke51ib7w3L5l9l3iTU05+1qOarwuNTXxjrC5s3U/249Q11trzeNMNTHwrW32LLYoX1sfruYp19RZ8DG3vzhbHzbQ0l5qGz0+qv9a4G23wqCK8ntg6wf1m2eT8t77wfLA7NA4ixYx5A5oFi68rUD4bPP3st2DjH+2OXS9i4cf0xPzViPjbmnWI+Oqzf+f42n4i1ZeoLOd3nbCzMU8z6durPZPW3tXfqBcLj7F+2Z+cpxLWPdTMfFfNHmuZU5gtjXn42tuaJabFi/lnTvYvtoQ+N7T3MKc1v6jye1tfTd7GPPvpoe5xxzuez7Rwb+ibau5d52Rqs36tXr67KbA/r9/Tp06uy5fus7/Pnz6/K5qdlPjvn/mHcWf5o72pWN8Yp236rVxjvx3XrnIPtvKz6hVBERERERERExJ3RB6GIiIiIiIiIiDujD0IREREREREREXfGo/AQWutak0d9nun5TOtJbeFX7RlkGmvW96wH5DHqoakN5LOsbqZFtL7l81k/e/60r03byfawPuaXYfXhcep4qUkm5uVyrg+9NqZ+RSxbnLPuvN40yaYX5/nmW2P+WaZfN3g+Nc4cW4stjq2tQzutLrE1hLAvTc9t+mpbF0zzbFhf8H4Wm6bpNl8b6vXNP2q65+z2CD7L5oGNDcfe4tT8o6aeJoy1XeyYZ128P85zzHKc6d5jZYtJljl/zTPI1vrdtdP5wfnN+/HZXNssx+LzbO00jx3ez45bLHA9muactraaJxH3qp0nEvuO+4S1lcfNJ9G8QGyftBzK5tHUV9HWAcuXf/iHf/iqzNggbL/55thc2nmZcQ1hW82Lz96VeL6tC69fv96eT8zT0rx0rX2WY7J+loPxfBvL6fuOzYVz/aY+xLbmWl5z9vB5iOk8s3WLflZnT6E8hCIiIiIiIiIi4jP6IBQRERERERERcWf0QSgiIiIiIiIi4s54FB5Cx3Fc6Zynnj+mlSTmM0MtIss8n/rBiR7eNMbmkWO6SmsbMY2z+dKwvqYfJ7f6O1n7zIeG7TXdL2H7+byzztc0uFMvEJap52bdqAk27b9h9aHGmX1tY2deA2yveUGYx9Hz58+vyuYpRB3vDo49627z2DxyWFdiscbnMTamnj+E92N9Web9WV9qtE2/z7G32GN9zetht+5O22prFr0HGMfsC9O/2/5rHkY774GJr1Z8/ziO42oNMm+O6TgyJmwfNU8vWx9t3z6Xp2stMS8RYvuI5RAcC67lT58+3d7Pcjxrr2Frra1HbK/V3zySuLee22e+Ixwb8zV58uTJVXnqpzT11rJ3Ebsf8wb2Bz2RWF/zJGLfmw+l7cvmk2P1PZfNb8jWAdsH2besG7F3tannEbF5b/PQfHTMQ+jZs2dXZXt3m3iBreXvE7v9zea55UDm7WU+j+adazkgx5454bk9u/e6fiEUEREREREREXFn9EEoIiIiIiIiIuLO6INQRERERERERMSd8Sg8hNa61uRRL2d6vqmW0HS7PH/qScT773TKpuOkbtS8P1hX86egntC091OPH/NIYn1ML271mep+zRvB7sf2EdZ351Vi2vi3b99u60q9tnnk2PPNS8B0tKZ7JebVNR2LqWcQ+9vWBdMJ79YdjpV5GViZcbbTEK/17jy3sbd5Yp4i5mvD+tOLwTyWzG+LsUM9P2OHsWLrrmnIz8dtzcnDuq4AACAASURBVCIWlxxrm5e3eoPZ8d28mu4f8f3hcrlcxbStlZbzcH6ZP8XUT47w/rZWn9c/86YjjGGr+9SXkdj5t3ogca0lNtbTtfaTTz7ZHre9h8e5lxGef+5P83vivadjYTkC78d8394Ppn5K5udEzNdmmt/bXmK+ORwP1t/at/OPMg8cyznMi4vHmc/z+ZwXt/oYEmuP5dP2rmmxZ15htgfYXNj1n/mymVfW1IeRx7kG2p5h+5n5R53P3+03/UIoIiIiIiIiIuLO6INQRERERERERMSd0QehiIiIiIiIiIg749F4CJ11baYdNL0etYjEfHVM7256euP8PGsLdZ3mJ0FM0zxty9TnZaqf5/n0DmH9qHM1bad5n5j/k+mOzcvgPH5TjS+hRtbimGWLDauf6Wqn9zcPItM0m18UY4mabJ5PzTPn3lTPfr4/n8V7m5fW1EOIx80rzLy6zAvA9ObmXTb1mrH+5P2ouTYfCmq+rT27sunVzSOPMI7ND2o6r4iNPftysubFDwaWc5nHFssW4+Y5ZDndzivG8k1rm/mkTPc5ywk5v8wzzPZJYr6HU09NixXzZDJPJOZBEx8b3pv7gu1jxLy3pvuoee1NsdizHM3eD1g/iwV6BPF5FvsW2xzPc33sXcL8mew9k/e32OL9bM0znxn2Fdtn59u8Z/14vXmDWZ5ie8TU9+ccm8+ePdvei5in5NSv1LCxthxxV97td2VnERERERERERF3Rh+EIiIiIiIiIiLujD4IRURERERERETcGY/CQ+g4jitdm3kqmA8B9Xd2fOpDY3pAHt95iUw9c0z/TegvQf2gtXXqOTTVTNtx1n+nC11rrr202DI9u2lPdzpb87wxfTfP/+ijj67Kpuc27wSLvamu1Tx6iGmebSx4f/N/YpmaaM7NqS74jOnPzbvK/KHYlrdv316VqWefemmZlwLHhu1lX1p7iXkL2Fzh86f6fov9rU5b1hjWlfPY9gBb46yuNtbT88/tnXpDxfeP8ziZ1555kdBzyzzLpn4VNodsrT/X37xCzNeEWA5l+/rU84dts5zFPJGs/ZZHsD70syP0GrG9je2zfXmXUzIuP/jgg6sy626+KNxHWDfez/Yp5r/mH2VlyzumPi+MHeZcloPxevavxSLL9CTiOnQeD96bdWVfm2ck28ax57zg8y3H4/XsK/rimM/NkydPrso21ub9ZeuoeQJZvs/xmeZw5/tbW81HmHHFsWDZ3nX4fBs782/a9e3u+0m/EIqIiIiIiIiIuDP6IBQRERERERERcWf0QSgiIiIiIiIi4s54FB5ChmmCzSPBfF6o76P+jpo7ahWp8zV94rl+1hbTylvbzY/J+tb07nY9y6b151hQh2q6Wuv7qTeC+cSY9pP1P59vem2DfUEdKTXCbKv5OFCTbHG/88payzXEhO1hX5pXmMWq+djYOsC5ZP5fZ1h3ao6phSfUMJvPAuFYmS+brUtTf6bnz59vn8e+nGqozYPI/AEYu0+fPt3eb+Jrx3nHsZyuWbammx8U+5rzzPTsdvyM9VO8Hy6XyztxyeNnGLPTtdLWZstbbG8xD4jz+eYhScyTxzx3bB9j39jaxH152hfmpULMx4X1Yx7B+rH+0314N7Zr7fday3cJ6zb16LSxtuexvvQGtPtN83Orv+1V1j8837zHrMy9jDnw2cOJ51pbLGe61SvMvGen3mLm20hsHnMsLU/g2Ni6Yx5F3J/Ms4jryjmWre9sjTIvLsPWeBt79jXjfPcemodQRERERERERER8Rh+EIiIiIiIiIiLujD4IRURERERERETcGY/CQ+hyuVzpNaca4ak+nVpE07ubh4OVqVc8axmp66RO03Sf5g9hulDrW+NWDyJqMac6W9P7mzeB1df0/qbt3I29aWItrlhX+s5Ql8rnUac69XPi/c3Xhnr3qW+NxTbbZx5F5ktj6wLvb7G78/VhX5pvgnn0TGPL2mr+T4w963v6SpgnkPW9+XhY/xH2H9vHMp+/62+u8S9fvrwqcx6Z95cd5xrEutK/yrwACI/v9pA8hB4nx3Fs5wTnH+OfMWbrmfk22j5r+zjLu7XZ1kLLmcy3xdamqa+j1dd8Fs1fbpp/m0eR7U22b9peaO8D5n+1exbj3O499bzkccvnOW+mfnK2d/D5u/z1oftbbNpYcm/k88zTaJKnsG1812KcsO8t7swHjW21vjGPI17PfZ1lG1vGnpXZfx9++OHawfO5Z1gsMZbpo7N7HzG/pmnc21hN36vNf8m8uGz/+zzKziIiIiIiIiIi7ow+CEVERERERERE3Bl9EIqIiIiIiIiIuDMehYfQWtf6StPnmU7WfArM88B8Zwj1eeatctYZ81zTCBO2deo5RM2z6V5NY0zMB8YwXxjz7DEtp3kjcDxMy2la0fP1vNZ8X8z7yjS+5rvCMuPWdKmMve985zvb800vPu1r838ybtVMEz7/3F7zDjDvAfM/4rpiXl3mJ2W+EhZr1Hc/f/78qsz2sG9tbFk2fydbN7jusj5sD5+/84+il5Z5fX300UdXZc4rztOpT4+tiYT1Zd/s9gS7d7wfjuO4ihvz6rD1aOrfZmu7+fgQ81Q4t491NV8Uq6v5Dto+R6ael/Z8O872mX+G+UKaz6OtzRZrdv9d/5hPCde2qfce12Zb680/zvbBab5q83yag9k6wPp985vf3D6Pe6XlTaw/vQp36xLbwvyUbXv16tVVmbEy9S+1dx32Hd9VeL3l31PPIvPXokcRx4o5E8vmIUQsD+H9z+PJvrF82vxP2VbGjr2X2hpuvm08zrHJQygiIiIiIiIiIh6kD0IREREREREREXdGH4QiIiIiIiIiIu6MR+EhRP266e2mHgmmQeb9qcXk+dRKTrWiZ9gWav+IeQyZdp5tYVunPi5TTyDrm6l/FDGNtV3P/jVdrN1vp9E2vyHCsWVfTn1fzEeFsWiaYupsTd899atirBKeT03xztfloePmy2P9Y3PxzHTsTHPMvmLdqX83/Tjh/WwdsHWEfWljZ55DrB9jlcctFs0nw3xIzu2nr4F5zlGPzrJ5kZn/y5s3b7bnmx7dYvF8PA+hx8t5bGzts5zHfF4szzEsT7D6ns/nteajYp48hvmwWNtsHzVfSMthbK2zfY73t72BOZfdn7FjedTOI8n2FfMnsmdxX7K+Z9u51pvvjOXv5hlk/qwcS15P3xY+j/1hewnzbzvf5v3uXGLvTtO45ViZ997U2/b169drB6+3HImYL6O9X/B5jJXpez1hrLG/z/3FY7aG2zv/7lkP1Y2Yb53l/zxu+f/n0S+EIiIiIiIiIiLujD4IRURERERERETcGX0QioiIiIiIiIi4Mx6Fh9DlcrnS3JmW0HSvpr8zvbudb/4e1AtSK3mun9XF9N1TX5nvfOc7a4d5YRDT9Jq2klh7zUfG9Op23DTdOz36QzA2zv3JtljZYsP61uJ6Om+ouzXfF2KxRo02+4OaafOFIaYRt/ZxXvN59FQ61286z01DzL40HwdbR8x3xvTkhF4IHFvzGKKGe+phZF4Idj/bI3bzfK3r/rSxMz8U8xwy7yrOI3py2H7KsbC+yTfo8cMczOaT+cl9keedsb2Oa6+t3TYfz2WuTeaFYfPDcibL+dgXHAtbe20vsb3HsOvZfvPPsLFie7l32PN39zfvDotz9i1jya63d5mpr6J5l0x9GKf5P8eGcO+a7i3m9fLhhx9eldnec3ssJ2JdCPuGvoz23sh3MfPqYuzweq6RzD+nPpW8H4+zf3i+eQjZukYsr2DssX/Ox6f5qq0LnGfTfNfWSMYGczbL18+xsHsv6hdCERERERERERF3Rh+EIiIiIiIiIiLujD4IRURERERERETcGY/CQ2ittfUQMr04oX7PtIlTf4ipN8oO07GaXp0a2akXCK+nltE8iey46bl5vZXZHjLVpZp/h5WtvjsNto21eRdYX5u2nmNv847w+U+fPr0qU1NMzbHFMucxNdHm3WDeENTlTueeeS5R53vWUFPfvNO6P1Q2fTrbxnnNsWB9zNvA6mfz3mJv+jz2NdvPss216TpiXhRnpj4P5qXF620NN1+KaVstNuPxcxzH1ldhmoNNvUrMP8PWWovpna+NzX1bCy0nsuunPjD2fHqKmded+VmYv5OtlRYLxNYT7uvm9bLLE6b+Q5Yjmf/UJC4fOs62sC/Mg4j14/1sHrLMvYllxobF4jRfZ47J8osXL67K5/bavGRbLF9kDkKsr2/15jJ/Js7DqRegxTKx9xdiOaztATz+5s2bq/J5rti8Yl3Z11xjd35FDz1v6gXGdys+fzL2eQhFRERERERERMRn9EEoIiIiIiIiIuLO6INQRERERERERMSd8Sg9hKjHI9QOUjdKPZ759Ew14lMPhp2Pj2l0TXf5vfbusDK1koTaRj6PfWteKrzemHqdGNNY2N2ffUNevXp1Vba2U3PL+1vdphpdYp5GU6+U6VhTT8/ydKw5j6kRt3Vlt86YXtu8wayu5pFjY8PnmXcAsTV251ey1ruxbOvGVF9PLwOeT6aeKbvxM20/62I+DeYZNPU5s3XArt/5bkznYLwfbL3g2mrznfOP1/M4yza/LUZZv/P5lr+Zl5x5a7Cu5jlEpt57llcYtjdZf0y9T6zM9W66l+3Gx3wI+WyLK7bdfFDMz8nuT3i+7YNsH4/zes5b2zeJeRmaZxPh9fRa2c17elLSl8V8X2wsyXSNMp8Zy+/t3YT343u0eYPZ+wH7j/cnNtY2N+kZtMtpLedinO/8iB66H79hcB0g9s3AfNjM74rz4vPoF0IREREREREREXdGH4QiIiIiIiIiIu6MPghFRERERERERNwZj8JD6DiOK82baZSpl6O2kNpK898gpkk2zTnZabJNp2qY9nCqUzWPBx43jyHDdK+sL7WRppOdlqf1N83zRBNtvgvTODMNMJnOK84Lm7eEfcH62djT08d0tubxY/1HzI+Kmulz+8wHYRq37Evz2LAy+8I8cljmWLFMDbatY+YjYR4kHAs+n14EhunlyXm8zOuK9zbvLfPwmHqK2LxgrNg6dW77dH2N7x+7GOYxzmfz0bEYoZ+H+UDa+ml70bn+PMa6WM5BbC0wn5npWm85HjH/tV1frTX3eeRYmf8cMU8irp98Ps8/t9/yP+4b1leWf5tfkuXjrM+zZ8+253PszF9p6nFk3mCsD59nHkCMFYtNq9+5PrzW3o2mPoKsu11vY2/7sMWWzRPLkSxnmvpYmicnx9rqY3nUuUxPIPMjoieQ+TFZTkYsXzYsHz0/f/eNoV8IRURERERERETcGX0QioiIiIiIiIi4M/ogFBERERERERFxZzwKD6HL5bLVXVNb+OTJk6sytZJkqpM1LxHTcJOdb4zpyc3PiHWjDtR8Vfh83n/qKWQ6UtO3mwZ5qsOden3c6onEsd7FztQnwTyArG3sC/OR4fM4NqyvaXqJ9a1pps0TyHwm7HzTp/N6aqpZPt9v6tNCDTPn6cuXL6/KXAd4PTGfBvNC2K1xD92fmm3zp7J5TWzNn3qeWH/YnnJuL9vOdYD6dR43rwHT6hMeZ9n8X8x74Rx7tkbF++FyuVzNuek+aJ5gu/mw1rsxZ/5sxGLS5tAZ2xetbpbT2f0MW9s4FtwLbK2/1SvQjlss8PzpPs/+Zf+f729xzmdZXaf+peZPZX5wrB/9r+x9gceZJ5hflHkucSz47sayvf/w/vZutot9xuHz58+vyjz++vXrq7Lll/Zeau+dNk/Nt8byVcvXuSZbjmfvwdZe805kmXOR92d7znODfcdzmYPZPCKMa8svCeN2GivWN59HvxCKiIiIiIiIiLgz+iAUEREREREREXFn9EEoIiIiIiIiIuLOeBQeQsT8NEyDbZ4G1KmaFpTaRl5v7DTU1lbT7JqW0dpi3gATT5y13tVG2vXUZtJ3hXAseX9eb7pS02aaV4rpfHexy3MtrqzviXmN2POtr9mX1J+bptm8E0xna55GbA91w+Y1YOsE62fXn8vmD8Vr/9E/+kdXZfNZYNyyrhwr8wKwdcL8rVgf03BzHtu6Zz4S1l88Tg22+QOYvv1cZttfvXq1Lds8tDWYbeVx8xAyfxfri08++eSz//6iWvZ4v1hewvli88vyAM5fzhHzFrG9ZeelyLpM7237lO3D1rfmm2Jro7XPckKWbR8lU/8nO275vflXna9n3Sz/tHzcvO94nGNrOct5LV1rrWfPnl2V2Xaeb/mvxRrrwzzCcjDWj/1tOZblbOZhtPPTs7himT4zrAt9ZDgWt/oy2tjYPH769OlV2bwNORa35ozT9tr7i+WY51i1/NPmscWxeWOxr6xvze+J9d3ls1v/vM89EhERERERERERvyHpg1BERERERERExJ3RB6GIiIiIiIiIiDvjUXgIHcdxpYc0nxbzUDDtJKHezjTe5oNAPSD1fWdMP826maaW2kLTb5uHjvW9nW/69enz7Hoy9Q7ZjdVa7hcwidWpz4JhfWmaXdPkUgNNHe7EP2mtd/ueUKN9a6yab4X50Nj5LO/qY2PPvqf2/eXLl1dl88wwryvzQzIfB3ue6eVtHtq85PPMv4r3s9gl5jn09u3bq/K5fziPPv744+211le3+pqZx575Wpge/6zdz0PoB4NbfRTNY+jW87kemefDzmPNPCgtP7S1j/B62zemHjq8P9cm2+csT7B91MbO8hr6a0z3WVv/ztdP92GLBWu7eWFNx54ePvQuYV9++9vf3tZ36j9lfnXmkcSy5dc8zvbaunBur/kVMecyP1KOHb0ALUeY5iDTnMZyMB5nfTmWBvuXY2VzxeprvpmcG+cyj3ENN/8o8/xkW1+8eLG9/vnz51dl5oi8v/m6kXOs7vanfiEUEREREREREXFn3PRB6DiOf+84jv/rOI6/ehzHnz6O4+vHcfzW4zj+8nEcv3gcx399HMf+k29ERERERERERHxf+dKSseM4fvNa699da/22y+XyyXEcf3at9fvWWr9zrfUfXy6XP3Mcx3++1voDa60/Zvezn8ftsJ/G2bNMWmN/KtJ+Wrf7iZb9BHXy51PXevenaPxpHLGfxk1/Om5jYTIck/1wLPizTdbHfmZoP1G1n/Taz6l30hnGif2U3MaKTP90MGPLflJqP282WY09z37GyeP282eTRdlY2tiaRO5ctp/t28+XWXdbR9j3VndifcEy5yXHymLFfv7LP/Npsg32p8UOsZ9T8378Ofi5P9g3/Hmw7T8m8eJYcp1hLHEspvspj7M9r1+//sL3ivfD5XLZyqisbPu87RW2PpBbZf7nOWByVGI5iUksbR+e7qMm/ba+MIkb629SDcsr+DzKK0xSZu01znsB+4J1NwsEW4un+byNlUkhLUfhvmnvB9Ye+zPvvJ7PJ/Z+wL1r92fkH7rfuf4WRyYLMjkfz7exstjj2HPs7E+V2/uGzQVbB6Z/Sp1/9t7etSz/n/yZe/Yd2zrNsdgWSsQoCWNfPXv27KrMeXLOoR66nmNNS4lze76XkrEfWmt94ziOH1prPVlr/fJa619da/25T4///Frrd9/4jIiIiIiIiIiI+Ar50h+ELpfL311r/Udrrb+9vvsh6OO11v+61vr25XL5x5/ifmmt9ZtvrWRERERERERERHx1fOkPQsdxfHOt9bvWWr91rfWb1lpP11q/Y3D9zxzH8QvHcfwCf94UEREREd8bysEiIiJirdv+7Py/ttb6fy+Xyz9Ya63jOP6btda/tNb68DiOH/r0V0K/Za31dx+6+HK5/Nxa6+fWWusnfuInLmdNnmkVp3/60fTrpunm/e1P1pmWcXdv89IwnSe1h1N99VRTbX9+dvqnwe160+FO/QAI9fnT57G/dxps80GZ/qltYr4O0z+fy7bYn4VkmX3LMueV+bRYrJqHkHkKUadrf46X7GLX4sr069QcG+ZDMf3zt+wbjo31tXkB2J+zNX27xcZUL8/+ombbYvV8nM/ivcw3wnwhzMPOxtb8qdg2zlv+aeM3b9587r3i/XHOwX78x3/8cp5j5u1nfg6MQVt/zKPB4sZyRvM0mtzLcirLmQxbm2yttjIxzyHzhzMPMvvz3eZlYt4njKXp9Wcs7sn0T39bjmWeQza2PH/qA2leeeYhavWbzstpjmu+Nuf+sfyWbac33tTnzP7MveXP5kk09QgyX0nLC+ibY+8btq6YPy5h/3J8mJec1y0eM88g9gXLU28s877i823sLVa+KLd4CP3ttda/cBzHk+O7kfRTa62/ttb6H9dav+fTc356rfXnb3hGRERERERERER8xdziIfSX13fNo/+3tdb/+em9fm6t9bNrrT94HMcvrrV+eK31x7+CekZERERERERExFfELZKxdblc/sha64/gn//mWuufu+W+ERERERERERHxveOmD0JfFZfL5UoDZ7pc0yaartagPo/3M68S0/3uzqWWkfDZLL99+3b7bNO3U9dqmma2ndhxYpph81ph+8w3xzx/phpn0/uf608t/lTrT271FDJ9N/vavABMz24eQuYVMPWjYn0Z66YJNz286fF3+nVimmV6CJk3gPU9j0+1/7YG2jpnZdaXevmzT81D9eH5hPp16tFZ5vnmDXG+3vxUbH9j3FK/zrbz+JMnT7bns+5sG/uahsTcg87XT/fi+P5xjjOuB+bhZfPLvD64vnFOTOE+buvfGVuLLP+z+Wu+kZxvtheQqS+N5VCsj3kOWQ5m9bOxM3a+jWtdj715+U299cxPyXIi81GxseL9uFabXx2fb+8bU98YPn861jxu3mI2Hrt7mY+M5Z8WW7fmWMR8HM2j1N7tWJ/nz59v78c1ljmr5QIWqzz++vXrq/JHH310VT7nKdN8l/sb22Iem8zBzE+K9WFOxbZP8tNdv9/iIRQRERERERERET+A9EEoIiIiIiIiIuLO6INQRERERERERMSd8Sg8hNa61ieaZtv0dlO/il1d1npXT2jazImu1rT6pt23svlPmJeJ+aJMvQCoy7X7m/58qk83zbF5CE3Z+QfwWeZDMvWsMa8B8vTp0+39rC/t+abPt/ra882/iWNBz6JvfetbV2Xqfnm+9Q/n9ln3a94ENq++8Y1vbOtiPmzUIJt+nLFqa/Q0dk1/zvabhw/PN804r+dYs2weJ+yvc/9YXUxfTn06PYF4P8bK1DvLvLyoX7c1Nh4fx3FcxQHHmPODa6PlBYxZy5nMB2bqHbjbK80XkJi3iM3vqY+Wnc/5bP5w5vlDzBeG95+uJ9b/07zBfHHOWF+wbRbn5sEz9eaavstwLeY+z+ebJ4/5SREbK8P88biOWM65iyUbK/NpMd9Gq8t03SDmt8T6GpZDse/pHch1kbHA+lg+bTkrPYI+/vjjq/Lufcre6yzfno71xF90LV+TzbeRfZWHUEREREREREREPEgfhCIiIiIiIiIi7ow+CEVERERERERE3BmPxkPoDPV91O9Rrzf1w7jVF8c8iMxP41w/88SZanBN723aRfNLIlY/02hPr596HplGeuqzQ0zPzuPnsmmALU4tzkzrb/OC8Hq2jRpixg7nqfnSmJeAab5NI87jvJ7rDjXUVr+d3t/WLPORudXzh2NJDfLUr2nqA2Gaaz6fY2X9ZbHK/qCefeoFMdGkW1tt7OmrwDKvt3lp+yn7hl4A9CzZrVPT/Sy+P1wul6uYtrWMMcy18c2bN9vn2d7AmLY8hnA9Y4yfy1P/IfMPs5zC1tKp35r5utjz2R4+n/3B9cRyLMuvCc9nrJhvjnmZ7GJn6uVhsWO+LOanZLFh3nbmzWf7quUZLHPeEraH6wSfZ+8ftg7t9mWLQ/aVeURybM2Llt5/r1+/vipbbJlfk8WOeQMytji2XLNfvXo1qs805+P9/+E//IdXZctLzve3HMvWEPaF5bfEYsk8Laf56c6/9Ey/EIqIiIiIiIiIuDP6IBQRERERERERcWf0QSgiIiIiIiIi4s74gfAQMl0u2Xl3rOUabeoH+XzTkJOd34R5KpiGmVjb+DzTh0+vp7aSbTffHBt7Pp/aSvPyMK8T81iy/jDO9TM/Javr1B+KOlnTQNvzTO9uPjJWH9Ovmz8V1wmLxVvXDYvdc/1M88s1xnxkpl4H1taJHnstjx3zpzJfCxsLegewTE8R85uy2CMWe+frp75qNo94P/MIsjVspz9/6PhknbA4iPfHeWw4hk+fPr0q8zj3YfO7Y8xxvu48f1jXteZeLbt7WY5D+Ozpvk3Mx8XyUctpzEvQci7L4cjUl8a8Ac0vbzp+Z9hXvPbWOJx6YbEt9NxhHkDfQ85LYu9W9m5kfnaMTdvXp89neyceTNN5xrE0T52pX+rUd8Zi0XxpbF5YjspYnL6r8ThzNMYGj3/00Ufb++/WOe5njCvuR8ypzM/JPIls3rFvOW8snydnv6rduPcLoYiIiIiIiIiIO6MPQhERERERERERd0YfhCIiIiIiIiIi7oxH4yG008BRC2g+N+blYZiXiXk6mGZ7osHe+Q+t9a6O1LwuTMdqfWkaabb91rExvya7v/ldTD2AjKnn0xmri+lWTac67Yup38dOr73Wu7paji3rbxrsW71WpusINeNTT6ezxtk8NOjhY54c07qaPtvWLIulW30pGBu8H8vme2PPM+8I9jfXPfPjOtdn6pPGZ5u2n2PButj15r1la/zu+XkI/WBg8WyeXhYjtp5N1wPOGfNIO8858xviWmIeXdO1eupPZuuDef2Z94g9f5qT2t5g3im2Plp5l5NN78U4ZN0ZG+aPNI1jns99ns9nXmH+q4RjZ/dnTkXMC8X6x3JW66/zeE39osz7in1DnxpezxxvmjPZmmr7vvW1eXkR1pc+OIw988GZ+tKZhynHZ4d5CvE4y/YszgP2DY+z73ncvinYGvuP6RdCERERERERERF3Rh+EIiIiIiIiIiLujD4IRURERERERETcGY/SQ8j0LHeNKwAAIABJREFU57f6vvB+5qFgWs2pLveM6UbNp8R0rob5P0x1ttQ2mn58+jzTkbI+1j8Wa1NfHmK61i967CFMq2+6UesLYt4nNlbsqydPnlyV6WNj/WGaaup4efwb3/jGVdnm4lTjvfNemHhcfBEsbgm9B6h355pGPbitAxZ75kthHibm8UNsnTMYS+wf1oea83PsTPcL9gXHgn3DeWVtZ32oV+dxm9c8fq7PNK7j+8d53MxPwvISrj/MC8zjjJivFbG97Nwemx/cJywfZdnWwunab2ujjZ3ta9Z3treY35vlHZYDMxZu6W/bsy1/tbFnX9n19E2xtpm/FeeZreVTrzDC2GR7WDZ/WMvxprF0LjPnMc+dV69eXZXZl9zzLcexWJvOW5btPZaYzyLHjkz9oaa+bwbHbzf3zP/J3mWIeY2ZxyXP57sQ/aZ4vdXvi/o4lp1FRERERERERNwZfRCKiIiIiIiIiLgz+iAUEREREREREXFnPBoPoYmng+l2qdeb6ttNV0z4PN6Pzz97KrAu1AryOJ9lviamqTVNsHn0sK9M/z31rbG4oPbT9PAWG1ONNMvW3vPzTFNrbTcPGxubW7247H7sS+rZqUnmWJoG2zTHU/8t86m51dPofH/TFJsPxDRWLBaof596elhfmw+Eje1Uj0/MC8LKrI/5aEzgvc2HgnHKvnj58uVV2TxJ6ElE/bqNle13z549+8J1iffHzkNouheZ/4X5ZVjeYh5Cth6dY3q6D3KfspzGvEmsrsR8Da09kxzly9xv6glEbG/bjeVD1+/2kqn/kvWF5SDW94wtrq2sH/dtxgaP21rOPMT6nvVj+3k/Wxfo18Xj5jPJ9rI/z2VbU9iX5jNjY0tvP/YV92Fi+S6fx7Fm7JgfFceW2DposWZ7jHmk2vXs73Ns2DcBi3P7RsB3G8Yl78fzb/Vx3H032K1hZWcREREREREREXdGH4QiIiIiIiIiIu6MPghFRERERERERNwZj8ZD6MxUxzvVgJv20jTRphU1reb5el779OnT7b2pDTRvDWLaR/PSIOatYd4hdr15DNnYmb7cYs3G3nSsO3+rqTbfxoKY5nh6PbG2EvOtMR2uwfpwLlBTzP40jbg9bzI+jEvTa1uc27yeevbY/Xi++ciY5471JZ/H9pie3dY1i01b0yf9Zb4MpsW32DF/FkK9On3sOA95nPWlD8S5LydegfH95RyH5n1iMTv10bEcz/wkbK/beQeatxzrwvnC87nWTX0Wbd81n0j2JdcyW6umeck0FiynMp9H8xqZvB9Y3fjsqW+jeQNanNvzpl6AlgcQiy3L2cxj6Pnz51dl+kza89keyynP51vc8NnMMT744IOrMj2AeL75M9neyLGyecz2Mf+lHxP73vqHMBZ5v6kPHZ9PbN3lu/TuvdvySx5n31m+y76fzkvGDufZ1D/28+gXQhERERERERERd0YfhCIiIiIiIiIi7ow+CEVERERERERE3BmPxkPorHmbaq5N00yoBzTvD97PPBmobZxqMc+Y7tK8MkyDbF4i5pFzq37czjeNNf0sTLM81YCbvt88nnaxaG23vra68HzT5ltsWKyZDwyfZ549vN50vayv1e/FixdXZWqerT/M12J3vc1r8+yZ+kyw7pwnFvc27ziWU58103Cbz4V5lrDM+rLMeUxvAsL6sH/P/T+d54R9Z3rzX/3VX70qs23Uo5te/dWrV1dlegbtvLryEHqcHMdxFeO2N1jOZF4j072FmE8P2a1Hti+atwXbZmuf1dXOZ9ttn7WxmPrmTP3rbJ+0/rMczO6/85mxZ9u+ZN50t+af5ifH5zGH4fEPP/zwqsx9jz44XOvNv87yCNaPe4d5pJrXosX+uX7ma8jjrKvln1O/Jtv3uS/b9earyPZMfRGn6xjHftr/1j7zfTzXxzwwmfPYu5a9q3DsWXd7Pj2LzJuMfXHu+10O1i+EIiIiIiIiIiLujD4IRURERERERETcGX0QioiIiIiIiIi4Mx6Fh9Dlctnq+0xXO9UQm0aZmNaRTDTXpq03HaXVnX1p/kd8PsvUgRLzeSHmi0NMozz1IrD62HHT3e5i1TS6pp/ms6ceQdO+ttixvmZ9zQuA9zO9unkomc8N5xpjeTpeLJ/vx7ZY39oaZl4Ftg6Yvp3PY1/a2E89R6Z6e1sHeT7Hmu0xzyGOhz3v3P6p9n7qUUL9OWPNYsM8g2zdeP369VX5+fPn2/PjcXCec5bz2Hpyq8+jzeepj83ON4drk3ns2FptHmHE9nXb5+18Gxvz2rO1mX4VXBvpd8H6kVs91Vjf3Xpn+6iNJZ+18y35InWztk/HlmWOlflFmWcR2fk1PXS9vU+YP555rTx9+vSq/OzZs8/+2/JB81ed5sMWx5ZT2NjaOsH2Wv0ttux8jq3lkMT8pcw3h+vq+XzGEfvePIR4/nTNtjK9vKZrItn5A17dd3TXiIiIiIiIiIj4gacPQhERERERERERd0YfhCIiIiIiIiIi7oxH4SF0HMdWn0itoelKzVPItJDENNamNd15nZgPimmWp546dr3pu03vbppk00KaPwW1m1ONtWnCzYfG6m+a613smf7cNM/mGcQ4tXlhZfal1Z/HbZ5SE2zzauo7wf6gbnjqEUR23gvf/va3r46Z3px1Nd8Ei3vTw9/a18TmEceaPjTWfotFiz2Wnzx5clWmHt7m1s6/yryxzEfO9hu7nmUbC3oAcY1jX7EcPxicY9b2wakP1tQ/gjFmMT+9/jwHbD6QqacNn205l/mqWM5kOY6N5a3egzv/tLXe7W/LgVk2fyvLU85M/ZGm/kqsq63V5tFj70KMJT7/5cuX2+exTB8X84E0jyT2p3mpTN+1eD5zuo8++uiz/+Y8+/DDD9cOi2tbFyxftL7hcb73Mo8wn0fu0+wrYvm8rQs279k+y1uI5cC7PYvnsm9tDbIyr//kk08+ty5ruQ8ymXrffu55X+isiIiIiIiIiIj4DUMfhCIiIiIiIiIi7ow+CEVERERERERE3BmPwkNorWs9IvV21EaaltH8NYj5QZivjvlj7DTY5tMy9QAy/bRpCal9pK6Ux01Xa5hXCvveNMbG1MPoVk+haX/s7m1xan5FHLtpLE3rY/cz/yk7PvU2MG8C9of5UHBu2Pnn9tiaxbZYX9hYURM9HavJmvbQccL7sf3Pnj27Kpu+nXr5p0+fXpXNC4HHba6Y78hubpnPGrGx5P5IzIvL1iiLBdO327yMx8d0bbeci/OTMW/z23y3bK/b5XDmD2H+amTiIbmWz3/Lf61v7Xnm42L+aLw/11K7H+FY2fsA1zdbX3d7k/kgTnMUYn1v8878oqY+LjyfscSy3d98cGyvIJajWSy9ffv2qnzuf8Yp68YcwnIgXv/mzZursvlHmd+SxT3XUMu3LYfh+exL5iG8n+W45glE7N2P7d/5yZrXle0nFpf2XmjPY1vt3cS8vc59s5vD/UIoIiIiIiIiIuLO6INQRERERERERMSd0QehiIiIiIiIiIg749F4CJ0xLxTTkVLfR30d9Xem75t6PJCdj495dxDWxfTtpmU0rST7imNj2kViGuqpF4np86ceQ+aFYPW3WN2NvenVTbPMZ9nYsK3mp8Tj9nyrv8XS1H9p6r9lc4VYLDIWqLE+X28eFzY2FgvUm9s8M58Yqw/h/Vm/r3/961dl8/Gw+/N+LNMLwfp7pz9fy/X21Pefr7e+Z13Mm4BjbT4YfL55cNg6wDinlv98f5tD8f44x6HtY+Z5YGsxy4xB88kyLxTbx3e+krZ2Tr3szLvDcgr2xZMnT67KU/+J6T479VSyHM7WTl7P8WD7WX/21y4HnuZc5vFDXxqbN9b39vyp16C11+prY2meQyxbbFqs2jzn886xYR6avDdzCst3zfvr1r63HGg6r8wb0HxszEPIYt88hix2LN8/9zf7nvPWvgFwjSFWF/PamvqhWn3Ox3c5WL8QioiIiIiIiIi4M/ogFBERERERERFxZ/RBKCIiIiIiIiLizngUHkLHcWw9KUwraHo60z7e6oMz9UU4ayVNN2qeP6YrtfuZfp2a4Kknj2kpp94pO2+Otd6tP5nq/629U9+cXWyZPtv04ubbYs+zsaLm2DS97AvTt0/1+6bJZvutTGzu8Tg1z7v62byyvppqjg1bV2wsbWzY19aX5gs39VozH49nz55dlc2TxLwRdnp8095P/ZNsXbC+Mw8S3t/8YnZeBbfGaXzvOI+NjanlPIxhizHD/CYs5nfX2z7EttAjyzy2rC7W1y9evNieT7i3cF/eedut9W5fmiePrY12f8uR2B7zNpmOxxmLU9vjp76O1nfm32T7tI3VNIe09Zs5IseOsWTz2vIier/w+eyv8/mffPLJ9lzey3wF7T2Tbbd5xedzbOkZZH5KFiss2zpCzF+LsL3MwYjFrr0bns+3fI0+ZTaPuSdM/Y2m7+k2L3e+ankIRURERERERETEZ/RBKCIiIiIiIiLizuiDUERERERERETEnfEoPIQul8uVrs08DMzvwrSGDz1/9zzDdLA77xLzc7C6TD2HCM+nttE0zhyrW/uemmnqfNmXNvbmc8PzzUNp6hlEJrFlOtSpb4Lp3QnbTs2yeSnYvNr5hj2E6cnt+ab5tucR1n+ihzevANMQ27MZx9bWt2/fXpVfv359VeY8tLG0dYj3Y2xN9wBbl2zeUc9venRr786zyLwB2DY+++nTp6PrGVuc16yraf+nPhdfVL8e74/L5XIVl4ypr9qb0Dy9OB9tftt6uIs7m9tsK9cGYp5dlnPZWmV5gXmTcP4b7Es7bjnXrX5w5qFmPpS79c1yhqm3h7V1EqdrzfPpiZfWWu/6uPA4Y5+x9urVq6vymzdvRvUjfL7FLo/vPJHoPzT1WzKvLdtnp2Np74rExtreB3j+8+fPr8rM4cxbkO3l83g9fXymHkncU87X27uCrXkcWz6LOZ69a9nxKTs/rN3+0i+EIiIiIiIiIiLujD4IRURERERERETcGX0QioiIiIiIiIi4Mx6Nh9BOQ2daSdN+Ut9HvSC1izzfPItMX0+94bmt5lVBnaZp780rZKrZJeYvcaunjunpbayn3gaEYzWNPdO7766d+muYdt/GwjTMprM13a35NU3nlT3PfFysf6deYhYbO+zeVhfTt9s6MPX+sliajhXnmem/uT/YusH2Ud9venbzp7LY2+n/p/4sdvxW7wMrsy0cO/Y1vcV294rHweVyuYozxgA9EXjcfK+IxbjtZVMPsd1abXs+/dXMe2Tqw2htYd9P788c89Z81vY921uItd88kfg8tpexc+5PW+en+57lVMTGgmM/9dZifdg3jH3Lj81Pa5pfs74WOzb25rtz7g/zC514Qq71bl/Yeyj3SbufrZHTPGCaj9/KztNnLfeHsj3HPJHOx81/iEy9c817lkzfPWzspt5k/5h+IRQRERERERERcWf0QSgiIiIiIiIi4s7og1BERERERERExJ3xKDyECLWGU/0eMa0lMY8Hu595rZyvf/Xq1fbZhPeiNnGq9zb9u91v6iViz5tqgq19NnZW/69//etX5YlH0EPn755t97bzWbaxNK8Q0+6bnnuq0zWfCJapr6dmmM+f+lCQaazdci8y1YMT9s1OX73Wu31FTbR5+PD+FpvTsbF1g7FBzCeO3m3mCzfB5iHbYmPF+3HeEtO37/z81nKPE47lua/zEHq8nONg6kFGbP2w+W0eXQbrv/NYM38yenmYP5Hte7YWmv+D5WDTPMLG0vzWzHeHZcvxpjmf7X22Pp2xnMn6cto3t+az9Ld6/vz5VZlttxyN7Nbyh65n/em3Zddb/dh+9rd5Ep37x/qedTW/KbaV7w7cly1WbE2cvnvYPLM11jyROFa25rN/zH/LcjDOhV0ssO+sLdP3Rj7b1gHLwcxj076JPHny5HOffVWvzz0SERERERERERG/IemDUERERERERETEndEHoYiIiIiIiIiIO+NReAgdx7HVAZvOlfo63muqWTYNMzGt5s4vg1pAqwsxLSN1mdQUmxcG+8704VO/JdPBTnWu0/4y3bDpeKfeKefrza+ImljzXSFTr6upV5fdzzTapqe39vK4zaVpLE89kOz4uX32bPb11EOIenXOc/aV6eV5P7aV57P8ne9856psHj1Tvysbq6lm3PrL1rHd3DB9usU1vQo49uYBxLGc+r5N1+R4/BzHceWVYz42Fu9TX0fOb/OdMWy9PNfXfGC4FtjaY/uoeWtM+57rg+1jlqNNczjzNrH1wvJ328en3oLn/pr6J9m7xNRPiWux7WMc67M3yEPX27yy9wHzzTGPzanPI8+3vIHXsz93/l/Wl7aG7daUtd7dpy2HYtuZ/5sXls2LaftYP/a9veuwvs+ePRs9z7wR+XweZ+yc+8eexbixdxX2ja0Du7o9VD97PyA8/xyru+8ZZXIREREREREREXeGfhA6juNPHMfxK8dx/NXTv33rOI6/eBzH3/j0/7/56b8fx3H8p8dx/OJxHH/lOI7f/r2sfEREREREREREzPkivxD6k2ut34F/+0Nrrb90uVx+fK31lz4tr7XWv7HW+vFP//cza60/9tVUMyIiIiIiIiIivirUQ+hyufzPx3H8U/jn37XW+lc+/e+fX2v9T2utn/303//U5bsCuP/lOI4Pj+P4scvl8sv2nJ2m2zyBzOdl6luzq9tD9zcfHXJun+mppx47hDpL07ebl8fUF2bqw2J+FdY/Uz8M8wSy8TH9Pvv/3N/UIE+fTY3uVL9uscHjNi9t7C12iHkXmLeDrQPmn8X+ZXumuuFzrJjHhWn/TT9uY8O283rqsXncNM82723N5LwynwrzEuD1b968uSpTX2/eCZwbjBVyfj7rat5W9GmwWLGxszXQ/J+m88jmeTwOznFnOZV5Gth6QOy47ZU2X3d5BNtC/whbu2wtNp9B28d2fhBrub+Z9a2N7dRDbOqrw/uzfyx/n/bXLge1fJN9aXHIunMttXlkfcGxJ+YfNc2nzSeG9aNvjPnCMKfi3DB/PevP8/m8Nz1/WLZ9zDx+uK6wrTw+8cRZ692+sXcbO5/weWyvrUM2VvZeb15h5jF0vt76wvJp8zWz/dO8w2y/m/qunfPd3TeFL+sh9KOnjzx/b631o5/+929ea/2d03m/9Om/RURERERERETEI+FmU+lPfw20/9nHAxzH8TPHcfzCcRy/8PHHH99ajYiIiIj4ApxzsJcvX77v6kRERMR74st+EPr7x3H82Fprffr/v/Lpv//dtdY/eTrvt3z6b+9wuVx+7nK5/OTlcvnJDz744EtWIyIiIiImnHOwFy9evO/qRERExHtCPYQ+h7+w1vrptdYf/fT///zp3//t4zj+zFrrn19rffxF/IOIeY+YLwz1ecQ0xsSeZz46O0236VKn+m3qNE3Xas83jTKZegiZdpJMY8PGbnp/07Wa59Pu+dShEtPqE2poDcaK+baYjtZi1bwNiOndzQvFfHN2evO13q3v06dPt8d33jDUi7OuVheLe/MgMp8M9i3vN/VXMt8N1sf07zavp94PbK/p0Y2dft7WDIvj6TpgWn3GFueJzRvzGjvXPz+hx8lxHNuxsbXf/O8mOdFD2PrD+5PdHJn4DT2ExbR52VnbrS/testXLe+wtZjzn+uJrdW219j5t3iCsq52b/rKcJ+Yem5OPX7Y1xb3PH+679u8pb+d1cf85zge5l/HWGF73759e1U+expxLPlsi2PzZTRfQvM95P3Md8beQ6f5Opn62Zof1nTNZvuYY1oecn6exT3vPfXKNU87e/cwf6apb/G5Pbtz9YPQcRx/en3XQPpHjuP4pbXWH1nf/RD0Z4/j+ANrrb+11vq9n57+3621fuda6xfXWm/XWr/f7h8REREREREREd9fvshfGfs3P+fQTz1w7mWt9W/dWqmIiIiIiIiIiPjecbOpdERERERERERE/GDxZT2EvnLOGjzzaZlqkHfPWutdPd/Us8Hqt9MyTvXg1Bqaxtf8Ikx7yOtNS0kdJ9szrf9Uz2/+UGyPabYtFslEQ27PMl+GW/04zAeCcGxNF0sNtD2f7TENMo9bbJmul0z9uchOH8+6mG+CeRWY9p59bfp404PbvLKxsjV66uth64LFBsfSvCQsVndeEdb2qbbevAzsOKE3lsUifRp2+3EeQo+Ty+VyFVdTjwLz6OJ6wBi09c/mp61Hu/WMz7J9zvJDy2FsH5mujXacY2H71jSn5PnmOcT2v379ens9Ma8QsstBp955hHFssWN1s7jl2Jkn0dRr0PYWy6Gm72rsP6sfPYsYK8x7zp5BPJ/3pr+StY19w32TbaEvjY0lY4H7rvmoWX5usUXYt3a+xd40x7N1zebueXzZF+aJafPM8kvGLePe1jx7F2Rs7GJlt172C6GIiIiIiIiIiDujD0IREREREREREXdGH4QiIiIiIiIiIu6MR+MhdIb6OPPZMa2heTLYcWJ6Qavf7nmmhyZsK7WQvJ95AlldTW9v9ade3PwsiOlOCXWkHDvTeE+x+uw8NEwjaxpg83Ew3xPTY7N+1KkSHudY0EuAmL7cvApsHSGmSSZ2nJr0c/+bHp2Y5495DLEvObZT7zK2zTTVNtbm2cOyeTVMvLweup56f5s71n9nn52pF4H5q9g8tnnD8osXL67KNq9sXThfP93f4v1gMTr11jMfRvPDsH186nn25s2bz/6baxn9HmytMe+K6fyzvuX8sjllnj92vuXTljeY78w037e9cOL/xrraum75Ma9nX5q3FsucFzx+juOHnmfeJdxrpv5P5rtjWN5g6xD3Ksszzv1p/qeWMxDG+fPnz6/KljPZGmc+NxZr1l6WydQ717zLOFb2bmr9z/7dvaebx4+9V5uHp61pjA2LWxtb1s/68vPoF0IREREREREREXdGH4QiIiIiIiIiIu6MPghFRERERERERNwZPxAeQnbc9OnT600DbpjPzVn/Zx495p1heuupJtfqbhpq9h21jd/4xje2z7Pnm2bbPJOo1WR9idVnWv9z/5vnz9RXZVrm2Jx9Th46PtWpsm9t7G2sqX83TyXzLjBN9HQdMe+Fc+zZmsW6897mK0MPHJ5PzfS0rxkrpvfmPCXmnzX1OLJ11Hw6rL4Wezt9vGn7+Wz6PBDuCVNPEurZGRtTDyGbB/H4OI5jm3fZfDBvQcYA/R6medB0frM+5/XQcgbLsWz+0vPH/CLMi8NyLPNCIayPefyYd4n5ythabh5JvD+x553vx3O/ao9Jmxds66tXr67K9Dgy7z/Wl/v0NAfj+TZXzMfF5g7vR3jcvAp369DO624t9/7iPkksH/7mN7/5uXVb691YsBzF8vNJ36z1bv/YOmexbXnF1BfuyZMnV2Vbx85l+8bAZ5nXreXPVmZs2H5o82Tnn7TbD/qFUERERERERETEndEHoYiIiIiIiIiIO6MPQhERERERERERd8aj8BC6XC5bfZ95i0y9P3b6uoeuN38MaifteTsNs2l6iWmcWVfTWVpfmr58qv2flomNlWmU6c9h+vNbx+d8P8aNPWvqOcTj5glEzS+Pv3nz5qq888Zay71QqDenXt28FywWTVNtvi98PjEvMl5/fr7FuWnx2XZqkl+8eHFVtnnM57Gv6EnEvmX9eXy6xhLzTJr6vk2PT33k2P7z3GDfm1+UxaHNO44tY8XmMbX/nCfmKXLGxjneD5fL5Wo/Mv8GxuR0XM0XhzFkMWu+VuT8vKkHFuOfdeE+af5mNv/M08j8nXb70EP3M19I893h2sfrbb2yfd3WZvPjOx+f5qvmp8QyPX+Y871+/Xp7veVg0/z91nyW15u3CnM69h/7g2XzvTHfy52vjXkIWY5h7xKE+zDvxzLPZ19M/Vun74qMBfal5dM8ztj91re+dVW2WJrmxLtYsLE1zx5bA22emaeQ5c/mnbXLIXdzvF8IRURERERERETcGX0QioiIiIiIiIi4M/ogFBERERERERFxZzwKD6G1rnVt1N+ZT83uXl+kbDpc047yfNMPnttjenFqBU2raN4k1nbTmRLTKNv5U/8nwtgwHappQ61/iI01tZ1nTTl1ooR1m9bVvEhMv24eR9P6vHz58qrMWP7www+3zyc2FzhvicW+eTPYOkTfnZ2G2TTAT5482daVsO3msTH1mSAce/rO2Lpgaz77h2PDeW8+FNYftq5abOw8WMyTh1hdrG3m42AeHYTPZ9/zeTvvrHgcXC6Xrc+V7VUWw+bVYr5a5g9neQX3kvP9pvkl10a23fpi6rFjfWVeIlOvQutrrn3mv2FrK+F6shu7h+5vvjjn+rEtln8Tnr/zrFnLPYV43GJl51W3ludk5ufEsXz27Nn2fBs78wJjf7J+9Axif7169Wp7/nk8WDdbg7hvm98RczbzPaNHDq+frrH2LmRjYTnm1CeS9TffTNbP1jXzjzrXx96tdtc+VDbfRbbNPPrMp9Fywi9LvxCKiIiIiIiIiLgz+iAUEREREREREXFn9EEoIiIiIiIiIuLOeJQeQlOPn8m9v0iZej57vvngTHxxpnp0qysxbSSxtpr+3rxRrH7ENN2G+d6YNtT09qzfzkfGtPSmDzdfF/P84f1tbDmW09ixsace3LwM6FVi64Z5Dk3LjFV6JHHsdz4c1BhPNcw2VqbftjWMbaE2n5iXgMUCx4rtMx836s15v+keYHPD6nuGY2Hae/PIsDhnXz19+vSqzLZxrE2vbv5T+Qb9YHAeJ4tR82sz35mpJ4Ltu+Ybw/aczzffFa5l03zP/M/Mr83WLsuxODbmLWLeIOY9wn2c+/RuLB6qr+UtxLwDJ36l3LdsHzBvPos162v2rXlt8XnsO3ruENunWT/en3mN1cfyDuZYH3300fZ+PP/Nmzef/bd5YRHe+4MPPrgq29iY19Y0x5n6MdkaOs2ppr5v5NYczfLxnS+Q7U8PsrWYAAAgAElEQVQsmw8bmb5H2jwzP1jOE97/PA9349IvhCIiIiIiIiIi7ow+CEVERERERERE3Bl9EIqIiIiIiIiIuDMehYfQcRxXekhqF013+9D9dvD6W8+/RQ9v9zbdp3mBmNbRPIfMs8fqb3r7W883Pb4x9ZcyLSiPU1d81nJO44aaYbaduljTqZr3iHmbmKeQjZX53JiXCttj9TcvMNMwU29P/brpeM/ja1p6xg3bylgwfTo10NaX5l81jRWLdT5v+nz6VJinkq07U680eiWYB8sZGzvrO5t3Ux8K06ubHwzPtz0m3j/HcVzFwTSv4PrC+WBMPLgeOt/mBOu39VEQ7zry9a9/fftsXs+1gfPTPICIeX2YDw5h+807hFh/cH0gtvbavs7+Y/lcP7bN1lLLL6djazmQ5QGWM5qPJKE34JMnT7b1Yc5jPjLmN7fz3Fzr2gPooTLv//r166vyeS7avsfjvDfL7Bvz6rJ5ZX1lTP2l2Ne8nuezfRb75stoHkhT/9fdOmXvxbam8nrz+rU1d/ouY31t+f7n0S+EIiIiIiIiIiLujD4IRURERERERETcGX0QioiIiIiIiIi4Mx6Fh9DlctnqtM2/YaoxJqbvM12u+fzsvEl4rekoJ94UD93PtIlTfwpqfs2HhfD51EKaLtTqP/UosutNS8ryLnap82SZ+m36Mpg+nHXl2JgPg7XFYo+YZw/LrP+0TGwdYP9Tf276edN477zDzLeFx58+fXpVZmxwHpkPBjH9ts076yv2ta075qHE/mJ/mAbbYGzxeh5n/c71YdvNp22630zXPNaHmEcHr+dYmJdCPA7OcTH1mbEYI+abZfPVYnZSf9sXbK5b3W1fmvpZTH0imUeYV4nty9McyvrD+m/qr0F2Xov0O2JfmB+beedNvT24bzL2zAfSfGHMR5Hljz/++KpMrz7Lz5l3mE8N2/fy5curMvMIltl/O59M84+ynMdyJOtrYu8aFmss23uweXpyLlh9bE/g8xkLt77bmifTuT4vXrzY3ttyLluzzD+JsO2W47GtXMf4vPNY7uKwXwhFRERERERERNwZfRCKiIiIiIiIiLgz+iAUEREREREREXFnPAoPoeM4rjwWqH+jLtS0jNRymk6WmK+P6fum2sfdsal+nNdPdZnmDWJaStOfm67VdLBTzyPTKJOpPxXvb3r8s+bZfFHM52SqUWbfUHdq+uupNwkxrwaOJX1gph5DxOpn7aFe3e63izU+i1p79gXbTv21rVG2rhDTq5t3gfnOTNdQ8xAxzyC2l74anAv2fPNS2Pl3caynYzf11OP1XKOmsTL1XbNYi8ePzV/zKjS/Oh7n82wOWJ6xW4unfmLmkcP78Xzzi7Dnce2begrZ+mEeP7Y+mOcQsRyV/WM5nXkhntd684mxfYp1t3eVqbcf72dxbW23fZv3577KHIjtZ9nyFrbPvAd5nLHI+nKfPrd36sE59dCZem9N1zTD3oNtnvO4+UGZR5HlDVZfYu8Du3XVxtLmHfNH8xqzdzuLY7aFHqLWd+e+2sVRvxCKiIiIiIiIiLgz+iAUEREREREREXFn9EEoIiIiIiIiIuLOeBQeQoT6PULt4lS7adpH0yyb/s/KZ0wXOvVfsPuZNp+wr6b6cPYldahTvwnziTE/i1v9Lsyvw/wDzuebRvhWPyPqVl+8eHFVpj6bsH7mC2M+KrzevAOsP6YeRpN5+RCmKedY7843fybzBph6brBs97d5YD4Vpo+3vrd1zHw3GPvUfPM4NdvUaNNjyOrD+5/HfurHQqZ7gnkDsK7mQ2E+Dna/ePxMPcfMH4Lzy9YfWx+n+/wkBm3tNO8KO9/mO3OuKfZ886uwsWb9rK8tPyd8Putn50/yAttnuO5b3HKfsbXy7du32+cxVt68ebO9n+UB7Bvzzpv6NLK+jBWWzZeG7bH3B4v9c85pflHTnGrqJWttNW8uq795l0191+g9yLKtI9P3G3Krn+y5veafNPUAYt9/8MEH2/NZN64bjI2dF9ZD9+M8OZd363u/EIqIiIiIiIiIuDP6IBQRERERERERcWc8CsnY5XK5+nma/aR0+ue2dz+fWst/Qm8/RzY5xO4nrbf+vHj6J4tNajGV35GpLMieT+yn5fwJrMmYiP1c236mabF7vn76p7KtLYzb6Z+V51jxJ6H8eTOxvrXYYv0t9uznydM/nW7rCutnc20na7B5z2dzbOwnryYxI/YTVOsr+xOi1le7Pwv9EPZTdvYX5xrnBn+Sa+fzeYw9+8nxGetL+zPTvLf9KWW2jfN8+udgTRZ8vn4q04zvH7uxmeYhtk/a9baP23o1kbTavmt/etssCKbSiKnU2vZ1W1un8l7uJewfux/XG5NLW314nOvhRK5s+SqPc62zsWXfUcZv8txvfvObV2WT/7F+fL7lHXY+903re3t/YI7JWJm+++3sPzjP7M/OW9xZDsbYmVoeTP8svc0r61vGHtc9i/2JjP4hGMtsr0lVd3mRvStQwsX9yL4R8PqddPGh47t8ca13x5L3+/a3v31VPo9df3Y+IiIiIiIiIiI+ow9CERERERERERF3Rh+EIiIiIiIiIiLujEfhIbTWtd7QNNT2ZzSpjaQW0jTKprud/lnPnQ7XPH1M02t/ttH067f+Ke6v2u9pCuvLsbb+m/4Z+ml7dsdNb233mvoxMe6pczUNtWmup7Fg7TcvFdN0m0cTudUzyDyNzsdN/03Y96btn/6Zd96f84gaZf75W/Odmf45WdaXz6d3gWmwWeb1z5492x43LwnTlJ/Pn64Z5gliHkLmbWBafkI9Pcv0gTjHkq0R8f44x7h5hu28OdbytdNi0nIww+p7vh/XOsYz88mp35nlg9M/Dz3dBy1vsD8NzrG1fZrY+jL90+H2PsD7mV/I7pittZb7m78RYds+/PDDqzJ9XOhBZD4xtvewfsyhuC8+f/58ez6x95Ppn0a3vGbiLWbzhGXzcbR3K46VrSs8Pm27zXPLG8zDaPouyVjj+4jlYOZduIs1279YZlzbn6G3NdNix/5MvflRcU/rz85HRERERERERMSD9EEoIiIiIiIiIuLO6INQRERERERERMSd8Sg8hI7juNLUUedJ/Rz1d3a+eZVQe2g6YtM0T7SW5qtiXh7Uu089gkz/zfOpP5z2jXkBmI7WtJn0rzAtJu/H4xYbpqNlf53HyzTHpmE2TxtienbzYaHG1zx0bCyI+dhY7FIzzONPnz7d1oexN9Uw2zpwro9phk3jPPUQMo8iYr405iNhmmmebx4i5ntjsczzzQOI5anX2O555hFkenHbz7gnmHcA72drIu/PecV5f/a9mPoVxfeP8zibp8LEl2Wt+V5n/hfTPGeXh1hOYeu67fnTHIhrH+cn+8L2MWL1tb2E7bG138bO7mc+OFO/jfNxPss8Pc2/iUzzQ57PnMtyGvPosXcjeqXs8teH7mfvA3b/6fsJvQzNQ/R8nHFh5ckev5bPc1tjeT/2HdtqWP0sJ7N8l1jOZLHOMmGssH8Yi+c8hMds7DlPOA/ME8jWOHuPt28crC/XhfP1Ww+1zz0SERERERERERG/IemDUERERERERETEndEHoYiIiIiIiIiIO+PRCPrPujbTkU51u6ZTNT27acyJ+Uvsjpk+3PTa5pFDqG00naf5tpDpWE3rx/6h78utY09dKrHrd94m5h3Ce+3iaC2PFZsXU28E83kwvTjrS+8E6nzNi4D3M12vjZW11zyGdp5GNs/MA8faQni+zWPzCrPzLXZt3poenPp5arBNDz/Vy9s6NWm/jb3NcxtLcqtHil3PseL557HNQ+hxQh9H82Ob+tnxes5/8zyz+Wp5j/ninKFnjfk7cG2aelxOPcVsn7K1yHxepuvPNMcyfyeuJzzffHmsv8/PY18a7LtpjsS2MrZsnzVvOz7PfFjMO4Wxw32a97exMX8stp/Hzz4wa73bf7w/2cX21F+JfWF7m+3T7DuOxYsXL67KjA32Dcvm+UPYl5bfW45ke4jlRbams36s//l8e9diXJnP4rRtxPYcm1c27/MQioiIiIiIiIiIB+mDUERERERERETEndEHoYiIiIiIiIiIO+NRCPovl8uVhs400uYrQ3g+9X+mTZxqqk2fuNMyEtN/U3tomuWpfwWP8/nmJUDsuGn9TTs51dPb81hf04BzPHaxY/5M7GsybcvUi8v6cuonNdUIUwd7q9cXNcW2rrB+5hlEzTfvdx5P80MybT5hrEw9f9i3vJ7ns6/Nv4r3Y9+wvRwrlu355jm001g/VD/uSdN1e4etwebxY2sW625rqh1nLNlYnq+3OI73w6/92q+tX/3VX/2s/Pz583eOnzFPM5sfNt/Mj4PHp14uuxxsWra5Pp1vzCmm/hOWV5jnju3TZLLvreV5i3kfEt6P/bHz+5jm7rbP2FrNtlhfsX7mZ0VfF/PU4f0sh7K9yjyWzHuFexXXIT7/29/+9lWZ7d/d39r69OnT7bNZV44l+366z5qXmK2xlvMRax+xvGOaY9oaPn1X3b3X2zu/xYbtT/auZH238z9a6911iPD4eZ7vru0XQhERERERERERd0YfhCIiIiIiIiIi7ow+CEVERERERERE3BmPwkPoOI4rvaJ5kZhme+r7Qi0jdajmmWBazp1vDp891X9Ty2iaZuoHeT3barpWYj4sxHSjNtY8n+2zsTFM88z2fvLJJ9v6nOtrYzv1/ri1Lw2LBWsPn8/6sb1T3xbzXqAu1/T609jnusGxP8+tN2/eXB1jW3kv03+bJtnGZjpPWF/zMLJ5xHWC9aEvjfnAsX70f7LYmq7x1p/n+5n23eb51NPOjtvzOJbsS655uz3M6hbvh6997WtXc4zz0+bvzqPnixy39eJWr5bd2m77FHMi85ewfYJYvkpsfhLzArT1wMbezr+1fna+rb07fzuLO1t7bR8xLz3LQcjU38m8UriW2/NufRey2DHs/YXt2fnacOyYP1o+xzLzS3pZWSyYVx/vZ7Fk64rNa469+VPZmmv9Y++2fP4t59u7CJmuA+wLtpXns+/t+eaHulsTd3O0XwhFRERERERERNwZfRCKiIiIiIiIiLgz+iAUEREREREREXFnPAoPobWuNXCmh5tqknn+1CfHylNfhPPzzD/CNLqm2zQ9OvuGz59qnKfa/il2P+s/K5uW0/qTz2d556dh+vWpzpRMvUvYVps3U98I0/Ha+YSabtaful3C/p1qvjl+O28E6tWnbbU4NC8yi/tpnBP2lXmOvH79enu+jS011Va2dcr08RxrsvNKsP2NmN8K+8aYeoHZWNrYfFH9erxfzuPG+OZ84F7AtZXnE8a0eSrweVMfGd5/11bzdSSsi62lliOYp475O5mPo/XV1NfFcirrP8unub7YWm151Pn41EtvunbbWJuPi/mdWj5unjvmh2XvE+b3au9eU59I2/sm70v0mKH/0NOnT6/K0zWRdWHdzVPHcpi3b99u78/6mMeQ5TSMDfY97/+d73znqsz+tpyX9ef1Ns937zMW57bfsO48n/PWPDOJ5WhTP1n7ZvLZfb/QWRERERERERER8RuGPghFRERERERERNwZfRCKiIiIiIiIiLgzHo2H0FkjZ7445q3C49QemtaQWk4yrd9OE256bdMG7rwqHnq23d+8R0zHSS2leQGYN4fpXKdaSzLtX7s/taNTT6cd5p9EjbHpTDmW5stinkfmHWDzZqq3N6+Fqe7WYtXOp6Z7p8+ftmXqi8bjLNu8NS2+6ePZN1xT7XmmubbYML361Ddj2j8cr3N9pj5npge3Nd7mGTGvLPNYsViNx890n7N99Nacjkz993YxbXX95JNPrspsu81fw9Zmmz82ny0/thxs54O41nz9muYtPG5+UxYLOw/PKeY9MvVzsrV66mdlXl23ePCs9W57zVOI+zIxPynbe8wn8tyf1rfTdw3LwWze8Po3b95clZlf8rj5tvH5Fms2z2zesWxehxZr0/N3flS2JlqZfffkyZOrMvuSx6f3t3ybfFmvxn4hFBERERERERFxZ/RBKCIiIiIiIiLiztAPQsdx/InjOH7lOI6/evq3//A4jr9+HMdfOY7jvz2O48PTsT98HMcvHsfx/xzH8a9/ryoeERERERERERFfji/iIfQn11r/2VrrT53+7S+utf7w5XL5teM4/oO11h9ea/3scRy/ba31+9Za/8xa6zettf6H4zh+4nK57AXh61ozZzrS3bVrvat9pJ7vO9/5zlXZ/CtMl2t6eHKuj2nlDeuraZl1p16eOtepxtm8QKaa6ml7pt4D5idlGnFyPn6rH5SNhbXdNM3mczPVJFusWnttXrHv7Xl2P+p0TbPN8k7jTE0x9dWcdzxuaxDHhmuizZtbfSlszZzG3nRu8Dj7z+aK1Zdjzfry+Pl+dm/zKrCxZl0MPs98IG7dc+Jxcp4Tu/h9CNtXp/PZ9pZpzsb5fz7Odd28LqaeWOYXwfk2zWmmXiWW89g+ar4zrM//3979xlpW3WUcf34BgWFmGKyYRhm0mBINNmob0mA0hrQmUiXgC2NpauwfTdOkidVomiIvGl/wwmj8F7VJUyo1IWCDVUkTTEltUt9ApZLUFqySYguEFhTLzDAJlbB8cTaw78O9+7lrn2HOnp7vJ2m4+55/a6+99l7r7N7fM+l64bkyKcMszdtp/8aPp8zLqddKOWsrre9TFl7a13Rt9WOZ8qpcOhY+FlK+XZq70ryeMoxSftf4cW+7r5FShmbvetTfz9vq3zU8u8y303meztPevkxrPF+zpjWsjxXf/9481zRWp86V3uzZlFeU5rs0X/ZmaKYc5P2uweJfCLXWPifpKfvdp1trL/T+PZKODj9fJ+n21tqzrbWHJT0k6Y37agkAAAAAAABOi1ORIfRuSXcNP18s6ZHRY48Ov3uZqnpPVd1XVfc9/fTTp6AZAAAASMZrsGPHjm26OQAAYEPWuiFUVTdKek7Srb2vba19pLV2RWvtiiNHjqzTDAAAAOzTeA12wQUXbLo5AABgQ/rCBkaq6p2SrpH05vZSAd1jki4ZPe3o8Lu1pFpN57WEXl+XMobS61P7vB5wKq/C6ypTrWFvHWtqa6o79fakOk5//1Qj7Z/nj3tdqx+b3mPZm8eR+ivVt0/Vfq6TPbXb61Pf9dajr5sDkbII0ljpzRRKdbapvtzfz2u+jx8/vmPbsydStsS4pjpldaV673VzJNJ56VL2gLfPtz0nwh8/efLk5OensbJudlraTtf0dXJzUp6R82tm2k61+GmspHymNDZT/gyWYXyc0zHzeTddf3wMecZBer+UCZZyuaby9tL4TfNOyo1J68OU9ZGkec6vvd7Xrvdan/SuI3pzdNIacGoNmdYo6VrnawSXru0pkyfl2KTzyPnrPU/Vx0rK00rP9/b7sfD2+jogzVWeq+NrMnfo0KEXf065K643F6Y3l3Aq52y356c8q/RdyV/vfed9e+DAgR3b6XtxkvrDj0ca6/5+3p/jsZfa3pu7ls67NMe4dXMZp95/6rWzjmhVXS3pA5Kuba2Nz+A7JV1fVedW1aWSLpP0+TmfAQAAAAAAgFdG/AuhqrpN0lWSLqqqRyV9SKt/VexcSXcPd5vuaa29t7X25ar6hKQHtCole99+/oUxAAAAAAAAnD7xhlBr7W27/PrmieffJOmmdRoFAAAAAACAV87sDKFTbVzz1pubk/IcUh2q1/elz0+1mqnefcxrbL0mN+U59NYipkyeVJeaMnVS3kTKWfFj4/xYpYwgfzzV83v7PdvF61hTXkBPlkh6bco1SVLNc++xcqkvU1/09JWU6/m9PX7sfP+8ft23vd7et9NYG/dvqmFO16iUTeDngb9fOu9786q8htrrz9PYTvlXvflSLmU39GaTpXNn6vikY5uOtV+TUkaQS2PB9y21J43l8bFdN3cAr4zW2o7rZ8q1SefLuuebj3GXMotSzs14f1JeW29WiF/LfF7y7d6MypR7k66Fqa/SPJ7mOZfmojRWXFpv+9wxtb8pl8V5X6TsK+eP+1hI513vGq53/9I8nbJQUjZhb3ZLynRKmUJT7UmZOum7T5pH0zhM54H3dZr307E6ceKEpqT9TzmXaWyl/kjr8ZSN2JMn23vN9c9ed5ynNVfKLkuZRlO5bVNtY3UGAAAAAACwZbghBAAAAAAAsGW4IQQAAAAAALBlFpEh1FrbUY+YMntcbwaQ681ESPV+zuv7xrWRBw4c2PGY1xR73aXXBqZaeZfqx1M9etr33mOXsk/S4y7V6breDKXezKT0+rFUQ5weT/uajlWq5e/NrVk3Myi1r/fY+bnkNcrHjh3bsX38+PHJ1yf++eMMo5QN4NcFzxZLx9Jrnv1x3/dUo+z12X6d8tf78z1vKWVx9Wb69Nb3916X/PW9uT9TGUIuHVsfhylzY93zvveaP9U3vTlhOD08QyjlyPj1xY+5n98pp8fHnI/xgwcPTr5fmnd71iXe9t7cFZeuhen5KS8irV/TvOhSfltvxmf6vN5j17tumMqdSWuI1BcujQWXMjGTlPvoYyl9V0rv3/t9wLdTLqMfe88E8nPT1yE9+VO9WbO9GZvrZs+ma573hUtjN2VsputG+u6Zrunp2Ht7Dx06NPm4j4WpsZo+K63nfF98fZ5y1tL6Me1L6vup9TwZQgAAAAAAAHgRN4QAAAAAAAC2DDeEAAAAAAAAtswiMoRcb/1dykBwqS421ZKmjIVU3zf+/PReXqufsjJcqiM977zzdmx77WLK9OnNDPL9STXbSW9Ns0s13L4/qZ4/ff74+amm2Pmx9mPXm7XlemuE07H3Y52yAtJ56/ufzttUf+5jLdWn+/N7sw7Gr0/74rX/vdeJ9P7+fN+XEydO7Nl26eVjNZ03qX3+eSkrLWUheHvTNTzVy6c5oyeHLo373nrydE3y7XQNTvlM/nhvvhOWp7W24xzpyWOQ8popnZ+7tWfMs0c8TyJlLU6tm1J+xLrrTz/ffd7uzeBZN5fRP995X6Zj733t15eUmZSOVW9eVFqzTfVf2te0ndZ0aZ5PmTjpu0Yay2nu8SwUl+aCXv5+KWvQH0/ZflPnVrrG+TXM+yZl+fnr05oo5T6ef/75k5+X+sb31/vWrwuHDx/WlHWzDNNY8v5LuZVuKi+3NwctjbM0//h5ntraOyf0ZoHt+bx9PQsAAAAAAADfMbghBAA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Odv6kqh7VWvvndflfbt6/tfbJS4D0RTUHEL9c818upuXYaq39X3W5fvqpNf+VYU8mfLEAPx9zhWN+p+a/qjxzeVX3k6rqvarq+/bav82fQj/WWntMa+3/rPmNlK9btn1wa+0vLdtuaa19ac1fhPhvy+HfXlWf3Vp79+W1139WyyuyV5GP51fVB7fWPnL5a98X1fxq7yuWaz+qzV5NN1XVTcs5dr9u9kGttSe31g612a/oG2o2jz6zbL9pOfZRVXVoObb/ZOn7L/s8rmajwxctbw7tfkr15pplZLUc+9jl0OfWHBi8z/LzjTW/bvzRy74f3lp7h+Ucb1/zV85e2F33vZbzHW6t/cOapWXfumz+uar6w6r6x0vZn1JVH15VP74c+wmttZPLuT+w5leoX7jU9e/WbDr5T9v8udZ3q/mV6Ct5FIQQQghX4lrFUOQ3quqTl33fpS7/otLr60//4FQ1x2D3V9XpJS/PrIeOxXOvr9kP6FBV1TRNr635gcW/aa3tLPHGO7fWesnagY7hOj6jqn5piRX6Yz+mzdK4Wtruy2qJK5aY6QU11//Tl4cx/bG3LvXRWmvvvlzzK3b3k9hyp+bY5henaXrGHmV6Qmvt8cu5P3jJ1zOX+nljzV8v+ztLrHSi5gcw/KPanmWuldiztXZHa+1prbWjS4z40TXL6H56N181f53sOdM0fSPzvexzS80PHb8Vv39ia+0pS33c3Fr7kpofkv3isn0t9gxhf0wHwNk6P/l5OH9q8wsZp2vWsx/utn9+Vf3HLv24mm/qd79I8bVV9dPLtptqfi32bM3yon9U3VcC6vKvjP16Vb1fd+6vqvkT6G+seSH82dr8wsZvVfdVsmtYB/fU/Brv/TV713xkt+3Tq+plXfqLag6QLtTsD/QB3ban1mxmeG4px89W1YfiWl9ccyB1tua/0jz2avKxbP/kmoPLs8t+79Fte1bND9T6n2ct2z6t5oDgwtIu315Vd3XHftYex35rt/0XujJ9U1UdQZ557KuuUM/Pqs2vjH1xzX8du6/mvy5+Q21+KeRf1fzK/fmag8h3wfneo2ZDzAuFr9LV/Gr2vcuxv11Vfw/HPqHm16jPV9XvVdXfvtFjMT/5yU9+8vPI+qlrGEMt2/uvjN1e80OWXY+ZZ9XmF8k+b1nTT9d8U/34JTY4X1W/U/NbKlMtXzmry79atvFV0pol3q9c/m/x3G1LbHCqqn5t+d3xmuVOr6n5baJfr6qndec/0DFct89vV9Vn7/H7f11z/HZhiRu+oqoevWx76lLX9y31v/uz+2W2Jy15u69mWeEX49xrseXTa/OLcrs/T1y2f+jSNvct1/h0nPt9lvo5VXN8/T1VdefVlHnZtmfsWXNf/tml/51d2vdvdcc9c8l3n+fzOPenLfXR8Pv3qPmh1YWaY7mfRp3cU1cZe+YnP6M/bZqiGgjbRWvtVTUHCD91he2PrXlR/4hp/gsQt39tzQ8XHtKXMgby+fFV9RnTNH3q9bxOCCGEEMLV8EiJoW40ieFCCI8U9vx0YAjbzDRNb66qd99NL6/JPqbmvwT8hZpfYf6cvY++pvn4wZqN+kIIIYQQDjwHJYa60SSGCyE8UsgDoRCcYzXLcR5f86uz/6Y635cQQgghhLAniaFCCOEAE8lYCCGEEEIIIYQQwpZx3b4y1lr7q621/9lae2Vr7TKH+BBCCCGEEEIIIYRwY7gubwgtn+n7nar6KzU77/9KVX3aNE0vv+YXCyGEEEIIIYQQQghDXC8PoQ+s+TOOv1dV1Vr7rqr6hJo/UXkZx48fn+66665L6dGHVK21jfTFixdXt1/rh2Cj5+/zx7wy/eCDD66m7fhRWJZDh9ZfIuP13va2t22kmV+rm0c/+tEb6cc85jGr22+66aaNNPPL8jBN9rud9bFWXm6z9GjfsLawtrG6s77BtnnUozanG56P+xPLj7XNaH2zfKPHr+XP8m7nvtbzgvU1YzT/lh69HrG2YF+z9hidx0jfl2wcjc5B13uOs740MuedP3++HnjggfUMhIed48ePT3feeeeltM0Ho+PPttvaZn3Mtq/NB3YumwutbkbjUau70Rhk9Hiu07aOMybjfGZpm6vJftf5ntF+M7ouWwxm19/vWjAaD1vfG12rjP2uZXY+0pdvNN7db3y+35hsNN608xn7iXH2So/eP4zOyyPj/lrHUKN9idicaFjf68/3wAMP1Fve8pY9M3S9Hgg9oape3aVfU1UfdKWd77rrrvrGb/zGS+k3v/nNG9u5ILGy+NDg/vvv30izsjlJ73cSHA2gH3jggUv/v++++za2nT9/fiN97ty5jfSZM2c20hcuXFhNE9Yl83bzzTdvpFm3LCuv98Y3vnEjzfy/9a1vXc3P7bffvpF+4hOfuJF+/OMfv5E+efLkRpr5t+Bl9IESz8e+w767FhCwLt7ylrdspNk27CvsC6dPn95Isy24P/uatQ3Lfsstt6xuP378+Eb6xIkTq8cfOXJkI826f+xjH7uRZlux7W1BZltxO6/H+uH+bD/2tb5+WLfcl/2Kc9rZs2c30mxb7t/POXvlnWXj8fawzB6gMM26Z93ZHGsPD5lf1u/hw4c30mxrnp/zHOuLsLz99Tin2pzLuh59aM45iGUlPJ7zBNuO21n2nhe96EWr1w43hjvvvLOe85znXEqzjbn22DrKPkc43nl+xg3czuM5Prmd+enHDMvKc9lcyLmA6yDzzu28PsePzX0Wg3A7j2f6tttu20jv7OxspO+4447V9NGjRzfSnN+YHn14z/qztcxujnqsLdiveC3GYPfee+9Gmus0+wavZ3GBxRGMqSyGsoeXXGtsLbF13B4mcjuxhxCkr1/Wtd0rsK0tZmJb25y2lte9rsc5jfkd/UPO6AMt1jXjeYtrLN7nWONYZf1ZjNuPnbXYfK/tdh/IumLd2H00x5GNQ17P+l5/vl/+5V+uK3HdPISM1trnttZe0lp7CTMfQgghhBCuD4nBQgghhFB1/d4Q+qOqevsufffyu0tM0/TcqnpuVdWTn/zkjcfe9hcQe91/9C0PPokc/YsEt/MvTGtPKvmUl3+h4FNP7s+nxvbanP1lnXlnfvjklPmzt1zsr9v26tyozMnKa0/BmT9ej+1h7dOff/SVUnvLwf6CQOwNJHsjxv7yx7q3NyOYtr5i49L+omF/qbU3flg//IsHt/fXZ17sjRPmhX8RYN653f4axb4yKtGwtrC/NNobRcTaltgbSvaXS5NR2F+l+/pl2e0vc9ZXRuVu9gYC05xnbM5mX+rXlP1KDMK1o4/BnvSkJ00jr9izza1Pcf/RtYHzH69nb/quzV+MYSzmIiwrj7e3IEx+sN832kelGYwBbV3jXGpv6lp57C1sWytHpDlWN7ZucTvfWLe52PqOxbfsW3xLg28e8G0vHm9rD9vG1jLLL7ezbe2Npv1Ic+zewCRdnJM4DmycWIwzKi+0eWpUZmtvwVjfZn4s3mZ7sH4tbXFUf357q5DnHn1L2+pmVKlCbFzxLc3+7bS1MXK93hD6lap619baO7bWHlNVT6uqvCseQgghhBBCCCGEcAC4Lm8ITdP0ttbaF1TVj1fVTVX1LdM0vex6XCuEEEIIIYQQQgghjHG9JGM1TdOPVNWPXK/zhxBCCCGEEEIIIYSHxnV7IDTK2qfYzX181HOBWkbTLJvWlJinQp9fXsu8QswnxvySRrwu9trfvrLAtjOvAPu6j2kxqUMla18X2Stt/lWmfx/5ZKx9RWD0ayHUj/N81KdT6z+qXzetvn1m3pz42TdMd2v5N034qA+OfZWN3itrX8ywr52YptnKZp4fZFRvbn3BvL3si277/Uz9qF8Xz2/zlK0xfd/muThuyajHiHkMsaz0CLH1lP342LFjG+m19TkeQo8MzPOHWLvaujk6N496gK3Nr9a/GYOZlwbHs83lVhc2F456ahLzobH8sW3pV2dzt/nEEKt/y2+//+hn14l9sY1eHuxL9qXS0c/KM80YhOlRf9RRj00y+gU9i8ft/mAtvrdrW/xt90KjnyK3mMQ8gojFfNZ3zD/K2tI8fcy3koze267Vt417S9sXi837luOQ+zMmsy8zcvub3vSmjXQ/x94ID6EQQgghhBBCCCGEcEDJA6EQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAPjIbSmaxv1x6A2kRpp87kxLaV5GhHqB3s9u+nTqU20NMtKXaXpXE23yrLbdtat+cDY/tRKMr+m0TbvFGI+OKNa0zWNNfvRhQsXVo81PyTqVHk+1i3LyrbgdvOZYH7NI8l8WHh+9j1LE7aNeUdY/kzD3LcP82a+CaMePSyL6bttDmPZiOm7DdOrE45j8y6wcWreDMwP5yH6bjB/vYab5zJ/IuaF/fLs2bMbac4DbFubs1k281mzObE/n/XzcDAwnxobTzY+meaY4PzFPkg4JtZ8G5nm3MZ1kox6BtnxFiOMxlA83tZpjnfGDSdPntxIs7w8n5Vn1G/KvFDM24/0+1uMY/Hkmi/gXudj3dLb6ty5c6vbbS62umLdrnnd7XU+82+1cc+2Yd+zvmxxgfX9frvdGzCv7Auj66CNO/OHtXE86vM26pW7FtNczf5Wnv36ZZmfVb//6HrG/a1shL5q5j9FDyHzK7W67X0e19o50VkIIYQQQgghhBDClpEHQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZB8ZDqNfsUZ9HzTL1ctQymobZNM373X/EB4gaYe5rXh+mJTTvkTWNbdXlZWXZRr1MqPPstY1VVcePH99IHz16dPX8zK/5ZfB8hFpN85syrwLzC1jbZm1pmlqmOU6oYeZ288QZbQt6M7CumF/r+yP+THud3zTjVl4eb35TfX45zlk2KyvnDTKqLzetPzF9t3kREPM6M422zVum97fymNeE+XH121k3HHfmsWH7m58KsbrgnMn8W1/Z2dm59H/rh+HGcPHixY05ZXS8jHqJmIeD+fSYr455Ba6tw+zfnKutD3PsW0w3GkPZ3GOePtyfvjasa6a5v8UVFhdwrjWfyNF4fS1t3nC8FtvSPHlG42HWpcWX58+frzU4dzP/Nq6J1ceobwy3270U1xrzoVkrL6/NtmC/47XpC2Nzpq3b5vto8a2ty+bFa9ezcci+P+rhaftbDGbzRl8/dl9m8SD3N29fto2tj/SFZNnYVzin0oOor7t4CIUQQgghhBBCCCGES+SBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfAQevDBBzf8RagNpL6O+jnTlfJ4O/9+tZTMD/WCvd7PdKqmted28x6xspnXh+nReTzr2vTo1DxTO8nzmVcAdalM02+D9cn8sn2s76xpnM2fyNpi1MeBmBcBda9k1JeGaepsqYc3nwgyqoc3/Tuvx75i9UON+ZkzZ66YN16bHkHmJcBxb/p087oa9SCiPp7XM/275Yd9lWPDfCmIeQ8YNs+tjSXOaZYX86oyL7Br3bd4fkv35YuH0CMDrk3mjcLxzbnRvFa4nesu1wb2eXomcEzRv65fx1lWG28WP5p3x6hHpdU188Pxx+1sG9Zl7/lV5R5FFjeY9wdhXxj1hxr1B1nL22jsb94gti6bj8yoZxH7Pfe3+wWOQ7tfMR8Y8wSy+mB+LKZc85VhXXCOMc/J0Xs3Yr6L1lbWN22c2r2flZfnN48kmxdH/W45T/Fekvnv7y3NE8jKamsGx8GpU6c20ryXGK0L1u2tt966mt++bdbm37whFEIIIYQQQgghhLBl5IFQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW8aB8BBqrW1o+qjvo+cB9XPULlJHyvOZptmON32f6Yb7/XkstYcsu+1v24npTHk+6lypnaRml2WnzpPaf9Ohjta96dvN84fnJ6N+VGveJpZXaohNt8r9zTuEfWHUQ8jGlXknWN83RjXR1DSbD4+dj/B8ffk4x5hXGM9l/kemeWbZyX69DGwcml8T69r05/v139qvH5f1lT5tc4rN6YR1z3HDOdX6An0nbNzS14Fzet+2o15N4eFhmqaNfmdeJzZ+6AV4+vTpy663djz7KOMGjhHGHezTa2upxThWVhuv5v9gMYf5ux6lBKcAACAASURBVLFtWBcsD9Mcv9xuHmHMz+jawuNZXs5n5idn9dtfbzS+tHsJ5pXrtMVw5kVnvi70LbT7AY5Tzt0836gvDMtvbWPzQu/DWOWeTWv5tzlt9F7LvGO53dZlG/c8P9tuv/6yLK/5Jtp9uXkisX4sVjB/Kc5j/RqyX8/I0brkOGOMZePW5kzm7/jx4xvpvi7WYuO8IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfEQ6vWG1J1SP37//fdvpM3vwbxFzp8/v5GmdpLaRNNIUz/I66+di9dmXZhm1zS/LDu1idR9cjvzZ94lvJ7p0akzNW8Vwu2sH2o/TZvJ85m/gB3f1wevbWkrO/uGeQZxf6bteiw7287qiphHD9uOML+mueb5qOs1nxrznWF992kbN6xLGxfmLWBtTSx/7JvM31rZ98LmaPalUa8Hlse8IMyDyNp67Xjua+uF1Q3bgnp1bic8P+cN6zvUq7NudnZ2rnit8MiAcQXHu41HYnO1jUfz8+D+jCH7uIoxlnmL2Fxi43V0XWVdMB41zyCb60brnnU9upaZ3wZZ82OrGp8f+/2tLOYdZz6I5ivDuiI83uJ781dimuezujN/J2tb807kvZj1Hdan+X315TFvLSurrdvWd8xHkdi8wLTFXKMxjI0NmydsLBC2PbF5ivNiX792bltfRr1ijx07tpFmX2NZ6JXFZx6EfXPtXmlt/ckbQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGUcCA+hixcvbmjkqO+j7pQaONvO9Llz5zbS5jlkGmfz1VnzCTJfE8sb0+blYVDbSO29eZWYNwB1nVa3pg83zTPLQ88iXn/UY8h8Zki/v+m/Tb/NujEPoJF+ybxeTf7YFqbnNm8D0+eb5tn8tqjLNc+lUV8MXr8vv+nHOU6srmzONG8D8+jgOLK+Y9p+8+ix61nftuubF4Hp4TlPWHn6/Ufb0vLCuuccR88Rm7PNW4vbeb413zvzDQgHA7Yh29jmfnqTcDzbOmueRBwz7POc29f8Jnht87qwPszzcV0x/wqbSyzGsu28vsUhNh+Zvx3LT0ZjOJvbyYhXi8UsFn/busR+OeqJyfOxbq0vW3zP63E729ruX6yvM3+2toz6SK7FrDaHML0Wz1WNxzwsi8V81hdsXScWr5sfq13f+oZdn9j5ORbW7sVGvWot/uR6aXMWz2frLbdzHjly5MhGeu3eaM0/NG8IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnEgPIQOHTq04XNgejrq/bg/MZ2qeY2YBpl6PeaH6f585hFknjWjOlZup06W2kpqIU0zPapnN58c84kh5hHENPdn/ZgnE+uHjOh6WRfWNuzH5pNiHjyjWn/zVuD196sZ5jg1Xa/ll+UdGbd7nZ/5YXv1+TN/Imu7nZ2djbR5j9n5iPk3Wd2z77IubdzbnGzlNZ8P6xvE9PRkrS+Yh4e1HffnnMo5idvtemwrjhPzxVhrq1FPu/Dw0FrbGBM2V496bu11vbXzjR5v/nZr85PFj9xueTefE1sXLcaxumLZ6SFGXxjzWSQ8nlhcwbYw/yjuz/yO+lD29Wd1b/EhsbxxLmZfMT9U81uy+HvUy8TiZ/MOtLY3L0KLwYj59vR9i3Vl48TuXbideWXZ2PYWz7MsrBubN+zeZzSm4pxrMeyoz6PdO1qMuHa/Y3OQ3efatWz9tJjOPIpsXKx5+K2NobwhFEIIIYQQQgghhLBl5IFQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW8aB8BCq2tTUUYdLPZ75EJgH0ag3iG03Xe2aHt40xjfffPMVj90rb9SBmraf/g9Hjx5dzZ/pWpl/bjctJs/P8rNvUMdq5TUNNnW1LA8xjyfSb7e8seymBzefF7Y1daamYTbfhlEfF9McE9O/j45j0zCbvt36NvXzfX3xXFa3PPeFCxdWt1vf4P7W1uw7No7t/DZH27w46rMz6qdlenyb49e82Kxsdu3R9dH8VcxLYb8eKf3+NsbDjaG1ttFPzL9hbW6r8vnN1k0bT4Tns/mz7+M29jlXmReIres83sYjsbpj3HDkyJHV7awrnp/Hm4fnqJffaFvz+NE4qc+PrWNsG2JeeOwbFnOdO3dudbt5HlnZbV20GGbU64uYF+Co74zlf619zAvM5jjmneuy+SEx71bXhPEpy8o0+wpjOp7P7u3sfoGYL48dT8xTdM2jiXXNsllMZvfBNs44D5gf0miMuPZMYs3HNm8IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnFgPIRG9IPmd0FM8z3qi8O06e3XdL7m38C8mm+KaXCpgzWtvdWtaaxZ9jVvjavJH+uLnj/Mr2nEqevl+UyHa/W9puU0/TWvbfp21o35pJg3wqhu1uqW+TVNMTFfHPOhML050+yb1rdY/xyr/f48t3lXmQeQ+VaYB4/5VlBvbnVh+nxiczr7DuvP9Pi2Blhft/Kxrdmeaxru/a43ZNQvyfoCfeXs+LVxbtr3cGOYpmm1nUZ9ZdhHzVPI5mrzh7O5fM1Xix40HMvmHWK+KJyLbK5n3XCuM19Fnn90+2hMZ2uVtZXN5bY2Wf7WYmTrh+ZbaD6DhNvPnz+/en3zBLKYyNrSfHNsHbe1jNjaMXr/YX1rzZvRYiLzsrJ+a3XDNOvevG7XfNH22s782jxl8T2xe8lR37jRe1uLY/ry0oPTPDw5juy+1MaN3dczPeotzP37/K+NqURnIYQQQgghhBBCCFtGHgiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZRwYD6E1Xa/paIlpH81/w3xtTC9v3i69htvywrRpE01TPOoDY35LprM1zx/WFdPHjh1bvR7zM6pTpW7W/DtG/KGqLq+/tX0tr6bdN68B07mav5N5BYx6n5hu1jBfHWJtZ34A5o1A1vT71k9Nn05Mn85xx+3Uq1u/Nm8wm1eYZt2z/NzOvj3q4zbqYWLzoM2ra2uW9Vuem3MW68rWBPNZ2K+f09q4GPEKDA8frbWNdrO4w+Z2zgc2nmz+srjGvPvW/CdsvJivofmr2Vxk57fzmSeRxa8WF5iHj3kSjfrwmB8UvVKsr6zN/RZTma8L+7GtE+bvxuvxfDbubN22cW2+jBajmdcKsb5u927E1uE1LxV65fFa5tvCstucxLY1n0Pz5LR7GfPNsXG3X19Iuxe1+3ybJyzG689n5xr1S+KcxDnR/I+sX9u45vXW7uXW4rO8IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfEQ6vV+1L+ZxptaSmq6Td9ObSNZ06HulR/TD/bH27UJdZHUlZrGmXnh/tR/m9bf9O9Ms+6ovaRO1zTeoz4w991332r+TCNt1ydrPjs81jxxrO5Zl9REnzhxYiPNsnLc2PVGNcXm02Kaa9PP71dvbnr50bG6ll+rW/OPMq287X/kyJHV67HsHLc2Lnk+zomcd6z8tgYQayu7nvVtq2/WX38967ejZTO9O/c3z5HR/LAuWb7++jYmw8HA1rW1Nq4a92iw+cr8LWytZB/u1zrORXZtxkhMj/qwjHje7IXl19qSjPq/mX8F88P6ZvltHbe+Z/Tls3ObRxB9WVg2bj9//vxGmvGo+axwO9fl/frAWN+0eymb383Pys43mr81/ztbw80Llv3UvLaIeXdZ/GvxMvse5ylidT06j5hX2VqMVHV5eUb9q0h/fl7r3Llzq9eyOYfjnvexvBezspqfkvVN60tXIm8IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnFgPIR6DZxpis1LxHSpps00fZ5prO34Bx544NL/zQPI/JBGfVlM69/nrepyX5pRPwrzFKKXiZ3PNNDUzZp+3zCd66h3S99+1PRa2Uy/zfMdPnx4I72zs7ORZluPav1N92raftO7m2+MjcPR/NlYYl8Y9Tjq28f6tenR2faE44gwbzbOqYkmll/zFjAvhFE/J2tb88Uh5tlkflRrPjrm42BzDo/nuLW8Wl8iln/SzzPxEHpkYN4lZDTusLWPjHq7ML02H5gvCeF4Mt9FmzvW5oq9zm9z46iH0H49zMxPbtTnhXGJ+eaYv93anGNeIMwLPX/oCcTjuZ3nY9uZtxb7Arez71ldE6s7iwHtXsvujQyLm+xecK385l3FspsPoaUtnrd12dp21HeSbcO+ZPGv3ZsyxrQ53WIqu3/gWFnzb2XeLB7lvGHzCNuSnkK8VzOfYntusHbvtOY5mTeEQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DLyQCiEEEIIIYQQQghhyzgwHkK9xs20kaZvp/6OWkLq7cwXxzTKPL9psns9IY81jTDzavpswuPpUUTNLTXT1EJSV2raS2IePFZ+08WeO3duI83y0BuFaZaH5aX20/pSnzbNsOnBzWuL16ZfE3WzNi5MD25eBeYJZL4vo9c3jTRhfdv19+vz08O88ljzOSPMK+cVG7fmGURG5ylqqDkP7VePb3PwqA+OeSVY3+/rw7ymzEuM29mWbGti2v7RspkPRj+u4iF0cOnbxubqUcxnZs3XoMq9T9b63F7b++ubP9LoPG9+bOZ9wetxPJuHDs/HGIj7M05g/pm2vsHrjfrPcW2wuXs0xlzra7bOMEZiPMmynzp1aiN9+vTpjTTnTtYVyz7qn2r3MsRiUPO3Ml9L8/SxGNTGAhnxUDI/Jl7LPCYtPrc5z7xrre1Zl8z/iO/hXttZvhHv3L3OZ/Vta5B5HPX1Z/Ex82ZevraGsGwsi/m/mp/TSLy7Nl/mDaEQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAMjGVt7LXf0s5j2ii9fReNrjvZKrknY7FXA/nr2Sqd92s+kD/a6MuvKXm2zTy/aJ1jt83z26p69/muvIfL69pl6vrpnfcskgP0rwdZ27BvMu70+PPqpXn4i1V5rtM9l2zixvmqSL5NuEpMtcDtf37a+aDKvNemNveo9+slQq3uTQprEjPll2SlPZNvap5L56r29njz66Whin8sd/Qz3moTNXs22cWHrk712z7bZr4TNJG1X+7pyuHFM07TRTywOsLlv9NPl7DP7/Xy2STz763Oet7HNslvdcG6yTwSbHHZ0buJcbuUb/ZS3zQejsidbR7m/ScVH5pxROR+hJIzrmH123uJpK6uty5Z/xh3Wd+z8FkPZ+YiV1z4H3q+VNo4tLxzHo7J2ayuz5zBsjrb9LX61/W0esHsl66vErtfXn1kMjFou2Bxm6xv7ksn/rG891DgrbwiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWcWA8hHrNm2miRzXWpjmmXs+0i/YZ3pHPX695zOy13T6zaJ9vNh2p+UUQaqL5iVLz8DEtp2kzra5Zf6wflo/5ZX3s7Oys5s/61tq5uS91qiOfT93reLaV+bqYbpVtbW05+tlN84mwujavBfOZsO2jn1Rd85ExHzLWtXkImbbePIvMF2P008fcbvMa64N9l4z6uJmHiXk22by2drx5BJl+nXXLcWpzpo1D+wy9zdGj81S48UzTtNEv2b+J9SHzPuHnuol5go1+qn0tv/YJYVsXzQ/C4kUez7nYxrvFdOZNaJ+VZ8xEbL6yGJI+jYT1N+rhueZ/Z956tn107rN+az6FbDtuZ9/l/seOHdtIW4xpc/+ot6HdyxHzg7J7P/Od7DEvr9HPoFteLGYzP1TCtjS/VvNMWvNdq7p83I7c++x1PjLqDWb1tVZ/o75svDbHmdUd2+rMmTOr+xO7T127F1m7r8obQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGUcCA+haZo2NHPU51HDbB4JxDTIpkM1jbKdj/T7m+6S2kSmTYdKraKVlec37w7WBb0BzJvE9Pn0VDKdL/enFpPlMS8DYrpg8zjqy2v7mr7a+hkxbwCmzduAdWm+M6YpHvWTMq+AEV1tlWuo6Y3A+rDzj3h08Nyj12bds22s7KPH8/qm9ec4Nd8abn/Tm960ml/Wz5qPxF75s7Fn9bGWHtXOc45h32HeTTtvPhVrc9ZejPgvjc5Z4cbA8XHhwoWNtI1nbuf8RWwtsTFjcRPpt9s6wrIzr7aO2ngzbxIb7xYf21xtfhTmgWQ+MaNrk3mzmG+P5b9vD1tXjxw5spFmWdjvuf+pU6dW82KYBw7zb+vmqO8iMZ8bps17hZifnvk8WlzU5898Cs33xeJZ6yvMq/m3mi/NqLctj7dxZ/dyVvdse9anxVysP/ODZfnW1iDb1+Y4Xpv3seZpZ8db25tv3dXGYHlDKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SFUtamJM+0m09TLUWtoPjumPTRNtvllrGkjTe9sHjWj1x71DjHNL7dbXZovjHkemRcB4f5MM39MUxdr5bF03/dMR0rod0Ttv/kjUWfKuub5zEvBPHeItR3Lb+OOrNX1XucjbGsrH8cmYXn6/PBaHHdHjx7dSJvXgWnrbdzRa8D02uwrzJ/p7bk/y8/8nD9/fiPNuj1z5sxGmuVjW9k8afMg88/ta94MbCvzZTAPoFHPDfMq4Dxi+RsZJzaGw41hmqaNduf4tT5mPjTmuTDiu1jl3ic23/TYeLOyW9lsO+e6/caXzL95hBGLGbn92LFjG2mWj2uJxZwW19j8x/pcW2vNj5THcv/Dhw9vpG2dZkzGvkEPIpZ91IONdTnq6zLq+2h+UbYWjd7fWN9ke/R9w3xXGDNYDMVrsd9b2urK5hHzEjM/WLuPHvWJNO8zu88e9TYzn7k1L8TRfs5xz75kc7jFTOxrbDuWhW3P8vR9OR5CIYQQQgghhBBCCOESeSAUQgghhBBCCCGEsGU85AdCrbW3b639TGvt5a21l7XWvnD5/a2ttZ9srf2v5d+T1y67IYQQQgghhBBCCGG/7MdD6G1V9Q+mafq11tqxqvrV1tpPVtVnVdVPT9P0Na21Z1TVM6rqS9dONE3Thh7SPIOonaRej/tTW2iaaurvTK9uWsg1XxnTQ5t+3bSIo14iVhfmtUGYf7aNaaqpweb5qPMd1W6aTpjXo66VjHg4mabWxsGoR5D5PRm8Hr0DeH5qkNmW7FvMr3mVmF6d2+1485sy3x7zo+rLx7Lx3Kw7y9tov7Z+avOKeRusaZj3SnN/aqK5P+uHfYdjgeczrwL2JfPV4PXW/KzMS8u0+la3tobY+W2OZ9r8W/q+HQ+hg8k0TRvtbnMZ10HOX6Prsnk4EI4h84Vc66O2r5XF1hWe38aPYV4jnOvM95DbLd61+rH4mW3HudM8hCymHZnbzfPRymrrpMWfNi5GvUSYP9b16L2SrYvE+rKtFaP3E9a36CfV1495/pinJsvC8zE+Ni+tUY8ei7dZdvYd+jKy7ekZSph/jmOWh+e3cWxewYT1v+blZnOw+RVZ/G4xmW23eN32J/3x18VDaJqm107T9GvL/89V1Suq6glV9QlV9W3Lbt9WVZ/4UK8RQgghhBBCCCGEEK4918RDqLV2T1W9b1X9t6q6c5qm1y6bXldVd16La4QQQgghhBBCCCGEa8O+Hwi11o5W1fdV1RdN03S23zbN7ybt+X5Sa+1zW2svaa295OzZs3vtEkIIIYQQrjF9DHbu3LkbnZ0QQggh3CD24yFUrbVH1/ww6Dunafr+5devb6293TRNr22tvV1VvWGvY6dpem5VPbeq6p3f+Z2nXq9pOlPTbnI7taCm6T5y5MjqdvMqMV1un2bezO+Iuk3TzFLXadp9ahF5PDGNNXWrZ86cWd3O8pgmemdnZyPN+jGNuPkHsL7Mq8T8BfrjR72n1rSfe13Lzse6PXr06EaafdP05Ox7THN/84EgVrfMH8tnOl9uZ/6Z31EPqL58pp1netSPyXwsiPUtGxfmrWX5szmVbcHy83j6aHC7tRUxjTbrZ83rwPTqlhebw9lPRz30bFya9wHbqt8/HkIHhz4Gu+eeeyYbwz3sIxY3sA8S84GxPmm+OmtxCo81r5D9emoRG88W04yuQ+ZnMRpz8mEij2e/Yto81SwmtDiD+/f1ZX5rlndb13htYuPI2tK8B7kOEluLRjwxr+b85n1oXilM83iL4fq+wTmKdXf48OFaw3wbzYNy1N/VYgxrG/OhpEcoy8e+z/Ozrs0zaO3eqOry8rEvW4y7FvdYP2Ne7T6VbWtzusV4dv1R/1eL73fZz1fGWlV9c1W9Ypqmr+s2vaiqnr78/+lV9cKHeo0QQgghhBBCCCGEcO3ZzxtCT6mqz6iq32qt/cbyu39SVV9TVd/TWvvsqvqDqvrU/WUxhBBCCCGEEEIIIVxLHvIDoWmafqGqrvS+4Ec81POGEEIIIYQQQgghhOvLvjyErhfm12B6cfOJoRbStIzmmWDazjWfIOoczY+B/kbUuZqO88KFCxvp8+fPb6RZlzy/+V1Q58nzUQvJujU/C5afdUsdr+lMra+YXwDLYz47fZrHmvcAz826s+PNp8U0zoZdn21jbWXj3LwbqAk3Hx7zDBr1DmP51urT9N7Mq3no7NcTyLwMbFxwHLFurO04zlnXHDvHjh1bPR/Hio1blte83swnpC+v+TZYv2Fd2no52o9HfdzMS6HPfzyEDiattY12NE+f0fFMzC9jdK637Xa+tWNZdltHzN+B1x6dyzm+eL3RucziCPNvsrna6sNiOGLxOa9Hb8S+77GsVlfmL2WeQqxL+tgw3rYYkL4vHFdcF81H0vyqbK0y/yv2dVubRj2FbO3sz89+a+OQmI+jee6M+j5a3dg4Hr2vZtuYfyrHDuuH+bN7RZvXzDOI9Nvtvo+Y1y4xX2PCujCPIfN9Wxvna3m5Jp+dDyGEEEIIIYQQQgiPHPJAKIQQQgghhBBCCGHLyAOhEEIIIYQQQgghhC3jQHgItdY2NHfUXo7qy0f9NLid1yfUQlIvSP3emg7XfFWoVx/VPhLWDT2FWHbTlRLzTxrV+lOHSt3qqP6c9Wd6dJ6POlXT6a75BfBc1q9HfWHM64qMjiPzFtiPz8pemD591OuBen3TPJs+faTvWT83jwzWvflG2PnYb03fbr4NHFfcbnXL/a28pidneXl905/Tc8j8AZi/ft5iWW19s7ISbre2p3eWrUHmmce0jftw47l48eJGn7a1xnwV2cc4H/D81kdsLbH8rs1nNl44N584cWIjbWWzdcpiDJ7ffG1svmAMxbay+cnGv5WH5+P8MxrnsPz0hlnzTOM25pV1xXWAHj4WT3IdM49PYus66559xeJ3i8/NE8k8g5g/83E0n0aL/y2u6LFxb3Vh/qR2vdH4ndu5TrPvMn8WH6/FMFVVZ8+eHdrf/LfsPpptbf5WazEpxxHPxboxfybm7dy5czWCzUN2b2ieRVfr3Zg3hEIIIYQQQgghhBC2jDwQCiGEEEIIIYQQQtgy8kAohBBCCCGEEEIIYcs4EB5CVa637DFPBG6n/s+0iKbfM021aSP7/I5q32276cHf9KY3reaVmmjT6Jpf0n49hEzrSZ3qWl1fTf7YV6hpNl3u8ePHN9JrvjI8FzHNMOvK/JFG/Y+sb9r5WD7TsZpXCdvW9OumV+d2no/beX5i3gt9fZmnjunHrV+b/5O17aivhPlI2BzLvjra1827gNczXw/zZqDXA+trzU+AeSPMC8cR6+b+++9fPZ+th1Z3o35Na34vI+t8ePg4dOjQxvxrvoqj3oU2v9l4trXQ/NvW5htuM08sXotedLfeeuvqtc2HheOdfhTmAWTlYduaL4utRSy/eTKxrTl/mZehnX9nZ2f1en1MZ/3Q/JcYL7Ms5r1njHpUmv+pjTOLiWztsPsRW2vMQ2jND2qv/I54jVnZLN6185nHjdUVsbqxeczmNV6f45zX49iwOIZ+XIbFtHb/suYbx7Kzbi0mGvURZtktP+w7tqaseWmt9du8IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfAQmqZpQ0Nn+nTTEFM/Z5pk6vtMM206XtOzr53LfFPIqNaeWkjzl6BXxmhdrXlp7HW8aS2pdzcPIZ6f56MWlD423J/1Y1rQtb5p2n7TFFNHSv042455PX/+/Or+5utCrb7pWs1ri23PtmT9cJyx7cwTyLaPehmYT0/ffjZHmD7dvFj2m9e1vO91Pqsr1rVpos1rbNRjaNSTieUb9Sqgl8SRI0euuC/nZFtPbBxZW5qPmo1D60trXmbmXRVuDK21jTFufWrUC5D7E/M+sbXPPCCYnz5uGPVdsbmxH+tVPn6YV65j3E7fGtYFYyLzBOJcxThhdL6x+Jgwv6PzF+OU06dPb6RZXydOnLj0f7bVmu/IXtfe7zpjdcOysa+ZjwvXFvaF0Xjdrm++NlYfNm+wvcwbcC3OGL0X4Xa7Nve3mGnUm9aONw9R87Hk+Tkvcf9Tp05tpK2v2bxqHqvE6qNvj9E5y+JfW5/sXsa8e5kfjmuL6db6/cZ1rrglhBBCCCGEEEIIIfyZJA+EQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DIOjIdQrxc07SX1edTEcX/TbppnEbWT5l3C7Wu+CXZt80fiuXt9dNXlWsPDhw+vno8a3TNnzmykzW+CulXz1DFNNdvadLHUvxPT75tumG1tWlRqS/v24L4si2n1WRdM0xOIXgHUrXI788Oy83rsa+aTY+Un1OFaW5nPi2m0iV2PmFfZ2rlMX23jiJgHj+m7TTPN85kefVRPb2mrD+aHfdf6Ast3TqG8+AAAIABJREFUyy23bKRZHp6/z4/1c+tXtiYwL+ZhQmx9NR+gtTk/HkIHl7W2YX8eHY8cf+yTHK+MM8wbxOIIrk19fs03xtY95o3pY8eObaQ5dzCv9PZjXfF4rvNHjx5dzS/XfWtLtgW9PWw+4vE295svo81fPH7Na/H48eMb29jWLJv5ooyuW8TiU4s3zavEYhJbx1i3Fg+PXs/GtcUZ5lVofWft2hYPE+vnFm9a2QnbajReteuzrs1/1fxgbR4xn0nz/VmblzhHjfpBcQ4e9bAjNq+Y/xMZ7WuXjruqvUIIIYQQQgghhBDCnxnyQCiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQt48B4CPWaOOrp6G1iulDTXlLvR305j+f+9OGhbpWs6YJNo2x6btMcU09uXiWsC+pCz507t5Gm3t00wdw+6ilhOlOrP/MqMX8Pyz+1p9T39+c3vTbbhuOAZWXeuT/bjsfz+oR1zb4y6tljdWk+L+ZtwvowTyMbS8Q02rxen3/TAPNcLMuoV8Go35Gdj2UzXwzzhRj1VrB5xrzWRss36kXAsdH3FfNJGPVjMX8k2855wPxYbNyxr/XnX/PRCjeOaZo2+oV5Fqz5slRd7kVo3ifmGcTrc76x+WNt/uRYt5iIMQ/HB/NGX0du53ixuZrxJ/Nnvotr47PK42fzqzOfSZafbU1s7TN4fO+hZNc2zxuLF62u7N7DYrLRdZ9tY76MNg/wfLZu7vd+gNuZf/P1WfMOs7bltczXjB45FjPYvYTtT8wDiFj5bFzbffLo/YPFQTb22Bf6/LBu2FZ2LubFfN5YV7we79PNR87aYm0NWes3eUMohBBCCCGEEEIIYcvIA6EQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAPhIdRa29C8me8LfVmo5+Px1POZ/s+8UMxTwfwuel2w+cb0eue90qZFNF3obbfdtro/887t1I3y/JZfltc8JswDyXxoCPNren7LL69H357eS8W8AlhXp06d2kibhxC3c9xwu/Vz07fTN4LnMw2zwbrmOCbm0cT8mS8Ny2/69zUPI16beTP99+i4MT32qG8N4XaW3eqa12fft3FpPhPmbWAeJ8Tag/Nw7+fFa5vnD+uKZWFf4Tjk8eYTYf2c12f+18aRjdlwY7h48eLGemB+aNYnzCuEaXqM0X/CfLaI+ej0Y8B8WGx8mLcd0yyr+VMcO3Zs9XibC82Lj+WlfwXPz/2ZH/P2MA8zi/+5P7G+0rfHmr/QXteyeNPWDfOrsr5i17N1eHTtsfje5nPzNBr1p7P7D4vZ1voO9zV/J7s3sHspYl64ts6yre3exKAHqXlwjnqN3Xrrravbeb9inkUWB/XPAayt7FqMTy0+Nl9i88gbjffX5pF4CIUQQgghhBBCCCGES+SBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfAQmqZpVdNNbSL1ddTzmXbRMF8Z8yYx35w+bXpr6rl5LvPioJcFtYssGzW41Nzu7OxspE3/blp/lsc00qb3Z1uYRtrg+a3tmV/WX69VpW6VmmXWDfXk7GfMG6896t/E/LBtud08g0wPzrpk3zJvB2L7mw7Y2przjHlNrJ3b/KRMP26+Z1ZXxHxmrO9Y3bFvmh+WeQfYmmF9n3171GOJ+Vtre6tL1oX5SVnf4f5cE6zuWVbzFCGjfS88/Bw6dGhjPmefoZ+E9QnrI5wfbK2wudfm4jW/PJubGIOZN595XzB+5brO7YTrrnnucH+2JWFbmJ+Fefowf7y+zcXmhcK519aCPq6wuWl0nbK5mvB49gXzxLF42O4X7H7A1lli282jyNZdmxesvtbGPc896pdk17bjzbvP7kNtXNq9kN2LEebH7hcI82dxCX3lmH/WH+8f+r5m6xHHJdO8NuvWfBZZV4R1xzmTdTF6H3wl8oZQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW0YeCIUQQgghhBBCCCFsGQfCQ+jQoUN15MiRS2nT9Y56fzA96nMz6pdx5syZjfTp06c30r33i/lFmN6bZaFu0nSXLAt1oSdPnlzdn7AuTOvP87Ftzc/C/DhYfjue16c+nVDzbRrvXltKbb55UbEvmMeQ9S3CvsE0da82rqwueLx5ElnfGdW3m/7ffGVsLJC+vKaNt35qjNaNeQaZV9ao35X5rBHmx7zPmDavNWJ6fRtLa/OqeX5YWxAbt5Y368dWVs6xbMu+LcxXINw4+rZhmx87dmwjzfHNNBmdK2188noWA3Lt7M9v6+y5c+dWtzMvXMc4dx4/fnwjTb8JjnfzFCL0MGJdjPo2Mo4wrxXub21va6HFeIQxMPfv22/NY6bK13ybm21/8yQynxvznTEvP1vnmB/zrWF+Rrz19trO+rexxvyR/nzmj2R1Tczzx2KSUR9D87lhW1tbjvppmU8lt1vcYfeyhvnYrcVgVpej8Z95TPL6XF/75yF77W++dmtz9Fre84ZQCCGEEEIIIYQQwpaRB0IhhBBCCCGEEEIIW0YeCIUQQgghhBBCCCFsGQfCQ6i1tqFro3ZwVK9Hjbcdb7pe05byevQMeu1rX7uR7nWw1MSazpR5PXHixEbadJlMU4PLujJdKPXto74y3H727NmNNOvavEZ4frYldcOE16MO1zTl1l799c3jx/yN6H3AvDLNsnOcsW1ZF8yvafV5fXoNmL+U+Uhwf9Pbs3zErrfWlqPnH9VbW1msH9ocZppqtj3rmnMgfSyYpgbavAhMv27eNOY7wb5p49w8n0byYuOWaerRzQuLsC5tzuYaYb5uXBP68lrewo2htbYxpkbnJ/MiMa8UW3dHfSCZZpzVn5/xGvelhxA9IlkXHC+33nrr6v48/6j/m9UVy2N+b/v1rSTmo7NfDzObe9f6nsWHtqaP+iOxbkd9Xux65j1onkKsa/OHGs2vrZsWP7Ov8XiLE/q1idt4LLF1b/TewNrSPIE4bi3m4vF2vVGvQ/PjIlZ+W2NGPZf6ec/KYnXNa5k3r/VjptkXef21GGuv/PTXX5svE52FEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWcWA8hHrNGzVu1EKO6lRNz079Hr1ZzFuFvjdveMMbNtJvfOMbN9K9HpB5t7KYppf6cNMymo6W26mtpJbRvE5MB0ptJ/X95mtjvjLmn2EaZ+tbpqnur89+ZprYUa8rq3vTy5ue3Hxn6KVA7HjzVDLvMPOL4na2ldUXx5r5cfXnt35rbU8fGfZzG/eEZeX+ax4cVZd7AnHcsq9bW5svhM2LNmebN5jV16iXw9qxNqeZPwr7LTEvMWt7m6PtfH15R7yWwsPHxYsXN8Yo5xf2SeujnIs5V5qPDPsQYzTz0eL51vLDfc2rj3Mh00ePHl09nnMV1w2bu7k/xzfzz7JbXZmfBucbjn/OD6wfizFZPs4Z5ldl5eu329zIvFgMxLwwbeOC52N+rG3MB8bGjXmjsC1t3TUfGfOHsns9Xp/1ZR5DPbbOHj58ePVadp9p3l52PPsOYy7OM3bfTNgX9jtPmJ+teQ5Z3xn1IlvLCxmN71h2uxfY7328jcO17fEQCiGEEEIIIYQQQgiXyAOhEEIIIYQQQgghhC0jD4RCCCGEEEIIIYQQtowD4SFE/brp50xbab4vTFNraTpb6vPOnTu3kaZnEPPXX9/8JJi3I0eObKTNI4fbqcXn9bidbWH6bXoK2f7EtJnWlmwb07ES08kybRroNe8SK5v5qrAuqSNlPz527Njq+c0/ydqGacL8UINtXio2VswPYL9+ATx+VD/f55/XNv02Md8ES5t+m3VtPhqcA+mrxv0J65L5s7R5N9i4N48k64u83trYtXHNfk2s7xA7P7fTA4XbOY5ZHq53NkeGG8+DDz64MYbNK8V8ZehvQcwPz3xobO41f7k+rrG5gjGRxWTmfceymI8jy0qYX65j995770baPIfMl8bWbfMyZH0y/6xP7j/qxbLmq2NeIdbPeC3WJduSeTWPn1H/OF6fdWteXeb7aH2X8b+V33wrR739LI5Ya2+L96wuLT5mjMQ5yeqK+3MeMs8iG9c83jx67F7OttucbfOe5Yfn6+ctu1cyb6rRurW+wXE7ep/MthuNKXdJpBZCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfEQ6vWR1NOZbpT6OWI63L3y00P/jNOnT2+k6Zdh+vleZ0uNq2kRuZ155f7My87OzkbaNNHmFUBtpflF8Hrmm0NtJzXOzB/Pz/ownxde3/ysTNPN7X1+bN9RnwTm/fDhw6tp0+gS6wvmfWBtbf5X1resbdgXmD/z2WGafZH1wfz0+7OspmEe9bYiljfzyKF+nfPKmTNnNtLUt5se3/q+6cNZfza2zFvANN48n/W9fp4c9SLguDefCPNRMG8wpjlvWF2v+clcrZY9PLxM07TRbjZe2cbmj2HrsJ3f1nkb72t+E6P+arfddttG2vwibPweP378qvNadfncyv0t/uRcbT40NhebZ5L5YfB4+tAwvebJeTXX769nx1rbmg+LjaNRjyKLwcjaOrTX9WwtsHspWzcZM3G7+VqOeruQ/vjRexumiXn88D7SvLu43c5nnp6WtnXc7sNHvcSI3Q9Y7GA+On152A/tPtDWBLs3sLa09YttxRiM6bXnCGv1mDeEQgghhBBCCCGEELaMPBAKIYQQQgghhBBC2DLyQCiEEEIIIYQQQghhyzgQHkJvfetb6/Wvf/2l9M0337yx3XxaHnjggY206WBHNdCmC6Y+kFB7evTo0Uv/N325+bSYLpRQb25eGtQmmqcO6878oMwzybSc9LcwXxfC+qQO2MpnaZav78ts+/3qu0f9kTjOzFOHmNcB65LlM58H8wzi9cx3Z1SPb+1jPjxrfZnnGm1LK4t54pg+nHOqeQjZvGB9l5jvDbE53fTxpk83bwPL75p+3tYfGyeE3gI2J5qPhc0D5g3Q9414CB1MHnzwwQ1vGfObM28Vm2/ME8Hm7lHfmrW4yPzJeOyRI0c20n08t9f+LJt5/3GMcC42uA7xeJaX8wUZ9eC04wnnJ9YP829xgvlv9Ntt3jbfQWJta548ZDQGsvjd8m9jwTxALf41v1eLGywGs7Wxn9cYszBvdi2WlT5qdm/Dfm11xxjMzm9z4ojfUpXfh1u8bHO4xRG2prB91s5n64vdO9kzhdG2Gh3n7Ds2B/blXRsjeUMohBBCCCGEEEIIYcvIA6EQQgghhBBCCCGELSMPhEIIIYQQQgghhBC2jAPhITRN04amjtpO6mDN/8H0fdzfdK3my0Oo56MWs9dWmhb+lltu2Uhb2agtND05fWTM58W8A0y3avmjnt307+ZNwrpfa4sq92oxna55PPX5G/X84TgwTyBq81l207ObHt48dHg95p+M+luZTwwxvT7b0jTjo3r8vn2PHTu2em6rS+5PzbJ5kZkem3p480dimn2Zbc+2oGfJzs7ORto8l9hWLL9puEfHno2VtfON+pqZr4LNueYJZN4ANseOzInxEDq49P3M1nkbX+zDPJ7bzZPL1hrrw9x/bTzaumh1w7ma67LNLYR1bTEU1wLz8htd54l5alp8Tex6FqNZfN+3n8VUFgOM+qLYOGBdjvpImheJ+axYnMC6tXszi/+tfrmdY8l8a9b6Hs9tc0jvsVZ1eT9nzGT3LnY9i9GIeWgS66s8/vjx46vHm4+ljZ393o+YV1tfPhv3+33GYN65xNp2NN4mff7XYrC8IRRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWkQdCIYQQQgghhBBCCFvGgfEQ6vWW1PNRi0nMT8K0mOaJwPNdS68T07GaN4WlTdc5mnfThZp+nG159uzZjbTp35ke9Qga1cebJpvXM+1pz6hmlrpXegRZ2jTC5i2y5odUdXld8HrmjcLymebY/K6YH9NMmy7X5gHzAerLx7o8ceLERto8MFhWjivz+DDPD5s3WDfmF0WPILYdvdK4vx1vnkq2Bph+3+rfxmrf9+xY6/emJ7eysO4I+w69E0w/v6aHj4fQwWSapo0+bjEV4f4cr+fPn99I0wuExxObv3i8+T6u+fbwWFvjiXn3ca4/evToRtpiHs5FHK/mH8cxaOvcaMxk3ijczvwSzm+WnxFvGOaNx9p21p2VnWUxD0uLkYj5zRErn/lnse3MV9FiJvNCsfsLejNyHlqL520cEa6L9BDiPMJr2zjkdusrxHxyiPkoHjlyZPV482lkW1na/LQsnmd99Wm7d7B7Fe5v917mKcS0eXfZvGL3ZlcibwiFEEIIIYQQQgghbBl5IBRCCCGEEEIIIYSwZeSBUAghhBBCCCGEEMKWcSA8hC5evLjhgUF9HDXWpv8zjbFpQ3l9no9p6gVNq9nrA03/bF4e1IlSO2i6VWr7qTW87777NtLUzXL/CxcubKSpqz19+vRGmh5CPJ7l5/7UjVIzTG8S5pf1YVpSYh5FPJ5azzXYlkzz2uaZQ0b14zwfr28aYl5v1O/KPI14Po5LGyvEdLujXmX9dnponDp1avXc5sdk+neezzxxzK+K21keztnU9q/pu/c6H8ex6cdZ9/RYMl8L7s8069M8lPr8mu/b6Jpg48706Dw/52zOuTYO1ua4eAgdXPq2sbmM45N9wMbzaB8aTY+sNeY7aDEV9+fYp/eG+aGxLlh35g83Wle2zpkHma2jo9581jcsprUYq7++efWxb1jMYTEKr8e2ZN2aJ455B1oMZWsNYf1YjEpsLbN4mti4Z330Y4nntnWOZee9Eb2+LO82j5jXn3mEmm8MYyqez+4vrK9YzMn6M08hYmvMiE+d3ffZemL+RtZPGXPde++9q9tHx7GNy13yhlAIIYQQQgghhBDClpEHQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZB8JDaJqmDU0d9XjUgZpW0vR1pjke1WKalwr1gn3atPOmn2beqDGmLtTKbhpc059TF2qeQvQw4nZi+aFW0tqOWlG2nfn2cDt1udQV92key35HzO/IvACIeQXYuDHdLLG+N6qDZX5H/aGsrYn5+Ni4X/PooDcX9c/WNuxnLKvpv+l7ceLEiY30zs7ORpr9nGn6ZtBTyNqS+Rn13WDdc3/zqeC8yfLxePMQ6s/Hfa3fmZeAeeSxb/B65stGuL/5VvR1FQ+hg8k0TRvzsc3l5iXC4zmfsU/aWjXqU2O+WX3+OL441i0vnOs4V5qvIccbfWHMr8n84EbHHOcuq1vOlZzfmH8y6inE+h71k+vztxabV7k3n8Ucdn7WrXmXmLee+asSK5/dH9j9C8sz6o1oa5/lh+fr70c4zrgv70UsPrW24rgyz0u2Lecdnp9tbb6PrFvOU3bvxHnA7k0t/ja/VfNYYn2tzWP7HRcsm6XZduanRO8wegoxPTpur0TeEAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHL2PcDodbaTa21X2+t/dCSfsfW2n9rrb2ytfbdrbXH2DlCCCGEEEIIIYQQwsPHtfAQ+sKqekVV7Qqnv7aq/u00Td/VWvvGqvrsqvqPaydorW1o7qhFNK2meQaNepWYHtC0mOYt0uv9qP0j1IFSd0odqem5zWPHNL3WFqbL5XZ6CZhXCrcT8zji9Qn3p67WdLNGvz/rzryr2HamR2dZePzouCLc3/qWeRswP2TUM8m8wWweYNty3LMvjOjnTUtP7y1ibce657zAfn38+PGN9OMe97iN9K233rqRNs8gzomsO5sjrS14ffPRsXmNmC8FsbHb14f5PJi3ANPM6+gcbD4MNm+YF4CN63Djaa1t9EtbO8zzy/qM9bFR7z6OAfpcrfm7MYaif4PFh2t+YXtt5/hmDMTjba40vybuz7rk8dbW9LuwtjIvQvMusbjE4g7WZ18ei0HYNrYOEOvX5o2135jJvENYHhvH1ldGfSzNV8bmDa7z7EtrMRk9gtivR71oeW3WFccFfRWZ5jg2v1jOEzyf3ddyf/PiYhzBurb6INZXrD7Zd7l/f36bU8z7y/qGPSNgvzUvMFtTTp06tZGmN6/dT+yyrzeEWmt3V9Vfq6r/tKRbVf3lqnrBssu3VdUn7ucaIYQQQgghhBBCCOHasl/J2NdX1T+qqt3HY7dV1elpmnYfFb6mqp6w14Gttc9trb2ktfYSPoUPIYQQQgjXhz4G41/HQwghhLA9POQHQq21j6uqN0zT9KsP5fhpmp47TdMHTNP0AfaZzxBCCCGEcG3oYzC+3h9CCCGE7WE/HkJPqaq/3lr72Kq6uWYPoX9XVSdaa49a3hK6u6r+yE5000031c7OzqU0tYmm66XezvR/TFOfx/OZZpzaS9O59n+NMw8dXot5MQ8hPmxjmucnoz40ZL+6UNaHeYfwfNTVWl+x+jANNfPD6/WBt3lbjWrzR31TzIuAaesL5tHD7VaXo55G1hdZ3+YBxLFmx3PeYnv0+TfN8qgvgnkEnThxYiNNz6Dbb799I93Px1WXewpxnHAeYnmsLxHWLfuOtRXzY7423M7zc54enRd5vhGYN5sXzNOEdTOKjQObZ8LBx9YWtqnNZzze4gLzg7D5gPvTU6EfExxP9EMzP4jRuc18DDnX3Hvvvav7Wzxr/nK2zto6aHEM68u8VNi25rNpnmhr3iXmkcN+br6OFhOZ36jVLevCvEssv6MxFctjcQph24x6glqMaj41/b0X34qkp9BoW5q/K30YGZMxbednDMeycx4w30nml+VnfZm/rc0j+/Xj4vEjfdPmvP2mzRvM4ne2hfnQWdn78q7l7SG/ITRN0z+epunuaZruqaqnVdV/mabp06vqZ6rqU5bdnl5VL3yo1wghhBBCCCGEEEII1559f3Z+D760qr64tfbKmj2Fvvk6XCOEEEIIIYQQQgghPESuxWfna5qmF1fVi5f//15VfeC1OG8IIYQQQgghhBBCuPZckwdC++XQoUMbmjnqSi1NXa1pus23xjyF6M9h2kge32v4qA20vJqO0vJGzLvE9N/ENL2mx6dm2Dx9zGuE9WneI6YD5vVMY72m7TSfhlGfFdPSm37cfFhML0+s7tk3eP1Rvfioh5ClWX88v3mnjLSnaYBNT04fBurR6QF0xx13bKTpm3Hs2LGNNPXqlj/zCGFbU5/O7Wtz6NVcf7/eA9YX2HdGNOWj3lrme0GsX3PNYN5tTrNx0NeV5TXcGFprG2OObci1hX2Ec535wdm6y/nIYjbzrVnzYLMYw3wabV01PzLWLccT50YbnzZ/8Pzcbn5pNp9wu8WoTPN81vfY9kyb38faNvNlsbY3j0trO/Zjnt+88cx7hOPC/FItJrVxOeoZxL7A/LM9eDzrp+/b5s3Htjh58uTqdsZcd91110b6zjvv3EjTU4hznt1b0E/VPIMsbXP2qI+j+coRu58Y9W9du5+wutiv36nF8/vJ+17XW/Mp5vnXvJsSnYUQQgghhBBCCCFsGXkgFEIIIYQQQgghhLBl5IFQCCGEEEIIIYQQwpZxIDyEWmsbWlVqIwm1i6bH43bqYk3Pbr401MWaxrvXTJvOkmUxbwye78KFC6t5NV3oqH+FaZC5nW1tmmtqJXk+06+b3t0016YhN8+lvn2o8+S1WFbm3fTqo549No6sr/F460vE8mt6ffMU2o+3QNW4BnqtvUyjbP2QnkHUo99+++0b6cc//vEbaerdzSfDvAeItQ3bmj4SNk+ZpxDPz7Y3/wCbp1g+86Hr06PjaNSri/2OZbV5gF4GpmfnPLVW9/EQOpi01jb6uPnGWJ8bjYk4fjj/mZ8d+zj7MOnzy7zSQ8fmQqZ5PNNnz57dSHP80DOH24nFp+atZ/D65lvDvsC2tHWUmN8c2898aPo5iMea15V55NjcPeIxudf5bV0zby+Lh1m+/fo8mk/jfmM6bjd/2b68nFNszjDPn9tuu20jTQ8h+jgyhrO10draYhyLO7i/zSt2H2x9edS/lvMw++KIZ6r1I4sv7ZmCxXR2L8e+yX7Mvsd4fs13ba2fJToLIYQQQgghhBBC2DLyQCiEEEIIIYQQQghhy8gDoRBCCCGEEEIIIYQt40B4CN100011/PjxS2nq90yjTe0jMQ216VipjTR/C+oJWZ61Y00bb9p96qu5ndpC6glNY2zeG9R5cn/TWo5qjNm25vnDtqM3iGnG+366V/54vTXdr3ncsC1NlzrqCcS6ImwLG5d2PPPPvjDqlTLqoTTiLbBX2nxq2Ld4fH891iU9dJje2dnZSFNDTH36HXfcsbrdfGJMM21918ateSsY1vac5+ilRmzcm9eZ6d378tmcbz4To1p8lm30eI5T06+v+TaMtnN4eGitbfRh9l9iawN9csz/gph3CTHPBfbhPoZkf+fcwbKY1x2vzbmH67rFVLbujcYJ1raE9cH5wnx3bP4w30xbt1l/XIfXfHG4Lxn1sLG03RtYvDrqKWQxiXkZjnoIWVva/QuxecLG4loMxzWdHkGMuegRxOOPHTu2kaaPo/mimT+qxZ8cp8TGyagvzqj/rcVMrA8y6h1M+v1tPbFxwuPNz8g8g+y+2zyOOKey7/Z9Ix5CIYQQQgghhBBCCOESeSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnEgPIQOHTq0occ0rxLzGjEtJTF9n+lgqcnj9akL7rWZpmU0nxLzzuD+5hdBHaflj21jvjHmZ0HvFMu/aS15Pep+eTx1s9QFHz16dPX8Vh9rWk5qfEe9r9Z8Eqpc48vjR8edaYZHx5X50rBuTSPN7ax/jlPTo9s8wPrr5zXW9ahH0MmTJzfSd95550aaenheb7+eQdxucyjndJvjeT62JfNPHw32fdPnm5eD9X0ez/P39cGymp7c+hkZnfNHPYsIxxX7Xn++NT+9cOM4dOjQxhzBPsI+x/HGtPmQglatAAAgAElEQVRjWB83LxTC/HF/i0N62J9ZNhsPLKt58Ni6ZXGCeWhyzHHdJFZ3FnOx7blWcTvzw/qzmNJ8I9dgPzCfEovvLJ61dYV9xbxLeD62DevS6s7iZ/YliwvsfBY3jM4LFo/3HqD0vqNHEGMq+ofy3oD9mjEX0xZPWl9k2bm/xUjcTq8z66s2D5lfrcWEvL71DYvR+u22LzHfRfOysnjX2p7XZ12z7/E+tT/f2tqXN4RCCCGEEEIIIYQQtow8EAohhBBCCCGEEELYMvJAKIQQQgghhBBCCGHLOBAeQlWbmj7q7czbxPR8o/4Xpu0cTa9poJk3Xtt8UqjTHNW7M6/03mDdU6vItjDNMo8nLK/pYtmW3E4Nt2mmmT8eTw8i6lzN46mvD9Pmj7ad6bVNi8/rcxyarwxhfnh+9l3mz/TxPN68B5hfls/GvWm4OTbXjue4Yl7Mu4rb2U9Nz27a+1GNtcG6IDbPcV7idvONoCcT25rj2rwIzJNkZKxY3RDzXSCjflHmm8HrsW+u+V5wWzgYTNO0MSeM+tJYHMDxaV4gZ86c2Uhz/rJ132KwfgyNruHmzUcvDvME4nbWnXkImb/SqN+braPE/DK4FjFt+efaZvE2WSufeeSMxpsWc7Duza/N5mLLP9PWt3l+i1OsvNY3rLzmk2Nr19r12Q/pfUdPIfo8su7tXsl8EYnF68TuHUf9sXi+0b7PmIrnt3nBPD2J3W/0+bN7G/MPZV5tjiXWFzguzp07t5FmX2B8y+P7toqHUAghhBBCCCGEEEK4RB4IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGUcGA+hXhNHjTTT1DiPenmYjwH1febtYprsNZ2saYqtbDzeNLWmK+X1R7WVVtejflCmSWZ9mKb65MmTq9sJtZmmix3xH7C2G/FBqHIfFWrxR/Xj5llknkSmmTbPnlEPIus7xLwSRvsq6a9vXlWj2nhidcG2ZVk4jsxnjfkzrwFCXw7r60ybdxnbjmPF9O2jfllra4CtD9eb0TmdmPfWWt2u6dfDjePixYsbY5DrqnmRsE9xfuH5LA5h2voY82eeRT0si81doz6Otq4RXp9zI+c65me/3h+2TvN41rX5PVkMaHEEt/N8Iz4+di9gMRPrwrxGRtcR84Vk3TI+5fWsb1pbMj8WQ1p5rTx2P0Ls3qxPj/oEjvo72f4W43DcE7t/4DzB8nLesnif220OtnnE7q0szjAvtjVPJbvvtnjX4tlROE+w7m+//faNtPnI0Q+rn8fiIRRCCCGEEEIIIYQQLpEHQiGEEEIIIYQQQghbxoF4f5ufPLVPkvJVOJMb2Ote9hlL5sdeb7bXFvv82CuoVhcmjbDPI5NR2Y+9cmqvF699GnCv84++qn7ixImNNF9TZH54Pb7GyPza57HX0uzHJnEa/Ww8j2ffMJmSyWBMmmmfyRz95CjT9mnH0deNLf/2CVSbN/r6NPkb6/78+fMbafZj64f2WebRurLXn5l/+5SxSUrs1Xx7hddeySVsO5N9WNuPflp+7Vq2Ho1KRkZlvzZPrPW1fHb+YDJN00Y/Mqk6sbjA+iT7sK2N3G6SMfa7fv60/m7xHufa06dPb6Tt09smm7FPHNtca9INYp9kZn1x/1EJmMmOGIMxP+yblEWRvm/YtRkjWb8yqbXFj7ye9R2T57Htmd9RqaWNBVsHWV6LeUfWlr1Y67uj9xL2WfbR+N3SJgVlzGh9z/LP65tckn2F+5t9Buc11rfdq1pcshYX2bi3fmhtbfcOvN6o9YlZHKyNs7XYPm8IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGXkgVAIIYQQQgghhBDClnEgPYSoyaYm2D5LP/p57NHP/pqnkWlB1zR8ppu0zzvb56P343uy1/lNz05to2mU7ROjrHuWjzpUXp96dPv8tkFvF6bX+op50hD7HK5poHk8fWhMU2wePqY3N48eq3vzWjA/Ll5v9DPz5qszcvzo51OtLdjvCPsC4TixcWaeOswv9e42Z1rb2rxj3mejXg+E12fa5rEeW5/I6Kd8R33YzAPEyk7iG3TwmaZpI04xX8bRT5Nz7bG4xdYOi7GYXvtkss01llfzUbT4j+PffGqYnwsXLqzmz/zMGAfY3Gg+MNaWlh9e3/qaeSeuzc0WTzNepE/KaL8mXLdH1zlifnIsj3kUWX7YF6ztLYZkXx/17bF1vI87WPfnzp3bSN92220bafMUGvUesznM2sLi+1HfSPPVsXsp66s2Lkd9G+1elfv386rVtcVz3J+Y/6mtb8R828znbW3O68kbQiGEEEIIIYQQQghbRh4IhRBCCCGEEEIIIWwZeSAUQgghhBBCCCGEsGUcGA+hXg9IfR/1d6ZhJtzfPBvMg4jX4/npl0FNeK/tNC0gdaCmW+Xx9Opg2agDZVl4vPlBmA7UdKrUs1O7ybo1TyPzMmF5zcPJtKHmZdDnj/3K2to0xNZ32NbmTUDs/Owr/z97bxOq2Zbf561d9xLUVXXOqapu1CjtQCQwgcxiRD4IhBBl4HgiD4xJRsIINMkHxBOJTDT1IBCSiYKIkygQ7AgRkAchYERCRhF0PkgcJ2DhYFuirdbVvbfq1K3bhK56M+hSsfdTp9ZzV597u476fR4Quqv21/r4r7X+e/f7+x3CvmffsT7WH6aRZn/ZOmG+PXwey+bBtK+fzWOOLdtm89J8Xlh302PbGklWfSZWvcHocWLrnnkv0BuC9ze9vq17++ev+keZp4+tyatafhtr85VYjZW4G+zHyXxfzIfK/CZW9zqWOT85383bZP8821eYv3Huc+0gq35KNt+5FtnazbHk2DDnYnvYt+x7ehhZDsbrbd9dLTM2Z2uxraVsC+/N2CC2T7DvV8fSsH2f9V99F+I8W/WLIuYraffj82eeRnYvew81zxzzSzKPHNunGTuE83bV68s8guzdy+aS1cf8Zm3uzvynzI+Ufc811951bM1f9TNd9bScralTP8vpUyIiIiIiIiIi4seOPghFRERERERERJwZfRCKiIiIiIiIiDgz7oyH0F6vSX0etZimJTRtpHkeENN0mzfJTJe7qqM0nSQxrxDz1GF9TAvJseL9zR/D+oOYFwHbQ0wrylhhe8wXZ+b7Y14C7FvzmzK996ofFTXK5vnz4sWLQ5ltpzcBr+dYm2ePYRpuYuuAebGwvTOdMtc49rXpo22emScRY8nG1soce665Ns/M48fuZ94O5ntjPhscD46t7TGzdXq1bsT8omy/sr63/c3mwd4zxO4V74fT6TT1GbC1kfN55t8whu999K8g5qHG41wf93Aum8cOY/jRo0eH8tXV1aF8fX09vd68ScyX0NZWtufi4uJQZl9z7bT1yHI4y+fZHsu3mUdwX2f7Zz40jDvzxOGzzaNn5iM4xtt9zbGgP5PtFYTHWX+2n/5O5pm56qF5Ww8g29tYH7K/v3n02LuQvUuZV5Z5k1mOZO92rC/n4erzzC/LxorHef9Vj9DVd7t92XIW8+QxzyFbE8mq99bMm3aM+ftBHkIREREREREREfGGPghFRERERERERJwZfRCKiIiIiIiIiDgz7oSH0MuXL8ezZ8/elKmbpR7OPBVM52p6ePN/4PU837SZ+/uZFtHqZsdNn866mleJ3c/05uZnYTpd9iXh/anrpY7WdLyrscLrZ7pf07Gyrx8+fHgomz8Ty6ybefSwr9lX7Euez/ryeraP9zMfHdP5Wixb7JoXmfl3zcaDY8+x5FjwfNbN9O6rflKrenHD5gW9E9ge7gl2PTHfnNV5bn5XrL/df1Y3YmNr64Ddz/Yk8+yjd8J+b1+Nm/jRsR9XruXm7cEYMZ8bW4+4/nB9tByN680sZzS/tdW1hp5B9CDi/bi2mTeGeW1wbGzfNP8nYvm4+anZXsbrbS9i/Vdiz7yyzKPGcjbW5fLy8lC2dwXLl9m31lc832LP8gqLJc5b2wdt71r1f7X67mHcse3Pnz+f3mv13ctih7Fh9SWW/5v/ld3P1pXVuUWsfeb3Ohv71ZzJ3nVWfRjN983WGbv/bI3OQygiIiIiIiIiIt7QB6GIiIiIiIiIiDOjD0IREREREREREWfGnfEQ+uSTTw7lPaafM18d0yKaNwifT60lNeXU6PH8n/iJn3jns3gudZ+sC+tObaLpRqmN5PU83/TZ5rdk+nPzBqBG2jwpzIfGdLCE7WV9+Dwe3z+PY8O+MZ2qaZg5VqyLtYV9a35TvN7mhfk7sWx6csaS6XZN87zqm7GybpnvCuFYcixYZt+ZjwPHcr9GjeFrqHmCWNn08uxL8yZY9R4zDTj7x8aD/T/z5eAx1oX3Mr8i8wJbjWtbw/n8p0+fHsrf/e533/nsuBucTqfDnLF9keNoPo3mS2PefeaHYXvDzA/v4uJiem/LqSwnWvV7M+8R22fMG8TKNr9t/bB8edVLxPIGji3zjlkss22WM5jfEevG81lX2zdYJry/+ZuyfqtjbXuV5RXMcyxns/eJ1Zx49izWjZ5BliOYR6fNY5bpRWb5t/nIEHu/WPUONF8ey7d5/eq7oc3d/XHLh80/ldi843HzS7XYIbYf72Np9s7cL4QiIiIiIiIiIs6MPghFRERERERERJwZfRCKiIiIiIiIiDgz7oSH0Ol0OmjeTMtIfR0xTTZ1sjxuul/qCem3wfN5v70ekG2hDtI0vcQ0tzxuOk1eb14fxO6/qm8njA3ThJu3AMummTb9O6/f95/5OzEu+WzG3apeneX79+9Pn8/zTeNsutoHDx4cyqbh5tia/5X5c1ls3ta7gezPn60JN5XNR8bWHItj06fzfuZHZXpz1n91LM3jyPT8xDTbtu6y/ozVfX/Ys2wNsrE03wjT3tu84DzlOrD3AxxjjE8//fTNf5vnW7wfmIOZP5x5AVrZfHXMZ8Z8aVb88cwziM+y+ba6lppHpuWI1lfWHtuLiI3FqpcI17vVnNPynpl/FfcR1tV8SVZ9VyynYJzaPmTP43Hz+jLvLvMCs/cVO77anwZjYT/e5uO4mp8/fPhwWhfzyGGZOYWtKzZ2fD7zffONszXV3qstNu3+5nVm77qzdY3HbB1g2fyUzDuLbeW7D3M4e/5nn312KH/88cfvPHdPvxCKiIiIiIiIiDgz+iAUEREREREREXFm9EEoIiIiIiIiIuLMuBMeQq9evTpo7Kino9fIqmbYdKfmd0F94de+9rVDmVrMi4uLQ5n6v7320Tx7qD00rw3Tm7NvTXdqfhfE7kcdLDXcq94fHHvqWNl/1J2yzP6l1tPaY9fv22Ma3VU9u2n52deMY/NlYV+ZhxFhe2f+Sjfdz3S65ptjHkLW34Z5O+zbz7q/ePHiUDbPIOsrYl4G1jfUx7Ns/lHmWWT1WfUuI+aLY35dxDTnM28Gi2vTvltb7P7md2Q+Dyw/ffr0UN57Bo0xxrNnz95Zl7gbnE6nw7ib3xzL5t1nHkHcx23tNY8x89nary+2T1jMci3k+fv4H8Pnq+3j7Cvej31tOaatF2TVg8h8aKy9thabnx6Ztc/i0OLMvLbML4nnW/7Ndw2OPd89butBRMwfyvYW7kV8l7J91Y7PckT2Bb3vWFfz/uKzmV+v+sXa880zh2s0Y89yPnv3Mn+o23p82jq26iW8f96qV9XquwjfOy3fpOcPn09PIasf77cv5yEUERERERERERFv6INQRERERERERMSZ0QehiIiIiIiIiIgz4054CG3bdtA7mjaQOlNqGamv4/nmKUSNHbWg1IybFwvbs9cXmibWvC3ME8f03uw76kxXPR9WvUqoIzU/qFWfF9OzU+fK9pp3iennyb5/OFYWl6tafNad+vZHjx4dyowtxgZ1sbz/9fX1TdV+5/1NZ8v2WOxYf9rYmEbc/LXIzLuBfWveBOY1YHpy1pWeRfR9Md8YlrnmmWeQ1df8tYjpz03zzf5cfb7Fyr5s+xXHxtYkrmFWN/pK8HmE57PM59OLYb8u5CF0N3n16tVhXM2Lw/Ya87lizHD+Ee5dXG/Mm4Xlff15rXltsC4G78d9z9ZO8wDi/c2HxXJMzm/W17z3Zn19U9lyQJ5v+7r5681yRo6F5Qw8n7HBOLccj33Bvv7mN785fT69Q9gXvL/FonmfcJ/lcXvf4FhYPr1anuVN1le81vJh81W0NczmqfmTmmeoeW6yPeYPZbFqY836rb7/mKfSzJPVPHgI+8b8nuxdh7HHvv3jP/7jQ5mxZO+CfN5sb9/TL4QiIiIiIiIiIs6MPghFRERERERERJwZfRCKiIiIiIiIiDgz7oSHEDF936r/hHl98HpqsKkN5XGWVzTg1NxSG2i6TGI6VGoLTbNLPbf5zHBsqJs1TTMxHa/1n/XnqncIyxZ77L+Z/4Bp4zlWjGvWjXH54MGDQ9l8GOglYH5NjC3C+pu239pr7edYsz3U/Vosm4aa9Zl5NVjbVn0ZWBfen3HOtpqPDGF92Jfmu2EeRCuePGO4t4F5iljs27y3+u3Hw+aR+S6Ydp9eXnY/8/Iynzp6Gzx9+vRQ3o+FtT3eH/s4sTG/vLw8lG3v4XHz7jMfSK4nnBPm4bA/bns8n2XeGqy7eUpyrbEcxXwWVz1+bN+z43Y+sRyJ7WHewvGw9WrmV2X+n5YDsG7Mby8uLg5lyzmsL1hmfW0eMNY+/vjjQ3nmPTKG+zWxfezf58+fH8rmk2Nzy3wdef2+fredJ5avsu2rnpZ8Hu/Pvlr1YWRf8Xn0uSEWq5bjMudk7PD+5jNHZu+WFkfW1/YezXlj+5utC/RlJOyrZ8+eHcr7nHC2PvcLoYiIiIiIiIiIM6MPQhERERERERERZ0YfhCIiIiIiIiIizow74yE001OazpT6O9Nkm88NdbnmxWI+ONRuUk+4hxpbanRZd+oqeW/Tw5uulDpS+inxfPN3Mt8Xax/HfjUWTPvJ57N+HA+W6a3A/trHlumjifkdmdeAae95vnn2mAcQ+5pl0whbrLL+fD5jwXx1zBuCY23Pn3mZmTeA+aBZW3g/0yxb3xqch+YhZJ5B5jnE9jC2eD7bS9hfHGvzJrB1eF9fHmPdbb8jdj9q9VfH1vT0Nu/3x/MQurvsY5QxY/sg56vtHcyhbD2jHwdzMvPZmvnsrHrjWf7I+WVl7oO39TIxrw2OpflEmt8T11bzb7P6W45oe4N5o+yPWxwT9tUsv7upzHnF/Jp15/W2L1ks83mWb7M/OLYW+6wv59Kq353FIuc51419fdl2e5cwLzHzXSOMa461+Raad5jl52yfvcda3mD1tXls71a83nJYsh9v8xCyWDBPId6PfTHzNbupzLbZuyA9hPbHZ98f+oVQRERERERERMSZ0QehiIiIiIiIiIgzow9CERERERERERFnxp3wENq27aDNpe8L9XfU59F3h8fNn4L3pz7ddMbUF5p2dOZnQX0fNcLUHpp+mnXn+dREW1+zfqaPN08e805hX5pOlsdNr05YX9Phmo8MY2XvMWS+JNY281civJ/5NtDngXpszjvz7CHUTFOHS0zHy763dcR8enh81e9q5s1g+mibV4Rtt3nCvjO9t/UF49w02uY7YX5W5hXG2CTmdWAacF7P+rD/9+czTujrYPPS4o7H2TemPzdPE9vf2Pb408F+jjGGzBOIORNjyGKM88f85oj5NLI9+7L5r9naO5vrN92Pz1v1P7O8gH3FtlvON8tZxvD1gPW1861/eD19Z2Zje9P9Zpi/0tXV1bTMfYRYjsB5dXFxcShbfm0enpYjMeczDx8e5zpAnjx5Mq0P5wLbax6ilqPt5475RVk+SDi2llNwnphHpeV0lm8zJ7J1y57P+vL9wHwZzQ/K2mO+jXz+/rj5tFmcWV8xjh8/fnwoM+czjyGbF2T2jWHmVdsvhCIiIiIiIiIizow+CEVEREREREREnBm3+iC0bdujbdt+a9u2/2fbtv9727Z/adu2J9u2/e1t2/7e6///2O8UERERERERERE/Km7rIfQfjzH++9Pp9Je2bfsnxhj3xxj/wRjjd06n01/btu1Xxhi/Msb45dlN7t27d9CeUldrulVCfSB1pfQuobbRdL3U85kmnOzrb14dph2k9pD6wFVfGl5vmmjra/PkMZ0u28fzTY9uOmFqqE1bau0zDfq+PjOPmTHc58W08hxbxpLpy00ny9gwDbT5W7H9LM90sWO8rcvl82xdYftX/bhMD7/XWJsnx0znO8bbfWO+aaYXZ9+ap5Bpqol5H3DsuEYTPt983Mw3h2Nr3gymr595lZl/kq3Rqz4S5qdCnj17diibj53NG5bj7sEczGKMfhGcr/SdMW9A+luYFwrnzKrn2n5O8F7mc2g5CNeSFy9eHMrMOXjcfBB5Pee3+blZ+wiPP3r0aPo821tsbNlerkeMNZbNa2VWF5bNR9H8pqzvzXOHsWR5Asvse8tp6FnE2DRvQPNxYaw8fPjwUDZPTvONWdn3WTfLr61MzNeQY2H+rOZ/SswXx7zLbN9mLLN95lVmOay9a9m6OdtzVn3fLJ+29/hPP/30UOZYmz+q5YScV7ye69a7+KF/IbRt29UY418ZY/z1McY4nU7/3+l0+nSM8fNjjN94fdpvjDH+4g/7jIiIiIiIiIiI+PK5jWTsp8cYfzTG+C+2bfvftm37z7ZtezDG+ObpdPrO63P+8RjjmzddvG3bL23b9u1t277NL30RERER8dWwz8H4v2ZHRETE+XCbD0IfjjH+3Bjj106n0z83xvhs/EAe9obTD37jdePv6k6n06+fTqefPZ1OP2s/m4yIiIiIL4d9DmYSzYiIiPjx5Tbi/t8fY/z+6XT63dfl3xo/+CD0h9u2/dTpdPrOtm0/Ncb4rt3o3r17Bw0ctYv8YEStommmqbejttF0v9T38RdN1Kmu+PiYxpjX8jjhcf4vf6t+Dqb9t7aaXpzHzVeH9efYm/bTnr/qvWK62JnO2DTM5mdkfW2+JowNvhRQY2w+K+ZBxLHl+dTrs/72S8JVzyP2t3mtsD7mjTCLPfOgMX8l8wZj3cyjx/Tw1jfmn7Qay+ZHZV4BFvurXmfmW7eyztmzzDfBfCDM18HazjLH0ry+VveseP98+OGH4+tf//qbMsfMvAZXvQI5P2w9svXDfKy49+zrZ94cnF9sG31X6N9g+4h5idjaQ7ivm/cdsbFjPm77KtcPli0HZI5nvpDmg7kvW1zavkosJ7OxMd8Xe19gfc1jx/YCPp9+Try/eQvy+YTrgPlz8bjl+/uy5SCr7xqW0zEHW31XMezdhfPOvMh4v9X3bPOdJLaH2Lvtyrpm97Yyx8bmMbH3WvMhJrY/z745HOq19NTjA//xGOMfbdv2z7z+p58bY/zdMcbfGmP8wut/+4Uxxm//sM+IiIiIiIiIiIgvn9v++Y9/d4zxX7/+C2N/f4zxV8YPPjL95rZtvzjG+AdjjL98y2dERERERERERMSXyK0+CJ1Op/99jPGzNxz6udvcNyIiIiIiIiIivjpu+wuhL4UPPvhgPHny5E2Z+jrzozDtpulmzW+CnkXEtJMzHavpRK2uq20z7b35V9j9qf82zFfGtJjmZcAydbDUbrJMzTjbz/rz+ll9zLvAWB1L0xibpphtZ2xwHponkGmmrf6s36qf06q+ndj5jI2Zxtl8JMyXxfTZNvbm+8DzzfPI9OXWd+YBQszbYdUDxTyJzBdntq6seggRO24+Z+wrxo75zJl3wsz/iefG3eDevXsHbxjzYbm+vj6UzaPL1mbzXuEcIebLs+KjY2sPj5u3HL3vWBfzd7O+5fPM99D2FmJ9yetZX/MEMl8dtpfHeX/Li2bnWl9xrbQcheebPxPbZnkB+477Fvva9mHzj2N51bvPrl/1oSSMldn7xGr+aHv+qheg+UHZu4StQ1x3LKezdcDWDfNas5zP5pJ5hBr7/jLfRfN3tXXDfIrt3cn+yIPNS17/lXsIRURERERERETEn076IBQRERERERERcWb0QSgiIiIiIiIi4sy4Ex5CH3744Xj8+PGbMvVw1O9Rv069nunXqa1c9WKhNtJ8bmY6Ydad2kLTU1M/blp800KaRtl0tObnZBpj04Wa9wf7ixpqtt90rdRisn68v3mp7DXlpiEmHOtVvyXW3fyTGAsz34Wbrqd+3rwATGPN+pge3epvOuDbejyR/fNZF8YRn82+Yhzzeva9eeSYxpmxx7L5znAseL3p+c0Xx663eWn6dPOjsnVgtq7Zmme+CKbdt3XB5iHHln3LNXLmD5OH0N1k27ZDLsIcyeY/Y4ZrM/Mcxox5LqzGsOU5+xg1PyPz8Pna1742rZutfXvvpptgX5nPzKoXiPlEruYBxPqLY2972+Xl5fR8y9f37WV+a158PM6xod+o9S3rfnV1dSiz7izzXchyJMu///AP//BQfvr06aHMdWHVM8jaY+8PlsfYXrafe5aDMC6tbJ4/Ni/Nj4nHLQcyvyvzybE8wt6NLDZs3TIPJ8tLZnuAeVzau5Llg6s+bbYHcZ3insH6sa/3a+jsPbNfCEVEREREREREnBl9EIqIiIiIiIiIODP6IBQRERERERERcWbcCQ+he/fuHfSIppOlBo7aSvMAuun5e6gHpFaU2kzTds78MMy3ZdVrw/wdzIPns88+m97PPH9YX0KNLzFPINPlmv6cZY4dj5uGmbFBfTzHd6a/t7FZ9V8yza15H5iXAsvmF2VeXeZTQX3+s2fPDmXODcYydbjmhbC6bth47NtvPg22xnEsOc8J22p6d/OnMi2/jf2qp5F5jJgfls0FsurTwf7nOjTD4ob3YhybZwbnkfmoWd+Y7xrH3tb8eP+cTqdDXsMch2ut5SW2r/J8mwOr64PN11mMm//Xar55cXFxKHN+mB8T/SKsr6w+Nn8J+9ZY9X+zHJPH2V+2ts/8O3gtx4aeOcRyDvP45LxY9UO1nMnqazkR78f+sH3fYtG8+NhffP7q/aJiOGAAACAASURBVGdeZKy7vQtxnbB82uKefbmaU632heVMqx4/5ttmazCvX62fea3t+8/8kVZ9hC0nm/kI33Q/HmfbuB/b2HxR37d+IRQRERERERERcWb0QSgiIiIiIiIi4szog1BERERERERExJlxJzyETqfTQXP3+eefH45Ty2n+GtTLmV6QmObZ9H92v/3z2Ta2hX1BbSLrYtpF9p1p8c3rxHxuVjS8Y7zd13w+27vqGWReA6YbNs8gPm+mPV3VCBPr29tqfFl3+koQtof6c/ateZ1QL865QG8x9h+P39ZDiPU1TTRjZX/9zNdgDNcQE/MWM18Mw/yU+PxV7y22z+a96fftfM5T86Fg/fg8Hp/p71c97jhW5nPBecZ5Y/srx4pja/5T7Ot9367GXfxoOJ1Oh3HmWm+eXzzfvESIrRdcu82LhWXOz/1xm1+si3l/0PPHPIc4v7g2cl+0fWvVI4jtN/808/yx9cPWCz6P3oGW15gf3/75K75/Y7jPzOq7BTEPH2J9y7J5fzFn4l7Bec55aX6q9v5hc43z2tad2bucPct8WCyn4bNtTbV5xb7m/VbnLevLvn3w4MH0foZ581qOynXU3q3YHt5vH/s2D3hv5li83rxj7d3I9kfzOLJ3N47Fu+gXQhERERERERERZ0YfhCIiIiIiIiIizow+CEVEREREREREnBl3wkNojKPmzbSPqz4EPJ96OtM6mjcL9emmEd9rP02DS92qaQGp+6Su0nSuxLSObBt1m6Z9NC8R08XyuLXXtJqm3yemg2X792NPDbB59NjYmw6WUB/OWOD9GJuMXZap/Wf72LcsW/+Ynp1l6oDNb8s03KYTnq0TjAtqkFefzWdxbHk9sTV31RuM1/P+1IebBpvrAq/numf3M48ie755k/H8/dwyLyrzfaBvhfmnmIcQz2es8PyZR9AYc/+k+NPBF/Uc+BNsLWTZvADN7828Xbge8P4zLxHGu63VhHVhW1m3jz/++FDmPmX7sPmmmA+krXVXV1eHMtcLrk+Wc5nPDtvP9rH91t6Zv4b5/hGLa2ureX2wzPtz7GyeWGw8e/bsUH769On0/hwb5mCsv/nysP8th7X22vP3sctnMa4tZ7B92dbEVW8yyxP4PPM5ZA51cXFxKF9eXo4Zq2Nr3mHmz2rjYZ5P+/GydwXL5W/rnUU4towt2z8ttvZ9N8vH+oVQRERERERERMSZ0QehiIiIiIiIiIgzow9CERERERERERFnxp3wEDqdTgcN3Ez3OYZrhs3nhsfNO8U0z7yeOlt6qez1h9QKmq6Uz6JOkvp305XyftSZmq8K67/qF0EtJHWtMy+OMdwTycbSYmPmPXAT5q2yHy/qrxk3pku9rd7d/J1YZmyx/oxzns/7mQeQabRn3lxjrHsPrGq2TdfL9u7rY54zpr82bf1tfSYYa+ZjwfrSJ4P69IcPHx7K5tHDdYHXs2weJTxuenZbh8xXbj9+t91PTF9u2nvCeUrYFov7mV4+P6G7ybZth7ExLz7OR64/FhPmN8H5z/lrOZ55nezvZ35p5nNivoe2z5vvjOUwnFM237k2s75cS3k+x4brje09jBXzTry+vj6UzeOJ48lYneVN1vfmo2ixw7rxeawb54l5FhnModi3qzmp5ZTmp2X7wcwH5qbn2/vKDNaNrOabrKvlGIR9aV6B9PzhvOQ8tpyK55t/K+9nvjmre4Dl4+bBtB8vy3c5luY3ZXFpHqAWp/ausrrfvot+IRQRERERERERcWb0QSgiIiIiIiIi4szog1BERERERERExJlxJzyExjhq+KinoxbS/CCo92OZmB7d9IXUD/J+1Pvtz+e51PrxWXacWkPqrU13yr403xbTPJu/E/X3HGvTpZrXyqzvx3hbB8yyxQKx4/vnU1dqem2Lc2s762YePbwfz7eyeQ6ZHt7uz/bbXDFfHPOPMg0zYXv31zOuV724Zv5EY/g8sTWR97N1iusAPYMeP358KF9dXU2vZ30fPXp0KLP/uG6Y/pxzieuQaa4ZGyuabdO+W1/b/Yh5Y9ErgPfjumT1n827PITuJtu2HeYA54PFKL1IOL/N+9DyGotx2xu4F+33XptfXCu4tpgHENcmwrqbL82q/xvrY2sf68v7r3oW2V7D42z/Sj49xtvtm/nv2dizLpYPr75L2Fq/6hdlvok8TsyrxOatjQ3bY9ev7jUrOZ+9m1jcWz5psbHqq8a+NM8dzmvu88y57Ho7bj6Y1t8839797L175qvDuLJ5ZPc2zyF792FbGFvmm7bi3TVb8/qFUERERERERETEmdEHoYiIiIiIiIiIM6MPQhERERERERERZ8ad8RDaa/So56OejlpG02qah4JdT/0fdcWrOuL9883rwrw6qBVkXU23yr6lzpTeHdQ6mnbSYPupzVwde9NQP3/+/FBme+iXQVg/xiq1njy+Hw/qPq0thMet7eQ2+uub7s/Yo97edLmE92f5y9bvmy7X/KHYfmqg9/fjszmvTWvPurDMNcrWVPP4MA8etpVl6svNA4j15XF6FK16jdk8Nh8OYn4Ds1hf9dUxLf6q1xZh38y0+Dfdn8zWvLgb3Lt377D3cy0jjAnOx9m+N4Z7oxDL2bjXcB/nerh/vvkVre57XKtYF5u/hvWl7Q18PtdOYt4oti+ax9Bt/eo4HszBZnmDeYmY14eNna29VjZ/JPOFZF9YLM/myU2YVwr7h2V7npU5XuZbsz/OdwkbC/OwsRzNYsVyMl5vfk/mtct5z3c/83O1HM7WbOsvWzds3WF/7O/38OHDwzHGLceW9zKvL6sL457Pt/feVe/b/djP+q1fCEVEREREREREnBl9EIqIiIiIiIiIODP6IBQRERERERERcWbcCQ+h0+l00LyZttD8IQj1d6u6WGqWCTXi5mWyr79pBc3nhc/i/UyTTPh86kLtfixT28++ZJn1n+lAx3ANM7WarJ9pMfl81tf8A3j+XldLja1pYs3rgMfZdvMOYWxRc2xxveq1QOx880YxHwrz5eH1Nj6mK2Z5Hxv01OHY2HG2fVVbz7i0ecyx5/24brDM9vA4Nd28P/Xq5nNjenyyqt+3sSb7+9m55hVge4Z5cjB2zD+J0JeC83S2zuQhdDd5+fLlePr06ZuyxSDHmDFkXoicr1yPbI6YTwyPsz779cR8Y2ytZV35bMtfbW01TyPz6jAvEfa95dPm62Ienpbfcz1b9QqxvXCP9eWqZ5B55lhOZe8SbKt5iF5fXy/dj5g3ob1PrMY+62OxurpX7vMQ1sXaZnHGuLV5zJyIdec6suptxufzecy56CHE42yf+V6a1xmxuWjrkvlc7vuH55qXrfmZrs4jy8ctzs37duaFO8uF+4VQRERERERERMSZ0QehiIiIiIiIiIgzow9CERERERERERFnxp3wENq27aBrM28U6uuo/6MWkZgm2TyEVjXRM28S09gS05WappbnmyaZ119cXBzK7CvWzzTX9AYxfTo10rw/j7M+rK9pvIn55phOdn9/81Uw/bq1xfrO4th8aHi+eRSZL8uqxnjmzTXG2/U1/T4xDbjp1Wd6eRt7Wwesrav+VNa3LJvPA720WDbPH/O9MD25+b4Zq35TPD5bF2yNXfVFMH26jZ15DnHsiPlRPX/+/M1/5yF0N3n58uX45JNP3pQZU7YecH7Sr8I8wMx7xWLe8pxZjmb7hOVIhM/ifLC1nudz/ll+a/mz9Z3lIeZRZGvhzFtvDF+/rP/omzMbX4tDy+1trV7NkczjkjC/pWfnqv8V28P7s34r82yM9RyM9zPPUev/PauePJavWk7Hutq72H7fvIlVz0uuwcwD6CFkfqjmqUksBzVWfRxtndxj7zarHnnmRWaxwr63PcK8cPd9MXvH7RdCERERERERERFnRh+EIiIiIiIiIiLOjD4IRUREREREREScGXfCQ+h0Oh30h9RWUr9HHS6Pm1bR9OjmqWDeKtToUXu51/Px3tRhrnoAEWoPVzxuxnjbC8DaZrpZ1tf8LFj/733ve9MytZXE9O5sL/vffHvIzBPJ4oY6Uhtr09iaV4npzc1Dh2NnnkWmgSbWnlXPI2sPY9v8uszzaT++jEPOA/MmMGyNM88Oex7ry77iPKI+nXp28xpY9SAx3w7Wd3WdND+A2bp+mzVjjLfbYv5KNtY8n33Hec2xt/165p0Vd4OXL18evFc4/23vIfQaJObRYP4cxGJ8ttcx3hnfts/wOK+nF4h5kdi+Zd5/ts+bvxrzAPaP1c/2fWLrC+F6Z3kL67Nfu7mvmB+ReYNYvsi2mqeQ+drYWm35sPWlzaNVHx2LPcv/LU/g/WexZV5+q2sg+87mIVn1c+X9LCdjrLNsXoGcR5Z3mO8cMQ8gGx97l97HqsUpMa9Z+0Zh77mEx81HzeYB17F3UXYWEREREREREXFm9EEoIiIiIiIiIuLM6INQRERERERERMSZcSc8hF69enXQupqmm34Uq14nprk23x5C/SDrS23m559//ua/V3SPN5VNa0jNLeti/kumteT51C7yuGmezf/JtJvmh2H1Ny+Q27Kvn/m6GKavZtk0veYtwvuZxtq8uFg2fTgxbwfej8cJ+5NzhWXWn14Rn3322aG8XxfMs4bzyOLa9Nmr+utVnwyuyTZPbWzoQWR6+VWPpVXPIB43nw3q7ff1X91vbD+zutq8Yt15PseC2Dqzf57NwXg/nE6nQ8yaL4uthYypFy9eHMrcK2y9IeblYl4k+/NX80XzyqCvIc/f539jvD2/WHfbGyyHYf1tXzZfHF7P9lgOZ34c5iVieY75Ue37e9ULxHIey7F4P8v32ReWI7I9trewveaTs7pXmTeh9b/luOZhOttreS7nLZ9tcUmsL9lXXCOJvWeyL8wD096FzJOU92d7Lb+3562+d5u32v78Vb9VW6NWfdRu++5n+zPru8/PZ+tzvxCKiIiIiIiIiDgz+iAUEREREREREXFm9EEoIiIiIiIiIuLMuBOCfurXVz0TDPNsMB0wMR3r/fv3D2Vqsmf6ddMGmq6SdaE+/eHDh9PzCbWQhPdn35jXB/vGPCaojTSvkVVNNDXeplPl80zfT63pHvbVbbX45u1hbbe6mw8E5wHPX/XLuq0vja0jNldYtuvps7HHtPWmh7Y1zbwP2LeMPc57ts3qM4vzMTwWzbeD7WNsWPv4PNbH/ALom8HxmvlwmJZ+db9j223/Mf8o9pWtoeZRtC9bXMT7Yz82XLvMS9DWI8L1xXxzzPuE8Djru3+e+byYJw3LnG+21ptfBeF8s32U5z99+nT6PK4fXA/MD4peLITttfWQz2d/sr6s3+x680+ysbYczeLW4t7uZ/uW+Tba81f9mSxnnPnL3XR/e5/hOsX7z/Iou9ZyEvMKs3lgOQyxdx/Lv82fibFhY8X6E/OTslix/H/1/jMPodvmrzYPLX+eeU7eVD/2hZ0/8y891OudRyIiIiIiIiIi4seSPghFRERERERERJwZfRCKiIiIiIiIiDgz7oyH0F67a34Pq7pe006aLpf6PGJ+F9T77XWxpg1k3aiJteupk726ujqUnzx5ciiz782HxfTrhvlZrF5vGmfzCzCPI461abBZn70PjZ1rGl/qTu1+jAXOE5tXq74z1FDbvGRf3nbemb7fvAqo+zWPJtaHx/extOI5M4bPQ4sVm5emDzfPDtbf/KLY1+ZJYr5u1h+2brJ/6EPB2GaZ92f/7WPJ5inL5lNmY8v7cayI6eFtv2X99s+3ORnvhw8++GA8ePDgTdm8BhlTNj953NY78yCy9WhlPTTfRvO8MQ9Ky3HMK2PVp4ZrD9cq80W0+7F93OefP39+KFuOythi2fw82D7z+vvss8/eeS0xPyjLASwnWI0l2xt4Pft61SOJY2e+O9YfNq95/mpOxvrNfHRsTVsdS84Dy6ns3cPWAfYN28r7sb2MFV7PvjavNItN1oc+jHy+xTb7g2Nv/ll77D2RmDeW5USr+T5ji33BseLz9rGWh1BERERERERERLyhD0IREREREREREWdGH4QiIiIiIiIiIs6MO+Eh9OrVq4OekPo5aiWpjzNd7qoenVpD6v1MX2j13+sVzY/BtPesK3WUjx49mpbNK2BV92p+FqbLNS2n+W1QW8n20BuEz+dx8wiy2GP99+VVDx3T1JoPg2mQzXfBnm9jb3p5Yl4Mq+ebP5bp33k+dbuml9+XLa54Lc8n5nGz6he16o/E+q16L1Avbz5sxHwyrP3X19eHMutvnkErviPmn8R7WRwS81GjHt08VAz2LefF/ngeQneTDz/8cPzkT/7kmzL3TY6p7buWtzAOVv3ibK8yD7J9jJsvoO175nljOZr1rc1380W09cT81+x6HudaaX4b5lnGnMzqaz6QFxcX77w3/Y8sbs2Hxjw32Vd7f6MxPK5t3zFvE8uZVvMAm9fmk2Pvepw79q7F8/fHzR901RuM8477LPuC9+M6wPuxryxnsnlqnkO2rphPJOtn7830FLJ1wXLIWY62+q6z6qtIVn0XzTdy7/d30/04lvvYykMoIiIiIiIiIiLe0AehiIiIiIiIiIgzow9CERERERERERFnxp3wEBrjqIGjFnCmebsJ81IxvfqqjpZl6v2urq4O5b3+zzS4pmMl5iFknkHmJ2G6UvMKWfUgMv38qq501ZvFvFaoezVt56wu5gPDWKAG2vqSY2PHV30arK9MP25jY7FmHkOrvjW2DrBMTfas/8zzZubDMsbbY0/tvOnZiWmirT4rnh1juMePjQXvP/NpG+Pt9lussX8593i+1X9ftv1k1fODa775t7BujFvz+TE/ppkPRB5Cd5N79+4d5jhjgmXzPOD8oFfLqj8HMY8vxuTMg8y8+Hhv88AxfwiWV/ch26dZ5vrAsTRPI9u3ba2nLw4xzyFbb4h5Je7rz2czrrhvsO9sX7S+sncR822xvcHmKd8HmM+y/fY8869if7D/mD+zv1lfzpWZZ9AYx/qbV6zlBNZW840x7y3WnVieYGNl6wzvzzXc8nt7d7P83a7n8zk3ZnORcW7vGsTWKM5zPs/ek9k2q495b+3vN9tb+4VQRERERERERMSZ0QehiIiIiIiIiIgzow9CERERERERERFnxp3xENpr6kxXa/4T5o1iWklix01PSH3fvsy28F6mXzfNMjW3LJs+3epHfTjraz4sqzpY8xow/T7HgrpTtpfHiWma+fx9fXlv9iXLHBuLa/NdoS51dV6teuyYP5Zpnlc9i2zsqVfn/c3bhVjs7TXYKz4HNx23sTBfB1sjGQsWexYrqx5BxNaF1VgyPyr236rPB70R9tjYrXpoWN8xDs3zjj4MNtYrnnqrfoDxo2M/NlwbLy4uDmXGDGGM8n4238wH6/r6+lA2/4jZemA5hrXV5q+txTyfaz3bzjJ9VsxPgmWODdcum998vuXvz58/P5S5tvI4sfWJ92P99ms/44Z9Y31tfUXMQ8jmgeUgvB9jgfkq4XHmoOwvzsNVzyCOJZ9v6w6vZ//P8hiO7eoaRH8l8+g0r7LZu8IYvq5wLJ48eXIom6+brXOEY8uyeZ3ZOmgeo+ZvO3u3Nf+n1W8KVhdiscGxMJ/H1b55F2VnERERERERERFnRh+EIiIiIiIiIiLOjD4IRUREREREREScGXfGQ2ivgTO/B/PNWfX8Ydn0gau+ONTx7vWA5nNCTx5qDdl26xtibTVWvUvM98bqs+pdYt4f1DRb/RmLvJ664lksU5NMvfbnn39+KJsm1/qC2NiZF4DpcO161tfG1u5nXgzU4Zr3Adtnz1vxSLJ5af5L5hvD+5sXFuE8MU2yla2+pic3TH9OvTz748vWu8/8uFb8TW4qk1WPH4stzpvVsZjFqrUl3g8ffPDBuLy8fFM2XxnLM8wfwzzNmPdwb+Q+y+fb/N3Xn/FOXxJiXiAsmw8Ly7bvGOwLtsc8xax+tpeYFyHr8+zZs0PZ8hDzPuRYs//347Oas1h+yL43nxq7/+rewL42zx6ez32S9efY8/7sP8ba1dXVoUyPIJZZXz6P97ecdN8enss1h8fZFzx/NUdi39vYWz7M94WPPvroUGa+a2smsXlm/quMVdsz7N2Q/WG+kHtWvxHYOsFnmzctz7fnmZ8Unzdr3+xYvxCKiIiIiIiIiDgzbvVBaNu2f3/btv9r27a/s23b39i27Se2bfvpbdt+d9u239u27b/Ztm3NujwiIiIiIiIiIr5SfmjJ2LZt3xpj/HtjjH/2dDp9vm3bb44x/s0xxl8YY/xHp9Ppb27b9p+OMX5xjPFrs3udTqfpz1BXZVD8edbqn4Az6Yn9ZNXKs5/m2c/i+BNK+9N/t/3JKn+GyJ/f8v5sm/3kdPXPylvf25+d5FjaT4D503mOB/uDP2md/YyUz+ZPUPmzePvT3Yb9WUj7ua1JIW0eceztJ7QmL7SfS/PnxKs/N16V6BG2b9/+VWkiy5yX1rerf4bdftJqscPy6p9oZf1tTbf+s/6yPztqsWXt2V9/W4nYqjzP1sDVeWQ/n57VJ8nY3WTbtsP+wDyDcgObPyZLYszYemJrr+3rrO/+fNbVZOWsi0lLWDeThrOuPJ+YfM5kPiaftTL72vZR5jkmXef97E8sW465H+/VupuczdpmOZKtjxw7nk/JlUmZGXtmz8FYNtkP67uXpY7xdn5tckOTEVl99nPTpJCUz1nOYXG8Kv22HM3mpclu7V3SxpL3Z9nyfz7P8hLLEXmc6+LK+5I9e/Xdy/ZHe9ewHIxwXu/n0VcpGftwjPG1bds+HGPcH2N8Z4zxr40xfuv18d8YY/zFWz4jIiIiIiIiIiK+RH7oD0Kn0+kPxhj/4RjjH44ffAh6Osb4X8YYn55Opz/5lPn7Y4xv3baSERERERERERHx5fFDfxDatu3xGOPnxxg/Pcb4J8cYD8YYf37h+l/atu3b27Z9234SGxERERFfDvsc7Pr6+n1XJyIiIt4Tt/mz8//6GOP/PZ1OfzTGGNu2/bdjjH95jPFo27YPX/9K6M+MMf7gpotPp9OvjzF+fYwxvvWtbx1EbaZPN38J02quegDN/nThGP7n+mb+GuYHQe2++ZbcVptPHSh1tDzOvjAfGJ5v9eX59mc+qYu19tufQqR2k+PDP6NpuuF9fUzrbr4J5tNg/krsy+fPnx/KbIt58NifMOXz+SGYY2N+V/anGO3Pb9v9rP9M3z7ztVn586g3nW/67VX/I1tXbA22P9drHkOcd6yP/alj3o+xZ/py84kzLzHTlO+Pr/7Z+VUvrdU/V7v6Z+ottkzfHneDfQ72Mz/zM6d9rmExxX3W9h6uZ8TWI/NUILa+7O9nf3aebbGcwLw76O9ALMey8w2213wPV32/WF/z/LT1jPXl+sPxsZxwHxsWtxbHLDM2VjxtxnAPT74PsG2rfqncV/lh2MbGfGC4b5pvjf0pdvaX5aQzH57Vtpk3l/kI2r5oOYqtO6wP+4r5PcfK8lnLeWwNt/ya2DrE8eA6OHuf4Lyf7Q833cvqYn9m3vyhODarf7ae82zfl7P1/DYeQv9wjPEvbtt2f/vBE35ujPF3xxj/wxjjL70+5xfGGL99i2dERERERERERMSXzG08hH53/MA8+n8dY/yfr+/162OMXx5j/NVt235vjPH1McZf/xLqGRERERERERERXxK3kYyN0+n0q2OMX8U///0xxj9/m/tGRERERERERMRXx60+CH1ZbNt20Bua18dtPRBMH09Mv256w5kO2XSsVlfTFpqmlvp26jBNp2o6VzufY8nzWT/qSk3/bv5SppM1rSj7m9fP9PPsa7aFmLaex1f9lMy3xfrWxobttb5l7LG+5q3C883vyjTLq+sI239xcTHeBeOcdeVYsG/4bOubVf8j8wgyfbxptFefR48gG2vzteC6RO8D8/eitwPHa2V/szXKtPfmQWTaf/OHsvJs/131I4kfDa9evTrMgYcPHx6Om+eC+Vtwftv85/05vwjPN6+UL+qpcFNdV/3XuO+xrrbWEWsb1zq2j2uneQGurtU8br46zGMsxyU8zliZxeYnn3xyOHbbnMS86Fb3aY7tqrcXcxB6EjEWLCeyvGN17Ljv2t7H9ljOzPbs+4f+n+wbziPW1eaFtd3eG+3+lvOw78ybjO2394tVjyGWGUuWg5lHEOfSbJ2zHGg1x+HYrObXPJ99b75zKx5DX5WHUERERERERERE/CmkD0IREREREREREWdGH4QiIiIiIiIiIs6MO+MhNPNaWfUIMh3sqibadMUGtZIzTwXTbZp+3LSELJtPDHWb1HlSh8rreb55FNlxO5/tow7VvEzI6viw/6lf3/cXfRqoE2VbzSPH9O4cG9McW9+Y7wPbY7FssWpjb/p7ay/LHDvzQuDYz/wCTP9sXgWm3zbMN8M8gdhW84UwPT3H0rwL2B/0YJqtuWN4bLM9dr15mMw8S8xXwo5b3czjw863PWK1PnE3ma1P5jfH+UbMy2/Vo8z2RsuD9s9jW+hBY/uU1ZVwreM+aR6U5j9h9bGx4tpt893WbvPH4NpJbxfzVrH78/x9++lBwxzB/NRsX1r1mWGOYR6ZbDvPt/qbx5DlHZZzmS8PMb9Wi13r331/cZ7T45F1tzVsNSfgWNi8WM3xWH/25ccffzytz+Xl5aHM/rJ1YuadO8bbflfsT4sVe/ecjdfqu5T5i9p+ZPul5WRsC8fKvLj28zwPoYiIiIiIiIiIeEMfhCIiIiIiIiIizow+CEVEREREREREnBl3wkPodDodtKfUKpqu1PR8LFOPZ/o/YlrOVc+HPeZLQkwLyWdRV0rNL8vUh5tnD/uSOk/2hR033S3rS1gfxhb7h/3P9hKebxrr2djzmOlGV9tuenLGrfUV5yHry1gzja9pjnk99e82F1a9GqiRNq8yns/+2T/PtPX27NV5SlbnmbWdY2H6crb3+fPnh7Kte+b1YLFgsWdeDXb+vISz3wAAIABJREFUzLfCPEZm/kM3Pds878y7i31pHnzWVnte3D1Op9NhjjAmWDa/NItB2xe5vvH+9J/gWktmz+cebmsjyy9evDiU6Wf27NmzQ9k8d2xftr4wnxvLMc2Dx7z5LCc0/wzC2DBfSMLn7cfHPH9me/hN92ZfMIficRuLR48eTetDH0ruw4w1Xs/YtXzWvEpY5r5u/cv6W95hzLzFOG8s37V5QlZ9Z1bnAec5Y5NjNctJvsj9GBss2xpsvpOE66iNPdelmc8Ox45lW4NtT7A13fxXzRPPjs/edWZx1i+EIiIiIiIiIiLOjD4IRUREREREREScGX0QioiIiIiIiIg4M+6Mh9BeY0d9nGmoTe9H7aHpXol5EJk2k/pEajNn917FvEVMs8vzzRvAxmLVS8DGglAPad4p7B/2N/XpFxcXh7LFEvuDOtj9+dT4su84VjyfulPq04lpghkLpm9n/cxLgJg3AY9zHplXCe9Hvbx5EJmmmpg3y/5+Nq9ML87zTYNsfWFrrJXZdsYqvQRYP44t22NrutXn8vLyUGZ/mIbcnmexu6+P7Vc8TlgX7jc2VjyfmE8G72eeRvtyfkJ3k1evXh32KvprcF9czZnsuHkwmKeaxewMm0+EaxufzbXu008/PZTpKWRrsfW1eXPYPs/7c+yJ+UuxP2zf5HGuH4w985kx78B9f7FvzGuO9+JYsS9s7eVxtpXnW/5OHxx7H7B9eNUfznwu7XzzSjGfRvMi2z+fz1rNPzk2vH41n2Qs2rq06ilqfWtrruXPHAuuI+a3tepNaDka77+vD4+x7pZvWw5kPozmXWvvA5YPz/yvZl5M/UIoIiIiIiIiIuLM6INQRERERERERMSZ0QehiIiIiIiIiIgz4054CG3bdtC1mT6OejxqMc2PwrSiph80rxA+j+fz+AzTOJvfBLWRpmU07SLvR001Mf27aYqN1dgwnS/vR50sY4Wwf8jep4F67pn3xhjrGmDzSTEdKzXAFkvUCLN+9F6w9rOvWR/2h3k7rPhK3HQ+62ca6BUvBbbFfFnMw4bnm3cLj1tssW+4DrA+5jPx8OHDQ5ntpzeCaap5P/YH72f+WDa2tubP1jXzQSDmFbDq92T7H+tn88L2z7h70MfRPAk4xuatwrL5z3HfNcyLcBaT5sfG+cYy9x22xXxj7Hnse9vHuZZxvltOw7XR8l3be2z9sbGyfZv3ow8P+2fvhWK5u+XHlhNwnzAvEo6N5QWWE5p/0+r5xGLN5g6fz/bbPmqxMsuDWBfz3jKfGRsLevDYvDFfw1WvMT7fckKu6Swzf7f7m58W+9u8eznX2H7Wb1+fVS9YmyccS8axjR25bT4+mzezce8XQhERERERERERZ0YfhCIiIiIiIiIizow+CEVEREREREREnBl3xkNorx+klpBaQbKqM13F9PN8vpX37bN7me7S9OXUXVLryOP03uDzqMs0LxNifcOxNj8KO876rWqqV7Wa5v8xu9Y0wNQwW1vNI8j8kxgb5gXA883/aqbxvel661s+3zyNbGwZy9RIm+Z7Fqs29qbNZ1+avp33px+TaftZNv27rTuE7bu6ujqUzcvM/KUsNszXwzxRrH37+zHurS4ca9OfGxYbq2NNZp4f5lsQ74cPPvjg4L1iPjW2Fpt/2moM8Xybn7a2z3wcbZ/gs5iz8PjFxcWhbGul7Q0r+8wY7sVh3im2/nDtNd8cy0OIjQf7g+XZ/dgXe4/HMd7uO+47T58+PZRtXthay/ubF4nti4w9ns/20ifGvE/Yfxx7zg3zbiHsf97P3hdmHqM2rxjXq956Vrb819ZIey8m7Ateb+8XrN/19fWhzPZcXl5O77/qb8tYtdikl9gs/171b13NF20NtHcVwvrYN4ov+g2kXwhFRERERERERJwZfRCKiIiIiIiIiDgz+iAUEREREREREXFm3AkPIUI9HXWp3/ve9w5l83MwzbHp1c3nx/R+M/291d00sabhNe2/+cBQZ0odJ7WQ1JESPo/t5dgS0yCb14dpL4n5fVh9qAHf18d8X8iq/tx0reYbM/MCGcP7ks+jppjXU7/O61f7x2Ld9OnmS8G5ZrGxv37Vd4z3YlwxTm3dMJ8HYvPWfGnYt/RjIoxl6sHNc8l8Mmxsba7ZujOLNY4Fx27mb3LT9avY/md7Ao9zLFne39/WlHg/bNt2iAvzoXn+/PmhzBi56f57VnOsVR9I86Pb38/2QeY8vPf9+/cPZVsL6NNo+Szh/Gf96WvDtZTY2PD+5sNoHj621pqPjnkGmV/e/v62rpsnDutmXiDmQ2PzyLxM7P3A/OrYV2wvY5VlPs/2afPL497I/uLctHx8vw5YjsK4Ms8a86C0d5PVfNLyAHuPNi8vttfWKZY5dqt5B8+3/jF/wn37zJ/U9h+bd3a9rRO2xjNWbU3d52SzvbRfCEVEREREREREnBl9EIqIiIiIiIiIODP6IBQRERERERERcWbcCQ+h0+l00NRRL0c9HbWM1HCbRwO1iKv6dNPhmr/Evn7WVtMWmkbYdJWmtWeZmmnTVhqm4TYt5qpvzKp2lLFiulmr/14DzThe9e4wPfpqX3Ke0NOH88w8i+j7Qr0378f6mm6WsH0Wi2w/x9p8fWyuUY+/b5/FMWFfELaVY8Gxsr41H4nVdcX8qfh8xqJ5FnGusO/NU8mw9pg/17695vtmPgvE9iOLYzvfYsXWwP06srrXxo+Gly9fHrxnuH6YTwtjmGu/7aO2V9naa+vnzO9j1c+N9+I+xhzJvERWPXyYN/B8jpXNZ661NrarOamx6qPD/lv1YNq3d3XtZN8yzm2ftDi19dF8ZMyrxLz4zLOHx5nTGZaDmtfgbG+56fjMw8jya7sXWXnvu6k8yxm+yPPs3dF824jFknkUmf+Uea+ZX5f176y9nMfmfWVjS+wbw6pn5+o3i9m7zCx37xdCERERERERERFnRh+EIiIiIiIiIiLOjD4IRUREREREREScGXfCQ2iMoybO/B1WdbmmPzc9OnW0pgFf0XKaztN0pOZBRC0hj1NLyefRi4OaXdP4mvbSvD9srGxszePIvD9My0mvBTLTa9pY8FmG9b1piM1ri3375MmTQ5l9abpX81Zg/5hHko01z6emme1j/ax/qXlmbOzrM9O231Q3ts3i3uLctPx8vsWCeXGteoWtarDNB8Nix/aQ1T1p5k9gvmbE4piY54f5S3FszWvBzl/1lYsfPafT6RA3jCHzf2NMcS00DzHGlPlBrPrFzfYaywHMk8fmJ9tq+7zlZB9//PH0eo4Fc7bVvWQ1pzN/OeZ4rK/1L9vL8WJ5tr6ab4utbfQ1Mf+jVW8+y8n4PK7l9i5isc3z9z5jY7i3H/veYontY+zyeWyveQXuy+ZJw2dZPm5eXfZuYvPS5pWNPcusD9/leNzyZZu3jFXLQyznmuXXNx3fx5qtYVZ3y19Xc6bV99DVWP2iPo5lahERERERERERZ0YfhCIiIiIiIiIizow+CEVEREREREREnBl3wkNo27aD3u/BgweH49TjsUy9+k3337OqPzddKqF20bxN9rBtM73fTVBLSOx+pkVk280vwvra/DPM18V0uDb2vJ9pP+3+jMWZN8KqP5J5gZi3iGG+NuYTQw3yqj6ez+M6YD435kVgXl+r/W/rxux80xSbnpp9xevZt+azwHnOsnn0mCZ61dvLYuvZs2eHsnkpGLbGm8eJeSPs+3s1jgzzmbM9hW1j39k8W2m7+SDE+2MfF9zHLEYtBm0tZYyavxzXJ1s/ZnmMeW2sxuyqr+Cq9x/n5+Xl5aHMvljNjy0n4v1sbyFsP71c6GvD9Yd+VtybeP7Mm5BjZfuq+b+t5rOsG9tieQLjmvukeZswlujvxH2WY2Peh/auRC8y1t+8C83varb3mW8M22I5D7F83fJry5HMr8rqY2umvTuxfxhLlo8Ty7EslsjM283mudXttr5q9h5ue5Dl67N3m9k49AuhiIiIiIiIiIgzow9CERERERERERFnRh+EIiIiIiIiIiLOjDvpIWTaTtPXmX5v1UOI+jyy6rWy1w+adwV1mKbX5v3M+4Nl8w7hcfOtua1W0nSs5iVAVseK97dYIiseQhZn1G9b39tYs+3U8rOvHj58eChTM/z5558fyqa/J2w/Y5v1I+Z9wOPm9bXqQcT+mK1bFtesC7X9bJvNA/MusLg2jfSqln/Fd2YM962g94B5LnEu8XzzLGHsWH/s728+ZGTVu2DVk4htszWSe4KV92Oz6okXPxq+//3vj48++uhNmfOfPjVc6zifzA+CcWDee9xbLG+xObWv78xrYoy322r+DfTWMK8P6xvbl8zHxrz4WLa9hedbHmN7G2OHY83Y4HGWycyjyNY+2xe5LzNWbJ/jWJlPCmOLfWO+jLy/7bM2zyx2OLfYP+YXS8xbzPbW/f3N09J8D1f8im46zr6wNXE1T1j1P2V76dVlnqCMZc671Xel1bzEcot9e2+bn5rP26ofFPt2tX683/X19aG8H8tZP/ULoYiIiIiIiIiIM6MPQhERERERERERZ0YfhCIiIiIiIiIizow74SF07969gxaXejjT2ZpW0jyDzP+CzPwhxnDt50zDZ3W3tpi+23Sz1G2u+txY281bgFjf2f1M08zrzaOJ92NsmtZz3z/UvrNvrW/Mw8Y0u+abQm2/td28RMz7ZNXXxeb9qubbNOOr3g2zWLS68V70KiCs26ommvUxzxz2la2JrJ9dbx5B9+/fn9bP1nCuU7yf+fCYN9vMC2HVh4ywr1a1+rYnmEeK7TH0ufj000/feW7cDV6+fHnwHbC1jGs14fVkNeYYU5xvnI98PvfG/fw0DyGuRZYTsW8s5s3/yPJhy8m4d7Dv2bfWXlvbie2j5h9lPjmWc87yfcsxWHfGEeH5HCvbp82HxfJzy7eJjTVzVHv/YOyzPfShNJ9Km4s8Tt8bMvPwtLZZjmb+U8Riyd4ricWurTOr79kWK/SHIuaXu7rGW8438/C0ebPqZWt9b31p3yjsvZUeQk+fPn3nvQ7PfeeRiIiIiIiIiIj4saQPQhERERERERERZ0YfhCIiIiIiIiIizow74SE0xlEzN/NfGONt7SG1haY3N62nlXk/6gNN8z271nScpsc2TTG1hdSlmp6bWsXnz58fyhwLaqB5PfuKY8P28nzqcE3DbP1JrxKLHfNqmfkNsC9MI8yxoDfAzJvqputNw0tfFfOxoYbYPHbMm4Bjy/7h83i9tc9Y9XQyDfr+OMd+1aPDPIg4VozTVR8286kgtkZyjSYce44d5yn72jxJ2D+sL59ventbd/fHea2t6bYGrnpnEa7ZhGO1ugft+37VLyl+NJxOp8P6yjE33xnbxxljPM68hDH7ySefHMqMQa6X5gW4j1G2jffis8zHhGsNcyBiOYPloxwLyyNW/ZpsfeFYcm3l/fg85pCWA/J+7C/zcNrnDZYTre4rfJbtIxyrVe+t1bXeciRrH8eG7WOZnkCMDT6fOeeqXxXHfubdwnlMODbWd2wbn8181XxpLKewPMB8Im0ecSzM55F9zzWf57N/6P/EWDI/LYv9ff+u9hXhsy2WVsfW/EyJjc1+f81DKCIiIiIiIiIi3tAHoYiIiIiIiIiIM6MPQhERERERERERZ8ad8BDatu2gnTVtpen3TK+3qsczTTfvZzrgvfaUmly2hfdi31DHOtNLj+G6VtOfs368ns83fyfzi5rpHcd4W7dK3St1p9bfpns1rxfef+a9wrE13wPTd5t3lfnIsC3URFPTy7Hh+ab55XGbd9Y+89Ghnt3G1nwybF2arTvmbcWx4LNtXnAer/Ylj68+n2uijS3HgrFm3gG8nrHFderq6upQZvsYKxwP9q/p12es+I7dhPmzmOeH+ciZ7wU9QOgFsJ9HtvfG+2M/BywGGUNcP8wvYxYjN11veYzNAa4n+/Nt7THPSq5NtpZeXl5O7899y3I0W+vNq4Njw/at+tJYf1gew/EwD6NVb7/981b9kcyviWXzwjNvOsbOzAtrDPc75f0ZaxbbfL75R7H93HcZi4TjYZ5MrA/nzn78LMcxLyp7l2CssK2s26o/FOtreYTlgDaPeT5h7Nu85vEHDx4cyoxV87e1+u/bzzi3/NbyVct3V/1dbd6Zf9KzZ88O5U8//fSdddvTL4QiIiIiIiIiIs6MPghFRERERERERJwZfRCKiIiIiIiIiDgz7oSH0Ol0OmjiqI8z3S0x7aXpA6kvNL26+VuQfXuoiTUfFx7ns01Pzr6hdtI01aZl5Pnmc8OxsefzevMKMA8h6k5ZNj8r04bO2rvqO2J9TdhWxg7nlfUdsefb/c27gbFsGmbTr1OjzOcb5hnE9nKd2McW62b6dfNJY11Mk2w+DrZGssx5Y33L4za21ONz7lj9CecGy+wv6v3ZXov1fftM/20eeKZft7aY9wHbavsd5+nHH398KH9R/Xq8P7ZtO8QF11LGDOerrSeMIcYsY4jnW15jfnD2/D2WY9izLWejlwj3pRcvXkyfZz6SbBvns3kVrs5R9i3LxPYSYv4cxizPYt9x3Sar/keMW+4TLLOtXGvZt9YX9Oy5uLg4lC3/JuavxeP2PM4Vy//NN8b2tv1ctPyY84ZcX19P62reX5az2L696iFkfWXeg6u+OYxtrnv0abR1zdZ080Ta39/ei22eWY5m+beVia07bCv7ej92s2f1C6GIiIiIiIiIiDOjD0IREREREREREWdGH4QiIiIiIiIiIs6MO+EhNMZRE0etonkimMaa5/P+9Kd49uzZO+s2hnsUEWob988zDa492zyAzGPHoJaSulh7nmG6WLu/6UZ5nGXTYLP/CetD3Szrs9cpW1wyrnhv6q9ZpgcQy9RMMzZu6zVienRr/239pKhRZvutfxkbFjumed63l20xLwHz5DA9+qonh80rWwPN44eYft309xZ7pq/n9ffv3z+UbV4ztmZeC7Zfse/N88PmBecZn8e+ff78+aFsscP9k9fvPYQsDuL9cO/evUPMM/4Zz4wpxgBjhL44Np/N28R8aFZ8Js2LwzCvD9bNcjruW+blQVb3Aruf7ePmm2P+GlxLDZ7P+zMWyT6WLadiXfluYDnMqrfd6ruKxQ7h9bbXEPP04TphnknWXvNW4f1sf9nXz3Ka1XzQvPasbZbvEosta4/1LY9zDWf7bY8wn0juOas5JZ83y9nMk8fec1dzLPaN5aPm02h7As/ft2fqn/fOIxERERERERER8WNJH4QiIiIiIiIiIs6MPghFRERERERERJwZd8JD6HQ6HXRtprk2fwvzEOL96XnAsmmeTU8404qa/tx0ktQMm+6SdTe9O7WJ1EKaZprlmbfGGO75Y1h9zZvAPIxMR7vircK+No2v+ZhwLFc9glhe9V1Z9YEwDbbp1a2+5stjGmf6CZh/lcXCHo4d+27vu3ITjEPC+z19+vRQ/uyzzw7lVZ8Lm5e2BtoaTlgf85Gwsnk02Tzn86nZ5tzbP998JBhn5s9iaz7vx3l2fX19KLPtLLM+fB6P78vmixDvh23bDuul+Wdwn+Vx85la9TKx9YHHza9jH7OMb/MKYbzTP8z2GfOH4NrPsTCvDjvfvE4sX7Z93rxZiI2t9Zd5sK14MFnfEvObWu1LwnnCfZtt5dhzrC2HMT85821ke5lD2bpiOSex53Nu7o9zj+az2XcW57ZOmEfoqieQ3W/13YnPNz8pHufzHjx4cChz7Dk29O0h5itp70P7+tl+xrpY/mp9b+/Nt12D2Rez/Xc2p/qFUERERERERETEmdEHoYiIiIiIiIiIM6MPQhERERERERERZ8ad8BDatu2g/zPtJTHtIMuEejvq86h9NN2q+f7sy3y2aX5NK0hPIdadfcnrrW283nxtqJ2kpnhVb8/+IaYvZ31ND28+Nav+HrM4J+aFxb6zMus281VgXcdw3whrD8feYtn09Ywt6nZXvcfu379/KJsXg40Pn7f3qrA1jmPHuDfvLnoNME5XPYhML87zbc013xsr25pr89r8AxiLxPy+Zh5N5nHB46Z3t7bamk9/KfNUob6esTnz7spD6E8Hth6Zx5j5wa3uVVx7LW9Y8R7hsxjfqzkEsX2DZeZw9NownxvzPjFvvdV93fJvwuPmxcL22N7DvW42PjyXY29xx9ixHMO87cx30fJT63vzRDLvL15PnxjmUNxnWT9bBwhjl/e39w+WZ8cYN/buwevN/8nyb3v3svdiW3fYV6trKMfKvHzZH+aTueona+9qe8yTx951LOda9TkmVh8b+9k6M1sD+oVQRERERERERMSZoR+Etm37z7dt++62bX9n929Ptm3729u2/b3X///x63/ftm37T7Zt+71t2/6Pbdv+3FdZ+YiIiIiIiIiIWOeL/ELovxxj/Hn826+MMX7ndDr92THG77wujzHGvzHG+LOv/++Xxhi/9uVUMyIiIiIiIiIivizUQ+h0Ov1P27b90/jnnx9j/Kuv//s3xhj/4xjjl1//+391+oFI7X/etu3Rtm0/dTqdvmPP2WvuTNNMTE9u+ndiulzTKlIvONOimoaYWkEep758VZ9tGmfe3zTM5j1gulbTSrL+q14i1Dibvt48j0zXOovdVb24+TSYN4m1za63+pk+3XS3jA32He/P2GWsmk+MaZR5Pzvf5vJ+rnDsbI1b9Rgyf6bVNdVig/ezNdO8FIjFDuc1sdgkq3PP6r9/vvkkWFv5LHoTMFao3TffM97P9kur394TxdbLeD/Qx5ExavvwbXM28ySitwv3MlubV3IwljlfuJaZz8zl5eWhbPuueYTx+eZ1Yn5yxDyOzIOIvjLm62IeneZ/x+O8//Pnz99ZZl9ZXS33N28Qa6v1Nfcxq99tc0buq+YhSri38HrLC6y9rD/vZzndHo49Yd0uLi4OZc7r7373u4cy+4p9Y31peYF5EhFbgy3nNL9Zrtm2Rtt7tM0dy1n39eNYrvqX2ruRvauYJ5HNA5b5rjOL+1kO9sNmZ9/cfeT5x2OMb77+72+NMf7R7rzff/1vERERERERERFxR7j1/1z3+tdAa//z6xhj27Zf2rbt29u2fZtf8CMiIiLiq2Gfg9lfHYyIiIgfX37YD0J/uG3bT40xxuv//ye/jfuDMcY/tTvvz7z+t7c4nU6/fjqdfvZ0Ov0s/8xmRERERHw17HMwk1xGRETEjy/qIfQO/tYY4xfGGH/t9f//7d2//zvbtv3NMca/MMZ4+kX9g/YaOGoXb6srNX2e+eLw/vY8017u689ziWkTWVdqCQm1ktaX5hvDtpu3xm08d266nvU3XxfzWKLGmHp486ey8drHonkXMDaoyaV2nnFOze1MPz2G67FNx7qqOV71pbD6mgab55uflcUKMT38rP85ltY23tuOm77cvAtMX25eXsTWARv7VVgf0+/zONc11o/rrsXWFz02xttjyXXg+vr6UDbPEPaFraHmc8G4Zl8+fvz4ncfibrBt22GczZ9hdd+z8znfiK2Hq+vffv0yjxrWzdZOto3zk/sIy+btwbWIa7H5xxH2neW7NnZsD3Mo86U0Hx+WOV6WA+770zwniXmDsG/oK8O6W0626glq+bl5DplPi53P/uHYmD+UeZGZ94p5q9heO8OuXd0XLQcyL1rLaVY9QTkWqz6KXIcY+zaWPJ/tt7ln/bnvL9uv7FmW61us2DcOew+2dzvWb+93xWOH66ZPHWNs2/Y3xg8MpL+xbdvvjzF+dfzgQ9Bvbtv2i2OMfzDG+MuvT//vxhh/YYzxe2OMF2OMv2L3j4iIiIiIiIiIHy1f5K+M/VvvOPRzN5x7GmP827etVEREREREREREfHX0N2AjIiIiIiIiIs6MH9ZD6Etl27aDtpX6O9NAm5bQPISoFzT4PNaX9+Pz9u0xzSt1lTR/NO2gefhQS0ltomknre9NG8nnm3+FeafwfqwfDcypb+f55pVi+v2ZttT8lvjX9+zZvJ+1xfTYjD3TFJt+3LwRWLbY5PNMQ014PmONx807bMWDyXwXzJds1Y9pdd6bb4yN/aoG2jTXjA3Tl9vc4PM4tmTFk2SMueeJ9SWfRW0/1wXOM3qWsG7mlWWeItxzLi8vD+W9Xp3HrZ/j/bBt22G9t/VjNa9gDNq+aXmHeYzZHNvPKfPc4fyzfZVl8zfi2mT5qq1t5nnEOch93nxtbK23nJb3Z39aXkLMp2fmg8Ox4dppOYnlw/ZuQMwba3WfXfXqslhZ9RKzvYywP7mu8HrzLpzlVWybxZn5KVnb+O7Guj19+vRQ5ruKrRucV+bjtrqG27rE/jRfHmuP5aD0UuQ6M3t3NO8pe/Zq2e5v77WMHa55LDMH27/nznwc+4VQRERERERERMSZ0QehiIiIiIiIiIgzow9CERERERERERFnxp3wECKmHzfdrmkXTRds+kJqLakXpBZypvGmdpBtoTbQ9OjmeWNeJaY/p06Tx3l/0zCzb8xbxDTGpl+nttJ8clhf060ydnj9/jjP5djRG4Rl1mXmV3RTXVZ9HUxXy7FgLJBVvymb13Y/jhXrx9iz/qJmm7E+09+zL6kR5rN4nNfzWas+Fqs+auYFY7Fg3l/mLba6Ztu6SMyXgs83D6Q95pFh+we9Clg2Xwlb0xlrXDMfPXo0Pf748eND+erq6s1/5yF0N7l3794hDm7rhWL7/sxX8abnm6ca4ZzhXrm/3rxDzJuCZfPIsbXCjpt/m61dlhOx/uxLy7nYn+wfwvM5Vqtr/8y/bYzj+mb+UJbfmuePvZvYPmfPN/8oe58wry17HzAvL/MmtLEzXxyez9iejQ/7wnwGbZ2wdwPzmKRnEJ9vOaJ5ktq8tdi0vmdf25ptOZp5gdn4zOrPZ/FaezaxeWFjz+vpbbvPocZ4e17wfOZo+/PzEIqIiIiIiIiIiDf0QSgiIiIiIiIi4szog1BERERERERExJlxZzyE9ho66uvmLPdYAAAgAElEQVSoNTSdrnkoUN9nng7UG9r9eZzazv3zWXdqF6lzNU3tqg7U+tL61jTO1E4S016ap5B5lbBsXiw2thwPnr+ic/3ss88Ox8wrZOaDcNP11reMcyvTa8A8edgXs3kwho814dibhpp6+JmWdoy322Nzb8WjyOYNx573ZiyYR8eqPp5liyUbe5unPG7PJ2y/6eW5Dlj7iennZ94RNjYcW/OJYN8wTs2njnsOr6e3wZMnTw7lb3zjG4cy14l92bT48X7Ytu0Qw+bTaPOb6xdhjFgOZuuVeRPO5hTXBsb/zIPmpvM5n+jvwL7j2s4y+4bPY9v4fNaXxzm/WV7dVy2HMmwv4HixfnZ8j42t3dvya/M1YVxzbDjvLFboM2m+LZYXmOePeQDxuPndmUcp+8f8sWaeQqs5inlsMpY++eSTQ9lii/c3PyVez/ZwXbAc03wlV9/lrMy5wPo8f/78UOY6yv7i/Wf+Vau+wMS8con5qtkazDI9gnic76n7vpvlYP1CKCIiIiIiIiLizOiDUERERERERETEmdEHoYiIiIiIiIiIM+NOCPq3bTto+EzLSUy3anr0VR8e6nRXdcR73St1kZeXl4cyNbCm7SfmVWJaSfNTMt2m6UjNp4Z9SVa9DVg2HSuhztY0zGzfXtdLDTLjyjS+vDePW1t5vmn/bSztfPNGMD8m0zizfeb9ZV4sHFvGNq9nffn8/f3M68r6ln1JPTnryuPmSUT9NsvmNWBeZTxua7Lp11d8226CY01s3bV1Zj9XGTfse3qL8Xy2xdY862t6Gzx+/PhQpmcQ9eq8nuWZd1bcDe7du3fwHbD1yNavmX/DTedzreQc4P3MT8/2wv18t32KORr9G3jcvEIsp7Mcw3wROd9ZP3qR2HE+z/zazH+N9WN76X9xfX09vR+f/0d/9Edf+HzLqezdgNczZzNvElvLzc/K/OMYa7Yvm4ePeRCZZ5L1J+cxMY9OMpv3xN4lONZsm3n+MDbYdq4r7AuWrS9YP85bYvmrPY/9Y55INtdYf+ag5ic1+46w6mm5mlNZLLHMNY851te//vWl82c+jrNvBv1CKCIiIiIiIiLizOiDUERERERERETEmdEHoYiIiIiIiIiIM+NOeAidTqeD3pAaN/ORMS8S05NTR0uoD6Ru1nSv1JLu9X7UAtJDiPpu1t18X0zzTMwTaNXPwnxZTKtJLBYI60/dqrVnVfdqmvH9+aZf57WmY13VwZpenbpUHrf6GeZ/taoxZn+u9t+KBnkM98mYxQKfZRpk88CxtrJvbF6YV4GtqatrNrF5Z94GhNdzDTfvB67Ttk7O1l3uF6wL/VN4Pp/FWDK/FvpEcJ7TM4ieQvQ6oIeJzaO4+9g+uBLvY7jnEGOGz7c8w9Ze1ne/l3FuM54Z75wv1haev+rFxxyFa5XlXGw768P2E2sfn8/6rvrsWF7CtZweQ7z/bG9b9byx47Zvm3eWje1KXN9UP5a5F5gvC5/Hverjjz8+lC0nW50LNl6Ws+3vb2uWjTXHhn3JdePZs2djBj1EzcOIY2EeRfRds9jkumA5mXl8rr5bWo7I57O/ZuO3+p5pcWb7kb2bcc+5uro6lG2PYuzZ/vwuytQiIiIiIiIiIs6MPghFRERERERERJwZfRCKiIiIiIiIiDgz7oyH0F6jR60htYIsU4dr3iS8P3W31ONRD0g9nmk3Z94s1PqZFnDVF4WYF4hBTbFphKm9NP8L8yYxXe+qV4p5FdDfg5hGe+ZRZBpkxpX5zFismB7cPIR4vWl+TTNsPi6m6TafCtOjW/9xPNg/1PXy+ll9bCwZG8T027Ym2ppmcW+Yh9FqbK/6yplPhXmS8P7sb469rQN7/T69Ali2vjdPIM5T1pXeBla+uLg4lG2erPpfxftn27bDONpaa75W5pNlnmDce/h8eijQy4RrNa/fz3fei8/mcbaN843zxdZywr5k3dlW2+ds3zLvDdvXzCuE2Nr7/PnzQ9n2EnoIMS+Z5ciMy1XfEnvXME9J66vb+q+t+jCaNwr3VV6/ms+z/xirNndsnWJ5Hxs21tY3nEezNWaMt9eV266R5tVn+Tnvb56jNs/N19FixTyFWLZ3ObJvv81b81eyMtti773m12q+b8zBGFv7sZj1U78QioiIiIiIiIg4M/ogFBERERERERFxZvRBKCIiIiIiIiLizLgTHkLbth00dtS4zfRwY7yt1TQ/CtMy8nzq9Xh/arp5f2rQ9/czbw/TIK/qPqm7ZNtv2/c8Tu3k6tgR85dif5rPjMWG6WxnGuUx3BdnVhfzP7KxNo8ie74dZ1+ZxndVL28+OdZ+80Kw/jFvFov9WWyYVp+w7jZPCetCvfrTp08PZYtbG0ubF+YPZR4gphdnf5jnifUnz2f/2Lq9r795CDHuzaOH5QcPHhzKjx8/PpSfPHkyLV9eXh7K5lXA58/mpcVNvD9WPM5sPjMnos8L78/5w/lGDwXWhzHLOTV7Ho8x3+NaxLJ54pifhMH6MZ80X0jzO7N91TyGeD3XC6sfY4lYjssyx4dr+f755u3Ba837btUTlGOx6ttia6/l16t5iHkambef+d/ZPLf7sX3Mc/axxn3W9nDra5ZtHpiXrc0rto37vnkgGbauWXv4PBtrjofNJa4b9i69bw+vtfdWYm2zec81mWNHH0cet1jkHrH3sJzRL4QiIiIiIiIiIs6MPghFRERERERERJwZfRCKiIiIiIiIiDgz7oSH0BhH/SF1u9T7UUtJvZ7pWE37SP2dacbNV8Y06HvMA4eYlwY1z6aNNK8Q87lhfVg2HxrT5dr9WV/rH+tv09uvasr33gqmWebx1dgwjxvC+/N8zkPzl7rtWJovjnkEmecP56FppK2+5qG0P998D0w7b3VjXNqaascJ68e2MhbMk8him/UzvymrL+tHPb75+FCTzdiZeTHMPC1uqpsdN8876tHpKUQtv3mGEJvX+Qbdfe7du3eIcc4HlhnDnA/mWcCYvri4OJQZw8zJzNOL6+nMi9G8J8x7jvE+8y25iZW1Ywz39OLzzGvDfGvMp2V1LV/dG+hHZbFj9dm3l2NlfkqW7xLzVbGxIJYjrnr8WM5mflHmxbLqCWr1Y+ybT8/M68zWND6b+yTXKI6FedPau4K9N9oabLFh/rSsP/vD3rutP3ic/Uss37bY34/3Sq4+hu8R7Avbr9hWa7utyfbutO/7WT/1C6GIiIiIiIiIiDOjD0IREREREREREWdGH4QiIiIiIiIiIs6MO+EhdDqdDvo+80ahPs60j6bjJaueB3ye+fDsn7+q3za/ImojqW2k9pF9ad4g5ktDrSSv5/3NJ4bXm+6Wx6191h7T819dXR3Kpk3d+3nYuSw/ePBgenw1NizWiNXX5pnp1U0/zvtzbKjpZpnt49zgPCbsT2q26YVA74N9f7Ht5otmvg3mxbXqk2HafvMWMG8FQn0878f+4Lw2zxGbW2w/28uxtlic6e9tf1mdx4RjzbqYvn11nlrsmXdZ3D0YQ1zLOMacH4xZizHGKPc68z7k+mB72b7+vNa8MWx+sK6cr+wr8zIx7w9buy2ns+PE8mdbr8wLxPzszHPI9or9/cwT0/yYzI+UY8vj3Pf4fPNFtLG3vMF8aCyPWPWjsrHn/WZ+qzdhvpcvXrx489/X19eHY2w768KxZN0Yd/SFuby8PJTN48fmga2Jlu/ae7N5pdlYcw23fJv9ZX5ZXKc4PhzPff25hti7lvlFmacQy7aGmlcZY8PeU+15b877QmdFRERERERERMSPDX0QioiIiIiIiIg4M/ogFBERERERERFxZtwZD6G9ho96dZapjaS+j3pA05ezTJ0t70etJPV81EaaFnJ2jG0n1BryemubefpYX5lmmX1vvjPmN2H6fcL62v1Nw03YHvOx2WuYCY+xbebLwuNPnz6d3o+YXpv3Z9tNY2xjbX1tXgqsP71SzOdl1fuEmmXO1efPnx/K+/4x7yrzKjDvLfMesHWCx01zTRgL5tVg/lOmd7f7c93jvOTxFS+CMd5u72yurq55xPTk5hPBsbR5seonZR4mcfd49erVIaZXfVvMN9G8U8wbhTFneQyZ+XXw3ub7wn2F0Jtkde1gXek9YvPTvEJ4f/M0s7XN8hIbO4411yv2p60/ZLZXMG44VnvPxzHe7jvzzrK1mWXzzjIfFvYNY82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0K2J45fRwDLKov417TA6/KYZA6L4xfBPx4zGY7j12PP6cGEwIvz2GTeeZ4/0vjeEBwh0xBDMHIuKfxBA4tYaKz46If3cR2uDPIuIXYnjD5dui/wsVD47hW67LIuJ/i0GOdfV47IYYNt8DMXzL9ksxBAmXjsevj8HA8UAMv/jwyYi4YTx2aQxvBr0qOubPMZgF/uSM//+zMXyDcsXYLzfHPb/m8Mi45xcZjo99+Prx2EoMD5+uiCHQ+8rx2qc1eb8xhm+6Lh/rf0vcY/z8E+OYuc+Y13fHEMQeo/Yaj3/52B5Xx/CtVmuc+I0RcZ/x318cwwPCn2iO/0YMpplrMYzfv4qIp4zHrozh29kbx/yfEMODtKua6+8/jrGZhtn+85///Oc///X+Yg9jqPF4ayr9MzF8+XM4Bl+ev5FzPx5T096fj0EGdnAsy/8TzQ9KxLkm1T8dES9q0o+NiPeO/6Z47gfGuq401786BiPq9TEeuD4iHtMcX/QYbudXxp4eQ7zzgyG/MhbDL5mdjIivnZF3Lya5IQZz7kfH4NP4krjHYPnw2M47f78Qw9tHV9O14/GHjnU/HMPDpA9ExOXjsceMdXz4mH5ERNy6M25ieGPqc8d/f14MceTvNnn/+7F9z/nF2xjitpMxyO4ORsTPxfRXxnpxaffa8ZyZMVoMNhNfNfbXoRh+3GQjIu633+uB/z47/va9AP7z3739F9NfyDgRgxTpcHP8n0bErzTpqyPiP8U9v0jxcxHxX8djByLi18djN8fw7dIHY/pAqP2VsbdHxJc1ef9MDB/aPxmDv9AfxzR4eUc0v0q2h21wXQyB0ukYHuA8tjn25Bi+7dhJPyOGb/m2YgiGvqI59rXj9VsxPDR5VUR8YXN85yfatyPiLyLi65tjj4khcNuO4VXanb/2V9quHTfHc37FLIbgZaft/y4intkce1IMAcLW2C+/ERH3HY+txPCK8G3j/f46hqCtyH3/cDz+/hj05zvHDsZgknnzeO+3RfOLHL32Go+/aRwLt8Xw0OlIc+wXxrpsjff9yRgfro3H12N4tXkjhm8nf1zK/ehxzGzGYKz4aLn3j0bnV9785z//+c9//uv9xR7GUOPx9oHQVTF8CbTjffOcmD4Q+oFx7z0xxhf3iyGW2dnLvz8u/IEQxXPHx/379oh42/j/jsYgw/pIDB/23x4RT2zyX+gYbjz+iBh+Ue10DPHMI+T4k2J4SFRmlINiku+K4QHgVgwPz67cpT7PieZXxujaGHx/bh/7/Q9CYsQYHmy9dyzX+yPih5pjPzP219b43+fH8JZ+xPCAqMbwZW0blz5Zxsxfje31hpj+St2ucSldOx6fGaPFEC//ZdwTO/5xRHz1fq8F/vvs+Su11jBmmSilfDCGAOGPdjl+eQyb+tfVWm+ecfznYni4cONFLue3RMR311q/82LexxhjjDHmfPhMiaH2G8dwxpjPFC7hU4xZLmqtn4rRDyYiopTyxTG8pvmOGMyEnxLTnxW9WOV4TQySKmOMMcaYhWdRYqj9xjGcMeYzBT8QMoZZi+HXse4XwyugvxjD66vGGGOMMWZ3HEMZY8wCY8mYMcYYY4wxxhhjzJJx0X52vpTyDaWU95RS3ltKedbFuo8xxhhjjDHGGGOMyXFR3hAqpRyIwe3/H8Tg4P7miHhSrfVde34zY4wxxhhjjDHGGJPiYnkIfWUMP+P4/oiIUsrLI+JbY/iJynNYX1+v11xzza6Z6UOrO++8c5I+c+ZMqnCa3913393NL/vQ7JJLps162WWX7Xq8lDI5RmlCy5pNZ9tSofJm65ttD+o7SmfLv5cPVFdWpi/s7XVfzjuute5aXk3r+QcOHEjlr2Tbh9D70djPtnevPHruvPNeobbK9t3FnifZ+93bUud51+HesXnbisbxxW47Kk97v1tuuSVOnjw53+A2e042Bpt3X9Xjd9111yRNexeVT9cz3XvatJ5L84fund0XsvNd0brRWk5Q39Heks1v3r2O7pfZu+YtSzbGyo41up7mCY0VPT7v3kHX7/XYyczVeT83ZtcFRdueyNb1Yh+fdywo2XWF6I21TMxyPnlnx3n2/vPur+3xXgx2sR4IXRsRH27SH4mIR+528jXXXBPPe97zds3s9OnTk/QnP/nJSXpzc3OSpomuD5S2tra6+VGwoovo8ePHJ+lrr712kr7qqqvO/vvyyy+fHNOHSbSAa1n04ZamP/WpT03Sn/70p7tprTttGDTptX6XXnppN33w4MHuca3f9vb2JK3to/XX63uB46z7a36ZRUzz1rGgZae+0nGt80bTej4Fslq+Q4cOTdKrq6uTtI7VK6+8MnrQBqn3077T+uhY1PO1PnfcccckTQ+i9Xzqn961WlYdZ7Tg61jSeUN117bV4zrO9bj2nc5zXUe0PPrQXI/TQw/6QKjQWNf7aX0UPb/3pQWVVeuuaFvpvbWvtOw6LukDn6Llpz2rrc8znvGMbt5mf9AYTPs4GyfoGNYxpfPjtttum6Q1JtO9i9bmw4cPT9JHjx6dpI8dO7bruRpD0FqsddeyZtuO1gedb0eOHJmkdR+mmEb7iua3rj/ZGJRiQC2f5k8fHHUsaHu396e2pb7Qsmnfa98qFAfQvq/307GrbaljQ9M6lrRv6Mt0Pa5jhdYJiuko7qC52uZH8Z6SHYe0BmrbaF9RW1KMQsfpS0F6sEwPI+lho0LxPeVPcVDb9/S5juJDHcfatjpuKZ6l++saRjFeL95/2tOeFrtx0TyEiFLKU0spbymlvOXUqVP7VQxjjDHGmKXCMZgxxhhjIi7eG0IfjYgHNOn7j//vLLXW50fE8yMirr/++qpPa1v0aRe9saNP5zStT9H12yhN69M5+lZBj+tT97W1tbP/1qe+Cj11nvfbHoWeetO3QfQklb4xoW/ftDz0xo9+g6LjjN4m0/bS9DwSNXqirW950BtAev3GxsYkreOevp1RdKxS2+u3T3q+PhUn6Ck7fbunaN/p9Vre7JsUSjtXqe70LSx9i6roty/0VoqONb2evh2iN+do3cq+wqvrhM4dKo+eT/XLvv7cprMSE/rmUI9rX9Gbc9TWNLbom0Va883+0MZgD3zgA2vbT7SPU4xF32jq3qV7lT6gojE6T8xGbzPSG3A63/S41pXeYqAYi+QCWn6NebJ7C63V2vcUn9O+Sm86KPPYHmjd6I14HUc6zqhtNE0xFK2ter2mKf7OvtFPbzbQmw5UH/osRfE3fb5py6PzIDuvsvHyvG/wUMx3sfd1WocoTtGxT3OPykdvOmfyy74VTWOFPotk36Kk8lF+5xuDXaw3hN4cEV9YSvn8UsplEfHEiPi9i3QvY4wxxhhjjDHGGJPgorwhVGu9q5TygxHx/0bEgYj49VrrOy/GvYwxxhhjjDHGGGNMjoslGYta6+9HxO9frPyNMcYYY4wxxhhjzIVx0R4IZThz5sxE50w/SUp69p67eMS5enP1ViFdcPYXNFTDrXr5FtWjqyaXNLik61Qdpepc1cU/2/bkV6HXk9Y/+9OQ9IsepLEm3SxpMUln3LaHtr3eW8eNlp3G1e233969nvyfdOxQXyvkuaMeQ/prL+Szk/1ZyqwGm34Vgfy/lPZ+Os+orbM+Mtl1gn7phbwHaN6Sfl7rS+VXtPzq26HX0zpHv1CX/Snotny0RpCHB/mWZX8Jkto22xa9PWavf2La7A211uh5CJGvzbz7bu9X+Wblp+NIj9P603qG6fgnP7Gs7wnNL1oPaN8mfwuqD/3iDkF+U9m+ofWNxibVr12PqK+oLBQfU9nol2Lpl6VonmmMSPEr+UAqFEO1fqkReT+77FxS6FfIWqhtst5W5AFEn+2y3oLUdlk/WYpBFfJ7pTiF9oCst1lvLmc/u5BPMdUt649EbUfzoNc2vc9N+/YrY8YYY4wxxhhjjDFmf/ADIWOMMcYYY4wxxpglww+EjDHGGGOMMcYYY5aMhfAQiphq9lRfp34QimoHycMn60ujkPZTdb2bm5uT9KlTp87+W70ssrpT8lFRTyLy6sj6tmh+1FekwSat5Ly6XdXNZv03tL6k99f69fw0yKuAfBu07dUbS8tG+ngdC+Qjoddr/XSsU1vSWKT7k4aaIG8w0jz3+o/GGc0zHcekr87ej/TYNC+p7el+mia9e9bXg/YA0nyTp5Pev+dPoHVVsn5NhPpS0PWk1VeorcziUWudrDE0X3V869quPo0UE1GafKx0byPfrXZv1LzVy07zophNoflGXoF6v6yXiHoLZutD+zCt9bQ2U/tQfK9xjtJbi/Xemp7X3037lmKIrMdP1ntL03Q/bQ/y0aE4geIWGtvZGLR3nD4rZOM7nSdaV/LsJMj/VSFvLvIuy85r8oOivqGxSH5cdP82f+oL+uylUPxKdaPPGtm6X+gzDr8hZIwxxhhjjDHGGLNk+IGQMcYYY4wxxhhjzJLhB0LGGGOMMcYYY4wxS8ZCeAiVUiYaONXf9bSAEexRoLpZ0g+qNlO1j6q5znoqtLpi1RiTLlTrTp44evzgwYOTNOkuycuDdLhav6xWUq9Xna72bVbnq+1NvjakwSafnrZ9MprXWWUjnxPtK21L6uvsvCEvLfWV0LFIPjnkZUB+W5o/eQqRDpj8s3qaa9L4kjZf65o9rtCaS/5QWW8vhTyCyNOE+nJebzKqL3mN9fYo0vpr3rQ/kU8c9S35PtD5Ok/aeTGvf5+5OJw5c2ayXmc9x3QMrK6uTtLq86JjVH1uyBeG5oiuxeoL1O4Neoz8kch3RtuGfF10H9T5o/fPeuXpPqhxAO1ztJeQpyaVT9tb24diRG1/LW/Pp5K8OSjmUPR82he0rlnPSoo/FfLeyn420rGb9QgiD0/aW7JxQHt/mkcUT9I+mfXmI98abXtti6znJ/kgKhRHUN/S52S6H0Hrbs/HJ1t2bWvy9iLvrWz8SX3XG5u9e/kNIWOMMcYYY4wxxpglww+EjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTIWwkNI9eukkyW9n6b1evJGmdePgzwX2vuRrwrpSBXVB2r+Cmklte1IH656d9Wrq2Y563ND9yfvA/JAUi8DLc+hQ4e652t+en5bPi0rlU11qaRD1bFy5MiRSVp9GvT+2vfkX0WeOjo2dB5mdbtZPypC66d9r+msb01bn6wnjdZd25LahtZIWuNozSX9u0L1y/YtlZ/2BB3LNLaz60pbXvJlIy8x8u6iviMvAYU8P2hstPUhrbxZTGh9ojGnx3UtpflNnkE6ho8ePTpJr62tTdKtx5HGXLpv0lqVjVHIo4d8Hynm0bba3NzsXq9kvQLJ64/2fc2f9jbyeCLa+mlfk2cQee2RjyOdT/5RtDdoTKdtTX1D+xh5cGqa9umsFwr5Xmb2Nj1GMQuNe/rsQft41hdSmXeNpn2d0uQzSZ8vaI2n/DPei3QufXagz/iKzhsaKzpvFfKJ07Ztx3avrH5DyBhjjDHGGGOMMWbJ8AMhY4wxxhhjjDHGmCXDD4SMMcYYY4wxxhhjloyF8BAqpUw0cKTTVQ2c+tKoHp38Myh/Rcun56v+r6eTJY0xaWgV0sWSRpq8NRTSXpIvjKJ9p5B+nPTo1J56ver3e9rMWcdbrwK9P+m3aVzrvXv3iuBxqsdVD66+D6SD1bGmban3I90uzWvSr5OmWyGfCvXn0vbsjT3ySaBxqeerVxV5E5C3APlM0BqZXVOzevSs3p7KQ+seacCz7dGD1ijtO+orhbwIaH8jH4iep4c9hBaTlZWVyRin8ax9rnuDro3qY6NruY5pnY9Zfwwtn66PrZ+e+gvptbqv0PzMrl0639Trj/zNNH+NGw4fPjxJa1uo7wx5Byo9n8RZ5VW0/Do2KAbTsUZ7a3s9jSttS1q/tO+17Fo3nRfaVrQWk2eQHl9fX5+kaZ+k4wq1J8V483oyKb2xqH1BHkHZmIbKpvcnryzyMyV/JYoTqK0pRqLP2QqNNW0Pei5Ank7t9eTjRr6LVHZqG6pb1neO9r/2/r1x6TeEjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyVgID6GVlZWJTpu8UlTfR34ZpG0krxE6X9OqOe/59pDvCmkbSUo+cyUAACAASURBVPdJ15NulnxoSGupUH4KeZtomjySCPJyIf8MPV/HQtu+5DWgaN+odl7HvWrpNa1eBao/J42zXq91VY2z6un1fNLFanvpcdX769ikuUB9qfkr2h56fdse2hfkK6E+ENr35D+lfUG+MKRJJl8JzV/HUtZDSaF1RsunY03vr2Of/APIO6y3btEaS/sReWXRukL7G/muUfkU+wYtPmfOnJnMAfLI0vSpU6cmafVGIY8gTdO+rnNI1zf1StF0O8bJU4vWPooRKD+a3wp5e9D8JT8MPZ/yo32c+k73BvVC1L1Mrz9+/Hi3fD2vxdOnT0+O0VpFeSuan8ZAWlctj/rGZMee3k/7guJl7RstH3ntad/1/OUizm3frKcofR5q86dYnj5rZD+HUv7aVhpP0me3rEcQHac4RetHXl/Zz5bzehJR//TKRr5mdD35CNM4z/pZZdO74TeEjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyVgID6Faa1ejp9pB8q9QvZzqVslvI6sjVt2sainV/6NNq06U9NqkuyStY9aTRzXSlCZvEtJSav1Vn67nU18rOnZUV6qabq0P+Vnpca1vWx8a16TvJr22tp2OQ9Wn6/Wqg+154swqD3kG6XHSyZIHUNb7S4+TB5GOLS2/5qft0V6vx8gfaXt7u1sW0gxr25E+Xa/XsaCQBlrrp/lpfXQekeeS5qfXZ73GyDOI9PbaHm15yUOE1hSdt9R2Ok8VLStp9RWtj84j8jYw+0+tdbL/0BjUvUrHpF6f9WBQdIzrmF5bW5ukr7jiikm6t1aTD4zWjWImijez+xxB80vbXvtG21Lbg3xnyEeGYkrygen5MM7Kn2LEdq/QvMh/TeumZdOxQv5sOi7JJ0bzo7ZQtK81DqC4hGJWHWs0rynuoHVHr6fyZ8pG85I++9D11JfkQUReuVRevZ8e15iT/Kgovqby0zqR9ZlU2vpS3ylZfyZtK4ofac2ivqS2aOvXO9eRmjHGGGOMMcYYY8yS4QdCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBkL4SEUMdXIqb6OvENI50r5kRcIeZ2QZ5D60rTHyYeFdJOqw9TjpFfPaiepbch3hfIjfTp5EFF9stfT2NHykrazPZ80s6SXJo8ebXvyHiG/JC2P9o3q0UnfTjpZ6qus9wLVj3S+pBGnudyWj/Tc5ClEayLptRX14KG+Js0y9U3Ww4fmNfUVzVMaGwr5AGW8HWhckceQevZQ22bHlt6f1sxeebP+RGZ/OH369CRNnkEbGxuT9NbWVio/8v7T47rXHDt2bJLWtbg3prP7SHbtzvqZ0dpO+Sman/q8kO+hpsl7ZV5vFa0vQb42vfvTPqbjTvOmvqC1NetDqPmRJ5CWT++n11O8TjGZQu1DHkTk60j7rtLbf2ifVWgsZHxdIs6tK8Vc5NtGnx3J70pRTyGKcXQskW+OkvXZJP+pXn+QHxKls35N5K+UHYv02aUdq71+9htCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBl+IGSMMcYYY4wxxhizZCyEh9CZM2cm+kTVn2ta9XHqf6Fp0vuRfl09gTR99OjRSXptbW2SVp1uq31UzW9WJ6maYK27ahdVo0taRdIwa1rrQ9pI8qdQXxytr9aH/KRIt6rHs+1Hut1W66m6T81bxyXpsUlnqm1JXgfkp6THta0ondXRkucP+eSQplvbU/sjS88Hh3wZsm1PaNvoOkHzWtuavL6yPjUK+ULQuqH5q4cJrQN6P/L9IL16b25SX9I8o7FB84K8CrI+Edr26o1gFo8zZ87E5ubm2TR5/mifttfOOj87xnV+aVyhMZem1bdRx2S7F9JaTP4QtHZm90GF1pLsXkExFnkaZX0WlWwcoWT3vt7aTG1D+wrFnz0vjwj2HSQfSfJf0us1f+17yo/8rBTyyaF4ndqXvF90LPX2Qoo/aR+leDz7WYXOp32a/JY0f/qsR+setV92bmQ9Q2kstPenzxaUt45jup78puizHu2nNDba/OwhZIwxxhhjjDHGGGPO4gdCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBkL4SF09913x8mTJ8+mVTuo+jlN33HHHefk10JaRdWbq65WPYHIQ0iPq56w1Q1n9dLkg6Jlz3oCkUcO6cO1bbV+6gGkkP8FlW9ebxGFykseQz2tJ2nhte9I3076cvIeUV8ZRe9H3iCkAab6kL5/Xm8V0jiTJlrvR5rrNj+6lu6tkKZYIR+LjCZ5FtQWumbTukT6cpoLNHayHk10fm8skS+DomsKeYBQ3TLjNOLcdUH17nQ/Grtm/7nrrrviE5/4xCTdg7xEdMxkYzQdU7oPa0ymHkM0Rts5o+fS2qLzjfY18hDSttJ9lTx8FL2efCWzPpG0flDMqdBYon2a4ozM+kPxeNbnUMeWnk99rR5D5JGjzOv7ko2/aSxkfSFpbyJ/u17fU9nIDzTr60JrHHmVUfxJ98t6i1HbKjS2svN2Xo+i3vnZZwzZutGaRvEqjU0aa7R/74bfEDLGGGOMMcYYY4xZMvxAyBhjjDHGGGOMMWbJ8AMhY4wxxhhjjDHGmCVjYTyETpw4setx1a2SjlVRvZ+mVa+nHkDHjh2bpFW/rjpf1Z7q8VZPSD4vpKnVsqu+XfXj6pekGmbVImpbk85V66rl0bbR6/V+6g2g2kgtv/adQjpcPa4eQ6TbJe1pmz/1NelG9XzyFiAvEvKHIt2s6uX1fpSfQt4nCvWlzgXyblF07JJ+X8/v3Y/6ViHNMWnt9ThpjmmNJZ828j7I+tBk9ehZvTxpvknv3rufXpvtS2Wvy0p7AmHPoM887rzzzrjlllvOprN7A+3jNCY1f/UM0n19fX29e5xivjYu0Zgl60dG+yIdJx9CnY/k66J9Qb6OWn+C1hPyVtE4QWNErS/tHdm1v01TTEExmaLjVmMO8tCh+DTrW6joWCTPS2177Ts9TvOevMSyMahCnqhteakvs35K5LuofUf5KRTfZmM4aksqP3mp0WdH7auslxnFTb34nGJ/Ok7xK/Wltp2ifUm+aBl/09649xtCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBl+IGSMMcYYY4wxxhizZCyMh9CpU6fOpknbSX4QqkUkTx/Vn6sOWM9XXxvVA5JWsk2TB41COlXVLGvdSd9NmuqsblbvT/cjPblqjlUrSf4bmp/eX4+TZxL5a/T07NTX5MNCbUll174kvbpCXiTkjUB9SZBHj+rVSb9PvhNaX10naCy05aN52Lt2VtkUGlsKeQfQPKbrtbykN9f89P6K1pe82Qjag7I+I219yMuq53cyq2y0JpEfk0Lnz+tXZRaPu+++O06ePHk2rfON5i/NBxojOj/Vt/GKK66YpK+88spJWr1NqHztnCI/CFqbyTNI763zX9Obm5uTNMUser1C+zT1raa1PFlvElorqT+ob+fx09C2pLqox6Siazf5Lek41jR5ElHbUHyv0Lwmfyra6/Q4xaC0z9NYaY/TPqf3yn42y3pgUn6UP3kMKdm2I/+p7Ge/bExL7UFxSgv5G5FfqOZN3l00tmgeU91pbLX1s4eQMcYYY4wxxhhjjDmLHwgZY4wxxhhjjDHGLBl+IGSMMcYYY4wxxhizZCyMh9CJEyfOplW/R7pYhfSApPFW3a+S0SjPOj+jbcx4U0SwLwppH7P6cvJtyXp5aP7aF1pe0jQr1Deku83S035SWyvkuUOePVnNrs4bzU+hsUV6cCof1U/16Or9RR5GNNdorGl/qv6/p1/XeaPXUluorwN5iWndSZufWcNmnZ89ruWh8pOenrwOyP9K0bGh5emtW7R/ZdsuqzfPeo7QOKd5c776dbN/1Fona5CuleThpWNCfRbX1tYmafVf0/ly9OjRSfrqq6+epFdXV7v5KTrm2/WS/BjIa4Tmm6Z1rd7e3p6k21g44ty9QentM7OOZz3BND/yzKQ4gDzGsv1Ba3GvPyhe1rx1Xui4o7Gi80ivp7GV/eyS9dbLev9RvJ/9fDGv5xF5EbZpmgdZ30S9d9bvSM+nz6HkvUXzhOYV7fPZsZL1YlOy64RCn18y12pbk/dttm1pbFLfXSh+Q8gYY4wxxhhjjDFmyfADIWOMMcYYY4wxxpglww+EjDHGGGOMMcYYY5aMhfEQ2tjYOJtWXS15pZAvAWkHNT/Vxepx0mTr+T1dLWkLKU0aXaq7nq91yXqZUP50XOuj6P01TT4xVF+F9O7qTUDa0PZ68rZSHWrWu4A8grJafPJfUnRsaFuRfl/zJ38oXTeoPeh+5GVG7a/lbduP8iJvAPKB0fx1Xmj+6vlB+dP9yIeGrifNdlZTTesEzXMa+9o/Pb09+cApmXE1C/L0IL267gG0Z2X9psz+U2ud9FN276D5T55COsaOHTvWvV6h+dnzeaS1IeuXpvNT59vp06e76axnkXoQZeNVWk8o3s7GDeTfRmu7kvXbaPub8qYYQu+l+0D2swftDRQv6/20bbK+jOSjQ5/NFBobepy8ArW82v69uID2LRob9NlC55XGVBR/Zj15qD50XNuO6pMtL5HtD/I2pHWlhca5ko2/aSxR3bLzouep2aub3xAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyVgIyVitdSKP0dedDh8+PEmTJIteFdPjKhGj1zZJnqA/maqvd7Wvp+m96RVVelVMZUYkD9BX5egV1uxrgtmfjaTXf7V+JO3QV+/otUdF70evxNJPQrf1ycrZaCxQX2fKFsGvuNJrlTQWsrIEkhNqefVVeYV+hpNkW1npTJumeUivgtOalP3pcq3LoUOHusezP8FK5SH5INWXxpJen/0Z+6y0VceitmcLyVvo52up7HQ/qqtKWkg225Nu0pph9oeVlZVJnEXSC5KAkhyafn5b5wvJH2i96cUBdG5W0kRlzc7X7M8/6/qhba37Iq2ltI/T+qVo+1J5Fb2frj+ZuIbiXS0rSZFpn5pH1jIL6vus7In2afpp9mxMpe2rew3tF7QO6TrWlpf6jvZhmjc0FkiKSZ8Fs+dT3EDzmD6PZNdBJSvfnMdCQ6+l+FOZVw6nZK1OSDrZe+bQW2P8hpAxxhhjjDHGGGPMkuEHQsYYY4wxxhhjjDFLhh8IGWOMMcYYY4wxxiwZC+EhFDHV5NFPrpF+nPTn9HOApHtVTyPSdOv5bXlJL00aZk2rBpc8cFRjSz/dTZpl8nfK6j617VX/Tj9jSfcn/wzS3Wb19m3+2rfaF6TppZ9ApbFDmt95vQ3I+4T09VnPI/oJUyXrs0P5ZTTQ2Z/+prpmf4KUyqprKv1cLa0LCvk+KKTf1/vrOkY/a6/r8F7r+WnstNC8Jc8N9aVT9vpntcm3ru0r/wT9YrKysjLZf+inwRWKY2htJr+47Hqp87M3p3Q+aIxBP7+s0FpGawfVVde2rJcJxVjZGIric4pTKIYjaK3txVHZn9amvs3GRLTP075La3M2bsj6uOj5tI/qXrG9vd09P7sPU/u05aUYQtcFhdaYnn/RrLJR2ZWsn2vWl4Y+61D7kScT3S/7s/MK+QD17kVoX2o8q2laV7QtqO80fzre82hu8RtCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBl+IGSMMcYYY4wxxhizZCyEh1ApZaLJU52pphXysSHt4erq6iSt2lE9n3S6pLdvr1ctYvZeJ0+enKS3trYmadUW6v3IT0L9jxTV25P3gGobqa/IS4C8C0hjrOUhjwvS5ys9bar2rfYFaYzp3lm9N/mckNaf9N/z+jllfXIUKh/p1XUdytavTWe9tLIaZ5pXpMUnfTn5NtDYUEgTPa9eXtcpWmeoP7J+WG37ZbX3Wd8KrYt6Cmma1h1Fy09+UD3vLLM4tP2m/URxiI4B8rXKxg269pJnmI5pTbf103upDyPNdVr7sp6Y2X1K2zrrXTKvrxf5xSnzeh0q2bW4HQt6TNuK/JUUWouznj3z+rJQ/ci3RvOj68kXRtH8yP+V5hp5jmbiqKwPI81D2vezMY3mp/dTsvE1+dpkPYqy6w75a2XXzd7npayHJ6F9qfuP7mfz+q7p/ru5uTlJt/PUHkLGGGOMMcYYY4wx5ix+IGSMMcYYY4wxxhizZPiBkDHGGGOMMcYYY8ySsTAeQq1mTn1rstpH1SSrzlX1eXS+3l815qqP1/x6HgvkV6Raw+3t7UlatYJ6/sbGxiStOlfV2FJb6/mkT9f6kVcJaYK1fUgvrlrLrN6dPIvI60Rp27enbY/I67O1bUlbr2OF9OQKaXaznkXkzaDQ2FC0Pej+Cvlfkb5f79+7ljTNVPbevWadr2nyFlOyHkfzeiaRpwhpuLPrmKZJA96bC+SvRONIj+v+Q/uTjmOtC61pWZ+I89Wvm/2lnTNZL0OdX6dOnZqk1d9Cx7R6h+h8zHr10Rhtx7xeq2XNrqV6b/V1zK4den9tG4rpyJtEIW8QTWvfzbuP0/kU12TWmKyvCe1b1FbZfY9iKspPyfq26FilGEnJ+kPp2KX8aG709s5s/Jz1Q1Kynp/kK0PzmPxTyZuXfGy0L6h+GncotOZr+ckzKRNTUl3IM48+y9E6oOfP63HZ+6xnDyFjjDHGGGOMMcYYcxY/EDLGGGOMMcYYY4xZMi74gVAp5QGllNeXUt5VSnlnKeXp4/+/spTyX0opfzP+94q9K64xxhhjjDHGGGOMmZd5PITuiogfqrW+rZSyFhFvLaX8l4j43oj4r7XWny2lPCsinhURP0KZZTwoSLer2kjV+6k2Uo+rh5F6MqiWkTwbVBfbll/vrXVRz6CTJ092733rrbdO0qpfV23hkSNHJmkqj7Z9VjdLfhhZjXXGu+N8yqPXa19S/uQPolrRltXV1e65pEvN+kPpcdLiZ+oyi73uO+rLrO+O5qcaZdINU/lbsnp18kfSupIXmLYVtQ1pmrNeDHo+zUNa00mDrXOL2ovqT15oSnuc9N5ZPxStu7aVpslziLwVsh5Cbf3sIbS49PztaG+g4+plSH4Sut6tra11j1N+vfWNfFvIn0zLovNJYzadf9rWGpPpPqTlO3jw4CStcQDtc9R3Wj/Nn/ymdG3X6/V+tI+TdwjFGZlrs14emRggIr9Pkm+itl02hiLfx3m9/7L3pxhV0zo2el4wtKeT1xeN02zfZv2k6HrygcyuA1QfGpsUY9I6S/XPkPXkoTVOIY9L8ijSz+20hlHMd75r4AW/IVRrvbnW+rbx3xsR8e6IuDYivjUiXjye9uKIePyF3sMYY4wxxhhjjDHG7D174iFUSrkuIh4REX8eEfeptd48Hvp4RNxnL+5hjDHGGGOMMcYYY/aGuR8IlVJWI+J3IuIZtdbJb43W4T2lme8qlVKeWkp5SynlLfTzd8YYY4wxZm9oYzCVMRljjDFmeZjHQyhKKZfG8DDopbXW3x3/99+VUj6n1npzKeVzIuKWWdfWWp8fEc+PiDh+/HhtddCkfSR/DdLnqTZRdaqK3k+1o6pf39zc7F7f00DrwzHVo2vet99+ezet+anukrSQej75V6ieXfXwWT151seGPIoU8gsgTTL5fyht+UiDnNVbZ3WqWc2wzhu9/tChQ5O0jhXy4CG9fdbXRse2znMa26TPJy8Dpa0/rTna1jpPaB6St5cen7dvyFNI66No2+q6RZ4hpAmn+9M8znpF9K7P+j0puifQnpH1EFLIM0XTvfqQ35C592hjsKuuuqq2a5L2qY4BXet1PpJPjnLq1OS7RJzv5LNDe117Pa1tivo7aF01RqPzyfeF/OVoPaG9gPzQsjGO5kcxWdbPTvc6WqspzumdS58tFB2H2tdZv6Ssdx/FNOQplPWJIR9LgupDMSb51PT88LIxDY1DRftWyXoG0djL9gX1vdaPykvrGNVP5w75QtIe1fv8pNfS50A9X8tGn6N1Hcg+s6DPguSp2fOwbJnnV8ZKRLwgIt5da31ec+j3IuLG8d83RsSrL/QexhhjjDHGGGOMMWbvmecNoUdFxHdHxDtKKX8x/r9nR8TPRsRvlVKeEhEfiojvnK+IxhhjjDHGGGOMMWYvueAHQrXWN0XEbu+Oft2F5muMMcYYY4wxxhhjLi5zeQjtFaWUrt5SNW/qiaD6OT1Oejz181CdqvriKKpd1PKolrPVr2f9iG677bZJ+sSJE5O0+kGQJlm1jeTRs729PUmvrq5O0kePHp2ktW00ndXJKno+6UpJQ63nU/vo+aTB7p1LbTWPNn4WpMmlvtF5QTpY8h7Ien+RVwJplhXyatD2yXortOfrtVljfeprGte0LtC6RPNU70deY7qukE8G3Y98Kqi+Opao7zP+V+T5Qd5b5BNBbUfnkz4+66vR808yi8Ell1wSx44dO5smH0cdgxp30L5MJtY0RnW91Pmq64F6DrXlo3Wb5oPWnTyDaC2h+ahtq2unxmDkxaFkPZWyvjgKlY+8XOaNY3rXkvecQvNGIU81LY+Oc/JfIv83mtcUU5FHUjbOUR8Z8gqk+vbif4rltSz02YjWEYU+m2h+2hYU/1PMRr451NbUtzRW6f70WY3mudK2B9WFYjBK01gh76usr6S2fW8d6nqode9ijDHGGGOMMcYYYz7r8AMhY4wxxhhjjDHGmCXDD4SMMcYYY4wxxhhjloyF8BA6cODAxItGfXHIH4I0wqTBJs22pknPrhpy1aKura2d/TfpSDUv0qeTlpG0jeTjQj42hw8fnqRVK6ltRZ5CpBcnDTbpUClNulTSiPd0stlrVZOrkJ6a+lY1vtQWpHvNeggRpPmmsUnlJe8Encd6PnkQ9XxkaB3QeaTo2MiWTdF1hMjej8Ye6efV903R/LTvaC5lxxatW23/0Twgbb16htC4pfuRX5TeX/cM8rFor896xJl7h0suuSSuueaas+msrxV5rWjcQjEarX/kj6HrQ6/85DtC8SF5zen5StbTi9Zmvb+ufTQHyZuE8sv6vSkUn2f985S2fSj+03tT3cmjh2Iq2lfoftn4lvZh8o/L+urovksxI+3D5B3Wi5uor2kf1uPknZWNxynmyHj3zUqTrxvNO/o8oGNB5w7FDZm4YlZ5eusMteW8PosUQ1Ff0BqvddXP1dqXbVv0xo3fEDLGGGOMMcYYY4xZMvxAyBhjjDHGGGOMMWbJ8AMhY4wxxhhjjDHGmCVjITyEVlZW4siRI2fTqnFTfRzpzVU/p8c1v0OHDnWPk6Za9YXq8XDs2LFdr9d7q+6RNLmkaSYPnqwuVNtWNbxaXtIIa/5ZXW3WNyarXyd/jqyfVZvO6r1VU5vVwZJHUFbzTPr3rH6eNMAX2z+K+pr0/aSXb8ub1XeTx46SLZseV88PmjdK1p9K6fmuRbBXmq5D6ilCnkfk2UT907ue5hm1nfbdvJ4lWR85vb+27cbGxiQ9r3eYufhccsklcfz48bNp8snROID8Nk6fPj1J6/py8ODBSVrHlM4Bms+0lmfmJ/lJzDtf6f661mnbKRRTKeShRNdT+2h76Ngh75VsnKHt1du3KUYh/7R59+Fs3yjZfZbGNkExkH52yvadrhNaf/Ig0vv3PIay8SN5Bmlbkz/qvB6bWT8nSmt+1BcUz+/12KXPvtTe7fGsBxAd1zVNz6c9Q+uqbU9tRft177NHi98QMsYYY4wxxhhjjFky/EDIGGOMMcYYY4wxZsnwAyFjjDHGGGOMMcaYJWMhPIQU9dVRPRz55qgenbSNqvcjna1qS0lr2dMNZz2BSG/dejHNKjtpFxXyFNK22NzcnKSvuOKKSZrahjx/tP6kX1fIu0XLo2NDNcnZ9mu9D8gbK6s/J42v5k/eJVQ3hfTidD4dp/pqX5HGmjTiRHYstWS9rWic01ihupEnzrzeXeTnRB4gWh4qX9bvibwW6HzVlPe8DrLeYaRH1/1HIT086elXV1e75eutcRHTtqA1wewP6uOongg6H7QfdcyQj5TmR2Myu57R/NX1o1eW7B6va7nGs+SzqPNV4wQtj+ZPa6HWneIAitloLCgUp9DaTT40tDe06yf5kSpZD87eOJtVtowH5azyUF/S/RUa2+QvRx6idD/6/DLvPr5X1846n+ZR1t9V14HsukFxBH0e0fvpPq9QTKv56zqmZGPS3rwnv1VtS/JforbL+j3RvKS+6d2/NwcdnRljjDHGGGOMMcYsGX4gZIwxxhhjjDHGGLNk+IGQMcYYY4wxxhhjzJKxEB5CKysrcfjw4bPpra2tyXHVDpJOVfV5dD5pM0lDTfdX/WDrs6Pntu0Qca62UbWBCmkLFW1b8vrQ89W/Qo9TeckXhs4nzbH2vZZHj+tYIM8mhfTsbfmynjukvSfdKenNaazR9aTnpvqRL8u896f7af2zGmXy2WjLq2XVulJeirYFzSPylVAPEdJAz+thpJAen/IjH7msxju7p/S8zLK+DuQ9oH1HHh40jsmnTX3uFPWxa/uCxqXZP9p+1vUou7brGNH5Ql4k5G+h/hU0JntxDcVM5DFJay/Fi+RHoW1BMRatbRTTUIxF/hpKdt/U4+TbSH56vbGa9QSispF3nkIxCu0VVH5qK/JrovxorNDnCdrn9zqmbcs3b4yR9RXUsunYoc8y5KdEHkLkNajrBI0tijO0PhpTav31s2/WV1LpxfNUF2277e3t7nHywNOykE8dxavZz9nnG3/7DSFjjDHGGGOMMcaYJcMPhIwxxhhjjDHGGGOWDD8QMsYYY4wxxhhjjFkyFsJDqJRyjueDHu+h+nH1INLrVUupevSsh4PqA1Ur2XoGRUQcOnTo7L9VC5jVn9P15Auj15N2kbwFtG20rdfX16MH6XhVy6l9R94fpPdv+2ZWeUgXrPQ00ZqXamizPg6K9gX1pc4j8okgPTiNZfLsofLr+TSPiZ7vS8S5ut+s31V7veqnqe1JY0xQ32nbwyrp4QAAIABJREFU0rwmH4ysv9O8fk4E+W9Rfan+Ga8I8i4gT5Be3hHsEaJtQT5yOjaz2v32+uy4NfcOtdbJ+qQxjJKdL+dz/xbdmzY2NiZpWi91jGvc0JLx/5p1Pq1ddD7Nf/K30OPkZ0HrA3kOKbRva/669xG0V1F/9dY3iu2zfm1Zf9FsDETX0z5OflT0eUCvJy8wheIQas/s5xWlvR/FJPRZgvZV/UyrddM1LhvTaH7kU0NeaPS5ltZorW/W21fRNT7rE9dbh2jNI1833R+z4z47r7K+cVqe843B/IaQMcYYY4wxxhhjzJLhB0LGGGOMMcYYY4wxS4YfCBljjDHGGGOMMcYsGQvjIdRq5LL6cPUMyupS9ThpOcmPgnx8elpGzYv8G7TsCmn9yYPoyJEjqfKQ7lPbgjx+yB+DNNGkQSbtJ2m69f6Z46q5JU8cKivpqcm/I+sZRJpq7Xstjx5XTXK2fLoOaPvp/cnroOeFMqs8GV8ZHec6j8mDg9o+67ugdSNvAFojyW+J/LFUs01trWn1AiPvMIXWRa2feqVpe/bmHnlgZD05qG7qVUYeIaRnpzWwvd4eQovJ3XffPfHp0flJfUzeHTqGdMyS34WOOfVw0PMzPjXz+rYoelzbktYuWut0bVSPSp3f2haazu41iq51FKcQWV8direVNr+sJxDFr+SzQvG3QvNOoZht3jSNhcxnn1nlpZiR9tl5vBZp3lNfU3xJbUGeRNnPpTrPKQYkTyOKgTJxwKzj1Hf0eYY+6/agvs36PFL8mY2hdM2nttDPPu1nzV67+A0hY4wxxhhjjDHGmCXDD4SMMcYYY4wxxhhjlgw/EDLGGGOMMcYYY4xZMhbCQ+jAgQOxtrZ2Nq36ONXPqX6ctIp6Pul8VRtJWk/ShvY8jUjvrBrZtp1m3UvrSjpR0p1Sen19fZJW7ST5W1Ca/DKymmHSt2e9VkjHqmO3rZ+WXetGulYad1mfFL1e759te23LrLcX+UtR/en+VF/ScGv+1D5tfnpvbQv1gZhXX67Xq8aYPDiybaP56bpEHiV0nMqr52t5dR0lXw3yVtD21Ot7Xm96LnkZkM9cb82ZlT/5OWU9jnrrmj2EFpe2b8jbhGIcHXOrq6uTtI6p7e3tbv6aH+3T5EPTO1d9ExUti/qaaNl07SO/JfLCIO+ObFtkfW3I15H2AtqraL1TaB/vra+0r1DbkG8M7ZNZHxmKj7NtkfVBpPiZ4hoqH8UZNLfIq6WtL92L2o72Sd1XyQctG58q9LmU6kMeQeTxqXGHeqRSTEmfH8h3hz7v9O6l15J/a9bPad79VGM8Kl/Pm6vXTn5DyBhjjDHGGGOMMWbJ8AMhY4wxxhhjjDHGmCXDD4SMMcYYY4wxxhhjloyF8BBaWVmZeM+oPk7T6lOj+jw9f3Nzc5JWLePGxsYkrVpJTZMfh+r/VFvZah+zWnzSOmrbaH6qWyUtpXqZaNtp26tuNKulJF0p6d8JzY/07ZQmjXhP96rHSAOb1TSTJjjb1uTRQ14jmr/WnzTS5LVC+n6qL6VprNHYastH45i8CRS6PqsvJ58LGks0ltUzRNclXTOpPHRc1zHVZJM3gq6zWZ+dlp6f0Ky8yeOOvA9oz6A1We8375ppFo+VlZXJuND5Qb5Tmia/CJ1vej/yf9PyqI+P5tfzuyMvvKNHj3bz0rqQ56XOHy2rth35k2X9lbJ+GNR3mj/t09nzs3thZn0knxbyQ6L4nNZC2ne0POQ7Q/t61rdSoZiKPvvQPM56FlEM2PVLgfiZ2oI8Nimm0s9qNG/n3VfJ45LmFflXERTvZ/23KO7plY+8rbStCO0rXbOp7WkPIn8qWpfatreHkDHGGGOMMcYYY4w5ix8IGWOMMcYYY4wxxiwZfiBkjDHGGGOMMcYYs2QsjIdQ6/GgejjVWm5tbXXzU62iplV/p/o91aOTFpQ8FXp+G6Q31+PaFlk/CNK5qr5QzycPIdK7Z3W76klEevis5xBpNTWt/UHt3dOxkpafNMKkc822jZ5PafKpUW+ErN5eIc0w6XCpvbNjs+cPNet4e31Wr531+KG+Jx8HalvyzNHjugbq2KV1j7wR6Liu+ephpOsqeZ9pe+g6pfdry0fjjvpOy0J6+Ow6Qtfr2CO/lzY/+wktJqWUyTggr5HV1dVJWuefxmjkSaTzXWMwTSs0Rntxg453LRv5M1BZyD9C20bnN+3z5F1H85W8PchLhfY9hbxHdC8gDyKF4ow2P62b9oWu41mfx3nXeu1LbRttC9q3qC/JH4q8A/U4fVbTfVehuZT1EO3tP+TtR56c1LfUF9T2FI9TW5H3rY518o2jz47k55T1maN1mWKLNj/qOyo77We0DmT9kLJjobdn9eaM3xAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyfADIWOMMcYYY4wxxpglYyE8hEopE02e6tPV70G1j5pWPwfVn6sOV7WMn/jEJyZp1dxpeUgT3tNekkeNpvXeWlctS+vNNKtspGvV43o/LV/WByfrT0F6fio/QV4hmta+1etJI96ibTmv/xLpUBXSJJMmWqHykg6W9PqkuSZvE9Kf09jN9k/vXJ2XWnaaZ5ofjdt5fSK0b+i43l/ro9DYyvpDka8b+Vro+dpfugf1PJf02qw/E9VF0bFE64Kuabpfkl69529F7Wz2B43BaN8nXxvycdTrNzY2Jmlam3VM6nqjx3teLOQTo/NVYyptG/J9oRiFfFJorSevP4ozNL+sT6XWL7s3kUcaxWg9zyDNL+vDkvV9If8oijF0bNL6qXUlPyXyzKS+1rGlY518JmkuZH0naSy1aZo3Ou50HlB8Sp5C2Xibztfy6LpEMR7dn7y8aB3Lfhajz35EL66ieUR+R7QmU9uRHxV9zqV50Muvt+b4DSFjjDHGGGOMMcaYJcMPhIwxxhhjjDHGGGOWDD8QMsYYY4wxxhhjjFkyFsJDKKKva1Pt4fr6+iStWsMjR45M0qprpbRqLW+//fZJWvV7W1tbk7TW5dChQ7vej/TW6n+kOlbVKqrenfTwpOElHatCfhSqayVIP673y+rX9bjq66k9yJ+j57tDdSMfBSXrPULHs54/5COjY48025ofea3Q+aQvp7FBPj/aXj2NOXllUd9n520WGgukmSYfDfKlIe8GamvSfJOXmpaf9PU09tvy0rikcUg+DOTrkJ1H2ha6J2W8B2gNMvtDKWUyB9THkbwKNb22tjZJ63zU+dfzeYngMZyNS3p+EuR9ocfVU0jT5P2RKeus+5NfBe3bul7o2qXHyXeSvAApZiOovhQjZtYjzZvavuefdj6Q1wh5d9HYJe888vTU4zovNX6m++lnJ/Ieo7mo7dXzb816ZNL52XiZxhbNQ8qPoBiP1mjyTyWvRIqhyOuQ5q6OxfZ6aitaQym+zH6OzvrAaVvo+b14u/c50W8IGWOMMcYYY4wxxiwZfiBkjDHGGGOMMcYYs2T4gZAxxhhjjDHGGGPMkrEwHkItqodT3xzV69Fx1TKqp4/qWFVjR34fpG/v+W+Qxw15aahGVyHdpWodVbdKmmKFvAXIY4i8ONS/gjTF2jdZHxstv5Yn237tcT1XxzH5yui9ta40T0iTTNr/nk/KrPtn/ahIY7y9vT1Jq2aYdMLkhaDtRZ5PpDlv24vKRv5Eih4nfTW1LZUv699EY1eheZbVu2tfkl6fxjJd31uXs95ZlKb9iPTntKdofrSG9/reHkKLycrKysT3h+IOhfzv6LiOKd3nT506NUlT3JDxaND5sbm5OUmrJxDFQOphSWut5qfzb94YsedlN+t+Pd+VWfentVnrS158en9F8ydfn16cQd50WleNMRQ9n+JzioGy+9y881TbvufDEpH37qK9hPqDvMgynkIaE9C9ybMn63Oo15OfaTamysYZWj76fKLropYv67mp9aG4hT6f0B7RQh5B5LNG8aNCcRDFYOQx1IsB7SFkjDHGGGOMMcYYY87iB0LGGGOMMcYYY4wxS4YfCBljjDHGGGOMMcYsGQvhIbSysjLx9SEt5qzrW7I+Oaq/U+2jaiXVc4h0xz1NNuk8FWoL8oVRbaSms/fPll/bgnS0mp+mSS9Oena6P/nqaPsperwdG3QtlS3rAURtRzpY8sxRyAtA0ftlvVG0fqrBprmg91evMa2PaqrX19cn6Z6fFM0TbVs9P+sT0RuHEay31uNZry0aa6R/p/PJO0DTusaTppvmAu1Z7dzVcavjlPpaj6vfiqapLwlak0nbT35UZv85cODAxPuG/B1ojM7Kv0Xn38GDBydp9eHR82l+k89PC/m+qFcdeQSRzwntc5SfQvnTWq9rGbU1xVgKrV+an5aHYjba+3rrE3lkkh8oeWYq5LmjUAyokGdnNn7P+kWRX5b6c+leRWNDYy5dN3Su6Plt+ujRo91zKcbKrnkKjSWdhwR57swb41CaPE9pT1G0/jpWtD76+aD32a/nozMrb1qjs58tCOpL+pxK8343HKkZY4wxxhhjjDHGLBl+IGSMMcYYY4wxxhizZPiBkDHGGGOMMcYYY8ySsRAeQrXWiXZU9XdZzbJCGmbV46l3iJ6vWkXy1+h5PpB+Wv0ltG1Uk5vVaWpdSCeb1UDr9Vp+ra+Wh/qeNNPkH0XtpWnyzaGx1tOvK1Q3LZtqoLWu2rZKdl6pple9FUhjrJDXic4FPa7pjY2Nbv6kwyVNuerXNX/1FGo10XpvnRfUVuQrkfVt0LFCbUWQTwV5ANEaTesI+UxkPVHIO0znqrZvO1f0XjSOyXdN70UeRQr5H2nd9P7qsdcbK9k1xtx7tGNc+1DXA1ofyIOB4gKNwVZXVydp9fVRdG/K+s606PzRmEvL1vOOi+AYQtsqG8No31CMp+Un75F5/TOyvjvUXtmYtK0feQRlxzV56c3rK0N1JY8fRfOj+JfaS8eOto/OnZMnT07SFKOS15i2r+5N7bpC3lvkqZP1l8p67ijZeaJ90fssMuv+2j7kCZT1U6U9RccmfR5Seu1Jeem9yeeRPHvIM5TKR/5SFIO11/faxW8IGWOMMcYYY4wxxiwZfiBkjDHGGGOMMcYYs2T4gZAxxhhjjDHGGGPMkrGQHkKqbSQNNXn26PmkgSa/CNW3q35P9e09jyHSIJOOUiHvES0b6UKzutWsV4hqIVUjnO07hbxA6HrS2VJ7aX3b/HSckm6U+kLbUiHvEfJRIb25jnNqG9K3k6Zax/LW1tYkTesCtb/q2dVTiOqj6bZ/VPveO3fWcRrHWR8IPU5tRX5Piuav85zWDao/abTJ24F8cmis69ig/mjRNZv07HpvvV79U/Q47Xd6fG1tbZKmNbrnR5XxbjH3Lu0co/WD/NNoPmU9zfR+mlaPB/XFId/HFvL20Lw0nfVfoniT/I+y8TD5TVAcQXEB9T3tmxSTURyTiVl1HGS99ahsVJasxw/tW+S5SWOP5in50mhMRp+FdK7RukDtRX5TbXtpDJaNuXTPV2jfJr8q8mmj41ofahs9n2IcJRtTZT2YdM3XdZfGZpu/5k0+ixRD0f6naNtSPK19d+zYsUla+67ncWkPIWOMMcYYY4wxxhhzFj8QMsYYY4wxxhhjjFky/EDIGGOMMcYYY4wxZslYCA+hiKnGjbSFpKnO+szo9aon1OOq11NPoZ5vTMS0PuSTojpI8ggiLw/SQpLuk3xkVGdK3iCqZ1e0rbMePuQtorpU0rlq/bU+1H6tVpT00VQWTdPYyGqWFdKbk1cC9ZW2h2qEVY9Oul3SBdM6QsdJ99vLT8elzhvS+s/rCUTeZdS3tMZS29FYIA8h0vdr/XRNJp85mmvzeOHouKRxq+NK54W2BfWtQnsQrdk6dntjh+5l9o+2n7TPs2u9Xp/x1IpgHx+NC8h3sgft6eS1p/OR8qf5qems9wilab3ReDbrj6H5ke8LkfUeIU+ltj+y8R7VnWI6TVN8SvNG5wF5Dun55BGkaN9qTLaxsTFJb25udu+n+zLtZfR5ROnFATpv6XOclp1iFC2b5keeN4r2HfWVHqcYU8uv5aX8aR3VdSwbl9A6RJ/Feu2l80R9GGldoL6g6+mzCvUN+Vu15eutx47OjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyVgID6GVlZVYXV097/NJr0f+FYpqD8lnh7xESCfcpslTQXWueu9bb711kta6kA6UdJikiVZIJ5rVtSqkj9fyav6k5dTyq26XdMKki22vJ98VhfyTFPKF0XGZ9SageUVtRX1NYznrKUQ6W80/63ei99d1pFdf8kUgn4l59dVKdmyQR1B2bGua9PeK1pfahzyb9LiWb2tra5JWTXi7juu41XGiaxCtmTqv1AtBy6Jl17Yi3wn1XSPPkbbv7CG0mNRaJ+MwG+PomFT0fJq/io5RHXOaJq+Stq5adp1/em/NW31SyPck6zeR9U1UyMNI5zP1NfmtkUcS+UJq+9Lel41b2vNp38qOU4ohyI8qU/ZZx3t+SeeTP409Rb1WaC6QBxKtIxRjaboXB+hnK90n1StP7037qkI+a1nmHSuKrnsUZ9BnH/KZy/pO6tg+derUJK392asvzcusb5pCa1g2Hqe20Lr32rZ3L0dnxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBlzPxAqpRwopby9lPLaMf35pZQ/L6W8t5TyilLKZZSHMcYYY4wxxhhjjLn32AsPoadHxLsjYn1M/1xE/Jta68tLKf8+Ip4SEb/Sy6DWOtHEqaZZtZrkDTKvd4nen/To5EvT0+VmryVPIdUmatuR9n99fX2SprYkDx7SsVJ+mta21/rT+TQWjhw5kjqftKCqac7o18krQPXbWpZ5PYkoTRrgXt1n0fN5mHU96XKz7UseQ1lNtM4FrU+vLOSboGsS+TZo3bK+FjS21AuAvBG0r6nvFFo3aexQX1L9FF1nte9bjyH1ENI1XetGvgrUVuQTQWuq3p/u19sfaQ0w+0MpZdLP5NNC+7RCx3XMZL1EyPdG52M7B3WtIsgHhuJD8ijS+UVto+msl0jP72zW/bW+ev+sJxHtVVmPJBq7bZrKpm1FddO2IU9NilEU3YdoLc/GdBTDUdyhe5uOLR3rWp+sZyn51vTWHfosomXP+p3SvkrzgOqejcf1fFqnqO8UrR958dKeoO2r65LOrcxnz0xsPuu4tk3PN3FWmryuKF7W8m9sbEzS+jm2zf+ieQiVUu4fEf8wIn5tTJeI+NqIeOV4yosj4vHz3MMYY4wxxhhjjDHG7C3zSsZ+KSJ+OCJ2HvUdj4gTtdadx8gfiYhrZ11YSnlqKeUtpZS3nDx5cs5iGGOMMcaY86GNwU6cOLHfxTHGGGPMPnHBD4RKKd8cEbfUWt96IdfXWp9fa/2KWutXHD169EKLYYwxxhhjErQx2LFjx/a7OMYYY4zZJ+bxEHpURPyjUso3RcTBGDyE/m1EHCulXDK+JXT/iPgoZbSysjLR5mb1c6prJf8NhbSbqo3U+5E2sudHQdpF8v5QsvmRhpg0z6RDzba9ajFVY6wabvIQIk+lQ4cOda9XyIuEdLtt/2Q9cchHhsYp5Z/1MKK6U1voPNa+1rGlulgtj37LrfXXvtfj2h56fN72aI9T3yjkiUPjVqG6kV4861NBa3rW74r0/3RcIe8vWvPJO6HVv+u5WR8I9TIgDw1dF8gHbV4fChoLZvHp7VsROZ/EWcfJp4o80XTfJm/Fw4cPx26cOnWqey9Fy6Zefnq97msUQ5HPCnmT0FpJvja6dvXabtb1tNZqX5GHkpZ/3ni8rZ/Gk9l9JFt3+mxB+WU9j2ht1rFMba1k/Z0opiUfGPIOo7ilheYJxXdZTx5tC/IlzMZ81Be0ryvZz4bZz3p0PBvTanl77atrnLa1jkP67KCQh6aWVdchhby1tPw9T6GL4iFUa/3RWuv9a63XRcQTI+J1tdYnR8TrI+IJ42k3RsSrL/QexhhjjDHGGGOMMWbvuRhf3f1IRDyzlPLeGDyFXnAR7mGMMcYYY4wxxhhjLpC9+Nn5qLW+ISLeMP77/RHxlXuRrzHGGGOMMcYYY4zZe/bkgdC8lFIm+kbV75FuVPV9pIPV61VbSbpd0ihT/q2+kHSYWnbyeyC/BtLsbm1tTdKq7df7aduT7pZ8W1SvTm1P+nzSo5OXCo0d8pXR4239tCzqRaC6UPJdIP8lGhukzc/q38nfqeexM+t88ntS3Sz1XVY/T9dTe7ftQ/OaPDmo7DROtayk1c+O8+z9SM9OGmraE7S9tf00rfmRt4HOXV1H2+NUNpon8/qyaf46z3VdIb260hsrWV8Asz/QGFFozGV9GnW90Px0jOoc0eM9jyHyHdG5T/tE1nuE9m0qj0K+LzQHKf6e1wuE8tOxRjGeQntj29/k7UFrJ7UFre00L+izSfazTa8tZqH50TxVLxTdB8m7RfuevMS0/OQL1IuBKSbL+jNp22T9ksjjk9YBWrMpriDvMS2PrrFK1g83601I/dWbqxTv0bjM+k9lvXzpGQjld77eWnZ7NMYYY4wxxhhjjFky/EDIGGOMMcYYY4wxZsnwAyFjjDHGGGOMMcaYJWMhPITuvvvuOHXq1Nk06WLVr4G8TlTLqXo60jhT/qpXX11dnaRVT9jqaPVepHklzS7pt1X7qJpehe6v+Wt+1HZZLWZWF6tjSTXVely1o9Q+5I1C3i89qO0uVCe6A2mGs2nS25OeXTXIev729vYkTZ5K2peaH60rVJ+eP1TEufVpj+u1WjetC4178s5SND+ad1nPH1rDtX4K+WvROqfo9ZrWvsp6ClF+bXtr3toW5G+kXlo0j2kPoOOk/dfy9OY5eW6Y/eHMmTOTNUjnN40R7VfyfSFfHR1TNKZ1Dml5tLzt/Mz6tFBZsvNR0bWZvDm0brTPUl/Rvq5rX3ZOa/01P41jNK31pZgrs3fpPkxeV+TjQj4ntM/q+eQdQjEL+UTSWKE4Q8ceeY5q+1H7a3l0ndDPQ3r/Nr/sGkefLRRaI8mvSqH4nupD8bBCPnIUA1L9svOcPitT/dvya9/puFGoLcjjR8n6xNE81rHV8y3u4TeEjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTL8QMgYY4wxxhhjjDFmyVgID6Faa9djQTXUPX+GiHP1eaqn22sfA9UuannOV78XwfpzupeeT/krmt/GxsYkTf4SpKWkvtja2urmr32rmmTyEqD6Z31j9Hy6X3s9eQ+QNp7anupK/kyk39bztTykl8/6OanOV9cBHRvq6aOQZlz17Fp+LY+Wt6evJ28qqht5h1Fbkp8S+Ujo2CDfCvLOonVNobGm69aJEycmafX8IW8D8gjS63u+FXpM8yZPE/Ig0bYgrwBtSy2ftgWt8TrvemueWQw0BiMfRVrryHeH9km9P8Udx44dm6RvvfXWSbq315A3HtWFYpSej8ms/LIxB609tM9qeTWGor0iG7Pq2klxEPnU0P16vju0L5JXCK2tCsVkWheKyWis0FjTvta+obhA13ry4NR5TOuItpeWV9Nant5eqG1Hdad9WMcCrQNUV5oH5CdFY4nSNLbI34o+P2T9bsnHR+mNRfrcrH1Hax6Na80/6xFE8XjGM7S3BvkNIWOMMcYYY4wxxpglww+EjDHGGGOMMcYYY5YMPxAyxhhjjDHGGGOMWTIWwkMoIlIeQqR1JL2dah9JZ6t6Qjp+6NChSVq9SNr6kSaYdKxZrSHpOLMeO9pX5BND+nQlq4kmDyUtr2qcyeNC75+lvV7HIWnpiayWnsZeNv/sWFKy3gfkO0HeCOrdovP0yJEj3fzJF0N9ftr66RpGPgwKrQMK+VjoWMz4ns26nnwtqH7kT6VeY7pHqIeQlkePk6+Fjg0dC1oepR0L2lY6LslPieaFti15G/T2p4j8PO5p//fav8/sHW3f0F6U7UeK0Wg90DGr6HxcX1/vHm/nq45/Wht1vuk6T/GgtgXlTzEH7Ut6f81f9zlN9/axWWldr8gLhK4nvwzKr+d9SDEHfXagsmXjRfIMorrR/SjuoPqTj87a2lr3/pq/zr3V1dVJmjyGaKxS3NOidVX0Wh3XOjYo/tSy63Eiez55ElE8S32v7ZH9bEvlJQ+hzc3NSbrn20PzjHwYtWx6vqJtQ/udxqv0OZo+97dpewgZY4wxxhhjjDHGmLP4gZAxxhhjjDHGGGPMkuEHQsYYY4wxxhhjjDFLxkJ4CJ05c2bi56HaS9LnkYaY9IGkw1WtperzSF/Y0+OTz4tqaFUPTmU/fPhw9zh5dWhbkRZS20LLqxphPa59r5pgLR95p5DOVdtX60NjTyGdbptW35Kszwr5OFB+pDeneUBkPY3o/uS9QOuGjiUde+SDo5Dvj5a/XeP0XjSPsn5F5E+l8z7r6aHHdV7pWNH7kZcBjRW6v6JeBTrvaS5S++nY69WP/Fh0XJJ+XMtKa2bGYyOC9zvygaOxZPafWus5c6KF1hNanyjmovlPHmc6/3Q90DnT8/SifU7nl+atMZe2jeavZSfPSorh9DitJ3o/jdG0ftr3unbqOCKvP/KxIe+SrE9P254UH5JvobYt9Q31FY1bWktpLJP/qkJzgcqnY0t9c8hvKuvbSDFZOzbJ00bzorGh6wL5sGW9Aim+pc9yCvlf6Tzu7Q+z7p9d4ym+p7FL3mK9sU7xN/k2atnIp5hiLi2PfhbJPrM4XxypGWOMMcYYY4wxxiwZfiBkjDHGGGOMMcYYs2T4gZAxxhhjjDHGGGPMkrEQHkK11omekbxFyH+DNNQKaQ+1PHr/jY2NSZr0fW15SRuoqI6TtPSkZ9f7kzZS21LPP3r06CStbaXHVXerWk29ntI0Fkj3qzph8kwinx7SaLeQ/xDdi/TlWY0ueXHRvCEfGPLayurbtS+1PgrVT+ca6dtJD9+mSeNLvg+aprKQtj/r70S+aqTvJg34kCGmAAAgAElEQVS1Qt5qmt7a2pqkW/+mCPYS0/tl/a56/lbat+qrsLa21s2b9O7q65AdG3Q/7Us9rvfvnWsWh7bfs2OO9ipay2nt1/VC1y89X+eYeoi10PjWNHnPkf+b7lMUA5Eviq5ler2uL+STqOWh88knh7xNKI4hj6Csr0xLdlwS85ad/OEoTfOI5hX5PVHMR/G4zh2KG7Q8+nmFPg/1Ys6sFxd52GhZtK3Um4tirmzbk08Nxd/kE6frjK6p+jlYxzKta+QXpce1vpp/rz9pjde20LqQty7FizSuNU0+blk/2N1wdGaMMcYYY4wxxhizZPiBkDHGGGOMMcYYY8ySsRCSsYjpK1b6+lX2FXZK06t1Cp1Pr5X2XmmlV+jptTi9Xl9L1NeF9XqVlOlrdvTqml6vcgc9n+QR1B70al/2J5Q1Ta9F9n6+dlb5Mq/O6yunmjdJF+mVS5JC0uu/9PqxvsZPsqLsq/DzvmpOr5wq9FPGWblgOxbp1WqF1jyFpKT06jn99C+tgTQWs5ISeiU3+zPx+lPJtC5kJXU9WZiu0evr65O0rqm05tOaQ3K5rHyP5HE0tsziUUqZjDNay3RMEllpjo5RRY/T+qS09aOf6qa1kOQEmta1gaQktLaRZJTkuiT/ozTJ7BW9nmwNaCxmpTHt9VkZDclYSPZPUg9a67MxkkJjWaG2JxsB2ve1PHpc4+/sZ7VemuJdktPROCdZq8b7JIslSVp2rOnxbFyh5aGYi+aSpknGr+2jn1V7cYxeq2laU2mcZa1DdA1UywNNzysN3Q2/IWSMMcYYY4wxxhizZPiBkDHGGGOMMcYYY8yS4QdCxhhjjDHGGGOMMUvGwngI9SAtZ1bnSzrarB4v6xvTlo/8icirQ+uiWkjVPupx0sWqx0/v55QjztXDqzaS9O3004RZDbRen/Uayf4UI/1UZXu+toX6mmhepJkl/biSLTvpv7X85FuT/flr+qlHmkukSdb8tX1pXaGfWm/JegXQz1TqPMv6nmV/LpfWRPLcId8amnfkCaRtr3NNfXX0foreT/29yCek9RBSzyBN089Q67zSvtO20LJrfurTkJ1X5OtGfktm/6m1TtZHHWPUp+RRQGNq3p9M1vx1fVTa+tDapfuGriU0HygmIw8f2gvoZ+cpzqD4laCYkLxSFL0++3Pg5O3Sq192H87uexR/Uzo7Nqh8CnmhaJr85rK+k9peujeRTwzNpTZ/vZZiJPLooXiT9nGKd+mzk94v60tJn23op9HJm5DGskK+jhS/axzVtre2JfnAZWMw2u9oHhPkgdlbM3vrr98QMsYYY4wxxhhjjFky/EDIGGOMMcYYY4wxZsnwAyFjjDHGGGOMMcaYJWMhPIRqrRPNG/k5ZPXlpBXV61WfR1pJTev99PpWq0llyepWSfNLXgB6PnmdqFeGepmo1lJ1naQjpfuTRprO1+PkaUTeK0pvbJJnEGlmqeykEaayU9vquCY9OGmKs14B5PWQ9UIgDXhWX99L65qR9bAhfbtqoknrT74X2XVH542OFVpjdexQX9L9tX11ndL+0PrSWGg9gmbdv10ndQ0kby7ygaN1gfxZ9Lj6wJGPW8afKjsnzf5A/m7k40jeIUrWkywbc2m656OQXXvoeNYLJLuP63wlbzuF4gzyb9M0+U/R2Ml6DpGHUM93M+uTSHXpedbMOp/yI8/L7Pl0vTKvRxB5m9D12blFvprt2KSy6rzTcUljhz57Ucyln60ovtS4QstLbU3103mlPowbGxuTtMZYSnZPyNK7P/m6Zec5eQhln1FQDEcxWC8+73qo7XrEGGOMMcYYY4wxxnxW4gdCxhhjjDHGGGOMMUuGHwgZY4wxxhhjjDHGLBkL4yHUau5Uv6fprN6O0qo7VW0kaUVJg93zWCD/Bc1L0bahtiCvDdJKkvZSda+kwSYvFPK/IA8m8gPQ8un9VQuqUHl7Y4W091o2KjuVjfTt5F+lZPXo85aP5l1W90s6Xh3LdD/ysWjvT32Z7Zus74Ten9YRrQv1haJto+VV/breT8uj+ZHvxdra2iStc03nOXkzaHvRvG/17DSvaB5pWcnXTdtWPUfUe0DbXqHya3navsmuEebeo+0b2nvIz4LWbp2f5Lmg811jNE1rfupZtrW1teu9FNrjtS7kV6bzjfyZaJ8i74+sD6O2He2bFINpe1GMRTFf1tuklx/towrNAxoLWU9L8nnRfYzQ+pGnD/WFQvOWxhqNHU1TX2filKzXHo0F8iPVz07ZdUEhfyry1tLrt7e3J+nNzc1JWj2D9DjNa41TaC7Q5yNqv7Y9KH6jz+U0rrSs5E9KMR35zWq65+PW8471G0LGGGOMMcYYY4wxS4YfCBljjDHGGGOMMcYsGX4gZIwxxhhjjDHGGLNkLISHUCllogfsadx2u76H6vVU76eeCaT3Iy+RjAaadJ2kySU/JYW0huQRpLrNnlYxgvXm5EVAWs5seynkD5BtTzre6ma17KRBJkhPTm2f9V/SsUH+UJqm8ik01smDSMtH+SvkN0A64J5+nfTmNE9oHNK8zK4zWj5Na1voGks+EKqZpjVWfTp0zVZUv07eDeS9QOtEW/6sFwD5RpBXltZV20rben19fZJWnwaF9Oxt32b3dnPvUGudjHny/lDI00v5/9l7l1DLssRMb+3Iwrgy4kZElkqIstpgDRqDZ26EHxiMsTxo90Q9aBp7JBqBJn6APZHwRNMeGIw9kRFu2zKYbgthUA+MoRE2HllQfmC324YWbbpbQtX1yHjciExhKuN4UKFgnS9uri9W3ayMW3W+D0Tljr3P3muv11776Pzf5fiy+YDzh3lz+Hn6LebzmeOG8Hi6Kzi++JzcdfDYc8Y+z7q2uY/Ys4XX271fzu10l9gaedc/t9pn97rrfzOHkK2JdteA9pwn5i7cHdfmrTF3iq0Zb+uPnY/ffVdhW9h60t6tbA1l603zMdm7k/mbuJ/jcvaw3XQ84RzM8u3WD+ddbrO+5nnptm7aXdeXnY/srvnMcZlDKCIiIiIiIiIibqQvhCIiIiIiIiIiLoy+EIqIiIiIiIiIuDDuhEPodDotPQPMBhLm93azk5bjNa+M5e1XOV7ztPBazH/zeCuLZQ3NIWQ5TZaXWUjWtZXf2nY3f8/95ioxL47lelkf8/ksR2rZfuvnlq/mfmZ0DZZ/1w9lfYF9j24AcwTZ2LLyEst0G3N5rJ9Zv9t1CllZbQ7k+dkWLK/l7c3LZuOUng6b1yyfT8xlxvOzPlYuhF3v2G7+nNc2V8LV1dXZtuXXWT7W7crfZPce74dXr16dOSF2XXy3fU6SXaeZrRNWY8bcFlzzWNnNN7Y7F9PdQaeXPVe5ZjTv4u5z0vxx5oHhNq9vTiTz3Kzuj2XfdUjuPnet7e1eye67i2HPaVuvW33xOW7+J96ftRf76sontfsc5nOQ88LKF3rTtr1bkV3vo/maOO+ZW9ccmbZGtfKyfjlW7N30/v37Z9urdY/NgdbPbI6y41n26+vrs23eO9dohHWzKk8OoYiIiIiIiIiIeENfCEVEREREREREXBh9IRQRERERERERcWHcGYfQnOmzjPFNn58xbw2zkXa+XV8H83urLKXlKLnfym6ZYstXm5fFcqPmLOLnmUsl3M/t2zopzAdg11s5gsZw38fqXFa35mWwz1vZd8tnfc/60q5bgFh5rD449m7rcGJ5Z++NOXesbc3jsjsn2TiwPD0/P/tIxnh73qIXg/utr5tHgvVhrjRzDpkvi/l/3s9cPmsL81DY8bvj3LxrzLvvOo3m6+UQupu8evXqzCnBNZTND+YQovOLWB82Dxa3OR/wOT47G8xBY89FW1/uuupYVnOD2LOE5aPHxXwXVl6Wx/xw3G9eHfNi8vPmfJrnfntOsC7sGW+ft37Le9n9vK2nrW2tL+xe397dzCHEvmMeG3vWzfvtuWrrVcLnJJ1BnAOt39ucZ2s4jnPbppuMazjWva0Bzdu423a2xt3x3Nm4I7vOO1sjsS55b/autfuuMt97DqGIiIiIiIiIiHhDXwhFRERERERERFwYfSEUEREREREREXFh3AmH0BjnGTnm63bzfvw8s5C7rhMeb74Pfp652Ll85vAx54LlKO3z5l+as/ZjvJ2L3XWdWB7ePs+2tbZheZmTtbz9bV0mq5wsP7tyTd10/G0ztZYf5/nNy8DyWZbfyrPrBLK2s+PNJbHr3WGmfOVSsUyw9XM7nuzOE8TcXpZPt3HP++H9sr7MBWHns3HNbfYNXp/zzsohZHOGzbG722wLYn3NxhXr3uaheP+8evXqrF+wj9h4sm3zVHH8kN3z01HG7Xn+4frMnFnsz6wrfp6Yk4hzI+dSmz94/d31st0v4X6Wl+e3+9/17JhDiczzlbUVy251a3OlPadtzcHr21y+67S0uZx9k+w6jHb38/qsH64LVs6k3fc4rmc5Z9ERZF4Ym1PN37Trd+K4tDlyx8kzxtv1Yw4kG6f2bszP73xvYG1v/djeFYi5aM2zZk7LHT/Taoz1C6GIiIiIiIiIiAujL4QiIiIiIiIiIi6MvhCKiIiIiIiIiLgw7oxDaMbyeZZxZv7OcrY8n+XZmcGjZ+fJkydn28xmzllOZnYts2x+iQcPHpxtW47VXCKW49z1YVhW0lwkVn7uJ5bFtMw3y8vcLH0EzBXP12PemfdiLg/L5pt3xeD12Td4/t2+Yee3jLRhjiAbezzenE2WEZ/rn3Vn92792sbtbet+1zfFcWLXY9/cdQpxnrTz7TqEzB9l9Tf3BfOhmAtr199CrC1szrZ+vqrrXVdVfDl8//vfH9/5znfebNv4MpegPWfZD8wNwj7F89m6YdXvWFZb//EZzzUX74Xjg2Vj3dpzlthzzLwq9pxkee1ZYm1j9b2aO2/atnXOyq9hbc22tOe21YWtN8muo9Pm1123ILHyWn0Qe07venLY11aOIXMA2ZrCxiWx5zq5bd/ZdWgS81ft+pusb9q6xerD7mfuG7vrXXv+EFsf27ubrXdtTrS6/Dz6hVBERERERERExIXRF0IRERERERERERdGXwhFRERERERERFwYd8IhdBzHWQbO/BfMcJv7w7Z5Pcvn8Xg6isyTM2/v+h52XSDmoeHxPL95bna3WVdsO/PksLyWGWZ2czePv5vn5/5VXzNXh+Vad10j3M9+bXltjju6sehTspytuUl4PtYt3QYcZ7uZY+u75jYgdj8z1nbs58TGNfPfNqcS88RY3e16cFh+c5ZwnuK4NyfQrjOI9bkzb1i/4rl4b+YC2PVPGVY3NufNx+cQupucTqezOYbzgW3fv39fzz9jayRinhmWx9yEc5+0edy8jrZeNZ+aeVPM1XfbdcLuc5DbrB+Wz57jNr+QXb8G5895P++Fn7X1o62Zdt17drw5jbjN4+25bX2DsO15PK9vayirT/PomF9qrm/WvXlYrGz2nmh1b23JOc7GNd+1rG1tzcX7sTUXMf8Ty2NrUnO1reqHZb+td5H3vuva2l2jsTzmoXvXff1CKCIiIiIiIiLiwugLoYiIiIiIiIiIC6MvhCIiIiIiIiIiLow74RC6d+/eePDgwZtt5lLN0WNuE8t+WnaR5WF28fr6+mz7xYsXy+vPeUOWhdlGy2maA4jnZy7Vso5W1+Yiefny5dk2vS+WnVz5KMZ4+/65/fz587Nty15ajtd8Hta35s/bve1m6bltbgRzZ1ne23Kz1lZWfo57G8e7/qldr82u+4H1NZeHdbfyHIzhHgnuNweOtYX5l6xu6Y3gnMn9nDfMo8H9Vn/m3SDm+SHmAVqVhfdmefDdurLr2zPI3AD2TDL/Vbx/TqfTWZ+/ravE+jjXAZzr7VlmHhuWh310vr55Smwu5XrTnDzzWncMn8vMRWd+C/MzmQPM3Cese3OZmKuF9//hhx8uj7c1GufT1WftOWNzq63pdp9Du15IW6PsOnnMu8jjOc5sjcm+uesitGfLas26+5wj1nd4Pr4L2XPS5kzzw1rfsfcDe9fhfvM8ch6wNRbrg/Vn7yNkPr+96+y+e/Hz5k+1uifm9rX3YHOB/Sn9QigiIiIiIiIi4sLoC6GIiIiIiIiIiAujL4QiIiIiIiIiIi6MO+EQ+uCDD5YOoZuOn2E+jp/nfmYxzY9B+HlmyAmvP+cHLQdqGV/Lq1sG2NwjlolmBpj+JPqV6L+wrKV5YixTvesIsuzlrkdm5aUxfxOx3CrLZnl1+7xlms0Ds+sU2u2Lxu79E3M7sLw7DibzKPCz7Mfmbdh1Bdg4InY8r8e6I1YfzKdbXn23bQ2bpywPP9+P9UPrt5ZvZ13ueh/MhcA53+ZI827E3WA1JnY9Uea5Mi+NebOIPcu4Lpn3767BzFHJujAXnT1nd58ztsZi+XefBSwvt3fmwjHeXkfY+n7Xe7k63vxpu2s0O7+tx805xHXA7nrd3nXs2WJjxVyC5vqzvsttPovMH7XypLGsu/6nXe+iOS/5ebatrffNicm6Mf+TPQPMBcb9bGtu29hg37H1+bxt3mDzE+06PIn1JXNgWt2Qd13v9guhiIiIiIiIiIgLoy+EIiIiIiIiIiIujFt9IXQcx+PjOH7nOI7/5ziO//s4jn/xOI6vHcfxt47j+Luv//ejL6qwERERERERERFxe27rEPqPxxj//el0+kvHcfxjY4wPxxj/wRjj906n0189juPXxhi/Nsb41dVJjuM4y//tenSYHWS+7uXLl2fblpHezWaaS2WVP2TZLWNr2UfmUJmT/fDDD5dl4/XsfMyjc9tyovQxWeaa5SOW16d7xPwZ1vaW59/x3piLg7Ds3DavDO/FXADE/Enmddh1E9g8YOW3TDbrw8YGj2ffWWW+d31GBuuW84h51Swvb3Mk5wVziFhmmuXnuOXxNk9a3n3XmbTrYltdy/wqlle3uuW2zXlWN7v5+NlVcNt+Hj8a7t27dzbGbO43p5aNv5uuP8Prf/rpp2fbNoa4DjG/x2qfOYLM7cG5ietFbu+ugTh+ba62NRfPb74nWy8Tukt2y0tszbqa/3ZdVsT6oT1HbQ1izx1i49b8b9YXzK1ifdc8NsQcR/YsYn3M7z/maTEHDuuOc47tt75i69fd5zznUHMo7fqidv2sNifbPMLy8n5X++090PqtvScSG1f27rC7Xl2VfzWn/dC/EDqO49EY418eY/y11wX8/06n09Mxxi+OMX7r9WG/Ncb4iz/sNSIiIiIiIiIi4ovnNpGxnxtjfGeM8V8cx/G/Hcfxnx3HcX+M8TOn0+mPXx/zrTHGz9z04eM4fuU4jm8ex/HNZ8+e3aIYEREREfGuzGsw+0upERER8ZPLbb4Q+soY48+NMX7jdDr9s2OMl+MH8bA3nH7wu6Ybf9t0Op1+83Q6/fzpdPr5R48e3aIYEREREfGuzGswRskjIiLicriNQ+gPxxh/eDqdfv/19u+MH3wh9I+O4/jG6XT64+M4vjHG+Lad6HQ6nWXedl0j3M883rtcf4b5O8s005dhGesZc3dY5pdZSP5/+pjRtdwoy277zQnE8lpWk23H8lrbE2Y1nzx5siyPbRuWPZ3rj23FujVXlvUVy2vveh3MJcK2s7YyP5VtmxeG2Ljkfta3zROWp5/Pv+v0MXeWzYHmi7J8OY+3cW99kXV7//79sYLX381cs+/bWCM2rs03tXoGmEOI92qOD/NM7DpCbJyZL8by9fH++cpXvjK+/vWvv9m2udz8FSuP1E1w/uF8YnM3P299eL6f3eeaPTc4Nz5//vxs+8GDB2fbHI/mDuH1zcljcxUdmzae7Vllazje364DyVwjLA+vP5+fx7IuWFc2V9u8z7JyXJkPyeZqe06aF8b2s3zmd7Lz39ajY+96q/crvtvYONp9Ll9fXy+PZ93ZepjXZ99kXdm7GM/H61lf3nUssa35eWLvK/y8+XHnz++6YFmXux5HG5eE5bF3H1u/zuVffh+xPMuC0+n0rTHGPzyO459+/U+/MMb4O2OMvznG+KXX//ZLY4zf/WGvERERERERERERXzy3/Stj/84Y479+/RfG/t4Y46+MH3zJ9NvHcfzyGOPvjzH+8i2vERERERERERERXyC3+kLodDr972OMn79h1y/c5rwREREREREREfGj47a/EPpCOJ1OZxk7yxwzj8dty5nu5lh3c63Ms6+ym5bjXDlobto25xCzkMzR8nhuM2P99OnTZXl4f8x5sm3oRHrx4sXZNvP5bHtzB5iLheWxnKx5cegJmvsK7203M8y6Ylua/4gZXXPwWLbf2oL3x77IvzbIurO2ZFuZN4b3azlgc7NwHliV1+raMsKWB7c5im1lDiCOu939xPab98K8Grt59932MEcT22P+PMepPV/Yz+15xvPtOvJYV9bvzakyt7WdK94Px3GczZdXV1dn+228s0+ZH8IcZeYs4xrL3CbmWJgxP5kdv+uN4X7zVdhzlth62OYXq7vduZRYW+96bszrM9eHrd9YFltj2JrN3CH2HDNfqa0hd32rbEs+u1gfXNNa32D5zWVofd2eRfN+PtN33VbmhWFdcVzbmoHYe6nNoYT3v9uXbU1q45af5/XNy0hsDTyXx9p+913B2pJ1YWswc5fZdxjkXZ9/t/krYxERERERERER8WNIXwhFRERERERERFwYfSEUEREREREREXFh3AmHEGG+jrlUukaYOzWPjWUfLYdqOdgd5wLvledmDtRynLs+JWZ0mdNkXe96byzrSKeQuQdYPvPMEN7/bbOa5jZg/cz1t5sPZ1nt8/fv3z/bZt+yjLRlis0hxP3mEGL5eT22teXb2ffZd8yxZH3F2oOfZ33O7PqLbE4zLxrr3uZQcxLZPMTPc5yb04htZ/6olTfiJm7rdNqB1zKHh5XFym5OIpszrS9Z35j336be4kfLPMeYI8ucCdYn7dnFud7WGXQb7riqbG4x35nN3XxOcLyYM8ieOyyfufbsWcK52HwYfO7afGFrKN6fuVdsTbta55h3xc5ljqAHDx6cbdP7yONt27wx9hzn8fYcJbYGJFZetj3fL1YOzpvKw77C41n/q3PtrsFs3BPzxth76a6rzOZoXt/m/N33ZmIuM5v3zMOz8v7YnEdsjWTfAZjb1q5nfc3G1TwuVmvhfiEUEREREREREXFh9IVQRERERERERMSF0RdCEREREREREREXxp1wCJ1Op7OMnWWsmY+z7KTlzy0/uHs8s4vmRllxW2fQjt/hh8GyjMQyx5YXJ6uc6E3l2c29MpPN67G8zDwzEz2Xj3lt86BYhpdlv76+Xh5PzKXAe6ETh3VlfcH6Ls9nmW5+3twKHJfMl7M9OBbNxbJyvfBcrAvzMBBzHVjdmWvAtq1v8fzmRjCHEccdr7/yNd2Etd1O2960PZfPvGTm/CBWFmt7Yp4IG2ecA+e223G7xJfH6XRaug3Ny7jrtTLnAbdvKu+MeWRW49eee+ZxNPcF68LWq8TKY3O9PTt225ZwTNMp9NFHH51tc13C5yzndrLrKLI18MyuZ8WeC+bs4fVsv7m2uEYz5yfr/urq6myb5edz1dbTLJ+tl1k+a1vWjzlZ5/KxLq0tze/Esuz6UG/7nmnPbXMJmp/JyrfrPrO2JdzP+t312M3Y+tfmUHJbz6PVDfebi2seV6s1WL8QioiIiIiIiIi4MPpCKCIiIiIiIiLiwugLoYiIiIiIiIiIC+NOOIQMy2gz72eZaMtWMp+3k0u96Xgy5wH5WeYwzVNiviRmdrltOVbLzVpOllhGmG3LbCTLby4TczBZXp31Y24TbvP6c/2xH5k7xO6NdcF7s0ww8+HmtWFO1dxYbEvru5YpJrt5dvNO2PnNL7DKDe/0kzF8TmHZLE9t3gxzEplXw9wDhH3D5ljOO/RW2PVZ/+YAMp+A9dX5fLt+lV3HCDEniHkYbNxym+Ns3r6tsy5+NBzHsXScsQ+YB4tYH+R44ni2Pso+Z88C+uJWZWFdEHNl2Hi7rb/M5n5bDxu2fub459zM+Y71a21nzyKbT1frfX521S/G2H8Ocw1mz/nduZWeRfqZvvOd7yzPx/tlXZtX0d6dXr58uTw/68dcg9bXWb6VW4XH8l7N4Wn+J3MD2ruFjXPWJbetL5mL194PWH72TR5v79Fsa1sr2LsnP7/jyLP3VNtvflhzAtm7GvuCucXm7dUc1i+EIiIiIiIiIiIujL4QioiIiIiIiIi4MPpCKCIiIiIiIiLiwriTDiFmEy3rablS5lSZt7N8njmFiPkndjLcli3ktXhv3F7lKm8qK2G2n/B6lrVkrtXy7+YyMK8Lt3l+9hXWh+VaeX8s35yrtQys+Zss12r9mPlzG0esO/NfmV/JMsW7bjDev41D3g/nFTqSrDz8PMfKXD88lvfCDLDl182VxbLaHGt1beOC+XGe3/qa+Z7Ydx4/fny2bW1lXg7Wt41VGwurc5vTzhwbH3744dm2+Vksu2+uAY4z81g8e/bszX/bszPeH3M/MCcC99vcyj7D+ef+/fvLz3N+MF+ejef587wW743jg3PbaqzfVBbzNO66TXb9Frbms/Wp+S3YNrZGtXWBeTN5vLlaVnMQ687cd7tONGsbltX6uXljzC1iz0lbUxF+ns8ClofY+455fex+Vp81v5M9N+38tm3jlNvEnEHW98wVyHUGxwbXuyzv8+fPz7bNl8vz8Rlha0wy933zJ3FNY++pbEvzoVpfsu8kuN/G+buuwfqFUERERERERETEhdEXQhERERERERERF0ZfCEVEREREREREXBh3xiE0Z+KYp2N20HK3zBbO+bmbPm/5QPNNmLtllQe07P1ujtVyp8zU8t5ZHqtby4ESZhvZFuYuIZbLZa6U5eX9Wn1ZtpOwPWcsK28Z5R0X1U3HWz+1ujRXl+XBLeNs7i87P7H7sQy25XQfPHhwts36nc/Hz3Lc2JxDLMNs3hqbh8xZxM8zg02srl++fHm2bTckdkoAACAASURBVPl2lo/eDT4DOI+x/s099rWvfe1sm/W78meZF83y6/b84b1z2/qt+ZZYvqdPn55ts65zCP14MM8Ju14XW4dwfNAPwfFnrkOOGT4rdvxz1t/NXWFzNe+N45FuDtYNt81rw7nTXHu76wxz81nbcf7gfLS7DiLm65jrz9Yctp42V5+tL817wvKx37Ot6T20NZE5fTiObL2762Ih1vbcb37XVV9kv+Bz0Jw2bDtra7aFub/sXY+f5zxl70ZsG/YFHs/6sL6/63nj+TkvPnr06Gyb9c95ceVitPc6W+vbepn3YnOY9S3CvsW6ffLkyefuX43hfiEUEREREREREXFh9IVQRERERERERMSF0RdCEREREREREREXxp1xCM15SMvnMW+36+HhfmYlmc1k5o75QOYLLbc7X9/yzry2+Y9YN5ZjJTw/s4yWlbS6N9eA5U6Z/SR2PsI8v3luDLbfqr4tj73jorrp2tZX2FbmaWDbsS2YgWbftvJbBtmcQzw/t9lX7fo8v/mozI0wZ9Svrq7O9rHt2DbmS9r9vDmLDHMlMI9vDhJzAbAtzE3AuiesL7YH51Hm83l+5tdZ3rl8nNPogWD+m23Nfvf8+fOzbdY9y8Zsvjn2eC+89+vr67Ntts38eZvz4v1wHMfZ/GbeKsL5gOOLmB+D2Hz38OHD5fEcI/Ozjf2d48O8V+Yj471xfD5+/Phs2/xKtoZiW3GNY+4QYm1v7kCy65Ehu+7ElYeG/Yj3Qqzf2hqH25xbiT33zLPI65lnxdZY9m7E+jHvI7H3iV1Pz8oJxXHGuuWagLCuWLfW1ram23WT2bsar8/95hDi9blusb7EdYM5Pa2vc15jeTiPz+ezdyXzLZnHjfduTiCra9tvzrzZ87gag/1CKCIiIiIiIiLiwugLoYiIiIiIiIiIC6MvhCIiIiIiIiIiLow74RA6nU5nuTbL9zG3yywlYX7P3CbmULCcMc+/8uiwbMydMudqXhXmQJnbZLaQ92qOIMtC8vysW56f5Vtlfm/a3nX8sG0sN8vrrfLoY7zdXqu8u9WVeVqsH7NuWHb2a8uD0xVieXRuM4fL89HzYPn5XcePuchs3uH1zT/A4+eMM+uajhrWrWXpzZdk/ZZ9xcadOT/MN2V9n/B4lofzCO+HbcVtnp99w9wHdvzcN6zfMwtvZWffsHFPhwn7CtuC44zlffHixdk2yz+XZ9f/EV8Ox3Gc9RP2CXPjcT/nr901lvkyzD9hPo15vJu/zFx85ndg2em2oAuD2xy/LA/nHmJrMHOImdeR45/7zbtj8w/nZratzSmrvsd7s7mPZbX1OI/n+WwNxv1cr3J711NjziMbt+YaY1uzL5gP1tp2911u3s/PWl1zjWZrCF7bHDs8nmsac+US87ey/Jx3WB7WB8c5t23esPJyXuG2OaA4T8z3w7rjNuuC2Hu3rY+t73D/7hzJ7XnNtypbvxCKiIiIiIiIiLgw+kIoIiIiIiIiIuLC6AuhiIiIiIiIiIgL4044hMY4z8xZXt0y0cyvE/PQMLvJzDezprw+z8984bzf3B+E92ZZRcsYW57bXCA83lwj5sWxvPzKzTGG9xVivg7WtzmUrG+yflb7zLPCurB8u/mRrO0t48zzmVOHLhW6DMxjQ8ydYi4DtpX5BIi5FVauB47Tx48fL8vKbasby5ubE8TmZPNb7W6zvKs8+Bje1pbvZ/1xv81TVv/zWLFsPPvZbp58t6zsl/b84jg2T8VcfpYt7gbHcZyNOY4/64PctrnUXCQ2XtkHV2usMd7u43N5eW6b68xpY+PBnjvmSyLmU7LnKOcbrol4POcrPre5LuAaiutpK595Ymxds1oXmFuPbWPrbX7ePDI217Nfm4fGngXm6jL/Fdl1idkazDyO5vLb8beyLszjwuPN70rYlubYsfUs+yYdRzvP5ZuuZ3My5xnzSZFd/5W1l/mw5vLseojpXSS760t7V7K6M4/k6juL1bn7hVBERERERERExIXRF0IRERERERERERdGXwhFRERERERERFwYd8IhdO/evbM8onlfiPkmLNfLbKLlci37aJnyOT9oHhdmcC2LTyz//fz587Nty8+by4RZSstzm2dmN4vJ85vbw1wklslm+/B6K/8Aj7V8tXkT2O/MF0VY1m9961vLz3Obbcm+QLcAXQX8PMe1eV128/67rpZdf8AqU01/Ej/LDDDZLQvZ7dfmq7Lz2Zxu+fivfvWrZ9vmOOL12bc4dswjYm4DtvXKrcZ75bi1urB7Na8bxx0x/5LNcSyvuRXi7sE2pEPB/HLWZ81rxfnRxq/5J3j+1XrT5i4+h3lu1pV5Ycz1wfFHzOtofgvzyHC+4JqR9UFs/WweH+63udbWsDM293I/65LPSfOw2La5TFg35uRkXXObHhw+Z+1ZYvD8tr62sWDvBzveGt6rOXPMKbn7LkbMa7Pr+OTx1pd5/7YmMoeR1RfLx3nF5iViY3deU3Mf+wLHtb2L8F7NB2UusV2vnN3P/PmVk65fCEVEREREREREXBh9IRQRERERERERcWH0hVBERERERERExIVxJxxCx3GcZU0tn2eZY8sQWz6e17c8Oq//7Nmzs23m/1ZZU56L17K6sdwmHUJWtkePHp1tm7uE5bUcrnlkmHm2urdcrfk3rL65vZvfn69n/dQytJbNZz+3e7P9zG/z+pZBZt/j8eaVMK/LbttbfVvfYz6e56MzaS6P+Z925zhi427Xj7Trb9r1MxEb17seHpsn2R67Hg9zpsz5dZaNvihzAdicZb4jG/f8PPP0zKfz8yu3gnkA4v0xt409e2z8so/f1ntlzxb2OfPRcXt1bs4FLJs9d81nZnOleWPMb2F1x+f6kydPzra5njVXoHkeOZ8YNp/Z+n7lGDV3Hvsxn/nmoLT1qrnx7Lm/6sfvgs3ttsbi9Vk/u+8v7Kvsm7bGI6s15u6awOra3lNtvck1DOdEc9Xas9WOZ11ZX9x9Jtia6urqarnfPDm2f+6b5s4yH6l52fh5vudav2Vd2Ti094F5zs4hFBERERERERERb+gLoYiIiIiIiIiIC6MvhCIiIiIiIiIiLow74RAa4zxrajlc81+YM4HZR+YJmdVklvT6+np5vp3spTl3rOzE8tuW+zQHETPH5gbh/pVT56bjd+uH5Tfnj2UzCctjGfNVeXguYhln86oQy+6zn3ObfcEyyLZNdv1Q5kgiLD+hG8Hq1+aplY+AZWfdmJeBmLdi121g48LGMeH5mPdmXbGtdstr9WnPAJ6P+fb79++fbZvDaD7/48ePz/bx3pg357g3jwXvjS4rYn4kcwewPBxHc/nNMxDvh1evXr3l65gxf515pHbdUeaBMY8Nx/9q/mD/5di2NZU9p82T8t3vfvdse+W+uOn6Nvewblh3nB+4vqX7j5+3uZhjftcfx/nN7tfeD+byrfxCN53LnDi2nub5ia1/eT3rG8ScSdxm/ewez/Ky75grzLw7tgZc1c/uu4J5XQjnwN13H3NsEtYFy2uYN8fmnV2/Lbft3ZL76V40j+P8eXqDea5dRyfhGo7vUvY85Txiz8Od43MIRURERERERETEG/pCKCIiIiIiIiLiwugLoYiIiIiIiIiIC+NOBPpfvXp1li1lNnHX82IOIcuCcps5VOYNmRc0h9Cc5zPXhbk3LDNs2X6WnXXF7KN5XFhXzH2yLlge82Hs5vktk225U3MlsP0s9zpvW780j4wdTxcB79UyzSyf5bvteJbf6soyzITHW96cOWL7vM1DrA/L068+a3l2c+pYftzmPGKuBHOT8fPW9nQN2FjhPEUvxq5LjfMYt+kUuo0/im3LOZ5z5O4cxXmA19t1EpnPZeXks2d3vB9evXp1thagR8c8OLbm2u3D7EOE491cMOx38/g1r4qNF36e60PWBecmGz8PHz482+Z43vWwmFOI+1l37BvmGGNbcHvH9zTG233J2nrlNLJ+Y89dntt8R/actuecuQF351fzO7GtuW2+Klsn0Ftm/in2FfPerDyYtt61stm7DevC6sba2taf5pWxNZPVJevDPHF2P5zneD3WL4+3sbUai/ZOb+5altV8Ufadwur5NMbbzwBzhq7eBVcOt34hFBERERERERFxYfSFUERERERERETEhdEXQhERERERERERF8adcQjN+URmB3edQJZ3tywlj2fG2q7HfB/vZz6/+Rosd2q5Usv6237WjWUhzfPCumBdGZbttP3mJDKXym5Ge5Wb5bXYz+h3slzqrm/JnESGjUvC8pp/yvLnu/l65oTpEGL5WN9k935W+fXdtmTZmO+2/Piug4jXY2bazmd5estoW31wnrK+vetOMG8I968y3uaesjnX6orX5rY9H63vmPOI15v7yiq/Hu+Pzz77bFxfX7/Z5vhmu9GDwz7LPsI+xHWA9TnCuZzPTsLzzfMlz2XPWZbdfEh8Lpq30dx5HF8219lczOs9evTobJvOImLnI+YQsmfLrv/O2mdVFnvuWVl2nTi2Pre52pxG5upi3+bYsL7A8tizgvOIYb47W0PO5TdnjvlX7d3N5rBdX9QXvW3vjqw7W2cQ1p+tucw1Zs8Icz7NfdHG1e671G5d8niOs48++uhsm+8q5uhkX52fz7y3s3J97p6IiIiIiIiIiPiJpC+EIiIiIiIiIiIujL4QioiIiIiIiIi4MO5EoP/Vq1dnuWpzEph/wpxAzO8xx8rj52z9TcdblnTlB7F8uPkjLKNsuVbzSTCLaPl58z+xPMwkm1eHfYPnY46U1zdXi2WQLfdqroS5fXczy6tc6E3n472bT4nbvFfei13PnELMzVrfMjcB6559y/LyvB7HgrkaLDO98mtZW5kryzLPVtZdx8/uHMvy8XhzkpgDaNcHwM+bX2p3XiBz+c0bYfPArrfCxrnl3Yl5Llj+eY5c5dfj/fHZZ5+NJ0+efO5+9gl7NplvwtYpNiZ4PMfr6l7GOO+zL168ONvH8cNnPPezbPZcNEelPffM82J+CfNA8vPmTzP3ic295jhi/Zjfyjw6K4en+dII22bXs2JrGluPm4/O3ifsuWl93d4fzMlk43y3L1v55/3mObP91nb2rNt1dtpzedfZw7ax65mLy96lbL29u47hGpHvfqs1M+dM87jZnMJt3qvd+9XV1XLb5hHrq3PdrZxr/UIoIiIiIiIiIuLC6AuhiIiIiIiIiIgLoy+EIiIiIiIiIiIujDvhEPr+978/vve9773ZZk6WGW7zWfDzK3fHTZ9n/o4Zc7Lr45jzhnZvlq1ntpBlscywZfNZHnOFWCabuVJ6XrhNt4hlos2Dw+ubx8acReaA4vXn+pi9WTed23xKbAuWlVhefCd/fVN5zZljvinmZne8LDdhLjKrL57f8u7mxZk/b23LPDTHhfmZiGX/LY9tHgx+npgLzdwD1jfteubD4rzA+2V92PVXzia2Ba9lfiaWfdenRHbnUJ7fjp+vb2M63g+fffbZmSvR5gv2AfNr7LpHzJlm25zbOb/O1+f6jnMBy2rjhetPm2tZNo4Rlo/3ZusAtpWtcdh2tt4mq/F/0/XMSWTPRvPvkbmvmM/NnJvWF7i+ZN8yJ6idn21rczPvb7ft7bm96yjd9U/Z+5Gtx+fr89rsV8T8qMTu1bD1t60T2Pd2fVi2JrLP2zxDzAFEzKm66ns2zs3/tHqvG8O9jKwLew81zHs3j4PVvfULoYiIiIiIiIiIC6MvhCIiIiIiIiIiLoy+EIqIiIiIiIiIuDDuRKD/+9///vj2t7/9ZvvZs2dn+5mnY15v5ei5adt8FYRZRWaa6QrZycObl8WyjJapNVcGsVwqr8dsJOvK6t6cPpahJpbdZMb48ePHZ9usL7s+PUA8P3Oxc/2YJ4VtYXXJumddmhPHcrHMpe56HVi39OLY/pUHYgx3FZh7xdwIxLwY7DurDDP7CcvK/Rw35msyzxq32a9nv8gYb9edjRPzL3E/y7OqyzE8A87P8/w2r61cBDftN4/Gqiysi908uz1TWPe7LjFijqL48WDuF+yDNr6tzxH2eXMX2nxhjqOVw8w8J+a9svWkOSzt+py7nz9/frZt45vseA7HeLttba41dwifXXZ9rgP4eT4bzTk0b9sz2+Y+wuNtrmVbm4OIdWG+OI6jXb+cPZtY1+aZsXnB1gHcZvnv37+/vN58fnP67Dp3zKdqc56Vx7wynCfML2WeRZbvwYMHZ9vmBOJ+82ba+4n1PVvDzfdjbipb/9kca/dudW3nJytn5U3n/zz6hVBERERERERExIXRF0IRERERERERERdGXwhFRERERERERFwYd8Yh9N3vfvfNNrOZlp1kNvNdrjdjWUU73rKMK38Gs4TcNveGwSyjZf95PctOMgNN9wg/f3V1dbbNujKvjmU7mbnm8cwYMxfLfDqzmczpsnzM97Nvzi4Wnsu8K+ZzMq+MOX3IrqeG98q24f1Zptny9KwPfp7lNwcSsZyxOZzMDTHDe+WcsupHY7zdjwnHJfsePQvm6DHPza63xpxBrHtzMbB8dvxu/t4cQmyP+frmQ7I5j/2a1zYHiOXXWT47H70WK8/Du2bZ4/2y65Wy/exDNt7t2WVOBts/jxlb79m9mO+I2BqLcMyYD87qguXj+axt+Rzl9bjf5lJbo5oHk9t0eu7AtrXnnnlfrK/YmsfmenMRmhvFPC7mxWHf2e1LNi/Yu535vVbtyX3m4rO2tOeotfXuc3e3bgn7lr0L2jpkd41mjiZzqNoalPPKvL3r0rJ+Zutf+zzH4a4X0rx19oz5U/qFUERERERERETEhXGrL4SO4/j3juP4v47j+NvHcfz14zj+8eM4fu44jt8/juMPjuP4b47jWKvIIyIiIiIiIiLiS+WHjowdx/GzY4x/d4zxz5xOp0+P4/jtMca/Mcb4C2OM/+h0Ov2N4zj+0zHGL48xfmN1rlevXp39ZIo/d2LcwbCfztk24U/buL17vhn7ua1FxuxnhfbnXO1nkHZ922bbPXny5GzbYjy7UZrdP3tpf5rc/pzfKgZ0U3nm8vLnvfZTcv5Ek/AnpPbTbPsTprf9qbn9jNJiCfYTVIvC2M+d7af99qfJuZ99YRUltXFqP6XmvTKqyM+zr+3+qXObd9g3rS9bX+D9sfzWtrt/xtrq2+ZNizHMx1td2M+NeW6Lx9gcvxs5s5+6Mxa8G+mOL5/jOM6efdbGFv9ln7Gf/9tzluz+aXUy90k+B8nunwq3+KpFtXdjlRZdtmi3zS9sm924xW7kzP7Uu82fFvNfrc+tLbmetba2uJ89l9gWrKvd9bxFSWzNtfscteN3/9y2xQ/tfWR1bc4DFhckrCtTNOzGgIiNG3t/sOj4LhY5M00Bt1l/XE/b9VfRVR67+65lazSLE3K/xWat79n+d+W2kbGvjDG+ehzHV8YYH44x/niM8a+OMX7n9f7fGmP8xVteIyIiIiIiIiIivkB+6C+ETqfTH40x/sMxxj8YP/gi6NkY438ZYzw9nU5/+vXXH44xfva2hYyIiIiIiIiIiC+OH/oLoeM4Phpj/OIY4+fGGP/EGOP+GOPPb3z+V47j+OZxHN/kT8kiIiIi4kfDvAazn+NHRETETy63+bPz/9oY4/89nU7fGWOM4zj+2zHGvzTGeHwcx1de/0roz4wx/uimD59Op98cY/zmGGN87WtfO835R/tz25b/sz+ZvPunzLnf/lz4zp9ItbIwd7rrRbFcqn0Zx7y5nd/cJeaB2XXy2PH2J0npuzCfB7Oe1tdWGfNdtxW3+WfYra9wPz9v2fzdP0XOvrL7p4GtrxPLHJsXwzLMzP2yL1lGer6/3bz27p+HXf3p7zG8rXg+9nvLWNuf2bQ/aWrX5/HctrbbdQRZeXg/bK/79++/+W+OI45DnptY37G6t7rjvZjrwL5M2HHqxZfHvAb7+te/fpr7JX0a5pPb9Sja+cjunxy2+WDu0/PY5L6bysp743OAnhm7N16fzxF+fncNRFhX9qe+2RdsPf7gwYOzbfM5WX1b/dmajc+qeb+tYcwRZGsiYs/d3T91bq4SexYQW9/b8TYuuc01qXkszS+148Vh3Zm30Nxh9i5n1zfnpz23d98V7V1l16FkzqCPP/54ud+cqjZWVn9mfozz+uCcZnXD/fZMsPWjjXNi61PWpXnqPo/bOIT+wRjjXziO48PjByPjF8YYf2eM8T+MMf7S62N+aYzxu7e4RkREREREREREfMHcxiH0++MH8uj/dYzxf74+12+OMX51jPHvH8fxB2OMnxpj/LUvoJwREREREREREfEFcZvI2DidTr8+xvh1/PPfG2P8c7c5b0RERERERERE/Oi41RdCXxT37t07yx2b38GcQZadtDyfZRHtepaLnffv5kC5bW4MZgv5efM/WAaY57PrEfu8eWgsx8tt1g9zq6u2uqk8LC+3LfO9ujaxLL25sMwRZE4fYuOCdcH9L168ONtm2/D81taWcbaxxM+zb9i29d257S0rbz4kblvfMS8NM8nW19iWPL9lpC0PvuuX4v1b/Zknx5xK7Is21ubymwvA2tLqxu6N+XmOO/OvWN8g8/3surPiy+E4jrO2sT5EzNlFbPxyvrA1oY3Xld+DDh+OD3NoXV9fL/dzruQ2HUQ2F9q9s67MoWlOIT7X+HmukXg9ti3rd9cHZ32H2yz/vH/XIcRt1hWfM7wX61s2zlje3fW5nc+cQLvPbcJ1gr3v8Hhbf5NVfdJ1xXvluDTvoa1frV8TtiVdgzZH2xrM+rL5XXl9mzc4T3Ie233347bd7zz2eG16xqxuzZVlc7aV1d5FzHm0chytfEW3cQhFRERERERERMSPIX0hFBERERERERFxYfSFUERERERERETEhXEnHEIffPDBePz48Ztt5u3MkcBsIjNyz58/X17f8uaEOWDmYHccEVdXV2f7mDu1PDRzm9xm3XA/7/3ly5dn27v5890sJMvDLCdzpLye+ZoM+gPMJcL72fVTzfstC0/MTWBl2fU1GdY23M/y7roWmKG+bdvw/OYIsow0Wc0Llr3f9UZY5tn8UZaBNm+NOUCIzSM2Dnl+lodta+UxnxSx8rLt520+Pzjnm9fB2s7KTneXeRus71lftrqP98+rV6/O5jOOZ+vf5vaw8WqOMxtv7KM8nteb1xl8rnBNxjWJjU96ZPjc4Nxkz12OZ7tXa4uVT2mM/WfPo0ePzrbNIXRbnx3LYz6N1fxjcxOvxTUB25LYXLjqlzd9nvfKurW6MC+MvXtZX+f5bE27+35h43qnfm3csZ9yHBN7LtqajXXHcbX7vmBrQl7P9purjGODDiabF22ds+uh5Lvd6tzktp5Gc+hxTrbyWF8xn9K7ehz7hVBERERERERExIXRF0IRERERERERERdGXwhFRERERERERFwYd8IhdO/evbNsJ7ObzPMxO8jM95MnT862mW20nC2zjcznMZtomWjm7efMOnOizIVanttymrw328/soWUX2Ra7mV6enw4jltfcKOwLloFmZpvlsdwr99PPscqYW3bfti3/vpvR5TjZ9cyY08hcBAbv1zLGluM1J5A5lnh/7EucJ+b9uw4h3ivvjeOAfYtY3e9mqNl3zFlkfZfls2cA4fGE9WfuL96POVZW92seBLLrGOE2r2e+p10vhvXV3XEeXz7HcZy1o7Uh+6zN7eaPs+vxOUvML7FyiXD9ZmPfPC7mczDfGTFXnT3neX1zlhHWvbWdrSnNcUSnk81XVr7VGprXtncFc33suDxu2ub52VbsK1z/2xrR/HLcb64TcxDZmtDexcyXZfMOzzfXj7mmbE1gz1Ueb+vXXQ+jOTJtXWHYmsv6Cuel3fW5jQ1rn9X7zq7X0OYBe35ZvyTcb88o80uZF/nNed7pqIiIiIiIiIiI+ImhL4QiIiIiIiIiIi6MvhCKiIiIiIiIiLgw7oRD6HQ6nWXsmH/76le/erbNPN719fXZtmUziWWamc+jE2iVTx/jbZfIgwcPbvzvMd7O+vFcvDfe+/Pnz8+2LbtPmJVk3dCRYxle7jf3CTPcPN68NOYOYeaa9c36sr5jme7V9cyTYrlP9kNr6926sr7AccnM8O797LpMdvPmlgO2fDvLT9cBx/LKD2ZOHstrm7di1+ti+WxzEZhHzRwfdn3OyXb/5tEwzGPH8vL8q+vZvZpjyJ5XxPq9ed/MEWIeiHl7N7sfXw7HcZyNKXuW8LlGbP7i+fncN88NnzXmXrH5cbXPngvEykJYV7w38xzaGsvGnLlLzCvJtrP7ofOH23yusv5s/uK6aOUws+cC99NRybnXnJn23LCy25px149qbhO2jXllOK65bU5O1o/VB/u6uVZWn+U233WI9UO2xa4rkHVnayAbJ/Yus+s0snXLbedNGyv2/kLm+zFXr70n25zIujLHptW1OYWsfPP5V/fWL4QiIiIiIiIiIi6MvhCKiIiIiIiIiLgw+kIoIiIiIiIiIuLCuBMOoVevXo2XL1++2bZ8302fn7HMtGUVzX3C8jHzvHIGjXGeQ6aHhZlZy9ITZnItU8vrWa6Wx1td8fOWi931zrDt5n500/XZdszn72bAd+9nPj/LYp4XtiXbejdvbZ4W3hvrim3D67HurC05jnh/xPLzxLwybHuOW36e49zcBzPW9uZtMbeWuQB47+apMaeO5d95PcuH83rmyzJvhnneWH7zxBHr63Pf2HWG2LjZ9R2Z9233ebpydIzh7oK4e5gPjvOJOYMI+6R5ZswfwW0bU/Ozjvs49m0u3n1u2XPG5jbeK4/f9ciYh8UcY7vl4fXMIcRttpetY1ZrXKsr3ju3uX7nvZgTx56jLI+5Rlj35hwy54+tIdmXrD7tfu19g9j7wuq5zbbiu4N5Ds3jYu4+W7/aepXlt7bcdXPtlse27f3Enhnm8uU8yvudj7c1E9n1KpJdDyPLY+8Lu+Ps8+gXQhERERERERERF0ZfXjHGTQAAIABJREFUCEVEREREREREXBh9IRQRERERERERcWHcCYfQZ599Nj7++OPP3W9+B3Mg7GYhLVPObWYVHz58eLbNbOPsHmE2kNvMKFvGltv8PDO/z58/HyuYRdzNk1tdE+ZeVw6eMd7OUhLrG8wN83rmYOL1zcUy90Wei/2UdUHMW7Lr3iLm/rDyMctvjiHzQuzm49k2vB+OpdntNYbPMzwfj1+1h7kBzOFBeK1PPvnkbNv6io0jG+e2zc/vZP3HeLs+WF82zs1RxPs3x5BlujmW5/Jbv7JxRcw9Zsebr8XmXJbXvGtx9zidTmf9hm246s9jvN2HzOFj3hfztZmDjOX99NNPP/f43We6ufO45jK/hPngWJfmKCK7Xhiy61U0Z9nTp0/PtrkG4/zI5zLX0+aZWTlJWZesK7t386Sw39uagey6S1geW9+a58acmLddQ3Ks7Hop7f55/bl9OSfYmoll5RrC/E72HmruMG6bF5GYm9fWz1b33M/6YfntfcXOx2cCt1fvbrbGYVvtvqvZM4VtYcfbuN31Ln8e/UIoIiIiIiIiIuLC6AuhiIiIiIiIiIgLoy+EIiIiIiIiIiIujDvjEJqzruZEYO7WXCPMCZvvgfuZWeY2HULMcq72Mztojh3LLDPnylwl64K5TsuXW86UmWPLEFsmmtvm8rBsKLdZH8wV73pqdrC8OrHMLvuC+amsbS0/bhlnjivLn9NDQawvsD7MdcBxScxrw4yyXX8F64p1yb7Ce2E/tiy9jRvbZluw75mDxPLo5ghhXzKn0G55bZv1veMLsHy6OULMQWTHv3jxYnm8Ofh2+8rcl22Oi/fH3DbWB22+sjUc53ru5+fNlWgeSM4P89xvz81dZw7hXGV+Mo5PtgWfy+a32PUssm24bc8Oc6nwWUbPDT/P++Uaje3H6/E5Pdc/jzVPi3lO2LZcj++6/Mx5aa481g37ko1j1h375q5rhfXD67F8u75XsnK92HPTnlXmSbR3h913HeuL1va2n21Ndt1l5iBa+Z1u+vzuPM31Pd/bZzjHmOPT3puJOSxtzjP/lL3Xztdf9et+IRQRERERERERcWH0hVBERERERERExIXRF0IRERERERERERfGnXAIjXGekWN+7vr6+mzbcqXMIjLvR8zHwdwss4jMsTLruMqzW05115PCzPIqN3lTWXk95rXNRcLrs+55PubTzTtjudldPxTL98knn4wVvD/LlK/uh21v98a6Zg7Vtq2tWVaOA7YV25JYW7DubSzw8zye9cW2YH3s+qHMnWBjc+XoMP8S50TzopmL7Dbuq5vOv+utsL5P2Hft/Ob9MM8c7499hZ9n+Vne1TPI+pH5iWxOY9/h+e15udu2/Lx55OL98+rVq7P5nHP7rjtvd36x+YBz90//9E+fbZu3ZjU3mxNy10dmayJ77nK9aM/l58+fL8vD8U8PzbNnz862zetinh3zb9hcb34pzlfmUuT9z+e7rbuOx5tX0D5v1999NhBzn9i6wxxHxNqefcuewxxLtsZcOYlsPWnOTL6XEvPGmH+K27aeN1eZvSvZmsfeo3fX22yb3Xc7W3Px/lfzDvuVzcn23sp7o8vXxpHNG6s5bQz3w34e/UIoIiIiIiIiIuLC6AuhiIiIiIiIiIgLoy+EIiIiIiIiIiIujDvhEDqdTmf5TWaEmUNlPo/HM1/HbKjlZi23y/wf9zOTvsqY/7BZv8+7NnObjx8/Xh7PumVdmsfGspXcZn6d5WVb7bYdsawmP29uEX7enEord4G1Pev+6urqbJu5V/MbWUaY25bZtXFgbhLzsLDu6bUwB9COR2KMt8epOY3Mk8H7m8trXgTWlXldeC/EXAN2r+ZS2HUZmP+J27x/1r2dz+bV3fy8uRNWfqxdP9FuXyDWVrtzrHnduD3fj/WzeH+s/Brm9iD8vD0X2S+4LuDn6fl59OjRsnz8/HyvfG7yOWAOLZsLuEay5wbvxTyMtqajY4jzizmT2Pa2vmbd8/55ftY/y7fr9bHyzO1pax6WxTyNvLaNG/Mt2b2wfOy79q5kfcv6uvlcud/6DrG25f3Zs23eNm+LzYG2JmDfoEeG2+YQsnc9q3tbA5k7jOy+DxCOe96vzfHs6za25uNt3Nu7A+t+tda/6fyE5zfvI+uG7lvOI+/q9OsXQhERERERERERF0ZfCEVEREREREREXBh9IRQRERERERERcWHcCYfQGOd5TebdmKezXKo5Fiw7uvI/jPF2fpBZRts/Zx15LO99NyPLzzNLyNwms4jMmzPX+fTp07HCys8cKJ1CllGmk8gyyOYCYXnZV3g9Hs/2Y/1ajnbGXBy73hfLf5tjiNscZ1YebrPu2Tdt3PF6ts3z2f2xr1ummm3N/av2Y7+wfm/3xraxPLfNiWTX3WXzknlr7PhdZxC3rf7J7jOGLob5GWB5cct7270Zu3Vr2yw/732eI3MI3U2O4zhrp93npj0bCM9nHpyVh3EM9+nx+HnM2DPa5iZzAdqazLw15qExz6F5ZIjNZeYUsnWGOTnN08O2NccQ63veb85Me+6aB5H91tYU3LbnFK9v27wfc/rYGo/1Zetp89HZWGRfZHnM5TJfjw4f9jNiz117Tu66+Fi3Ngda25Db+ln5ea6fzftG7w2hM/Xhw4dn27Z+X90/63LXhWX3Zv3a+gr7Js9njqFdz92b67zTURERERERERER8RNDXwhFRERERERERFwYfSEUEREREREREXFh3BmH0Jx5Y57OMtCWeeZ+5u0sa8l8oGWcV3l1Yt4R88gQy1PzXliXzGdbxtqykubUsWyjZYRZ99fX12fbrC9ra3M2MffKTDj3s77n85nLg/fGujaXFuveMs/mHrBt89JY3t5cCKwP+p0sH0/MxWDnt/ZbZcatrXczwLwX9kt+3tqe8F5YXvMv2bxmDiWen/Vj3g67X5bXfFbm0Vn5qnhuYuOU2PPM9ltfs77D8vL+5nH0rln2+HK5d+/eW8+qGXMscM3DudM8POyjHO/cpgPC/BWrZ6nNJeZDszXNrt/MnGB8jrLd+BwzZxHndh7PuiXmOGJfMN+FPRt5fq5zdrjtXGl9Z3cuZ13yXcbmYnt34nPK1rO7Lr5dRxD7mr07mV/LnvPz+WwNYB4yW++y71g/trLb+tzGkV1vd31vDiPO0dzPvmD+LXMq2Rp03rZxa+/ZhGUhu+6tXZ+TlX++99Uc1C+EIiIiIiIiIiIujL4QioiIiIiIiIi4MPpCKCIiIiIiIiLiwrgzDqE5A8fsIrOGzOsxb8dsI7OEzKFaDnY3v0dW7hHL6DJbaNjxli8nlue23Kndz9XV1dbx3GbbPnjw4GybdW/lNdjXmMHmfmbA577HjLL1Y8vsmtvD+jnr0uqKeXRz9li+3PL8rGvWh2Wk6TLg/Vp98PzmuWHfntubZWdfYL/hnGcuLasbyyzbuLM8PbHMNuH52HasL+tbu2OF98e+x+vxfOw7c3tav7F+bHO8eRqs7q2t2Dd3fFXm0Ij3x047mRPBvDXsUzZ/mS+C+3k9Hj+PX1vvsSy760Ubbyvf2Bhv3wufszy/zSe2riDmIrT6WTnFxth3p5irz8638kjyXOZPsn5t98K6Ydva+tW8MTyfrfFsv7m5uE1YHlt3sP7Ntci2XY1lWx/v+qW4n9zWGfTy5cvl+XffJXe9ilxzGrZ+NqeS1TfL++LFi7Pt+/fvf+7xu27c3fXj7rzA8pgvyryN5F2f7f1CKCIiIiIiIiLiwugLoYiIiIiIiIiIC6MvhCIiIiIiIiIiLow74RA6juMsL8g8nTkKLIPNvJ1lvC3fZ9lKy/utcra8tmUZid0791uWknX14YcfLvfv5lS5zfNbzpflp0uAuVsez/Lu+gFYfnOpzOUx7wjPxbIz229tY9n+3X5vTiFej9uWHyfmYrBxxjy65eeJuQpY/lXm2hw/lsWn04bHsy6s33Pc2JxJj4z5sKxuiY0N9jXzbuy0zU3XY1+xeXq1veuZsHPbHGTjktjz1bwNK3+MjeF4P5xOp7M+z/HBPmVrLlsXcL5a+dZuwp4Vu769Fbam4ngxx6V93tZMtmaxNZM5xoj5oLjf5g/2LfNn0DnEvsPP25p3nst5LcJ5n/3UXCHmjeHcbHVh63XzsOy6As1lYn3bHEPWN3k/5gozx9Kqvc1jZs89G3dW1+awtH5uHpvddxlumz/W+jbr89GjR8v9Nq+xfDze5tHVvpVn7KZz27sUMUemjTNe38bNu7qIW51FRERERERERFwYfSEUEREREREREXFh9IVQRERERERERMSFcSccQsRyt5ZTNa+M5XQtu8m8H30avN4qj2i5T3NvWP7bMsOsO+Y+eS+PHz9efp51RYfP8+fPz7ZZd+Y6sfKzvizXy6zmbr6dmBtmhvdunhTWJZ04Dx8+XG5b1t/6npWP49TqzvLfxDLOPB/rx443D471pR2vhtWN+aPMkWH91OZIy2eby8s+b33N8vGWf9+dV82vZf4o9jVur65l++35xf3ctnszF5Y9g3ivLN88R+cQ+vHA3CG742F3P+H1nz59erZtDrTVXGx90tafdPeZX8J8Rnyump/C5kp7DvL6rFuuscwXxfUxy8/P83jev90f69/cI3N5zatiLjn2u/v37y+3bY1k7q7d57aNW3MUkXd1kbzr8Vxzsa+aB9LWFexLc18zx6Y5aW673+6F2Bpo13XLtmddsS7pz+I4tvdo81lZX9xdU/L4ec2861O19aQ9v3brnu+G5quy9b/1rTfnfaejIiIiIiIiIiLiJ4a+EIqIiIiIiIiIuDD6QigiIiIiIiIi4sK4Mw6hOfPGPJxlFZmTZb6PeTvzvPD6n3zyydn2bs535cfgPuahuX/HUcNr3XS8ZaJ5PHOvLC/rihlglp9OIZaH27dtO16P+5lnZ/2x7/H67Hss73w+y52yLLw291s+3NqWZbfc627+3cYl2fXq8Hj2Pd4/8+s2luz8LN/KscR97Cfm6Nj1GZkrYTezfFvXgHknzJFkc/pq3N2E9WXC+mHf4rw4z5s27uz5x3Pz85YXNy+bHW91++DBg7Ptua13HRTx5XAcx1m/secex4f1WfPZmROB6yDOF9zm3LzyYu2uqcyxZeNx18W36zhi+fmc4/Gc+80Lab4N8zzac9Tqx7yZrG+6T+br8TlETyO3zX9kPiTzypj7j/u5fjWPC4+39bbN/bvuLrYF5wWej9czVx/bk59fPRttzrNxZi4+m/M4zrif45TYms58WNy2OXl3nWHuX9aPudeIrRnn+tn1p5qvaHeOtPdI8zKaV23lUVutwfqFUERERERERETEhdEXQhERERERERERF0ZfCEVEREREREREXBh3wiF0HMdZnpD5O25bxpu53d1MuLlJmE1kbpiZbZZ/5v79+8uyWY6TuVFmB82Fwf3MLvJ8rAvL31smmNvMvbItWT62HeuDOVXeD7etPObF2c0Rrz7LsrEumPl98eLFsqx2r9Z32BfN68DrE3MjENal5XCJ5fPZV+x+bSyuMtssqzmAdp1B5qkxTwOx81vf3c3ns7zWtqs59qbPs604luwZQo8P+w7nqXmet35vnh1zkhBznFhbmt9qJ3+fQ+hucu/evbM+bW4UtiOf848fPz7b5ngwr43NFxxvhM9tPsvmPmlrgl2Xhj03ON44vli3u44he3Zwm21j/gti8wextjdfh5XPHEm8/uratgYzbyLLwn5r64Bd952Vj8+53ecm+wqxcc73HW7b+wKfu+ZkWvU11g37mfmUzHNmTk9i1991eLLf83y2Xt31OPLz7At0C5oLjPe7+76wGnvmUbQ5xca5OYr4+d31PrHPz3WdQygiIiIiIiIiIt7QF0IRERERERERERdGXwhFRERERERERFwYd8Ih9MEHH4yrq6s328zjMftouV3L71nezxxGVh7Laq58SbuOBfNJMEto2UXLVpo/wtwCrGtuW36cudOVr2KMt90HloO1PD/Lay6SVe7V+iXbxrwJzI/z3lkWy2tbJtquT3i/Vh62Ja9vffm27gP7/O68Md8/68LcXdzPLL05bdgvP/nkk7Ntc25Ynt7aatdBxM+bU2R3XjEPheXnrX3s8zM2B/Pere535/Bdx549X1fj0Pwi8X64d+/emePh+vr6bL+5ATme+Hny8OHDs22OF/Pi2PxEVvMB++SuL4JlY1nsfLbG2XXl8Xx8FrDu6HHZ9WtwnbF7vV2Hkq3BzP82l9fWHCwr53k+N4k5e3h981UR86qwrlh+ayvzTtp62jw1PD+dP9zP8pqHZuWEsvWkPed4L1xTmeOHnzd/KrHzmSPJ1mTsm9y2vmQOIZvTifln7d1zvp7NuSyLOS5tjiQ27u2Zs8u7fq/QL4QiIiIiIiIiIi6MvhCKiIiIiIiIiLgw+kIoIiIiIiIiIuLCuBMOIebXmbu1TLb5H4hlwJk75X7LNNv1VnlAnsvcGZZltGwj79Wuz7pg1pF1b+4RyzZa7tXqh7lV5lxfvnx5ts37YT6e5+PnmQ1lX5mzqZanNneI9TvCfsd7Y1m5vetWsNyt9RVmkO1+V3nxm8rL+mffsIy55Xp5vrk8zChbNt7mKGbvzT/F89OV8OzZs7Nt9gUbl7veDJsHzPmzM8feBMvP+mFftPz86hliLgJzBNm25d/t+WhwjuP5Vy6tHEJ3k+M4zvoRn3PsU3x20J9hnhfzWLFP27Pr+fPnZ9vso5wf5/FqzwV77r548eJse9fLaI4vrgNsfWrzg61RzJvD63388cdn2+YMYlvSf0es7Xfrf65fW6NY3Zmbzp6D5qDc9cvZGoaYu8scRbz/22J9f7e92Hfn9uO5re047tnP2VZ8N7A5kdgax9ZgHFf27mR9m3XJOdUcQ/Z+Y+879q5HVu3FsrItzQ9qc7j5TG2O5vHmprU15bvSL4QiIiIiIiIiIi4M/ULoOI7//DiObx/H8benf/vacRx/6ziOv/v6fz96/e/HcRz/yXEcf3Acx/9xHMef+1EWPiIiIiIiIiIi9nmXXwj9l2OMP49/+7Uxxu+dTqc/O8b4vdfbY4zxr48x/uzr//uVMcZvfDHFjIiIiIiIiIiILwoNgJ5Op//pOI5/Cv/8i2OMf+X1f//WGON/HGP86ut//69OPwg7/s/HcTw+juMbp9Ppj1fXuHfv3lmmj7lWcyRYBttyuOY1sLye5Q9Xn7drm1uDn7fMreVizQXC4y3HaXVn5bMspmWoLSdr52dOlnl15mSZ9WTf/fTTT9/8t7Wd5cktv20ZaWK+J2b3uc265/XMXcK65vkNtgXH5SpPPoZntLnf7nfl3+Jn6Vlg2/J43ov1c7ue3Rvz8MR8UzYPsO/d1jl0WzcE8/fsm5aHZ/nZF1dls35lz4xdp93u89Dy8tZ2cfe4d+/e2ZzC8cD5gm1svgtzEJkvgs9dWxOyvBxT8/E23ti/ie0nu55HYmskc/OZc9PWFTaXmjuFbcvrm5OMfitbN1hfXJXV+rW5rmwutDWZraHMy8jy8Dltc7eVz8atPSfNq2PHs/w7fjv2G9Yd64rwWvy8bXNO4zi1dxl7rtq4NifQrouMbWHvVtzmmsveDW17tR6f38PGeLuu7TsD+w7C3sPNb8ry2Lxha7p3XYP9sA6hn5m+5PnWGONnXv/3z44x/uF03B++/reIiIiIiIiIiLgj3Foq/frXQNt/OuQ4jl85juObx3F8k3+hIiIiIiJ+NMxrMPsFYERERPzk8sN+IfSPjuP4xhhjvP7fb7/+9z8aY/yT03F/5vW/vcXpdPrN0+n086fT6eftz05GRERExBfDvAZjxCoiIiIuB3UIfQ5/c4zxS2OMv/r6f393+vd/+ziOvzHG+OfHGM/MHzTGD/Jtc96R2URmG5mHYxbTsCym+S3Md2H+iDk/aM4eZgV3s4y7Pgiez+6N5ePnmcPl51mXdjyzl6w/5mKZxST8MpIL4+fPn59tX11dnW3z/7PKvshfv819j/2Qx1p+3TK61q/NBWL5cR7PtreM726e3PLldAewbXk/7GvsOzY2ibkZ5nmNcxr7qeXHzSfFz7MuOMeyblg+jgPCvsu22nUZcJvzBOvW2sq8Fawv1g+3rT1WXrldz9uuo8Q8E/ZMsXForqxVnt3GULwfXr16dTbGrA8QcxpwPuH5+Xlej84HjkdjtS6wuYhj2Zw9u3MRt3mv5pfg+TlX3mauuul6Dx8+PNtm3bL8LA/hs8vmI94v12zX19fL4+f2trKa92TX92TYGs7WWLu+K/Z9W8/vrkFtnJu3ktjnzc0yl9/GpWFrCnP22HuuOYZY9ywP13x2fY5zlsfqx95F7Rmx68Mlti6Z3w/YNla35im2dytzaxF717H19Op8q3rUL4SO4/jr4wcC6a8fx/GHY4xfHz/4Iui3j+P45THG3x9j/OXXh/93Y4y/MMb4gzHGJ2OMv2Lnj4iIiIiIiIiIL5d3+Stj/+bn7PqFG449jTH+rdsWKiIiIiIiIiIifnTcWiodERERERERERE/XvywDqEvlOM4zvKLzC4yH8dtZjeZkbNcK/N55gCynC3ziKu/osayWybZsoe7985tcwDZ+XnvVjf0vrBu7X4tb87Pr7wuN+3n59mWdv0V5hKwurFsPeueWF6bdUFYXsub83jzRjDHa14aXt/6Hu/PcsSE5TfPzMrDwbawfmgZZ+tbzIvz/HRlMX/Oe3n8+PHZtjmFdl0FBuueY8c8FeYwYvlt3l61j7muWDYez2tbvnzXCcT9vL7NcSuX1s78GF8ep9PprJ3ZJ9gHzKNo/ghzBRJen04z+8MknB/m8pm3hWWns8aeS+Z1ND+Fjc/duZR1Tw8iz8e5n+tzbvNZwPq1v2jHZ9Nue5g78NmzZ597bvOamGOT8LljHhZej5+361v5WRfWt2xcsy/ZOsacTTz+o48+Git4PvYtlmeuT16Lax4+wzmHmLeRmDPTXGRzv72pPLy+vetwm+P8wYMHY4X1LXtG2Bpv19NjbsS5/lk2aztbU9mcvuv2Zfls3HIcrtaEqzVYvxCKiIiIiIiIiLgw+kIoIiIiIiIiIuLC6AuhiIiIiIiIiIgL4044hMY4z0ta7tayhZbDtZwsj7ecrTmOmHGeM3zMxjOPbb4IZh+ZPSSWJzd3gOXXd703vL75nSy7ad4by3wzt8tcLa9vOduVw4htx2sxH20uD9YlP7+bdydsCx5vrhLzRtj1iblO2DfseI49to95e3h/q7Y3t5V5IJj/trIRc23x8w8fPjzbNrcY78cy0NZWPN78TtxvzwBi8y4x/9VqXmJb716LsC75TNidV+hlYF1afn6+Hyt7vB9Op9NZP7H5h32I/ZvbXAOZe8+eJdxmea6vr8+2V3O5jTeWnc8J88zYetR8b8ScQebO4/aLFy+W+82fZi6TXS+OufjYN+gMYvvQZzefj2suc9HtelLMC2PrWXsOmsvO1tPE1gX2Lmb+VI5Le67Tm2PzitX33L50BpkTkthz0Po9+y3HIfsmxwnv1d4nuN/W84Rztvms7H3E+qZ5eIh5IOfPmyvXnJzsKzaH27sC645rrt13q9V6frUG6xdCEREREREREREXRl8IRURERERERERcGH0hFBERERERERFxYdwZh9Cca2PejllJ8zUwb2e+CZ6P+ULyve9972ybeT16ZZjvW5XHMru7nhXLt7OuLBNt2UdejxlsK79lqFlecxiZ28T8F1Y+5oDZXswhz8fv5q2ZweW9W6bZPC4Gz897szw5YfkI22K37zCTzVyuZa7Ztla/7Jvsa7PbwJwYrFtus252HULmJOK9mhOH5ec44OdtnNucvOsA4rZlxK1+uW33O+9n3XJcs252nSI8H8vCuuS8wzme+82Tseo7HCNxN7h3796Zj4PtxLmS7g72KZs/busm2XWrrPwZtsbiNscHsTUFt82zSFgX5hTi+DUPzu4ainVrbkD7vPUNqz9b463q1+Y280MZvFeW3eZeWwOZ75TjkOWxdYP5oWw9zr5m71pcs7Ev8F3L+s58vLn06Biye2Hd7Pqj7DlvdW/ns/dgtt3uvGHnM2/k7prL1nCrNaa9p3I/6551Y2XjfhsnPP/KgzaGv4e/q8exXwhFRERERERERFwYfSEUEREREREREXFh9IVQRERERERERMSFcWccQnPGznwVH3300dn28+fPP/dcY3hWkf6MXY8Oc650vzDXOl+f+8zFwZwoM7h2vOVazRfBurOsv5XH7tdysubB2XWFmAPI+hK9M2yfue/wXthvZufMGG9nni1vzrLRqWNtZW2360qw8+16JrjNvsDcr/Udy0Rb3zQvz3w9th3bZuWeGuPtOefZs2dn28zWsy/xfNzm8SsHxxjuErC6Z99mX7Z8vM1LNg+at473T3by7Dan2hxpfhZ7XjGvbs8zto25DjjnzfeXQ+hu8sEHH4yHDx++2Tb3oO2/7Xgybw37LK/PPszPz744fpbPAX7WnDW7z8ldlx7Hu43nXeeQzR+2vuWzi88Se05afRJz+/HZNt8f743XZtlsHJgvycaNOYK4f3f9b+tlW4Ptto25/lgf7KtsH44FrhPY11btx+ca3yO5huL2kydPltea59Mx9t1W5kkzJ485gGzNtLtNdt/Dbd7kfptHV34uW4PxXE+fPl0ev3rHv+l87Ofsi5xjzaVlTs/5+qs5oF8IRURERERERERcGH0hFBERERERERFxYfSFUERERERERETEhXEnHEL37t07y1+a34F5O2YViWUPzU9hWcjdjPW8zWsxW897ZU7V8uXcZn6QWUPLy5u3xTLHdKUQy3Tv5mhZn6wP5svJqu3GeDuzzPpkvn++vnkVfuqnfupsmxln1hUzzZbJ5b1Yhpn3wm1zCLHv8vy7bUPMY2F93TLO5iogrM+5vfhZm9PYttzPvDr7Jfffv3//bJttc3V1dbb96NGj5fWtbc0ZxIyYSDkAAAAgAElEQVS0uRpsniPmnzJflWXEd+al3X6zm91nPp3brFtzihB7/q0cQzmE7ib37t07c2RYO5nLY9cTs7vOYB9lea3Pz8ebW8O8i+ZB5L3vPndtPNvcw/Pv+tis7vls4rOG2PrevDc293MdtXIDzi6pmzDHD+/F3h2433xVPJ5rQJZvd+7m9e05a21j98e2ZluZU3V3jbZ6rrNsXM9yTcJ3F/MV0evIvsay2buZOSut75gH0lxitoZiXzGvIstr9UHMZbjqq+ZNtPdKe57xXsz3an2P+60uV/6l1bO9XwhFRERERERERFwYfSEUEREREREREXFh9IVQRERERERERMSFcWccQrMvxDLCzNMxn8dtywNaLtX2Wx6R2c05e8p9zKla3puY62I3A2x5d5af5+P9WP7d3CMGs51sG/PAWGaanyeWj5/LZ5lcc+zw3qzuuM2yMtdqfc0yyOZKMf8Vz2euA3MkmWfGymNOol3fwAzvjePGxiXdAnQEcT/HLbfpDJr9ImO87YngfnOXse9yXjBPjsHzWf3ZPMj6Ibwf9sX5/DYHmePH5lDzRPD8LLtd3zwQ9ryMu8dxHGfPJs4f5rHieKcjzMbXrluKc6k5hbh/XkPa3GVj3zwmu24Nq2ubD2xdYGtK87iYp8a8NlyjsfysL/PQmAeIx89+PLuWrefMy2J1y7rg+VYewjHeXrOxrWxuv+2azfqWPZtWa6Kbrmefp9eHfWM+nzl6bA1hdW1eSFsHmAts163FvsZtey9meen4NH+UudV2XcDmzWHfnsuz61nktW/rN+W4tfWvrYftPfldHZb9QigiIiIiIiIi4sLoC6GIiIiIiIiIiAujL4QiIiIiIiIiIi6MO+EQ+uCDD86cFZZlfPz48dk2s5v0ZTD/xzwec7vmzbFsJ7OjKzeJ5VZ5Lu5n/p3ZRuY0LbdquVHLp3/RMHvJurQcq2We6aNifTGDzJyr5ZCZaV5lmInlQnlv9LpYP+U4YVnpwrJ+zb7CurW+xLqjR8f8WNaXLZ9v437XQcTtOfO8640xb5nNUTyedWE+KfYNzjvctnFjdWmuLnN9WR6ffdmcKezLbC+eb1X+6+vr5bEr38kYb7eNjSPzPPB6PJ59byevzvPtumLiy+E4jrMxteujsPnH+qz1KXN6PXv27Gx7x1tjTi5zAnHu5PE2l7HuzGVnTiJinka7/q5L0Fwp5LbzD/sS53I+u+bzm8fEXH483txYVhe7Ljuuf/l58zBa3zVPpPUlWxOai2y1hhrj7ba29pzvx/yqxOY4wnu1vmPXszWNufvMIWR1zXHENY+9Z5s7bNeJZB451v8Mx7W5uHgtexeyccHrGTbPWF+Yj88hFBERERERERERb+gLoYiIiIiIiIiIC6MvhCIiIiIiIiIiLow74xCa/SfMFjKPxywjc7TMFPN8zNBZltO8OszvcZvOiPl+mHOcXUpjvJ2LJCzLbp7dvCeEWUjLjZrXxbYtT2/lY/3u+jWs/ixzvapvHsuysN8yP866YF9hvpplYaaX/ZTX57iyvDz7Iuue+23c2zgkq6zsGN5Xdl1irM+Vz8rGEdvaHELE+rnlv3l++qTYVzgn0yFkDqLdccT95pFg/THDzfre9fbsOqBm2G94Lebd2XY8nmXhNttilTe/id15Yd7OIXQ3OY5j6Tjj/GHbNn7NjWiYi4Tl5xiZ/XmcK/hZXstcdLtuO2LPTc6trEvzsJj7hOx6Xcy5ZH3DyrfrKFr5NfgcMm/JjqvjXY5nv7Xnuq3h2DdsnWHn5/2Y45Tb9q7FZ5PVJ8eerQPMybQD791cfOYb3b13mzc4T/HeWX4ez3HMvmReHcKxZX4rls/ebdnW9t4/X5/rWWtLznncb+8G1va8N26zL9k8sur3OYQiIiIiIiIiIuINfSEUEREREREREXFh9IVQRERERERERMSFcSccQvfu3TvzGlg2kXlwZh2ZM+X2TddfYRloyzxz/+yEsNwoc5TMu1tZWZe7Zbd8vOXliTmBdh1Clh83f9Runt4y21Yfc3nZFmwrurEsl8q6oSuEmPtqN6NrPqbdujdnko1r5nRXbpMx3m47tjXLa16MlY/AvAu8N/Mv8drEfE88v/mV2Nbcz+uxLz558uRsm/Ma697cZ+ZAsrHxrW99a3m85d15/dW8w7rhOH/27NnZNp93zL+bf4VtxfPZPGTZfo6LVV/YnW/jy2MeU+bKIzvuvDF8vLLPmsOB8yXnw9Wza/YJ3bQ9+y3HeLt/s2y2JjDfETGvIV0d5nnh3MbzW1sScwbZGs6Ot+f0rldyrm/zEvI5sbvmYb+87fxn/qarq6uzbdYN537reyw/n8vmOmR97TpK7Xr2ed7f3J7m9rL1oq2BWPbd89t7qe1nX7P3WML74Txi63Ne3+YZYu9u3DZ34vwMsLW8+UN5bXveEHN18RliPine68rlxX57dp3P3RMRERERERERET+R9IVQRERERERERMSF0RdCEREREREREREXxp1wCI1xni1lFpF5Oma6zX3CzBzzeHQyMBtp2U7LWDPvN5eHmV7eO3OozAZaztOykcweWnafWB6edWV5cXOl7Pqc7Py7DiTLTN8Gy8oTy9qz7zCTa74pbu/eqx3P8lpe3XxY5sEh3L/ryzK3A/0D8/1Y/nqVf77pWjwfP299ZbfvmZeGmIeG85h5Lqz+zItjc/hue5gTap6XWLcff/zx2Tbn8Nv6iziObM42V5j1pdUcbPNrvB+O4zjrw2xTOr52XSnsc7NHcQz3P3AuZR+0+Y/jeS6PlY1OIXMCEa5fWTe7ax7uZ1vwejze3IO7zh8rL7G51/xTu06m1frb7tV8puYKMfcIsecy+7n53ux9wdYV9lyzdYi1Hfsqy89xz+PZ9vTxreYle8+zdyVre7K7nrfnLOuC27YGNP+VuQF5PrbV06dPx4rddy/uN//s6no2R+w661iXtmay93x7N7F3n1V5VvXUL4QiIiIiIiIiIi6MvhCKiIiIiIiIiLgw7kxkbMZ+Osefa/GncvwznPy5l/3JOB5vP2E1Vj/pt596Mc5mf/bc4muM2/F63LY/b23RDvvZJX+WaNGO1U+/x/A/+cry8Cemuz+fJvazxbm89mfY7afQFomyn6JzHDH2Y/3e6sr+tDDHKf9kKtvGfr5sfdn+jOVuBM1+wrqapziOif2c1376zXFGdv/kKOch+4mt/UlWizHYuOf9sn4sQmbzgvUtjlX7qfxcXqt7tp39XNj+FPHun7m2n3KzPHZ+mzPj/XPv3r2z+Ylzsa0zrq+vz7Y5X9if2d39k8sW91iNP8L+zPHHCBmfI48fPz7btrnBniMWxbBoC9uKcT9ebzcSetv9FlmzvsLPs34tAjtjfxrb2srWVDw/65rbfE7yeqwb+/PTFn1h37Y1n0VHLapp0c/dmL71Nd7P/P6zG1XkftYd340sSmrrVXsPtbrYjVTZc962bR1g7zd2v/buZu++c3nZT6zfsuwW++X6fne9yfJxnrDr8xkw38/q2dovhCIiIiIiIiIiLoy+EIqIiIiIiIiIuDD6QigiIiIiIiIi4sK4Mw6hOeNmuVBmF5ndZM7Vcrv2Zzx5fsv1Wh5+vleey/7E6e6fXf/GN76xLDszz3YvzEbyeswuPnv2bLmfnzd3h2WazWtjvilezzLT5jpZZdQtx2o50t0/q25/KpH5bnohiHljrG+wbpkJppuBfc+cP3QHmJfCfFjmu7I/2Tpvs+5sTiK8N/qfWFbzuNBdwLZi3zHvmjmNdvPvuz4tmzesL9j5zJ1A5r5h9767bc8Ay+7f9s9Gm/9pvvdd/168Hzg3sw9ZH7D5zP40OrE/02s+uNX8xLmMcymvxbmQ8F74XGXdmBvDvCiGPadYHnN0mtOT+9l3yO6fsbf7t7l47qs2r/Patv6243edm9w2d4h5aawu2Re4BtydB8ynxc/bWOH5rS+snE9c+1u/Z7+kj9XWPNzmmsvmIVv/2xxqa7Bdh+muK8ze4wnLY2sHW7PN2yvHzk1l3V0/2hpr993OXGBWvrlvr+qxXwhFRERERERERFwYfSEUEREREREREXFh9IVQRERERERERMSFcSccQsdxnGVBLb/H/N9HH320PD+ziMxucvvly5dn25azNV8Ft+fzrRwzY4zx4sWLs23mOO3zzEYy98oc7aqsY3jm1/L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" ] From 288633f4382a3b4ad15e8109589dfaf41940b281 Mon Sep 17 00:00:00 2001 From: Soan Kim <39689481+SoanKim@users.noreply.github.com> Date: Wed, 3 Jul 2024 18:41:04 +0900 Subject: [PATCH 05/25] =?UTF-8?q?#=20blurry=5Fvision.ipynb:=20=E2=80=9Ccat?= =?UTF-8?q?s-and-dogs.zip=E2=80=9D=20has=20zero=20bytes,=20and=20the=20dow?= =?UTF-8?q?nloadable=20link=20doesn=E2=80=99t=20exist.=20404=20-=20File=20?= =?UTF-8?q?or=20directory=20not=20found?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit # cellular_segmentation.ipynb: "numpy long type issue" # lunar_lanter: swig should be installed before gym[box2d] to avoid the error --- projects/Neuroscience/blurry_vision.ipynb | 4 ++-- projects/Neuroscience/cellular_segmentation.ipynb | 9 ++++++--- projects/ReinforcementLearning/lunar_lander.ipynb | 2 +- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/projects/Neuroscience/blurry_vision.ipynb b/projects/Neuroscience/blurry_vision.ipynb index 320e6d77d..ee68c5f6d 100644 --- a/projects/Neuroscience/blurry_vision.ipynb +++ b/projects/Neuroscience/blurry_vision.ipynb @@ -60,7 +60,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Building wheel for torch-intermediate-layer-getter (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + " Building wheel for torch-intermediate-layer-getter (setup.py) ... \u001B[?25l\u001B[?25hdone\n" ] } ], @@ -1877,7 +1877,7 @@ "# # Download the Data\n", "# if \"cats-and-dogs.zip\" not in os.listdir():\n", "# !wget --no-check-certificate \\\n", - "# \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\" \\\n", + "# \"https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_5340.zip\" \\\n", "# -O \"cats-and-dogs.zip\"\n", "\n", "# local_zip = 'cats-and-dogs.zip'\n", diff --git a/projects/Neuroscience/cellular_segmentation.ipynb b/projects/Neuroscience/cellular_segmentation.ipynb index 7903fa871..39ea6c19b 100644 --- a/projects/Neuroscience/cellular_segmentation.ipynb +++ b/projects/Neuroscience/cellular_segmentation.ipynb @@ -304,14 +304,14 @@ "labels_train = np.zeros((len(masks_train), 2,\n", " masks_train.shape[-2],\n", " masks_train.shape[-1]),\n", - " np.long)\n", + " np.longlong)\n", "labels_train[:, 0] = masks_train == 0\n", "labels_train[:, 1] = masks_train > 0\n", "\n", "labels_test = np.zeros((len(masks_test), 2,\n", " masks_test.shape[-2],\n", " masks_test.shape[-1]),\n", - " np.long)\n", + " np.longlong)\n", "labels_test[:, 0] = masks_test == 0\n", "labels_test[:, 1] = masks_test > 0" ] @@ -831,12 +831,15 @@ " for ibatch in np.arange(0, n_train, batch_size):\n", " # augment the data\n", " inds = np.arange(ibatch, min(n_train, ibatch+batch_size))\n", + " train_data = train_data.astype(np.float32)\n", + " train_labels = train_labels.astype(np.float32)\n", " imgs, lbls, _ = random_rotate_and_resize(train_data[inds],\n", " train_labels[inds])\n", "\n", " # transfer to torch + GPU\n", " imgs = torch.from_numpy(imgs).to(device=device)\n", " lbls = torch.from_numpy(lbls).to(device=device)\n", + " lbls = lbls.long()\n", "\n", " # compute the loss\n", " y = net(imgs)\n", @@ -1379,7 +1382,7 @@ "from tifffile import imread\n", "\n", "fname = \"gt1.tif\"\n", - "url = \"https://www.suite2p.org/test_data/gt1.tif\"\n", + "url = \"https://www.suite2p.org/test_data/gt1.tif\" # This URL does not exist.\n", "\n", "if not os.path.isfile(fname):\n", " try:\n", diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 8106a6c89..7622f5b5a 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -102,12 +102,12 @@ "!pip install rarfile --quiet\n", "!pip install stable-baselines3[extra] --quiet\n", "!pip install ale-py --quiet\n", + "!pip install -q swig --quiet\n", "!pip install gym[box2d] --quiet\n", "!pip install pyvirtualdisplay --quiet\n", "!pip install pyglet --quiet\n", "!pip install pygame --quiet\n", "!pip install minigrid --quiet\n", - "!pip install -q swig --quiet\n", "!pip install -q gymnasium[box2d] --quiet\n", "!pip install 'minigrid<=2.1.1' --quiet\n", "!pip3 install box2d-py --quiet" From 02925083085e48890ca6b848180484490a68725d Mon Sep 17 00:00:00 2001 From: Soan Kim <39689481+SoanKim@users.noreply.github.com> Date: Sat, 6 Jul 2024 06:50:14 +0900 Subject: [PATCH 06/25] suppressed excessive root-user warning messages --- projects/ReinforcementLearning/human_rl.ipynb | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/projects/ReinforcementLearning/human_rl.ipynb b/projects/ReinforcementLearning/human_rl.ipynb index ae9d35d7e..e72d4367f 100644 --- a/projects/ReinforcementLearning/human_rl.ipynb +++ b/projects/ReinforcementLearning/human_rl.ipynb @@ -64,24 +64,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.1 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "chex 0.1.81 requires numpy>=1.25.0, but you have numpy 1.23.3 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" + "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.1 which is incompatible.\u001B[0m\u001B[31m\n", + "\u001B[0m\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "chex 0.1.81 requires numpy>=1.25.0, but you have numpy 1.23.3 which is incompatible.\u001B[0m\u001B[31m\n", + "\u001B[0m" ] } ], "source": [ "# @title Install dependencies\n", - "!pip install jedi --quiet\n", - "!pip install --upgrade pip setuptools wheel --quiet\n", - "!pip install dm-acme[jax] --quiet\n", - "!pip install dm-sonnet --quiet\n", - "!pip install trfl --quiet\n", - "!pip install numpy==1.23.3 --quiet --ignore-installed\n", - "!pip uninstall seaborn -y --quiet\n", - "!pip install seaborn --quiet" + "!pip install jedi --quiet --root-user-action=ignore\n", + "!pip install --upgrade pip setuptools wheel --quiet --root-user-action=ignore\n", + "!pip install dm-acme[jax] --quiet --root-user-action=ignore\n", + "!pip install dm-sonnet --quiet --root-user-action=ignore\n", + "!pip install trfl --quiet --root-user-action=ignore\n", + "!pip install numpy==1.23.3 --quiet --ignore-installed --root-user-action=ignore\n", + "!pip uninstall seaborn -y --quiet --root-user-action=ignore\n", + "!pip install seaborn --quiet --root-user-action=ignore" ] }, { From 36080adc1539e96ecc4e7ba493dd2199ce7dd996 Mon Sep 17 00:00:00 2001 From: dalia-nasr Date: Sat, 6 Jul 2024 17:55:20 +0300 Subject: [PATCH 07/25] restarted kernel and cleared output cells --- .../sentiment_analysis.ipynb | 4641 ++++++++++++++++- 1 file changed, 4640 insertions(+), 1 deletion(-) diff --git a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb index a6f073666..eb393529a 100644 --- a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb +++ b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb @@ -1 +1,4640 @@ -{"cells":[{"cell_type":"markdown","metadata":{"execution":{},"id":"view-in-github"},"source":["\"Open   \"Open"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"D_fgc45VfjDz"},"source":["# Twitter Sentiment Analysis\n","\n","**By Neuromatch Academy**\n","\n","__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n","\n","__Production editors:__ Amita Kapoor, Spiros Chavlis"]},{"cell_type":"markdown","metadata":{"execution":{}},"source":["---\n","# Welcome to the NLP project template\n","\n",""]},{"cell_type":"markdown","metadata":{"execution":{}},"source":["---\n","# Step 1: Questions and goals\n","\n","* Can we infer emotion from a tweet text?\n","* How words are distributed accross the dataset?\n","* Are words related to one kind of emotion?"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"Vd1qdNW9fjD1"},"source":["---\n","# Step 2: Literature review\n","\n","[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n","\n","[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"oOYDQElpfjD2"},"source":["---\n","# Step 3: Load and explore the dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":103706,"status":"ok","timestamp":1720042135196,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"EZpxSExUfjD2","outputId":"19b01445-9b83-4a93-9cc2-7830ab0dcf5b"},"outputs":[],"source":["# @title Install dependencies\n","!pip install pandas --quiet\n","!pip install torchtext --quiet\n","!pip install datasets --quiet"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":9008,"status":"ok","timestamp":1720042144200,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"DxqD3Tk5fjD3","outputId":"451d68c5-7894-4f93-9f54-bf0b7f482e20"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/torchtext/data/__init__.py:4: UserWarning: \n","/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n","Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n"," warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n"]}],"source":["# We import some libraries to load the dataset\n","import os\n","import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","\n","from datasets import load_dataset\n","\n","from collections import Counter\n","from tqdm.notebook import tqdm\n","\n","import torch\n","import torch.nn as nn\n","import torch.optim as optim\n","import torch.nn.functional as F\n","from torch.utils.data import TensorDataset, DataLoader\n","\n","import torchtext\n","from torchtext.data import get_tokenizer\n","\n","from sklearn.utils import shuffle\n","from sklearn.metrics import classification_report\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split\n","from sklearn.feature_extraction.text import CountVectorizer"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"63Eg1SLbfjD4"},"source":["You can find the dataset we are going to use in [this website](http://help.sentiment140.com/for-students/)."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":567,"referenced_widgets":["fbb4191426bd485e8e965b6d432eecae","df7eba182d1b4c21bc21d157eac6b996","6d64402d9da74516ab4e1d46ae9f1ee3","d9ca809f7b1c49e595a05458251f3ab2","90908b6f69524a72860214ef8bd2d946","db432a2cd6244a7592fc9732f0ca4738","84485541f3a14c65a67d10a97b72bbad","5fa7ab2ab2004e5cb692199e2bd27d6b","ab71bd2b452146829e973d6cf99f31ed","55ba92cfe0724286ac1c2bbe6577e5c8","67a4fa49ca5349d58512a16a3742d401","afd671543846468abfe37669a72845c3","057e918ace004506aedc4e4b9942c3a8","325387f6b62d47b0b21bea61676cea72","ea1e3eb0e6ec4f8d82cf9b12cfe6e700","96c2d7ee644a438982e1792b7ec0453c","9baa1a735c0646b89953bf4a7c7fc92c","0ac9711c8ece4c5397a8cd810713adfb","a8d69769921241b8b1081e84f7770858","d189f24b0e964d1a9fc86379bad38cca","db9bf44dec914db793cc4f73751c272c","1cf3ba0f756f4aa5ad1dcb675a791cfa","c432c4efcb794ce781fcb6f176f1b60d","510eeffb32694e7798f23e3931d7a943","a8b3dfaa2831416582d8eeef01451386","db1cdafaf36f4c339476f3221abc17b3","ffd3778a96e046718828bbc5aa73f173","49c5a3fbe87b491cb3f0f450a0af0659","252949e8784c4878a62eb2e30b1e3466","7bcef602e7f441308472bc145b12dcd3","97fb30a5a31742efa1d188b9361f9938","9b34daddb9cc48bba109e547177ec654","fd2b5a6533794a2794579956c25247fb","f3a9667c8c994324a2409f227bd0a1e9","6e6c5372ffe045c0b72587989567429e","2ead0216695e4227aef44552f4ec3cc9","53843f49adda4bce8450fd91fa9fd587","40262cb3eefa45fcbe37aaafccb69f5f","b54b826314ea4b3a92eebd218c093fc1","8cd7be688b8c4818be48915db14a0792","a9a0f6ce71ed415c8c8513f68e34e162","7f638a6deacd42e88c031fa47797516b","849e39cc86f64e558ff94bf542a5121a","67b0b03c391c414bad5ea9fb3c947a2f","1cef38981af6457dbaeb393f9936a389","b0b5cfae51214c60bbca9a09b196c217","5ee2a4b33be04c6db8ee4d7995c2376d","403fffb635c2409ebeabc90063750ed3","6279343019064572adedf34cfbd437fa","2715d00db77545f9aa5eace8a0eb2839","942ce490d87347c789e229589b1b9c9f","f04df4daeb6049ab85d3d75b472ccf6e","fd0b3c53b66543cea0c396d8047445a8","2c42e2fef6314c9e842a7e9641af3cab","913d95e58aa94e4a8009768a23fbf304"]},"execution":{},"executionInfo":{"elapsed":189390,"status":"ok","timestamp":1720042333586,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"3HLOsd3rfjD4","outputId":"7653fee1-a871-472b-a978-d8ec0250dc84"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fbb4191426bd485e8e965b6d432eecae","version_major":2,"version_minor":0},"text/plain":["Downloading builder script: 0%| | 0.00/4.03k [00:00\n","
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polarityuserdatequeryusertext
00_TheSpecialOne_Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
10scotthamiltonMon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
20mattycusMon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
30ElleCTFMon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
40KaroliMon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n"," \n"],"text/plain":[" polarity user date query \\\n","0 0 _TheSpecialOne_ Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n","1 0 scotthamilton Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n","2 0 mattycus Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n","3 0 ElleCTF Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","4 0 Karoli Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n","\n"," user text \n","0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n","1 scotthamilton is upset that he can't update his Facebook by ... \n","2 mattycus @Kenichan I dived many times for the ball. Man... \n","3 ElleCTF my whole body feels itchy and like its on fire \n","4 Karoli @nationwideclass no, it's not behaving at all.... "]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["# We load the dataset\n","\n","dataset = load_dataset(\"stanfordnlp/sentiment140\", trust_remote_code= True)\n","\n","train_data = dataset[\"train\"]\n","df = pd.DataFrame(train_data)\n","df = df.rename(columns={'sentiment': 'polarity'})\n","df = df[['polarity', 'user', 'date', 'query', 'user', 'text']]\n","df.head()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"fuKShcfjfjD4"},"source":["For this project we will use only the text and the polarity of the tweet. Notice that polarity is 0 for negative tweets and 4 for positive tweet."]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{},"executionInfo":{"elapsed":1059,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"GXHQOn6gfjD5"},"outputs":[],"source":["X = df.text.values\n","\n","# Changes values from [0,4] to [0,1]\n","y = (df.polarity.values > 1).astype(int)\n","\n","\n","# Split the data into train and test\n","x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"7kr3TO_LfjD5"},"source":["The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"FsL-xY03fjD5","outputId":"655f0ef8-c177-4f42-c024-1d628241401a"},"outputs":[{"name":"stdout","output_type":"stream","text":["1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n","0: worst headache ever \n","0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n","1: doesn't know how to spell conked \n","0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n"]}],"source":["for s, l in zip(x_train_text[:5], y_train[:5]):\n"," print('{}: {}'.format(l, s))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"4cPGXSc-fjD5"},"source":["An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first."]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":9,"status":"ok","timestamp":1720042334642,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"U1OugpZ0fjD5","outputId":"9e6cb4e3-8d8c-4db0-c113-bdd4fe87db5f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before Tokenize: worst headache ever \n","After Tokenize: ['worst', 'headache', 'ever']\n"]}],"source":["tokenizer = get_tokenizer(\"basic_english\")\n","\n","print('Before Tokenize: ', x_train_text[1])\n","print('After Tokenize: ', tokenizer(x_train_text[1]))"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["e1348a02ceeb4af19fbd63d52b7d843b","fbf51b14e6b34d0485ddf59c43d22c49","c29e06a72ac9401b8c41f4195021071e","48b812211db04284bfbbf02823fb879a","5455119809c74916acc50e1905903ded","2475bd62a3224bacb38a6334d07d6a8c","3d29947b5d2d4e2abc1355d900096642","3f7a8f56f15c434da70029366a37167a","3610a2db297f4686bf9043f2b7ee55b5","a1bd0616199e44538977ee2ea6049690","835fb9a91b34471fa6d61adf37616f52","d8de1a85076b453a92295e79110ba8fd","78d48ee2fb9f42089f475fcf5fc368c8","b0ca3012d0b84c5a9d7c1fc176251af7","39fa73efcbf54d8dad225d8380061dbf","6b6cc35257fe433e93736d02e898b6b8","e0fc900d8b5940a6bd6a97e58adb4651","6b7286d74e0f4a0199dbfcaf3dd0d622","a4bbd3df99cd4acab5e1b3ba5cd7c114","9a7140a6197945d5bac5c48b820dfb04","0bdc146792a64853ae06a9d185aa2b15","768da964ffcd44fea1af09e81f5621f3"]},"execution":{},"executionInfo":{"elapsed":29122,"status":"ok","timestamp":1720042363757,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"7ZggzGCXfjD6","outputId":"ae19f8d6-224d-4224-d3a0-d00c659ec9b2"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"e1348a02ceeb4af19fbd63d52b7d843b","version_major":2,"version_minor":0},"text/plain":[" 0%| | 0/1280000 [00:00"]},"metadata":{},"output_type":"display_data"}],"source":["plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n","plt.show()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"o9IYA0cZfjD7"},"source":["It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"5FQIOqoRfjD7"},"source":["Usually the number of words in the dictionary will be very large.\n","\n","Here are some thing we can do to reduce that number:\n","\n","* Remove puntuation.\n","* Remove stop-words.\n","* Steaming.\n","* Remove very uncommon words (the words that appears in fewer than N occations).\n","* Nothing: we can use a pretrain model that handles this kind of situations.\n","\n","\n","We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n","\n","* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n","* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n","* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"_ul5MgYcfjD7"},"source":["---\n","# Step 4: choose toolkit\n","\n","Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n","\n","* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n","* A simple Embedding + RNN."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"GteI1PxTfjD7"},"source":["## Logistic regression\n","\n","We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)."]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{},"executionInfo":{"elapsed":22699,"status":"ok","timestamp":1720042396408,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"S_ei2qu8fjD7"},"outputs":[],"source":["vectorizer = CountVectorizer(binary=True)\n","x_train_cv = vectorizer.fit_transform(x_train_text)\n","x_test_cv = vectorizer.transform(x_test_text)"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":17,"status":"ok","timestamp":1720042396409,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"iK_zfqnLfjD7","outputId":"9b3f6db3-01bf-4246-b943-359620c717a2"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before Vectorize: doesn't know how to spell conked \n"]}],"source":["print('Before Vectorize: ', x_train_text[3])"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042396409,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"wKxY8e38fjD8","outputId":"19530135-070d-4259-d6a9-7ba06b519763"},"outputs":[{"name":"stdout","output_type":"stream","text":["After Vectorize: \n"," (0, 528584)\t1\n"," (0, 165468)\t1\n"," (0, 300381)\t1\n"," (0, 242211)\t1\n"," (0, 489893)\t1\n"," (0, 134160)\t1\n"]}],"source":["# Notice that the matriz is sparse\n","print('After Vectorize: ')\n","print(x_train_cv[3])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"QTPPEMd9fjD8"},"source":["Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)."]},{"cell_type":"code","execution_count":14,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"execution":{},"executionInfo":{"elapsed":127277,"status":"ok","timestamp":1720042523682,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"2vEPOQS6fjD8","outputId":"3be77fc0-76e6-40b8-8847-5f6e7c6c0ce0"},"outputs":[{"data":{"text/html":["
LogisticRegression(solver='saga')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
"],"text/plain":["LogisticRegression(solver='saga')"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["model = LogisticRegression(solver='saga')\n","model.fit(x_train_cv, y_train)"]},{"cell_type":"code","execution_count":15,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1720042523683,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"37bUbqB6fjD8","outputId":"7eb9178d-6130-47d0-bdf4-ce4be164bc97"},"outputs":[{"name":"stdout","output_type":"stream","text":[" precision recall f1-score support\n","\n"," 0 0.81 0.79 0.80 160000\n"," 1 0.79 0.81 0.80 160000\n","\n"," accuracy 0.80 320000\n"," macro avg 0.80 0.80 0.80 320000\n","weighted avg 0.80 0.80 0.80 320000\n","\n"]}],"source":["y_pred = model.predict(x_test_cv)\n","\n","print(classification_report(y_test, y_pred))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"161kDLhofjD8"},"source":["## Explainable AI\n","The best thing about logistic regresion is that it is simple, and we can get some explanations."]},{"cell_type":"code","execution_count":16,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":1105,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"EILTmxzifjD9","outputId":"b7ce6853-7385-4a24-d4eb-e6d0843ca5d5"},"outputs":[{"name":"stdout","output_type":"stream","text":["(1, 589260)\n","589260\n"]}],"source":["print(model.coef_.shape)\n","print(len(vectorizer.vocabulary_))\n","\n","words_sk = list(vectorizer.vocabulary_.keys())\n","words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])"]},{"cell_type":"code","execution_count":17,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"NGjVPON6fjD9","outputId":"d40443bc-476d-4f5a-ce90-4b5b17e47933"},"outputs":[{"name":"stdout","output_type":"stream","text":["roni: -3.8625952420933984\n","inaperfectworld: -3.5734321547933936\n","dontyouhate: -3.5002133484207576\n","xbllygbsn: -3.4126303898325787\n","anqju: -3.3363997631497493\n","sad: -3.200516823534637\n","pakcricket: -3.1949062976331675\n","condolences: -3.132503698316079\n","heartbreaking: -3.0665219866881297\n","saddest: -3.042020604188048\n","sadd: -3.029036146667248\n","heartbroken: -3.0287524416643463\n","boohoo: -3.0226033087262802\n","sadface: -2.991829110065316\n","rachelle_lefevr: -2.925076661509848\n","disappointing: -2.902522686643491\n","lvbu: -2.8947109582208865\n","saddens: -2.8855187276040715\n","bummed: -2.836500453805889\n","neda: -2.792917726280752\n"]}],"source":["for w in words_sk[:20]:\n"," print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"oxQ_jlNRfjD9","outputId":"363de58b-817a-4205-f019-2379d0d64e0d"},"outputs":[{"name":"stdout","output_type":"stream","text":["iamsoannoyed: 2.8493838469077013\n","myfax: 2.7974330510971424\n","jennamadison: 2.5667217237933104\n","yeyy: 2.4780234846131646\n","tryout: 2.438315611477797\n","goldymom: 2.4374072779309204\n","wooohooo: 2.402957513257194\n","thesupergirl: 2.356525094856456\n","iammaxathotspot: 2.3116551216589682\n","londicreations: 2.3074264075299316\n","smilin: 2.2991796213822497\n","worries: 2.2899555142510084\n","sinfulsignorita: 2.27989578448778\n","finchensnail: 2.2642827277181063\n","smackthis: 2.237672991997692\n","kv: 2.2157591386122775\n","tojosan: 2.2117938132889696\n","russmarshalek: 2.20953890861265\n","traciknoppe: 2.1768232307222153\n","congratulations: 2.1715901103136876\n"]}],"source":["for w in reversed(words_sk[-20:]):\n"," print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"9KSSAC3qfjD9"},"source":["What does this mean?\n","\n","Remember the `model.coef_` is the $W$ in:\n","\n","$$h(x)=\\sigma(WX + b)$$\n","\n","where the label 1 is a positive tweet and the label 0 is a negative tweet."]},{"cell_type":"markdown","metadata":{"execution":{},"id":"oDHjTP2_fjD9"},"source":["## Recurrent Neural Network with Pytorch"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"TbgpKy95fjD9"},"source":["In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n","\n","In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n","\n","Let's start by importing the relevant libraries.\n"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"7nmUJV99fjEB"},"outputs":[],"source":["def set_device():\n"," device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n"," if device != \"cuda\":\n"," print(\"WARNING: For this notebook to perform best, \"\n"," \"if possible, in the menu under `Runtime` -> \"\n"," \"`Change runtime type.` select `GPU` \")\n"," else:\n"," print(\"GPU is enabled in this notebook.\")\n","\n"," return device"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"chI-18LcfjEB","outputId":"7f633079-6548-48f3-802e-94bc9cfada93"},"outputs":[{"name":"stdout","output_type":"stream","text":["GPU is enabled in this notebook.\n"]}],"source":["# Set the device (check if gpu is available)\n","device = set_device()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"01UtIN7ofjEC"},"source":["First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences."]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042524784,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"afus9SyUfjEC","outputId":"bb4eb869-e2f0-4ccd-f64c-e55908272345"},"outputs":[{"data":{"text/plain":["['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']"]},"execution_count":21,"metadata":{},"output_type":"execute_result"}],"source":["# From previous section, we have a list with the most used tokens\n","sorted_words[:10]"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"6vfQFjaufjEC"},"source":["Let's select only the most used."]},{"cell_type":"code","execution_count":22,"metadata":{"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"tGLkxaGcfjEC"},"outputs":[],"source":["num_words_dict = 30000\n","# We reserve two numbers for special tokens.\n","most_used_words = sorted_words[:num_words_dict-2]"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"AzhQvekCfjEC"},"source":["We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)."]},{"cell_type":"code","execution_count":23,"metadata":{"execution":{},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"73Wrb-lEfjEC"},"outputs":[],"source":["# dictionary to go from words to idx\n","word_to_idx = {}\n","# dictionary to go from idx to words (just in case)\n","idx_to_word = {}\n","\n","\n","# We include the special tokens first\n","PAD_token = 0\n","UNK_token = 1\n","\n","word_to_idx['PAD'] = PAD_token\n","word_to_idx['UNK'] = UNK_token\n","\n","idx_to_word[PAD_token] = 'PAD'\n","idx_to_word[UNK_token] = 'UNK'\n","\n","# We popullate our dictionaries with the most used words\n","for num,word in enumerate(most_used_words):\n"," word_to_idx[word] = num + 2\n"," idx_to_word[num+2] = word"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"kMHVkEisfjEC"},"source":["Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model."]},{"cell_type":"code","execution_count":24,"metadata":{"execution":{},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1720042524785,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"tkCIu3PKfjED"},"outputs":[],"source":["# A function to convert list of tokens to list of indexes\n","def tokens_to_idx(sentences_tokens,word_to_idx):\n"," sentences_idx = []\n"," for sent in sentences_tokens:\n"," sent_idx = []\n"," for word in sent:\n"," if word in word_to_idx:\n"," sent_idx.append(word_to_idx[word])\n"," else:\n"," sent_idx.append(word_to_idx['UNK'])\n"," sentences_idx.append(sent_idx)\n"," return sentences_idx"]},{"cell_type":"code","execution_count":25,"metadata":{"execution":{},"executionInfo":{"elapsed":9346,"status":"ok","timestamp":1720042534127,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"aHru4vpzfjED"},"outputs":[],"source":["x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n","x_test_idx = tokens_to_idx(x_test_token,word_to_idx)"]},{"cell_type":"code","execution_count":26,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":8,"status":"ok","timestamp":1720042534127,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"Ofj3OD7zfjED","outputId":"b2788d03-dbfa-41d7-8231-5011206baa59"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before converting: ['worst', 'headache', 'ever']\n","After converting: [721, 458, 237]\n"]}],"source":["some_number = 1\n","print('Before converting: ', x_train_token[some_number])\n","print('After converting: ', x_train_idx[some_number])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"NcCicvb-fjED"},"source":["We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:"]},{"cell_type":"code","execution_count":27,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1720042534128,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"BSjhdyYUfjED","outputId":"82e49be9-7868-44ac-b496-c7a48da1efee"},"outputs":[{"name":"stdout","output_type":"stream","text":["Max tweet word length: 229\n","Mean tweet word length: 15.0\n","99% percent under: 37.0\n"]}],"source":["tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n","print('Max tweet word length: ',tweet_lens.max())\n","print('Mean tweet word length: ',np.median(tweet_lens))\n","print('99% percent under: ',np.quantile(tweet_lens,0.99))"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"t311WY6ZfjEE"},"source":["We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter."]},{"cell_type":"code","execution_count":28,"metadata":{"execution":{},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1720042534128,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"r4S8KTWLfjEE"},"outputs":[],"source":[" # We choose the max length\n"," max_length = 40\n","\n","# A function to make all the sequence have the same lenght\n","# Note that the output is a Numpy matrix\n"," def padding(sentences, seq_len):\n"," features = np.zeros((len(sentences), seq_len),dtype=int)\n"," for ii, tweet in enumerate(sentences):\n"," len_tweet = len(tweet)\n"," if len_tweet != 0:\n"," if len_tweet <= seq_len:\n"," # If its shorter, we fill with zeros (the padding Token index)\n"," features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n"," if len_tweet > seq_len:\n"," # If its larger, we take the last 'seq_len' indexes\n"," features[ii, :] = np.array(tweet)[-seq_len:]\n"," return features"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{},"executionInfo":{"elapsed":4762,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"Z-Cw-bBxfjEE"},"outputs":[],"source":["# We convert our list of tokens into a numpy matrix\n","# where all instances have the same lenght\n","x_train_pad = padding(x_train_idx,max_length)\n","x_test_pad = padding(x_test_idx,max_length)\n","\n","# We convert our target list a numpy matrix\n","y_train_np = np.asarray(y_train)\n","y_test_np = np.asarray(y_test)"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":12,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"8eC3YswlfjEE","outputId":"3bb0ea7f-518f-4545-9241-feb783f48122"},"outputs":[{"name":"stdout","output_type":"stream","text":["Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n","After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n"," 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n"," 150 4 22 5 18 667 3 138 61 7 3296 4]\n"]}],"source":["some_number = 2\n","print('Before padding: ', x_train_idx[some_number])\n","print('After padding: ', x_train_pad[some_number])"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"SzDhnauUfjEE"},"source":["Now, let's convert the data to pytorch format.\n"]},{"cell_type":"code","execution_count":31,"metadata":{"execution":{},"executionInfo":{"elapsed":10,"status":"ok","timestamp":1720042538886,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"--Yd22YWfjEF"},"outputs":[],"source":["# create Tensor datasets\n","train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n","valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n","\n","# Batch size (this is an important hyperparameter)\n","batch_size = 100\n","\n","# dataloaders\n","# make sure to SHUFFLE your data\n","train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n","valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"jQ5qPOWTfjEF"},"source":["Each batch of data in our traning proccess will have the folllowing format:"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":598,"status":"ok","timestamp":1720042563992,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"S1mqhk1hfjEF","outputId":"c97e7edd-695f-4336-a2e6-f6bed4852a63"},"outputs":[{"name":"stdout","output_type":"stream","text":["Sample input size: torch.Size([100, 40])\n","Sample input: \n"," tensor([[ 0, 0, 0, ..., 32, 203, 86],\n"," [ 0, 0, 0, ..., 1, 1, 4661],\n"," [ 0, 0, 0, ..., 169, 43, 34],\n"," ...,\n"," [ 0, 0, 0, ..., 2, 2961, 4076],\n"," [ 0, 0, 0, ..., 2319, 1325, 2],\n"," [ 0, 0, 0, ..., 7, 253, 1]])\n","Sample input: \n"," tensor([0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n"," 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n"," 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n"," 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n"," 0, 1, 0, 1])\n"]}],"source":["# Obtain one batch of training data\n","dataiter = iter(train_loader)\n","sample_x, sample_y = dataiter.__next__()\n","\n","print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n","print('Sample input: \\n', sample_x)\n","print('Sample input: \\n', sample_y)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"jn0PzZdGfjEF"},"source":["Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n","\n","* Embedding Layer\n","> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n","\n","\n","* LSTM layer\n","> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":34,"metadata":{"execution":{},"executionInfo":{"elapsed":433,"status":"ok","timestamp":1720042567199,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"vfzcowAxfjEF"},"outputs":[],"source":["class SentimentRNN(nn.Module):\n"," def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n"," super(SentimentRNN,self).__init__()\n","\n"," self.output_dim = output_dim\n"," self.hidden_dim = hidden_dim\n"," self.no_layers = no_layers\n"," self.vocab_size = vocab_size\n"," self.drop_prob = drop_prob\n","\n"," # Embedding Layer\n"," self.embedding = nn.Embedding(vocab_size, embedding_dim)\n","\n"," # LSTM Layers\n"," self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n"," num_layers=no_layers, batch_first=True,\n"," dropout=self.drop_prob)\n","\n"," # Dropout layer\n"," self.dropout = nn.Dropout(drop_prob)\n","\n"," # Linear and Sigmoid layer\n"," self.fc = nn.Linear(self.hidden_dim, output_dim)\n"," self.sig = nn.Sigmoid()\n","\n"," def forward(self,x,hidden):\n"," batch_size = x.size(0)\n","\n"," # Embedding out\n"," embeds = self.embedding(x)\n"," #Shape: [batch_size x max_length x embedding_dim]\n","\n"," # LSTM out\n"," lstm_out, hidden = self.lstm(embeds, hidden)\n"," # Shape: [batch_size x max_length x hidden_dim]\n","\n"," # Select the activation of the last Hidden Layer\n"," lstm_out = lstm_out[:,-1,:].contiguous()\n"," # Shape: [batch_size x hidden_dim]\n","\n"," ## You can instead average the activations across all the times\n"," # lstm_out = torch.mean(lstm_out, 1).contiguous()\n","\n"," # Dropout and Fully connected layer\n"," out = self.dropout(lstm_out)\n"," out = self.fc(out)\n","\n"," # Sigmoid function\n"," sig_out = self.sig(out)\n","\n"," # return last sigmoid output and hidden state\n"," return sig_out, hidden\n","\n"," def init_hidden(self, batch_size):\n"," ''' Initializes hidden state '''\n"," # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n"," # initialized to zero, for hidden state and cell state of LSTM\n"," h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n"," c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n"," hidden = (h0,c0)\n"," return hidden"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"YfrLPa9mfjEF"},"source":["We choose the parameters of the model."]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{},"executionInfo":{"elapsed":471,"status":"ok","timestamp":1720042569608,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"rOm-xoFkfjEG"},"outputs":[],"source":["# Parameters of our network\n","\n","# Size of our vocabulary\n","vocab_size = num_words_dict\n","\n","# Embedding dimension\n","embedding_dim = 32\n","\n","# Number of stacked LSTM layers\n","no_layers = 2\n","\n","# Dimension of the hidden layer in LSTMs\n","hidden_dim = 64\n","\n","# Dropout parameter for regularization\n","output_dim = 1\n","\n","# Dropout parameter for regularization\n","drop_prob = 0.25"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":465,"status":"ok","timestamp":1720042571776,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"xapxpe7ufjEG","outputId":"51c90159-7d2b-4fc4-f34a-98e9901d40e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["SentimentRNN(\n"," (embedding): Embedding(30000, 32)\n"," (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n"," (dropout): Dropout(p=0.25, inplace=False)\n"," (fc): Linear(in_features=64, out_features=1, bias=True)\n"," (sig): Sigmoid()\n",")\n"]}],"source":["# Let's define our model\n","model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n"," embedding_dim, drop_prob=drop_prob)\n","# Moving to gpu\n","model.to(device)\n","print(model)"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1720042571776,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"GEvTs3uwfjEG","outputId":"2e15f6df-2aa8-4665-b2da-7363d2bfa09e"},"outputs":[{"name":"stdout","output_type":"stream","text":["Total Number of parameters: 1018433\n"]}],"source":["# How many trainable parameters does our model have?\n","model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n","params = sum([np.prod(p.size()) for p in model_parameters])\n","print('Total Number of parameters: ',params)"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"Pc2OC5GDfjEG"},"source":["We choose the losses and the optimizer for the training procces."]},{"cell_type":"code","execution_count":38,"metadata":{"execution":{},"executionInfo":{"elapsed":1740,"status":"ok","timestamp":1720042574210,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"iBWjPADUfjEG"},"outputs":[],"source":["# loss and optimization functions\n","lr = 0.001\n","\n","# Binary crossentropy is a good loss function for a binary classification problem\n","criterion = nn.BCELoss()\n","\n","# We choose an Adam optimizer\n","optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n","\n","# function to predict accuracy\n","def acc(pred,label):\n"," pred = torch.round(pred.squeeze())\n"," return torch.sum(pred == label.squeeze()).item()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"OZgMwOe2fjEG"},"source":["We are ready to train our model."]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"execution":{},"executionInfo":{"elapsed":304614,"status":"ok","timestamp":1720042880244,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"3B6YhEocfjEH","outputId":"76276a1f-7775-4b98-aab0-0e199aa133e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1\n","train_loss : 0.4366412344621494 val_loss : 0.3881208170717582\n","train_accuracy : 79.485546875 val_accuracy : 82.475\n","Validation loss decreased (inf --> 0.388121). Saving model ...\n","==================================================\n","Epoch 2\n","train_loss : 0.3760281792609021 val_loss : 0.3713956154882908\n","train_accuracy : 83.186328125 val_accuracy : 83.4575\n","Validation loss decreased (0.388121 --> 0.371396). Saving model ...\n","==================================================\n","Epoch 3\n","train_loss : 0.3574051411205437 val_loss : 0.36425656544510276\n","train_accuracy : 84.19953125 val_accuracy : 83.80375\n","Validation loss decreased (0.371396 --> 0.364257). Saving model ...\n","==================================================\n","Epoch 4\n","train_loss : 0.344456663565943 val_loss : 0.3613302929420024\n","train_accuracy : 84.89265625 val_accuracy : 84.00874999999999\n","Validation loss decreased (0.364257 --> 0.361330). Saving model ...\n","==================================================\n","Epoch 5\n","train_loss : 0.33407817618339325 val_loss : 0.3601334386831149\n","train_accuracy : 85.444921875 val_accuracy : 84.03625\n","Validation loss decreased (0.361330 --> 0.360133). Saving model ...\n","==================================================\n"]}],"source":["# Number of training Epochs\n","epochs = 5\n","\n","# Maximum absolute value accepted for the gradeint\n","clip = 5\n","\n","# Initial Loss value (assumed big)\n","valid_loss_min = np.Inf\n","\n","# Lists to follow the evolution of the loss and accuracy\n","epoch_tr_loss,epoch_vl_loss = [],[]\n","epoch_tr_acc,epoch_vl_acc = [],[]\n","\n","# Train for a number of Epochs\n","for epoch in range(epochs):\n"," train_losses = []\n"," train_acc = 0.0\n"," model.train()\n","\n"," for inputs, labels in train_loader:\n","\n"," # Initialize hidden state\n"," h = model.init_hidden(batch_size)\n"," # Creating new variables for the hidden state\n"," h = tuple([each.data.to(device) for each in h])\n","\n"," # Move batch inputs and labels to gpu\n"," inputs, labels = inputs.to(device), labels.to(device)\n","\n"," # Set gradient to zero\n"," model.zero_grad()\n","\n"," # Compute model output\n"," output,h = model(inputs,h)\n","\n"," # Calculate the loss and perform backprop\n"," loss = criterion(output.squeeze(), labels.float())\n"," loss.backward()\n"," train_losses.append(loss.item())\n","\n"," # calculating accuracy\n"," accuracy = acc(output,labels)\n"," train_acc += accuracy\n","\n"," #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n"," nn.utils.clip_grad_norm_(model.parameters(), clip)\n"," optimizer.step()\n","\n","\n"," # Evaluate on the validation set for this epoch\n"," val_losses = []\n"," val_acc = 0.0\n"," model.eval()\n"," for inputs, labels in valid_loader:\n","\n"," # Initialize hidden state\n"," val_h = model.init_hidden(batch_size)\n"," val_h = tuple([each.data.to(device) for each in val_h])\n","\n"," # Move batch inputs and labels to gpu\n"," inputs, labels = inputs.to(device), labels.to(device)\n","\n"," # Compute model output\n"," output, val_h = model(inputs, val_h)\n","\n"," # Compute Loss\n"," val_loss = criterion(output.squeeze(), labels.float())\n","\n"," val_losses.append(val_loss.item())\n","\n"," accuracy = acc(output,labels)\n"," val_acc += accuracy\n","\n"," epoch_train_loss = np.mean(train_losses)\n"," epoch_val_loss = np.mean(val_losses)\n"," epoch_train_acc = train_acc/len(train_loader.dataset)\n"," epoch_val_acc = val_acc/len(valid_loader.dataset)\n"," epoch_tr_loss.append(epoch_train_loss)\n"," epoch_vl_loss.append(epoch_val_loss)\n"," epoch_tr_acc.append(epoch_train_acc)\n"," epoch_vl_acc.append(epoch_val_acc)\n"," print(f'Epoch {epoch+1}')\n"," print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n"," print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n"," if epoch_val_loss <= valid_loss_min:\n"," print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n"," # torch.save(model.state_dict(), '../working/state_dict.pt')\n"," valid_loss_min = epoch_val_loss\n"," print(25*'==')"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":364},"execution":{},"executionInfo":{"elapsed":894,"status":"ok","timestamp":1720042881135,"user":{"displayName":"Dalia Nasr","userId":"11103095680145801589"},"user_tz":-180},"id":"ttJazP-nfjEH","outputId":"992bed02-611e-4614-c60f-77223d5b801a"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAABkoAAAIQCAYAAADO7zKKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADxbklEQVR4nOzdd3xUVfrH8c/MpJNGKgECCZ1QlRKkWQhVIyAqooKg2HFV1sYKIipi2WWxrfhTEAsuVhQXBAII0psivSeEmkKAhASSycz8/ggMxIQSJuGmfN+vFy+Yc8+997kPE5h7nznnmBwOhwMREREREREREREREZEqyGx0ACIiIiIiIiIiIiIiIkZRoURERERERERERERERKosFUpERERERERERERERKTKUqFERERERERERERERESqLBVKRERERERERERERESkylKhREREREREREREREREqiwVSkREREREREREREREpMpSoURERERERERERERERKosFUpERERERERERERERKTKUqFERERERERERERERESqLBVKREQqsP/85z+YTCZiY2ONDkVERERERKTSmjZtGiaTiXXr1hkdioiIlAEVSkREKrDp06cTFRXFmjVr2L17t9HhiIiIiIiIiIiIVDgqlIiIVFCJiYmsWLGCiRMnEhoayvTp040OqVjZ2dlGhyAiIiIiIiIiInJBKpSIiFRQ06dPp3r16tx8883cfvvtxRZKjh8/ztNPP01UVBSenp7Url2bIUOGkJ6e7uxz+vRpXn75ZRo1aoSXlxcRERHcdttt7NmzB4DFixdjMplYvHhxoWMnJSVhMpmYNm2as23o0KH4+vqyZ88e+vTpg5+fH/fccw8AS5cu5Y477qBOnTp4enoSGRnJ008/zalTp4rEvX37du68805CQ0Px9vamcePGvPjiiwD8+uuvmEwmZs6cWWS/r776CpPJxMqVK0ucTxEREREREVf88ccf9O7dG39/f3x9fenWrRurVq0q1MdqtTJu3DgaNmyIl5cXwcHBdO7cmYSEBGefI0eOMGzYMGrXro2npycRERH07duXpKSkq3xFIiJVh5vRAYiIyJWZPn06t912Gx4eHgwaNIgPP/yQtWvX0q5dOwBOnjxJly5d2LZtG/fffz/XXnst6enpzJo1iwMHDhASEoLNZuOWW25h4cKF3HXXXTz55JNkZWWRkJDA5s2bqV+/fonjys/Pp2fPnnTu3Jl//vOf+Pj4APDtt9+Sk5PDo48+SnBwMGvWrOG9997jwIEDfPvtt879N27cSJcuXXB3d+ehhx4iKiqKPXv28PPPPzN+/HhuuOEGIiMjmT59Ov379y+Sk/r163Pddde5kFkREREREZGS2bJlC126dMHf35/nnnsOd3d3PvroI2644QaWLFniXFfy5ZdfZsKECQwfPpz27duTmZnJunXr+P333+nevTsAAwYMYMuWLTzxxBNERUWRmppKQkICycnJREVFGXiVIiKVlwolIiIV0Pr169m+fTvvvfceAJ07d6Z27dpMnz7dWSh5++232bx5Mz/88EOhgsLo0aNxOBwAfP755yxcuJCJEyfy9NNPO/u88MILzj4llZubyx133MGECRMKtb/55pt4e3s7Xz/00EM0aNCAf/zjHyQnJ1OnTh0AnnjiCRwOB7///ruzDeCNN94AwGQyce+99zJx4kROnDhBQEAAAGlpacyfP9858kRERERERORqGT16NFarlWXLllGvXj0AhgwZQuPGjXnuuedYsmQJALNnz6ZPnz783//9X7HHOX78OCtWrODtt9/mmWeecbaPGjWq7C9CRKQK09RbIiIV0PTp0wkPD+fGG28ECooHAwcOZMaMGdhsNgC+//57WrVqVWTUxdn+Z/uEhITwxBNPXLDPlXj00UeLtJ1fJMnOziY9PZ2OHTvicDj4448/gIJix2+//cb9999fqEjy13iGDBlCbm4u3333nbPt66+/Jj8/n3vvvfeK4xYRERERESkpm83G/Pnz6devn7NIAhAREcHdd9/NsmXLyMzMBCAwMJAtW7awa9euYo/l7e2Nh4cHixcv5tixY1clfhERUaFERKTCsdlszJgxgxtvvJHExER2797N7t27iY2NJSUlhYULFwKwZ88emjdvftFj7dmzh8aNG+PmVnoDDN3c3Khdu3aR9uTkZIYOHUpQUBC+vr6EhoZy/fXXA3DixAkA9u7dC3DJuJs0aUK7du0Krcsyffp0OnToQIMGDUrrUkRERERERC4pLS2NnJwcGjduXGRb06ZNsdvt7N+/H4BXXnmF48eP06hRI1q0aMGzzz7Lxo0bnf09PT158803+eWXXwgPD6dr16689dZbHDly5Kpdj4hIVaRCiYhIBbNo0SIOHz7MjBkzaNiwofPXnXfeCVDsou6uuNDIkrMjV/7K09MTs9lcpG/37t2ZPXs2zz//PD/++CMJCQnOheDtdnuJ4xoyZAhLlizhwIED7Nmzh1WrVmk0iYiIiIiIlGtdu3Zlz549TJ06lebNm/PJJ59w7bXX8sknnzj7PPXUU+zcuZMJEybg5eXFmDFjaNq0qXMkvoiIlD6tUSIiUsFMnz6dsLAwPvjggyLbfvjhB2bOnMnkyZOpX78+mzdvvuix6tevz+rVq7Farbi7uxfbp3r16kDBXLnn27dv32XHvGnTJnbu3Mlnn33GkCFDnO0JCQmF+p0dpn6puAHuuusuRo4cyX//+19OnTqFu7s7AwcOvOyYRERERERESkNoaCg+Pj7s2LGjyLbt27djNpuJjIx0tgUFBTFs2DCGDRvGyZMn6dq1Ky+//DLDhw939qlfvz5///vf+fvf/86uXbto3bo1//rXv/jyyy+vyjWJiFQ1GlEiIlKBnDp1ih9++IFbbrmF22+/vcivESNGkJWVxaxZsxgwYAB//vknM2fOLHKcswu1DxgwgPT0dN5///0L9qlbty4Wi4Xffvut0Pb//Oc/lx23xWIpdMyzf37nnXcK9QsNDaVr165MnTqV5OTkYuM5KyQkhN69e/Pll18yffp0evXqRUhIyGXHJCIiIiIiUhosFgs9evTgp59+IikpydmekpLCV199RefOnfH39wfg6NGjhfb19fWlQYMG5ObmApCTk8Pp06cL9alfvz5+fn7OPiIiUvo0okREpAKZNWsWWVlZ3HrrrcVu79ChA6GhoUyfPp2vvvqK7777jjvuuIP777+fNm3akJGRwaxZs5g8eTKtWrViyJAhfP7554wcOZI1a9bQpUsXsrOzWbBgAY899hh9+/YlICCAO+64g/feew+TyUT9+vX53//+R2pq6mXH3aRJE+rXr88zzzzDwYMH8ff35/vvvy92ccJ3332Xzp07c+211/LQQw8RHR1NUlISs2fPZsOGDYX6DhkyhNtvvx2AV1999fITKSIiIiIicgWmTp3K3Llzi7S//PLLJCQk0LlzZx577DHc3Nz46KOPyM3N5a233nL2i4mJ4YYbbqBNmzYEBQWxbt06vvvuO0aMGAHAzp076datG3feeScxMTG4ubkxc+ZMUlJSuOuuu67adYqIVDUqlIiIVCDTp0/Hy8uL7t27F7vdbDZz8803M336dHJzc1m6dCljx45l5syZfPbZZ4SFhdGtWzfnYusWi4U5c+Ywfvx4vvrqK77//nuCg4Pp3LkzLVq0cB73vffew2q1MnnyZDw9Pbnzzjt5++23L7no+lnu7u78/PPP/O1vf3POs9u/f39GjBhBq1atCvVt1aoVq1atYsyYMXz44YecPn2aunXrOtdgOV98fDzVq1fHbrdfsHgkIiIiIiJSWj788MNi24cOHcrSpUsZNWoUEyZMwG63Exsby5dffklsbKyz39/+9jdmzZrF/Pnzyc3NpW7durz22ms8++yzAERGRjJo0CAWLlzIF198gZubG02aNOGbb75hwIABV+UaRUSqIpPjr3OZiIiIVBD5+fnUrFmT+Ph4pkyZYnQ4IiIiIiIiIiJSAWmNEhERqbB+/PFH0tLSCi0QLyIiIiIiIiIiUhIaUSIiIhXO6tWr2bhxI6+++iohISH8/vvvRockIiIiIiIiIiIVlEaUiIhIhfPhhx/y6KOPEhYWxueff250OCIiIiIiIiIiUoFpRImIiIiIiIiIiIiIiFRZGlEiIiIiIiIiIiIiIiJVlgolIiIiIiIiIiIiIiJSZbkZHUBpsdvtHDp0CD8/P0wmk9HhiIiIiIiUKYfDQVZWFjVr1sRs1vef5NJ0zyQiIiIiVc3l3jdVmkLJoUOHiIyMNDoMEREREZGrav/+/dSuXdvoMKQC0D2TiIiIiFRVl7pvqjSFEj8/P6Dggv39/a/6+a1WK/Pnz6dHjx64u7tf9fNXdMqfa5Q/1yh/rlH+XKP8uUb5c43y5xqj85eZmUlkZKTzc7DIpeieqWJT/lyj/LlG+XON8uca5c81yp/rlEPXGJ2/y71vqjSFkrNDx/39/Q370O/j44O/v79+YK6A8uca5c81yp9rlD/XKH+uUf5co/y5przkT1MoyeXSPVPFpvy5RvlzjfLnGuXPNcqfa5Q/1ymHrikv+bvUfZMmMxYRERERESkDH3zwAVFRUXh5eREbG8uaNWsua78ZM2ZgMpno16/fBfs88sgjmEwmJk2aVDrBioiIiIhUYSqUiIiIiIiIlLKvv/6akSNHMnbsWH7//XdatWpFz549SU1Nveh+SUlJPPPMM3Tp0uWCfWbOnMmqVauoWbNmaYctIiIiIlIlqVAiIiIiIiJSyiZOnMiDDz7IsGHDiImJYfLkyfj4+DB16tQL7mOz2bjnnnsYN24c9erVK7bPwYMHeeKJJ5g+fbqmfhARERERKSWVZo2Sy2G328nLyyuTY1utVtzc3Dh9+jQ2m61MzlGZVZT8eXh4YDarvigiIiIiF5aXl8f69esZNWqUs81sNhMXF8fKlSsvuN8rr7xCWFgYDzzwAEuXLi2y3W63M3jwYJ599lmaNWtWJrGLiIiIXC02mw2r1Wp0GJdUUZ5blldlnT93d3csFovLx6kyhZK8vDwSExOx2+1lcnyHw0GNGjXYv3+/FtS8AhUlf2azmejoaDw8PIwORURERETKqfT0dGw2G+Hh4YXaw8PD2b59e7H7LFu2jClTprBhw4YLHvfNN9/Ezc2Nv/3tb5cVR25uLrm5uc7XmZmZQMHNqhEPJc6esyI8ECmPlD/XKH+uUf5co/y5RvlzTXnMn8PhIDU11fnZpLw7+9wyOTm5XD+3LK+uRv78/f0JCwsr9viX+96vEoUSh8PB4cOHsVgsREZGlsmIALvdzsmTJ/H19dWIgytQEfJnt9s5dOgQhw8fpk6dOvqHUURERERKRVZWFoMHD+bjjz8mJCSk2D7r16/nnXfe4ffff7/sz6ETJkxg3LhxRdrnz5+Pj4+PSzG7IiEhwbBzVwbKn2uUP9cof65R/lyj/LmmPOXPz8+P6tWrExISgoeHh56xyRVzOBzk5eWRlpbGzp07ycrKKtInJyfnso5VJQol+fn55OTkULNmzTK7ITg7rZeXl1e5fdBfnlWU/IWGhnLo0CHy8/M1J7SIiIiIFCskJASLxUJKSkqh9pSUFGrUqFGk/549e0hKSiI+Pt7ZdnYkvJubGzt27GDp0qWkpqZSp04dZx+bzcbf//53Jk2aRFJSUpHjjho1ipEjRzpfZ2ZmEhkZSY8ePfD393f1MkvMarWSkJBA9+7d9Vn6Cih/rlH+XKP8uUb5c43y55rylj+bzcbevXsJDQ0lODjY6HAui8PhICsrCz8/PxV1rsDVyJ+Xlxeenp507NixyDRclztyqUoUSs7OfabpksRVZ99DNputXPznIiIiIiLlj4eHB23atGHhwoX069cPKCh8LFy4kBEjRhTp36RJEzZt2lSobfTo0WRlZfHOO+8QGRnJ4MGDiYuLK9SnZ8+eDB48mGHDhhUbh6enJ56enkXa3d3dDf0sa/T5KzrlzzXKn2uUP9cof65R/lxTXvJns9kwmUzlelaZvzr7BRaTyVRhYi5Prkb+fH19SU9PByjyPr/c932VKJScpYqfuErvIRERERG5HCNHjuS+++6jbdu2tG/fnkmTJpGdne0sagwZMoRatWoxYcIEvLy8aN68eaH9AwMDAZztwcHBRb516e7uTo0aNWjcuHHZX5CIiIhIKdIzNilNpfF+qlKFEhERERERkath4MCBpKWl8dJLL3HkyBFat27N3LlznQu8Jycn6xuJIiIiIiLlhD6ZVzFRUVFMmjTJ6DBERERERCq9ESNGsG/fPnJzc1m9ejWxsbHObYsXL2batGkX3HfatGn8+OOPFz1+UlISTz31VOkEKyIiIiJXVWk8p9Wz3tKjQkk5ZTKZLvrr5ZdfvqLjrl27loceeqhUYvzvf/+LxWLh8ccfL5XjiYiIiIiIiIiIiJQnFouF6tWrY7FYyu1zWnGdpt4qpw4fPuz889dff81LL73Ejh07nG2+vr7OPzscDmw2G25ul/7rDA0NLbUYp0yZwnPPPcdHH33Ev/71L7y8vErt2CIiIiIiIiIiIiJGO3jwIFlZWfj5+fHtt9+Wy+e04jqNKCmnatSo4fwVEBCAyWRyvt6+fTt+fn788ssvtGnTBk9PT5YtW8aePXvo27cv4eHh+Pr60q5dOxYsWFDouH8djmUymfjkk0/o378/Pj4+NGzYkFmzZl0yvsTERFasWMELL7xAo0aN+OGHH4r0mTp1Ks2aNcPT05OIiAhGjBjh3Hb8+HEefvhhwsPD8fLyomXLlsydO/fKEyYiIiIiIiIiIiJSymrUqEF4eHi5fU57vuTkZPr27Yuvry/+/v7ceeedpKSkOLf/+eef3Hjjjfj5+eHv70+bNm1Yt24dAPv27SM+Pp7q1atTrVo1mjVrxpw5c648cRVMlRxR4nA4OGW1leox7XY7p/JsuOXlX3RRRm/3giFapeGFF17gn//8J/Xq1aN69ers37+fPn36MH78eDw9Pfn888+Jj49nx44d1KlT54LHGTduHG+99RZvv/027733Hvfccw/79u0jKCjogvt8+umn3HzzzQQEBHDvvfcyZcoU7r77buf2Dz/8kJEjR/LGG2/Qu3dvTpw4wfLly4GCXPXu3ZusrCy+/PJL6tevz+bNm8nNzS2VvIiIiIiIiIiIiEj5VxbPaS9XZXlOe5bdbncWSZYsWUJ+fj6PP/44AwcOZPHixQDcc889XHPNNXz44YdYLBY2bNiAu7s7AI8//jh5eXn89ttvVKtWja1btxYaLVPZVclCySmrjZiX5hly7q2v9MTHo3TS/sorr9C9e3fn66CgIFq1auV8/eqrrzJz5kxmzZpVaDTHXw0dOpRBgwYB8Prrr/Puu++yZs0aevXqVWx/u93OtGnTeO+99wC46667+Pvf/05iYiLR0dEAvPbaa/z973/nySefdO7Xrl07ABYsWMCaNWvYtm0bjRo1AgoqqJmZmVeSBhEREREREREREamA9Jy2sJI+pz3fwoUL2bRpE4mJiURGRgLw+eef06xZM9auXUu7du1ITk7m2WefpUmTJgA0bNjQuX9ycjIDBgygRYsWANSrV68EGaj4NPVWBda2bdtCr0+ePMkzzzxD06ZNCQwMxNfXl23btpGcnHzR47Rs2dL552rVquHv709qauoF+yckJJCdnU2fPn0ACAkJoXv37kydOhWA1NRUDh06RLdu3Yrdf8OGDdSuXdtZJBERERERkarBajc6AhEREZHSZ9Rz2vNt27aNyMhIZ5EEICYmhsDAQLZt2wbAyJEjGT58OHFxcbzxxhvs2bPH2fdvf/sbr732Gp06dWLs2LFs3Ljxss5bWVTJESXe7ha2vtKzVI9pt9vJyszCz9/vklNvlZZq1aoVev3MM8+QkJDAP//5Txo0aIC3tze33347eXl5Fz3O2eFVZ5lMJuz2C9/BTJkyhYyMDLy9vZ1tdrudjRs3Mm7cuELtxbnUdhEREZHyyuFwsCftJKsTM1i5O51VOy10624r8nlKRArLOm1l/OytLN9q4ebedvQjIyIiIlA2z2lLcu7SYtRz2pJ6+eWXufvuu5k9eza//PILY8eOZcaMGfTv35/hw4fTs2dPZs+ezfz585kwYQL/+te/eOKJJ0rt/OVZlSyUmEymUhtWdZbdbiffw4KPh9tFCyVlafny5QwdOpT+/fsDBZXLpKSkUj3H0aNH+emnn5gxYwbNmjVztttsNjp37sz8+fPp1asXUVFRLFy4kBtvvLHIMVq2bMmBAwfYuXOnRpWIiIhIuWazO9h+JJPVezNYk5jB2qQMjmaff3Nj4s+DJ+jU0MuwGEUqglNWG79sPsKJUyamrtjH4zfpPkBERETK5jlteXA1ntP+VdOmTdm/fz/79+93jirZunUrx48fJyYmxtmvUaNGNGrUiKeffppBgwbx6aefOuOMjIzkkUce4ZFHHmHUqFF8/PHHKpRIxdOwYUN++OEH4uPjMZlMjBkzplQrjgBffPEFwcHB3HnnnUUWO+rTpw9TpkyhV69evPzyyzzyyCOEhYU5F25fvnw5TzzxBNdffz1du3ZlwIABTJw4kQYNGrB161ZOnTrFbbfdVqrxioiIiJSE1WZn88ETrE48VxjJOp1fqI+nm5lr61SnXd1A7Ck7aVkrwKBoRSqOMD8vXujVmFEzt/Duoj30aVmL6JBql95RREREpAK6Gs9p/youLo4WLVpwzz33MGnSJPLz83nssce4/vrradu2LadOneLZZ5/l9ttvJzo6mgMHDrB27VoGDBgAwFNPPUXv3r1p1KgRx44d49dff6Vp06ZlGnN5okJJJTJx4kTuv/9+OnbsSEhICM8//3ypL5A+depU+vfvX6RIAjBgwAAGDx5Meno69913H6dPn+bf//43zzzzDCEhIdx+++3Ovt9//z3PPPMMgwYNIjs7mwYNGjB69OhSjVVERETkUk5bbWzYf5w1Zwoj6/cd45TVVqiPr6cbbaOq0z46iNjoIFrUCsTDzYzVamXOnB14leKQfZHKbMA1Nfl00SZ2noBRP2zkvw92KPa+QkRERKSiuxrPaf/KZDLx008/8cQTT9C1a1fMZjO9evXivffeA8BisXD06FGGDBlCSkoKISEh3HbbbYwbNw4omDHo8ccf58CBA/j7+9OrVy/+/e9/l2nM5YkKJRXA0KFDGTp0qPP1DTfcgMPhKNIvKiqKRYsWFWp7/PHHC73+6xCv4o5z/PjxC8ZysUV87rzzTu68807n64cffpiHH3642L5BQUHOxd+hYOqysv7HQkRERCQ7N5/1+445CyMb9h8nz1b4m12BPu60jwo6UxgJpmmEH24WY6ZWFalMTCYTA+vZ+edmN1btzWDG2v0Mal/H6LBERERELlt5ek5b3DHq1KnDTz/9VGxfDw8P/vvf/17wWGcLKlWVCiUiIiIiUmmdyLGyNimDNUkZrE7MYPPBE9jshW9AQv08iT0zWqR9dDANw3wxm/Utd5GyEOIFT3VrwIS5O3l9zjZuahJGuL/W+BERERERY6lQIiIiIiKVRlpWbkFhJLGgMLL9SCZ//WJW7erezmm02kcHExXso+l/RK6i+66ry5wtqfy5/zijf9zM/w1uo59BERERETGUCiUiIiIiUmEdOn7KWRRZk3iUPWnZRfrUC612pihSUBipFehtQKQicpbFbOLNAS245d1lJGxNYc6mI9zcMsLosERERESkClOhREREREQqBIfDwb6jOecKI0lH2Z9xqlAfkwkah/s5R4u0jw4i1M/ToIhF5EKa1PDnsRvq8+6i3YydtZlODYIJ9PEwOiwRERERqaJUKBERERGRcsnhcLAr9eSZ0SIFI0ZSMnML9bGYTTSv6e9ceL1tVHU9bBWpIB6/qQFzNh9hd+pJXpu9jX/e0crokERERESkilKhRERERETKBZvdwbbDmc5ptNYmHSMjO69QHw+LmVaRAc7CyLV1q+PrqY+0IhWRp5uFNwe04PbJK/lu/QH6tq5Jl4ahRoclIiIiIlWQ7ipFRERExBB5+XY2HTzhHC2yLukYWbn5hfp4uZtpU7c67aOCia0XROvIQLzcLQZFLCKlrU3dIIZ0qMtnK/cx6odNzH+6Kz4euk0VERERkatLn0BFRERE5Ko4bbXxR/LxM2uMHOX35GOcttoL9fHzdKNtVHXaRxcURprXDMDDzWxQxCJyNTzbqwkJW1M4cOwU/5q/kzG3xBgdkoiIiIhUMSqUiIiIiEiZOJmbz/p9x1iTeJTVezP488BxrDZHoT7Vfdyd02i1jw6iaYQ/FrPJoIhFxAi+nm6Mv60Fwz5dy6fLE7mlZQTX1KludFgiIiIiUoXo63mV3A033MBTTz3lfB0VFcWkSZMuuo/JZOLHH390+dyldRwRERGpGI7n5JGwNYXxs7dy6/vLaDVuPvdNXcMHv+5h3b5jWG0Owv09ubVVTV7r15yEp7uyfnR3Phrclvs7R9O8VoCKJCJV1I2Nw+h/TS3sDnjh+03k5dsvvZOIiIhIBVLZn9O+/PLLtG7dukzPUZY0oqScio+Px2q1Mnfu3CLbli5dSteuXfnzzz9p2bJliY67du1aqlWrVlphAgU/BD/++CMbNmwo1H748GGqV7863wQ7deoUtWrVwmw2c/DgQTw9Pa/KeUVERKqy1KzTrE08M2IkMYPtR7KK9IkM8naOFomNDqJOkA8mk4ohIlLUmFtiWLIzjR0pWXy4eA9PxjU0OiQRERERbr31Vk6fPs38+fOLbNNz2spDhZJy6oEHHmDAgAEcOHCA2rVrF9r26aef0rZt2xL/8AGEhoaWVoiXVKNGjat2ru+//55mzZrhcDj48ccfGThw4FU7t4iISFVx8Pgp5zRaaxIz2JueXaRP/dBqxNYLJjY6iHZRQdQM9DYgUhGpiIKqeTA2PoYnZ2zg/V930adFDRqG+xkdloiIiFRx999/P3fccQcHDhygTp06hbbpOW3loam3yqlbbrmF0NBQpk2bVqj95MmTfPvttzzwwAMcPXqUQYMGUatWLXx8fGjRogX//e9/L3rcvw7p2rVrF127dsXLy4uYmBgSEhKK7PP888/TqFEjfHx8qFevHmPGjMFqtQIwbdo0xo0bx59//onJZMJkMjlj/uuQrk2bNnHTTTfh7e1NcHAwDz30ECdPnnRuf+yxx+jfvz///Oc/iYiIIDg4mMcff9x5rouZMmUK9957L/feey9Tpkwpsn3Lli3ccsst+Pv74+fnR5cuXdizZ49z+9SpU2nWrBmenp5EREQwYsSIS55TRESkMnM4HCSmZ/P12mRGfr2BTm8sotMbi3j66z+ZsXY/e9OzMZkgJsKfoR2j+PCea1k3Oo6Ff7+B1/u3oG/rWiqSiEiJ3dqqJjc1CcNqc/D89xux2R2X3klERESkDN1yyy2EhITw2WefFWqvzM9phw4dSr9+/a7oOe1ZdrudV155hTp16hAeHs61115baPakvLw8RowYQUREBF5eXtStW5cJEyYABfejL7/8MnXq1MHT05OaNWvyt7/97bLPfSWq5ogShwOsOaV7TLu94Jh5FjBfpP7k7gOXMd2Em5sbQ4YMYdq0abz44ovOKSq+/fZbbDYbgwYN4uTJk7Rp04bnn38ef39/Zs+ezeDBg6lfvz7t27e/jJDt3HbbbYSHh7N69WpOnDhRaJ68s/z8/Jg2bRo1a9Zk06ZNPPjgg/j5+fHcc88xcOBANm/ezNy5c1mwYAEAAQEBRY6RnZ1Nz549ue6661i7di2pqakMHz6cESNGFCoGLV68mJo1a/Lrr7+ye/duBg4cSOvWrXnwwQcveB179uxh5cqV/PDDDzgcDp5++mn27dtH3bp1ATh48CBdu3blhhtuYNGiRfj7+7N8+XLy8/MB+PDDDxk5ciRvvPEGvXv35sSJEyxfvvyS+RMREalM7HYHu1JPsvrMNFprEjNIy8ot1MdiNtGiVgCx0UG0jw6ibd0gAnzcDYpYRCojk8nEa/2a0+Pfv/F78nG+WJnE0E7RRoclIiIiZaUsntNerhI8px04cCCfffYZo0ePrjLPaX/99VciIiJK9Jz2fO+88w7/+te/+PDDD2nYsCHffvstt956K1u2bKFhw4a8++67zJo1i2+++YY6deqwf/9+9u/fDxTMHvTvf/+bGTNm0KxZM44cOcKff/55Wee9UlWzUGLNgddrluohzUDg5XT8xyHwuLy55+6//37efvttlixZwg033AAUDOcaMGAAAQEBBAQE8Mwzzzj7P/HEE8ybN49vvvnmsn4AFyxYwPbt25k3bx41axbk4/XXX6d3796F+o0ePdr556ioKJ555hlmzJjBc889h7e3N76+vri5uV10CNdXX33F6dOn+fzzz51z773//vvEx8fz5ptvOoeaVa9enffffx+LxUKTJk24+eabWbhw4UV/AKdOnUrv3r2d8+z17NmTTz/9lJdffhmADz74gICAAGbMmIG7e8HDnEaNGjn3f+211/j73//Ok08+6Wxr167dJfMnIiJSkeXb7Gw+mMmvh0z8PP0P1iUf53hO4W8HebiZaR0Z6CyMXFunOtU8q+bHRxG5emoGevN8r8aM+WkLb83bQVxMOLWr+xgdloiIiJSFMnhOe9lK8Jz23nvv5b333qsSz2nDw8OBK3tOe75//vOfPP/889x1111kZmbyxhtvsHjxYiZNmsQHH3xAcnIyDRs2pHPnzphMJueX3gGSk5OpUaMGcXFxuLu7U6dOncvKoyt0p1uONWnShI4dOzJ16lRuuOEGdu/ezdKlS3nllVcAsNlsvP7663zzzTccPHiQvLw8cnNz8fG5vJuIbdu2ERkZ6fzhA7juuuuK9Pv6669599132bNnDydPniQ/Px9/f/8SXcu2bdto1apVoQWKOnXqhN1uZ8eOHc5CSUxMDBaLxdknIiKCTZs2XfC4NpuNzz77jHfeecfZdu+99/LMM8/w0ksvYTab2bBhA126dHEWSc6XmprKoUOH6NatW4muR0REpKLJy7ez6eBxVp1ZX2T9vmOczM0HLEAaAD4eFtrUrU77qILCSKvIQLzcLRc9rohIWbgnti6z/jzE2qRj/GPmZj4b1s757U0RERGRq61Ro0ZV5jnt2UJJs2bNSvSc9nyZmZkcOnSITp06FWrv1KmTc2TI0KFD6d69O40bN6ZXr17ccsst9OjRA4A77riDSZMmUa9ePXr16kWfPn2Ij4/Hza3syhlVs1Di7lNQMSxFdrudzKws/P38MF9q6q0SeOCBB3jiiSf44IMP+PTTT6lfvz7XX389AG+//TbvvPMOkyZNokWLFlSrVo2nnnqKvLw8Vy6lkJUrV3LPPfcwbtw4evbs6RyZ8a9//avUznG+vxYzTCYTdrv9gv3nzZvHwYMHiyzebrPZWLhwId27d8fb+8Lzo19sm4iISEV2Ks/GH8nHnNNo/Z58jNz8wv+n+nm5Ucc7j5vbN+a6+qE0rxWAu0VL2ImI8cxmE28MaEnvd5by2840Zv5xkNuurW10WCIiIlLayuA5bYnOXQLDhg3jySef1HPaUnLttdeSmJjIL7/8woIFC7jzzjuJi4vju+++IzIykh07drBgwQISEhJ47LHHnDMvFfdl+NJQNQslJtNlD6u6bHY7uNsKjnuxQkkJ3XnnnTz55JN89dVXfP755zz66KPOb1ItX76cvn37cu+9954Jwc7OnTuJiYm5rGM3bdqU/fv3c/jwYSIiIgBYtWpVoT4rVqygbt26vPjii862ffv2Ferj4eGBzWa75LmmTZtGdna2s1q5fPlyzGYzjRs3vqx4izNlyhTuuuuuQvEBjB8/nilTptC9e3datmzJZ599htVqLfKD5OfnR1RUFAsXLuTGG2+84jhERESMlnXayrp9x1hzpjCy8cBxrLbCiyAHV/Og/ZlptNpHB1E/2Jt5c3+hT+foMvuwKSJypeqH+vJkt4a8PW8Hr/xvK10bhRLi62l0WCIiIlKayuI5bRm58847efrpp/Wc9jL4+/tTs2ZNli9fTpcuXZzty5cvLzSFlr+/PwMHDmTgwIHcfvvt9OrVi4yMDIKCgvD29iY+Pp74+Hgef/xxmjRpwqZNm7j22mtLJca/qpqFkgrE19eXgQMHMmrUKDIzMxk6dKhzW8OGDfnuu+9YsWIF1atXZ+LEiaSkpFz2D2BcXByNGjXivvvu4+233yYzM7NIwaFhw4YkJyczY8YM2rVrx+zZs5k5c2ahPlFRUSQmJrJhwwZq166Nn58fnp6Fb2Duuecexo4dy3333cfLL79MWloaTzzxBIMHDyY8PPyKqpFpaWn8/PPPzJo1i+bNmxfaNmTIEPr3709GRgYjRozgvffe46677mLUqFEEBASwatUq2rdvT+PGjXn55Zd55JFHCAsLo3fv3mRlZbF8+XKeeOKJEsckIiJytRzLzmNNUoazMLLl0Anshesi1PD3IrZeQVEkNjqY+qHVCk1dY7VaEREpzx7qWo//bTzMtsOZjPt5K+8NusbokERERKSKqirPaUvLs88+y9ixY4mOjqZBgwZ89913bNiwgenTpwMwceJEIiIiuOaaazCbzXz77bfUqFGDwMBApk2bhs1mIzY2Fh8fH7788ku8vb0LrWNS2jS3QgXwwAMPcOzYMXr27FlonrrRo0dz7bXX0rNnT2644QZq1KhBv379Lvu4ZrOZmTNncurUKdq3b8/w4cMZP358oT633norTz/9NCNGjKB169asWLGCMWPGFOozYMAAevXqxY033khoaCj//e9/i5zLx8eHefPmkZGRQbt27bj99tvp1q0b77//fsmScZ6zCw4Vt75It27d8Pb25ssvvyQ4OJhFixZx8uRJrr/+etq0acPHH3/s/Obsfffdx6RJk/jPf/5Ds2bNuOWWW9i1a9cVxyUiIlIWUjNP8/Ofhxjz42Z6/vs3rnk1gYe/WM+UZYlsOlhQJKkb7MMdbWrz9u0tWfrcjawcdRPv3HUN98TWpUGYr+b3F5EKx91i5s0BLTCb4Oc/D7FwW4rRIYmIiEgVpue0l+9vf/sbI0eO5Nlnn6VTp07MmzePWbNm0bBhQ6Bgpp+33nqLtm3b0q5dO5KSkpgzZw5ms5nAwEA+/vhjOnXqRMuWLVmwYAE///wzwcHBpRrj+UwOh8Nx6W7lX2ZmJgEBAZw4caLIAjanT58mMTGR6OhovLy8yuT8drudzMxM/P39L75GiRSrouTvaryXroTVamXOnDn06dNHU6dcAeXPNcqfa5Q/11Tm/O3PyHGOFlmTlEFienaRPg3DfAtGi9QLpn1UEDUCSvZ/U2XO39VgdP4u9vlXpDhGv2dc+Zl5fc42/u+3vUQEeDH/6a74eVW9f7OM/jenolP+XKP8uUb5c43y55rylr/y+mztYirKc8vy6mrk72Lvq8v9DKypt0REREQM5nA42Juefa4wkpjBweOnCvUxmSAmwt85jVa7qOoEa65+Eakino5rxLwtR9h3NIc3527ntX4tjA5JRERERCoRFUpERERErjK73cGOlCxnUWR1YgbpJ3ML9XEzm2hRO4DY6GBio4O4tm51AryN/waYiIgRvD0sTLitBXd/vJovVyUT37ImsfXKbuoFEREREalarqhQ8sEHH/D2229z5MgRWrVqxXvvvVdotfq/mjRpEh9++CHJycmEhIRw++23M2HCBOcwmJdffplx48YV2qdx48Zs3779SsITERERKVfybXa2HMp0FkXWJmVw4lThhdQ93MxcExlIbL2Cwsg1dQLx8dB3WkREzupYP4S72kUyY+1+Rv2wiTlPdsHL3WJ0WCIiIiJSCZT47vvrr79m5MiRTJ48mdjYWCZNmkTPnj3ZsWMHYWFhRfp/9dVXvPDCC0ydOpWOHTuyc+dOhg4dislkYuLEic5+zZo1Y8GCBecCc9ODAREREamYcvNtbDxwwlkYWZ+UQXaerVCfah4W2kQFERsdRPvoIFrWDsDTTQ/8REQuZlSfpizansre9GzeXbiL53o1MTokEREREakESlyNmDhxIg8++CDDhg0DYPLkycyePZupU6fywgsvFOm/YsUKOnXqxN133w1AVFQUgwYNYvXq1YUDcXOjRo0aV3INIiIiIobKycvnj+TjrE7MYE3iUf5IPk5uvr1QnwBvd9qdVxhpVtMfN4sWAhQRKYkAb3de6ducR75cz0e/7eXmlhE0qxlgdFgiIiIiUsGVqFCSl5fH+vXrGTVqlLPNbDYTFxfHypUri92nY8eOfPnll6xZs4b27duzd+9e5syZw+DBgwv127VrFzVr1sTLy4vrrruOCRMmUKdOnSu4pAtzOBylejypevQeEhERgMzTVtYnHWNV4lHWJGaw6cAJ8u2F/48I8fUgNjqY9mcKI43D/TCbTQZFLCJSefRqXoPezWvwy+YjPP/9Rn58rJMKzyIiIhWM3W6/dCeRy1Qa76cSFUrS09Ox2WyEh4cXag8PD7/geiJ333036enpdO7cGYfDQX5+Po888gj/+Mc/nH1iY2OZNm0ajRs35vDhw4wbN44uXbqwefNm/Pz8ij1ubm4uubnnFj3NzMwEwGq1YrVai90nLS2N4OBgTKbSf0jhcDjIy8vj1KlTZXL8yq4i5M/hcHD06FHn6wu9z4xwNpbyFFNFovy5RvlzjfLnmquVv4zsPNbtO8aapGOsTTrG9iNZ/KUuQkSAF+2jqtMuqjrt6lYnOsSn0P9pNls+Nhvlit5/rjE6f/p7k6psXN9mLN+dzuaDmUxZlsjD19c3OiQRERG5DB4eHpjNZg4dOkRoaCgeHh7l9lngWXa7nby8PE6fPo3ZrC9nlFRZ5u/sM+W0tDTMZjMeHh5XfKwyXwhk8eLFvP766/znP/8hNjaW3bt38+STT/Lqq68yZswYAHr37u3s37JlS2JjY6lbty7ffPMNDzzwQLHHnTBhQpEF4AHmz5+Pj49PkXYPDw+CgoJIS0srpSuTqig/P5+MjAx27txpdCjFSkhIMDqECk35c43y5xrlzzWlnb8TebAn08TuTBN7Mk0cOVX0g3uol4P6/md++TkI9joJnITU/WxPheK/QlI+6f3nGqPyl5OTY8h5RcqDMD8vRt8Sw3PfbWRiwk56NqtBVEg1o8MSERGRSzCbzURHR3P48GEOHTpkdDiXxeFwcOrUKby9vct9Uac8uhr58/HxoU6dOi4VYkpUKAkJCcFisZCSklKoPSUl5YLri4wZM4bBgwczfPhwAFq0aEF2djYPPfQQL774YrHBBwYG0qhRI3bv3n3BWEaNGsXIkSOdrzMzM4mMjKRHjx74+/sXu4/NZiM/P79Mpk/Kz89nxYoVdOzYUQvRX4GKkD+TyYSbmxsWS/lbaNdqtZKQkED37t1xd3c3OpwKR/lzjfLnGuXPNaWRP4fDwYHjp1ibdG7ESHLGqSL9GoX5FowWiapO27qBhPt7uRq+4fT+c43R+Ts7olqkqrqjTW1mbTjEst3pvPDDRv77YAc9vBAREakAPDw8qFOnDvn5+djK27D7YlitVn777Te6du2q+6YrUNb5s1gsuLm5ufw5sERPpD08PGjTpg0LFy6kX79+QMHQmYULFzJixIhi98nJySlSDDn7oPlCBYuTJ0+yZ8+eIuuYnM/T0xNPT88i7e7u7hdMeFm+ka1WK/n5+fj6+uoH5goof6XjYu9/uTTlzzXKn2uUP9eUJH8Oh4M9admsScxg9Zk1Rg6fOF2oj9kEzWoGONcXaRcVRFC1Kx/CW97p/ecao/KnvzOp6kwmE6/3b0HPSb+xam8GM9buZ1D70l3nUkRERMqGyWSqMPchFouF/Px8vLy8KkS85U1FyV+Jv7o/cuRI7rvvPtq2bUv79u2ZNGkS2dnZDBs2DIAhQ4ZQq1YtJkyYAEB8fDwTJ07kmmuucU69NWbMGOLj450Fk2eeeYb4+Hjq1q3LoUOHGDt2LBaLhUGDBpXipYqIiEhVZLM72HEky1kUWZOYwdHsvEJ93C0mWtYOdBZG2tatjp9X+f0AJyIiBeoE+/D3Ho14bfY2Xp+zjZuahFWKEX8iIiIicnWVuFAycOBA0tLSeOmllzhy5AitW7dm7ty5zgXek5OTC40gGT16NCaTidGjR3Pw4EFCQ0OJj49n/Pjxzj4HDhxg0KBBHD16lNDQUDp37syqVasIDQ0thUsUERGRqsRqs7P54AlnUWRtUgaZp/ML9fF0M3Ntneq0jw4iNjqIa+pUx9uj/E2tKCIilzasUzQ//3mIPw+cYMyPm/locBtNwSUiIiIiJXJFi0GMGDHiglNtLV68uPAJ3NwYO3YsY8eOveDxZsyYcSVhiIiIiGC1w5qkDH5PzmRNUgbr9x0jJ6/wPLe+nm60qVtQGOlQL4jmtQLwdFNhRESkMrCYTbwxoCXx7y1j/tYUftl8hD4tIowOS0REREQqkPK5araIiIjIRSQfzWHBthQSth5hbaKF/NXrCm0P9HGnXVTBaJHY6GCaRvjhZjFf4GgiIlLRNY3w59Eb6vPeot289NMWOtYPJtCn8q4tJSIiIiKlS4USERERKffsdgd/HjjOgm0pLNiayo6UrPO2mgj19aB9vWA6RAfRPjqYhmG+mM2adkVEpCoZcVMD5mw6zJ60bMbP3sbbd7QyOiQRERERqSBUKBEREZFy6VSejeW70wuKI9tSST+Z69xmMZtoHxXEjY1D4PAWht7WHQ8PfXNYRKQq83Sz8NbtLbl98kq+XX+AW1vXpEtDrXspIiIiIpemQomIiIiUG2lZuSzankLC1lSW7U7jtNXu3Obn6cb1jUPpHhPODY3CCPBxx2q1MmfOFi3aKyIiALSpG8SQDnX5bOU+Rv2wiflPd8XHQ7e9IiIiInJx+sQoIiIihnE4HOxOPUnCthQWbE3hj/3HcTjOba8V6E33mHDimobTPjoIDzetMyIiIhf3bK8mJGxN4cCxU/xr/k7G3BJjdEgiIiIiUs6pUCIiIiJXVb7NztqkY2em1Eph39GcQttb1Q4grmk4cTHhNKnhp9EiIiJSIr6eboy/rQXDPl3Lp8sTiW9Vk9aRgUaHJSIiIiLlmAolIiIiUuayTlv5bWfBeiOLtqdy4pTVuc3DzUyn+sHExYTTrUk4NQK8DIxUREQqgxsbh9GvdU1+3HCI57/byM9PdNaoRBERERG5IBVKREREpEwcPH6KhdtSSNiawqq9R7Hazs2pFVTNg5uahBHXNJwuDUOo5qmPJCIiUrpeim/Gb7vS2ZGSxeQle/hbt4ZGhyQiIiIi5ZSeSoiIiEipcDgcbD6Y6VxvZOvhzELb64VWo3tMON2bhnNNnepYzJpSS0REyk5QNQ/Gxsfw5IwNvL9oN72b16BhuJ/RYYmIiIhIOaRCiYiIiFyx3HwbK/YcZcHWFBZuS+VI5mnnNrMJ2tYNIi4mjG5Nw6kf6mtgpCIiUhXd2qomP204xKLtqTz//Ua+faSjCvUiIiIiUoQKJSIiIlIiGdl5/Lo9lQXbUvhtZxrZeTbnNh8PC9c3CiWuaTg3NgkjqJqHgZGKiEhVZzKZeK1fc3r8+zd+Tz7OFyuTGNop2uiwRERERKScUaFERERELmlv2kkWbEthwdZU1u3LwH5uuRHC/T2JaxpO95hwOtQLxsvdYlygIiIif1Ez0JvnezVmzE9beGveDuJiwqld3cfosERERESkHFGhRERERIqw2R38kXyMhK0pJGxLYW9adqHtMRH+xJ1Zb6R5LX9MJk1jIiIi5dc9sXWZ9ech1iYd48WZm5k2rJ3+7xIRERERJxVKREREBIDs3HyW7kpnwbYUFm1PJSM7z7nN3WKiQ71guseE061pOLUCvQ2MVEREpGTMZhMTbmtJn3eWsmRnGj9uOEj/a2obHZaIiIiIlBMqlIiIiFRhKZmnz0yplcLyPUfJy7c7twV4u3NTkzDimobTtVEIfl7uBkYqIiLimgZhvvytWwP+OX8nr/y8la4NQwn29TQ6LBEREREpB1QoERERqUIcDgfbDmcVFEe2pbDxwIlC2+sE+dA9Jpy4puG0jaqOu8VsUKQiIiKl7+Hr6/O/jYfZfiSLcT9v5d1B1xgdkoiIiIiUAyqUiIiIVHJ5+XbWJGawYFsKCVtTOHj8lHObyQTXRAY61xtpEOarOdtFRKTScreYeev2lvT7YDmz/jxE39Y16dY03OiwRERERMRgKpSIiIhUQidyrCzemUrC1hSW7EgjKzffuc3L3UznBqH0iAnnxiZhhPpp2hEREak6WtYOZHiXevzfb3sZ/eNm2kcHaXpJERERkSpOhRIREZFKIvloDgln1htZk5SBze5wbgvx9SSuacF6I50ahODtYTEwUhEREWM9HdeIeVuOsO9oDm/O3c5r/VoYHZKIiIiIGEiFEhERkQrKbnfw54HjZxZjT2VHSlah7Y3D/YiLKSiOtKodiNmsKbVEREQAvD0sTOjfgrs/Wc2Xq5K5tVUt2kcHGR2WiIiIiBhEhRIREZEK5FSejeW7088sxp5K+slc5zaL2UT7qCDneiN1gn0MjFRERKR869gghIFtI/l63X5e+H4jc57sgpe7RlyKiIiIVEUqlIiIiJRzaVm5LNqeQsLWVJbtTuO01e7c5ufpxvWNQ+keE84NjcII8NEc6yIiIpfrHzc35dcdqexNz+a9Rbt4tmcTo0MSEREREQOoUCIiIlLOOBwOdqeedK438sf+4zjOLTdCrUBvuseEE9c0nPbRQXi4mY0LVkREpAIL8Hbnlb7NeeTL9Uxespc+LSJoVjPA6LBERERE5CpToURERKQcyLfZWZd89MyUWinsO5pTaHur2gHENQ0nLiacJjX8MJm03oiIiEhp6NW8Br2b1+CXzUd4/vuN/PhYJ9ws+hKCiIiISFWiQomIiIhBsk5bWbT1CJ/vMvPShsWcOJXv3ObhZqZT/WDiYsLp1iScGgFeBkYqIiJSuY3r24zlu9PZfDCTKcsSefj6+kaHJCIiIiJXkQolIiIiV9HB46dYuC2FhK0prNp7FKvNAZiBfIKqeXBTkzDimobTpWEI1Tz137SIiMjVEObnxeibY3ju+41MTNhJz2Y1iAqpZnRYIiIiInKVaDyxiIhIGXI4HGw6cIKJCTvp885SOr2xiJd+2sLSXelYbQ7qhfjQraadGcPbsfbFOP55Ryt6Na+hIomISCXwwQcfEBUVhZeXF7GxsaxZs+ay9psxYwYmk4l+/fo526xWK88//zwtWrSgWrVq1KxZkyFDhnDo0KEyir7quaNtbTo1CCY3386oHzbhOH+BMBERERGp1PQURkREpJTl5ttYsecoC7amsHBbKkcyTzu3mU3Qtm4QcTFhdGsaTp1AT+bMmUObutWxmLXuiIhIZfH1118zcuRIJk+eTGxsLJMmTaJnz57s2LGDsLCwC+6XlJTEM888Q5cuXQq15+Tk8PvvvzNmzBhatWrFsWPHePLJJ7n11ltZt25dWV9OlWAymZjQvyU9Ji1h5d6jfL12P3e1r2N0WCIiIiJyFahQIiIiUgoysvP4dXsqC7al8NvONLLzbM5tPh4Wrm8USlzTcG5sEkZQNQ/nNqvVakS4IiJSxiZOnMiDDz7IsGHDAJg8eTKzZ89m6tSpvPDCC8XuY7PZuOeeexg3bhxLly7l+PHjzm0BAQEkJCQU6v/+++/Tvn17kpOTqVNHD/RLQ51gH57p0ZjXZm9j/Jxt3NgkjHB/rRMmIiIiUtmpUCIiInKF9qadZMG2FBZsTWXdvgzs583QEe7vSVzTcLrHhNOhXjBe7hbjAhURkasqLy+P9evXM2rUKGeb2WwmLi6OlStXXnC/V155hbCwMB544AGWLl16yfOcOHECk8lEYGBgsdtzc3PJzc11vs7MzAQKivRGFOrPnrO8f0ng3va1mbXhIBsPZjJ65iY+GNQKk8n4UZ8VJX/llfLnGuXPNcqfa5Q/1yh/rlMOXWN0/i73vCqUiIiIXCab3cEfycdI2JpCwrYU9qZlF9oeE+FPXEw43ZuG07yWf7l4qCIiIldfeno6NpuN8PDwQu3h4eFs37692H2WLVvGlClT2LBhw2Wd4/Tp0zz//PMMGjQIf3//YvtMmDCBcePGFWmfP38+Pj4+l3WesvDXkTHlUa9g2HzIQsK2VCZ8OZfWweVnvZKKkL/yTPlzjfLnGuXPNcqfa5Q/1ymHrjEqfzk5OZfVT4USERGRi8jOzWfprnQWbEth0fZUMrLznNvcLSY61Aume0w43ZqGUyvQ28BIRUSkosrKymLw4MF8/PHHhISEXLK/1WrlzjvvxOFw8OGHH16w36hRoxg5cqTzdWZmJpGRkfTo0eOCxZWyZLVaSUhIoHv37ri7u1/185fUyeq7+c+Svfx8yJvHBnQi0MfYmCta/sob5c81yp9rlD/XKH+uUf5cpxy6xuj8nR1VfSkqlIiIiPxFSubpM1NqpbB8z1Hy8u3ObQHe7tzUJIy4puF0bRSCn5c+JImISGEhISFYLBZSUlIKtaekpFCjRo0i/ffs2UNSUhLx8fHONru94P8eNzc3duzYQf369YFzRZJ9+/axaNGiixY8PD098fT0LNLu7u5u6E2+0ee/XE92b8S8rSnsScvmrfm7ePuOVkaHBFSc/JVXyp9rlD/XKH+uUf5co/y5Tjl0jVH5u9xzqlAiIiJVnsPhYNvhrILiyLYUNh44UWh7nSAfuseEE9c0nLZR1XG3mA2KVEREKgIPDw/atGnDwoUL6devH1BQ+Fi4cCEjRowo0r9JkyZs2rSpUNvo0aPJysrinXfeITIyEjhXJNm1axe//vorwcHBZX4tVZmnm4U3B7Tkjo9W8u36A/RtXYvODS894kdEREREKh4VSkREpErKy7ezJjGDBdtSSNiawsHjp5zbTCa4JjLQud5IgzBfrTciIiIlMnLkSO677z7atm1L+/btmTRpEtnZ2QwbNgyAIUOGUKtWLSZMmICXlxfNmzcvtP/ZBdrPtlutVm6//XZ+//13/ve//2Gz2Thy5AgAQUFBeHh4XL2Lq0LaRgUxuENdPl+5j1EzNzLvqa74eOg2WkRERKSy0Sc8ERGpMk7kWFm8M5WErSks2ZFGVm6+c5uXu5nODULpERPOjU3CCPUrOlWJiIjI5Ro4cCBpaWm89NJLHDlyhNatWzN37lznAu/JycmYzZc/QvHgwYPMmjULgNatWxfa9uuvv3LDDTeUVujyF8/1asKCrSnszzjFxPk7GX1LjNEhiYiIiEgpU6FEREQqteSjOSScWW9kTVIGNrvDuS3E15O4pgXrjXRqEIK3h8XASEVEpLIZMWJEsVNtASxevPii+06bNq3Q66ioKBwOR/GdpUz5erox/rYWDPt0LVOXJ3JLq5q0jgw0OiwRERERKUUqlIiISKVitzv488DxM4uxp7IjJavQ9sbhfsTFFBRHWtUOxGzWlFoiIiJycTc2DqNf65r8uOEQz3+3kZ+f6IyHm9YsExEREaksVCgREZEK71SejeW7088sxp5K+slc5zaL2UT7qCDneiN1gn0MjFREREQqqpfim/HbrnR2pGQxecke/tatodEhiYiIiEgpUaFEREQqpLSsXBZtTyFhayrLdqdx2mp3bvPzdOP6xqF0jwnnhkZhBPi4GxipiIiIVAZB1TwYGx/DkzM28P6i3fRpUYMGYX5GhyUiIiIipUCFEhERqRAcDge7U0+SsC2FhK0pbNh/nPOnaq8V6E33mHDimobTPjpI02GIiIhIqbu1VU1+/OMgv+5I4/nvN/Htw9dpGk8RERGRSkCFEhERKbfybXbWJh07M6VWCvuO5hTa3qp2AHFNw4mLCadJDT9MJj2oEBERkbJjMpl4rX8Lekxcwvp9x/hi1T7u6xhldFgiIiIi4iIVSkREpFzJOm1lyc40FmxN4dcdaZw4ZXVu83Az06l+MHEx4XRrEk6NAC8DIxUREZGqqFagNy/0bsKYn7bw1tztdGsaRu3qWgNNREREpCJToURERAx38PgpFp6ZUmvV3qNYbefm1Aqq5sFNTcKIaxpOl4YhVPPUf10iIiJirHti6zLrz0OsTTrGizM3M21YO41sFREREanA9LRJRESuOofDweaDmSRsS2HB1hS2Hs4stL1eaDW6x4TTvWk419SpjkVzf4uIiEg5YjabmHBbS/q8s5QlO9P4ccNB+l9T2+iwREREROQKqVAiIiJXRW6+jRV7jrJgawoLt6VyJPO0c5vZBG3rBhEXE0a3puHUD/U1MFIRERGRS2sQ5svfujXgn/N38srPW+naMJRgX0+jwxIRERGRK6BCiYiIlJmM7Dx+3Z7Kgm0p/LYzjew8m3Obj4eF6xuFEtc0nBubhBFUzcPASEVERERK7uHr6/O/jYfZfiSLcT9v5d1B1xgdkoiIiIhcARVKRESkVO1NO8mCbSks2JrKun0Z2M8tN0K4vydxTcPpHhNOh3rBeLlbjAtURERExEXuFjNv3d6Sfh8sZ9afh+h3TU1uahJudFgiIiIiUkIqlIiIiEtsdgd7MuHNeTtZtCONvWnZhbbHRPgTd2a9kea1/LXQqYiIiFQqLWsHMrxLPf7vt728OHMz858Ows/L3eiwRERERKQEVCgREZErkpGdx/RV+/h8ZRJpJ92AJADcLSY61Aume0w43ZqGUyvQ29A4RURERMra03GNmLflCPuO5vDW3B282q+50SGJiIiISAmoUCIiIiWyO/UkU5cn8v36A+Tm2wHwsTjo3rwmPZpF0LVRiL5FKSIiIlWKt4eFCf1bcPcnq/li1T7iW9WkfXSQ0WGJiIiIyGVSoURERC7J4XCwcu9RpixNZOH2VGd781r+DLuuLqb9fxB/Swvc3VUgERERkaqpY4MQBraN5Ot1+3nh+43MebKL1mMTERERqSBUKBERkQvKy7fzv42H+GRpIlsPZwJgMkG3JuE82CWa9tFB5OfnM+fgHwZHKiIiImK8f/RpyqIdqexNz+a9Rbt4tmcTo0MSERERkcugQomIiBRxPCeP6auT+WxFEqlZuQB4uZu5o00kwzpFUS/U1+AIRURERMqfAB93Xu3bjEe+/J2Pluzl5hY1ianpb3RYIiIiInIJKpSIiIhTYno2ny5P5Nt1BzhltQEQ5ufJfR2juLt9HapX8zA4QhEREZHyrVfzCHo1q8HcLUd4/vuNzHysI24Ws9FhiYiIiMhFqFAiIlLFORwO1iRm8MmyRBZsS8HhKGhvGuHP8M7RxLeqiYebbu5FRERELtcrfZuxYk86mw6eYOryRB7qWt/okERERETkIlQoERGpoqw2O3M2HWbKskQ2HjjhbL+pSRjDO0dzXf1gTCaTgRGKiIiIVExh/l6MvjmG577fyL/m76RHTA2iQqoZHZaIiIiIXIAKJSIiVcyJU1ZmrElm2ookDp84DYCnm5nbrq3NA52jaRCm9UdEREREXHVH29r89OdBlu8+yqgfNvHVg7H6EoqIiIhIOaVCiYhIFZF8NIepyxP5Zt1+cvIK1h8J8fVgyHVR3BNbh2BfT4MjFBEREak8TCYTE/q3pMekJazce5Sv1+7nrvZ1jA5LRERERIqhQomISCXmcDj4PfkYnyxNZN6WI9jPrD/SONyPB7pEc2urmni5W4wNUkRERKSSqhPsw9+7N2b8nG2Mn7ONG5uEEe7vZXRYIiIiIvIXV1Qo+eCDD3j77bc5cuQIrVq14r333qN9+/YX7D9p0iQ+/PBDkpOTCQkJ4fbbb2fChAl4eRX9gPjGG28watQonnzySSZNmnQl4YmIVHn5Njtztxzhk6WJbNh/3NnetVEowztH06VhiKZ+EJGKwW4Hhw3stjO/55/5s/28tvN/t5e8rz3/AvtfvK8530qjw9shJxYCahidKREpp4Z1iuLnjYfYeOAEL/20mY8GtzU6JBERERH5ixIXSr7++mtGjhzJ5MmTiY2NZdKkSfTs2ZMdO3YQFhZWpP9XX33FCy+8wNSpU+nYsSM7d+5k6NChmEwmJk6cWKjv2rVr+eijj2jZsuWVX5GISBWWddrK12v38+nyJA4ePwWAh8VM/2tqcX/naBrX8DM4QpEqwuEo5mG9rZiH/n9tv8y+hR7sX+IB/0Ue+pvzrTQ9tBPzorWA4+IFghIc91yf/AvsX8y1FXs8m9F/kxdlAZoC1pxnVCgRkQtys5h5c0BL4t9bxrwtKfyy6TC9W0QYHZaIiIiInKfEhZKJEyfy4IMPMmzYMAAmT57M7NmzmTp1Ki+88EKR/itWrKBTp07cfffdAERFRTFo0CBWr15dqN/Jkye55557+Pjjj3nttdeu5FpERKqsA8dymLY8iRlr93MyNx+AoGoeDO5Ql3s71CXUT+uPSAXncEBuJpxMg+xUOJkCJ9MwZx6h+YHNmOctBZPjIg/iLzayoLi+lyponD+yoJi+OIzO2GWxAI0AUgwO5IqZwGwBk+W8383nXpvdirYV29et+P2dv5vPe32urx3Yt/8gtT1VhBaRi2sa4c+jN9TnvUW7GfPTFjrWDyHAx93osERERETkjBIVSvLy8li/fj2jRo1ytpnNZuLi4li5cmWx+3Ts2JEvv/ySNWvW0L59e/bu3cucOXMYPHhwoX6PP/44N998M3FxcZdVKMnNzSU3N9f5OjMzEwCr1YrVai3JZZWKs+c04tyVgfLnGuXPNRU5fxv2H+fTFfuYtzUV25kFSOqFVOP+TnXp2yrCuf5IWV5bRc5feVCl8+dwwOkTkJ2GKTv13O8nz70uaDvzuy23yCEsQH2AtKsdvGscF3qwf9GH+Wdem8w4Cj20L7q96EN+t2L3t2Mief8B6tSNxuzmfpHCQnExXKS4UKSw8NftFhzFXfdf475QweJsnAZPIWi1WtmYkEC4VwgY+PlTRCqGETc1YM6mw+xJy+a12Vt5+45WRockIiIiImeUqFCSnp6OzWYjPDy8UHt4eDjbt28vdp+7776b9PR0OnfujMPhID8/n0ceeYR//OMfzj4zZszg999/Z+3atZcdy4QJExg3blyR9vnz5+Pj43PZxyltCQkJhp27MlD+XKP8uaai5M/ugE0ZJn49bCYx69xDwkYBdm6McNAk8ATm1I0sSth4VeOqKPkrrypN/hwO3G0n8bJm4pl/As/8TDytZ38/jmd+Jl5nXnvkZ2Jx5Jfo8FazF7nuAeS6+ZPrFkCuuz/5Zm/sJgsOkxkH5oKH+JgKXp9pK/T7ZbWZcJgshdownWkvsq/l3D4XO+55xykXzg56qQ2bbUCpz3J19qB5pX3gcseon9+cnBxDzisiV8bTzcKbA1pyx0cr+Xb9Afq2rkXnhiFGhyUiIiIiXOFi7iWxePFiXn/9df7zn/8QGxvL7t27efLJJ3n11VcZM2YM+/fv58knnyQhIaHYxd0vZNSoUYwcOdL5OjMzk8jISHr06IG/v39ZXMpFWa1WEhIS6N69O+7uGkJdUsqfa5Q/11SU/J3Mzef73w8ybWUyB44VrD/ibjER3zKCodfVpWmEMVO/VJT8lVcVIn8OO+RknDe6I/XcKI/sNDiZem4ESE46JnvJih8OT3+oFoqjWihUCzvzeygO3zD4Sxvu3ngCZyeTqxD5K8eUP9cYnb+zI6pFpOJoGxXE4A51+XzlPkbN3Mi8p7ri41Hmt+UiIiIicgkl+kQWEhKCxWIhJaXwRNYpKSnUqFH8ApZjxoxh8ODBDB8+HIAWLVqQnZ3NQw89xIsvvsj69etJTU3l2muvde5js9n47bffeP/998nNzcVisRQ5rqenJ56eRefcd3d3N/RG3+jzV3TKn2uUP9eU1/wdOn6Kz1Yk8dWaZLJOFzyADvRx597Yugy5ri5h/pdfZC5L5TV/FcVVz5/dBjlH4WTqmTU/zq79cabgcTL13Lbs9JIvqu0VCL5hUC0MfEPP/e4bXritWigm94L3sCuTKOn95xrlzzVG5U9/ZyIV03O9mrBgawr7M04xcf5ORt8SY3RIIiIiIlVeiQolHh4etGnThoULF9KvXz8A7HY7CxcuZMSIEcXuk5OTg9lceIqJs4UPh8NBt27d2LRpU6Htw4YNo0mTJjz//PPFFklERKqCjQeO88nSRGZvOlx4/ZHO0Qy4tjbeHvr3Uf7Clg856RcvfmSnFSyEnnO0YKRISXgHnSl+hBZTBAkvVPzAzaNsrlFERKSC8/V0Y3z/FgybtpapyxOJb1WTVpGBRoclIiIiUqWVeIzvyJEjue+++2jbti3t27dn0qRJZGdnM2zYMACGDBlCrVq1mDBhAgDx8fFMnDiRa665xjn11pgxY4iPj8diseDn50fz5s0LnaNatWoEBwcXaRcRqexsdgcLt6XwybJE1iRmONuvqxfM8C7R3Ng4DLPZ2MWL5SqzWQtGdJxMOa/YcYEiSM5Rzi08cTlM4BN8keJH2Lm2aiFg0bfXRURESsONTcLo27omP204xPPfb2TWiM54uJWTNaxEREREqqASF0oGDhxIWloaL730EkeOHKF169bMnTvXucB7cnJyoREko0ePxmQyMXr0aA4ePEhoaCjx8fGMHz++9K5CRKSCy8nL57v1B5i6LJGkowWL87qZTcS3qskDnaNpXivA4AilVOXnFRQ3slMxHT9EnaNLMC/fCaeOFi1+nMq49PHOZzKDT8hfih8XGAHiEwwWzYsuIiJihJduiWHprnS2H8nioyV7eKJbQ6NDEhEREamyrujpyIgRIy441dbixYsLn8DNjbFjxzJ27NjLPv5fjyEiUlmlZJ7msxVJTF+dzIlTVgD8vdy4p0Nd7rsuihoB5WP9EbkM+bmFR3ucTPnLyI/zRoCcPu7czQ24BiD5Isc2WQpGdDiLHRcZAeITDGZNyyYiIlLeBft6MjY+hidnbOC9Rbvp3aIGDcL8jA5LREREpErS10hFRAyw5dAJpixN5OeNh7DaCqZKqhvsw/2dorm9TW2qeeqf53LBeuov63tcpAiSe6Jkxza7QbVQHNVCSc2B0KhmmP3Ciy9+eAeBWdNxiIiIVDa3tqrJj38c5NcdaTz//Sa+ffg6TbMqIiIiYgA9iRMRuUrsdge/7kjlk6WJrNx71NnePiqIB7pEE9c0HItujMteXnbxxY/s1IICyPnFj7yskh3b7H7eaI/wwsWOQiNAwsArEMxm8q1WVs2ZQ58+fTC7aw0QERGRqsRkMvFa/xb0mLiE9fuO8cWqfdzXMcrosERERESqHBVKRETK2Kk8Gz/8cYApyxLZm5YNgMVs4uYWETzQOZpWkYHGBljRORyQd/IixY/Uwm3W7JId3+JZuNBxsemvvALBpGKXiIiIXL5agd4837sJL/20hbfmbicuJpxagd5GhyUiIiJSpahQIiJSRlKzTvPFyn18uWofx3IK1h/x83RjUGwd7usYpRvgi3E4IDezcLHjYkWQ/FMlO76bdzGjPcKLL354+qv4ISIiImXq3ti6zNpwiHX7jvHizE18OrQdJn3+EBEREblqVCgRESll249kMmVpIj9tOESezQ5A7ere3N8pmjvbReJbVdcfcTjg9Inzih0XKn6c+T3/dMmO716t+KmuihsB4uGr4oeIiIiUG2aziTcGtKTPO0tZvCONnzYcot81tYwOS0RERKTKqKJP60RESpfD4WDJzjSmLEtk6a50Z/u1dQJ5sEs9useE42appItx52bhe/ogpn3L4NTRixc/bHklO7aH33nFj0uMAPGoVjbXJyIiInIVNAjz5W/dGvDP+TsZ9/MWujQMIdjX0+iwRERERKoEFUpERFxw2mrjxz8OMmVZIrtSTwJgNkHv5hHc3zmaNnWrGxxhKbHlw/F9cHQ3pO+Co7sgfTcc3YX7yRS6AWy7zGN5Blx+8cNd05OJiIhI1fHw9fX538bDbD+Sxbift/LuoGuMDklERESkSlChRETkCqSfzOXLVfv4YuU+jmYXjJLw9XRjYLtIhnaMIjLIx+AIr1BOxnmFkF3nCiMZe8FuveBueZZquAfWxOQbXnwR5Oz0V9VCwd3rKl6QiIiISMXhbjHz1u0t6ffBcmb9eYh+19TkpibhRoclIiIiUumpUCIiUgK7UrKYsiyRH/44SF5+wfojNQO8GNYpmoHtI/H3cjc4wsuQnwfHEouMDCF9F5zKuPB+bt4Q3ABCGkBwQwhpCMENsAZE8cvCpfTp0wd39wpw/SIiIiLlWMvagTzQOZqPlyby4szNzH86CL+K8BlTREREpAJToURE5BIcDgfLdqfzydJEluxMc7a3qh3A8C716N28Rvlbf8ThKFgn5K8jQ47ugmP7wGG78L4BkWcKIg3PFETOFEb8a4G5mOu0XnikiYiIiIiU3MjujZm3JYXkjBzemruDV/s1NzokERERkUpNhRIRkQvIzbcxa8MhpixLZPuRLABMJugRE86DXerRpm51TCaTsUFaT8HRPUVHhhzdDbmZF97Pw7f4YkhwfS2KLiIiImIwbw8LE25rwT2frOaLVfu4tXVNWtfyMzosERERkUpLhRIRkb/IyM5j+qp9fLZyH+kncwHw8bBwZ9tIhnWKom7wVS4kOByQebDoyJD03XBiP+Aofj+TGQLrFJomy1kY8atRUPURERERkXKpU4MQBraN5Ot1+3n++43MerSD0SGJiIiIVFoqlIiInLEnLZvPV+/n+/UHyD2z/kgNfy+GdopiULs6BPiU8dzQuVlnCiHnjwzZVTBixJpz4f28AouODAlpCEH1wM2zbGMWERERkTLzjz5NWbQjlb1p2XywZC9NjA5IREREpJJSoUREqjSHw8GqvRn833YzW1Yud7Y3r+XP8M71uLllBO6luf6I3QbHk/8yMuTMSJGswxfez+wG1aOLjgwJaQg+wRodIiIiIlIJBfi482rfZjzy5e98vDSJkVqqRERERKRMqFAiIlVSXr6d/208xCdLE9l6OBMwYzJBtybhDO8STWx0kGvrj5w6VnRkSPpuyNgLttwL71cttOjIkOCGUL0uWMp4RIuIiIiIlDu9mkfQq1kN5m45wn/3WBhms+Ouj4UiIiIipUqFEhGpUo7n5DF9dTKfrUgiNaugYOHlbqZtUD4v3dWFRhGBl38wmxWOJRUdGZK+C3LSL7yfxbNg0fS/jgwJbgDeJTi/iIiIiFQJr/Rtxoo96ezPzmfaymQevbGh0SGJiIiIVCoqlIhIlZCYns2nyxP5dt0BTlltAIT5eXJfxyjuuDaClYsXEB1SzCLtDgdkpxcdGXJ0V0GRxJ5/4ZP61Sw6MiSkAQREgtlSNhcqIiIiIpVOmL8XL/RqxD9+3Mo7i3bTp2VN6gYX89lVRERERK6ICiUiUmk5HA7WJGbwybJEFmxLweEoaG8a4c/wztHEt6qJh5sZq9WK2Z4HqdvgRGLhkSFHd8HpExc+ibtP8SNDghuAp+/VuVARERERqfRuv7YWny7azK5MGPXDJqYPj3VtqlgRERERcVKhREQqHavNzpxNh5myLJGNB84VOW5qHMqjbarR1jcd09F5sKCgGOKWvotbju/D9KfjAkc0QWBk4ULI2cKIf00tpC4iIiIiZc5kMnFXfTtvb3ZjxZ6jfLNuPwPb1TE6LBEREZFKQYUSEak0TpyyMmNNMjOWb8c7K4l6psPEuR+ha/Bxmrin4HV4L/xwssh+Z8scDk9/TOdPkXW2MBJUD9y9r+7FiIiIiIj8RYgXPNWtAW/M3clrs7dxQ+Mwwv29jA5LREREpMJToUREKia7HTIPQPouMpK3snvbH+Sn7uQWDvGw6Sh4ntf3+Hl/Nlmget1Co0Pyq9djwR/76HbrXbh7eFzlCxERERERuXz3dajDnM0pbDxwgpd+2sxHg9saHZKIiIhIhadCiYiUb6czCy+gfmb9EMfRPZjyTwEQBLSHc0NDAId3UPGjQ6pHg1vhYojDaiV383FNoSUiIiIi5Z6bxcybA1oS/94y5m1J4ZdNh+ndIsLosEREREQqNBVKRMR4tnw4vq/wAupnCyMnU4rdxQTkOSzsc9RgryMCa/X6NGx6DY2aXYMppBEmn6Crew0iIiIiIldJ0wh/Hrm+Pu//upuXZm2hY/0QAnzcjQ5LREREpMJSoURErp6cjPMKIbvOFUYy9oLdeuH9fMPJr16fXbYIElL9+CMnlL2OCFLN4dx6TR3u7xxN4xp+V+86REREREQMNuKmBszZfJi9admMn7OVt25vZXRIIiIiIhWWCiUiUrry8+BYYtGRIem74FTGhfdz84LgBgW/zpsy66ClFlPXZfD12v2czM0HIKiaB4M71OXeDnUJ9fO88DFFRERERCopL3cLbw5oyR2TV/LNugP0bV2LTg1CjA5LREREpEJSoURESs7hgJOpRUeGHN0Fx/aBw3bhff1rF14z5GxhxL82mM3Obn8kH+OTJYn8smk9dkdBW/3QagzvUo/+19TCy91SxhcpIiIiIlK+tYsKYsh1dfl85T5G/bCJuU91wcdDt/kiIiIiJaVPUCJyYdZTcHRPsYupk5t54f08fIuMDCG4IQTXB49qF9zNZncwf8sRPlmWyPp9x5ztnRuE8ECXaK5vGIrZrAXXRURERETOeq5XExZsTSE5I4eJ83cy+pYYo0MSERERqXBUKBGp6hwOyDxYdGRI+m44sR9wFL+fyQyBdYqODAluCH41wHT5BY2Tufl8u24/U5cnsj/jFADuFhO3tqrFA52jianpXwoXKiIiIiJS+fh6ujG+fwuGTVvL1OWJxLeqSavIQKPDEhEREalQVCgRqSpyTxYzMmRXwYgRa86F9/MKLDoyJKQhVI8Gdy+XQjp0/BSfrUjiqzXJZJ0uWH8k0Mede2PrMuS6uoT5u3Z8EREREZGq4MYmYfRtXZOfNhzi+e83MmtEZzzczJfeUUREREQAFUpEKhe7DTIO/GVkyJmRIlmHL7yf2Q2qR/2lGNKooCDiE1yi0SGXY+OB43yyNJHZmw5jO7MASb2QatzfOZoB19bG20Prj4iIiIiIlMRLt8Tw2840th/J4qMle3iiW0OjQxIRERGpMFQoEanIHA5IWopl/WfcuHslbhsfBFvuhfv7hBSdJiukYUGRxOJepqHa7A4Wbkvhk2WJrEnMcLZ3qBfE8M71uKlJmNYfERERERG5QsG+noyNb8ZTX2/gvUW76d2iBg3C/IwOS0RERKRCUKFEpCKynoJN38LqjyBlM2bAuYqHxQOC6heeJuvsSBHv6lc91Jy8fL5bf4CpyxJJOlowxZeb2UR8q5o80Dma5rUCrnpMIiIiIiKVUcH0Wwf5dUcaL3y/iW8evk5fRhIRERG5DCqUiFQkJw7C2k9g/TQ4dWZUhrsPthYDWXM8iLa9BuEeUg/Mxk9dlZJ5ms9WJDF9dTInTlkB8Pdy4+7YugztGEWNAK0/IiIiIiJSmkwmE6/1b0GPiUtYt+8YX67ex5DroowOS0RERKTcU6FEpLxzOGD/Glj9IWydBQ5bQXtgHWj/EFxzL3Y3X1LnzCmYQsvgIsmWQyeYsjSRnzcewmorWH+kbrAP93eK5vY2tanmqX92RERERETKSq1Ab57v3YSXftrCm79sp1vTcGoFehsdloiIiEi5pieWIuVVfi5smQmrJ8OhP861R3WB2IehcZ9zRRGr1ZgYz7DbHfy6I5VPliaycu9RZ3v7qCAe6BJNXNNwLBryLyIiIiJyVdwbW5dZGw6xbt8xXpy5iU+HtsNk0udxERERkQtRoUSkvDmZCuumwtopkJ1a0GbxhJZ3QOwjUKOFsfGd51SejR/+OMCUZYnsTcsGwGI2cXOLCB7oHE2ryEBjAxQRERERqYLMZhNvDGhJn3eWsnhHGj9tOES/a2oZHZaIiIhIuaVCiUh5cegPWDUZtvwAtryCNr8IaDcc2gyFaiGGhne+1KzTfLFyH1+u2sexnILRLH6ebgyKrcN9HaM0tF9ERERExGANwnx54qYG/CthJ+N+3kKXhiEE+3oaHZaIiIhIuWQ2OgCRKs2WD5t/gCk94f9ugI0zCooktdvDgCnw1Cbo+ky5KZJsP5LJs9/+Sec3fuW9Rbs5lmOldnVvXrolhpX/6MY/+jRVkURERETkjA8++ICoqCi8vLyIjY1lzZo1l7XfjBkzMJlM9OvXr1C7w+HgpZdeIiIiAm9vb+Li4ti1a1cZRC6VxcPX16dJDT+O5Vh55X9bjQ5HREREpNzSiBIRI+RkwPppsPYTyDxY0GZ2h2b9ocMjUKuNoeGdz+FwsGRnGlOWJbJ0V7qz/do6gTzYpR7dY8Jxs6jmKiIiInK+r7/+mpEjRzJ58mRiY2OZNGkSPXv2ZMeOHYSFhV1wv6SkJJ555hm6dOlSZNtbb73Fu+++y2effUZ0dDRjxoyhZ8+ebN26FS8vr7K8HKmgPNzMvDmgJf3/s5yfNhyib+ua3NQk3OiwRERERModFUpErqaULQWLs2/8BvJPF7RVC4W29xf88qthbHznOW218eMfB5myLJFdqScBMJugd/MI7u8cTZu61Q2OUERERKT8mjhxIg8++CDDhg0DYPLkycyePZupU6fywgsvFLuPzWbjnnvuYdy4cSxdupTjx487tzkcDiZNmsTo0aPp27cvAJ9//jnh4eH8+OOP3HXXXWV+TVIxtYoM5IHO0Xy8NJHRMzcz7+kg/LzcjQ5LREREpFxRoUSkrNltsHNuQYEk8bdz7TVaQodHofkAcCs/cwWnn8x1rj9yNLtgrRRfTzcGtotkaMcoIoN8DI5QREREpHzLy8tj/fr1jBo1ytlmNpuJi4tj5cqVF9zvlVdeISwsjAceeIClS5cW2paYmMiRI0eIi4tztgUEBBAbG8vKlSuLLZTk5uaSm5vrfJ2ZmQmA1WrFarVe8fVdqbPnNOLclYEr+XvihnrM3XyE/cdO8cacbbwc37S0wyv39P5zjfLnGuXPNcqfa5Q/1ymHrjE6f5d7XhVKRMrK6RPwx5ew5v/gWFJBm8kCTW+B2EehTgcwmQwN8Xy7UrKYsiyRH/44SF6+HYCaAV4M6xTNwPaR+OtbZyIiIiKXJT09HZvNRnh44SmOwsPD2b59e7H7LFu2jClTprBhw4Zitx85csR5jL8e8+y2v5owYQLjxo0r0j5//nx8fIz78ktCQoJh564MrjR/8REm/nPMwvQ1+wnOTqS+fykHVkHo/eca5c81yp9rlD/XKH+uUw5dY1T+cnJyLqufCiUipS19F6z+CDZ8BdbsgjavQGgzFNoNh8BII6MrxOFwsGx3Op8sTWTJzjRne6vaAQzvUo/ezWto/RERERGRMpaVlcXgwYP5+OOPCQkJKbXjjho1ipEjRzpfZ2ZmEhkZSY8ePfD3v/pPya1WKwkJCXTv3h13d30Jp6RczV8fIHXmFr77/SD/S/Fn1oDr8HS3lH6g5ZTef65R/lyj/LlG+XON8uc65dA1Rufv7KjqS1GhRKQ02O2wZ1HB9Fq7z6uOhjYtWJy9xZ3gUX6mrMrNtzFrwyGmLEtk+5EsoGBwS4+YcB7sUo82datjKkejXUREREQqkpCQECwWCykpKYXaU1JSqFGj6Jp0e/bsISkpifj4eGeb3V4wwtfNzY0dO3Y490tJSSEiIqLQMVu3bl1sHJ6ennh6Fp3i1d3d3dCbfKPPX9G5kr8xtzRjya509qbnMHnpPp7p2biUoyv/9P5zjfLnGuXPNcqfa5Q/1ymHrjEqf5d7ThVKRFyRexL+/G/BCJKju840mqBRr4ICSfT15Wp6rYzsPKav2sdnK/eRfrJgvmofDwt3to1kWKco6gZXMzhCERERkYrPw8ODNm3asHDhQvr16wcUFD4WLlzIiBEjivRv0qQJmzZtKtQ2evRosrKyeOedd4iMjMTd3Z0aNWqwcOFCZ2EkMzOT1atX8+ijj5b1JUklEeDjziu3NuPR6b8zeckebm4ZQdOIKjoHl4iIiMh5VCgRuRLHkmDNx/D7F5B7oqDN0x+uuRfaPwhB9QwN7692p55k6vJEvl9/gNwz64/U8PdiaKcoBrWrQ4CPquEiIiIipWnkyJHcd999tG3blvbt2zNp0iSys7MZNmwYAEOGDKFWrVpMmDABLy8vmjdvXmj/wMBAgELtTz31FK+99hoNGzYkOjqaMWPGULNmTWcxRuRy9G4RQa9mNZi75QjPf7+RHx7tqOl2RUREpMpToUTkcjkckLQUVk2GHXMAR0F7UH2IfQRaDwJPP0NDPJ/D4WDl3qNMWZrIwu2pzvbmtfwZ3rkeN7eMwF03RCIiIiJlYuDAgaSlpfHSSy9x5MgRWrduzdy5c52LsScnJ2M2l+yz2HPPPUd2djYPPfQQx48fp3PnzsydOxcvL6+yuASpxF7p24wVe9LZeOAEny5P4sGu5euLXiIiIiJXmwolIpdiPQWbvi2YXitl87n2+jdB7KPQIA5KeJNblvLy7fxv4yE+WZrI1sMFixWZTNCtSTjDu0QTGx2k9UdEREREroIRI0YUO9UWwOLFiy+677Rp04q0mUwmXnnlFV555ZVSiE6qsjB/L168uSnPf7+JfyXsoEezcE3DKyIiIlWaCiUiF3LiIKz9BNZPg1MZBW3uPtBqEMQ+DKHla+HDbCtMXrKXL1bvJzWrYP0RL3czd7QpWH+kXqivwRGKiIiIiEh5cWfbSH7acIgVe44y6odNTB8eqy9UiYiISJWlQonI+RwO2L8GVn8IW2eBw1bQHlgH2j9UsAaJd3VjY/yL3Hwbb/yyg+m/W8iz7wYg1M+ToR2juLt9HapX8zA4QhERERERKW9MJhMTbmtBz0m/sWLPUb5Zt5+B7eoYHZaIiIiIIVQoEQHIz4UtM2H1ZDj0x7n2qC4Fo0ca9wGzxbj4LuKDX/fw6Yp9gIkm4b482LU+t7SKwNOtfMYrIiIiIiLlQ93gaozs3ojX52zntdnbuLFxGGH+WvNGREREqh4VSqRqO5kK66bC2imQfWbBc4sntLyjYIH2Gi2Mje8S9qadZPLiPQDcVc/GK0Ovw8NDI0hEREREROTy3N8pmp//PMymgyd46actTB7cxuiQRERERK46FUqkajr0B6yaDFt+AFteQZtfBLQbDm2GQrUQQ8O7HA6Hg5d+2kKezU7XhsF0CE7RnMIiIiIiIlIibhYzbw5oya3vL2PuliPM3XyYXs0jjA5LRERE5KpSoUSqDls+bJsFqz+C/avOtdduXzC9VkxfsLgbF18JzfrzEMt2p+PpZualW5qyZVWK0SGJiIiIiEgFFFPTn0eur8/7v+5mzE9buK5eCAE+FefeSERERMRVKpRI5ZeTAeunwdpPIPNgQZvZHZr1hw6PQK2KN7Q887SV12ZvA2DEjQ2oG+TDFoNjEhERERGRimvETQ2Ys/kwe9OyGT9nK2/d3srokERERESuGhVKpPJK2VKwOPvGbyD/dEFbtVBoe3/BL78axsbngn/N20FaVi71Qqvx0PX1wGE3OiQREREREanAvNwtvDmgJXdMXsk36w7Qt3UtOjUo/1MSi4iIiJQGFUqkcrHbYOfcggJJ4m/n2mu0hA6PQvMB4OZpXHylYNOBE3yxah8Ar/VtjqebBatVhRIREREREXFNu6ggBneoyxer9jHqh03Me6or3h4Wo8MSERERKXMqlEjlcPoE/PElrPk/OJZU0GayQNNbIPZRqNMBKsFC5za7gxd/3ITdAX1b16SjvuElIiIiIiKl6LlejVmwLYXkjBwmJuzgxZtjjA5JREREpMypUCIVW/qugsXZN3wF1uyCNq9AaDMU2g2HwEgjoyt1X63ex8YDJ/DzcuPFm5saHY6IiIiIiFQyfl7ujO/fnPunrWPKskRuaVmTVpGBRoclIiIiUqbMV7LTBx98QFRUFF5eXsTGxrJmzZqL9p80aRKNGzfG29ubyMhInn76aU6fPu3c/uGHH9KyZUv8/f3x9/fnuuuu45dffrmS0KQqsNth1wL48nZ4vy2s/bigSBLaFG6ZBCO3Qfdxla5Ikpp1mrfm7gDg2Z6NCfPzMjgiERERERGpjG5qEk7f1jWxO+D57zditWmqXxEREancSjyi5Ouvv2bkyJFMnjyZ2NhYJk2aRM+ePdmxYwdhYWFF+n/11Ve88MILTJ06lY4dO7Jz506GDh2KyWRi4sSJANSuXZs33niDhg0b4nA4+Oyzz+jbty9//PEHzZo1c/0qpXLIPQl//rdgBMnRXWcaTdCoF3R4BKKvrxTTa13I+NnbyMrNp2XtAO6JrWt0OCIiIiIiUom9dEsMv+1MY/uRLD5asocRNzU0OiQRERGRMlPiESUTJ07kwQcfZNiwYcTExDB58mR8fHyYOnVqsf1XrFhBp06duPvuu4mKiqJHjx4MGjSo0CiU+Ph4+vTpQ8OGDWnUqBHjx4/H19eXVatWXfmVSeVxLAnmvQgTY2DOMwVFEg8/6PAYPLEe7p4B9W6o1EWS5bvT+WnDIcwmGN+vBRZz5b1WERERERExXrCvJ2PjC764+O7C3exOzTI4IhEREZGyU6JCSV5eHuvXrycuLu7cAcxm4uLiWLlyZbH7dOzYkfXr1zsLI3v37mXOnDn06dOn2P42m40ZM2aQnZ3NddddV5LwpDJxOCDxN/jv3fBOa1j5PuSegKD60Pst+Ps26DUBgusbHWmZy823MebHzQAM7lCXFrUDDI5IRERERESqgr6ta3JD41DybHZe+H4TdrvD6JBEREREykSJpt5KT0/HZrMRHh5eqD08PJzt27cXu8/dd99Neno6nTt3xuFwkJ+fzyOPPMI//vGPQv02bdrEddddx+nTp/H19WXmzJnExMRcMJbc3Fxyc3OdrzMzMwGwWq1YrdaSXFapOHtOI85dGTjzl5OJaecsLGs/xpS6xbndXu9G7O0ewlG/G5jMZ3cyItSr7sPFe9mbnk2orwdP3lSv2PeY3n+uUf5co/y5RvlzjfLnGuXPNUbnT39vIlLWTCYT4/u3oMfEJazbd4wvV+9jyHVRRoclIiIiUupKvEZJSS1evJjXX3+d//znP8TGxrJ7926efPJJXn31VcaMGePs17hxYzZs2MCJEyf47rvvuO+++1iyZMkFiyUTJkxg3LhxRdrnz5+Pj49PmV3PpSQkJBh27orMKy+DpukLsbz7GG62kwDkmz3YH9SZvaHdOelVC3ZaYedcgyO9utJPwwcbLICJ3hGnWLro4u8vvf9co/y5RvlzjfLnGuXPNcqfa4zKX05OjiHnFZGqpVagN8/1asLYWVt485ftdGsaTq1Ab6PDEhERESlVJSqUhISEYLFYSElJKdSekpJCjRo1it1nzJgxDB48mOHDhwPQokULsrOzeeihh3jxxRcxmwtGB3h4eNCgQQMA2rRpw9q1a3nnnXf46KOPij3uqFGjGDlypPN1ZmYmkZGR9OjRA39//5JcVqmwWq0kJCTQvXt33N3dr/r5KySHA9PBtZjXfIRp+/8wOWwFzQGR2Ns+gKPVvdT2DqS2wWEaxeFwMPyL37E6jtKxXhCjB7fBdIF1WPT+c43y5xrlzzXKn2uUP9cof64xOn9nR1SLiJS1wR3q8vOfh1i37xijZ25i6tB2F7w3EREREamISlQo8fDwoE2bNixcuJB+/foBYLfbWbhwISNGjCh2n5ycHGcx5CyLxQIUPAi+ELvdXmhqrb/y9PTE09OzSLu7u7uhN/pGn79CyM+FLTNh9WQ49IezOd23CYE9R+HWLB6L2YLFwBDLgzmbDvPbrqN4WMy81r8FHh4el9xH7z/XKH+uUf5co/y5RvlzjfLnGqPyp78zEblazGYTbwxoSZ93lvLrjjRm/XmIvq1rGR2WiIiISKkp8dRbI0eO5L777qNt27a0b9+eSZMmkZ2dzbBhwwAYMmQItWrVYsKECQDEx8czceJErrnmGufUW2PGjCE+Pt5ZMBk1ahS9e/emTp06ZGVl8dVXX7F48WLmzZtXipcqhjuZCuumwtopkJ1a0GbxhJZ3YL32AZb/vp8+TfqAuaqXSOBkbj6v/LwVgEduqE+9UF+DIxIRERERkaqsQZgvT9zUgH8l7OTlWVvo3CCEYN+iX14UERERqYhKXCgZOHAgaWlpvPTSSxw5coTWrVszd+5c5wLvycnJhUaQjB49GpPJxOjRozl48CChoaHEx8czfvx4Z5/U1FSGDBnC4cOHCQgIoGXLlsybN4/u3buXwiWK4Q79Aasmw5YfwJZX0OYXAe0egDbDoFrImYXZ9xsaZnny74SdHMk8Td1gHx67ob7R4YiIiIiIiPDw9fWZvekw249k8cr/tvLOXdcYHZKIiIhIqbiixdxHjBhxwam2Fi9eXPgEbm6MHTuWsWPHXvB4U6ZMuZIwpDyz5cO2WbD6I9i/6lx77XYQ+wjE9AWLposoztZDmUxbkQTAK32b4+WuETYiIiIiImI8Dzczbw5oSf//LOenDYfo27omNzUJNzosEREREZddUaFE5IJyMmD9NFj7CWQeLGgzu0Oz/gUFktptDA2vvLPbHbz44yZsdgc3t4jg+kahRockIiIiIiLi1CoykPs7RfPJskRGz9zM/JHB+Hrq0YKIiIhUbPo0I6UjZUvB4uwbv4H80wVtPiHQ9v6CKbb8ahgbXwUxY+1+/kg+TjUPC2NuiTE6HBERERERkSJG9mjEvK1H2J9xirfmbueVvs2NDklERETEJSqUyJWz22Dn3IICSeJv59prtIQOj0Kz28Ddy7j4Kpj0k7m8OXc7AH/v0ZgaAcqdiIiIiIiUPz4ebrxxW0vu+WQ1X6zax62tatI2KsjosERERESumAolUnKnT8AfX8Ka/4NjSQVtJjM0jYfYR6FOBzCZDA2xIpowZzsnTlmJifBnyHV1jQ5HRERERETkgjo1COHOtrX5Zt0Bnv9+I7P/1kXrK4qIiEiFpUKJXL70XQWLs2/4CqzZBW1egdDmPmj3IARGGhpeRbZ671G+//0AJhOM798cN4vZ6JBEREREREQu6sU+Mfy6I409adm8v2g3z/RsbHRIIiIiIldEhRK5OLsd9iwqmF5rd8K59tCmEPswtBwIHj7GxVcJ5OXbGf3jZgAGta/DNXWqGxyRiIiIiIjIpQX4uPPKrc14dPrvTF6yh5tbRtA0wt/osERERERKTIUSKV7uSfjzvwUjSI7uOtNogka9oMMjEH29ptcqJVOWJbIr9STB1Tx4vmcTo8MRERERERG5bL1bRNCzWTjztqTw/Pcb+eHRjhohLyIiIhWOCiVS2LEkWPMx/P4F5J4oaPPwg2sHQ7vhEFzf0PAqm/0ZObyzcCcA/+jTlAAfd4MjEhERERERKZlX+jZnxZ6jbDxwgk+XJ/Fg13pGhyQiIiJSIiqUCDgckLQUVk2GHXMAR0F7UP2C6bVa3w2efoaGWFmN+3kLp612YqODuO3aWkaHIyIiIiIiUmLh/l682KcpL/ywiX8l7KBHs3DqBlczOiwRERGRy6ZCSVVmPQWbvi2YXitl87n2+jdB7KPQIA7MGjJdVuZvOcKCbam4W0yM798ck6YyExERERGRCmpgu0h+2nCIlXuPMuqHTUwfHqt7HBEREakwVCipik4chLWfwPppcCqjoM3dB1oNKhhBEtrY0PCqgpy8fMb9vBWAB7vUo0GYRuyIiIiIiEjFZTKZeGNAC3pO+o0Ve47y7boD3Nku0uiwRERERC6LCiVVhcMB+9fA6g9h6yxw2AraA+pA+wcL1iDxrm5sjFXIOwt3cfD4KWpX9+aJmxoaHY6IiIiIiIjL6gZXY2T3Rrw+Zzuvzt7KDY1DCfP3MjosERERkUtSoaSyy8+FLTNh9WQ49Me59rqdocMj0LgPmC3GxVcF7TiSxZSliQCMu7UZ3h7Kv4iIiIiIVA73d4rm5z8Ps+ngCV76aQuTB7cxOiQRERGRS1KhpLI6mQrrpsLaKZCdWtBm8YSWd0D7hyGipbHxVVF2u4PRP24i3+6gR0w43ZqGGx2SiIiIiIhIqXGzmHlzQEtufX8Zc7ccYe7mw/RqHmF0WCIiIiIXpUJJZXPoD1g1Gbb8ALa8gja/CGj3ALQZBtVCjI2vivvu9wOsTTqGt7uFsbc2MzocERERERGRUhdT05+Hr6/HB7/uYcxPW7iuXggBPu5GhyUiIiJyQSqUVAa2fNg2C1Z/BPtXnWuv3Q5iH4GYvmDRh1KjHcvOY8KcbQA83b0htQK9DY5IRERERESkbDxxU0N+2XyEvWnZvD5nG2/erlkNREREpPxSoaQiy8mA9dNg7SeQebCgzewOzfoXFEhqay7Y8uTNuds5lmOlcbgfwzpFGx2OiIiIiIhImfFyt/DmgJbcMXklX6/bT9/WNenYQDMciIiISPmkQklFlLKlYHH2jd9A/umCNp8QaHt/wRRbfjWMjU+KWL8vgxlr9wMwvn9z3C1mgyMSEREREREpW+2ighjcoS5frNrHCz9sYt5TXfH2sBgdloiIiEgRKpRUFHYb7JxbUCBJ/O1ce42W0OFRaHYbuHsZF59ckNVm58WZmwG4s21t2kYFGRyRiIiIiIgBUrdisZ02Ogq5yp7r1ZgF21JIzshhYsIOXrw5xuiQRERERIpQoaS8O30C/vgS1vwfHEsqaDOZoWk8xD4KdTqAyWRoiHJx05Ynsf1IFoE+7rzQu6nR4YiIiIiIXH12O27f3UfPzBTMXr9Dh4chqJ7RUclV4Oflzvj+zbl/2jqmLEvklpY1aRUZaHRYIiIiIoWoUFJepe8qWJx9w1dgzS5o8wqENvdBuwchMNLQ8OTyHDp+in8v2AnAqN5NCKrmYXBEIiIiIiIGyDoEJhPuthxYMxnWfASNekLsw1DvRn35q5K7qUk4t7aqyaw/D/H89xv5+YnOmo5YREREyhUVSsoTux32LCqYXmt3wrn20KYFNxAtB4KHj3HxSYm98vNWcvJstK1bnTvaqLglIiIiIlVUQG3yH1nFuhlvEmv6E/OeBQVTC++cCyGNoP1D0GoQePoaHamUkbHxMSzdlcb2I1l8tGQPI25qaHRIIiIiIk76Ckd5kHsS1nwMH7SH6QPOFElM0Kg3DPkJHlsJbYepSFLB/Lo9lblbjmAxm3itf3PMZn1LTkRERESqMJOZ1IBW2O6aASPWQ+wj4OEH6TthzjMwsSnMHQVH9xgdqZSBYF9PxsY3A+DdhbvZnXrS4IhEREREzlGhxEjHkmDeizAxpuDG4Oiu/2/vzsOjKs83jn9nJpOVJJCELIRAIGFNwr7IHpRFQQS1v1ptEbFqVWjRtLYuIKJYahfEWgSrVaxotVbBBUQQCPsimyQQAlkgLFkJZCX7/P4YCERACBM4We7PdZ0r5uTMzHMex5g3d973tQ8Ubnocfr0T7vsI2kdrGnoDdKaskue/sG/g/svB7egc6GVwRSIiIiIi9YhfONz2CsTsh9v+Ar7hUJoPW9+A13vDBz+FpNX2WffSaIzv0YroTi0pq6zimc/2UlVlM7okEREREUBLb914Nhsc3gBbF0LicuDsD4Y+YfbltXrcBy6ehpYojpu/NomjuWdo5e3KtFs0pVxERERE5JJcvaD/I9D3IUhZY9+n8dBKOPSN/fDtYF+Wq8e9Gic1AiaTiZfvjGLU3HV8d/gUH2w7wsQBoUaXJSIiIqKg5IYpPwNxn9h/8M+MP38+7Gbo/xiEjwCzJvg0BklZBby53r5cwPPjIvBw0X9mIiIiIiI/ymy2j4nCR9iX3tr+Fuz5wD7r/uunYPWL0PPn9tDEN8zoasUBwc3d+P2tnZn5xT7+9PUBbu4SQHBzN6PLEhERkSZOv5m/3vKOw7ez7MtrffFre0hidYc+v4Qp22HiEug4SiFJI2Gz2Zi+NJ7yShs3d/ZndESA0SWJiIiIiDQsvmFw25/sy3KN+at9VklZAWxbCK/3gsU/gUPfalmuBmziTW3p3bYFRWWVTF8Sh82mJbhERETEWPpT9+vBZoOj22HbAtj/Bdgq7ee920C/h6HXRHBrYWyNcl0s3XOcrSm5uFrNzLojApP2lxERERERuTYunvbxU59fQspa2P5POPgNJK2yH+eWL+5+r30JL2kwzGYTr9wdxZjXNrI2MZsvvj/B+B7BRpclIiIiTZiCkjpkrirHFPdf2PEWnNh9/gttB8NNj0KnMWC2GFegXFd5xeW8vCwBgF/f3IEQH3eDKxIRERERaQTMZgi/xX7kpsD2t2H3+5CbDF//Hla/ZN/rsd8j9k3ipUEI9/dk6s3hzF11kFlf7mdIh5b4eDgbXZaIiIg0UVrvqS4UZmFe/2dG7ovB6YvH7SGJxQV6/gJ+tQEmL4Mu4xSSNHJ/WXmAnMIywv2b8fCQ9kaXIyIiIiLS+Pi0h1v/CDEJMPZv4NfJvizX9jfhH71h8d1wcKWW5WogHh0WRudAT3KLynjxy31GlyMiIiJNmIKSurDpNSwb/oxrRR62ZoFw83T7errj50NQN6Orkxtgz9HTfLAtDYCXxkfi7KT/tERERERErhuXZtD3IZiyDSYuhY63ASZI+hY+/D97aLJ1AZTkGV2p/AhnJzOv3N0NswmW7jnB2sQso0sSERGRJkq/za0LfR+iqnU/doQ+TsXU3TD0KfDwM7oquUEqKqt4bkkcNhvc1TOYAWG+RpckIiIiItI0mEwQNhzu+wh+sxsGTAUXb/sSXSuehrldYdnvIPug0ZXKZXQPac6Dg9oB8NxncRSWVhhckYiIiDRFCkrqgk87Kict53iLm8BiNboaucHe33qEfSfy8XJ14tmxXYwuR0RERESkafJpB6Nfts/uHzsXWnaGskL47i2Y3xfev9O+GbyW5ap3YkZ1JMTHjRN5Jfx5xQGjyxEREZEmSEGJiAMy80v420r7X6f94bbO+DVzMbgiEREREZEmzqUZ9P0lPL4V7v8cOo0FTJC8Bj78KbzeC7a8oWW56hF3Zyfm3Glftvr9rUfYcTjX4IpERESkqVFQIuKAl77aT2FpBT1CmnNv3zZGlyMiIiIiIueYTNA+Gu79EKbtgYG/BldvOJUK3zwDf+sCy34L2YlGVyrA4A5+/F/v1ths8IdP91JSXml0SSIiItKEKCgRuUYbDmXz1d50zCaYPSESs9lkdEkiIiIiInIpLUJh1GyISYDb50HLLlBeBN+9DfP7wb8nQOLXUKVfzhtp+tiu+DVzITm7iPlrk4wuR0RERJoQBSUi16CkvJIZS+MBmDQwlMhgb4MrEhERERGRK3L2gD6T4fEtMOlL6Hw7mMyQshb+8zP7slyb/wFnThtdaZPk7W7lpfERACyITSYhPd/gikRERKSpUFAicg0WxCZz+GQxAV4uxIzsaHQ5IiIiIiJSGyYTtBsKP/sAfrMHBv4GXJvDqcOw8jmY2wW+ehKytLH4jXZbVBCjIwKoqLLxh0/3UlFZZXRJIiIi0gQoKBGppdScIhbEJgMw4/aueLpaDa5IRERERESuWYu2MOol+7Jc414D/wgoL4Yd78Ab/eG9O+DAci3LdQO9OD4ST1cn9h7L491Nh40uR0RERJoABSUitWCz2Xj+83jKKqsY2rElY6OCjC5JRERERETqgrM79H4AHtsEk76CLuPsy3KlroOP7oW/94TNr8OZU0ZX2ugFeLny3JguAPxtVSJHThYZXJGIiIg0dgpKRGrhq73pbDiUg7OTmRfviMBk0gbuIiIiIiKNiskE7YbAPYth2vcw6AlwawGnj8DK6TC3K3z5BGQlGF1po3ZP3xAGtPelpLyKZz6Lw2azGV2SiIiINGIKSkSuUkFJOS99tR+AKdHhhPp5GFyRiIiIiIhcV83bwMhZ8OR+uON1CIi0L8u181144yZ4bxwkfKVlua4Dk8nEnLuicHEyszn5JJ/sOGZ0SSIiItKIKSgRuUp/W3mQrIJS2vl58Gh0e6PLERERERGRG8XZHXrdD49uhAeWQ5c7zi7LtR4+/jn8vQdseg2Kc42utFEJ9fMgZmRHAGYv209WfonBFYmIiEhjpaBE5CrEH8/j31sOA/DS+EhcnCzGFiQiIiIiIjeeyQShg+Ce92HaXhj8JLj5wOk0WPW8fVmuL34DmfuMrrTR+OXgdkQFe5NfUsHML9RXERERuT4UlIhcQWWVjeeWxFFlg3HdWzG4g5/RJYmIiIiIiNGah8CIFyBmP9zxDwiIgoozsOs9WDAQFt0O+7+AygqjK23QnCxmXrm7G05mE1/HZ7AiPt3okkRERKQRUlAicgUfbk/j+2N5eLo4MWNsF6PLERERERGR+sTqBr0mwqMbYPLX0HUCmCxweAP8d6J9Wa6N87QslwO6tvLiV8Psyx/P+HwfecXlBlckIiIijY2CEpEfkV1Qyp9XHADgd6M74e/lanBFIiIiIiJSL5lM0HYg/PQ9eGIvDPmtfVmuvKPw7UyY2wU+nwoZcUZX2iD9+uYOtG/pQXZBKX9cnmB0OSIiItLIKCgR+RF/XJ5AQUkFUcHe/OKmtkaXIyIiIiIiDYF3a7jleYhJgPFvQGA3qCiB3e/DwsHw7ljY/7mW5aoFV6uFP93VDYCPdxxlc1KOwRWJiIhIY6KgROQyNifnsGT3cUwmePnOSCxmk9EliYiIiIhIQ2J1hZ4/h1+thwe/gYg77ctyHdkI/70fXusOG+ZC0UmjK20Q+rXz4Rc3tQHg6c/iOFNWaXBFIiIi0lgoKBG5hNKKSqYvjQfgF/3b0q11c2MLEhEREZEGZf78+YSGhuLq6kr//v3Zvn37Za/97LPP6NOnD82bN8fDw4MePXrw/vvv17imsLCQqVOn0rp1a9zc3OjatSsLFy683rchdcVkgjY3wf8tgifiYMjvwN0P8o/B6lnwalf4fAqk7zW60nrvD7d2JsjblbTcYl799qDR5YiIiEgjoaBE5BLeWp9CSnYRfs1c+N3oTkaXIyIiIiINyMcff0xMTAwzZ85k165ddO/endGjR5OVlXXJ6318fHjuuefYsmULe/fuZfLkyUyePJlvvvmm+pqYmBhWrFjB4sWLSUhI4IknnmDq1Kl88cUXN+q2pK54B8MtM+DJfTBhAQR1P7ss12J4cwi8cxvsW6JluS7D09XKy3dGAvD2hhT2HjttbEEiIiLSKCgoEfmBtJPFvL4mCYAZt3fB281qcEUiIiIi0pDMnTuXhx9+mMmTJ1fP/HB3d+edd9655PXR0dHceeeddOnShbCwMKZNm0a3bt3YuHFj9TWbN29m0qRJREdHExoayiOPPEL37t1/dKaK1HNWV+hxHzyyDh5cCZF3g9kJ0jbDJw/Aa91gw9+0LNcl3Nw5gDu6t6LKBr//317KK6uMLklEREQaOCejCxCpT2w2GzO/iKe0oopB4b7c0b2V0SWJiIiISANSVlbGzp07eeaZZ6rPmc1mRowYwZYtW674eJvNxpo1a0hMTOSVV16pPj9w4EC++OILHnzwQVq1akVsbCwHDx7k1VdfvexzlZaWUlpaWv15fn4+AOXl5ZSXl1/L7Tnk3Gsa8dr1XlAvGP8m3PwC5l2LMO96D1P+cVj9IrbYV7BF3E1FzwcA9e+cZ2/ryIZD2RzIKOCNNYd4PLr9j16v959j1D/HqH+OUf8co/45Tj10jNH9u9rXVVAicoFv9mWwNjEbZ4uZF8dHYjJpA3cRERERuXo5OTlUVlYSEBBQ43xAQAAHDhy47OPy8vIIDg6mtLQUi8XCG2+8wciRI6u//vrrr/PII4/QunVrnJycMJvNvPXWWwwdOvSyzzlnzhxmzZp10fmVK1fi7u5+DXdXN1atWmXYazcM3TF3eIVWp7fTPnslLYpTMe39ELe9HzLYoyP7Tm0jvXlvbCYN58e2MrE4ycLf1xzC7eQBAtyu/Bi9/xyj/jlG/XOM+ucY9c9x6qFjjOpfcXHxVV2nn6xEziosrWDWl/sB+NWw9oS1bGZwRSIiIiLSVHh6erJnzx4KCwtZvXo1MTExtG/fnujoaMAelGzdupUvvviCtm3bsn79eqZMmUKrVq0YMWLEJZ/zmWeeISYmpvrz/Px8QkJCGDVqFF5eXjfitmooLy9n1apVjBw5EqtVy9te2XiwzabixE7M3/0TU8IX+BYdxLfoIDbPIKp6Taaq5/3g4Wd0oYa5zWbj6Pu7WXcoh5Wn/Pjgzr6YzZf+Yze9/xyj/jlG/XOM+ucY9c9x6qFjjO7fuVnVV6KgROSseasOkp5XQhsfd6YMDze6HBERERFpgPz8/LBYLGRmZtY4n5mZSWBg4GUfZzabCQ+3/wzao0cPEhISmDNnDtHR0Zw5c4Znn32WJUuWMHbsWAC6devGnj17+Otf/3rZoMTFxQUXF5eLzlutVkMH+Ua/foMTOgBCB1Cem0byf2fQqWAzpoJ0LOv+iGXjXyHyJ9D/EWjV0+hKDfHyXVGMenU9O46c5r+7TjBxQOiPXq/3n2PUP8eof45R/xyj/jlOPXSMUf272tfUZu4iQEJ6Pu9uPgzArPERuFotxhYkIiIiIg2Ss7MzvXv3ZvXq1dXnqqqqWL16NQMGDLjq56mqqqreX+TcniJmc83hm8VioapKm1g3GZ5BJAbdRcXU3XDnP6FVL6gsg+8/hH9Gw79GQdz/oLJprZ/euoU7f7i1MwB/+voAJ06fMbgiERERaYgUlEiTV1Vl47klcVRW2RgTFcjwTv5GlyQiIiIiDVhMTAxvvfUW7733HgkJCTz22GMUFRUxefJkAO6///4am73PmTOHVatWkZKSQkJCAn/72994//33+cUvfgGAl5cXw4YN46mnniI2NpbU1FQWLVrEv//9b+68805D7lEM5OQC3e+BR9bCQ6sh6qdgtsLRbfDpL2FeFKz7CxRmG13pDTPxprb0btuCorJKpi+Nx2azGV2SiIiINDBaekuavP/uOMqutNN4OFt4/vYIo8sRERERkQbunnvuITs7m+eff56MjAx69OjBihUrqjd4T0tLqzE7pKioiMcff5xjx47h5uZG586dWbx4Mffcc0/1NR999BHPPPMMP//5z8nNzaVt27a8/PLLPProozf8/qQead3HfoyaDTvfhe/+BQXpsHY2rP8zRN4N/R6B4F5GV3pdmc0mXrk7ijGvbWTNgSy++P4E43sEG12WiIiINCAKSqRJyy0q408rDgDw5MiOBHq7GlyRiIiIiDQGU6dOZerUqZf8WmxsbI3PZ8+ezezZs3/0+QIDA3n33XfrqjxpbDwDIPppGBwD+z+HbQvh+A74/j/2o3Vf6P8odLkDnJyNrva6CPf3ZOrN4cxddZBZX+5nSIeW+Hg0znsVERGRundNS2/Nnz+f0NBQXF1d6d+/P9u3b//R6+fNm0enTp1wc3MjJCSEJ598kpKSkuqvz5kzh759++Lp6Ym/vz8TJkwgMTHxWkoTqZU5yxM4XVxOlyAvHhgYanQ5IiIiIiIi187JGbr9Hzy8Gh5aA93usS/Ldey7s8tyRULsK1CYZXSl18Wjw8LoFOBJblEZL365z+hyREREpAGpdVDy8ccfExMTw8yZM9m1axfdu3dn9OjRZGVd+getDz/8kKeffpqZM2eSkJDAv/71Lz7++GOeffbZ6mvWrVvHlClT2Lp1K6tWraK8vJxRo0ZRVFR07XcmcgXbU3P5ZOcxAGZPiMTJoi17RERERESkkWjdG+76Jzy5D6KfhWaBUJgJsX+EuV3hs0fg2E6jq6xTzk5mXvlJN8wmWLrnBGsTG2cgJCIiInWv1r8Znjt3Lg8//DCTJ0+ma9euLFy4EHd3d955551LXr9582YGDRrEfffdR2hoKKNGjeLee++tMQtlxYoVPPDAA0RERNC9e3cWLVpEWloaO3c2rh/apP4or6xi+tI4AO7tF0Lvti0MrkhEREREROQ68AyA6D/AE3Fw97+gdT+oKoe9H8PbN8Nbt8De/0JFmdGV1okeIc2ZPKgdAM99FkdhaYXBFYmIiEhDUKs9SsrKyti5cyfPPPNM9Tmz2cyIESPYsmXLJR8zcOBAFi9ezPbt2+nXrx8pKSksX76ciRMnXvZ18vLyAPDx8bnsNaWlpZSWllZ/np+fD0B5eTnl5eW1ua06ce41jXjtxuBG9++fG1I5mFlIC3crMbeEN/h/b3r/OUb9c4z65xj1zzHqn2PUP8cY3T/9exORWnFyhqif2I/ju2D7PyH+U/teJp/tgJXTofdk6DMZPAONrtYhvx3VkZX7Mziae4a/rDjArPGRRpckIiIi9VytgpKcnBwqKysJCAiocT4gIIADBw5c8jH33XcfOTk5DB48GJvNRkVFBY8++miNpbcuVFVVxRNPPMGgQYOIjLz8DzNz5sxh1qxZF51fuXIl7u7utbirurVq1SrDXrsxuBH9yy2F1/ZYABNjgkrYHNt4/p3p/ecY9c8x6p9j1D/HqH+OUf8cY1T/iouLDXldEWkEgnvBnQth5EuwcxF89zYUZsC6P8GGv0HEBPvm7637GF3pNXF3dmLOnd34xb+28e+tR7ijRyu6tfI0uiwRERGpx2oVlFyL2NhY/vjHP/LGG2/Qv39/kpKSmDZtGi+99BIzZsy46PopU6YQHx/Pxo0bf/R5n3nmGWJiYqo/z8/PJyQkhFGjRuHl5VXn93El5eXlrFq1ipEjR2K1Wm/46zd0N7J/j32wm7KqbPqGtmDmpD6YTKbr+no3gt5/jlH/HKP+OUb9c4z65xj1zzFG9+/cjGoRkWvWrCUMewoGPwEJX8C2N+HoNoj7xH606mUPTCImgJOL0dXWyuAOfvxf79Z8svMYv//fXj5/7CajSxIREZF6rFZBiZ+fHxaLhczMzBrnMzMzCQy89NTcGTNmMHHiRB566CEAoqKiKCoq4pFHHuG5557DbD6/TcrUqVP56quvWL9+Pa1bt/7RWlxcXHBxufgHNavVauhA3+jXb+iud/9W7c/k2wPZOJlNvHxnFM7OztfttYyg959j1D/HqH+OUf8co/45Rv1zjFH9078zEakzFitE3m0/TuyGbf+E+P/BiV2w5BFY+Rz0edB+NKBluaaP7craxGySs4t4Y10qnYwuSEREROqtWm3m7uzsTO/evVm9enX1uaqqKlavXs2AAQMu+Zji4uIaYQiAxWIBwGazVX+cOnUqS5YsYc2aNbRr165WNyFyNYrLKnjhi30APDSkPR0DNPVaRERERESkhlY94c4F8OR+uHk6eLaComxY9wq8GgH/+yUc3Q5nx/P1mbe7lRfHRwD2fSqPFxlckIiIiNRbtQpKAGJiYnjrrbd47733SEhI4LHHHqOoqIjJkycDcP/999fY7H3cuHEsWLCAjz76iNTUVFatWsWMGTMYN25cdWAyZcoUFi9ezIcffoinpycZGRlkZGRw5syZOrpNEfj76iSOnz5DcHM3fnNLuNHliIiIiIiI1F/NWsLQp+CJvfCTd6HNAKiqsM80+ddIeGs47PkPVJQaXemPui0ykFFdA6iosvGfZAsnC+t3vSIiImKMWu9Rcs8995Cdnc3zzz9PRkYGPXr0YMWKFdUbvKelpdWYQTJ9+nRMJhPTp0/n+PHjtGzZknHjxvHyyy9XX7NgwQIAoqOja7zWu+++ywMPPHANtyVS08HMAt7ekALArDsicHe+7tvziIiIiIiINHwWK0TeZT/Sv7cvyxX3iX2JrqWPwqoZ0PsB+7JcXq2MrvYiJpOJlyZEsiXlJEeLKhj2tw3c268NDw9tT3BzN6PLExERkXrimn5bPHXqVKZOnXrJr8XGxtZ8AScnZs6cycyZMy/7fLYGMGVXGi6bzcb0pfFUVNkY2TWAEV0DjC5JRERERESk4QnqDhPmw8hZsOs9+O5fkH8c1v8FNr4KXe6wb/4e0g9MJqOrrRbg5crbE3vx+/9s40hhFYs2H2bx1iPc2TOYR6PDCGvZzOgSRURExGC1XnpLpKH5dNdxtqfm4ma18MIdEUaXIyIiIiIi0rB5+MGQ38K0vfB/70GbgfZlufZ9Bu+Mgn8Ogz0fQnmJ0ZVW69WmOU9GVvLeA70ZGOZLRZWNT3YeY8TcdUz5YBfxx/OMLlFEREQMpKBEGrXTxWX8cXkCANNGdNDUahERERERkbpicYKICfDg1/CrDdDzF+Dkal+ia+lj8GpXWP0S5J8wulLAPsllYJgvHz58E0seH8jIrgHYbLAsLp3bX9/IA+9uZ3tqrtFlioiIiAEUlEij9sqKA+QWldExoBm/HNzO6HJEREREREQap6BuMH4+PLkfbpkJXq2h+CRs+Cu8GgmfPABHtkA9WXq7Z5sWvHV/H755YigTerTCbILYxGx++uYW/m/hZtYmZmmZcBERkSZEQYk0WjuPnOI/248CMHtCFFaL3u4iIiIiIiLXlYcvDImBad/DT/8NbQeDrRL2LYF3b4U3h8LuxfVmWa5OgZ7M+1lP1v4umvv6t8HZYua7w6eY/O533P76RpbtTaeySoGJiIhIY6ffHEujVFFZxfSl8QD8X+/W9GvnY3BFIiIiIiIiTYjFCbqOh8nL4NGN0Ot++7JcGXvh8yn2Zbm+nQV5x4yuFIC2vh788c4oNvxhOA8PaYe7s4V9J/KZ8uEuRs5dx393HKWsosroMkVEROQ6UVAijdKizYdJSM+nubuVZ8Z0MbocERERERGRpiswCu54HWISYMQs8A6xL8u1cS7M6wb/vR+ObK4Xy3IFeLny3NiubPrDzUy7pQPeblZScor4/f/2Ev2XtSzalMqZskqjyxQREZE6pqBEGp30vDO8uuogAE/f2hkfD2eDKxIRERERERHcfWDwE/CbPXDPYggdYl+Wa//n8O5tsHAI7Hofys8YXSktPJx5cmRHNj19M8+O6UxLTxdO5JXwwpf7GfzKGuavTSK/pNzoMkVERKSOKCiRRuelr/ZTVFZJrzbN+WmfEKPLERERERERkQtZnKDLOHjgK3hsM/SaBE5ukBkHX0yFuV3h2xfg9FGjK6WZixOPDA1jw++H8/KdkYT4uHGyqIy/fJPIoDlr+Ms3B8gpLDW6TBEREXGQghJpVNYmZrE8LgOL2cTLd0ZhNpuMLklEREREREQuJyAC7vg7xOyHkS+Cdxs4kwsbX4XXusHHE+HwRsOX5XK1Wvh5/7as/W008+7pQceAZhSUVjB/bTKDX1nDC1/s48Rp42fCiIiIyLVRUCKNRkl5JTM/3wfA5IGhdAnyMrgiERERERERuSruPjBoGkzbA/d8AO2Ggq0KEr6ARWNh4WDY+R6UFRtappPFzISewayYNpR/TuxN99belJRXsWjzYYb+eS1PffI9ydmFhtYoIiIitaegRBqN+WuTSMstJtDLlSdGdjS6HBEREREREaktswW63A6TvoTHtkDvyWeX5YqHL38Dr3aFVc/D6TRjyzSbGBURyNIpg/jgof4MDPOlosrGJzuPMWLuOqZ8sIv443mG1igiIiJXT0GJNArJ2YUsXJcMwAt3dKWZi5PBFYmIiIiIiIhDArrCuHnw2wQYNRuat4Ezp2DTa/Bad/jo55C6wdBluUwmE4PC/fjw4Zv47PGBjOgSgM0Gy+LSuf31jTzw7na+O5xrWH0iIiJydRSUSINns9mYsTSe8kobwzu1ZHREoNEliYiIiIiISF1xawEDfw2/2QM/+w+0G2ZfluvAV/De7bBgIOxcZPiyXL3atODtSX1Y8cQQxvdohdkEsYnZ/N/CLfx04RZiE7OwGbzXioiIiFyaghJp8L74/gSbk0/i4mRm1h2RmEzawF1ERERERKTRMVug8xiY9AU8vhX6PAhWd8jaD19Og7ldYOUMOHXE0DI7B3rx2s96svZ30dzbrw3OFjPbD+fywLvfcfvrG1kel05llQITERGR+kRBiTRoeWfKeemr/QD8+uZw2vi6G1yRiIiIiIiIXHf+XeD2VyFmP4x6GVqEQslp2Px3+HsP+7JcKesMXZarra8Hc+6KYsMfhvPwkHa4O1vYdyKfxz/YxchX1/HJjqOUV1YZVp+IiIicp6BEGrS/fpNITmEZ7Vt68PDQ9kaXIyIiIiIiIjeSWwsYOBV+vQvu/QjaDz+/LNe/74A3BsCOd6CsyLASA7xceW5sVzb94Wam3dIBbzcrKdlFPPW/vUT/JZZFm1I5U1ZpWH0iIiKioEQasO+PnmbxNvuU6tnjI3FxshhckYiIiIiIiBjCbIFOt8H9S2HKduj7EFg9IDsBvnoSp9e7EXX035ji/gsZcVBRdsNLbOHhzJMjO7Lp6Zt5dkxnWnq6cPz0GV74cj+DX1nD/LVJ5JeU3/C6REREBJyMLkDkWlRW2Zi+NB6bDe7sGczAcD+jSxIREREREZH6oGUnGPs3uHkG7PkQtr+J6dRh2pd8C198a7/GbLVfFxAJAREQGAkBUdCs5XUvr5mLE48MDeP+AaH8b+cxFq5L5tipM/zlm0QWxiZz/8C2TB7UDr9mLte9FhEREbFTUCIN0uKtR4g7noeXqxPPjulidDkiIiIiIiJS37g1hwGPQ/9HqTjwNWlr3iHUrRBzVgKU5kFmvP24ULMAe3ASEAmBUfaPfh3AYq3z8lytFn5xU1t+1jeEL/ee4I21yRzKKmT+2mT+tTGVn/VtwyND29OquVudv7aIiIjUpKBEGpys/BL++k0iAE/dap+uLCIiIiIiInJJZjO2DqOIO1RByJgxmJ2cIO8oZMSfD0sy4iE3BQoz7UfymvOPtzifnX0SdXbmSYT9nz1866Q8J4uZO3u2Znz3YFYlZPLG2iS+P5bHos2H+WDbEe7sGcyjw8Jo37JZnbyeiIiIXExBiTQ4Ly1LoKC0gu6tvbmvXxujyxEREREREZGGxGSC5m3sR+cx58+XFkJWQs3wJHMflBXY9zXJiIPvL3gez6ALlu46O/vENxws1/arFrPZxOiIQEZ1DWBT0kneiE1ic/JJ/rvjGJ/sPMaYqCAejw4jopW3Y/cvIiIiF1FQIg3KhkPZfPn9CcwmePnOKCxmk9EliYiIiIiISGPg0gxC+tqPc6qqIC/t/OyTjDh7eHIqFQrS7UfSqvPXW1zAv/MPZp9EgrvPVZdhMpkY3MGPwR382JV2ijfWJvNtQibL9qazbG86wzu15PHh4fQNvfrnFBERkR+noEQajJLySp7/fB8A9w8IJTJYf0UjIiIiIiIi15HZDC1C7UeX28+fLy2AzP2QeTY4OTf7pLwI0r+3HxfyCr5443jfMDBbfvTle7VpwduT+nAgI58Fscl8+f0J1iZmszYxm36hPjw+PIxhHVtiMumPCEVERByhoEQajDfXpZCaU4S/pwsxozoaXY6IiIiIiIg0VS6e0Ka//TinqgpOH75g9snZj6ePQP5x+3Hom/PXO7mCf5eaG8cHRNg3of+BzoFevPaznsSM7MjCdSl8uvMY2w/nsv3dXCJaeTFleDijIwK16oKIiMg1UlAiDcLhnCLmxyYBMOP2rni5Wg2uSEREREREROQCZjP4tLcfXe84f74k7+zskwsClKz9UF4MJ3bbjwt5h5wNTy7YON6nHZgttPX1YM5dUUy7pQNvb0jhg21p7DuRz+Mf7KJ9Sw8eGxbGhJ7BWC3mG3vvIiIiDZyCEqn3bDYbMz6Pp6yiiiEd/Li9W5DRJYmIiIiIiIhcHVdvaDvAfpxTVQm5qT/YOD4e8o6ePw5+ff56qzv4d63eOD4wIJLpIyKYMjycdzcfZtGmVFKyi3jqf3uZ9+0hHhnannv6huBq/fGlvURERMROQYnUe8vi0tlwKAdnJzMvjo/U2qsiIiIiIiLSsJkt4BduPyImnD9/5rR9r5MLN44/N/vk+A77cYEWzdsQExDF1EFdiT3tz4JEN/acbs7ML/bx+ppDPDi4Hb+4qa1WZRAREbkCBSVSrxWUlPPil/sBeGxYGO38PAyuSEREREREROQ6cWsOoYPsxzlVlXAy+Qcbx8fb9zw5nQan03BOXMYoYBRQ7uHOAVsIe0tak7CqDY/Htqdvv8H8YmgEvs1cDLoxERGR+k1BidRrc1cdJKuglFBfdx6LDjO6HBEREREREZEby2yBlh3tR+Td588X514w+yTeHqRkHcBaWUwUiUQ5JZ6/djukbfMnq0UXgjv3xattT/syXs3b2vdWERERaeIUlEi9FX88j/c2HwbgxfGRWltVRERERERE5Bx3H2g3xH6cU1kBJ5Oql+6yZe6j9Nj3uJZk0caUBaezYOs62Hr2emfPsxvGR5zdPD4KArqCs1ZzEBGRpkVBidRLlVU2nlsaT5UNbu8WxNCOLY0uSURERERERKR+sziBf2f7EfUTTIArYCvKIX7XJnZ/twn3Uwl0MR0h3HQcl7ICOLrVflQzgU87CIiEwCj7R99OYLMZdFMiIiLXn4ISqZf+sz2N74+eppmLEzNu72p0OSIiIiIiIiINlsnDj6gh44kaMp6dR07xamwSsQknaG9Kp4vpCKP9chjULAPv/EQozITcFPuR8AUAVmCMxR3LyW4Q1O3sLJQo8O8Czu7G3pyIiEgdUFAi9U52QSl/XnEAgN+O6kiAl6vBFYmIiIiIiIg0Dr3btuDtSX1JSM9nQWwyX+4N4fMsIAv6tfPhiVEtGOB+AlPW+Y3jbdmJWCuLLz37xDfs7OyTc0t3RYB3azCZjLpFERGRWlNQIvXOnOUJ5JdUENHKi4k3tTW6HBEREREREZFGp0uQF3+/tycxIzvy5vpk/rfzGNtTc7kvNZfIYC+mRP+E0TdNxWw2UVFSxMal7zCkow9O2fvPbyBfnGPfE+VkEuxfev7JXZtfEJ5E2P/ZvwtY3Yy6XRERkR+loETqlW2puXy2+zgmE7x8ZxROFrPRJYmIiIiIiIg0WqF+Hsy5qxvTbunI2xtS+GBbGvHH83nsg12EtfTgsehwxkS0JN+tDbaoMWC1nn9wQSZkxp2debLPHqDkHISS03Bko/04x2QG3w41N44PjATPIM0+ERERwykokXqjogpmfpkAwM/7t6FHSHNjCxIRERERERFpIgK9XZl+e1ceHx7Oos2HWbQpleTsIn73yffMXenKgBYmbi6vxHphUOIZYD/CR5w/V1EK2QfswUlG/Pkg5Uwu5CTaj32fnb/ezedseHJ24/iACGjZGaxahltERG4cBSVSb6xNN5GcXYRfM2eeGt3Z6HJEREREREREmhwfD2diRnbk4SHt+HBbGm9tSOVEXgmf5lmI/dsGfjmkHb+4qS1ertZLP4GTCwR1tx/n2GxQkHF2ya44+8fMfZBzyB6gHN5gP84xWcCv4wVLd52dfdIsQLNPRETkulBQIvXC0VPFfHPUvszWc2O74O12mR+4REREREREROS683S18qthYUwaGMpH24/w95X7OVlUxp9XJLIgNplJA0KZPCgU32YuV34ykwm8guxHh5Hnz5eXQHZCzaW7MuLsS3dlJ9iPuE/OX+/ud+nZJ07OdX7/IiLStCgoEcPZbDZe/OoA5TYTN7VrwYQewUaXJCIiIiIiIiKAq9XCz/uF4JUVR2XrHry54TBJWYX8Y20Sb29M4d5+bXh4SHtaNb+GjdqtrtCqp/04x2aD/ONnl+6KO79xfG6yffP41HX24xyzE/h1qrlxfGAUNPN3/OZFRKTJUFAihvtmXyaxB3OwmGy8MK4rJk2jFREREREREalXLGYY16MVd/duw8r9mbwRm8TeY3m8u+kwi7ce4a6erXk0Oox2fh6OvZDJBN6t7UfH0efPlxVfYvZJPJTmQdY++3EhD/+LN4736wgWrWAhIiIXU1AihioqrWDWl/YfZm5uZSOspYM/UImIiIiIiIjIdWM2m7g1MpDREQFsTMrhjbXJbEk5ycc7jvLJzqOMiQri8ehwurbyqtsXdnaH4N724xybDfKOXrxxfG4KFGVBShakrL2geKt9qa7AyPNLdwVGgYdf3dYqIiINjoISMdRrqw+RnldC6xZujAouMLocEREREREREbkKJpOJIR1aMqRDS3YeOcWC2CS+Tcjiq73pfLU3nZs7+/N4dBh9Qn2uZxHQvI396HTb+fNlRZCVUHPj+Mx9UJpvD1My42o+T7PAizeO9+0AFv3aTESkqdB3fDHMgYx8/rUxFYCZt3emOOk7gysSERERERERkdrq3bYFb0/qS0J6Pgtik/lq7wnWHMhizYEs+rXzYcrwcIZ28LtxS207e0DrPvbjHJsNTh+5YOmus7NPTqVCYQYkZUDSt+evt7hAy041N44PjAL36xj8iIiIYRSUiCGqqmw8tySeyiobt0YEEt2xJcuTjK5KRERERERERK5VlyAv/n5vT2JGduTN9cn8b+cxtqfmsj11O5HBXkyJDmd0RCBmswF7k5pM0CLUfnS5/fz50oKas08y4iFrP5QVQsZe+3Ehz1YXbxzvE6bZJyIiDZy+i4shPtl5lJ1HTuHubOH5cV2NLkdERERERERE6kionwdz7urGb27pwNsbUvlwWxrxx/N57INdhLX04LHocMb3aIXVYja6VHDxhJB+9uOcqio4ffjs7JOzM1Ay4uwzUgpO2I9DK89f7+R6wd4nUec3kXdrccNvR0REro2CErnhcovKmPP1AQCeHNGRVs3dKC8vN7gqEREREREREalLQd5uzLi9K1OGh7NoUyqLNh8mObuI333yPa+uOsivhrXnp31CcLVajC61JrMZfNrbj653nD9fkn922a74mrNPyoshfY/9uJBXawiMxOzXheDcM5hSPcCzJbj5gLuvfYN6ERGpFxSUyA33p68TOF1cTudATx4YFGp0OSIiIiIiIiJyHfl4OBMzqhMPD23PB9vSeHtDKsdPn+H5z/fx99WH+OXg9vzipjZ4ulqNLvXHuXpB2wH245yqSjh1uObSXZn7IC8N8o9B/jEsB1fQB+DIwprP5+R6NjTxsc8+cT8boFSf+8FHdx9w8bYHOSIiUqcUlMgNteNwLv/dcQyAl++MrB/TbEVERERERETkuvN0tfLosDAeGBjKJzuPsTA2meOnz/DKigO8EZvEAwNDeWBgKL7NXIwu9eqZLeAbZj8iJpw/f+b02dkn+6hK/57cpO/wdTNhOnMKinOhqhwqSs4v5XW1TGZ7qFIdnpwLVlpcIlg5+zW3FuDkXNd3LiLSqCgokRumvLKK55bEA/CzviH0butjcEUiIiIiIiIicqO5Wi1MvKktP+sbwpffn+CN2GSSsgp5fU0Sb21I4d5+bXhkaHuCvN2MLvXauTWH0EEQOojK8nI2LV/OmDFjsFqtYLPZN4svPmkPTc7kQvGpsx9z7efP/fOFXysrBFvV2cedhJO1qMfZ84IwxffyM1YuPOfsASbT9eqQiEi9oqBEbph3NqaSmFmAj4czf7i1s9HliIiIiIiIiIiBrBYzd/VqzYQewazcn8n8tUnEHc/j3U2HWbz1CHf1bM2j0WG08/MwutS6ZTLZN5F38YQWoVf/uIrSC8KTS3y85NdOATYoK7Afp9Ou/vUszj8IVlpcesbKhedcm2tpMBFpkBSUyA1x/PQZ5n17CICnb+tMCw9N+RQRERERERERMJtN3BoZyOiIADYm5TB/bRJbU3L5eMdRPtl5lDFRQTweHU7XVl5Gl2osJxfwCrIfV6uqEkryLhOsnKwZqFR/7SRUltmPwgz7cdVMFwcql5uxcuFHpwa03JqINEoKSuSGmPXFPs6UV9I3tAU/6dXa6HJEREREREREpJ4xmUwM6dCSIR1asvNILm+sTWb1gSy+2pvOV3vTubmzP1OGh2kp79owW86HFFfLZoOyoqucsXLB18oKAJv93Jnc2tXp3OzivVbOzlgxu3gTnHsEU7IreLY8/zXnZloaTETqjIISue5WJ2Sycn8mTmYTsydEYTbrf2IiIiIiIiIicnm92/rwrwd82H8inwXrklm29wRrDmSx5kAW/dv5MGV4OEM6+GHSL8rrnskELs3sR/M2V/+4ijL7zJQaIcoV9mE5c8q+70pZof3Iu3hpMAvQB+DIwppfMFt/MDulxY/sv3JuY/vm9vBIROQHFJTIdXWmrJKZX+wD4JdD2tEp0NPgikRERERERESkoejayovX7+3Jb0d25M31yfxv5zG2peayLXU7UcHeTBkexqiugfqjzPrAyRk8A+zH1aqqgtK8mst//WAz+6qik5w8egg/dxOmc0FMRQlUlUNhpv24aiZw9b4gRLkwWLl4Nkv116yutW6HiDQsCkrkunp9zSGOnTpDcHM3pt3SwehyRERERERERKQBCvXzYM5d3fjNLR14e0MqH25LI+54Ho8u3kVYSw8ejw7njh6tsFq0kXiDYjbbZ4K4tbjsJZXl5WxevpwxY8ZgtVrtJ8uKLzNz5dRl9mE5ZQ9ksEHJaftBytXXaXX/8b1WqoOVC8IWFy8tDSbSgCgokevmUGYB/1xv/5/OzHFdcXfW201ERERERERErl2Qtxszbu/KlOHhLNqUyqLNh0nOLuK3n3zP3FUH+dWw9vy0TwiuVi2v1Kg5u9sP71rsg1tZccHSYCcvs+fKqYu/ZquE8mL7kX/s6l/P7HR+ObAay4NdajbL2Y9uLcCi35+JGEH/5cl1YbPZmL40nooqGyO6+DMqItDokkRERERERESkkfDxcCZmVCceHtqeD7al8faGVI6fPsPzn+/j76sP8cvB7fnFTW3wdLUaXarUFxYnaNbSflwtmw1K86+wmf0l9mEpL4aqCijKth+14ep98YyV6mClxaVns1jdavcaInIRBSVyXXy26zjbUnNxtZqZOS7C6HJEREREREREpBHydLXy6LAwHhgYyic7jrJwXQrHT5/hlRUHWBCbxKSBoUwe1A4fD2ejS5WGyHR2TxNXb/Bpd/WPKy/5kZkrpy7ah4Xi3LPLgQElefbjVOrVv56T2yX2WrHPWDG7eBNyMg1TQjm4eICTCzi52j9aLvjnCz9arFo2TJocBSVS504Xl/HH5QkA/OaWDoT4uBtckYiIiIiIiIg0Zq5WCxMHhPKzfm34Ys8J3ohNIjm7iNfXJPH2hlTu7deGh4e2I8hbf3kvN4DVFaytwKvV1T+mssIellxx5soP9mGpKoeKM5B/3H78gAXoBZD2z9rdwyUDFJeLzzm5XHC4XiKIOff5hV9zvsy1F76Wfm0tN9Y1vePmz5/PX/7yFzIyMujevTuvv/46/fr1u+z18+bNY8GCBaSlpeHn58dPfvIT5syZg6urKwDr16/nL3/5Czt37iQ9PZ0lS5YwYcKEa7ohMd6fv0nkZFEZHfyb8dDg9kaXIyIiIiIiIiJNhNVi5u7erbmzZzAr92cwf20yccfzeGdTKu9vPczdvVrzq2FhtPPzMLpUkZosTuDhZz+uls0GpQUXz1i5YDP7qqIcso8m0bKFJ+bKMqgohYoS+8fKC/65oqTmc1eUnD2XV6e3edVMlsuHKk6uYPnh+ToMbc6FQmazMfcuhqh1UPLxxx8TExPDwoUL6d+/P/PmzWP06NEkJibi7+9/0fUffvghTz/9NO+88w4DBw7k4MGDPPDAA5hMJubOnQtAUVER3bt358EHH+Suu+5y/K7EMLvSTvGf7WkAzJ4QibOTvqGIiIiISNNTmz8u++yzz/jjH/9IUlIS5eXldOjQgd/+9rdMnDixxnUJCQn84Q9/YN26dVRUVNC1a1c+/fRT2rRpcyNuSUSkQTGbTdwaGcToiEA2HMrhjdgktqbk8tF3R/nvjqOM7daKx6PD6BLkZXSpItfOZAJXL/vRIvSSl1SWl7N1+XLGjBmD2foje/bYbFBZdjYgKasZoJz7WFl68bmK0guOkh9cf5nnudzzV5VfUE8llBfZD6OYreDkipOTCyPLq3A68sKlQ5WLgpgLA5krBT0/MjvH4qwl0G6gWgclc+fO5eGHH2by5MkALFy4kGXLlvHOO+/w9NNPX3T95s2bGTRoEPfddx8AoaGh3HvvvWzbtq36mttuu43bbrvtWu9B6omKyiqeWxKPzQZ392pN//a+RpckIiIiInLD1faPy3x8fHjuuefo3Lkzzs7OfPXVV0yePBl/f39Gjx4NQHJyMoMHD+aXv/wls2bNwsvLi3379lXP0hcRkUszmUwM7diSoR1bsvNILm+sTWb1gSy+/P4EX35/gps7+zNleBi92/oYXaqIsUym87+kN0pV1cWzXC4brlw4K6auwpszYKu6oJ5yKCvHVFaAO0Bu7o3vyZX2k7nqGTc/DGOucsZNE1oCrVZ3WlZWxs6dO3nmmWeqz5nNZkaMGMGWLVsu+ZiBAweyePFitm/fTr9+/UhJSWH58uUX/XWUNHzvbTlCQno+3m5Wnh3T2ehyREREREQMUds/LouOjq7x+bRp03jvvffYuHFjdVDy3HPPMWbMGP785z9XXxcWFnb9bkJEpBHq3daHfz3gw/4T+SxYl8yyvSdYcyCLNQey6N/OhynDwxnSwQ+T/oJbxBhmM5jdwGrgXkKVFRfNoikvKWLTutUM7t8HJyrqdhbNVS+BZhCT+Spnz1zunCtmkxP++WeAMcbdx1WoVVCSk5NDZWUlAQEBNc4HBARw4MCBSz7mvvvuIycnh8GDB2Oz2aioqODRRx/l2WefvfaqgdLSUkpLS6s/z8/PB6C8vJzy8vLLPey6OfeaRrx2fZCRX8LclYkA/G5kB7xczLXqRVPvn6PUP8eof45R/xyj/jlG/XOM+ucYo/unf2/107X8cdmFbDYba9asITExkVdeeQWAqqoqli1bxu9//3tGjx7N7t27adeuHc8888yP7u2oMVPjov45Rv1zTGPrX4eWbsz9SSS/Gd6OtzYcZsmeE2xLzWVb6nYiW3nx6NB2jOzij9lcN4FJY+vfjab+OUb9uwZmF3B2AWf70nzlruXkuadQFtgL248tX1YXqpdAu3i2jKmiFCovCF0u+LqpxvUXPqbk/PP98DE1Qp0LHlNjCbQqKC+2H9fIArTyGVLvx00mm81mu9onPXHiBMHBwWzevJkBAwZUn//973/PunXraiyndU5sbCw/+9nPmD17Nv379ycpKYlp06bx8MMPM2PGjIsLMpmuajP3F154gVmzZl10/sMPP8Td3f1qb0nqyLsHzew5aSa0mY1pkZXU0c8SIiIiInIZxcXF3HfffeTl5eHlpfXV64trGTMB5OXlERwcTGlpKRaLhTfeeIMHH3wQgIyMDIKCgnB3d2f27NkMHz6cFStW8Oyzz7J27VqGDRt2yefUmElE5OqcLoU16Wa2ZJooq7L/QiPAzcaI4Cp6+9qwaPtVEWlKbFWYbRVYqsox28rPfizDXFWBxVaOuar8oo/nrzv/8cKv5zbrSJrvpX9mvd6udtxUqxklfn5+WCwWMjMza5zPzMwkMDDwko+ZMWMGEydO5KGHHgIgKiqKoqIiHnnkEZ577jnM5mv7v80zzzxDTExM9ef5+fmEhIQwatQoQwaK5eXlrFq1ipEjR2K93sliPbPhUA57tuzCYjbx9/sH0CXIs9bP0ZT7VxfUP8eof45R/xyj/jlG/XOM+ucYo/t3bnaANA6enp7s2bOHwsJCVq9eTUxMDO3btyc6OpqqKvta2ePHj+fJJ58EoEePHmzevJmFCxdeNijRmKlxUf8co/45pin07z7gZFEZ/96Sxvvb0sg8U8EHSRZic1x5eHAod/cKxtVquabnbgr9u57UP8eof45TDx1TXl7OngYwbqpVUOLs7Ezv3r1ZvXp19YyPqqoqVq9ezdSpUy/5mOLi4ovCEIvF/j+WWkxmuYiLiwsuLhdvLmS1Wg19wxr9+jdaSXkls5bZl117YGAo3do4tvlZU+tfXVP/HKP+OUb9c4z65xj1zzHqn2OM6p/+ndVP1/LHZWBfnis8PBywhyAJCQnMmTOH6Oho/Pz8cHJyomvXrjUe06VLFzZu3HjZ59SYqXFS/xyj/jmmsfcvsLmV39/WhceGh7N4axr/2pjK8dMlvPDVAf4Rm8pDQ9rx8/5t8HS9th409v5db+qfY9Q/x6mHjqnv46ZaT+eIiYnhrbfe4r333iMhIYHHHnuMoqKi6o0K77///hrr8Y4bN44FCxbw0UcfkZqayqpVq5gxYwbjxo2rDkwKCwvZs2cPe/bsASA1NZU9e/aQlpZW2/LkBntjbRJHThYT6OXKkyM7Gl2OiIiIiIihLvzjsnPO/XHZhUtxXUlVVVX1/iLOzs707duXxMTEGtccPHiQtm3b1k3hIiJSzdPVymPRYWz8w3BeGh9BcHM3cgpL+dPXBxj0pzXMXZlIblGZ0WWKiEgdqtWMEoB77rmH7Oxsnn/+eTIyMujRowcrVqyo3uA9LS2txgyS6dOnYzKZmD59OsePH6dly5aMGzeOl19+ufqaHTt2MHz48OrPz00PnzRpEosWLbrWe5PrLCW7kIXrUgB4flxXmrnU+u0kIiIiItLoxMTEMGnSJPr06UO/fv2YN2/eRX9cFhwczJw5cwCYM2cOffr0ISwsjNLSUpYvX87777/PggULqp/zqaee4p577mHo0KHVe5R8+eWXxMbGGnGLIiJNgqvVwsQBofysXxu+2HOCN2KTSM4u4u9rknhrQyr39mvDw0PbEeTtZnSpIiLioGv6zfbUqVMvu9TWD39Qd3JyYubMmcycOfOyzxcdHe3QMlxy49lsNmZ8Hk9ZZRXDOrbktsjLLyMgIiIiItKU1PaPy4qKinj88cc5duwYbm5udO7cmcWLF3PPPfdUX3PnnXeycOFC5syZw29+8xs6derEp59+yuDBg2/4/YmINDVWi5m7e7fmzp7BrNyfwfy1ycQdz+OdTam8v/Uwd/dqzaPDwgj18zC6VBERuUaaAiDX5IvvT7Ap6SQuTmZeHB+ByWQyuiQRERERkXqjNn9cNnv2bGbPnn3F53zwwQd58MEH66I8ERG5BmaziVsjgxgdEciGQznMX5vEttRcPvruKP/dcZSx3VrxeHQYXYK8jC5VRERqSUGJ1Fp+STmzlyUAMHV4OG199RcTIiIiIiIiItI0mEwmhnZsydCOLdlxOJc3YpNZcyCLL78/wZffn+CWzv48Pjyc3m1bGF2qiIhcJQUlUmt/+yaR7IJS2rf04JFh7Y0uR0RERERERETEEH1CfXjnAR/2nchjQWwyy+PSWX0gi9UHsripvQ9ThofTv6230WWKiMgVKCiRWtl77DT/3noEgNnjI3FxshhckYiIiIiIiIiIsSJaefOP+3qRmlPEm+uS+XTXMbam5LI1ZTtRwV708TAxoqIKq9XoSkVE5FLMV75ExK6yysZzS+Kx2WB8j1YMDPczuiQRERERERERkXqjnZ8Hf7q7G+t/P5wHB7XD1Wom7ng+7x60cNMrscT8dw+rEzIprag0ulQREbmAZpTIVftg2xHijufh6erEc2O7GF2OiIiIiIiIiEi9FOTtxvPjujJleBhvb0jmg80p5JdU8Nmu43y26zierk6M7BLAmKggBnfww9WqFTtERIykoESuSlZBCX9ZkQjAU6M74e/panBFIiIiIiIiIiL1m28zF2JGdKBj6SECIgawMiGb5XHpZBWU8tnu43y2+zjNXJwY0cWfMVFBDO3YUqGJiIgBFJTIVXl5WQIFpRV0a+3Nz/u3NbocEREREREREZEGw2yCvqEtGNjBn+dv78rOtFMs25vOivgMMvJLWLrnBEv3nKCZixO3dPHntsggojspNBERuVEUlMgVbUrK4fM9JzCb4OUJUVjMJqNLEhERERERERFpkMxmE31Dfegb6sPzt3dl99FTLNubwdfx6aTnlfD5nhN8vucEHs4Wbu4SwNioQIZ19MfNWaGJiMj1oqBEflRpRSUzlsYDMPGmtkS19ja4IhERERERERGRxsFsNtG7rQ+92/owfWwXdh89zddx6Xwdn8Hx02f48vsTfPn9CdydLQzv7M/YqCCGd1JoIiJS1xSUyI96c10KKTlFtPR04bejOxldjoiIiIiIiIhIo2QPTVrQu20LnhvbhT1HT7M8Lp3lcfbQZNnedJbtTcfNauHmzv7cFhXIzZ39cXfWr/dERByl76RyWUdOFvGPtUkATB/bBS9Xq8EViYiIiIiIiIg0fiaTiZ5tWtCzTQueHdOFvcfyWB6XzrK4dI6dOsOys//sajUzvJN9I/ibO/vj4aJf9YmIXAt995RLstlsPP/5PsoqqhgU7ssd3VsZXZKIiIiIiIiISJNjMpnoHtKc7iHNefq2zsQdz2N5XAbL49JJyy3m6/gMvo7PwMXJTHSnloyJCuKWLgE0U2giInLV9B1TLunr+AzWHczG2WLmpfGRmEzawF1ERERERERExEgmk4lurZvTrXVz/nBrJ/adyGdZXDrL49I5crKYb/Zl8s2+TJydzER3PBea+OOpVUJERH6UghK5SGFpBbO+3AfAo9FhtG/ZzOCKRERERERERETkQiaTichgbyKDvfn96E7sT8+v3tMkNaeIlfszWbnfHpoM7dCSMVGBjOgaoKXVRUQuQUGJXGTuyoNk5pfS1tedx6PDjC5HRERERERERER+hMlkIqKVNxGtvPndqE4cyCio3tMkJbuIbxMy+TYhE2eLmSEd/BgTFcSIrgF4uyk0EREBBSXyA/tO5LFocyoAL46PxNVqMbgiERERERERERG5WiaTiS5BXnQJ8iJmZEcSMwuq9zRJyipk9YEsVh/IwmoxMTjcHpqM6hqIt7tCExFpuhSUSLWqKhvTl8ZTZYOxUUEM69jS6JJEREREREREROQamUwmOgd60TnQHpoczCxg2V77niaHsgpZm5jN2sRsnrXEMSjcjzGRQYyKCKC5u7PRpYuI3FAKSqTaR98dZXfaaTycLcy4vavR5YiIiIiIiIiISB3qGOBJx5GePDmyI4cumGmSmFlAbGI2sYnZPLvExMBwP8ZGBTKqayAtPBSaiEjjp6BEAMgpLOWVFQcAiBnViUBvV4MrEhERERERERGR66VDgCfTAjyZNqIDSVmFfH12T5MDGQWsP5jN+oPZPLsknoFhvoyJCmJ0RCA+Ck1EpJFSUCIAzFl+gLwz5XQN8mLSgLZGlyMiIiIiIiIiIjdIuH8zfn1LB359SwdSsgvPbgSfQUJ6PhsO5bDhUA7Tl8YzoL0vt0UFcmtEIL7NXIwuW0SkzigoEbamnOTTXccwmeDlOyNxspiNLklERERERERERAzQvmUzpt7cgak3dyA1p4jlcfY9TfadyGdjUg4bk3KYsTSem9rbZ5rcGhmIn0ITEWngFJQ0cWUVVUxfGg/Avf3a0LNNC4MrEhERERERERGR+qCdnwdThoczZXg4h3OK+DrevqdJ3PE8NiefZHPySZ7/PJ5+7XwYGxXE6MhA/D21nLuINDwKSpq4tzemkJRViK+HM38Y3dnockREREREREREpB4K9fPgsegwHosOI+1kMcvj7TNN9h7LY2tKLltTcnn+i330C/VhTFQQt0UG4u+l0EREGgYFJU3Y0dxi/r76EADPjumCt7vV4IpERERERERERKS+a+PrzqPDwnh0WBhHc4v5Ot6+p8n3R0+zLTWXbam5vPDlPvq29WFMVCC3RgYR6K3QRETqLwUlTdisL/dRUl5F/3Y+3NUr2OhyRERERERERESkgQnxceeRoWE8MjSMY6eKWRGfwbK4dHannWb74Vy2H87lhS/306dtC/tMk6hAgrzdjC5bRKQGBSVN1Mp9GXybkIXVYuLlOyMxmUxGlyQiIiIiIiIiIg1Y6xbuPDSkPQ8Nac/x02f4+uxG8LvSTrPjyCl2HDnFi1/tp1eb5oyJCmJMVBCtmis0ERHjKShpgopKK3jhi30APDykPeH+ngZXJCIiIiIiIiIijUlwc7fq0CQ97wxfx9k3gt9x5BS70k6zK+00s5cl0LNNc8ZGBXFrZCCtW7gbXbaINFEKSpqgv68+xIm8Elq3cOPXN3cwuhwREREREREREWnEgrzdeHBwOx4c3I6MvBK+jk/n67gMvjuSy+600+w+G5p0D2nO2KhAbosMIsRHoYmI3DgKSpqYxIwC/rUxFYBZd0Tg5mwxuCIREREREREREWkqAr1dmTyoHZMHtSMzv4QV8faZJtsP5/L90dN8f/Q0f1x+gG6tvRkTFcSoLn5GlywiTYCCkiakqsrG9KVxVFTZGNU1gFu6BBhdkoiIiIiIiIiINFEBXq5MGhjKpIGhZBWU8M3ZjeC3p+ay91gee4/l8aevIcTDwtFmqYzrEUxbXw+jyxaRRkhBSRPyv13H+O7wKdysFmbeEWF0OSIiIiIiIiIiIgD4e7oycUAoEweEkl1Qyjf77DNNtqac5GiRib+uOsRfVx0iopUXY6KCGBsVRKifQhMRqRsKSpqIU0VlzFmeAMCTIzsQ3NzN4IpEREREREREREQu1tLThV/c1JZf3NSWjFOFzP1kDcdMLdmWeop9J/LZdyKfv3yTSJcgL8ZGBTImKoj2LZsZXbaINGAKSpqIP319gFPF5XQK8GTyoHZGlyMiIiIiIiIiInJFvs1cGBRgY8yYPuSXVrFyfybL49LZnHyShPR8EtLz+evKg3QO9GRsVBC3RQUR7q/QRERqR0FJE7DjcC4f7zgKwMt3RmK1mA2uSEREREREREREpHZ8m7lwb7823NuvDaeKyli5P4NlcRlsTsrhQEYBBzIK+Nuqg3QK8GRMVBBjogLpEOBpdNki0gAoKGnkyiurmL40HoCf9mlNn1AfgysSERERERERERFxTAsPZ+7p24Z7+rbhdHEZK/dlsjw+nY2HckjMLCAxs4BXvz1IB/9m9j1NugXRUaGJiFyGgpJGbtGmwxzIKKC5u5Wnb+tidDkiIiIiIiIiIiJ1qrm7Mz/tG8JP+4aQV1zOyv32jeA3JuVwKKuQ11Yf4rXVhwj3b8aYyEDGdAuiU4AnJpPJ6NJFpJ5QUNKInTh9hle/PQjAM7d1xsfD2eCKRERERERERERErh9vdyv/1yeE/+sTQt6Zcr49u6fJhkM5JGUV8vc1Sfx9TRLtW3rY9zSJDKJLkEITkaZOQUkj9uKX+ykuq6RP2xb8X+8Qo8sRERERERERERG5YbzdrNzduzV3925Nfsm50CSD9QezScku4vU1Sby+Jol2fh6MiQpkTFQQXYO8FJqINEEKShqpNQcyWbEvA4vZxOw7IzGb9Q1eRERERERERESaJi9XK3f1as1dvVpTUFLO6oQslsWls+5gNqk5Rcxfm8z8tcmE+rpzW1QQY6OCiGil0ESkqVBQ0gidKavk+c/3AfDLwe3oHOhlcEUiIiIiIiIiIiL1g6erlQk9g5nQM5iCknLWHMhieVw6sYnZHD5ZzILYZBbEJtPGx50xUUGMiQokKthboYlII6agpBH6x9pDHDt1hlberky7pYPR5YiIiIiIiIiIiNRLnq5WxvcIZnyPYApLK1hzIIuv49JZm5hFWm4xC9cls3BdMiE+boyJDGJMVBDdWis0EWlsFJQ0MklZBfxzfQoAz4+LwMNF/4pFRERERERERESupJmLE3d0b8Ud3VtRVFrB2kT7TJM1B7I4mnuGN9en8Ob6FIKbu1XvadIjpLlCE5FGQL9Fb0RsNhvTl8ZTXmnj5s7+jI4IMLokERERERERERGRBsfDxYnbu7Xi9m6tKC6rIDYxm2Vx6axJyOL46TO8tSGVtzakEtzcjdsiA7ktKoieIc21T7BIA6WgpBFZuuc4W1NycbWamXVHhNJsERERERERERERB7k7O53dqySIM2WVrDuYxbK4DFYnZHL89Bne3pjK2xtTCfJ25bbIIMZ2C6RnSAuFJiINiIKSRiKvuJzZXyUA8OubOxDi425wRSIiIiIiIiIiIo2Lm7OFWyODuDUyiJLySmITs/k6Pp1v92eSnlfCO5tSeWdTKoFertwaGcjYbkH0bqPQRKS+U1DSSPz5mwOcLCoj3L8ZDw9pb3Q5IiIiIiIiIiIijZqr1cKtkYHcGhlISXkl6w9mszwunW8TssjIL2HR5sMs2nwYf08Xbou072nSJ9QHi0ITkXpHQUkjsOfoaT7cngbAS+MjcXYyG1yRiIiIiIiIiIhI0+FqtTAqIpBREfbQZOOhHJbHpbNqfyZZBaW8t+UI7205QssLQpO+Ck1E6g0FJQ1cRWUVzy2Jw2aDu3oGMyDM1+iSREREREREREREmixXq4URXQMY0TWA0gp7aLLsbGiSXVDKv7cc4d9bjuDXzIVbIwMYExVE/3a+Ck1EDKSgpIF7f+sR9p3Ix8vViWfHdjG6HBERERERERERETnLxcnCLV0CuKVLAGUVVWxKsocmK/dlkFNYyuKtaSzemoZfM2dGRQQyNiqI/u18cLJoxRiRG0lBSQOWmV/C31YeBOAPt3XGr5mLwRWJiIiIiIiIiIjIpTg7mRne2Z/hnf0puzOKzcn25bm+2ZdJTmEZH25L48Ntafh6nA9Nbmqv0ETkRlBQ0oC9+NV+Cksr6BHSnHv7tjG6HBEREREREREREbkKzk5mojv5E93Jn5fvrGJz8kmW703nm/0ZnCwq4z/b0/jP9jRauFsZHWHf02RAmC9WhSYi14WCkgZq/cFslu1Nx2yC2RMiMWsNQxERERERERERkQbHajEzrGNLhnVsyezKSLamnKyeaZJbVMZH3x3lo++O0tzdyqiu9j1NBoX7KTQRqUMKShqgkvJKnv88HoBJA0OJDPY2uCIRERERERERERFxlNViZkiHlgzp0JKXxlexLTWXZXHpfBNvn2ny3x3H+O+OY3i7nQ1NugUxKMwPZyeFJiKOUFDSAC2ITebwyWICvFyIGdnR6HJERERERERERESkjjlZzAwK92NQuB8v3hHB9nOhyb4McgrL+GTnMT7ZeQwvVydGdg1kbLdABoe3VGgicg0UlDQwqTlFLIhNBmDG7V3xdLUaXJGIiIiIiIiIiIhcT04WMwPD/RgY7seL4yPZnprL8rh0vo7PIKewlE93HePTXcfwdHViZBf78lxDOvqhyETk6igoaUBsNhvPfx5PWWUVQzu2ZGxUkNEliYiIiIiIiIiIyA1kMZsYEObLgDBfXrgjgh2Hz4cmWQWlfLb7OJ/tPo6nixPDO7XEv9TEsNIKmlv1B9cil6OgpAH5cm86Gw7l4Oxk5sU7IjCZtIG7iIiIiIiIiIhIU2Uxm+jf3pf+7X2ZOS6CHUdOnQ1N0snML+WLvemAhUV/XEuvNi0YFO7H4A6+dGvdXJvBi1xAQUkDkV9Szktf7QdgSnQ4oX4eBlckIiIiIiIiIiIi9YXZbKJfOx/6tfPh+du7sivtFF9+f5wvdx0htxS2H85l++FcXv0Wmrk4cVN7H3twEu5HuH8z/VG2NGkKShqIuSsPkl1QSjs/Dx6Nbm90OSIiIiIiIiIiIlJPmc0m+oT60D3Yk16kEHlTNNsO57EpKYdNyTmcLi7n24Qsvk3IAsDf06V64/hB4b4EebsZfAciN9Y1za+aP38+oaGhuLq60r9/f7Zv3/6j18+bN49OnTrh5uZGSEgITz75JCUlJQ49Z1MSfzyPf285DMBL4yNxcbIYW5CIiIiIiIiIiIg0GG183Lmvfxvm/7wXu6aP5KtfD+bp2zozpIMfLk5msgpKWbL7OL/75HsGzFnDLX+LZebn8azcl0F+SbnR5Ytcd7WeUfLxxx8TExPDwoUL6d+/P/PmzWP06NEkJibi7+9/0fUffvghTz/9NO+88w4DBw7k4MGDPPDAA5hMJubOnXtNz9mUVFbZeG5JHFU2GNe9FYM7+BldkoiIiIiIiIiIiDRQZrOJyGBvIoO9eXRYGCXllew6coqNSTlsSsph7/E8krOLSM4u4r0tRzCboHtIcwafnXHSs01z/SG3NDq1Dkrmzp3Lww8/zOTJkwFYuHAhy5Yt45133uHpp5++6PrNmzczaNAg7rvvPgBCQ0O599572bZt2zU/Z1Py4fY0vj+Wh6eLEzPGdjG6HBEREREREREREWlEXK0WBob7MTDc/gfaecXlbEnJORucnCQ1p4jdaafZnXaa19ck4Wa10K+dT3Vw0jnQE7NZ+5tIw1aroKSsrIydO3fyzDPPVJ8zm82MGDGCLVu2XPIxAwcOZPHixWzfvp1+/fqRkpLC8uXLmThx4jU/J0BpaSmlpaXVn+fn5wNQXl5OefmNnw527jXr8rWzC0r584oDADw5IpwWbhZD7u1GuB79a0rUP8eof45R/xyj/jlG/XOM+ucYo/unf28iIiIicj14u1u5NTKIWyODADh++ox9b5OzR05hGesOZrPuYDYAvh7ODAz3Y3C4L4PC/Wjdwt3I8kWuSa2CkpycHCorKwkICKhxPiAggAMHDlzyMffddx85OTkMHjwYm81GRUUFjz76KM8+++w1PyfAnDlzmDVr1kXnV65cibu7cf8xrlq1qs6e69+HzBSUmAnxsNHiZDzLl8fX2XPXV3XZv6ZI/XOM+ucY9c8x6p9j1D/HqH+OMap/xcXFhryuiIiIiDQtwc3d+GmfEH7aJwSbzUZiZgEbD9lDk22puZwsKuPL70/w5fcnAAj1da/eGH5Ae19aeDgbfAciV1brpbdqKzY2lj/+8Y+88cYb9O/fn6SkJKZNm8ZLL73EjBkzrvl5n3nmGWJiYqo/z8/PJyQkhFGjRuHl5VUXpddKeXk5q1atYuTIkVitVoefb0vKSXZu2YnJBK9NvImoYO86qLL+quv+NTXqn2PUP8eof45R/xyj/jlG/XOM0f07N6NaRERERORGMZlMdA70onOgFw8NaU9ZRRV7jp6u3t9kz9HTHD5ZzOGTaXywLQ2TCSJbeTMo3I/B4X70CW2Bq1X7m0j9U6ugxM/PD4vFQmZmZo3zmZmZBAYGXvIxM2bMYOLEiTz00EMAREVFUVRUxCOPPMJzzz13Tc8J4OLigouLy0XnrVaroQP9unj90opKXvjKPpvmF/3b0iu06WzgbvS/v4ZO/XOM+ucY9c8x6p9j1D/HqH+OMap/+ncmIiIiIkZzdjLTr50P/dr5EDOyIwUl5WxLya0OTg5lFRJ3PI+443ksXJeMs5OZvqEtqoOTiFbeWLS/idQDtQpKnJ2d6d27N6tXr2bChAkAVFVVsXr1aqZOnXrJxxQXF2M2m2ucs1jsqaHNZrum52zs3lqfQkp2EX7NXPjd6E5GlyMiIiIiIiIiIiJyRZ6uVkZ0DWBEV/s2C5n5JWxOzmHjoZNsTMomM7+UTUkn2ZR0kj+TiLeblYFhvtXBSVtfd0wmBSdy49V66a2YmBgmTZpEnz596NevH/PmzaOoqIjJkycDcP/99xMcHMycOXMAGDduHHPnzqVnz57VS2/NmDGDcePGVQcmV3rOpiTtZDGvr0kCYMbtXfB2018KioiIiIiIiIiISMMT4OXKnT1bc2fP1thsNpKzi9iUlMPGpBy2Jp8k70w5X8dn8HV8BmDfD2VwuB+DOvgxMMwXv2YXrygkcj3UOii55557yM7O5vnnnycjI4MePXqwYsWK6s3Y09LSaswgmT59OiaTienTp3P8+HFatmzJuHHjePnll6/6OZsKm83G81/EU1pRxaBwX+7o3srokkREREREREREREQcZjKZCPdvRrh/MyYNDKWisoq9x/PYdMgenOxKO8Xx02f4eMdRPt5xFIAuQV4MDrfPOOnXzgd35+u+5bY0Udf0zpo6depll8WKjY2t+QJOTsycOZOZM2de83M2FSviM4hNzMbZYubF8ZGaZiYiIiIiIiIiIiKNkpPFTK82LejVpgW/vqUDxWUVbE/NPTvj5CQJ6fnVx1sbUrFaTPRs08I+4yTcj+6tvXGymK/8QiJXQRFcPVFYWsGsL/cD8Kth7Qlr2czgikRERERERERERERuDHdnJ6I7+RPdyR+AnMJSNiefrJ5xcvz0Gban5rI9NZe5qw7i6eJE//a+DA73ZXAHP8JaNtMfnss1U+RWT8xbdZCM/BLa+LgzZXi40eWIiIiIiIiD5s+fT2hoKK6urvTv35/t27df9trPPvuMPn360Lx5czw8POjRowfvv//+Za9/9NFHMZlMzJs37zpULiIiImI8v2Yu3NG9Fa/8pBsb/zCc2N9FM3tCJGOiAvF2s1JQWsG3CZm88OV+Rsxdz01zVhPz3z18tusYmfklRpcvDYxmlNQD+0/k8+7mwwDMGh+Bq9VibEEiIiIiIuKQjz/+mJiYGBYuXEj//v2ZN28eo0ePJjExEX9//4uu9/Hx4bnnnqNz5844Ozvz1VdfMXnyZPz9/Rk9enSNa5csWcLWrVtp1Up7GoqIiEjTYDKZCPXzINTPg1/c1JbKKhv7T+SzMSmHTUk5bD+cS2Z+KZ/tOs5nu44D0MG/GYPC/Rgc7kf/9j54uloNvgupzxSUGKyqysb0pXFUVtm4LTKQ4Z0uHjSJiIiIiEjDMnfuXB5++GEmT54MwMKFC1m2bBnvvPMOTz/99EXXR0dH1/h82rRpvPfee2zcuLFGUHL8+HF+/etf88033zB27Njreg8iIiIi9ZXFbCKqtTdRrb15LDqMkvJKdh45VR2cxB3P41BWIYeyClm0+TAWs4keIc0ZFO7HoDBferZpgbOTFluS8xSUGOzjHUfZlXYaD2cLz4/ranQ5IiIiIiLioLKyMnbu3MkzzzxTfc5sNjNixAi2bNlyxcfbbDbWrFlDYmIir7zySvX5qqoqJk6cyFNPPUVERMQVn6e0tJTS0tLqz/Pz8wEoLy+nvLy8NrdUJ869phGv3Riof45R/xyj/jlG/XOM+ueYptI/C9CvrTf92noTc0sYp4vL2Zqay+bkk2xOzuVIbjE7j5xi55FT/H31IdydLfRt24KBYT4MDPOlU8Dl9zdpKj28Xozu39W+roISA50sLOVPXx8A4MmRHQnydjO4IhERERERcVROTg6VlZUEBATUOB8QEMCBAwcu+7i8vDyCg4MpLS3FYrHwxhtvMHLkyOqvv/LKKzg5OfGb3/zmquqYM2cOs2bNuuj8ypUrcXd3v8q7qXurVq0y7LUbA/XPMeqfY9Q/x6h/jlH/HNNU+3eTE9zUCU6WwME8U/VRWFbJukM5rDuUA0Azq42OXjY6NbfR0duGj8vFz9VUe1hXjOpfcXHxVV2noMRAc74+QN6ZcroEefHAwFCjyxEREREREQN5enqyZ88eCgsLWb16NTExMbRv357o6Gh27tzJa6+9xq5duy77144/9MwzzxATE1P9eX5+PiEhIYwaNQovL6/rdRuXVV5ezqpVqxg5ciRWq9YIry31zzHqn2PUP8eof45R/xyj/l2sqsrGwazC6tkm2w/nUlhexa6TJnadtF8T6utun23S3pfeIZ5s3xirHl4jo9+D52ZVX4mCEoNsT83lfzuPATB7QiROFq2JJyIiIiLSGPj5+WGxWMjMzKxxPjMzk8DAwMs+zmw2Ex4eDkCPHj1ISEhgzpw5REdHs2HDBrKysmjTpk319ZWVlfz2t79l3rx5HD58+KLnc3FxwcXl4j+HtFqthg7yjX79hk79c4z65xj1zzHqn2PUP8eofzVFhfgQFeLDr6KhrKKK3Wmn2JSUw8akHL4/lsfhk8UcPlnMh9uPYTZBa3cLCdbDDO3oT6+2LXC1Woy+hQbHqPfg1b6mghIDlFVUMX1pHAD39guhd9sWBlckIiIiIiJ1xdnZmd69e7N69WomTJgA2PcXWb16NVOnTr3q56mqqqreY2TixImMGDGixtdHjx7NxIkTqzeMFxEREZHac3Yy07+9L/3b+xIzqhP5JeVsS8mtDk6SsgpJKzKxcH0qC9en4uJkpl87HwaF+zE43I+uQV6YzVc341fqLwUlBvjXxlQOZhbi4+HMH27tbHQ5IiIiIiJSx2JiYpg0aRJ9+vShX79+zJs3j6KioupQ4/777yc4OJg5c+YA9v1E+vTpQ1hYGKWlpSxfvpz333+fBQsWAODr64uvr2+N17BarQQGBtKpU6cbe3MiIiIijZiXq5WRXQMY2dW+39zRkwUsXLKWIo/WbE7JJauglA2Hcthwdn+T5u5WBoX5VQcnbXyN2wtOrp2Ckhvs2Kli/r76EADPjulCc3dngysSEREREZG6ds8995Cdnc3zzz9PRkYGPXr0YMWKFdUbvKelpWE2n19+t6ioiMcff5xjx47h5uZG586dWbx4Mffcc49RtyAiIiIiQKCXK/1a2hgzJgonJyeSsgrZmJTDpqQctqbkcrq4nGVx6SyLSwegdQs3Bofbg5OBYb74NrvEzvBS7ygoucFe+GI/Z8or6dfOh7t7BRtdjoiIiIiIXCdTp0697FJbsbGxNT6fPXs2s2fPrtXzX2pfEhERERG5fkwmEx0CPOkQ4MnkQe0or6xi77G86mW6dqed4tipM3z03VE++u4oAF2DvBjcwR6c9Av1wc1Z+5vURwpKbqBV+zP5NiETJ7OJ2RMiMZm0dp2IiIiIiIiIiIhIQ2S1mOndtgW927bgN7d0oKi0gu2Hc9l0yB6cHMgoYH96PvvT8/nn+hScLWZ6tW1ePeMkKtgbJ4v5yi8k152CkhukuKyCF77YB8BDQ9rTMcDT4IpEREREREREREREpK54uDgxvJM/wzv5A5BdUMrmZPsyXRsP5XAir4StKblsTcnlrysP4unqxID2vtUzTtr7eeiP6w2ioOQG+fvqJI6fPkNwczd+c0u40eWIiIiIiIiIiIiIyHXU0tOF8T2CGd8jGJvNxuGTxfb9TQ7lsDk5h/ySClbuz2Tl/kwAgrxdGRTux6BwXwaF+eHv5WrwHTQdCkpugMSMAt7ekALAC3dE4O6stouIiIiIiIiIiIg0FSaTiXZ+HrTz82DiTW2prLIRfzyPjUn20OS7w6dIzyvhfzuP8b+dxwDoGNCMQeF+DA73o397X5q56PfK14s6e53ZbDZmLI2nosrGyK4BjOwaYHRJIiIiIiIiIiIiImIgi9lE95DmdA9pzpTh4ZSUV7Lj8Cn7jJOkHOJP5HEws5CDmYW8u+kwTmYTPUKa24OTDn70CGmOVfub1BkFJdfZ/3YeY/vhXNysFl64I8LockRERERERERERESknnG1WhjcwR6CAJwqKmNLysnq4OTIyWJ2HDnFjiOneG31ITycLfRv71s946RjQDPtb+IABSXX0amiMuZ8fQCAaSM6ENzczeCKRERERERERERERKS+a+HhzJioIMZEBQFwNLfYvil8Ug6bk0+SW1TGmgNZrDmQBYBfMxcGh/ue3ePEj1b6XXStKCi5jv78zQFyi8roGNCMXw5uZ3Q5IiIiIiIiIiIiItIAhfi487N+bfhZvzZUVdlIyMg/G5ycZHvqSXIKS1m65wRL95wAoH1LDwafDU1uau+Lt5vV4Duo3xSUXCc7j+Tyn+1HAZg9IUrrxYmIiIiIiIiIiIiIw8xmExGtvIlo5c0jQ8Morahk15HTbE62zzj5/uhpUrKLSMku4t9bjmA2QbfWzRkc7sfAcF96t22Bi5PF6NuoVxSUXAcVlVU8tyQegJ/0bk2/dj4GVyQiIiIiIiIiIiIijZGLk4UBYb4MCPPlt6M6kXemnG0pJ6uX6krOLmLP0dPsOXqaf6xNwtVqpm+oT/WMk65BXpjNTXt/EwUl18GizYc5kFFAc3crz9zW2ehyRERERERERERERKSJ8HazMioikFERgQCk551hU9L54CS7oJQNh3LYcCgHgBbuVgae3RR+cLgfIT7uRpZvCAUldSw9r4RXVx0E4OlbO+PbzMXgikRERERERERERESkqQryduMnvVvzk96tsdlsHMoqZOOhHDYl5bA15SSnistZtjedZXvTAWjj486gs6HJgDBffDycDb6D609BSR17efkBisoq6dWmOT/tE2J0OSIiIiIiIiIiIiIiAJhMJjoGeNIxwJMHB7ejvLKK74+eZmOSPTjZnXaatNxi0ran8Z/taZhMENHKqzo46Rvqg6u18e1voqCkDu0/ZeKbA1lYzCZevjOqya/rJiIiIiIiIiIiIiL1l9Vipk+oD31CfXhiREcKSyv4LjW3Ojg5kFFA/PF84o/n8+a6FJydzPRp24JBZ/c3iQr2xtIIfg+uoKSOlJRX8r9UMwCTB4bSJcjL4IpERERERERERERERK5eMxcnhnf2Z3hnfwCyCkrYknySjYfs+5uk55WwOfkkm5NP8pdvEvFydWJAmG/1xvDt/DwwmRpecKKgpI68sS6Fk6UmArxceGJkR6PLERERERERERERERFxiL+nK+N7BDO+RzA2m43UnKLqTeE3J58kv6SCb/Zl8s2+TABaebval+nq4MfAMD+au5oNvoOro6CkDiRlFfL2xsMAzBjTmWYuaquIiIiIiIiIiIiINB4mk4n2LZvRvmUzJg4IpaKyivgT+fbg5FAOO4+c4kReCZ/sPMYnO48B0CmgGZ1cTIwxuPYr0W/068C/txymvNJG1+ZVjOrqb3Q5IiIiIiIiIiIiIiLXlZPFTI+Q5vQIac6U4eGcKavku8O51TNO9p3IJzGzEL+A+r8Ul4KSOjBzXARtfdzgRHyDXH9NRERERERERERERMQRbs4WhnZsydCOLQHILSpj48FM0vbvMriyK2sYC4TVcxaziftvaoOfq9GViIiIiIiIiIiIiIgYz8fDmdsiAwlpZnQlV6agREREREREREREREREmiwFJSIiIiIiIiIiIiIi0mQpKBERERERERERERERkSZLQYmIiIiIiIiIiIiIiDRZCkpERERERERERERERKTJUlAiIiIiIiIiIiIiIiJNloISERERERERERERERFpshSUiIiIiIiIiIiIiIhIk6WgREREREREREREREREmiwFJSIiIiIiIiIiIiIi0mQpKBERERERERERERERkSZLQYmIiIiIiIiIiIiIiDRZCkpERERERERERERERKTJUlAiIiIiIiIiIiIiIiJNloISERERERERERERERFpshSUiIiIiIiIiIiIiIhIk6WgREREREREREREREREmiwnowuoKzabDYD8/HxDXr+8vJzi4mLy8/OxWq2G1NCQqX+OUf8co/45Rv1zjPrnGPXPMeqfY4zu37mfe8/9HCxyJRozNWzqn2PUP8eof45R/xyj/jlG/XOceugYo/t3teOmRhOUFBQUABASEmJwJSIiIiIiN05BQQHe3t5GlyENgMZMIiIiItJUXWncZLI1kj9Bq6qq4sSJE3h6emIymW746+fn5xMSEsLRo0fx8vK64a/f0Kl/jlH/HKP+OUb9c4z65xj1zzHqn2OM7p/NZqOgoIBWrVphNmtFXbkyjZkaNvXPMeqfY9Q/x6h/jlH/HKP+OU49dIzR/bvacVOjmVFiNptp3bq10WXg5eWl/2AcoP45Rv1zjPrnGPXPMeqfY9Q/x6h/jjGyf5pJIrWhMVPjoP45Rv1zjPrnGPXPMeqfY9Q/x6mHjqnv4yb96ZmIiIiIiIiIiIiIiDRZCkpERERERERERERERKTJUlBSR1xcXJg5cyYuLi5Gl9IgqX+OUf8co/45Rv1zjPrnGPXPMeqfY9Q/kdrRfzOOUf8co/45Rv1zjPrnGPXPMeqf49RDxzSU/jWazdxFRERERERERERERERqSzNKRERERERERERERESkyVJQIiIiIiIiIiIiIiIiTZaCEhERERERERERERERabIUlIiIiIiIiIiIiIiISJOloKQW5s+fT2hoKK6urvTv35/t27f/6PWffPIJnTt3xtXVlaioKJYvX36DKq2fatO/RYsWYTKZahyurq43sNr6Y/369YwbN45WrVphMplYunTpFR8TGxtLr169cHFxITw8nEWLFl33Ouur2vYvNjb2oveeyWQiIyPjxhRcz8yZM4e+ffvi6emJv78/EyZMIDEx8YqP0/c/u2vpn77/nbdgwQK6deuGl5cXXl5eDBgwgK+//vpHH6P33nm17Z/eez/uT3/6EyaTiSeeeOJHr9N7UJo6jZkcozHTtdO4yTEaN107jZkcozGT4zRucozGTXWnoY+ZFJRcpY8//piYmBhmzpzJrl276N69O6NHjyYrK+uS12/evJl7772XX/7yl+zevZsJEyYwYcIE4uPjb3Dl9UNt+wfg5eVFenp69XHkyJEbWHH9UVRURPfu3Zk/f/5VXZ+amsrYsWMZPnw4e/bs4YknnuChhx7im2++uc6V1k+17d85iYmJNd5//v7+16nC+m3dunVMmTKFrVu3smrVKsrLyxk1ahRFRUWXfYy+/513Lf0Dff87p3Xr1vzpT39i586d7Nixg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"]},"metadata":{},"output_type":"display_data"}],"source":["fig = plt.figure(figsize = (20, 6))\n","plt.subplot(1, 2, 1)\n","plt.plot(epoch_tr_acc, label='Train Acc')\n","plt.plot(epoch_vl_acc, label='Validation Acc')\n","plt.title(\"Accuracy\")\n","plt.legend()\n","plt.grid()\n","\n","plt.subplot(1, 2, 2)\n","plt.plot(epoch_tr_loss, label='Train loss')\n","plt.plot(epoch_vl_loss, label='Validation loss')\n","plt.title(\"Loss\")\n","plt.legend()\n","plt.grid()\n","\n","plt.show()"]},{"cell_type":"markdown","metadata":{"execution":{},"id":"iUyaF-EbfjEH"},"source":["---\n","# What's Next?\n","\n","You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n","\n","* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n","* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n","* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n","* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. 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+{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "view-in-github" + }, + "source": [ + "\"Open   \"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "D_fgc45VfjDz" + }, + "source": [ + "# Twitter Sentiment Analysis\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n", + "\n", + "__Production editors:__ Amita Kapoor, Spiros Chavlis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "axvz0SUsfjD0" + }, + "source": [ + "---\n", + "# Welcome to the NLP project template\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "2Vfm0ThbfjD1" + }, + "source": [ + "---\n", + "# Step 1: Questions and goals\n", + "\n", + "* Can we infer emotion from a tweet text?\n", + "* How words are distributed accross the dataset?\n", + "* Are words related to one kind of emotion?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "Vd1qdNW9fjD1" + }, + "source": [ + "---\n", + "# Step 2: Literature review\n", + "\n", + "[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n", + "\n", + "[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "oOYDQElpfjD2" + }, + "source": [ + "---\n", + "# Step 3: Load and explore the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EZpxSExUfjD2", + "outputId": "19b01445-9b83-4a93-9cc2-7830ab0dcf5b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m60.3 MB/s\u001b[0m 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\u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", + "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", + "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install pandas --quiet\n", + "!pip install torchtext --quiet\n", + "!pip install datasets --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DxqD3Tk5fjD3", + "outputId": "451d68c5-7894-4f93-9f54-bf0b7f482e20" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/torchtext/data/__init__.py:4: UserWarning: \n", + "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", + "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", + " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n" + ] + } + ], + "source": [ + "# We import some libraries to load the dataset\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from datasets import load_dataset\n", + "\n", + "from collections import Counter\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "\n", + "import torchtext\n", + "from torchtext.data import get_tokenizer\n", + "\n", + "from sklearn.utils import shuffle\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.feature_extraction.text import CountVectorizer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "63Eg1SLbfjD4" + }, + "source": [ + "You can find the dataset we are going to use in [this website](http://help.sentiment140.com/for-students/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 567, + "referenced_widgets": [ + "fbb4191426bd485e8e965b6d432eecae", + "df7eba182d1b4c21bc21d157eac6b996", + "6d64402d9da74516ab4e1d46ae9f1ee3", + "d9ca809f7b1c49e595a05458251f3ab2", + "90908b6f69524a72860214ef8bd2d946", + "db432a2cd6244a7592fc9732f0ca4738", + "84485541f3a14c65a67d10a97b72bbad", + "5fa7ab2ab2004e5cb692199e2bd27d6b", + "ab71bd2b452146829e973d6cf99f31ed", + "55ba92cfe0724286ac1c2bbe6577e5c8", + "67a4fa49ca5349d58512a16a3742d401", + "afd671543846468abfe37669a72845c3", + "057e918ace004506aedc4e4b9942c3a8", + "325387f6b62d47b0b21bea61676cea72", + "ea1e3eb0e6ec4f8d82cf9b12cfe6e700", + "96c2d7ee644a438982e1792b7ec0453c", + "9baa1a735c0646b89953bf4a7c7fc92c", + "0ac9711c8ece4c5397a8cd810713adfb", + "a8d69769921241b8b1081e84f7770858", + "d189f24b0e964d1a9fc86379bad38cca", + "db9bf44dec914db793cc4f73751c272c", + "1cf3ba0f756f4aa5ad1dcb675a791cfa", + "c432c4efcb794ce781fcb6f176f1b60d", + "510eeffb32694e7798f23e3931d7a943", + "a8b3dfaa2831416582d8eeef01451386", + "db1cdafaf36f4c339476f3221abc17b3", + "ffd3778a96e046718828bbc5aa73f173", + "49c5a3fbe87b491cb3f0f450a0af0659", + "252949e8784c4878a62eb2e30b1e3466", + "7bcef602e7f441308472bc145b12dcd3", + "97fb30a5a31742efa1d188b9361f9938", + "9b34daddb9cc48bba109e547177ec654", + "fd2b5a6533794a2794579956c25247fb", + "f3a9667c8c994324a2409f227bd0a1e9", + "6e6c5372ffe045c0b72587989567429e", + "2ead0216695e4227aef44552f4ec3cc9", + "53843f49adda4bce8450fd91fa9fd587", + "40262cb3eefa45fcbe37aaafccb69f5f", + "b54b826314ea4b3a92eebd218c093fc1", + "8cd7be688b8c4818be48915db14a0792", + "a9a0f6ce71ed415c8c8513f68e34e162", + "7f638a6deacd42e88c031fa47797516b", + "849e39cc86f64e558ff94bf542a5121a", + "67b0b03c391c414bad5ea9fb3c947a2f", + "1cef38981af6457dbaeb393f9936a389", + "b0b5cfae51214c60bbca9a09b196c217", + "5ee2a4b33be04c6db8ee4d7995c2376d", + "403fffb635c2409ebeabc90063750ed3", + "6279343019064572adedf34cfbd437fa", + "2715d00db77545f9aa5eace8a0eb2839", + "942ce490d87347c789e229589b1b9c9f", + "f04df4daeb6049ab85d3d75b472ccf6e", + "fd0b3c53b66543cea0c396d8047445a8", + "2c42e2fef6314c9e842a7e9641af3cab", + "913d95e58aa94e4a8009768a23fbf304" + ] + }, + "id": "3HLOsd3rfjD4", + "outputId": "7653fee1-a871-472b-a978-d8ec0250dc84" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading builder script: 0%| | 0.00/4.03k [00:00\n", + "
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polarityuserdatequeryusertext
00_TheSpecialOne_Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
10scotthamiltonMon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
20mattycusMon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
30ElleCTFMon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
40KaroliMon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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Notice that polarity is 0 for negative tweets and 4 for positive tweet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "GXHQOn6gfjD5" + }, + "outputs": [], + "source": [ + "X = df.text.values\n", + "\n", + "# Changes values from [0,4] to [0,1]\n", + "y = (df.polarity.values > 1).astype(int)\n", + "\n", + "\n", + "# Split the data into train and test\n", + "x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "7kr3TO_LfjD5" + }, + "source": [ + "The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "FsL-xY03fjD5", + "outputId": "655f0ef8-c177-4f42-c024-1d628241401a" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n", + "0: worst headache ever \n", + "0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n", + "1: doesn't know how to spell conked \n", + "0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n" + ] + } + ], + "source": [ + "for s, l in zip(x_train_text[:5], y_train[:5]):\n", + " print('{}: {}'.format(l, s))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "4cPGXSc-fjD5" + }, + "source": [ + "An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "U1OugpZ0fjD5", + "outputId": "9e6cb4e3-8d8c-4db0-c113-bdd4fe87db5f" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Before Tokenize: worst headache ever \n", + "After Tokenize: ['worst', 'headache', 'ever']\n" + ] + } + ], + "source": [ + "tokenizer = get_tokenizer(\"basic_english\")\n", + "\n", + "print('Before Tokenize: ', x_train_text[1])\n", + "print('After Tokenize: ', tokenizer(x_train_text[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 81, + "referenced_widgets": [ + "e1348a02ceeb4af19fbd63d52b7d843b", + "fbf51b14e6b34d0485ddf59c43d22c49", + "c29e06a72ac9401b8c41f4195021071e", + "48b812211db04284bfbbf02823fb879a", + "5455119809c74916acc50e1905903ded", + "2475bd62a3224bacb38a6334d07d6a8c", + "3d29947b5d2d4e2abc1355d900096642", + "3f7a8f56f15c434da70029366a37167a", + "3610a2db297f4686bf9043f2b7ee55b5", + "a1bd0616199e44538977ee2ea6049690", + "835fb9a91b34471fa6d61adf37616f52", + "d8de1a85076b453a92295e79110ba8fd", + "78d48ee2fb9f42089f475fcf5fc368c8", + "b0ca3012d0b84c5a9d7c1fc176251af7", + "39fa73efcbf54d8dad225d8380061dbf", + "6b6cc35257fe433e93736d02e898b6b8", + "e0fc900d8b5940a6bd6a97e58adb4651", + "6b7286d74e0f4a0199dbfcaf3dd0d622", + "a4bbd3df99cd4acab5e1b3ba5cd7c114", + "9a7140a6197945d5bac5c48b820dfb04", + "0bdc146792a64853ae06a9d185aa2b15", + "768da964ffcd44fea1af09e81f5621f3" + ] + }, + "id": "7ZggzGCXfjD6", + "outputId": "ae19f8d6-224d-4224-d3a0-d00c659ec9b2" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/1280000 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "o9IYA0cZfjD7" + }, + "source": [ + "It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "5FQIOqoRfjD7" + }, + "source": [ + "Usually the number of words in the dictionary will be very large.\n", + "\n", + "Here are some thing we can do to reduce that number:\n", + "\n", + "* Remove puntuation.\n", + "* Remove stop-words.\n", + "* Steaming.\n", + "* Remove very uncommon words (the words that appears in fewer than N occations).\n", + "* Nothing: we can use a pretrain model that handles this kind of situations.\n", + "\n", + "\n", + "We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n", + "\n", + "* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n", + "* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n", + "* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "_ul5MgYcfjD7" + }, + "source": [ + "---\n", + "# Step 4: choose toolkit\n", + "\n", + "Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n", + "\n", + "* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n", + "* A simple Embedding + RNN." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "GteI1PxTfjD7" + }, + "source": [ + "## Logistic regression\n", + "\n", + "We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "S_ei2qu8fjD7" + }, + "outputs": [], + "source": [ + "vectorizer = CountVectorizer(binary=True)\n", + "x_train_cv = vectorizer.fit_transform(x_train_text)\n", + "x_test_cv = vectorizer.transform(x_test_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iK_zfqnLfjD7", + "outputId": "9b3f6db3-01bf-4246-b943-359620c717a2" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Before Vectorize: doesn't know how to spell conked \n" + ] + } + ], + "source": [ + "print('Before Vectorize: ', x_train_text[3])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "wKxY8e38fjD8", + "outputId": "19530135-070d-4259-d6a9-7ba06b519763" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "After Vectorize: \n", + " (0, 528584)\t1\n", + " (0, 165468)\t1\n", + " (0, 300381)\t1\n", + " (0, 242211)\t1\n", + " (0, 489893)\t1\n", + " (0, 134160)\t1\n" + ] + } + ], + "source": [ + "# Notice that the matriz is sparse\n", + "print('After Vectorize: ')\n", + "print(x_train_cv[3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QTPPEMd9fjD8" + }, + "source": [ + "Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 74 + }, + "id": "2vEPOQS6fjD8", + "outputId": "3be77fc0-76e6-40b8-8847-5f6e7c6c0ce0" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LogisticRegression(solver='saga')" + ], + "text/html": [ + "
LogisticRegression(solver='saga')
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" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ], + "source": [ + "model = LogisticRegression(solver='saga')\n", + "model.fit(x_train_cv, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "37bUbqB6fjD8", + "outputId": "7eb9178d-6130-47d0-bdf4-ce4be164bc97" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.79 0.80 160000\n", + " 1 0.79 0.81 0.80 160000\n", + "\n", + " accuracy 0.80 320000\n", + " macro avg 0.80 0.80 0.80 320000\n", + "weighted avg 0.80 0.80 0.80 320000\n", + "\n" + ] + } + ], + "source": [ + "y_pred = model.predict(x_test_cv)\n", + "\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "161kDLhofjD8" + }, + "source": [ + "## Explainable AI\n", + "The best thing about logistic regresion is that it is simple, and we can get some explanations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EILTmxzifjD9", + "outputId": "b7ce6853-7385-4a24-d4eb-e6d0843ca5d5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "(1, 589260)\n", + "589260\n" + ] + } + ], + "source": [ + "print(model.coef_.shape)\n", + "print(len(vectorizer.vocabulary_))\n", + "\n", + "words_sk = list(vectorizer.vocabulary_.keys())\n", + "words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NGjVPON6fjD9", + "outputId": "d40443bc-476d-4f5a-ce90-4b5b17e47933" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "roni: -3.8625952420933984\n", + "inaperfectworld: -3.5734321547933936\n", + "dontyouhate: -3.5002133484207576\n", + "xbllygbsn: -3.4126303898325787\n", + "anqju: -3.3363997631497493\n", + "sad: -3.200516823534637\n", + "pakcricket: -3.1949062976331675\n", + "condolences: -3.132503698316079\n", + "heartbreaking: -3.0665219866881297\n", + "saddest: -3.042020604188048\n", + "sadd: -3.029036146667248\n", + "heartbroken: -3.0287524416643463\n", + "boohoo: -3.0226033087262802\n", + "sadface: -2.991829110065316\n", + "rachelle_lefevr: -2.925076661509848\n", + "disappointing: -2.902522686643491\n", + "lvbu: -2.8947109582208865\n", + "saddens: -2.8855187276040715\n", + "bummed: -2.836500453805889\n", + "neda: -2.792917726280752\n" + ] + } + ], + "source": [ + "for w in words_sk[:20]:\n", + " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oxQ_jlNRfjD9", + "outputId": "363de58b-817a-4205-f019-2379d0d64e0d" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "iamsoannoyed: 2.8493838469077013\n", + "myfax: 2.7974330510971424\n", + "jennamadison: 2.5667217237933104\n", + "yeyy: 2.4780234846131646\n", + "tryout: 2.438315611477797\n", + "goldymom: 2.4374072779309204\n", + "wooohooo: 2.402957513257194\n", + "thesupergirl: 2.356525094856456\n", + "iammaxathotspot: 2.3116551216589682\n", + "londicreations: 2.3074264075299316\n", + "smilin: 2.2991796213822497\n", + "worries: 2.2899555142510084\n", + "sinfulsignorita: 2.27989578448778\n", + "finchensnail: 2.2642827277181063\n", + "smackthis: 2.237672991997692\n", + "kv: 2.2157591386122775\n", + "tojosan: 2.2117938132889696\n", + "russmarshalek: 2.20953890861265\n", + "traciknoppe: 2.1768232307222153\n", + "congratulations: 2.1715901103136876\n" + ] + } + ], + "source": [ + "for w in reversed(words_sk[-20:]):\n", + " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "9KSSAC3qfjD9" + }, + "source": [ + "What does this mean?\n", + "\n", + "Remember the `model.coef_` is the $W$ in:\n", + "\n", + "$$h(x)=\\sigma(WX + b)$$\n", + "\n", + "where the label 1 is a positive tweet and the label 0 is a negative tweet." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "oDHjTP2_fjD9" + }, + "source": [ + "## Recurrent Neural Network with Pytorch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "TbgpKy95fjD9" + }, + "source": [ + "In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n", + "\n", + "In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n", + "\n", + "Let's start by importing the relevant libraries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "7nmUJV99fjEB" + }, + "outputs": [], + "source": [ + "def set_device():\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " if device != \"cuda\":\n", + " print(\"WARNING: For this notebook to perform best, \"\n", + " \"if possible, in the menu under `Runtime` -> \"\n", + " \"`Change runtime type.` select `GPU` \")\n", + " else:\n", + " print(\"GPU is enabled in this notebook.\")\n", + "\n", + " return device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "chI-18LcfjEB", + "outputId": "7f633079-6548-48f3-802e-94bc9cfada93" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "GPU is enabled in this notebook.\n" + ] + } + ], + "source": [ + "# Set the device (check if gpu is available)\n", + "device = set_device()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "01UtIN7ofjEC" + }, + "source": [ + "First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "afus9SyUfjEC", + "outputId": "bb4eb869-e2f0-4ccd-f64c-e55908272345" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']" + ] + }, + "metadata": {}, + "execution_count": 21 + } + ], + "source": [ + "# From previous section, we have a list with the most used tokens\n", + "sorted_words[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "6vfQFjaufjEC" + }, + "source": [ + "Let's select only the most used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "tGLkxaGcfjEC" + }, + "outputs": [], + "source": [ + "num_words_dict = 30000\n", + "# We reserve two numbers for special tokens.\n", + "most_used_words = sorted_words[:num_words_dict-2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "AzhQvekCfjEC" + }, + "source": [ + "We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "73Wrb-lEfjEC" + }, + "outputs": [], + "source": [ + "# dictionary to go from words to idx\n", + "word_to_idx = {}\n", + "# dictionary to go from idx to words (just in case)\n", + "idx_to_word = {}\n", + "\n", + "\n", + "# We include the special tokens first\n", + "PAD_token = 0\n", + "UNK_token = 1\n", + "\n", + "word_to_idx['PAD'] = PAD_token\n", + "word_to_idx['UNK'] = UNK_token\n", + "\n", + "idx_to_word[PAD_token] = 'PAD'\n", + "idx_to_word[UNK_token] = 'UNK'\n", + "\n", + "# We popullate our dictionaries with the most used words\n", + "for num,word in enumerate(most_used_words):\n", + " word_to_idx[word] = num + 2\n", + " idx_to_word[num+2] = word" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "kMHVkEisfjEC" + }, + "source": [ + "Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "tkCIu3PKfjED" + }, + "outputs": [], + "source": [ + "# A function to convert list of tokens to list of indexes\n", + "def tokens_to_idx(sentences_tokens,word_to_idx):\n", + " sentences_idx = []\n", + " for sent in sentences_tokens:\n", + " sent_idx = []\n", + " for word in sent:\n", + " if word in word_to_idx:\n", + " sent_idx.append(word_to_idx[word])\n", + " else:\n", + " sent_idx.append(word_to_idx['UNK'])\n", + " sentences_idx.append(sent_idx)\n", + " return sentences_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "aHru4vpzfjED" + }, + "outputs": [], + "source": [ + "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", + "x_test_idx = tokens_to_idx(x_test_token,word_to_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ofj3OD7zfjED", + "outputId": "b2788d03-dbfa-41d7-8231-5011206baa59" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Before converting: ['worst', 'headache', 'ever']\n", + "After converting: [721, 458, 237]\n" + ] + } + ], + "source": [ + "some_number = 1\n", + "print('Before converting: ', x_train_token[some_number])\n", + "print('After converting: ', x_train_idx[some_number])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "NcCicvb-fjED" + }, + "source": [ + "We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BSjhdyYUfjED", + "outputId": "82e49be9-7868-44ac-b496-c7a48da1efee" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Max tweet word length: 229\n", + "Mean tweet word length: 15.0\n", + "99% percent under: 37.0\n" + ] + } + ], + "source": [ + "tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n", + "print('Max tweet word length: ',tweet_lens.max())\n", + "print('Mean tweet word length: ',np.median(tweet_lens))\n", + "print('99% percent under: ',np.quantile(tweet_lens,0.99))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "t311WY6ZfjEE" + }, + "source": [ + "We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "r4S8KTWLfjEE" + }, + "outputs": [], + "source": [ + " # We choose the max length\n", + " max_length = 40\n", + "\n", + "# A function to make all the sequence have the same lenght\n", + "# Note that the output is a Numpy matrix\n", + " def padding(sentences, seq_len):\n", + " features = np.zeros((len(sentences), seq_len),dtype=int)\n", + " for ii, tweet in enumerate(sentences):\n", + " len_tweet = len(tweet)\n", + " if len_tweet != 0:\n", + " if len_tweet <= seq_len:\n", + " # If its shorter, we fill with zeros (the padding Token index)\n", + " features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n", + " if len_tweet > seq_len:\n", + " # If its larger, we take the last 'seq_len' indexes\n", + " features[ii, :] = np.array(tweet)[-seq_len:]\n", + " return features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "Z-Cw-bBxfjEE" + }, + "outputs": [], + "source": [ + "# We convert our list of tokens into a numpy matrix\n", + "# where all instances have the same lenght\n", + "x_train_pad = padding(x_train_idx,max_length)\n", + "x_test_pad = padding(x_test_idx,max_length)\n", + "\n", + "# We convert our target list a numpy matrix\n", + "y_train_np = np.asarray(y_train)\n", + "y_test_np = np.asarray(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8eC3YswlfjEE", + "outputId": "3bb0ea7f-518f-4545-9241-feb783f48122" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n", + "After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n", + " 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n", + " 150 4 22 5 18 667 3 138 61 7 3296 4]\n" + ] + } + ], + "source": [ + "some_number = 2\n", + "print('Before padding: ', x_train_idx[some_number])\n", + "print('After padding: ', x_train_pad[some_number])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "SzDhnauUfjEE" + }, + "source": [ + "Now, let's convert the data to pytorch format.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "--Yd22YWfjEF" + }, + "outputs": [], + "source": [ + "# create Tensor datasets\n", + "train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n", + "valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n", + "\n", + "# Batch size (this is an important hyperparameter)\n", + "batch_size = 100\n", + "\n", + "# dataloaders\n", + "# make sure to SHUFFLE your data\n", + "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n", + "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "jQ5qPOWTfjEF" + }, + "source": [ + "Each batch of data in our traning proccess will have the folllowing format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "S1mqhk1hfjEF", + "outputId": "c97e7edd-695f-4336-a2e6-f6bed4852a63" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Sample input size: torch.Size([100, 40])\n", + "Sample input: \n", + " tensor([[ 0, 0, 0, ..., 32, 203, 86],\n", + " [ 0, 0, 0, ..., 1, 1, 4661],\n", + " [ 0, 0, 0, ..., 169, 43, 34],\n", + " ...,\n", + " [ 0, 0, 0, ..., 2, 2961, 4076],\n", + " [ 0, 0, 0, ..., 2319, 1325, 2],\n", + " [ 0, 0, 0, ..., 7, 253, 1]])\n", + "Sample input: \n", + " tensor([0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n", + " 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n", + " 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n", + " 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", + " 0, 1, 0, 1])\n" + ] + } + ], + "source": [ + "# Obtain one batch of training data\n", + "dataiter = iter(train_loader)\n", + "sample_x, sample_y = dataiter.__next__()\n", + "\n", + "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n", + "print('Sample input: \\n', sample_x)\n", + "print('Sample input: \\n', sample_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "jn0PzZdGfjEF" + }, + "source": [ + "Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n", + "\n", + "* Embedding Layer\n", + "> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n", + "\n", + "\n", + "* LSTM layer\n", + "> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "vfzcowAxfjEF" + }, + "outputs": [], + "source": [ + "class SentimentRNN(nn.Module):\n", + " def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n", + " super(SentimentRNN,self).__init__()\n", + "\n", + " self.output_dim = output_dim\n", + " self.hidden_dim = hidden_dim\n", + " self.no_layers = no_layers\n", + " self.vocab_size = vocab_size\n", + " self.drop_prob = drop_prob\n", + "\n", + " # Embedding Layer\n", + " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n", + "\n", + " # LSTM Layers\n", + " self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n", + " num_layers=no_layers, batch_first=True,\n", + " dropout=self.drop_prob)\n", + "\n", + " # Dropout layer\n", + " self.dropout = nn.Dropout(drop_prob)\n", + "\n", + " # Linear and Sigmoid layer\n", + " self.fc = nn.Linear(self.hidden_dim, output_dim)\n", + " self.sig = nn.Sigmoid()\n", + "\n", + " def forward(self,x,hidden):\n", + " batch_size = x.size(0)\n", + "\n", + " # Embedding out\n", + " embeds = self.embedding(x)\n", + " #Shape: [batch_size x max_length x embedding_dim]\n", + "\n", + " # LSTM out\n", + " lstm_out, hidden = self.lstm(embeds, hidden)\n", + " # Shape: [batch_size x max_length x hidden_dim]\n", + "\n", + " # Select the activation of the last Hidden Layer\n", + " lstm_out = lstm_out[:,-1,:].contiguous()\n", + " # Shape: [batch_size x hidden_dim]\n", + "\n", + " ## You can instead average the activations across all the times\n", + " # lstm_out = torch.mean(lstm_out, 1).contiguous()\n", + "\n", + " # Dropout and Fully connected layer\n", + " out = self.dropout(lstm_out)\n", + " out = self.fc(out)\n", + "\n", + " # Sigmoid function\n", + " sig_out = self.sig(out)\n", + "\n", + " # return last sigmoid output and hidden state\n", + " return sig_out, hidden\n", + "\n", + " def init_hidden(self, batch_size):\n", + " ''' Initializes hidden state '''\n", + " # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n", + " # initialized to zero, for hidden state and cell state of LSTM\n", + " h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", + " c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", + " hidden = (h0,c0)\n", + " return hidden" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "YfrLPa9mfjEF" + }, + "source": [ + "We choose the parameters of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "rOm-xoFkfjEG" + }, + "outputs": [], + "source": [ + "# Parameters of our network\n", + "\n", + "# Size of our vocabulary\n", + "vocab_size = num_words_dict\n", + "\n", + "# Embedding dimension\n", + "embedding_dim = 32\n", + "\n", + "# Number of stacked LSTM layers\n", + "no_layers = 2\n", + "\n", + "# Dimension of the hidden layer in LSTMs\n", + "hidden_dim = 64\n", + "\n", + "# Dropout parameter for regularization\n", + "output_dim = 1\n", + "\n", + "# Dropout parameter for regularization\n", + "drop_prob = 0.25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xapxpe7ufjEG", + "outputId": "51c90159-7d2b-4fc4-f34a-98e9901d40e4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "SentimentRNN(\n", + " (embedding): Embedding(30000, 32)\n", + " (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n", + " (dropout): Dropout(p=0.25, inplace=False)\n", + " (fc): Linear(in_features=64, out_features=1, bias=True)\n", + " (sig): Sigmoid()\n", + ")\n" + ] + } + ], + "source": [ + "# Let's define our model\n", + "model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n", + " embedding_dim, drop_prob=drop_prob)\n", + "# Moving to gpu\n", + "model.to(device)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GEvTs3uwfjEG", + "outputId": "2e15f6df-2aa8-4665-b2da-7363d2bfa09e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Total Number of parameters: 1018433\n" + ] + } + ], + "source": [ + "# How many trainable parameters does our model have?\n", + "model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n", + "params = sum([np.prod(p.size()) for p in model_parameters])\n", + "print('Total Number of parameters: ',params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "Pc2OC5GDfjEG" + }, + "source": [ + "We choose the losses and the optimizer for the training procces." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "id": "iBWjPADUfjEG" + }, + "outputs": [], + "source": [ + "# loss and optimization functions\n", + "lr = 0.001\n", + "\n", + "# Binary crossentropy is a good loss function for a binary classification problem\n", + "criterion = nn.BCELoss()\n", + "\n", + "# We choose an Adam optimizer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + "\n", + "# function to predict accuracy\n", + "def acc(pred,label):\n", + " pred = torch.round(pred.squeeze())\n", + " return torch.sum(pred == label.squeeze()).item()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "OZgMwOe2fjEG" + }, + "source": [ + "We are ready to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3B6YhEocfjEH", + "outputId": "76276a1f-7775-4b98-aab0-0e199aa133e4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1\n", + "train_loss : 0.4366412344621494 val_loss : 0.3881208170717582\n", + "train_accuracy : 79.485546875 val_accuracy : 82.475\n", + "Validation loss decreased (inf --> 0.388121). Saving model ...\n", + "==================================================\n", + "Epoch 2\n", + "train_loss : 0.3760281792609021 val_loss : 0.3713956154882908\n", + "train_accuracy : 83.186328125 val_accuracy : 83.4575\n", + "Validation loss decreased (0.388121 --> 0.371396). Saving model ...\n", + "==================================================\n", + "Epoch 3\n", + "train_loss : 0.3574051411205437 val_loss : 0.36425656544510276\n", + "train_accuracy : 84.19953125 val_accuracy : 83.80375\n", + "Validation loss decreased (0.371396 --> 0.364257). Saving model ...\n", + "==================================================\n", + "Epoch 4\n", + "train_loss : 0.344456663565943 val_loss : 0.3613302929420024\n", + "train_accuracy : 84.89265625 val_accuracy : 84.00874999999999\n", + "Validation loss decreased (0.364257 --> 0.361330). Saving model ...\n", + "==================================================\n", + "Epoch 5\n", + "train_loss : 0.33407817618339325 val_loss : 0.3601334386831149\n", + "train_accuracy : 85.444921875 val_accuracy : 84.03625\n", + "Validation loss decreased (0.361330 --> 0.360133). Saving model ...\n", + "==================================================\n" + ] + } + ], + "source": [ + "# Number of training Epochs\n", + "epochs = 5\n", + "\n", + "# Maximum absolute value accepted for the gradeint\n", + "clip = 5\n", + "\n", + "# Initial Loss value (assumed big)\n", + "valid_loss_min = np.Inf\n", + "\n", + "# Lists to follow the evolution of the loss and accuracy\n", + "epoch_tr_loss,epoch_vl_loss = [],[]\n", + "epoch_tr_acc,epoch_vl_acc = [],[]\n", + "\n", + "# Train for a number of Epochs\n", + "for epoch in range(epochs):\n", + " train_losses = []\n", + " train_acc = 0.0\n", + " model.train()\n", + "\n", + " for inputs, labels in train_loader:\n", + "\n", + " # Initialize hidden state\n", + " h = model.init_hidden(batch_size)\n", + " # Creating new variables for the hidden state\n", + " h = tuple([each.data.to(device) for each in h])\n", + "\n", + " # Move batch inputs and labels to gpu\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + "\n", + " # Set gradient to zero\n", + " model.zero_grad()\n", + "\n", + " # Compute model output\n", + " output,h = model(inputs,h)\n", + "\n", + " # Calculate the loss and perform backprop\n", + " loss = criterion(output.squeeze(), labels.float())\n", + " loss.backward()\n", + " train_losses.append(loss.item())\n", + "\n", + " # calculating accuracy\n", + " accuracy = acc(output,labels)\n", + " train_acc += accuracy\n", + "\n", + " #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n", + " nn.utils.clip_grad_norm_(model.parameters(), clip)\n", + " optimizer.step()\n", + "\n", + "\n", + " # Evaluate on the validation set for this epoch\n", + " val_losses = []\n", + " val_acc = 0.0\n", + " model.eval()\n", + " for inputs, labels in valid_loader:\n", + "\n", + " # Initialize hidden state\n", + " val_h = model.init_hidden(batch_size)\n", + " val_h = tuple([each.data.to(device) for each in val_h])\n", + "\n", + " # Move batch inputs and labels to gpu\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + "\n", + " # Compute model output\n", + " output, val_h = model(inputs, val_h)\n", + "\n", + " # Compute Loss\n", + " val_loss = criterion(output.squeeze(), labels.float())\n", + "\n", + " val_losses.append(val_loss.item())\n", + "\n", + " accuracy = acc(output,labels)\n", + " val_acc += accuracy\n", + "\n", + " epoch_train_loss = np.mean(train_losses)\n", + " epoch_val_loss = np.mean(val_losses)\n", + " epoch_train_acc = train_acc/len(train_loader.dataset)\n", + " epoch_val_acc = val_acc/len(valid_loader.dataset)\n", + " epoch_tr_loss.append(epoch_train_loss)\n", + " epoch_vl_loss.append(epoch_val_loss)\n", + " epoch_tr_acc.append(epoch_train_acc)\n", + " epoch_vl_acc.append(epoch_val_acc)\n", + " print(f'Epoch {epoch+1}')\n", + " print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n", + " print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n", + " if epoch_val_loss <= valid_loss_min:\n", + " print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n", + " # torch.save(model.state_dict(), '../working/state_dict.pt')\n", + " valid_loss_min = epoch_val_loss\n", + " print(25*'==')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {}, + "colab": { + "base_uri": "https://localhost:8080/", + "height": 364 + }, + "id": "ttJazP-nfjEH", + "outputId": "992bed02-611e-4614-c60f-77223d5b801a" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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+ }, + "metadata": {} + } + ], + "source": [ + "fig = plt.figure(figsize = (20, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epoch_tr_acc, label='Train Acc')\n", + "plt.plot(epoch_vl_acc, label='Validation Acc')\n", + "plt.title(\"Accuracy\")\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epoch_tr_loss, label='Train loss')\n", + "plt.plot(epoch_vl_loss, label='Validation loss')\n", + "plt.title(\"Loss\")\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "iUyaF-EbfjEH" + }, + "source": [ + "---\n", + "# What's Next?\n", + "\n", + "You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n", + "\n", + "* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n", + "* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n", + "* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n", + "* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. Do you belive that a sentiment analysis model trained on some corpus (Twitter dataset) will perform well on another type of data (for example, youtube comments)?\n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "provenance": [] + }, + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "fbb4191426bd485e8e965b6d432eecae": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + 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index eb393529a..87c305bde 100644 --- a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb +++ b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb @@ -1,4640 +1,1671 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "view-in-github" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "D_fgc45VfjDz" - }, - "source": [ - "# Twitter Sentiment Analysis\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n", - "\n", - "__Production editors:__ Amita Kapoor, Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "axvz0SUsfjD0" - }, - "source": [ - "---\n", - "# Welcome to the NLP project template\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "2Vfm0ThbfjD1" - }, - "source": [ - "---\n", - "# Step 1: Questions and goals\n", - "\n", - "* Can we infer emotion from a tweet text?\n", - "* How words are distributed accross the dataset?\n", - "* Are words related to one kind of emotion?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "Vd1qdNW9fjD1" - }, - "source": [ - "---\n", - "# Step 2: Literature review\n", - "\n", - "[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n", - "\n", - "[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "oOYDQElpfjD2" - }, - "source": [ - "---\n", - "# Step 3: Load and explore the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EZpxSExUfjD2", - "outputId": "19b01445-9b83-4a93-9cc2-7830ab0dcf5b" - }, - "outputs": [ - { - 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This behaviour is the source of the following dependency conflicts.\n", - "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 16.1.0 which is incompatible.\n", - "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n", - "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 16.1.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "# @title Install dependencies\n", - "!pip install pandas --quiet\n", - "!pip install torchtext --quiet\n", - "!pip install datasets --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "DxqD3Tk5fjD3", - "outputId": "451d68c5-7894-4f93-9f54-bf0b7f482e20" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/torchtext/data/__init__.py:4: UserWarning: \n", - "/!\\ IMPORTANT WARNING ABOUT TORCHTEXT STATUS /!\\ \n", - "Torchtext is deprecated and the last released version will be 0.18 (this one). You can silence this warning by calling the following at the beginnign of your scripts: `import torchtext; torchtext.disable_torchtext_deprecation_warning()`\n", - " warnings.warn(torchtext._TORCHTEXT_DEPRECATION_MSG)\n" - ] - } - ], - "source": [ - "# We import some libraries to load the dataset\n", - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from datasets import load_dataset\n", - "\n", - "from collections import Counter\n", - "from tqdm.notebook import tqdm\n", - "\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import TensorDataset, DataLoader\n", - "\n", - "import torchtext\n", - "from torchtext.data import get_tokenizer\n", - "\n", - "from sklearn.utils import shuffle\n", - "from sklearn.metrics import classification_report\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.model_selection import train_test_split\n", - "from sklearn.feature_extraction.text import CountVectorizer" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "63Eg1SLbfjD4" - }, - "source": [ - "You can find the dataset we are going to use in [this website](http://help.sentiment140.com/for-students/)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 567, - "referenced_widgets": [ - "fbb4191426bd485e8e965b6d432eecae", - "df7eba182d1b4c21bc21d157eac6b996", - "6d64402d9da74516ab4e1d46ae9f1ee3", - "d9ca809f7b1c49e595a05458251f3ab2", - "90908b6f69524a72860214ef8bd2d946", - "db432a2cd6244a7592fc9732f0ca4738", - "84485541f3a14c65a67d10a97b72bbad", - "5fa7ab2ab2004e5cb692199e2bd27d6b", - "ab71bd2b452146829e973d6cf99f31ed", - "55ba92cfe0724286ac1c2bbe6577e5c8", - "67a4fa49ca5349d58512a16a3742d401", - "afd671543846468abfe37669a72845c3", - "057e918ace004506aedc4e4b9942c3a8", - "325387f6b62d47b0b21bea61676cea72", - "ea1e3eb0e6ec4f8d82cf9b12cfe6e700", - "96c2d7ee644a438982e1792b7ec0453c", - "9baa1a735c0646b89953bf4a7c7fc92c", - "0ac9711c8ece4c5397a8cd810713adfb", - "a8d69769921241b8b1081e84f7770858", - "d189f24b0e964d1a9fc86379bad38cca", - "db9bf44dec914db793cc4f73751c272c", - "1cf3ba0f756f4aa5ad1dcb675a791cfa", - "c432c4efcb794ce781fcb6f176f1b60d", - "510eeffb32694e7798f23e3931d7a943", - "a8b3dfaa2831416582d8eeef01451386", - "db1cdafaf36f4c339476f3221abc17b3", - "ffd3778a96e046718828bbc5aa73f173", - "49c5a3fbe87b491cb3f0f450a0af0659", - "252949e8784c4878a62eb2e30b1e3466", - "7bcef602e7f441308472bc145b12dcd3", - "97fb30a5a31742efa1d188b9361f9938", - "9b34daddb9cc48bba109e547177ec654", - "fd2b5a6533794a2794579956c25247fb", - "f3a9667c8c994324a2409f227bd0a1e9", - "6e6c5372ffe045c0b72587989567429e", - "2ead0216695e4227aef44552f4ec3cc9", - "53843f49adda4bce8450fd91fa9fd587", - "40262cb3eefa45fcbe37aaafccb69f5f", - "b54b826314ea4b3a92eebd218c093fc1", - "8cd7be688b8c4818be48915db14a0792", - "a9a0f6ce71ed415c8c8513f68e34e162", - "7f638a6deacd42e88c031fa47797516b", - "849e39cc86f64e558ff94bf542a5121a", - "67b0b03c391c414bad5ea9fb3c947a2f", - "1cef38981af6457dbaeb393f9936a389", - "b0b5cfae51214c60bbca9a09b196c217", - "5ee2a4b33be04c6db8ee4d7995c2376d", - "403fffb635c2409ebeabc90063750ed3", - "6279343019064572adedf34cfbd437fa", - "2715d00db77545f9aa5eace8a0eb2839", - "942ce490d87347c789e229589b1b9c9f", - "f04df4daeb6049ab85d3d75b472ccf6e", - "fd0b3c53b66543cea0c396d8047445a8", - "2c42e2fef6314c9e842a7e9641af3cab", - "913d95e58aa94e4a8009768a23fbf304" - ] - }, - "id": "3HLOsd3rfjD4", - "outputId": "7653fee1-a871-472b-a978-d8ec0250dc84" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Downloading builder script: 0%| | 0.00/4.03k [00:00\n", - "
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polarityuserdatequeryusertext
00_TheSpecialOne_Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
10scotthamiltonMon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
20mattycusMon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
30ElleCTFMon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
40KaroliMon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
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\n", - " \n" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "dataframe", - "variable_name": "df" - } - }, - "metadata": {}, - "execution_count": 3 - } - ], - "source": [ - "# We load the dataset\n", - "\n", - "dataset = load_dataset(\"stanfordnlp/sentiment140\", trust_remote_code= True)\n", - "\n", - "train_data = dataset[\"train\"]\n", - "df = pd.DataFrame(train_data)\n", - "df = df.rename(columns={'sentiment': 'polarity'})\n", - "df = df[['polarity', 'user', 'date', 'query', 'user', 'text']]\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "fuKShcfjfjD4" - }, - "source": [ - "For this project we will use only the text and the polarity of the tweet. Notice that polarity is 0 for negative tweets and 4 for positive tweet." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "GXHQOn6gfjD5" - }, - "outputs": [], - "source": [ - "X = df.text.values\n", - "\n", - "# Changes values from [0,4] to [0,1]\n", - "y = (df.polarity.values > 1).astype(int)\n", - "\n", - "\n", - "# Split the data into train and test\n", - "x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "7kr3TO_LfjD5" - }, - "source": [ - "The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "FsL-xY03fjD5", - "outputId": "655f0ef8-c177-4f42-c024-1d628241401a" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n", - "0: worst headache ever \n", - "0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n", - "1: doesn't know how to spell conked \n", - "0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n" - ] - } - ], - "source": [ - "for s, l in zip(x_train_text[:5], y_train[:5]):\n", - " print('{}: {}'.format(l, s))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "4cPGXSc-fjD5" - }, - "source": [ - "An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "U1OugpZ0fjD5", - "outputId": "9e6cb4e3-8d8c-4db0-c113-bdd4fe87db5f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Before Tokenize: worst headache ever \n", - "After Tokenize: ['worst', 'headache', 'ever']\n" - ] - } - ], - "source": [ - "tokenizer = get_tokenizer(\"basic_english\")\n", - "\n", - "print('Before Tokenize: ', x_train_text[1])\n", - "print('After Tokenize: ', tokenizer(x_train_text[1]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 81, - "referenced_widgets": [ - "e1348a02ceeb4af19fbd63d52b7d843b", - "fbf51b14e6b34d0485ddf59c43d22c49", - 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wAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAq/oVRqqqqpSSkqLIyEhlZmZqz549F+y/evVqff3rX9fw4cOVnJysBx54QH/605/6VTAAAAgujsPI5s2bVVJSovLycu3du1epqanKycnR8ePHe+2/ceNGLV68WOXl5frggw/09NNPa/PmzXrkkUcuuXgAABD4HIeRyspKLViwQIWFhZo0aZKqq6s1YsQIrV27ttf+b731lmbMmKG5c+cqJSVF3/nOdzRnzpyLrqYAAIChwVEY6e7uVkNDg7Kzs788QGiosrOzVVdX1+uYG2+8UQ0NDd7w0dTUpO3bt+v2228/73m6urrkdrt9NgAAEJzCnXQ+efKkenp6FB8f79MeHx+vDz/8sNcxc+fO1cmTJ3XTTTfJGKPPP/9cCxcuvOBlmoqKCi1btsxJaQAAIEAN+NM0tbW1WrFihZ588knt3btXL7zwgrZt26bHHnvsvGNKS0vV3t7u3VpaWga6TAAAYImjlZHY2FiFhYWpra3Np72trU0JCQm9jlm6dKnuvPNO3XPPPZKkKVOmqLOzUz/60Y+0ZMkShYaem4dcLpdcLpeT0gAAQIBytDISERGh9PR01dTUeNs8Ho9qamqUlZXV65izZ8+eEzjCwsIkScYYp/UCAIAg42hlRJJKSkpUUFCgjIwMTZ8+XatXr1ZnZ6cKCwslSfPnz1dSUpIqKiokSfn5+aqsrFRaWpoyMzN16NAhLV26VPn5+d5QAgAAhi7HYWT27Nk6ceKEysrK1NraqmnTpmnHjh3em1qbm5t9VkIeffRRhYSE6NFHH9XRo0f1ta99Tfn5+Vq+fLn/vgUAAAhYISYArpW43W5FR0ervb1dUVFRfj12yuJtPvtHVub59fgAAAxVff332/HKyFBAQAEAYPDwojwAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVP0/QBT9cAADBwWBkBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABY1a8wUlVVpZSUFEVGRiozM1N79uy5YP/Tp0+rqKhIY8aMkcvl0vjx47V9+/Z+FQwAAIJLuNMBmzdvVklJiaqrq5WZmanVq1crJydHBw4cUFxc3Dn9u7u7deuttyouLk7PP/+8kpKS9PHHH2vUqFH+qB8AAAQ4x2GksrJSCxYsUGFhoSSpurpa27Zt09q1a7V48eJz+q9du1anTp3SW2+9pWHDhkmSUlJSLq1qAAAQNBxdpunu7lZDQ4Oys7O/PEBoqLKzs1VXV9frmJdeeklZWVkqKipSfHy8Jk+erBUrVqinp+e85+nq6pLb7fbZAABAcHIURk6ePKmenh7Fx8f7tMfHx6u1tbXXMU1NTXr++efV09Oj7du3a+nSpVq1apV+8pOfnPc8FRUVio6O9m7JyclOygQAAAFkwJ+m8Xg8iouL01NPPaX09HTNnj1bS5YsUXV19XnHlJaWqr293bu1tLQMdJkAAMASR/eMxMbGKiwsTG1tbT7tbW1tSkhI6HXMmDFjNGzYMIWFhXnbJk6cqNbWVnV3dysiIuKcMS6XSy6Xy0lpAAAgQDlaGYmIiFB6erpqamq8bR6PRzU1NcrKyup1zIwZM3To0CF5PB5v28GDBzVmzJhegwgAABhaHF+mKSkp0Zo1a/SrX/1KH3zwge699151dnZ6n66ZP3++SktLvf3vvfdenTp1SosWLdLBgwe1bds2rVixQkVFRf77FgAAIGA5frR39uzZOnHihMrKytTa2qpp06Zpx44d3ptam5ubFRr6ZcZJTk7Wq6++qgceeEBTp05VUlKSFi1apIcffth/3wIAAAQsx2FEkoqLi1VcXNzrZ7W1tee0ZWVl6e233+7PqQAAQJDj3TQAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwql8/Bw8pZfE2n/0jK/MsVQIAQGBjZQQAAFhFGAEAAFYRRgAAgFWEEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFjFW3v9hLf4AgDQP6yMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMCqfoWRqqoqpaSkKDIyUpmZmdqzZ0+fxm3atEkhISGaNWtWf04bcFIWb/PZAADAuRyHkc2bN6ukpETl5eXau3evUlNTlZOTo+PHj19w3JEjR/Tggw/q5ptv7nexAAAg+DgOI5WVlVqwYIEKCws1adIkVVdXa8SIEVq7du15x/T09GjevHlatmyZrrnmmksqGAAABBdHYaS7u1sNDQ3Kzs7+8gChocrOzlZdXd15x/3rv/6r4uLidPfdd/fpPF1dXXK73T4bAAAITo7CyMmTJ9XT06P4+Hif9vj4eLW2tvY6Zvfu3Xr66ae1Zs2aPp+noqJC0dHR3i05OdlJmQAAIIAM6NM0Z86c0Z133qk1a9YoNja2z+NKS0vV3t7u3VpaWgawSgAAYJOjF+XFxsYqLCxMbW1tPu1tbW1KSEg4p/8f/vAHHTlyRPn5+d42j8fzxYnDw3XgwAFde+2154xzuVxyuVxOSgsYf/lUDS/TAwDA4cpIRESE0tPTVVNT423zeDyqqalRVlbWOf0nTJig9957T42Njd7tu9/9rmbOnKnGxkYuvwAAAGcrI5JUUlKigoICZWRkaPr06Vq9erU6OztVWFgoSZo/f76SkpJUUVGhyMhITZ482Wf8qFGjJOmcdgAAMDQ5DiOzZ8/WiRMnVFZWptbWVk2bNk07duzw3tTa3Nys0FB+2BUAAPSN4zAiScXFxSouLu71s9ra2guOXb9+fX9OCQAAghRLGAAAwCrCCAAAsIowAgAArCKMAAAAqwgjAADAKsIIAACwijACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwKp+vbUX/pOyeJvP/pGVeZYqAQDADlZGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFYRRgAAgFWEEQAAYBU/B38Z+sufiOfn4QEAwY6VEQAAYBUrIwGgt5fpfXX1hBfuAQACFSsjAADAKsIIAACwijACAACsIowAAACr+hVGqqqqlJKSosjISGVmZmrPnj3n7btmzRrdfPPNuvLKK3XllVcqOzv7gv0BAMDQ4jiMbN68WSUlJSovL9fevXuVmpqqnJwcHT9+vNf+tbW1mjNnjt544w3V1dUpOTlZ3/nOd3T06NFLLh4AAAQ+x2GksrJSCxYsUGFhoSZNmqTq6mqNGDFCa9eu7bX/M888o/vuu0/Tpk3ThAkT9Mtf/lIej0c1NTWXXDwAAAh8jsJId3e3GhoalJ2d/eUBQkOVnZ2turq6Ph3j7Nmz+uyzzxQTE3PePl1dXXK73T4bAAAITo7CyMmTJ9XT06P4+Hif9vj4eLW2tvbpGA8//LASExN9As1XVVRUKDo62rslJyc7KRMAAASQQf0F1pUrV2rTpk2qra1VZGTkefuVlpaqpKTEu+92uwkk/cA7bgAAgcBRGImNjVVYWJja2tp82tva2pSQkHDBsY8//rhWrlyp//7v/9bUqVMv2NflcsnlcjkpDX3AT8YDAC5Hji7TREREKD093efm0z/fjJqVlXXecT/72c/02GOPaceOHcrIyOh/tQAAIOg4vkxTUlKigoICZWRkaPr06Vq9erU6OztVWFgoSZo/f76SkpJUUVEhSfrpT3+qsrIybdy4USkpKd57S0aOHKmRI0f68asAAIBA5DiMzJ49WydOnFBZWZlaW1s1bdo07dixw3tTa3Nzs0JDv1xw+cUvfqHu7m5973vf8zlOeXm5/uVf/uXSqgcAAAGvXzewFhcXq7i4uNfPamtrffaPHDnSn1NgkHz1JlfuKwEADDbeTQMAAKwijAAAAKsIIwAAwCrCCAAAsGpQf4EVgYmbXAEAA4kwAr/gp+cBAP3FZRoAAGAVKyMYEL1dymH1BADQG8IIrOHeEwCARBjBZYbVEwAYerhnBAAAWMXKCC5rXMoBgOBHGEHA4VIOAAQXwggCHqsnABDYCCMISqyeAEDg4AZWAABgFSsjGBIu9iNsvWFFBQAGB2EEuADuRwGAgUcYARwgnACA/xFGgEtEQAGAS0MYAfysL/en9PbiQEINgKGKMAJcxvoSWHiMGUCgI4wAQaa/qzCEGgC2EEYA9IpLSwAGC2EEgF/1ZxWG1RxgaCOMAAga/f1xO1Z4ALsIIwDwFf4MNVzaAi6OMAIAltm8tMXlL1wOCCMAgAu63MKRP/r0hndW2UMYAQDAgcspVAXLClio1bMDAIAhr19hpKqqSikpKYqMjFRmZqb27Nlzwf5btmzRhAkTFBkZqSlTpmj79u39KhYAAAQfx2Fk8+bNKikpUXl5ufbu3avU1FTl5OTo+PHjvfZ/6623NGfOHN19993at2+fZs2apVmzZmn//v2XXDwAAAh8jsNIZWWlFixYoMLCQk2aNEnV1dUaMWKE1q5d22v/J554QrfddpseeughTZw4UY899phuuOEG/ed//uclFw8AAAKfoxtYu7u71dDQoNLSUm9baGiosrOzVVdX1+uYuro6lZSU+LTl5ORo69at5z1PV1eXurq6vPvt7e2SJLfb7aTcPvF0nfXZd7vd57R9VX/7fLWNPpdPn97w/0Lw9ekNf85Ds09vhvqf80D483GNMRfuaBw4evSokWTeeustn/aHHnrITJ8+vdcxw4YNMxs3bvRpq6qqMnFxcec9T3l5uZHExsbGxsbGFgRbS0vLBfPFZflob2lpqc9qisfj0alTpzR69GiFhIT4/Xxut1vJyclqaWlRVFSU34+PLzHXg4N5HhzM8+BhrgeHv+fZGKMzZ84oMTHxgv0chZHY2FiFhYWpra3Np72trU0JCQm9jklISHDUX5JcLpdcLpdP26hRo5yU2i9RUVH8Tz5ImOvBwTwPDuZ58DDXg8Of8xwdHX3RPo5uYI2IiFB6erpqamq8bR6PRzU1NcrKyup1TFZWlk9/Sdq5c+d5+wMAgKHF8WWakpISFRQUKCMjQ9OnT9fq1avV2dmpwsJCSdL8+fOVlJSkiooKSdKiRYv0rW99S6tWrVJeXp42bdqk+vp6PfXUU/79JgAAICA5DiOzZ8/WiRMnVFZWptbWVk2bNk07duxQfHy8JKm5uVmhoV8uuNx4443auHGjHn30UT3yyCO6/vrrtXXrVk2ePNl/3+ISuVwulZeXn3NpCP7HXA8O5nlwMM+Dh7keHLbmOcSYiz1vAwAAMHB4Nw0AALCKMAIAAKwijAAAAKsIIwAAwCrCiKSqqiqlpKQoMjJSmZmZ2rNnj+2SAlpFRYW+8Y1v6IorrlBcXJxmzZqlAwcO+PT505/+pKKiIo0ePVojR47U3//935/z43hwZuXKlQoJCdH999/vbWOe/ePo0aP64Q9/qNGjR2v48OGaMmWK6uvrvZ8bY1RWVqYxY8Zo+PDhys7O1kcffWSx4sDU09OjpUuXaty4cRo+fLiuvfZaPfbYYz7vNWGundu1a5fy8/OVmJiokJCQc94N15c5PXXqlObNm6eoqCiNGjVKd999tzo6OvxX5MXeRxPsNm3aZCIiIszatWvN//7v/5oFCxaYUaNGmba2NtulBaycnByzbt06s3//ftPY2Ghuv/12M3bsWNPR0eHts3DhQpOcnGxqampMfX29+eY3v2luvPFGi1UHtj179piUlBQzdepUs2jRIm8783zpTp06Za6++mrzj//4j+add94xTU1N5tVXXzWHDh3y9lm5cqWJjo42W7duNb/73e/Md7/7XTNu3Djz6aefWqw88CxfvtyMHj3avPLKK+bw4cNmy5YtZuTIkeaJJ57w9mGundu+fbtZsmSJeeGFF4wk8+KLL/p83pc5ve2220xqaqp5++23zf/8z/+Y6667zsyZM8dvNQ75MDJ9+nRTVFTk3e/p6TGJiYmmoqLCYlXB5fjx40aSefPNN40xxpw+fdoMGzbMbNmyxdvngw8+MJJMXV2drTID1pkzZ8z1119vdu7cab71rW95wwjz7B8PP/ywuemmm877ucfjMQkJCebf/u3fvG2nT582LpfLPPvss4NRYtDIy8szd911l0/bHXfcYebNm2eMYa794athpC9z+v777xtJ5t133/X2+a//+i8TEhJijh496pe6hvRlmu7ubjU0NCg7O9vbFhoaquzsbNXV1VmsLLi0t7dLkmJiYiRJDQ0N+uyzz3zmfcKECRo7dizz3g9FRUXKy8vzmU+JefaXl156SRkZGfr+97+vuLg4paWlac2aNd7PDx8+rNbWVp95jo6OVmZmJvPs0I033qiamhodPHhQkvS73/1Ou3fvVm5uriTmeiD0ZU7r6uo0atQoZWRkePtkZ2crNDRU77zzjl/quCzf2jtYTp48qZ6eHu+vx/5ZfHy8PvzwQ0tVBRePx6P7779fM2bM8P7qbmtrqyIiIs55+WF8fLxaW1stVBm4Nm3apL179+rdd9895zPm2T+ampr0i1/8QiUlJXrkkUf07rvv6p//+Z8VERGhgoIC71z29vcI8+zM4sWL5Xa7NWHCBIWFhamnp0fLly/XvHnzJIm5HgB9mdPW1lbFxcX5fB4eHq6YmBi/zfuQDiMYeEVFRdq/f792795tu5Sg09LSokWLFmnnzp2KjIy0XU7Q8ng8ysjI0IoVKyRJaWlp2r9/v6qrq1VQUGC5uuDy3HPP6ZlnntHGjRv113/912psbNT999+vxMRE5jrIDenLNLGxsQoLCzvn6YK2tjYlJCRYqip4FBcX65VXXtEbb7yhq666ytuekJCg7u5unT592qc/8+5MQ0ODjh8/rhtuuEHh4eEKDw/Xm2++qZ///OcKDw9XfHw88+wHY8aM0aRJk3zaJk6cqObmZknyziV/j1y6hx56SIsXL9Y//MM/aMqUKbrzzjv1wAMPeF+8ylz7X1/mNCEhQcePH/f5/PPPP9epU6f8Nu9DOoxEREQoPT1dNTU13jaPx6OamhplZWVZrCywGWNUXFysF198Ua+//rrGjRvn83l6erqGDRvmM+8HDhxQc3Mz8+7ALbfcovfee0+NjY3eLSMjQ/PmzfP+N/N86WbMmHHOo+kHDx7U1VdfLUkaN26cEhISfObZ7XbrnXfeYZ4dOnv2rM+LViUpLCxMHo9HEnM9EPoyp1lZWTp9+rQaGhq8fV5//XV5PB5lZmb6pxC/3AYbwDZt2mRcLpdZv369ef/9982PfvQjM2rUKNPa2mq7tIB17733mujoaFNbW2uOHTvm3c6ePevts3DhQjN27Fjz+uuvm/r6epOVlWWysrIsVh0c/vJpGmOYZ3/Ys2ePCQ8PN8uXLzcfffSReeaZZ8yIESPMr3/9a2+flStXmlGjRpnf/OY35ve//73527/9Wx437YeCggKTlJTkfbT3hRdeMLGxsebHP/6xtw9z7dyZM2fMvn37zL59+4wkU1lZafbt22c+/vhjY0zf5vS2224zaWlp5p133jG7d+82119/PY/2+tt//Md/mLFjx5qIiAgzffp08/bbb9suKaBJ6nVbt26dt8+nn35q7rvvPnPllVeaESNGmL/7u78zx44ds1d0kPhqGGGe/ePll182kydPNi6Xy0yYMME89dRTPp97PB6zdOlSEx8fb1wul7nlllvMgQMHLFUbuNxut1m0aJEZO3asiYyMNNdcc41ZsmSJ6erq8vZhrp174403ev07uaCgwBjTtzn94x//aObMmWNGjhxpoqKiTGFhoTlz5ozfagwx5i9+2g4AAGCQDel7RgAAgH2EEQAAYBVhBAAAWEUYAQAAVhFGAACAVYQRAABgFWEEAABYRRgBAABWEUYAAIBVhBEAAGAVYQQAAFhFGAEAAFb9P4h3gyYUwy6PAAAAAElFTkSuQmCC\n" - }, - "metadata": {} - } - ], - "source": [ - "plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "o9IYA0cZfjD7" - }, - "source": [ - "It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "5FQIOqoRfjD7" - }, - "source": [ - "Usually the number of words in the dictionary will be very large.\n", - "\n", - "Here are some thing we can do to reduce that number:\n", - "\n", - "* Remove puntuation.\n", - "* Remove stop-words.\n", - "* Steaming.\n", - "* Remove very uncommon words (the words that appears in fewer than N occations).\n", - "* Nothing: we can use a pretrain model that handles this kind of situations.\n", - "\n", - "\n", - "We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n", - "\n", - "* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n", - "* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n", - "* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "_ul5MgYcfjD7" - }, - "source": [ - "---\n", - "# Step 4: choose toolkit\n", - "\n", - "Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n", - "\n", - "* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n", - "* A simple Embedding + RNN." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "GteI1PxTfjD7" - }, - "source": [ - "## Logistic regression\n", - "\n", - "We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "S_ei2qu8fjD7" - }, - "outputs": [], - "source": [ - "vectorizer = CountVectorizer(binary=True)\n", - "x_train_cv = vectorizer.fit_transform(x_train_text)\n", - "x_test_cv = vectorizer.transform(x_test_text)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iK_zfqnLfjD7", - "outputId": "9b3f6db3-01bf-4246-b943-359620c717a2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Before Vectorize: doesn't know how to spell conked \n" - ] - } - ], - "source": [ - "print('Before Vectorize: ', x_train_text[3])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wKxY8e38fjD8", - "outputId": "19530135-070d-4259-d6a9-7ba06b519763" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "After Vectorize: \n", - " (0, 528584)\t1\n", - " (0, 165468)\t1\n", - " (0, 300381)\t1\n", - " (0, 242211)\t1\n", - " (0, 489893)\t1\n", - " (0, 134160)\t1\n" - ] - } - ], - "source": [ - "# Notice that the matriz is sparse\n", - "print('After Vectorize: ')\n", - "print(x_train_cv[3])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "QTPPEMd9fjD8" - }, - "source": [ - "Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "2vEPOQS6fjD8", - "outputId": "3be77fc0-76e6-40b8-8847-5f6e7c6c0ce0" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "LogisticRegression(solver='saga')" - ], - "text/html": [ - "
LogisticRegression(solver='saga')
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" - ] - }, - "metadata": {}, - "execution_count": 14 - } - ], - "source": [ - "model = LogisticRegression(solver='saga')\n", - "model.fit(x_train_cv, y_train)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "37bUbqB6fjD8", - "outputId": "7eb9178d-6130-47d0-bdf4-ce4be164bc97" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " precision recall f1-score support\n", - "\n", - " 0 0.81 0.79 0.80 160000\n", - " 1 0.79 0.81 0.80 160000\n", - "\n", - " accuracy 0.80 320000\n", - " macro avg 0.80 0.80 0.80 320000\n", - "weighted avg 0.80 0.80 0.80 320000\n", - "\n" - ] - } - ], - "source": [ - "y_pred = model.predict(x_test_cv)\n", - "\n", - "print(classification_report(y_test, y_pred))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "161kDLhofjD8" - }, - "source": [ - "## Explainable AI\n", - "The best thing about logistic regresion is that it is simple, and we can get some explanations." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "EILTmxzifjD9", - "outputId": "b7ce6853-7385-4a24-d4eb-e6d0843ca5d5" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "(1, 589260)\n", - "589260\n" - ] - } - ], - "source": [ - "print(model.coef_.shape)\n", - "print(len(vectorizer.vocabulary_))\n", - "\n", - "words_sk = list(vectorizer.vocabulary_.keys())\n", - "words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NGjVPON6fjD9", - "outputId": "d40443bc-476d-4f5a-ce90-4b5b17e47933" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "roni: -3.8625952420933984\n", - "inaperfectworld: -3.5734321547933936\n", - "dontyouhate: -3.5002133484207576\n", - "xbllygbsn: -3.4126303898325787\n", - "anqju: -3.3363997631497493\n", - "sad: -3.200516823534637\n", - "pakcricket: -3.1949062976331675\n", - "condolences: -3.132503698316079\n", - "heartbreaking: -3.0665219866881297\n", - "saddest: -3.042020604188048\n", - "sadd: -3.029036146667248\n", - "heartbroken: -3.0287524416643463\n", - "boohoo: -3.0226033087262802\n", - "sadface: -2.991829110065316\n", - "rachelle_lefevr: -2.925076661509848\n", - "disappointing: -2.902522686643491\n", - "lvbu: -2.8947109582208865\n", - "saddens: -2.8855187276040715\n", - "bummed: -2.836500453805889\n", - "neda: -2.792917726280752\n" - ] - } - ], - "source": [ - "for w in words_sk[:20]:\n", - " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "oxQ_jlNRfjD9", - "outputId": "363de58b-817a-4205-f019-2379d0d64e0d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "iamsoannoyed: 2.8493838469077013\n", - "myfax: 2.7974330510971424\n", - "jennamadison: 2.5667217237933104\n", - "yeyy: 2.4780234846131646\n", - "tryout: 2.438315611477797\n", - "goldymom: 2.4374072779309204\n", - "wooohooo: 2.402957513257194\n", - "thesupergirl: 2.356525094856456\n", - "iammaxathotspot: 2.3116551216589682\n", - "londicreations: 2.3074264075299316\n", - "smilin: 2.2991796213822497\n", - "worries: 2.2899555142510084\n", - "sinfulsignorita: 2.27989578448778\n", - "finchensnail: 2.2642827277181063\n", - "smackthis: 2.237672991997692\n", - "kv: 2.2157591386122775\n", - "tojosan: 2.2117938132889696\n", - "russmarshalek: 2.20953890861265\n", - "traciknoppe: 2.1768232307222153\n", - "congratulations: 2.1715901103136876\n" - ] - } - ], - "source": [ - "for w in reversed(words_sk[-20:]):\n", - " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "9KSSAC3qfjD9" - }, - "source": [ - "What does this mean?\n", - "\n", - "Remember the `model.coef_` is the $W$ in:\n", - "\n", - "$$h(x)=\\sigma(WX + b)$$\n", - "\n", - "where the label 1 is a positive tweet and the label 0 is a negative tweet." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "oDHjTP2_fjD9" - }, - "source": [ - "## Recurrent Neural Network with Pytorch" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "TbgpKy95fjD9" - }, - "source": [ - "In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n", - "\n", - "In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n", - "\n", - "Let's start by importing the relevant libraries.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "7nmUJV99fjEB" - }, - "outputs": [], - "source": [ - "def set_device():\n", - " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", - " if device != \"cuda\":\n", - " print(\"WARNING: For this notebook to perform best, \"\n", - " \"if possible, in the menu under `Runtime` -> \"\n", - " \"`Change runtime type.` select `GPU` \")\n", - " else:\n", - " print(\"GPU is enabled in this notebook.\")\n", - "\n", - " return device" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "chI-18LcfjEB", - "outputId": "7f633079-6548-48f3-802e-94bc9cfada93" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "GPU is enabled in this notebook.\n" - ] - } - ], - "source": [ - "# Set the device (check if gpu is available)\n", - "device = set_device()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "01UtIN7ofjEC" - }, - "source": [ - "First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "afus9SyUfjEC", - "outputId": "bb4eb869-e2f0-4ccd-f64c-e55908272345" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']" - ] - }, - "metadata": {}, - "execution_count": 21 - } - ], - "source": [ - "# From previous section, we have a list with the most used tokens\n", - "sorted_words[:10]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "6vfQFjaufjEC" - }, - "source": [ - "Let's select only the most used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "tGLkxaGcfjEC" - }, - "outputs": [], - "source": [ - "num_words_dict = 30000\n", - "# We reserve two numbers for special tokens.\n", - "most_used_words = sorted_words[:num_words_dict-2]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "AzhQvekCfjEC" - }, - "source": [ - "We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "73Wrb-lEfjEC" - }, - "outputs": [], - "source": [ - "# dictionary to go from words to idx\n", - "word_to_idx = {}\n", - "# dictionary to go from idx to words (just in case)\n", - "idx_to_word = {}\n", - "\n", - "\n", - "# We include the special tokens first\n", - "PAD_token = 0\n", - "UNK_token = 1\n", - "\n", - "word_to_idx['PAD'] = PAD_token\n", - "word_to_idx['UNK'] = UNK_token\n", - "\n", - "idx_to_word[PAD_token] = 'PAD'\n", - "idx_to_word[UNK_token] = 'UNK'\n", - "\n", - "# We popullate our dictionaries with the most used words\n", - "for num,word in enumerate(most_used_words):\n", - " word_to_idx[word] = num + 2\n", - " idx_to_word[num+2] = word" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "kMHVkEisfjEC" - }, - "source": [ - "Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "tkCIu3PKfjED" - }, - "outputs": [], - "source": [ - "# A function to convert list of tokens to list of indexes\n", - "def tokens_to_idx(sentences_tokens,word_to_idx):\n", - " sentences_idx = []\n", - " for sent in sentences_tokens:\n", - " sent_idx = []\n", - " for word in sent:\n", - " if word in word_to_idx:\n", - " sent_idx.append(word_to_idx[word])\n", - " else:\n", - " sent_idx.append(word_to_idx['UNK'])\n", - " sentences_idx.append(sent_idx)\n", - " return sentences_idx" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "aHru4vpzfjED" - }, - "outputs": [], - "source": [ - "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", - "x_test_idx = tokens_to_idx(x_test_token,word_to_idx)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ofj3OD7zfjED", - "outputId": "b2788d03-dbfa-41d7-8231-5011206baa59" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Before converting: ['worst', 'headache', 'ever']\n", - "After converting: [721, 458, 237]\n" - ] - } - ], - "source": [ - "some_number = 1\n", - "print('Before converting: ', x_train_token[some_number])\n", - "print('After converting: ', x_train_idx[some_number])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "NcCicvb-fjED" - }, - "source": [ - "We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BSjhdyYUfjED", - "outputId": "82e49be9-7868-44ac-b496-c7a48da1efee" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Max tweet word length: 229\n", - "Mean tweet word length: 15.0\n", - "99% percent under: 37.0\n" - ] - } - ], - "source": [ - "tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n", - "print('Max tweet word length: ',tweet_lens.max())\n", - "print('Mean tweet word length: ',np.median(tweet_lens))\n", - "print('99% percent under: ',np.quantile(tweet_lens,0.99))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "t311WY6ZfjEE" - }, - "source": [ - "We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "r4S8KTWLfjEE" - }, - "outputs": [], - "source": [ - " # We choose the max length\n", - " max_length = 40\n", - "\n", - "# A function to make all the sequence have the same lenght\n", - "# Note that the output is a Numpy matrix\n", - " def padding(sentences, seq_len):\n", - " features = np.zeros((len(sentences), seq_len),dtype=int)\n", - " for ii, tweet in enumerate(sentences):\n", - " len_tweet = len(tweet)\n", - " if len_tweet != 0:\n", - " if len_tweet <= seq_len:\n", - " # If its shorter, we fill with zeros (the padding Token index)\n", - " features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n", - " if len_tweet > seq_len:\n", - " # If its larger, we take the last 'seq_len' indexes\n", - " features[ii, :] = np.array(tweet)[-seq_len:]\n", - " return features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "Z-Cw-bBxfjEE" - }, - "outputs": [], - "source": [ - "# We convert our list of tokens into a numpy matrix\n", - "# where all instances have the same lenght\n", - "x_train_pad = padding(x_train_idx,max_length)\n", - "x_test_pad = padding(x_test_idx,max_length)\n", - "\n", - "# We convert our target list a numpy matrix\n", - "y_train_np = np.asarray(y_train)\n", - "y_test_np = np.asarray(y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8eC3YswlfjEE", - "outputId": "3bb0ea7f-518f-4545-9241-feb783f48122" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n", - "After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n", - " 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n", - " 150 4 22 5 18 667 3 138 61 7 3296 4]\n" - ] - } - ], - "source": [ - "some_number = 2\n", - "print('Before padding: ', x_train_idx[some_number])\n", - "print('After padding: ', x_train_pad[some_number])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "SzDhnauUfjEE" - }, - "source": [ - "Now, let's convert the data to pytorch format.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "--Yd22YWfjEF" - }, - "outputs": [], - "source": [ - "# create Tensor datasets\n", - "train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n", - "valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n", - "\n", - "# Batch size (this is an important hyperparameter)\n", - "batch_size = 100\n", - "\n", - "# dataloaders\n", - "# make sure to SHUFFLE your data\n", - "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n", - "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "jQ5qPOWTfjEF" - }, - "source": [ - "Each batch of data in our traning proccess will have the folllowing format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "S1mqhk1hfjEF", - "outputId": "c97e7edd-695f-4336-a2e6-f6bed4852a63" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Sample input size: torch.Size([100, 40])\n", - "Sample input: \n", - " tensor([[ 0, 0, 0, ..., 32, 203, 86],\n", - " [ 0, 0, 0, ..., 1, 1, 4661],\n", - " [ 0, 0, 0, ..., 169, 43, 34],\n", - " ...,\n", - " [ 0, 0, 0, ..., 2, 2961, 4076],\n", - " [ 0, 0, 0, ..., 2319, 1325, 2],\n", - " [ 0, 0, 0, ..., 7, 253, 1]])\n", - "Sample input: \n", - " tensor([0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1,\n", - " 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0,\n", - " 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,\n", - " 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,\n", - " 0, 1, 0, 1])\n" - ] - } - ], - "source": [ - "# Obtain one batch of training data\n", - "dataiter = iter(train_loader)\n", - "sample_x, sample_y = dataiter.__next__()\n", - "\n", - "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n", - "print('Sample input: \\n', sample_x)\n", - "print('Sample input: \\n', sample_y)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "jn0PzZdGfjEF" - }, - "source": [ - "Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n", - "\n", - "* Embedding Layer\n", - "> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n", - "\n", - "\n", - "* LSTM layer\n", - "> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n", - "\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "vfzcowAxfjEF" - }, - "outputs": [], - "source": [ - "class SentimentRNN(nn.Module):\n", - " def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n", - " super(SentimentRNN,self).__init__()\n", - "\n", - " self.output_dim = output_dim\n", - " self.hidden_dim = hidden_dim\n", - " self.no_layers = no_layers\n", - " self.vocab_size = vocab_size\n", - " self.drop_prob = drop_prob\n", - "\n", - " # Embedding Layer\n", - " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n", - "\n", - " # LSTM Layers\n", - " self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n", - " num_layers=no_layers, batch_first=True,\n", - " dropout=self.drop_prob)\n", - "\n", - " # Dropout layer\n", - " self.dropout = nn.Dropout(drop_prob)\n", - "\n", - " # Linear and Sigmoid layer\n", - " self.fc = nn.Linear(self.hidden_dim, output_dim)\n", - " self.sig = nn.Sigmoid()\n", - "\n", - " def forward(self,x,hidden):\n", - " batch_size = x.size(0)\n", - "\n", - " # Embedding out\n", - " embeds = self.embedding(x)\n", - " #Shape: [batch_size x max_length x embedding_dim]\n", - "\n", - " # LSTM out\n", - " lstm_out, hidden = self.lstm(embeds, hidden)\n", - " # Shape: [batch_size x max_length x hidden_dim]\n", - "\n", - " # Select the activation of the last Hidden Layer\n", - " lstm_out = lstm_out[:,-1,:].contiguous()\n", - " # Shape: [batch_size x hidden_dim]\n", - "\n", - " ## You can instead average the activations across all the times\n", - " # lstm_out = torch.mean(lstm_out, 1).contiguous()\n", - "\n", - " # Dropout and Fully connected layer\n", - " out = self.dropout(lstm_out)\n", - " out = self.fc(out)\n", - "\n", - " # Sigmoid function\n", - " sig_out = self.sig(out)\n", - "\n", - " # return last sigmoid output and hidden state\n", - " return sig_out, hidden\n", - "\n", - " def init_hidden(self, batch_size):\n", - " ''' Initializes hidden state '''\n", - " # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n", - " # initialized to zero, for hidden state and cell state of LSTM\n", - " h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", - " c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", - " hidden = (h0,c0)\n", - " return hidden" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "YfrLPa9mfjEF" - }, - "source": [ - "We choose the parameters of the model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "rOm-xoFkfjEG" - }, - "outputs": [], - "source": [ - "# Parameters of our network\n", - "\n", - "# Size of our vocabulary\n", - "vocab_size = num_words_dict\n", - "\n", - "# Embedding dimension\n", - "embedding_dim = 32\n", - "\n", - "# Number of stacked LSTM layers\n", - "no_layers = 2\n", - "\n", - "# Dimension of the hidden layer in LSTMs\n", - "hidden_dim = 64\n", - "\n", - "# Dropout parameter for regularization\n", - "output_dim = 1\n", - "\n", - "# Dropout parameter for regularization\n", - "drop_prob = 0.25" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xapxpe7ufjEG", - "outputId": "51c90159-7d2b-4fc4-f34a-98e9901d40e4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "SentimentRNN(\n", - " (embedding): Embedding(30000, 32)\n", - " (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n", - " (dropout): Dropout(p=0.25, inplace=False)\n", - " (fc): Linear(in_features=64, out_features=1, bias=True)\n", - " (sig): Sigmoid()\n", - ")\n" - ] - } - ], - "source": [ - "# Let's define our model\n", - "model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n", - " embedding_dim, drop_prob=drop_prob)\n", - "# Moving to gpu\n", - "model.to(device)\n", - "print(model)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "GEvTs3uwfjEG", - "outputId": "2e15f6df-2aa8-4665-b2da-7363d2bfa09e" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Total Number of parameters: 1018433\n" - ] - } - ], - "source": [ - "# How many trainable parameters does our model have?\n", - "model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n", - "params = sum([np.prod(p.size()) for p in model_parameters])\n", - "print('Total Number of parameters: ',params)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "Pc2OC5GDfjEG" - }, - "source": [ - "We choose the losses and the optimizer for the training procces." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "id": "iBWjPADUfjEG" - }, - "outputs": [], - "source": [ - "# loss and optimization functions\n", - "lr = 0.001\n", - "\n", - "# Binary crossentropy is a good loss function for a binary classification problem\n", - "criterion = nn.BCELoss()\n", - "\n", - "# We choose an Adam optimizer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", - "\n", - "# function to predict accuracy\n", - "def acc(pred,label):\n", - " pred = torch.round(pred.squeeze())\n", - " return torch.sum(pred == label.squeeze()).item()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "OZgMwOe2fjEG" - }, - "source": [ - "We are ready to train our model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3B6YhEocfjEH", - "outputId": "76276a1f-7775-4b98-aab0-0e199aa133e4" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1\n", - "train_loss : 0.4366412344621494 val_loss : 0.3881208170717582\n", - "train_accuracy : 79.485546875 val_accuracy : 82.475\n", - "Validation loss decreased (inf --> 0.388121). Saving model ...\n", - "==================================================\n", - "Epoch 2\n", - "train_loss : 0.3760281792609021 val_loss : 0.3713956154882908\n", - "train_accuracy : 83.186328125 val_accuracy : 83.4575\n", - "Validation loss decreased (0.388121 --> 0.371396). Saving model ...\n", - "==================================================\n", - "Epoch 3\n", - "train_loss : 0.3574051411205437 val_loss : 0.36425656544510276\n", - "train_accuracy : 84.19953125 val_accuracy : 83.80375\n", - "Validation loss decreased (0.371396 --> 0.364257). Saving model ...\n", - "==================================================\n", - "Epoch 4\n", - "train_loss : 0.344456663565943 val_loss : 0.3613302929420024\n", - "train_accuracy : 84.89265625 val_accuracy : 84.00874999999999\n", - "Validation loss decreased (0.364257 --> 0.361330). Saving model ...\n", - "==================================================\n", - "Epoch 5\n", - "train_loss : 0.33407817618339325 val_loss : 0.3601334386831149\n", - "train_accuracy : 85.444921875 val_accuracy : 84.03625\n", - "Validation loss decreased (0.361330 --> 0.360133). Saving model ...\n", - "==================================================\n" - ] - } - ], - "source": [ - "# Number of training Epochs\n", - "epochs = 5\n", - "\n", - "# Maximum absolute value accepted for the gradeint\n", - "clip = 5\n", - "\n", - "# Initial Loss value (assumed big)\n", - "valid_loss_min = np.Inf\n", - "\n", - "# Lists to follow the evolution of the loss and accuracy\n", - "epoch_tr_loss,epoch_vl_loss = [],[]\n", - "epoch_tr_acc,epoch_vl_acc = [],[]\n", - "\n", - "# Train for a number of Epochs\n", - "for epoch in range(epochs):\n", - " train_losses = []\n", - " train_acc = 0.0\n", - " model.train()\n", - "\n", - " for inputs, labels in train_loader:\n", - "\n", - " # Initialize hidden state\n", - " h = model.init_hidden(batch_size)\n", - " # Creating new variables for the hidden state\n", - " h = tuple([each.data.to(device) for each in h])\n", - "\n", - " # Move batch inputs and labels to gpu\n", - " inputs, labels = inputs.to(device), labels.to(device)\n", - "\n", - " # Set gradient to zero\n", - " model.zero_grad()\n", - "\n", - " # Compute model output\n", - " output,h = model(inputs,h)\n", - "\n", - " # Calculate the loss and perform backprop\n", - " loss = criterion(output.squeeze(), labels.float())\n", - " loss.backward()\n", - " train_losses.append(loss.item())\n", - "\n", - " # calculating accuracy\n", - " accuracy = acc(output,labels)\n", - " train_acc += accuracy\n", - "\n", - " #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n", - " nn.utils.clip_grad_norm_(model.parameters(), clip)\n", - " optimizer.step()\n", - "\n", - "\n", - " # Evaluate on the validation set for this epoch\n", - " val_losses = []\n", - " val_acc = 0.0\n", - " model.eval()\n", - " for inputs, labels in valid_loader:\n", - "\n", - " # Initialize hidden state\n", - " val_h = model.init_hidden(batch_size)\n", - " val_h = tuple([each.data.to(device) for each in val_h])\n", - "\n", - " # Move batch inputs and labels to gpu\n", - " inputs, labels = inputs.to(device), labels.to(device)\n", - "\n", - " # Compute model output\n", - " output, val_h = model(inputs, val_h)\n", - "\n", - " # Compute Loss\n", - " val_loss = criterion(output.squeeze(), labels.float())\n", - "\n", - " val_losses.append(val_loss.item())\n", - "\n", - " accuracy = acc(output,labels)\n", - " val_acc += accuracy\n", - "\n", - " epoch_train_loss = np.mean(train_losses)\n", - " epoch_val_loss = np.mean(val_losses)\n", - " epoch_train_acc = train_acc/len(train_loader.dataset)\n", - " epoch_val_acc = val_acc/len(valid_loader.dataset)\n", - " epoch_tr_loss.append(epoch_train_loss)\n", - " epoch_vl_loss.append(epoch_val_loss)\n", - " epoch_tr_acc.append(epoch_train_acc)\n", - " epoch_vl_acc.append(epoch_val_acc)\n", - " print(f'Epoch {epoch+1}')\n", - " print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n", - " print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n", - " if epoch_val_loss <= valid_loss_min:\n", - " print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n", - " # torch.save(model.state_dict(), '../working/state_dict.pt')\n", - " valid_loss_min = epoch_val_loss\n", - " print(25*'==')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/", - "height": 364 - }, - "id": "ttJazP-nfjEH", - "outputId": "992bed02-611e-4614-c60f-77223d5b801a" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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- }, - "metadata": {} - } + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "execution": {}, + "id": "view-in-github" + }, + "source": [ + "\"Open   \"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "# Twitter Sentiment Analysis\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Juan Manuel Rodriguez, Salomey Osei, Gonzalo Uribarri\n", + "\n", + "__Production editors:__ Amita Kapoor, Spiros Chavlis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# Welcome to the NLP project template\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# Step 1: Questions and goals\n", + "\n", + "* Can we infer emotion from a tweet text?\n", + "* How words are distributed accross the dataset?\n", + "* Are words related to one kind of emotion?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# Step 2: Literature review\n", + "\n", + "[Original Dataset Paper](https://cs.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf)\n", + "\n", + "[Papers with code](https://paperswithcode.com/dataset/imdb-movie-reviews)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# Step 3: Load and explore the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "execution": {} + }, + "outputs": [], + "source": [ + "# @title Install dependencies\n", + "!pip install pandas --quiet\n", + "!pip install torchtext --quiet\n", + "!pip install datasets --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# We import some libraries to load the dataset\n", + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from collections import Counter\n", + "from tqdm.notebook import tqdm\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "\n", + "import torchtext\n", + "from torchtext.data import get_tokenizer\n", + "\n", + "from sklearn.utils import shuffle\n", + "from sklearn.metrics import classification_report\n", + "from sklearn.linear_model import LogisticRegression\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.feature_extraction.text import CountVectorizer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "You can find the dataset we are going to use in [this website](https://huggingface.co/datasets/stanfordnlp/sentiment140)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "dataset = load_dataset(\"stanfordnlp/sentiment140\", trust_remote_code= True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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polarityiddatequeryusertext
001467810369Mon Apr 06 22:19:45 PDT 2009NO_QUERY_TheSpecialOne_@switchfoot http://twitpic.com/2y1zl - Awww, t...
101467810672Mon Apr 06 22:19:49 PDT 2009NO_QUERYscotthamiltonis upset that he can't update his Facebook by ...
201467810917Mon Apr 06 22:19:53 PDT 2009NO_QUERYmattycus@Kenichan I dived many times for the ball. Man...
301467811184Mon Apr 06 22:19:57 PDT 2009NO_QUERYElleCTFmy whole body feels itchy and like its on fire
401467811193Mon Apr 06 22:19:57 PDT 2009NO_QUERYKaroli@nationwideclass no, it's not behaving at all....
\n", + "
" ], - "source": [ - "fig = plt.figure(figsize = (20, 6))\n", - "plt.subplot(1, 2, 1)\n", - "plt.plot(epoch_tr_acc, label='Train Acc')\n", - "plt.plot(epoch_vl_acc, label='Validation Acc')\n", - "plt.title(\"Accuracy\")\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "plt.subplot(1, 2, 2)\n", - "plt.plot(epoch_tr_loss, label='Train loss')\n", - "plt.plot(epoch_vl_loss, label='Validation loss')\n", - "plt.title(\"Loss\")\n", - "plt.legend()\n", - "plt.grid()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "iUyaF-EbfjEH" - }, - "source": [ - "---\n", - "# What's Next?\n", - "\n", - "You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n", - "\n", - "* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n", - "* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n", - "* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n", - "* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. Do you belive that a sentiment analysis model trained on some corpus (Twitter dataset) will perform well on another type of data (for example, youtube comments)?\n", - "\n" - ] + "text/plain": [ + " polarity ... text\n", + "0 0 ... @switchfoot http://twitpic.com/2y1zl - Awww, t...\n", + "1 0 ... is upset that he can't update his Facebook by ...\n", + "2 0 ... @Kenichan I dived many times for the ball. Man...\n", + "3 0 ... my whole body feels itchy and like its on fire \n", + "4 0 ... @nationwideclass no, it's not behaving at all....\n", + "\n", + "[5 rows x 6 columns]" + ] + }, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "provenance": [] - }, - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.2" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "fbb4191426bd485e8e965b6d432eecae": { - "model_module": "@jupyter-widgets/controls", - "model_name": "HBoxModel", - "model_module_version": "1.5.0", - "state": { - 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Notice that polarity is 0 for negative tweets and 4 for positive tweet." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "X = df.text.values\n", + "\n", + "# Changes values from [0,4] to [0,1]\n", + "y = (df.polarity.values > 1).astype(int)\n", + "\n", + "\n", + "# Split the data into train and test\n", + "x_train_text, x_test_text, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42,stratify=y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "The first thing we have to do before working on the models is to familiarize ourselves with the dataset. This is called Exploratory Data Analisys (EDA)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1: @paisleypaisley LOL why do i get ideas so far in advance? it's not even june yet! we need a third knitter to have our own summer group \n", + "0: worst headache ever \n", + "0: @ewaniesciuszko i am so sad i wont see you! I miss you already. and yeah! that's perfect; i come back the 18th!\n", + "1: doesn't know how to spell conked \n", + "0: "So we stand here now and no one knows us at all I won't get used to this I won't get used to being gone"...I miss home and everyone -a\n" + ] + } + ], + "source": [ + "for s, l in zip(x_train_text[:5], y_train[:5]):\n", + " print('{}: {}'.format(l, s))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "An interesting thing to analyze is the Word Distribution. In order to count the occurrences of each word, we should tokenize the sentences first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before Tokenize: worst headache ever \n", + "After Tokenize: ['worst', 'headache', 'ever']\n" + ] + } + ], + "source": [ + "tokenizer = get_tokenizer(\"basic_english\")\n", + "\n", + "print('Before Tokenize: ', x_train_text[1])\n", + "print('After Tokenize: ', tokenizer(x_train_text[1]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "499e7fb54aa048afb3cba78dd8d6bb0e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1280000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fff9bd0ae74e46b0ad97ad980a834a58", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=320000.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "x_train_token = [tokenizer(s) for s in tqdm(x_train_text)]\n", + "x_test_token = [tokenizer(s) for s in tqdm(x_test_text)]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We can count the words occurences and see how many different words are present in our dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of different Tokens in our Dataset: 669284\n", + "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it', 'and', 'you', '?', 'is', 'for', 'in', 's', 'of', 't', 'on', 'that', 'me', 'so', 'have', 'm', 'but', 'just', 'with', 'be', 'at', 'not', 'was', 'this', 'now', 'can', 'good', 'up', 'day', 'all', 'get', 'out', 'like', 'are', 'no', 'go', 'http', '-', 'today', 'do', 'too', 'your', 'work', 'going', 'love', 'we', 'got', 'what', 'lol', 'time', 'back', 'from', 'u', 'one', 'will', 'know', 'about', 'im', 'really', 'don', 'am', 'had', ')', 'see', 'some', 'there', 'its', '&', 'how', 'if', 'still', 'they', '"', 'night', '(', 'well', 'want', 'new', 'think', '2', 'home', 'thanks', 'll', 'oh', 'when', 'as', 'he', 'more', 'here', 'much', 'off']\n" + ] + } + ], + "source": [ + "words = Counter()\n", + "for s in x_train_token:\n", + " for w in s:\n", + " words[w] += 1\n", + "\n", + "sorted_words = list(words.keys())\n", + "sorted_words.sort(key=lambda w: words[w], reverse=True)\n", + "print(f\"Number of different Tokens in our Dataset: {len(sorted_words)}\")\n", + "print(sorted_words[:100])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Now we can plot their distribution." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The 0.13970153178620734% most common words account for the 80.00532743602652% of the occurrences\n" + ] + } + ], + "source": [ + "count_occurences = sum(words.values())\n", + "\n", + "accumulated = 0\n", + "counter = 0\n", + "\n", + "while accumulated < count_occurences * 0.8:\n", + " accumulated += words[sorted_words[counter]]\n", + " counter += 1\n", + "\n", + "print(f\"The {counter * 100 / len(words)}% most common words \"\n", + " f\"account for the {accumulated * 100 / count_occurences}% of the occurrences\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.bar(range(100), [words[w] for w in sorted_words[:100]])\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "It is very common to find this kind of distribution when analyzing corpus of text. This is referred to as the [zipf's law](https://en.wikipedia.org/wiki/Zipf%27s_law)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Usually the number of words in the dictionary will be very large. \n", + "\n", + "Here are some thing we can do to reduce that number:\n", + "\n", + "* Remove puntuation.\n", + "* Remove stop-words.\n", + "* Steaming.\n", + "* Remove very uncommon words (the words that appears in fewer than N occations).\n", + "* Nothing: we can use a pretrain model that handles this kind of situations.\n", + "\n", + "\n", + "We used one of the simplest tokenizers availables. This tokenizer does not take into account many quirks of the language. Moreover, diferent languages have different quirks, so there is no \"universal\" tokenizers. There are many libraries that have \"better\" tokenizers:\n", + "\n", + "* [Spacy](https://spacy.io/): it can be accessed using: `get_tokenizer(\"spacy\")`. Spacy supports a wide range of languages.\n", + "* [Huggingface](https://huggingface.co/): it has many tokenizers for different laguages. [Doc](https://huggingface.co/transformers/main_classes/tokenizer.html)\n", + "* [NLTK](https://www.nltk.org/): it provides several tokenizers. One of them can be accessed using: `get_tokenizer(\"toktok\")`\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# Step 4: choose toolkit\n", + "\n", + "Our goal is to train a model capable of estimating the sentiment of a tweet (positive or negative) by reading its content. To that end we will try 2 different approaches:\n", + "\n", + "* A logistic regression using sklearn. **NOTE**: it can probaly work better than an SVM model.\n", + "* A simple Embedding + RNN." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "## Logistic regression\n", + "\n", + "We will represent our senteces using binary vectorization. This means that our data would be represented as a matrix of instances by word with a one if the word is in the instance, and zero otherwise. Sklean vectorizers can also do things such as stop-word removal and puntuation removal, you can read more about in [the documentation](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "vectorizer = CountVectorizer(binary=True)\n", + "x_train_cv = vectorizer.fit_transform(x_train_text)\n", + "x_test_cv = vectorizer.transform(x_test_text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before Vectorize: doesn't know how to spell conked \n" + ] + } + ], + "source": [ + "print('Before Vectorize: ', x_train_text[3])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After Vectorize: \n", + " (0, 528584)\t1\n", + " (0, 165468)\t1\n", + " (0, 300381)\t1\n", + " (0, 242211)\t1\n", + " (0, 489893)\t1\n", + " (0, 134160)\t1\n" + ] + } + ], + "source": [ + "# Notice that the matriz is sparse\n", + "print('After Vectorize: ')\n", + "print(x_train_cv[3])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Now we can train our model. You can check the documentation of this logistic regressor [here](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html?highlight=logistic#sklearn.linear_model.LogisticRegression)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", + " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", + " multi_class='auto', n_jobs=None, penalty='l2',\n", + " random_state=None, solver='saga', tol=0.0001, verbose=0,\n", + " warm_start=False)" + ] + }, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "model = LogisticRegression(solver='saga')\n", + "model.fit(x_train_cv, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 0.81 0.79 0.80 160000\n", + " 1 0.79 0.81 0.80 160000\n", + "\n", + " accuracy 0.80 320000\n", + " macro avg 0.80 0.80 0.80 320000\n", + "weighted avg 0.80 0.80 0.80 320000\n", + "\n" + ] + } + ], + "source": [ + "y_pred = model.predict(x_test_cv)\n", + "\n", + "print(classification_report(y_test, y_pred))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "## Explainable AI\n", + "The best thing about logistic regresion is that it is simple, and we can get some explanations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 589260)\n", + "589260\n" + ] + } + ], + "source": [ + "print(model.coef_.shape)\n", + "print(len(vectorizer.vocabulary_))\n", + "\n", + "words_sk = list(vectorizer.vocabulary_.keys())\n", + "words_sk.sort(key=lambda w: model.coef_[0, vectorizer.vocabulary_[w]])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "roni: -3.862597673594883\n", + "inaperfectworld: -3.5734362290886375\n", + "dontyouhate: -3.500197620227523\n", + "xbllygbsn: -3.412645372640648\n", + "anqju: -3.336405291553548\n", + "sad: -3.200522312464158\n", + "pakcricket: -3.1949158120163412\n", + "condolences: -3.132498019366488\n", + "heartbreaking: -3.066508733796654\n", + "saddest: -3.041999809733714\n", + "sadd: -3.029070563580306\n", + "heartbroken: -3.0287688233900174\n", + "boohoo: -3.022608649696793\n", + "sadface: -2.9918411285807234\n", + "rachelle_lefevr: -2.925057253107806\n", + "disappointing: -2.902524113779547\n", + "lvbu: -2.894705935001672\n", + "saddens: -2.8855127179984654\n", + "bummed: -2.83650014970307\n", + "neda: -2.792944556837498\n" + ] + } + ], + "source": [ + "for w in words_sk[:20]:\n", + " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "iamsoannoyed: 2.8494314732277672\n", + "myfax: 2.797451563471618\n", + "jennamadison: 2.5667257393706113\n", + "yeyy: 2.478028598852801\n", + "tryout: 2.4383315790116677\n", + "goldymom: 2.4374026022205535\n", + "wooohooo: 2.40297322137544\n", + "thesupergirl: 2.3565118467330004\n", + "iammaxathotspot: 2.311648368632618\n", + "londicreations: 2.3074490293400993\n", + "smilin: 2.2991891636718216\n", + "worries: 2.2899429774914717\n", + "sinfulsignorita: 2.2798963640981817\n", + "finchensnail: 2.264302079155878\n", + "smackthis: 2.2376679263761083\n", + "kv: 2.2158393907798413\n", + "tojosan: 2.211784259253832\n", + "russmarshalek: 2.2095374025599384\n", + "traciknoppe: 2.1768297770350835\n", + "congratulations: 2.171590496227557\n" + ] + } + ], + "source": [ + "for w in reversed(words_sk[-20:]):\n", + " print('{}: {}'.format(w, model.coef_[0, vectorizer.vocabulary_[w]]))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "What does this mean?\n", + "\n", + "Remember the `model.coef_` is the $W$ in:\n", + "\n", + "$$h(x)=\\sigma(WX + b)$$\n", + "\n", + "where the label 1 is a positive tweet and the label 0 is a negative tweet." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "## Recurrent Neural Network with Pytorch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "In the previous section we use a Bag-Of-Words approach to represent each of the tweets. That meas that we only consider how many times each of the words appear in each of the tweets, we didnt take into account the order of the words. But we know that the word order is very important and carries relevant information.\n", + "\n", + "In this section we will solve the same task, but this time we will implement a Recurrent Neural Network (RNN) instead of using a simple Logistic Regression.Unlike feedforward neural networks, RNNs have cyclic connections making them powerful for modeling sequences.\n", + "\n", + "Let's start by importing the relevant libraries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "def set_device():\n", + " device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + " if device != \"cuda\":\n", + " print(\"WARNING: For this notebook to perform best, \"\n", + " \"if possible, in the menu under `Runtime` -> \"\n", + " \"`Change runtime type.` select `GPU` \")\n", + " else:\n", + " print(\"GPU is enabled in this notebook.\")\n", + "\n", + " return device" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU is enabled in this notebook.\n" + ] + } + ], + "source": [ + "# Set the device (check if gpu is available)\n", + "device = set_device()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "First we will create a Dictionary (`word_to_idx`). This dictionary will map each Token (usually words) to an index (an integer number). We want to limit our dictionary to a certain number of tokens (`num_words_dict`), so we will include in our ditionary those with more occurrences." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['.', 'i', '!', \"'\", 'to', 'the', ',', 'a', 'my', 'it']" + ] + }, + "execution_count": 22, + "metadata": { + "tags": [] + }, + "output_type": "execute_result" + } + ], + "source": [ + "# From previous section, we have a list with the most used tokens\n", + "sorted_words[:10]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Let's select only the most used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "num_words_dict = 30000\n", + "# We reserve two numbers for special tokens.\n", + "most_used_words = sorted_words[:num_words_dict-2]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We will add two extra Tokens to the dictionary, one for words outside the dictionary (`'UNK'`) and one for padding the sequences (`'PAD'`)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# dictionary to go from words to idx\n", + "word_to_idx = {}\n", + "# dictionary to go from idx to words (just in case)\n", + "idx_to_word = {}\n", + "\n", + "\n", + "# We include the special tokens first\n", + "PAD_token = 0\n", + "UNK_token = 1\n", + "\n", + "word_to_idx['PAD'] = PAD_token\n", + "word_to_idx['UNK'] = UNK_token\n", + "\n", + "idx_to_word[PAD_token] = 'PAD'\n", + "idx_to_word[UNK_token] = 'UNK'\n", + "\n", + "# We popullate our dictionaries with the most used words\n", + "for num,word in enumerate(most_used_words):\n", + " word_to_idx[word] = num + 2\n", + " idx_to_word[num+2] = word" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Our goal now is to transform each tweet from a sequence of tokens to a sequence of indexes. These sequences of indexes will be the input to our pytorch model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# A function to convert list of tokens to list of indexes\n", + "def tokens_to_idx(sentences_tokens,word_to_idx):\n", + " sentences_idx = []\n", + " for sent in sentences_tokens:\n", + " sent_idx = []\n", + " for word in sent:\n", + " if word in word_to_idx:\n", + " sent_idx.append(word_to_idx[word])\n", + " else:\n", + " sent_idx.append(word_to_idx['UNK'])\n", + " sentences_idx.append(sent_idx)\n", + " return sentences_idx" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", + "x_test_idx = tokens_to_idx(x_test_token,word_to_idx)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before converting: ['worst', 'headache', 'ever']\n", + "After converting: [721, 458, 237]\n" + ] + } + ], + "source": [ + "some_number = 1\n", + "print('Before converting: ', x_train_token[some_number])\n", + "print('After converting: ', x_train_idx[some_number])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We need all the sequences to have the same length. To select an adequate sequence length, let's explore some statistics about the length of the tweets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Max tweet word length: 229\n", + "Mean tweet word length: 15.0\n", + "99% percent under: 37.0\n" + ] + } + ], + "source": [ + "tweet_lens = np.asarray([len(sentence) for sentence in x_train_idx])\n", + "print('Max tweet word length: ',tweet_lens.max())\n", + "print('Mean tweet word length: ',np.median(tweet_lens))\n", + "print('99% percent under: ',np.quantile(tweet_lens,0.99))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We cut the sequences which are larger than our chosen maximum length (`max_lenght`) and fill with zeros the ones that are shorter." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + " # We choose the max length\n", + " max_length = 40\n", + "\n", + "# A function to make all the sequence have the same lenght\n", + "# Note that the output is a Numpy matrix\n", + " def padding(sentences, seq_len):\n", + " features = np.zeros((len(sentences), seq_len),dtype=int)\n", + " for ii, tweet in enumerate(sentences):\n", + " len_tweet = len(tweet)\n", + " if len_tweet != 0:\n", + " if len_tweet <= seq_len:\n", + " # If its shorter, we fill with zeros (the padding Token index)\n", + " features[ii, -len(tweet):] = np.array(tweet)[:seq_len]\n", + " if len_tweet > seq_len:\n", + " # If its larger, we take the last 'seq_len' indexes\n", + " features[ii, :] = np.array(tweet)[-seq_len:]\n", + " return features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# We convert our list of tokens into a numpy matrix\n", + "# where all instances have the same lenght\n", + "x_train_pad = padding(x_train_idx,max_length)\n", + "x_test_pad = padding(x_test_idx,max_length)\n", + "\n", + "# We convert our target list a numpy matrix\n", + "y_train_np = np.asarray(y_train)\n", + "y_test_np = np.asarray(y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Before padding: [1, 3, 71, 24, 122, 3, 533, 74, 13, 4, 3, 102, 13, 209, 2, 12, 150, 4, 22, 5, 18, 667, 3, 138, 61, 7, 3296, 4]\n", + "After padding: [ 0 0 0 0 0 0 0 0 0 0 0 0 1 3\n", + " 71 24 122 3 533 74 13 4 3 102 13 209 2 12\n", + " 150 4 22 5 18 667 3 138 61 7 3296 4]\n" + ] + } + ], + "source": [ + "some_number = 2\n", + "print('Before padding: ', x_train_idx[some_number])\n", + "print('After padding: ', x_train_pad[some_number])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Now, let's convert the data to pytorch format.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# create Tensor datasets\n", + "train_data = TensorDataset(torch.from_numpy(x_train_pad), torch.from_numpy(y_train_np))\n", + "valid_data = TensorDataset(torch.from_numpy(x_test_pad), torch.from_numpy(y_test_np))\n", + "\n", + "# Batch size (this is an important hyperparameter)\n", + "batch_size = 100\n", + "\n", + "# dataloaders\n", + "# make sure to SHUFFLE your data\n", + "train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size,drop_last = True)\n", + "valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size,drop_last = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Each batch of data in our traning proccess will have the folllowing format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample input size: torch.Size([100, 40])\n", + "Sample input: \n", + " tensor([[ 0, 0, 0, ..., 4, 4, 4],\n", + " [ 0, 0, 0, ..., 7447, 14027, 2],\n", + " [ 0, 0, 0, ..., 100, 22241, 4],\n", + " ...,\n", + " [ 0, 0, 0, ..., 2702, 4409, 2],\n", + " [ 0, 0, 0, ..., 162, 17, 1],\n", + " [ 0, 0, 0, ..., 67, 12904, 49]])\n", + "Sample input: \n", + " tensor([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n", + " 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n", + " 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n", + " 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n", + " 0, 0, 1, 0])\n" + ] + } + ], + "source": [ + "# Obtain one batch of training data\n", + "dataiter = iter(train_loader)\n", + "sample_x, sample_y = dataiter.__next__()\n", + "\n", + "print('Sample input size: ', sample_x.size()) # batch_size, seq_length\n", + "print('Sample input: \\n', sample_x)\n", + "print('Sample input: \\n', sample_y)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "Now, we will define the `SentimentRNN` class. Most of the model's class will be familiar to you, but there are two important layers we would like you to pay attention to:\n", + "\n", + "* Embedding Layer\n", + "> This layer is like a linear layer, but it makes it posible to use a sequence of inedexes as inputs (instead of a sequence of one-hot-encoded vectors). During training, the Embedding layer learns a linear transformation from the space of words (a vector space of dimension `num_words_dict`) into the a new, smaller, vector space of dimension `embedding_dim`. We suggest you to read this [thread](https://discuss.pytorch.org/t/how-does-nn-embedding-work/88518/3) and the [pytorch documentation](https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html) if you want to learn more about this particular kind of layers.\n", + "\n", + "\n", + "* LSTM layer\n", + "> This is one of the most used class of Recurrent Neural Networks. In Pytorch we can add several stacked layers in just one line of code. In our case, the number of layers added are decided with the parameter `no_layers`. If you want to learn more about LSTMs we strongly recommend you this [Colahs thread](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) about them.\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "class SentimentRNN(nn.Module):\n", + " def __init__(self,no_layers,vocab_size,hidden_dim,embedding_dim,drop_prob=0.1):\n", + " super(SentimentRNN,self).__init__()\n", + "\n", + " self.output_dim = output_dim\n", + " self.hidden_dim = hidden_dim\n", + " self.no_layers = no_layers\n", + " self.vocab_size = vocab_size\n", + " self.drop_prob = drop_prob\n", + "\n", + " # Embedding Layer\n", + " self.embedding = nn.Embedding(vocab_size, embedding_dim)\n", + "\n", + " # LSTM Layers\n", + " self.lstm = nn.LSTM(input_size=embedding_dim,hidden_size=self.hidden_dim,\n", + " num_layers=no_layers, batch_first=True,\n", + " dropout=self.drop_prob)\n", + "\n", + " # Dropout layer\n", + " self.dropout = nn.Dropout(drop_prob)\n", + "\n", + " # Linear and Sigmoid layer\n", + " self.fc = nn.Linear(self.hidden_dim, output_dim)\n", + " self.sig = nn.Sigmoid()\n", + "\n", + " def forward(self,x,hidden):\n", + " batch_size = x.size(0)\n", + "\n", + " # Embedding out\n", + " embeds = self.embedding(x)\n", + " #Shape: [batch_size x max_length x embedding_dim]\n", + "\n", + " # LSTM out\n", + " lstm_out, hidden = self.lstm(embeds, hidden)\n", + " # Shape: [batch_size x max_length x hidden_dim]\n", + "\n", + " # Select the activation of the last Hidden Layer\n", + " lstm_out = lstm_out[:,-1,:].contiguous()\n", + " # Shape: [batch_size x hidden_dim]\n", + "\n", + " ## You can instead average the activations across all the times\n", + " # lstm_out = torch.mean(lstm_out, 1).contiguous()\n", + "\n", + " # Dropout and Fully connected layer\n", + " out = self.dropout(lstm_out)\n", + " out = self.fc(out)\n", + "\n", + " # Sigmoid function\n", + " sig_out = self.sig(out)\n", + "\n", + " # return last sigmoid output and hidden state\n", + " return sig_out, hidden\n", + "\n", + " def init_hidden(self, batch_size):\n", + " ''' Initializes hidden state '''\n", + " # Create two new tensors with sizes n_layers x batch_size x hidden_dim,\n", + " # initialized to zero, for hidden state and cell state of LSTM\n", + " h0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", + " c0 = torch.zeros((self.no_layers,batch_size,self.hidden_dim)).to(device)\n", + " hidden = (h0,c0)\n", + " return hidden" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We choose the parameters of the model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# Parameters of our network\n", + "\n", + "# Size of our vocabulary\n", + "vocab_size = num_words_dict\n", + "\n", + "# Embedding dimension\n", + "embedding_dim = 32\n", + "\n", + "# Number of stacked LSTM layers\n", + "no_layers = 2\n", + "\n", + "# Dimension of the hidden layer in LSTMs\n", + "hidden_dim = 64\n", + "\n", + "# Dropout parameter for regularization\n", + "output_dim = 1\n", + "\n", + "# Dropout parameter for regularization\n", + "drop_prob = 0.25" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SentimentRNN(\n", + " (embedding): Embedding(30000, 32)\n", + " (lstm): LSTM(32, 64, num_layers=2, batch_first=True, dropout=0.25)\n", + " (dropout): Dropout(p=0.25, inplace=False)\n", + " (fc): Linear(in_features=64, out_features=1, bias=True)\n", + " (sig): Sigmoid()\n", + ")\n" + ] + } + ], + "source": [ + "# Let's define our model\n", + "model = SentimentRNN(no_layers, vocab_size, hidden_dim,\n", + " embedding_dim, drop_prob=drop_prob)\n", + "# Moving to gpu\n", + "model.to(device)\n", + "print(model)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Number of parameters: 1018433\n" + ] } + ], + "source": [ + "# How many trainable parameters does our model have?\n", + "model_parameters = filter(lambda p: p.requires_grad, model.parameters())\n", + "params = sum([np.prod(p.size()) for p in model_parameters])\n", + "print('Total Number of parameters: ',params)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We choose the losses and the optimizer for the training procces." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [], + "source": [ + "# loss and optimization functions\n", + "lr = 0.001\n", + "\n", + "# Binary crossentropy is a good loss function for a binary classification problem\n", + "criterion = nn.BCELoss()\n", + "\n", + "# We choose an Adam optimizer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n", + "\n", + "# function to predict accuracy\n", + "def acc(pred,label):\n", + " pred = torch.round(pred.squeeze())\n", + " return torch.sum(pred == label.squeeze()).item()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "We are ready to train our model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1\n", + "train_loss : 0.4367361353733577 val_loss : 0.39174133955966683\n", + "train_accuracy : 79.530625 val_accuracy : 82.3628125\n", + "Validation loss decreased (inf --> 0.391741). Saving model ...\n", + "==================================================\n", + "Epoch 2\n", + "train_loss : 0.3765802335098851 val_loss : 0.3724124691961333\n", + "train_accuracy : 83.19140625 val_accuracy : 83.42031250000001\n", + "Validation loss decreased (0.391741 --> 0.372412). Saving model ...\n", + "==================================================\n", + "Epoch 3\n", + "train_loss : 0.35746844720793886 val_loss : 0.365050206175074\n", + "train_accuracy : 84.16882812499999 val_accuracy : 83.7440625\n", + "Validation loss decreased (0.372412 --> 0.365050). Saving model ...\n", + "==================================================\n", + "Epoch 4\n", + "train_loss : 0.34491546426317654 val_loss : 0.36467386982403693\n", + "train_accuracy : 84.879140625 val_accuracy : 83.77\n", + "Validation loss decreased (0.365050 --> 0.364674). Saving model ...\n", + "==================================================\n", + "Epoch 5\n", + "train_loss : 0.33429012800217606 val_loss : 0.36189084346871825\n", + "train_accuracy : 85.44296875 val_accuracy : 84.0221875\n", + "Validation loss decreased (0.364674 --> 0.361891). Saving model ...\n", + "==================================================\n" + ] + } + ], + "source": [ + "# Number of training Epochs\n", + "epochs = 5\n", + "\n", + "# Maximum absolute value accepted for the gradeint\n", + "clip = 5\n", + "\n", + "# Initial Loss value (assumed big)\n", + "valid_loss_min = np.Inf\n", + "\n", + "# Lists to follow the evolution of the loss and accuracy\n", + "epoch_tr_loss,epoch_vl_loss = [],[]\n", + "epoch_tr_acc,epoch_vl_acc = [],[]\n", + "\n", + "# Train for a number of Epochs\n", + "for epoch in range(epochs):\n", + " train_losses = []\n", + " train_acc = 0.0\n", + " model.train()\n", + "\n", + " for inputs, labels in train_loader:\n", + "\n", + " # Initialize hidden state\n", + " h = model.init_hidden(batch_size)\n", + " # Creating new variables for the hidden state\n", + " h = tuple([each.data.to(device) for each in h])\n", + "\n", + " # Move batch inputs and labels to gpu\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + "\n", + " # Set gradient to zero\n", + " model.zero_grad()\n", + "\n", + " # Compute model output\n", + " output,h = model(inputs,h)\n", + "\n", + " # Calculate the loss and perform backprop\n", + " loss = criterion(output.squeeze(), labels.float())\n", + " loss.backward()\n", + " train_losses.append(loss.item())\n", + "\n", + " # calculating accuracy\n", + " accuracy = acc(output,labels)\n", + " train_acc += accuracy\n", + "\n", + " #`clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.\n", + " nn.utils.clip_grad_norm_(model.parameters(), clip)\n", + " optimizer.step()\n", + "\n", + "\n", + " # Evaluate on the validation set for this epoch\n", + " val_losses = []\n", + " val_acc = 0.0\n", + " model.eval()\n", + " for inputs, labels in valid_loader:\n", + "\n", + " # Initialize hidden state\n", + " val_h = model.init_hidden(batch_size)\n", + " val_h = tuple([each.data.to(device) for each in val_h])\n", + "\n", + " # Move batch inputs and labels to gpu\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + "\n", + " # Compute model output\n", + " output, val_h = model(inputs, val_h)\n", + "\n", + " # Compute Loss\n", + " val_loss = criterion(output.squeeze(), labels.float())\n", + "\n", + " val_losses.append(val_loss.item())\n", + "\n", + " accuracy = acc(output,labels)\n", + " val_acc += accuracy\n", + "\n", + " epoch_train_loss = np.mean(train_losses)\n", + " epoch_val_loss = np.mean(val_losses)\n", + " epoch_train_acc = train_acc/len(train_loader.dataset)\n", + " epoch_val_acc = val_acc/len(valid_loader.dataset)\n", + " epoch_tr_loss.append(epoch_train_loss)\n", + " epoch_vl_loss.append(epoch_val_loss)\n", + " epoch_tr_acc.append(epoch_train_acc)\n", + " epoch_vl_acc.append(epoch_val_acc)\n", + " print(f'Epoch {epoch+1}')\n", + " print(f'train_loss : {epoch_train_loss} val_loss : {epoch_val_loss}')\n", + " print(f'train_accuracy : {epoch_train_acc*100} val_accuracy : {epoch_val_acc*100}')\n", + " if epoch_val_loss <= valid_loss_min:\n", + " print('Validation loss decreased ({:.6f} --> {:.6f}). Saving model ...'.format(valid_loss_min,epoch_val_loss))\n", + " # torch.save(model.state_dict(), '../working/state_dict.pt')\n", + " valid_loss_min = epoch_val_loss\n", + " print(25*'==')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "execution": {} + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize = (20, 6))\n", + "plt.subplot(1, 2, 1)\n", + "plt.plot(epoch_tr_acc, label='Train Acc')\n", + "plt.plot(epoch_vl_acc, label='Validation Acc')\n", + "plt.title(\"Accuracy\")\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "plt.subplot(1, 2, 2)\n", + "plt.plot(epoch_tr_loss, label='Train loss')\n", + "plt.plot(epoch_vl_loss, label='Validation loss')\n", + "plt.title(\"Loss\")\n", + "plt.legend()\n", + "plt.grid()\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {} + }, + "source": [ + "---\n", + "# What's Next?\n", + "\n", + "You can use this project template as a starting point to think about your own project. There are a lot of ways to continue, here we share with you some ideas you migth find useful:\n", + "\n", + "* **Work on the Preproccesing.** We used a very rudimentary way to tokenize tweets. But there are better ways to preprocess the data. Can you think of a suitable way to preprocess the data for this particular task? How does the performance of the model change when the data is processed correctly?\n", + "* **Work on the Model.** The RNN model proposed in this notebook is not optimized at all. You can work on finding a better architecture or better hyperparamenters. May be using bidirectonal LSTMs or increasing the number of stacked layers can improve the performance, feel free to try different approaches.\n", + "* **Work on the Embedding.** Our model learnt an embedding during the training on this Twitter corpus for a particular task. You can explore the representation of different words in this learned embedding. Also, you can try using different word embeddings. You can train them on this corpus or you can use an embedding trained on another corpus of data. How does the change of the embedding affect the model performance?\n", + "* **Try sentiment analysis on another dataset.** There are lots of available dataset to work with, we can help you find one that is interesting to you. Do you belive that a sentiment analysis model trained on some corpus (Twitter dataset) will perform well on another type of data (for example, youtube comments)?\n", + "\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "include_colab_link": true, + "name": "sentiment_analysis", + "provenance": [], + "toc_visible": true + }, + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From d159bb5065981f035e2e3bf9444322d47fdcc16e Mon Sep 17 00:00:00 2001 From: Soan Kim <39689481+SoanKim@users.noreply.github.com> Date: Tue, 9 Jul 2024 01:40:18 +0900 Subject: [PATCH 09/25] Update lunar_lander.ipynb deleted duplicated minigrid installation --- projects/ReinforcementLearning/lunar_lander.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 24a8e0d37..b02af964d 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -92,7 +92,6 @@ "!pip install pyvirtualdisplay --quiet\n", "!pip install pyglet --quiet\n", "!pip install pygame --quiet\n", - "!pip install minigrid --quiet\n", "!pip install -q gymnasium[box2d] --quiet\n", "!pip install 'minigrid<=2.1.1' --quiet\n", "!pip3 install box2d-py --quiet" From 3edc9188881fd43a197c245582c442862888cd7f Mon Sep 17 00:00:00 2001 From: Zoltan Date: Mon, 8 Jul 2024 17:01:52 -0400 Subject: [PATCH 10/25] Update notebook-pr.yaml ci:execute --- .github/workflows/notebook-pr.yaml | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index 5d3a3f64c..b14d492da 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -15,7 +15,22 @@ jobs: runs-on: ubuntu-latest steps: - + - name: Free up disk space + uses: jlumbroso/free-disk-space@main + with: + # this might remove tools that are actually needed, + # if set to "true" but frees about 6 GB + tool-cache: false + + # all of these default to true, but feel free to set to + # "false" if necessary for your workflow + android: true + dotnet: true + haskell: true + large-packages: true + docker-images: true + swap-storage: true + - name: Checkout uses: actions/checkout@v3 with: From 8fa1bd6412ef53d8e10a7a59720be896b8db73e8 Mon Sep 17 00:00:00 2001 From: Zoltan Date: Mon, 8 Jul 2024 17:16:32 -0400 Subject: [PATCH 11/25] Update requirements.txt ci:execute --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index ab36879c3..4b78c17c3 100644 --- a/requirements.txt +++ b/requirements.txt @@ -10,3 +10,4 @@ torchvision pathlib xkcd decorator==5.0.9 +pyvirtualdisplay From 93cccbfb8aa2cb6089d3821193341190a6c16310 Mon Sep 17 00:00:00 2001 From: Zoltan Date: Mon, 8 Jul 2024 18:36:26 -0400 Subject: [PATCH 12/25] Update notebook-pr.yaml ci:execute --- .github/workflows/notebook-pr.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index b14d492da..5cbdfd03d 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -63,6 +63,7 @@ jobs: - name: Install dependencies if: "!contains(env.COMMIT_MESSAGE, 'skip ci') && contains(env.COMMIT_MESSAGE, 'ci:execute')" run: | + sudo apt-get update && sudo apt install xvfb -y python -m pip install --upgrade pip wheel pip install -r requirements.txt pip install jupyter_client==7.3.5 # downgrade jupyter-client to fix hangs From 942e35c5229f79ea2c6ae113731692eaff2b76b2 Mon Sep 17 00:00:00 2001 From: Zoltan Date: Mon, 8 Jul 2024 19:33:12 -0400 Subject: [PATCH 13/25] update reqs and use of np.inf ci:execute --- projects/NaturalLanguageProcessing/sentiment_analysis.ipynb | 2 +- requirements.txt | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb index 87c305bde..70b008cb9 100644 --- a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb +++ b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb @@ -1504,7 +1504,7 @@ "clip = 5\n", "\n", "# Initial Loss value (assumed big)\n", - "valid_loss_min = np.Inf\n", + "valid_loss_min = np.inf\n", "\n", "# Lists to follow the evolution of the loss and accuracy\n", "epoch_tr_loss,epoch_vl_loss = [],[]\n", diff --git a/requirements.txt b/requirements.txt index 4b78c17c3..b8d8390a9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -11,3 +11,5 @@ pathlib xkcd decorator==5.0.9 pyvirtualdisplay +tensorboard +moviepy From df5adecea19a0bfb2180d84fc127b601a4ec701f Mon Sep 17 00:00:00 2001 From: Mobin Nesari Date: Tue, 9 Jul 2024 09:21:43 +0330 Subject: [PATCH 14/25] Updating notebook and adding new links --- .DS_Store | Bin 0 -> 6148 bytes projects/.DS_Store | Bin 0 -> 6148 bytes projects/ComputerVision/em_synapses.ipynb | 2773 +++++++++++---------- 3 files changed, 1443 insertions(+), 1330 deletions(-) create mode 100644 .DS_Store create mode 100644 projects/.DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..988d1b44537a728310113a45890b2741a8e33794 GIT binary patch literal 6148 zcmeHKO>fgM7=GP#kZeNq0Maf+&_E3`+!3iPcH?kjpypJ6}cI=plL}xN=5!HyuL13(IAzEWx&tb(X zreqr^xQ*jPo`#7Gk>>`jSp}>D|F#1B?N%uyiRY~J{kBop_^wP1Vv~;OA+_lN^-7+f zOP<#$qLeNurZL)0>0IS=YS4S6lOkK(T4CR(WAxQSZvo;vNI#-3QVp<&CHBV>dy9(f zb)0V{jzc5$rf=|`r<7Ax?4$DPcmClt8=0N&OE^m6EN?cyipoZH^U77nakiZo!D~4U z@Yx15FMwXY$!!J5UCuHR^l3kkU}NIC8X&A2@q;fIRFaT?2;~Q*3o_tp(s+$ z@E`aKT=^yZ7f$fTcBS2oL@)R65Y>suL1C<3L3NJtIF}8p zn4TS=P%(1KD4>K+DBSS29aaIW!2hNIf4kS{kR(lMM#b+p#WyS|eiX^E7fBC(yh%Jn zY5i1&)?c{ZJe?WtH*Xe&Y1VFk7nQB*rR~d(P3)aSz>i6a?L;KwnlxHQtXkUIQzJ-}MguV`MbMoH#Q+0Ya5qN-g?` z-l!a@!fF*WsL?SUVQuy439to*UuLLv_ocAz8ejx!c5jhEqUJkQd1p{0z}pkd;hfnM z{}RPW+e&%PJEU>>txEf%FQ^yD%$j$253ptxunJfOiVE=l;G!^g4X!k*tpk<10syP% zR)#kJO<;~|uxoIo5hF06sX$E?=87RS9sREH>l$2X)N~T&@*&Kdg}I>!^>)N}l{<;9 zMq672tO8{Pw)A72&;O&}-~Y=b`(zcc3S1}!M72NY_b?@Mwr)(0&srDd4GJ6ktu!hM kDsvpG1|P+HD9SMAas${kxYCFonEfN5WU!T0;GZh+3tH;kegFUf literal 0 HcmV?d00001 diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb index 58d92b82f..32441053b 100644 --- a/projects/ComputerVision/em_synapses.ipynb +++ b/projects/ComputerVision/em_synapses.ipynb @@ -1,1374 +1,1487 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "2d9f0b20", - "metadata": { - "colab_type": "text", - "execution": {}, - "id": "view-in-github" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "id": "renayVUI7b9x", - "metadata": { - "execution": {} - }, - "source": [ - "# Knowledge Extraction from a Convolutional Neural Network\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Jan Funke\n", - "\n", - "__Production editors:__ Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "id": "U6wofKujWp6X", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", - "\n", - "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", - "\n", - "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." - ] - }, - { - "cell_type": "markdown", - "id": "zO4YN6W8W0Cp", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fO1IZwvkW9Me", - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @title Install dependencies\n", - "!pip install scikit-image --quiet\n", - "!pip install pillow --quiet\n", - "!pip install scikit-image --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "gKkHjjTGWzUk", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "import glob\n", - "import json\n", - "import torch\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "\n", - "from skimage.io import imread\n", - "from torchvision.datasets import ImageFolder\n", - "from torch.utils.data import DataLoader, random_split\n", - "from torch.utils.data.sampler import WeightedRandomSampler\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "bd7d427d", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Project Ideas\n", - "\n", - "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n", - " * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n", - " * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n", - " * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n", - " * (medium) Other networks: Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n", - " * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n", - " \n", - " \n", - "2. Explore the CycleGAN.\n", - " * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n", - " * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n", - " \n", - "\n", - "3. Try on your own data!\n", - " * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n", - "\n", - "### Acknowledgments\n", - "\n", - "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "5642d709", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Train an Image Classifier\n", - "\n", - "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine." - ] - }, - { - "cell_type": "markdown", - "id": "c61a11c6", - "metadata": { - "execution": {} - }, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "821dc497", - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Data are downloaded.\n" - ] - } - ], - "source": [ - "# @title Download the data\n", - "import requests, os\n", - "from zipfile import ZipFile\n", - "\n", - "# @markdown Download the resources for this tutorial (one zip file)\n", - "fname = 'resources.zip'\n", - "url = 'https://www.dropbox.com/sh/ucpjfd3omjieu80/AAAvZynLtzvhyFx7_jwVhUK2a?dl=1'\n", - "\n", - "if not os.path.exists('data/'):\n", - " print('Data downloading...')\n", - " with requests.get(url, stream=True) as r:\n", - " r.raise_for_status()\n", - " with open(fname, 'wb') as fh:\n", - " for chunk in r.iter_content(chunk_size=16384):\n", - " fh.write(chunk)\n", - " print('Download is completed.')\n", - "\n", - " # @markdown Unzip the file\n", - " with ZipFile(fname, 'r') as zf:\n", - " # extracting all the files\n", - " print('Extracting all the files now...')\n", - " zf.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # @markdown Extract the data\n", - " fnames = ['data.zip', 'checkpoints.zip']\n", - "\n", - " for fname in fnames:\n", - " with ZipFile(fname, 'r') as zh:\n", - " # extracting all the files\n", - " print(f\"\\nArchive: {fname}\")\n", - " print(f\"\\tExtracting data...\")\n", - " zh.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # @markdown Make sure the order of classes matches the pretrained model\n", - " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", - " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", - " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", - " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", - " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", - " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", - "\n", - " # @markdown Remove the archives\n", - " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", - " if os.path.exists(i):\n", - " os.remove(i)\n", - "\n", - "else:\n", - " print('Data are already downloaded.')" - ] - }, - { - "cell_type": "markdown", - "id": "0b84ec7b", - "metadata": { - "execution": {} - }, - "source": [ - "## Classifier Training" - ] - }, - { - "cell_type": "markdown", - "id": "a79ab567", - "metadata": { - "execution": {} - }, - "source": [ - "### Create and Inspect Datasets\n", - "\n", - "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ae50b16a", - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "id": "2d9f0b20", + "metadata": { + "execution": {}, + "id": "2d9f0b20" + }, + "source": [ + "\"Open   \"Open" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of images per class:\n", - "\t0_gaba:\tn=15855\tweight=6.30715862503942e-05\n", - "\t1_acetylcholine:\tn=4911\tweight=0.00020362451639177357\n", - "\t2_glutamate:\tn=3550\tweight=0.00028169014084507044\n", - "\t3_serotonin:\tn=2297\tweight=0.00043535045711797995\n", - "\t4_octopamine:\tn=951\tweight=0.0010515247108307045\n", - "\t5_dopamine:\tn=4649\tweight=0.00021510002151000216\n" - ] - } - ], - "source": [ - "def load_image(filename):\n", - "\n", - " image = imread(filename)\n", - "\n", - " # images are grescale, we only need one of the RGB channels\n", - " image = image[:, :, 0]\n", - " # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n", - " image = image.astype(np.float32)/255.0\n", - " image = (image - 0.5)/0.5\n", - "\n", - " return image\n", - "\n", - "\n", - "# create a dataset for all images of all classes\n", - "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n", - "\n", - "# randomly split the dataset into train, validation, and test\n", - "num_images = len(full_dataset)\n", - "# ~70% for training\n", - "num_training = int(0.7 * num_images)\n", - "# ~15% for validation\n", - "num_validation = int(0.15 * num_images)\n", - "# ~15% for testing\n", - "num_test = num_images - (num_training + num_validation)\n", - "# split the data randomly (but with a fixed random seed)\n", - "train_dataset, validation_dataset, test_dataset = random_split(\n", - " full_dataset,\n", - " [num_training, num_validation, num_test],\n", - " generator=torch.Generator().manual_seed(23061912))\n", - "\n", - "# compute class weights in training dataset for uniform sampling\n", - "ys = np.array([y for _, y in train_dataset])\n", - "counts = np.bincount(ys)\n", - "label_weights = 1.0 / counts\n", - "weights = label_weights[ys]\n", - "\n", - "print(\"Number of images per class:\")\n", - "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n", - " print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n", - "\n", - "# create a data loader with uniform sampling\n", - "sampler = WeightedRandomSampler(weights, len(weights))\n", - "# this data loader will serve 8 images in a \"mini-batch\" at a time\n", - "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)" - ] - }, - { - "cell_type": "markdown", - "id": "e9010bdc", - "metadata": { - "execution": {} - }, - "source": [ - "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d8c6f3a", - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "id": "renayVUI7b9x", + "metadata": { + "execution": {}, + "id": "renayVUI7b9x" + }, + "source": [ + "# Knowledge Extraction from a Convolutional Neural Network\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Jan Funke\n", + "\n", + "__Production editors:__ Spiros Chavlis" + ] + }, { - "data": { - "image/png": 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", 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" + "cell_type": "markdown", + "id": "U6wofKujWp6X", + "metadata": { + "execution": {}, + "id": "U6wofKujWp6X" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", + "\n", + "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", + "\n", + "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def show_batch(x, y):\n", - " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", - " for i in range(x.shape[0]):\n", - " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", - " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", - " plt.show()\n", - "\n", - "# show a random batch from the data loader\n", - "# (run this cell repeatedly to see different batches)\n", - "for x, y in dataloader:\n", - " show_batch(x, y)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "f882416f", - "metadata": { - "execution": {} - }, - "source": [ - "### Create a Model, Loss, and Optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "54f177cc", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "class Vgg2D(torch.nn.Module):\n", - "\n", - " def __init__(\n", - " self,\n", - " input_size,\n", - " fmaps=12,\n", - " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", - " output_classes=6):\n", - "\n", - " super(Vgg2D, self).__init__()\n", - "\n", - " self.input_size = input_size\n", - "\n", - " current_fmaps = 1\n", - " current_size = tuple(input_size)\n", - "\n", - " features = []\n", - " for i in range(len(downsample_factors)):\n", - "\n", - " features += [\n", - " torch.nn.Conv2d(\n", - " current_fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Conv2d(\n", - " fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.MaxPool2d(downsample_factors[i])\n", - " ]\n", - "\n", - " current_fmaps = fmaps\n", - " fmaps *= 2\n", - "\n", - " size = tuple(\n", - " int(c/d)\n", - " for c, d in zip(current_size, downsample_factors[i]))\n", - " check = (\n", - " s*d == c\n", - " for s, d, c in zip(size, downsample_factors[i], current_size))\n", - " assert all(check), \\\n", - " \"Can not downsample %s by chosen downsample factor\" % \\\n", - " (current_size,)\n", - " current_size = size\n", - "\n", - " self.features = torch.nn.Sequential(*features)\n", - "\n", - " classifier = [\n", - " torch.nn.Linear(\n", - " current_size[0] *\n", - " current_size[1] *\n", - " current_fmaps,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " output_classes)\n", - " ]\n", - "\n", - " self.classifier = torch.nn.Sequential(*classifier)\n", - "\n", - " def forward(self, raw):\n", - "\n", - " # add a channel dimension to raw\n", - " shape = tuple(raw.shape)\n", - " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", - "\n", - " # compute features\n", - " f = self.features(raw)\n", - " f = f.view(f.size(0), -1)\n", - "\n", - " # classify\n", - " y = self.classifier(f)\n", - "\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5da43245", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# get the size of our images\n", - "for x, y in train_dataset:\n", - " input_size = x.shape\n", - " break\n", - "\n", - "# create the model to train\n", - "model = Vgg2D(input_size)\n", - "\n", - "# create a loss\n", - "loss = torch.nn.CrossEntropyLoss()\n", - "\n", - "# create an optimzer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" - ] - }, - { - "cell_type": "markdown", - "id": "01688095", - "metadata": { - "execution": {} - }, - "source": [ - "### Train the Model" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fa65090d", - "metadata": { - "execution": {} - }, - "outputs": [ + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Will use device cuda for training\n" - ] - } - ], - "source": [ - "# use a GPU, if it is available\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "model.to(device)\n", - "print(f\"Will use device {device} for training\")" - ] - }, - { - "cell_type": "markdown", - "id": "ecbab4f7", - "metadata": { - "execution": {} - }, - "source": [ - "The next cell merely defines some convenience functions for training, validation, and testing:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1a8c7fe9", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "def train(dataloader, optimizer, loss, device):\n", - " '''Train the model for one epoch.'''\n", - "\n", - " # set the model into train mode\n", - " model.train()\n", - "\n", - " epoch_loss, num_batches = 0, 0\n", - " for x, y in tqdm(dataloader, 'train'):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - " optimizer.zero_grad()\n", - "\n", - " y_pred = model(x)\n", - " l = loss(y_pred, y)\n", - " l.backward()\n", - "\n", - " optimizer.step()\n", - "\n", - " epoch_loss += l\n", - " num_batches += 1\n", - "\n", - " return epoch_loss/num_batches\n", - "\n", - "\n", - "def evaluate(dataloader, name, device):\n", - "\n", - " correct = 0\n", - " total = 0\n", - " for x, y in tqdm(dataloader, name):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - "\n", - " logits = model(x)\n", - " probs = torch.nn.Softmax(dim=1)(logits)\n", - " predictions = torch.argmax(probs, dim=1)\n", - "\n", - " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", - " total += len(y)\n", - "\n", - " accuracy = correct/total\n", - "\n", - " return accuracy\n", - "\n", - "\n", - "def validate(validation_dataset, device):\n", - " '''Evaluate prediction accuracy on the validation dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(validation_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'validate', device)\n", - "\n", - "\n", - "def test(test_dataset, device):\n", - " '''Evaluate prediction accuracy on the test dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(test_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'test', device)" - ] - }, - { - "cell_type": "markdown", - "id": "68bcfbbf", - "metadata": { - "execution": {} - }, - "source": [ - "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d0af7638", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "def train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device):\n", - "\n", - " for epoch in range(num_epochs):\n", - " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", - " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", - "\n", - " accuracy = validate(validation_dataset, device=device)\n", - " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "id": "45e31b87", - "metadata": { - "execution": {} - }, - "source": [ - "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", - "\n", - "Unceck the box below and run the cell to train a model." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "W5KA7zDIa3Lw", - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @markdown\n", - "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53fb8dda", - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# Load a pretrained model or train the model from scratch\n", - "\n", - "# set this to True and run this cell if you want a shortcut\n", - "\n", - "if yes_I_want_the_pretrained_model:\n", - " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", - " map_location=device)\n", - " model.load_state_dict(checkpoint['model_state_dict'])\n", - "else:\n", - " train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f6e3663", - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "id": "zO4YN6W8W0Cp", + "metadata": { + "execution": {}, + "id": "zO4YN6W8W0Cp" + }, + "source": [ + "---\n", + "# Setup" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "test: 0%| | 0/216 [00:00" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "def show_batch(x, y):\n", + " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", + " for i in range(x.shape[0]):\n", + " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", + " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", + " plt.show()\n", + "\n", + "# show a random batch from the data loader\n", + "# (run this cell repeatedly to see different batches)\n", + "for x, y in dataloader:\n", + " show_batch(x, y)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "f882416f", + "metadata": { + "execution": {}, + "id": "f882416f" + }, + "source": [ + "### Create a Model, Loss, and Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "54f177cc", + "metadata": { + "execution": {}, + "id": "54f177cc" + }, + "outputs": [], + "source": [ + "class Vgg2D(torch.nn.Module):\n", + "\n", + " def __init__(\n", + " self,\n", + " input_size,\n", + " fmaps=12,\n", + " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", + " output_classes=6):\n", + "\n", + " super(Vgg2D, self).__init__()\n", + "\n", + " self.input_size = input_size\n", + "\n", + " current_fmaps = 1\n", + " current_size = tuple(input_size)\n", + "\n", + " features = []\n", + " for i in range(len(downsample_factors)):\n", + "\n", + " features += [\n", + " torch.nn.Conv2d(\n", + " current_fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Conv2d(\n", + " fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.MaxPool2d(downsample_factors[i])\n", + " ]\n", + "\n", + " current_fmaps = fmaps\n", + " fmaps *= 2\n", + "\n", + " size = tuple(\n", + " int(c/d)\n", + " for c, d in zip(current_size, downsample_factors[i]))\n", + " check = (\n", + " s*d == c\n", + " for s, d, c in zip(size, downsample_factors[i], current_size))\n", + " assert all(check), \\\n", + " \"Can not downsample %s by chosen downsample factor\" % \\\n", + " (current_size,)\n", + " current_size = size\n", + "\n", + " self.features = torch.nn.Sequential(*features)\n", + "\n", + " classifier = [\n", + " torch.nn.Linear(\n", + " current_size[0] *\n", + " current_size[1] *\n", + " current_fmaps,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " output_classes)\n", + " ]\n", + "\n", + " self.classifier = torch.nn.Sequential(*classifier)\n", + "\n", + " def forward(self, raw):\n", + "\n", + " # add a channel dimension to raw\n", + " shape = tuple(raw.shape)\n", + " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", + "\n", + " # compute features\n", + " f = self.features(raw)\n", + " f = f.view(f.size(0), -1)\n", + "\n", + " # classify\n", + " y = self.classifier(f)\n", + "\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5da43245", + "metadata": { + "execution": {}, + "id": "5da43245" + }, + "outputs": [], + "source": [ + "# get the size of our images\n", + "for x, y in train_dataset:\n", + " input_size = x.shape\n", + " break\n", + "\n", + "# create the model to train\n", + "model = Vgg2D(input_size)\n", + "\n", + "# create a loss\n", + "loss = torch.nn.CrossEntropyLoss()\n", + "\n", + "# create an optimzer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" + ] + }, + { + "cell_type": "markdown", + "id": "01688095", + "metadata": { + "execution": {}, + "id": "01688095" + }, + "source": [ + "### Train the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "fa65090d", + "metadata": { + "execution": {}, + "id": "fa65090d", + "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Will use device cuda for training\n" + ] + } + ], + "source": [ + "# use a GPU, if it is available\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "model.to(device)\n", + "print(f\"Will use device {device} for training\")" + ] + }, + { + "cell_type": "markdown", + "id": "ecbab4f7", + "metadata": { + "execution": {}, + "id": "ecbab4f7" + }, + "source": [ + "The next cell merely defines some convenience functions for training, validation, and testing:" + ] + }, { - "data": { - "image/png": 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" + "cell_type": "code", + "execution_count": 18, + "id": "1a8c7fe9", + "metadata": { + "execution": {}, + "id": "1a8c7fe9" + }, + "outputs": [], + "source": [ + "def train(dataloader, optimizer, loss, device):\n", + " '''Train the model for one epoch.'''\n", + "\n", + " # set the model into train mode\n", + " model.train()\n", + "\n", + " epoch_loss, num_batches = 0, 0\n", + " for x, y in tqdm(dataloader, 'train'):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + " optimizer.zero_grad()\n", + "\n", + " y_pred = model(x)\n", + " l = loss(y_pred, y)\n", + " l.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " epoch_loss += l\n", + " num_batches += 1\n", + "\n", + " return epoch_loss/num_batches\n", + "\n", + "\n", + "def evaluate(dataloader, name, device):\n", + "\n", + " correct = 0\n", + " total = 0\n", + " for x, y in tqdm(dataloader, name):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + "\n", + " logits = model(x)\n", + " probs = torch.nn.Softmax(dim=1)(logits)\n", + " predictions = torch.argmax(probs, dim=1)\n", + "\n", + " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", + " total += len(y)\n", + "\n", + " accuracy = correct/total\n", + "\n", + " return accuracy\n", + "\n", + "\n", + "def validate(validation_dataset, device):\n", + " '''Evaluate prediction accuracy on the validation dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(validation_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'validate', device)\n", + "\n", + "\n", + "def test(test_dataset, device):\n", + " '''Evaluate prediction accuracy on the test dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(test_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'test', device)" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "markdown", + "id": "68bcfbbf", + "metadata": { + "execution": {}, + "id": "68bcfbbf" + }, + "source": [ + "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 19, + "id": "d0af7638", + "metadata": { + "execution": {}, + "id": "d0af7638" + }, + "outputs": [], + "source": [ + "def train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device):\n", + "\n", + " for epoch in range(num_epochs):\n", + " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", + " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", + "\n", + " accuracy = validate(validation_dataset, device=device)\n", + " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "markdown", + "id": "45e31b87", + "metadata": { + "execution": {}, + "id": "45e31b87" + }, + "source": [ + "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", + "\n", + "Unceck the box below and run the cell to train a model." ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 20, + "id": "W5KA7zDIa3Lw", + "metadata": { + "cellView": "form", + "execution": {}, + "id": "W5KA7zDIa3Lw" + }, + "outputs": [], + "source": [ + "# @markdown\n", + "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 21, + "id": "53fb8dda", + "metadata": { + "execution": {}, + "id": "53fb8dda" + }, + "outputs": [], + "source": [ + "# Load a pretrained model or train the model from scratch\n", + "\n", + "# set this to True and run this cell if you want a shortcut\n", + "\n", + "if yes_I_want_the_pretrained_model:\n", + " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", + " map_location=device)\n", + " model.load_state_dict(checkpoint['model_state_dict'])\n", + "else:\n", + " train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device)" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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qtDn+3mOMx23+OvPCmjfkX9wc+5DNp+O1GQ+fX5tffxizN9R31PxXyOddD4SiPe+t+cVWVdU9Y9tv6kVV9UljjHcYY5yp2XT8ep12aWsREZE75bhipuSnq+qjNue+Y1V9chx/bW3/0eN8zfvrw5uyfEHdORS/vbZmP6Drf0h6dc0P6H93jHFhE1/83jHGUrJ2omO2BZ9Qs4nzK+O/f0NVffIY4w+OWQr2V2sTT4wxnjnGeP9NvveOMT675pdNP7o5/vQxxu8ZM+9Xc2x0o3/GGO+2ue7MGOOzqupt6s2xyv9XVe86xnjuJib6a1X1s4u466PHGOc2bf6cqvrTNb9ouZ1473Nrltg/+6gXW2vxHrTHarw3xnjXmmV0f2Gapu854r63LNeYJWjfX/Mf956f14rszHQCnK39579H819t/2LGwzXr288sjv+5qvrqRfqpNb+ouP4LFV9WVT+wOfa4qvrHm2OvrvmvTb9Sm19hqJt/Zeynquq9Fnl/SVW9oWYN9JfXrOVe/uLGz9XiV8mOsQ0erPnXOK7WrO1+9uLYx1fVzy/Sn15zwHRYs2fN+yyOfWDNuufLm3r8u6r6gLjX/7i59jyU6Qtr8Stjm//2p6rqv22u/+6qesri2BNq/hn6h2v+BPkr682/fPJxNf/s6uGmX76hqt76duq0Of4jizq9oKrOLo79nZoDxcOaf+b+r1fVExbtMVXVlZo/Nb7+b/nLc1P+i3t/0aZsr6tZk/7k425r//nPf/7zn/9u518dY8y0Ob78lbEHan7Jct375gtr+xfJPm2zhz9c85c8v6fm2OWgqn6p5q8ybvzCVN38q2VfXFVft0g/u6pesfn/FL/dv4kFHqqql23+2301y7BeVfPXRD9VVR+7yP9Ex2yLc36xbv0rrX+p5hjnUs1/pLpn89/fpeYvwg5r/vrnB2o7HvyATftd2ZTx4yPfv71py4OaX5q9Yxx/9qZcVzd1fXBx7N9v2vtSzXHQss0p3ptq/kPsMib7vMXxW8Z70B6r8d7m3Gtx7Odvp1w1y9imevMv7F3/98xHez3w3++Of2Oa9BiV/WaM8Ss1Bwz/5hbH76l5k//gaf6LUB7/spo3mzv65YxGOT+iqj5hmqaPuZv3ERERETmKx0rM9GhjzCYijxWO/GlkEXkz0zS9sar+4PX05pPnJ9b8l58/VPMnzZ9y9NXHWo7vqdmoTkREROTEcVJipkcbYzYReazgCyGRPuer6ltr/lT5tVX1d2vjGSMiIiIiNzBmEhE5wSgZExERERERERHZM+7ar4yNMf74GOM/jzFeMcbQEV1ERERERERE5IRwV74QGmM8rma3/z9as/P+T1bVx03T9PJjv5mIiIiIiIiIiLS4Wx5Cf7jmn3H85aqqMcY/qaqPrPknKm/i3Llz0/33338jPcZYzTyP0/lJvgTbNb+7eT69sMvjmb527drq8Te96U1b6d/5nd9p5ZdQWz7+8Y9fPX7q1KnV492x0T1O7UntQ/kt24/ulWTZu9cTlF+3fEn27eMe97jV67tjoTsPE5orBPXPWt/vuuYd9/Xdvtx1bFDbEbv2fdKtzy5reqaPu21prHXXsFwDk5xHy/u9/vWvr4ODg+PtLNmZs2fPTk95ylNupLvzNccssev6R/nR8U55Ke8c75SmmCpjMlovuvtmksdzX16bz7dTPqK7HlLfdcYC9c1x/8F8132qOxa6Y4Pamtq+2zfdudGNSzrsusZ089t13hLH/Zy7K8d9/+4eskve3T0gybLRHkHssk49/PDDdXh4eGRj3a0XQk+vql9bpF9VVe97q5Pvv//+ev7z36wqe+ITn7h1nDasTFNAnRvuE57whNU05ZfH86VHsixvXpvB9m//9m+v3jvPz7o98sgjq+mHHnpoK33x4sWt9NWrV7fSb3zjG1fLk22XbfHAAw+snn/69OnV4/fee+9Wml44JRTsZDrre+nSpa00PRzl9ct0Hsu+S3KsZFlzrHQXobw+y0P507zMvr1w4cJWOvs21wGapzQ2kqzfb/3Wb62mad5ToHzlypUb/z/bkl6O5fFsmxz3eX4ez7bL8++5555aI+9PgRsFPzQvE8ov2z7rT/M2j1N/0LqzvD7HXaazbWnzz7Ik1Df0kjvLd3BwsHp+7hnLtvnSL/3S1bLKo8NTnvKU+ozP+IwbaVqPcq2l9SLnZ66tNObzeI5ZerDM87P8a9dmWZPlun5UOvf5zO/w8HAr/brXvW4rTTFetk3WjfaG3AvOnTu3lc7y5/VZvpz/SV6f+efemOU5c+bMVprimOzP5fGMh7NvaJ+mmCrbnv4gSmt9lo/GQt4vx0bGZJk+e/bsVjrbPlmbV1U3lzfHSqazvvSsl/XP85ft2Y1BuvE5PRfmvKN4txvz5LykmKX7h6iEnjdyj+i+AKOXhZ3ndDqX1oG8N+0RtOZdvnx5K03rQt6PYrhl+gUveMEty3nXPISIMcanjjFeMsZ4SQaYIiIiInJ3WMZg+VJCRERE9oe79YXQf6+qt12kn7H5bzeYpumFVfXCqqoHH3xwyrehS7qfJ+eb3O5bZPqLKv31mr4sWL5l737W15VwEXT//AsKvXWmN/j09RV9BZL1zb5M8nz6MoC+iulK7PL65fm7yv3oDTt9VUJtcbc/Bc+/BmVb0V+zaOzSX1joLX++xad1KNuT/oq9hP4iTmvWrp+KU9vkcVrj6K9Ru36enWMl//rU+WLnqPt1112aS8v26O4v9DVW9m1+5Zl/5aUvlOivuNn3WZ618+mvrPKWYxmDPfOZz5zWvjSj+UpjNsdErs00//KFFcWAFBctj9Ncpq+26QugrBt9lZJkW3T3wZz/Xak2xXSZpr+mJxTD0Vjpjr1lffNc+uIm60bxK8VA9GUcrZfZdxSf0pcF9MUTtQ999U750xdQeX62d/bP2lftFFPR11VU9q7UetcvfRNaI/P6bKusT/fZlJ716AsqOr/7xdAyTeOKykbzLI/T9QnN0+48WV6/Ng7v1hdCP1lV7zTGePsxxhOr6mOr6sV36V4iIiIiIiIiItLgrnwhNE3Tm8YYf76qvr+qHldV/3iapp+/G/cSEREREREREZEed0syVtM0/Yuq+hd3K38REREREREREbkz7toLoS5L/WRX05x0f/ow8+tqoOl45xduSA9OHjekb6dfwKBf50m6nkF5fupy17ykjsqffvEi+yb1+9Q33V99y/tl+da0o13fFPKF6Wpy6focK6SJTugXI0gP3vFnqqo6f/78TtdnmjyDSG+/PE7eW91fhknIlybrnvejtqJ1gtatXX/BousP1f01F9LLd3/idFl/2n9oHGWafgGKfM5ojaM9hfT0y+uP+2ec5fhYzmHyOCCvE/pVr9wLcj7T2tv1j1jzzaFfRs2YIen+MlSWNWMyiklyPtIvS3U9OGmvobU2PYvIsLzrK0meRZ1fmKRrc60jujFY18+puy+Tn2r+oE+en2OT4vXs+2583fVEpb1xbSzTHk6/+pXrAj0LdX/tkzxEKT8aa/SrwUneL8cCeQh1f/WY1qlu+y7ncvdXLHf1ayLfOIr5KH6lGG15/loM9qj9ypiIiIiIiIiIiDw6+EJIRERERERERGTP8IWQiIiIiIiIiMiecSI8hE6dOrWlN0yNG3mfdH1cyEOBtJekl6f7La8nHWSm816pFUxtYqbJB4a0/V1flzNnzmylUwtJulvS53fbizyYUvN89erV1v063iVdXxLS2FLbkcaYPIO6Xlsdb5Gj7k++EuSNkrpg0myTj0/Xy2XNT6Db9+S9Qhpm0uZTW5KePdcNapts2+wr8lgi/6uEyp/1J++0hNqfPFnW6PqW5b1yHpFPRM572mNo/1z2LbWTPHos+5H2hu5ammP49OnTW+kcUwntZeS5trbeduM/2ncTmq/kWZTX33PPPavlo/ypPOSfkWs1rfUUI2X5aS9IaH1b659sy258l9A8oBiN9jXyZaF9mOII6vucx9l+FP8nu9aXvFXW1rHMm7yqsi0pvqQ9n+ZJ91mH1iWK8Wgu5PnkfUYxXEIx2HH6S3W9u6gtuu8Y6DmU5i35n661/VqsbHQmIiIiIiIiIrJn+EJIRERERERERGTP8IWQiIiIiIiIiMiecSI8hMYYW/pN0jompE0kvSDp/Tq61CrWB66Vl8qSeZHfQ6ZT90k+MIeHh6vHs7yk+yQvAOrrbvukpjqPd3xfqm7WDVP+axrobt0zr66mNtsq69LV8JKuNenOE9LpUt9lfllf0q+TjwydvzZWyZeBNMm0JtFYIg+QTNNYI71712ejq0envup6O5C+v3t8WT/q24TWPPJAIV85SlPbJV2PFTkZLPuJ1hPyGsm9Kte3XedTli/vl6yNwe4+mtDaTL6P5OO45sl1VPrs2bOrx8nDh/ZZWpsJiq+zL8mvjnxm1uIOGnfZV9R25O3R3We6+xr5X3U9ScmHhmKsvN+99967mk6oPkm2Z+a/HCvkAbSrX1TXI2hXH0caCwnF9xSn0Lqx63M71acbky6vz76ntkzoWYPmWXfPofPJv+p2PTGN1ERERERERERE9gxfCImIiIiIiIiI7Bm+EBIRERERERER2TNOhIfQNE1bGr7UCqYejvTlpHEmHWzXA6Hr17E8P+uS6ax7anwfeeSRrfTVq1e30qm3zvy6/hVUvtTsdv2YiK5OlbSb5EOT56f2NMdOjr3sn8x/DRqHpFdP6N6kKU7It4E0wjl2SJdLPjPkaUQa74R8arrr0Fre5NlBHhrkR0X6a7o++6rr85ZtQW1FfZPHc50j76+Eyp9Qf62t6129etL1e9p1zaN5R2NnzTdNTganTp3a2rvJtyXHQO4FNP9ojNLeQb49NMaXc4bWRkrnPkP7csZwGbNl3bNt77nnnq007Ut5/4zRMj/a97seQl2/jexL8vvI9qEYd1nebHvy3sixktcnFONk32Rf5PGMJ8nPlMZS5kfzKMl1IaHyUV/S+UnWN8f6Mr+sW9eXsOthQ21Nz7ldTyHyHqO4oTvPydeNYqauXxSVd23s5DHyPyWPnoTaluJ/6tuE+vp24y6/EBIRERERERER2TN8ISQiIiIiIiIismf4QkhEREREREREZM84ER5CY4wtTV/q80hbSH4a5LlA3iLkd9H1bFjqAcn3JHWn3TT5JZHHUNfbgzTTpG/v6uGpr1LHSlpR0mKm/p88jdZ8f6ituj4l1NYJzQPyicjryeOIxlbXuyEh3wvyQqG+X/MiOCq9puNNrwAaC13PjG5bkuaZ1lDSVFN+Xc0zeRMk5HVAfZmQ3n8tTXWjeZ6QDxvtl10PELo/+arJyWQ5DnK+dNe6PE4+jTRGMj/yWOj4YtG55BFEnjjdmKPrFUJ7R0L17frSUNyS0PGMsdIHhjyD8vzDw8Ot9HLv6frIZNkS8ocjP9Mk65bpbItMnz59eitNMSPFTBR/U5zS9clJsm8p3l8bO+Rxk3XNtul451WxP9Rxp7vPsTS2svzduKALjaWuj+ZyrGVfduPjbtvnOMx1gMrT9c5NbtfH0S+ERERERERERET2DF8IiYiIiIiIiIjsGb4QEhERERERERHZM06kuL/rybOrJ0NqJ1Ovl9pJ0nyTTnh5Pnn8pCfQpUuXttKpae56fZD+nTS6eTy9BDKdulXSKJN/RtdbIMuTkM9MR6d61P2W+R0cHKyWJSGdKXmDkC42xzmN+ytXrmylu/pu8rtKyIshofpT+5GXQvZttlfHl+bMmTNbadJLdz1/qO26/lXkbUD50Rqe51P9ku79ul4GCa1jy3Tei7ysqCy0/9F+lPfLcUy+EtR21Ffy6DPG2BoHOSZy7aZ9keII8lah+Zt015+1+ZjjPffp9KTJfSz3xYzZMj8qK/nEdPuGPDpp/SEvEvIYon2Z8svzc++k9W1Znl09IskHMaEY5OrVq6vXk39SljdjsoTmDc1TGhu0F2RcQvmRnxT5AuXetgblnW239px31Pk51qjt6FmNPHdyXaN5T8/Ju+4J5ENH9euWf5mm2Jy8djPvHFfkt9p9jsw9JaHng9v1c/ILIRERERERERGRPcMXQiIiIiIiIiIie4YvhERERERERERE9owT4yG01NCl/q6rU+1qO0kHnP/qV7YAACAASURBVPnR9aR5Xss7daWpHUyNcWodqaxdSC+efZV67tRW5vWZTkhznZCOlHxtul4lpGlOv4Bl/qT5pXFOdaX8u14gWbdz585tpXPsZvny+OnTp7fSpF8nPTrpbEnjTGOdvF5obizzJw0z+T6QHpvmDY0FoqvnJi8xGuvdNHkrdOcW+XORXn5Nv05+KaRHJ314N//cU3INJ1+6tbG2634kd4dpmlpjtOszk+d345Rd/SSS5fEsS/pFXL58eSudHkI5X2gf7PrO0F6Q8zFjklx7aT2gfbbrGURjIaG1lPYOqu8yTsi1Lds247fb9eK4TrZN+k1RW5O3SfY17YNnz57dSlNfdX0bE/IcomethMpDY3N5f5oH3ZiB4leqW9L1uUm6azbF+zT21zwyjzpOfrJdv6oO3XFIvmu0f3X9lXKPIb/THGtrzx5r7eYXQiIiIiIiIiIie4YvhERERERERERE9gxfCImIiIiIiIiI7BknwkNomqYtDVxXp5ukni413Unq7RLSDyakb19q1El/numsW+rd8zjpYsmvKcuemuVMpyabjnf19Em3vt38su+zv7reKcvyZd2zr/PeXQ0zaXJJg7umQz0qv/QESr+rbAvSw+fY7noU5fXUXqRpJk+j7K9sv2X+pNcm76tse/JxSfL+2VYJ6e2zb8nHgfyZaGySp0jS9RwhP4CENOFrkB8Uef7Qvanseb+ch5Rfx8tg171d7g5jjK1xQPsqeZ3Qepbk+kOeXXS8sz7QPkMxV6bTJya97fJ8WiupLuS1QX5qtD4Q5GlGHkJU3yxvxhXd8i/bO/uKfE6SXPuor7q+Ml3PTPKPyvJm32R8Tl4p9KxFPjU5F/L+3XibPJCW61K2LT0L5digtqXr8/zuPk0+OORpmdDzQ5a364WYkK9cN1YgP6y18lB83R139NxJY4PGwtq4Pqo8t3vM6ExEREREREREZM/whZCIiIiIiIiIyJ7hCyERERERERERkT3jRHgIJeQHQTpc8vogrSTpBak85Lmw1M2S7wl52JAONctOx1N3mtrGs2fPbqXTNyavJ8+grsaa+qKjGz3qfl0vEtLxrmnASYNMPjKkL6frSR+f16c+PfsivRIy/6wveShRW3d9LmgupA43xx75/mR6zecn24J8zrIvuhpmamtaw8gjiNaNPJ5jidaFhLwXqO+SXf25kqzPsn3JIyMhbwDSu9Ma1dXe07xZK58eQieT9BBKcgzRGMy9jPaCHJPkdUL+Fsla/oeHh1vHcm3MvClG63p7kB8b7Uvk+0K+NuTl0fGnOCq/XWKkKh5L5EO55iuTbZ1rW/oV0VqYdaWYphtzZF3JSyvbLsdK1p/85DK/jPnIvyrJ9qG9LMnjWb58PlmOjTNnzmwdo3gxybZMqO/yevIqo7FD8Xzmn89yXZ8cqn/ej2I8iu+760onBsx7ZZrWZFpjac0lP1aC1p21e28da91VREREREREREQe8/hCSERERERERERkz/CFkIiIiIiIiIjInnEiPITGGFu6NvIGSb1c6u/y/NQSUn7kH0E64rxfakGX+r6uXv2RRx5ZPU66z4S0jeQBlPnn9am1pPt39emkKyU9fddjKdsj+5r8Apb557jNvMkfisb1rn3f9RhKnS21fdYn9erJmhfAUfntui5k/uRbkenMf9me5NnTnTc0L8ljg7T75AlE3gbkP0XlyfZKqK/Ip4c8gWgs0Vxb9m93zaK+Jf07zUPyNOqOhTXPET2ETibTNG2tV+Thk2OA1iPaF8kPg/YO8o3JOGuZzrpmjHX58uXV4xQ/0nxKL5OuTwytRbkvd70IKcajdNcXJyFfGuqPtb2U1m3y+qO1l7yxMuajtZXi66xP+sTk/c6dO7eVpn0wPZVoXmZ+9LyS11PcQj6c2V/L9ujGj7Tm5fHsm7xfnk9rGD0vUPmz7TOdYyGhGC7zIygWII+ghNa15XF6Dsy2pTWeytJ9Ls+2zrFE605ev6zvWixsdCYiIiIiIiIismf4QkhEREREREREZM/whZCIiIiIiIiIyJ5xIjyEqrY1eakRJv0d+biQ5joh7ShppDOdOtYrV67c8limU5dJ2kTSOpI/RWpyT58+vXqc0qSJJo01eX8kpOFOaGyQfp3G6poXA2mOyTOIyOup7TNN45g0xeSJlNd3vbyo/WhdIO8I8m7JuZn3z+PL+5MHT0L6cNLD01gg3xkqL/lYkIcIlSfzI+8E8hzale463PHOIe08eRnk+aR3p/snXQ+RZVvR3iuPDtM0bY0b8n3JMXbhwoXV87u+VzS/aA6Qn9vyeK7Ty/is6uZ9gbw+siy5D+b5ufaRrwzdL9fGzK/r30b3p/ic7kdxDR2nmHNtPcy2ybHQ9Q4h/1Dax7Pvuj6NFEdkPE9eJQmNBfIAopiM5lqWn+ZSpteeJ2geUqxP8XTX75X6IscCxTzd8lIc0fWzSigmTbrPZmvPfuQhlGUhT72sez4rUV8m9OyUdHwm1/rFL4RERERERERERPYMXwiJiIiIiIiIiOwZvhASEREREREREdkzToSHUOrXU+OW+jnSk5N2kfweKD/yIqH0Us9HPi2psyQNM9Wd9OWkRczzz5w5s5UmPwnyCiHvga6nD3kNkHdKQuXv+n0sIb+obtskWbYse449mgd5PPXoXX0+aazJI4j0+6QxJg8l6tv0nrjvvvvqVmTdSP9NfUe+arTG0byn+5PXF3mQZH6kue764hC7+mRQ+65BPmdJ7ifkaUJlp3nQ8T86Kv/j7is5fqZp2lr/r169unWcvDwODw+30uTdQZ5jXX8J2tczvfQquXz58taxTOc+kun08qC653zK63Pto7U6yeOUX7ZdXk/xcpJ9mfXL6zPuIM8g6uvOXkfee3Qv8lmhvqOx0Y2vydOT+jrnddKNp3MuUd9THJPpXHcy5sz6LNc1mrfUlzROc2yQJxHF91leiq8zf/IopTiEPDS7a3qSc5H8oHLsUH8t1/zuMz95BFFb53FqK4qXk46Pmx5CIiIiIiIiIiJyA18IiYiIiIiIiIjsGb4QEhERERERERHZM06Eh1DVtkYu9XOpLUx9Xlfj3PU0IC3kmj696ma94bI+pKsk/wbyEiE9OPm0dP2Zdr2edLzU1l2fnSxfprt+HeS/sSx/t+/JV4V0ql0dKum7SR+fnkKp5yYdbdY3NdTZNzkWsjzpi0Fjq6sLzvZN/fz58+dv/P/UX2ddsu3yOM0j8kNKjnvNzPvRvE49PHk5UN/RupPlI186WodpXVmWj+YhHae2pLJ2PYLIZ432Xxqr8ugzTdPWGkO+jbmW5ninMUZ7G8URmc71lPzxlmnaN9PH5ezZs6v3Im+QXNspnu36MuY+SWtbsqsfHB2nOCIh30byAVqL3/Na2mcovk66npQ0FsjrjzyBcuzR8wH50S1jmir220q6MWpCcc3DDz+8lV6Wl/xIycMm5xmNc9q3s+9p36R4mTyPqO26+3Z3naK5RzFg91lseZzyojTFROQ3Su8QyKur+5y7bHs9hERERERERERE5Aa+EBIRERERERER2TN8ISQiIiIiIiIismecCA+haZq2NHSpbSQtYR5PbSdplJPUA6Y3CenTSc++THd1muTlQRrc1JmmrjPbKr09SMNMmmM6n7wISPeakIabyks6WPIBWtPXk/aefBZI50o62a53AWmuyfsr9euZf3o1kEdPzkvSFHd1w+RNkPnlunPlypVblof6sqOHPoqu586a5vio+1Nb0tjN47lmku8FzdusH2m6qb1zLJLGe21u0Dzv7gm0JtGa2tWjd8urh9DJJ2Mwmt855nItzriB/DG6ewmtn8na2p5rT9dPouuPRGtFQmtzzu/ch+j8hNaHrp8ctVc3PyoPlX/NKyvpxn9dz8zMP/suPYCob/M4+VXRPMzryTcn88+YjrxTyM8rn7W6MfJyncpxkX5I5B1LvjFZFvK4zPzz+jye19M6RusQxed5fo7NjHdzLlAM2PXZyfpS3LKc6+R/Sn2bPnLpqUfznupK+2E3vba3L/ELIRERERERERGRPcMXQiIiIiIiIiIie8aJkIxdu3Zt63M6+vyXPp2jnwAmeQD9JHQez88YSeqyvD9JG0g+1/1pxCTvl+fTp9/Utl25AUk/6KcW6XNh+nyafpqRfv6bPgNd++nDrgyo+9nhrj+9TT/FnfMixxJ91tgdW/TZJ80t+vnaLE/Oc+q//Ix0mV+2fbZVfl5MdSU5Hc2zhMY1zbOEZAO0bpCsoispo7GQ+eXY6MpIluWh/Y1ktSRz6P5s9HFL2Lo/ay8ng+UcyPWIYiBae1NeQGt53i/zp5iLjq+VhWKirHt3nyQpSqZTypJtmfKFjNm6a+GuP09NcgeSjnTlzZlfjh36eesl3X2EYjCSnKVMJ/syj3ctF2hfTXIs5DyivSDrl2OxK4/sStVTtpRzZdk+XdkpxYvd50waG7Rvkgw+IWkr2V9kfbKtKWbrxpC0DtCz2Nq62pXB5jim/YXKTvvhrrLctXXBn50XEREREREREZEb+EJIRERERERERGTP8IWQiIiIiIiIiMiecSI8hKq2NXZdrSF5KqT+L+lqkEkvSJrs5XHyecm2oHuTfp1+CpDY9SeGu14iWd7sy66XSfdnQ/P8hHSza/Xten2Q7wlBXiFd74Osa/YNeSOQnxNpqmksp9dD1j91wOQVQfr9zjrV/clS+rlYyo98LhIae3T/7Dvy/so0+VvROkJeBeQJ1F13yU9qmaa6ku8Z1ZXqktDP0lNf0zyQk88YY2uc0FpN/my5ltLPQydrPxN/FLmWHx4ebqXTU20t3kzflsyb6PpTUMxHPytPP03e9Wej+b6rzwvtFRT/Z3mzb2ntX/b9rp48VBeaF9l3GUNRXya7enTSvKWxmvXJsZD1o7FIc4k8VvNn75cxHf0M+8WLF7fS586d20rTmkTxdsaX9BxK+z7Fu9kWSfYd7ePkI5flzXmaUN+Tjx2dv+bdS/EwefPSmpM+cNQWCcXzFB/fbkzmF0IiIiIiIiIiInuGL4RERERERERERPYMXwiJiIiIiIiIiOwZJ8ZDaKmBW9P+5blVN2svyf+BvELy/NSQk8cQeQ4t6XrykMY306nZJZ0n+dSQHp40x5kf1T/LSxribB/yWEpI757phM5f1r+rvc+yk3cIeYmQDpV8VtJrIfs2y5fpri8OeaEkpLnu+uok5OF09erVrfSyfqS9Jy+DhLT95ANBencqD92P+jLbiuZ93p/WpRy7qeEmvX7mn31PY21Zn6xbavvpXgmtsWtlOep68kPqroHy2GBtfcr50fUIovWB/OnWPICOSmfMtuY3QTEI7bsEtQV5huU+m+tF5k97Be3rSfY1nU/rB/nskDdI0vW3WuZHayXFw7QvZttlX5EvYjdGoXmR5el6JFGckHEMefwkmT89m1F/HRwcbKWXc4nmdc47elbq+onms1mucbQu0TyjeUV+rOTjmOsQ9TXFbFk+8pHLvs25dfny5a30cuzTuM28Mj6leJHiW4q5Ejo/77e2X67dy8hNRERERERERGTP8IWQiIiIiIiIiMie4QshEREREREREZE948R4CC01buTH0NXt5vGubpj07KlXJy+YNW0oaetT90ltkWnybUnyOJ2fbUuaaWorup78KkjvT9rPLA/p6bP+a/lR2WmcJtRWCXkOETnuySsgId+IrtcC3Z801Hk825P8A65cubKVvnDhwi2vp3HUrTvp07t6d+o78iAiX4vUYKeGO9dY2hOoPrTO0BpO3gw0VpbHM++8d7ZVd42h/S6hsZF0/abo/nLyobU2IW8U8qdIyPMr0x3PoZx/RHqL5PW59pLfWu4FSeaXUAzT8dQ5Kk3zm9ZmijOyfbK+FP+nP0eytvd0xxWVndY6aks6P8nrya+K4mvKP8mxSz48tE9medMnprvvr607NO5p36VnmRw7SdeXkPyjKGak51B6liSP0a6/bMbbmd/h4eHq+eQvlfH42ljuevTQftj1P815kXWjeUzj/naf7fxCSERERERERERkz/CFkIiIiIiIiIjInnHHL4TGGG87xvjBMcbLxxg/P8b4i5v//pQxxr8eY/yXzf8++fiKKyIiIiIiIiIiu7KLh9Cbquozp2l62RjjfFW9dIzxr6vqE6vqB6Zp+ltjjOdX1fOr6nM6GaeWMvV0efzMmTNb6a5uNrWGeT/So5OWck2P2NWNkj9D3ivPJ68R0hQnXQ+jrF/HX+mo87v6efLlIf18t73X+ou0/DTOcpxSX2RdSaeaadK/Z/7k65D5pd6cdLDkJ0V6/9S/59jPNPkJkD/Xmgac6kKePDSPSGvfLTv1RULn51gkj6WE1gHyiuj6yHX9B5Zpyot838hzhPYMGtcJeYB010g5mSz7kXxhkhxjNEbz/K53S/rGpI9PlndtznW983L+UNuQp2X6p+U+SPtUQusLeY7lWkz+dgn5a9A+n32Z9c/jud5kfjlWlt4kmRftC+QTQ/EuHe96du4aA1EMmPl192WKW7J9uzEZ+WNl/yyvz7rnc+DZs2dX8066voUJHSfPTtqXyTeRYkLypaSx2vVxTP+oPJ7lo+eLZX9TWbIt7rvvvtV70/6X45S8gOk5NucRxZC3yx1/ITRN06unaXrZ5v9frqpfqKqnV9VHVtXXb077+qr6E3d6DxEREREREREROX6OxUNojPFgVb1nVf1EVT1tmqZXbw69pqqedhz3EBERERERERGR42HnF0JjjHNV9c+q6tOnabq0PDbN3zEd+b34GONTxxgvGWO8JD8NExEREZG7gzGYiIiIVO3mIVRjjCfU/DLom6dp+s7Nf37tGONtpml69RjjbarqN466dpqmF1bVC6uqHnzwwWmpuUstJ3l/kHcJaS0zTb4zqT3N+6V2cc1DgTxryFeGfGgS0uSS5pe0juStQZrpvB/5QZF3CXmnkDfKcXqHUNlIv07jmKC2p7GT16cOltqS5hmd3x3rCenzydMo5z2NjazfUgPd9VWgvqN5QPM4oXmTx1NvT74aa9r+qpv7Otun6zlC85p8b8g3pONV1l2DyW+F6prXpxa/O7bI9667rsijwzIGe+Yznzkt+436jPzmEvIKoX01x/za2nrU/dbiBppfOb7zeO6DmU6PS/K87M4n6ivyJOv6ItLeRND6kZ5KtJ7l9ZcvX16935qPI42bhPY5mhddv7VdPTcpHifvsJy31PfdOIXmQvd5JFnWJ8/NeZ1rCsUkVNeMHwlaA7t1T7p+VfT8QV6H6fFz6dLW9yM3eQZlmtoj191keX+qC/mQ0f639Cmrqjp37txWOud918OTnjvXfI3X1phdfmVsVNXXVtUvTNP05YtDL66q523+//Oq6rvv9B4iIiIiIiIiInL87PKF0PtX1SdU1c+NMX56898+r6r+VlX90zHGJ1fVr1bVx+xWRBEREREREREROU7u+IXQNE0/UlW3+sbsg+80XxERERERERERubvs5CF0XFy7du0mfeES0iynljNJvR5pRVPrSdpF8g7J48v8Sb+eZetq+0lXSWRbk2fQrpB+nnSuCWmmE/IoIv09aVPXvAu6HjukI039d5Y95w3VNX1iSENNutxu/btjgXxwuh5MWX7ysVnzocl5mW2deZG2PvXaXZ+XTFPbJ+S7keVPTXbeLzXX1JdJ13ci88/y0h5E+v7l/kbae/JnIr+l3DOyLDlWaF5219xED6GTzzRNW2OU1hfyNEgfGForcz0gzyDyNOt4/2W8RzFZtkWuFd19MPNLaN9OuvsieZGQNyD5X5AXIuVPexONjTW6XnRUF4rJyCOT4n/aKxJqu65vJHnrdedhliehsUHk/daeh3INSo+b8+fPb6VpHnafPWjdID/VhGI+8hJM6Hli7Tn3qOuzPN3n7uzLrE+yHEs0r7Ms5GGZvnDkB9v1WyUPy139Za9zLD87LyIiIiIiIiIijx18ISQiIiIiIiIismf4QkhEREREREREZM84ER5CVduaN/IySc016XhTz5f6QEqTHpA0y1mfpdYx60JaRvLm6HpjkN48z0/Im4P6pqt331X/nprl1A1n+cnXhrxE1spP55LONe9NGuCcR+RlkLpZ0r+Txw6NlS6kic77p08Z6Xq7adLjr2mYcxxmXyXdtuwe73rmUPly3nTnKc0N8i6gdZTq223vNT18zrMrV65spe+///7VvLv7De2X3bp11+RdPYjkLcNyDtBe0fXcunjx4lY6x3DO91zLc33MNHmV5Hq1LB/5mtDakP4R6W2Rx3M+Zn55Pe3z3ZgooevJs6x7fjduIE808tnM/lzGARQTUJr6kryw6Fkn2ybHRh6nZxda27vPE3Sc9mmC9hpal9bK0/UT7Za9691HHjzk2bP2nHnU9ZnO8zP+p7GbcyPnFvlFZXuR/y3lt/asm3Wn50BaBw4PD7fSmT/5P3U87466vhPDrcVjfiEkIiIiIiIiIrJn+EJIRERERERERGTP8IWQiIiIiIiIiMiecWI8hJaQV0fq8VJ/R54F5CmUesA8n7Sl5JexLF+WPfNOHSX5znT15aQ3J/06+dCQFpLKS+Wntqe+yuPdviUd65qXAem9M2/SmWZfpI719OnTq8ez7zOdmuIcm9l2Xc8h6lvydcn2TG8W8qVJdvU+yeuX/UcaYMqLPHO6bUl0PXZonuQ6QWtsjrUcS6kBp77O63Nu0NwkD6g134wsG92b2o58JXb1IOn6qmX55LHBchxQDJN9nHvJQw89tHp9jtH0m6D1ZM2j66jrs3zLMd31V8u80iOI9uFujJXzm/yX6P45fxPKP/OjOIW8D7PvKcYk3x8qz9IrhcpK8yDJcUh+UDmPyGcm6fpdJVkf8pmhsUv5U4ybx+l5h/z01p5vaF6T78uaL9ntkOdn/rTPp+cPjS2Kd3MdI78sevbJ8ua8pbmSfUfPahSnLPsr75XPNuTXlFC8nXXP+5PHJcVcyVrb6iEkIiIiIiIiIiI38IWQiIiIiIiIiMie4QshEREREREREZE940R4CI0xtjR7qd8jLST5vqQn0MHBwVb60qVLW+nUZpLvDukHk6X+jzTJqTMlf4lsi9SFpu6V9Ouk48y+Iu0/tV1COlE6nzyJyOeGdLoJ1X95/yxr3ov07FmXrv47dbPpZZI6V/JxSZ1slpd0sTT2yG+LNN5dr7EulN+aRwetadSW3XlB85j08Hk9+VvlWMvykkdIQuXLsUlrckLrJHmY5FxaXt/1yMtxlWs6+cDR/tSdB+StsHb+cc85OR6madpav3P8Up/nmMoxmn5utBbn/KK4p+ulsuYlkmU5e/bs6nHyUSRPLvKZ6fo+7rr20V5Da3OS12e8nfWn8tL6Rl6CS783GlfdmIViroT2MYqBEvL2on0+79f1dSHW/Jyq2LuPPJ+Stf6gedL1l+r6n1LbdZ/9iO6zVJY/xxbFQDS2yOOUvAmpP9bW/BxXFy5cqDW6nj7k60ht1+2bbFuKt2+FXwiJiIiIiIiIiOwZvhASEREREREREdkzfCEkIiIiIiIiIrJnnAgPoaptDRxpLckrJPV8eTzTpP0kbWJCfiBLPWHX+4P04qR1PHfu3FY6tY6p08zjpFslnxfSRKfWsqt/J78M0mqmtpN0ql3/qGV7kA6V2pK8SKgvyYsgdaikuc77UX1SQ5yQxprmNbUvXd8dCwlpzNfKSpCHEGmMyaeC8kto3mb90m+KPIk6/kxHnZ9jszu2aB1Z8wzK+3V9HRKaR10Pjq5+vev9tba3y8ng1KlTW145tJZnn9PamHtJ+rqQx0J3rc/88v5Lcj7ktTm3aa4vPWqqbm5L8jei+UtrEXmfkK8N3T89OM+fP7+VTl+YJOufewH50SV5P3peWOZP53Y9NmnfoXFO8TvFXBRTkT8r+WNRTJXly75Z83OqunksZJr8q8jDc5mmfbbrtUcxBfmt0nMvjUXqi5xXlF+uE+RXS/E+xRk0lvP5JccSxePL/s7nYPLc6cZoWZfcr7p0fevWxtraOPYLIRERERERERGRPcMXQiIiIiIiIiIie4YvhERERERERERE9owT4SF06tSpLQ1f6t9Sh0oaadJYZ36kV08t5S6+MXn9msa1iv0mMp3axzNnzmylyYOIdLLdtiCdKulkyS+K7k8a8RwL1B9df4+18uSxzJvqmm2V1+fYSJ1sanIzTV4jpGvtapBJL08a5ZzHdH72PaV3HXvL9si2Ij05+UyQxwf5RZGvBdWd8icvMep7Ok7lp/qQr0aen3OL1rFl/tT2NC+WXi9HlS3z7+rX6Xrq6zX/p64nnLzlWI5Z8p+g+UYxVR7PNK0P3X15bUynJxDtazSGc37m9eQB1vUqyfyS7r6cUF9cunRpK00eZFle8pPK9s6xRPv0mu9O1xuPykb7Ou3j5BuZYyXnTY5liukohqTy0TxPj6D0n8rjORYoPk+yvTLmXdav66tIz5U0lsh/tOtN270feRzRsxuVv5sfje3sO1qXqb7LuZDzIu+VZc+y0ZpL3mBd/ycir+8+198oV+uuIiIiIiIiIiLymMcXQiIiIiIiIiIie4YvhERERERERERE9owT4SFUta0HJC0g6VrJK4S0jF1vEGJN305aRSoreQIlpCHOupOnEbUV6c/p+uzL1CiTzvSRRx5ZTWf98niWlzTW5FWyrE9XH01eANk3eXypnT8qnbravF/qYknnmn2R5UtIo0xjIcuXfZnHu+3f1ZSvtX+2BWn9yTeC1o2EPIjIE4c8i7r+UV1/JtJk07qT7ZXly/6g83fRcFPeNM/oXjnPybOI7k9+LZS/nDxOnTq15T9CnkA5xnL+5pijtTi9RDKdazWNSRqjy7qm70py4cKFrTTtU7l20FqUbUVrZeaXMRCtpZQf3T/Xq+wr2jvIdybTtFdkebM8a3tVtj3Fz+RnRL6FOTao7lmeHKtdT0/aRxOKx7Ots2/TBZRQ+AAAIABJREFUM+jixYtb6cPDw9X8uzFm9udaXEDzNtu66/VHY6n7nNvt22wLuj/FhOTbRjEfjb08nu1PMSjFGWsenuTzluP4vvvu20rnPKDnRCorrcmUX+63NBau4xdCIiIiIiIiIiJ7hi+ERERERERERET2DF8IiYiIiIiIiIjsGSfCQ2iMsarvI91unk8ab9Je5vmpiyV9esf/IvMiL47UAqae+8yZM6tlJV1m6mipbUjrmOUnjXTXg6iraSb9emqaSddLOto1jXmWPcuWdDSyVayPp3mWbU8a4YR8XkhvT+2TOtn0mSDfCmpPWlfIR2dtnemucaRHJz076d9Jq5+QHpzWiW75qG3Ja43W/KR7nHx6lvWncZ5rMO0J5IVFa2y37ck/hu4nJ5NlP2cf5xghL78ck3l+Hs99+8qVK1vprq9VxkVr6azr2bNnaw2K/8iXJec3He/uO11fmVx/yLsvY6QsT/YtrcV5fu7TXU+1y5cvb6XJ52dJd5/M4zRuKaaisUM+Lnn9rjEg9S3FYDlWKN31m8vnmVyHci4tn49yTaC2ojT1RULzmHwhyVOT1o2k6y+75otYxXPlbvtErvnzZl7kKZR7Qo77tXiv6uZxfv78+a10rnlUV2LNd21t/fQLIRERERERERGRPcMXQiIiIiIiIiIie4YvhERERERERERE9owT4SFUta1rIy1haj1Tz5daydTTka6XNMukhSS/j7XjqRtNjSxpHTPv1NBm22U6Id8X0qWSN0qmsy/J1+bixYtbadKjZ/seHBxspVOXS/4cSebfaS8aV6SR7XplZV2prKSZzr6k8iddvXr6TpBPBXm3JFlf6h9KL9uL2obWsKTrqUN1oXWFxhb5Lx03u/pBUX3IWyHbb23PorJQX9G4pb6nPSShsUGeQ8s1WD+hk8mpU6e2Yo1ca8k/g2KkHBOZf9f/gvaiZM3zi/wZcr7R2piQJ0+2BXlnUEySbZ/+F+RXQfskrYXZl+QZlOeT72PX5ybTa2OFYqzuswWVLcdGxo+0r3THXjeez7FJnkHpoZRp2rtonpNHKj0/LZ+HyD+J1jDy6kooRkvIU7S7l1Lf07pAz4prvjVVN/c9rTtdzyJi2d/klUtrVpY967p276Pyp2cN8hSiZ8FlX+shJCIiIiIiIiIiN/CFkIiIiIiIiIjInuELIRERERERERGRPeNEeAiNMbb0iamfS70feSSQDw35P5DuljwVMr12fmpc77333q10agtTx5l55/VdnSZpHTNN/k2ZTk1xan5Jf57p1L1me2b903eGPIMyf9J2JmveCN22J98Z8n3ItqY0eQfkWMzrybOIvBXIEyj7hs4n3xia59Q+5CW2vD7v1fV7yrxJH07jlO5H3grkFZaQ9wJB5aE1mXx5yMenq+nOubl2LvUt7VekRyf/F/JBS/L8vN/y+K4+AHL3WNubut54tB7k3kE+LxkDJrRXro15mqsZU9Fakfei87Ntun5xNN/JA6kb71LfZoyX19O+TD6TtLeQT82y/Dmus2wJ7Qs0zunZI/sm+4JixKx7emRmebrx/eHh4VY6+zrja4rZ8n4517J/8nj6Y6XHUD4PLOdK17OG5tGuvokUE9I8oRivez+qP8Vw1PcUs9K6Sl5uOdaX51P8SH5H5MtIa2hC8zrbhuLZO8UvhERERERERERE9gxfCImIiIiIiIiI7Bm+EBIRERERERER2TNOhIdQ1bZGjrSQpHkm/V4eJ08F0iST9jT1fkttY7cuqYskvwnSSqbOM48n5E2SmuPUjXZ9XLLtsrxZ/9Q4U/26XimkcyVd7fJ49n32bTdv8tQh/ybyBCIPo7yePIFIr57procQ9VVX19vxpTjq+DK/PHdXDTB5GWRZyBeiq3+ndSOhedb1mumu4VS/rmdSsrYHdP2ISGufdU+6niBUN9L2r+nd9RA6uSz7hnxfku4YW/OZquK1l9ba9Bxa82jIuuW1eZzivVx7yV8i5wt5m+T1WT7yiUnWvDaqqi5fvrya36VLl7bSuS9T3JG+M3l/8lii9St9ZJbtnX3d9fIjyPuP/JCyfHk+jY1u2+X52TfkGUQxWbZHjoXsqwsXLmyl0zMo01T/Zbob61M8nHT3cYL8p+j8hOJ5GmsJxVjkeUrxOa3THX+t7nM2rQP0HNv1AuvG57TG5/1uhV8IiYiIiIiIiIjsGb4QEhERERERERHZM3whJCIiIiIiIiKyZ5wYD6E1nwHSVJNWc1ctZ1ffRx5Iy/PJE4iOk69MloU8c6gtyBcm0+QhlKQuNOub+aWutOtNkvmRl0tXE07+HkuyrKn7TH+kNa+Oqpu1/eQ9knps8kLo6mqzrckjKOtLY4s00KkBz/rfe++9q+fTOtLxaujqv8kfKcn8s627nh90P2oLmoeUzr6gvqY1mPTqXS+2jkcKrcnd/azrt0J9QV4FdH6yPN71mpK3DNM0bfVTjkla+9NLpLt30PpGY7y7ni7vTz6IGYORByXNb/LSS6gtKEaitSvrSzFN7stdf7YsH/laku9MHs99POuzvD77lnxV0kOH9p2sa9dLhPzZumtzevSQ52cez74nHxhqn4w5z58/v5V+4IEHttLUP+StsuYdRs8S2Vf5rJLjjnxkOn6jR5HzgOYFjb1cw+n6ru8leQgRXf/ZtRgv50G37brn07gluvHyncZZfiEkIiIiIiIiIrJn+EJIRERERERERGTP8IWQiIiIiIiIiMiecSI8hMYYW1pZ8qNIrSd5IpCWk7SbpP/r+tYs75fXkvcFeYEkqeHNtkvyOOnBSe/d9SIhDTTlT94F5DVAXiCZX1dbutSs57GE9Nc0L8hPKjXPqY8nz50k9fipR8/8U7Oc5x8cHGylyZOIvB2yfKTBTn07eSvQ2FqbqznOut5WXX11Qppj0khn2xKkgc55ltA8pDWY2pP2jIR8RJb3X/O0qOI1s+v509HWH0XHl+Go/JfH9RA6mZw6dWprP8gxSjEX9WteT54Hu+7TtDYv60d7POWVa3XOZ5p/ubaSf1P3+rW6V/V9FikmI8+jrmcaebOQx1Pmv/R+oXGYZU3vkfTUoRiE+irHWvZ1xiTUV2fOnNlKkydQXp8xGp1P+2r67pCHEO3z5EuT5Vu2b7Y19U2SdaFns84+edT15KNI+ZEfFvnLZpr8tzI/8g6jmI/matanGwMvybZOaA1LunFPN56n+98ufiEkIiIiIiIiIrJn+EJIRERERERERGTP8IWQiIiIiIiIiMiecSI8hKq2NXLkq5O6XPJK6fpVkH4vyfNTy5jlXeqQ89rUoWbZyMeFdLCk+SUdKflXkB8T9Q3dr6MDPep6gnxiyP8jj2d7L/Xw2dfZdnlv0rWSXxT5wNBYIK+TvF+WJ+ub+vQ8Tr4VWV7ycUnNN3kI0bqR3gZUvjXvMJonXX03+U+Rvp3StGaSlwD5WpA+naDzu5pv8iKj9ur4xtEaR/cmj7yE5k23vGv75XFp3eV4maZpa73N9SMh3xhK51q/a0xGe1veb7m+5LnpXUceNTT/6HqC/CrIR4Xi59z3Mmah8lD+5NWS15OXSJ6fPjk5VnKfXvNxzHtnW6SHTvYtxTw0L86dO7eVzrpevny51qD4mvyfMubK+mbMluRcoPg4j1P7ZPle//rXb6WzvdfKl2UlvycqWz735Vgib0CKR7M89JxJ86rrb7Wr7+KucUnXq3ft+YnWjLyW4tHuc22y9qxwVH5dfyS6/418bussERERERERERH5XYMvhERERERERERE9gxfCImIiIiIiIiI7Bkn0kMo9XOp402dKOnjUgtJOlfSp3e1jMlaXfNeCR3vavm7OlnS3Xa9O8h7g/T6mR/pbqm8mV76PVWxDpfqv7w+dayXLl3aSmddU9eafUP+ReQxRG2fenKaV6Snz3SWl3S52Rep8810jo28njTcXb+trP/a2CBPIPLoyXtlW1JdyYeN5h3pwWnNpXWm66tDY4V8Kroa8Jx7a3p+2j+6PmxJty+6+na6fu188uOTR4dr165trRnk4dP1kaK9isYYsaun0JKsO8Wb5N2RUN0T2vdpLaZ9P/fJhGIaijuyPc+ePdu6H42dTKdn0Nq+nnUnj82MBzMmyvPJVzEhT6O8f5af/KPSAyjLk8cphiT/KPIKJC+xbL8sD/lQJsv+yTUjPSazrhT/ZX7ZN12fSFpzkxwrtE7lPMl52X3eoLiAfBgpZut6JK3FvF0vQ/J7opgroTW/+1zc9Xe65Xm3dZaIiIiIiIiIiPyuwRdCIiIiIiIiIiJ7hi+ERERERERERET2jBPhITRN05YGjzyESLtJ2svU/6VWMrWKeT5pyjuabjqXtPykwyTPoF21+3k/0iqSB1Hqbm9X+3ir69OnJyG9eULtTb4zy/pkX2TZL1++vJW+cuXKVjqvz3lCfk2pf+/6P2V5EvIQ6nql5FghX5zs2xwLeT3djzyVaK4t+4fGNem/u2tkpskLICGPENLbk7cX6cupr6i85EeV9aH70Tq5tifQOO+ODYLqQh5A2VbdsdPV68tbnlOnTm3tP924Iud3rke0r5MHEPlrkL/HWkxHMRbdm7zvyFOH5gfFbFm3hPwo0qclvVSy7yjmIy8RWouzPrTe5PW57yfL8pEXSOZ9cHCwlSYvP9onyMsuYyx6dsn8cuxlX2dMljFNevTQXpBjh7wHyXOp6ytDLM/Ptsm6d8ve9bzseu5kW2c6/aVyrGR5aJ6TNxjFLeQLSTEX7RnddeTcuXM3/n/WvftcTfFjQmWlvk+oPMntPtf7hZCIiIiIiIiIyJ7hCyERERERERERkT3DF0IiIiIiIiIiInvGifAQGmNs6R1Jz5fk+aRdJD3fUeVbg7Smax5HqQMlSGtP2kRqG9K9JuSh0/V3ynTen/T3qTvNdGpHycModblZ/oS8Upb5pz6cNMZZ19Rb51gi/Tf1bd4/NcSkgc7ydH0gsi1zbKRXQPZtegadPXt29X6Zf+rrs70zTd4uy+PZNl3NMs3jhMYSrYm7+lxkW5G/E+nZu2O363VG6xJ5KK31D/mddD13un2/q+dPZ5zfzv3kZLDsd/J7yDGb87vryUD7PsU1WZ5kba8iX5KuX0Q35unOJ9pXaZ8nT7GuTyJ57SXk+0KeQDQWaa1e5k9rHfmakJ9pd5/IGCtjlq6vTXoeZUz40EMPbaVzHmd5si8opiNPzYTGNs3V8+fP33b+WRfy6Oy2fTeGI7KuNI+yPksPnaPKk9A8pOvJj5X8ZLsxWl6f5V2OnW7fdveAzjuAo+jGTB1Py7VzjdRERERERERERPYMXwiJiIiIiIiIiOwZvhASEREREREREdkzToSH0DRNW1pV0oenrjWPk641Id+bvB9BnkZr+sC8V2p6k67Okrw3EvKfID16pklXSjpZ0nSnbpTyy/PJu4Q0zeSNsMwv9eGkO83j5BmUbU2+DumZQ2OFPI/IvyrHOmmIsz6ZTo10ti/pz7P+qcEmDyWaC8uxRp4bu3jUHHXvhLyzaFxnOtsq60c+FdmX5A/V9UBKsn7ZnjnPaWxmOq/PublG9iV5ae3qC0fzmOpO83wtbzkZTNO01Y/Up+SHQV4hRF5P3iy7+HVQTELn51wnf7iEPG/In432IYpp8v7ku0jeHbRv01jq+rXt4rlGHp5ZlmyLbOvc9/J88nvKulJ+b3jDG1bzOzw8XM0/96WMcWifz77N9rxw4cJWOtsr70d7W8YFSZZ3zTM0885x311zcl7SGtr1QUzIW4zWwGz7bCt6luqu8cfti0PPdmvP8d2YhnzKup6e5GGUfZHs4lelh5CIiIiIiIiIiNzAF0IiIiIiIiIiInvGzi+ExhiPG2P81Bjjezfptx9j/MQY4xVjjG8bYzyR8hARERERERERkbccx+Eh9Ber6heq6rpY9Muq6u9N0/RPxhhfU1WfXFVfvZbBqVOn6vTp0zfS5JeRulrSYpLejrSh5E9B3ilr59O1lFdCuk7yo0iy7VN7SW1FXiQJefysaYKrWC9PXiXUH5k/tfeaHj7bgvLOdOrDs21yHmWa6ka62K7Gl3xZyLvgzJkzW+n0DMp0np99kfp5WgfIC4H8BtbmWvZF6ru7+vQsa9Yl2z4hjXX2ZfZdQj4XdLy77tA6R5pxWqfI127tevJbIQ8R8jrI8xMaGwl58JFn3nLd0UPoZDJN05aHWq5dXU8FWvtpL8l01ycrWfPNorWB/NlyPtHanWQ8S/ej8uX6QP5w5H1HnmK0l1BcQZ5CXY/Prp/H2rXJ8jmlqurg4GArnfFkxhgUc+U8ybGRHj5Jxnjkp5r5dz0yya8qz8+YLPsm69f1YMryrO3r5M1FMQo9O9FzIsVUSY69PP/8+fNbafK87HrwkNdYQvOO1pldn8vX1k2aB0nOK3pOpLrQfkh+UxS/dvfHG/ne1lm3YIzxjKr6sKr6R5v0qKr/paq+Y3PK11fVn9jlHiIiIiIiIiIicrzsKhn7f6rqL1fV9ddT91fVw9M0XX/99aqqevpRF44xPnWM8ZIxxksuXry4YzFERERE5HZYxmCXL19+tIsjIiIijxJ3/EJojPHhVfUb0zS99E6un6bphdM0vc80Te9z33333WkxRERERKTBMgZLuYGIiIjsD7t4CL1/Vf2vY4wPrap7a/YQ+oqqetIY4/Gbr4SeUVX/nTKapmlLo9fV4+X5pLkmDwfS9yVdX4Tl/UiLT7pWaivSqXb9kUibSFrH1INnOq9PDTHp4fN+qenO+yVdL4Muy/LtqkPNtsi6kScReXFlmuYFnU9jLctLnkBnz55dTZO3WEI64qQ7lpfpbDvSKCe0TqR+POcR5Uf6boI8ebo+NjTP8/pdfd7o/jR213zw1jx2jiprQp4ku46d7n5J++cyTWWXR4cxxtYYJh9H8iqhvaLj93A753f3mmWaYpg83vVryDT5unTXIooDqG3JO4RiHvLByXR6oWR503eH/PG66+cS6itaKxPqm9yHl75dVTf3RcYkeT6NVerbbnyffZcxWabJhyfbM/Mn38ssH8VwHf/W7ppE8XlC11OMRM8y5ClE+3bXW5DWyV19H3Ms59gglmOju0bnvKN1IcuWbZ90fdjoPcDaHrW2ht3xF0LTNH3uNE3PmKbpwar62Kr6t9M0fXxV/WBVffTmtOdV1Xff6T1EREREREREROT42fln54/gc6rqL40xXlGzp9DX3oV7iIiIiIiIiIjIHXIcPztf0zT9UFX90Ob//3JV/eHjyFdERERERERERI6fY3khtCvTNK1qO0m7mZDmmTTjqWFOXWrqCyn/Nb1h5p3nEqR7Tbq6WdJaUl3Jlya1k910arJJU026U/KdIZ0ttcdybJKGN8ch+TYk5AWQulTyjcm2Jq+RvD77Lq9PD6C834ULF7bS5A9FmmvSBXd9Y2jsLNuju0Z1vQGorck74ODgYPV6ahvSg2ffZVtm+fN4d48gvXvWh3xzyKdibR0gLT+NDfLu6vYVzVtaQ2kdW+bf9aKStwzXrl3bWg+zT3MvIp8YGjPkN0GeDLQ+JmtxBM118ubL67trEfnS0Nrf9Tqh+1P9ct/MtTx9YMgLhPaOXL/IjyOvXxtbXe888sai67Nsefzw8HA1v7x/xkjkr9T19su+zRgtPYPyfOrr7tjNdYjOz/qv+aQl9KxB+2RC1yfkM5n5kX8S+dTkPKfzuzFY99mQ5lKOtaz/WpyUbUmeeRSjUUy363N2zvuMz5O1mG9t3N0NyZiIiIiIiIiIiJxgfCEkIiIiIiIiIrJn+EJIRERERERERGTPOBEeQmOMLc0baaZJJ7urPi/1gZQmr5a18pOXB5U1Ib04eQyRxpj07qQTpb4hDXPq27uaZNKA5/EsP7UPtdeyP0iHmuM6NbKZJt1q188p6foz0VjLvs506tVJ0036fPJeybF25cqVrTT5ZiRrc4XmEc2bpDsPdk2vafOr2HeDytv1CKH8aF0l356ExlaOpY6PDvlBUVt0fRlo3SDfOdoP1/Z2ORlM07S1n+zqG5XQvkoeDnk9eTLQvr3Mn/zOKJ3X575BXiXZ1l2fLVqLuvll26cn0Pnz57fS1NbZPknGPd31KqG4ZjmWuuOIfBNpnOb5uU8kVL6sW9f7jmKq7GvyhyJvvsyfypsxbnoWpecS+U8t5yI9Z5JH5doef1Sa1sw1r9ks+1Hp7rNI15eR+iLbh3zlsr1p3tLzUMdTNfNKb6pdY7Au1NfdZ481v1M9hERERERERERE5Aa+EBIRERERERER2TN8ISQiIiIiIiIismecCA+haZpWvVVIi9nVn+fxvD41zaRZzvxIE748nzS3Xa0+aRtTC5k6UqpLV2/f1ceTz01C/lLZl+QjQ2ODNNnJmu6XxmXqo9Ob4ODgYCudmtpu35GHUELzjvTvWZ7Up6dGm3S85EXQ1VyTDw2NrY4PTldfTV4G5D2WbZ99lfpw0qNn3TN/8jqgedRdY0mPT14PWV7q24S8xtbY1TMoOe627s5rPYROPhmD0dpG3ny0PtBaSn50tI/T2rtM53jP8Ut5ZVkypkqybdJXJaG1J/OjfZD6MvfdLB+1NXmB0BpAfhnke0Pr3bK9KJ6mcUn+a1mWLGvmn1Bbkn9p93kg+z7jgDye9c3j2b4Zo9LY7PZtsvY8QWsSxUBdDx7yKyXfmPS5oTWaxlYepxiR+oI8lrr+q92+zfqsPcvRs1LXW5bicYqZEvKLov12zSNTDyEREREREREREbmBL4RERERERERERPYMXwiJiIiIiIiIiOwZJ8JDqGpbE0faytRS5vmkDSVda96PdLoJaSOXer/UXWY6y5KaX/JvyLLn9ZQmjyBqm9TZUn273inklUJjgXxwuufn8RxbyzTpTPPaHPePPPLIapp8FqhtyZuAyk/3P3v27GqaNMxd/f+al0AVz+u8P+mC8/iafp28vWheUtvTvO36SpA+nTTZ3XUn60frHM1bGguZzv7IsdfxWMpxRHrxHJfk9dX1O6L9sZvf2rqih9DJZIyx6tdH8yn97fL8XD9orc31Zc2X6nZY87mhmIJ8FmntJY8dun/Xx7CbP52f98++zL7peihRDElrM7X3mlcj+Y3mOCRfR7o+ybbKdPYN+U5m3TOGzL5L38b0zaGx0d2Lsu8yZqV9PWNg8gZc8+ciT02K9+j67Euax1k38rRc84k5Kn96Nsm+zvLkcRor5DmUUDye7UnP7Wt7VNfjrrsmk6dP108pIQ+hNS9iPYREREREREREROQGvhASEREREREREdkzfCEkIiIiIiIiIrJnnAgPoWmaVj2EUmea2sLUVh4cHKyen7pe8kZJSO9H+vbl8dTgkh6czu96AKUuk7w/KL9MU/nJQ4jalvpiTS9ZdbMWlLSZOVby/NTdrmnMSQ+eZct7k4cNzZNsG5oHVN7Mj7wFUs9Omm3yNiHvsRw7NDbIc4nG4prmnPTo2RakrU9o3mbfZV1y3GZfkddX5t/Vj9M8prFH3gU0dsiboevZtLye5km3rSi/7rgnvXwe7/o5ycljmqatcUf+FOTfln2eMRztRRQHkFdK5r8G5U0eXt21juYj7etU3q5nUPrI5D6dx7sx265+d3l+0o0DlnEO+Y5kTJTjLD2FMv7renwmtNaStx2df+7cua00eQJRfEx91fVGyfYnPyuKI5ZkW3SflTJGIx9IipGyr7LuFy9e3ErTs2LXJ4fWDfIWI6816nsaO12vxTW/qSxLnktly75KaP+h+HbNe/Z2rl/r27V+8AshEREREREREZE9wxdCIiIiIiIiIiJ7hi+ERERERERERET2jBPhIXTt2rUtz4rUTqbmjfToXR0q6e9IS5nk/dc03Lvem3xMUuvY9RCidFfvnuT12TfU1qTRzvxJ70++NZlObWeOzTy+vL47LqmsCelcux5GeX62PWmoz5w5s3qc9PTk/UV6/xyLu/pnkT5/be6Stxa1LZWNvASor9M3ouslRp4gWb5ue9C6kPXLsZD3y76jsU9zIa+nubokvQnIv4V8H8hXobvHUP5rbaGf0MlkjLE1jmj9oPnUHaO0Nu8aw635+tBa0vX8oflF/kuZH62NSR4nvzfydcw01Y/Oz76kNYHW/iw/+Twu0+kdkuM6vfTSM4g8hLIvMwZKaC3O62mfpn2KIK8uetaidYA8iPL8s2fPbqWzvbN91uYmeVXRsxLFu+QDSc8KNC/Pnz+/ml/Oc/IWzLFEz5b0PJL1obiG1nAaa3m/tbiJvMK68WVCMRQ9m3T3N1qzl2k9hERERERERERE5Aa+EBIRERERERER2TNOhGRsmqatz806n3xW9WVKJA3p/qRb95P7NSkPSTO6P0vfTXd/4pQ+8aTP/ro/nUg/l01jh6QbmX/ml59lUnpt7OZngfkZIf1sJH3imufTzzSSNIXGdf4EaH6+m2n6tLz7SSp9gkqfYdKn+t1P59fmZvfnaGleJPQJaVcGlG2X8zz7nn4GuivHy/NJ4kXrEo01koDRWFvLnyQWx/1TvllWkjbSz2zTnrO2xisZO7msSVoTkul0ZYw0hrp7E0lVlmO+K2vpxoMJrYW0z9DPvnfltQTJBylm7Mhlq25ea5NufmvSHJIiUoyWkrJMk1S7Kw+kmCTXdoqZ6NmKJGO071LMRfWjn2rP43S/ZfuQ7CchiRhJ0JKUv3Wf9Q4ODrbS586d20rnWEzJVpYvJWM0NjM/6ltaJ2nd7FpErMX/NO/JzqJjC3PU+fRcSntCQvLC28UvhERERERERERE9gxfCImIiIiIiIiI7Bm+EBIRERERERER2TNOhIdQ/ux8ajvJl4U8F0hD3fWTyPNTv5daxtRmLvWJqWVML46uHpz8Ibo/b92FNNJ0f/o52+5PuJJPDvV912eHdK3L9iAfEhrHNC67PyNP/h7Zd6khJg+hHJsJzeNO2x6VJh8Z+qniHIu5LiWdn37s+sR0ryfPje4ameTPv5L3piYoAAAgAElEQVQPBv2Ea9dzKNM5FrvebNm3tG7Q+cvy0P5FfdH1vtp17FBb0v1onsjJg3xr6CeEcz2gmGpXbynyOlkbw+RTSGtZQvsGXZ9QX2TdaN/rxnjk70ZrMfk7UTqhuICeF5bnkyfQpUuXVtO0dlNfU8xB0LzpehHm+dke5KVHx+mnyClGy2cp8vrL+i7jDvJpIT+jtZ+0v53jef+cx+Sjlh5ECcVYazFK1c19RXsAxXA0r/N43j/3lEzn+Vn/5XF6zsw1pevzRuM+n42yLjT28v4dT721NcMvhERERERERERE9gxfCImIiIiIiIiI7Bm+EBIRERERERER2TNOpIdQ6kAznaR+LjXEpGUkHW3q95I8TtrKZZp0pZk3aZRTN0lax0zT/VJbmW2XbZv5Udtn22VfUvnJ4yehviUtaKbJp2dZ366XCPmgkBdCQn2f45bGVmqQsy+7bU1zg/TzeT+6flf/LPIDWI6VvBe1fY4z8iagsZPpzC/7krwCcixk+XPdIC8AWgdz7uT98/ocizlXqD5ZvoODg9b1y/6jeZBkW9I4o3lBnh3k45Bjg3zdup4p8pZnmqab1pgl5AuT453igu7eR/cj35417xLy+CEvjV33LZrflF/Xf47W5q4H0HF7JGX5ut4jGTMeHh5upZfPGpcvX946dvHixa10Hqe1MmOkXCvJ45O8Qro+iJTuxrfU9xST7upfRcfTVye9WpZxB8VgFL/mvWkfzr6nmCbzo+fKhPyUKGaheU3xNfna5LpDz3K0p2RMtubhRGt4riFZt2w78oPKedT1/qJnr258fyv8QkhEREREREREZM/whZCIiIiIiIiIyJ7hCyERERERERERkT3jRHgIVW1r3sgHhvwsSN9H3iprZati7WlH90qa5CR1pHkv8qcgrT9pG6lvSOeZkO8N6Vq72k7yaklS+0n6dPIQWtaPfBXIHyrrlmXr+id1PYO6mmNKJzQWaexkeboa8IS8FEhjvRwbqTGmeUneYbQOZJrmPXn6UN9TeUlvTmOFvAqoPlTevD716efOndtKP/zww6vlXabT1yDXmJyH3b4n/XiOc1oDyQuA9qTlOqKf0MllOYfI7yGPk7dJzh8aU+RVkvM992HaO9bGYR7rxjTd+Uh1pZiO/NTIL60bEyUUh1B7UHyf5aeYLMda+gItx8rST6iKPYPIy4qeDWjtphgiybbo+q6QB+aaD8tRaWofihsyTZ5M6RlE5y/3qu4+2523mV+2ba6p3TUr912KobrPdjSWu8+OWd8cCzkXr169uppfN45ZljfbJtcQmjdZtq7vHO2n5IFH87Lrg3yjHLd1loiIiIiIiIiI/K7BF0IiIiIiIiIiInuGL4RERERERERERPaME+EhNE3TlkZuV5+arkaZtKR5nNKktVxqP1MH2vWNIc0u6dez7FnWXbWKeX1CGui8X2ov13xaqm7ue/K7yPtn+TI/8q9aG7t5L+qrrHvXO4u8AjKdY4n6PuvTnWeZP9Unyfyob3Ns0tzparTX5lqem2Xp9hXN2xzHSbZNzpMsT2rzydtrV2jdo3WGypf1pbGRmvPMf81bgvKmutH5tB92PfXyft15vpwH5K8nJwNaK2kM0PnksUA+PZlfrkfkP7EsX+4zuW5n3hQT0T5B0FrV9YHseodkecn/gnxyaGyQZ1PeL9OXLl3aSufYyrGwPJ7HyGMn2+LChQtb6fS0oX09ybqlr0o+D+zqvZf1y/vT3kBxCcU5Wb7Tp09vpc+fP7+VzrmY56c/39o+TM8OmaZ4lda4PE77PD2HUsxDey3FAV1/2q4nKHmo5v1ortI6vBZ/57ineJDGMXnh0vXkrZVji9bgnDe3wuhMRERERERERGTP8IWQiIiIiIiIiMie4QshEREREREREZE948R4CC31fuTTQp5CpHslzTJpJ/N88vlJ/eCyPqmBTe0i+ZKQ1wjpRPN80pMnpHfP4+Trkm3Z1d1SfXMs0fXdsdXxUiBNcldzmzpY8ovq3r/btqQJpvKRHp48f7reC6TLpbHe9WpYQnUnX5g8n+rS1f535x21VXdeJ7Qu7ZpfXp/rErG2h3THDc0bKjutYXQ9lYf2z2X+XT8Vectw7dq1Vd8d8mEk38fcm3LM53pFY5bGaMZRa75bNJ7Jr6i7llEMt+v1NJ9p/aB9MfeK9FMjPygaC3n88PBwK53jNH120lNo7XzyzMlxdO7cua107gsZAyUUP2Z+2RfZFrSv0T6fkFcKzQ3yU+3GoBT30P3W0uRZSfemeJLWzITmdeZPfUn7fNcjk+LvhGLChDyDyFss/buyP5frVN7rvvvu20rnmkfevt2Yas3fqIrXRPIOvlP8QkhEREREREREZM/whZCIiIiIiIiIyJ7hCyERERERERERkT3jRHgIXbt27SYd8hLyKun65Pz/7L1f6G1pnpf3rsOQ6TrnVHXPRJBxRoiKCZiryGASAkEygRgJ0QsRvTJGGAImgeQio8nFXAmGBEJCwDBE4wiiGSRBL5KQiSR4paHzB6PG4KCoI6Mj9tSfc05VBrt2LrqmsvZTv3qf861d1fXr3s8DQ9c6a+213vWu99/asz/PzzLXzCpOcqkPnY/lO+cPeS/MJjKvPs2xWuZ36sQxx45lgC0fz6wkHUv8/LQtWK6V5zM3gvmuds/DfBqWSba6JSzb1JFjOVd+fuqFMI+OeW0sp8ttMn0etn93PqtL87KQqWvMvBWW7bdnZ+PQ1NU1HddY/t0YvJaPI5bZ5vWtfs+Yx8Luhdh+Mp3PCD0brFveXzw+vvnNb64XL158vG1jubUJ8z9M+6+5SIiV/3w9c9mZz8HKZvPqzjH5UPk4j5lLz9ZANvbv6u6h/eazszUW97Mt0RHE53Fux2vt12h8VqxLPjtbn9u92Txq85Bt2/ltDUTs8zYO2P3QyWS+Kb4P2Pp7N45M1/ZT75k5c2z9PnUYsW6mflVrO9P37Kkr0e6P+9n3bP19Pp7Phu3MxmRbn9/qhbS2xHHI1nDne9+tRfuFUERERERERETEndEXQhERERERERERd0ZfCEVERERERERE3BmPwiG01nXmzpwD5vKY+jMM5gkt78eMHrOV5/30L1g23lwVdrw5g+zz5gAyl8g0A215en7ecrdsW5bPZ07Ytnm/LP+5Ldm9W9bfPCx8Frw3erssE2xuBCsf64oZYMvFWi6XdU2svJZB5uft+F15bQyxc5nfifdm44bdGzEHiDH1XBDWj3lAyPR6bJsvX7682uY4zra9c5ZY2Thm2bOZ1p3NV8TGhV3bnvqN4tvDhx9+uF69evXxts0tNh6Zc4GYI83aHLG549xf2b+ePXs2OhfXh+bksbokLJ/Ns9M1lq0TzLdGxw/ri04fwnUIt1keXu/cbtf6ZP3y+uf757Mjb7311tX21P1hnhruN3eJtRVzitrcY2O5eWc4D1rbMzcf+6K5+9gWdk4ie3cw75lt83ysW9aVtR32Q9Yl7/3WNZWNydxPhxH7CuuD+1kfU8fpz//8z2/Lcy6veRtZF6x7Pivrd+YAMmcnj2db4n72m9f1OPYLoYiIiIiIiIiIO6MvhCIiIiIiIiIi7oy+EIqIiIiIiIiIuDMehUPocrlc5QvNA2M5WOb5zE1ime9phtxytefymH/IMsT0srDubJtlt5ypZS2Z/2b5zDVA94blcLnfnEPWtsyzwywm74/weZ7Pbxlbtg2WxfLrzNpbRtl8UWwr5ovi59kP7frWNs0HxbZk3hrC58P7tYz2JJNt+WrzVBDun+azbRyyvLw5SIjVlfmhLONtPi4b4+mhsPpneXfHs53SBWBjlNWdOYJYd8R8LeyHrMupXyq+/Vwul6vnaH6MqduEcDyzNZSNh9bmeL6dy8/WHNw2r4n5G8z9wc+bS4TPxvxxNs9y7DOnEevnF37hF662ORfx/FxTmSfG1mC7uWHqezKHkLn4zF1i57d51jyQNs/b3ELYdmwumPZz6wt89tNxY/dZYp4XWy/eusZ6XQ/Mp33etqfrCNbl570uYfk4bvD9ht4cXv/cVqbfMUydPvbuYz4pMnUQ7a6/a+f9QigiIiIiIiIi4s7oC6GIiIiIiIiIiDujL4QiIiIiIiIiIu6MR+kQYm7Uso6Wx5tmOS37OHUQMe83ybFaPpp1ZdlIy/5bBtpcILy+lY9eHHN9TJ1LvB/zzlhG2TLNbHs7zw/rglim2PLj5iQyhw/Lbo4f+7x5Z4j5r/hs2BZYHrpa7FlNfQHEvDRnpr4mO7e1FWJtaeous+vZGH5r/t0y4ebNYP0+ffr0atvGmZ13h2PGdH4zP5H5oWybmMeC8xv3n/uZeRricWBzCbH+Z+OZOYEm/euh8pDz580xaWMv64prGvOf8Xjzuth48fLly235bE1l87y1BZtLOP5Zee3z5rHkvH9+flMHkDl6WDd2b+bmsrrm51me6frU+o3Nq7ZmItP3BTveXC67ccPWCGxHHDfs3cXGGVtj8Vna9Wz9bf3c1tMGrzcdR3g8+waf5fPnz7fnP98P69LGlOl3CDyefiO2Be639bvNp7yfr371q5967Jl+IRQRERERERERcWf0hVBERERERERExJ3RF0IREREREREREXfGo3AIrXWdmZvmVJnH4zY/zywo83/mk2AGj9u8Pjmfj1m/3bFrfTJHyc9PvTGW85x+nuWzTDTP9/7772/LY64Uy15OvT12v5Z3Z/nPbXGa/bcMrtX91BdFzA1gHgjLkzMzbfdjz9r66dQ9Zp4M2z6Xdzqm2LnNY8a6snGHWL81f9T0/MTmBHMW2Rg/dS5NnU67Y82DQHbZeLv265zPrs/5jf1m1xZyCD1eJmswYv4K8yiav8OOnzrFzm3Uxl5zWbD/mJ/CvCi2frQ1Ca/HZ8N1Bvebq4T3b/O+eW6mnkqb+2xsPu+3dwtbP9q7xnRetnnUymP7p/3Y+jXXbLwfvmvZuxfX//Y+w/uli4Wc27Z5Ymx9PX3vJOaHvXUcIna/1nbNKWrH2/uHtT1+nm3J3kXPz+dW/5KtmdgWWFY6Ka3t8F645mL5zaf0afQLoYiIiIiIiIiIO6MvhCIiIiIiIiIi7oy+EIqIiIiIiIiIuDMejUPonOmzLCPzc2+88cZ2v+XTiV3P3CiWHT1nL5llZH7bvCnM3Fqu0zK508wxy2s5zg8++OBqm9lKO5/5Mcx9YNlPa3vm+7DPn+uDdUMsT277zQ1g/ihrS9xvXhxrG4TnIzwfjzcPhblSCO+fsD52uV3zHFjdmsPH2qV5IqbODnN42Jg59TFZ/pzlsWdn9WVuMe63TPoZ3ot5LMyrZnVjz8LKM/Woncs39RvFt4fjOK7alc2jts3+ZG3QxmZr48Q+f4YuD/ss97948eJqm2ucqdfRPDXmEGL5WB6uZ7nfxkJzHJmLxdYF5NZ10s55ZOO0rZ/Zdrge535+3sZa+p5sHWAOIT5ra9tWP+a1ZFsz35WtSbltLpXdOGVjkq0ppu4vW0PYGm46d97q7Jk6QM35M12j8vPm2bG2c+6bdPjYu4SN4TafmUvL5jPeu72n23r80+gXQhERERERERERd0ZfCEVERERERERE3Bk3fSF0HMfXjuP4U8dx/NXjOP7v4zj+2eM4vv84jp8+juOvffS/3/d5FTYiIiIiIiIiIm7nVofQf7rW+h8ul8tvO47jH1lrPV1r/ftrrT97uVz+4HEcv2+t9fvWWj82OSmziJYb5TaPn2ZFzYXC45nvM7/GOTfLXCUztcwK2v6p34HbhnlkCOuS2UmWl/t3Dp6HPm+uEMu5si3Z/d3iPGLdMG9umWS7F8sQW17esv3kdXOqv4Rliq2tsn64zfNxXLDcL883bYs7B5Pl0bltHhnzzrCsbGvTfmPPyvqleWum45K5DaxtWn1bPt3qe3cuYll5c1/Zvdv8Rlg3U6/adByJbz/HcWznG3Oamf+BbeLZs2fb/dZmrE3T5bKb558/f769Ftv/q1evtvvNM2NjtXlmiM0N5tSkg9OuZ2PzdE1mHhoy9c1NXCz2bFiXtsbis7d+ZHBesbZijiFz33F9a/M+P8++Qqa+PB5va77du+GtPlLWDTGXlr1H2pqE2/Y+cKvX0d4XzCtn44CtuYiti3ZeHXOD8Vx8NuYhtvXo9PPE3g3J6/qnPvMvhI7j+Opa659fa/3hjy74i5fL5e211m9Za/3kR4f95Frrt37Wa0RERERERERExOfPLZGxX7XW+vtrrf/qOI7/4ziO//I4jmdrrV9+uVx+7qNj/u5a65c/9OHjOH70OI6vH8fxdZrvIyIiIuKL4bwG4y9qIiIi4n645Quh71lr/fq11h+6XC7/1Frr5fpWPOxjLt/6ndKDv1W6XC4/cblcfvhyufwwY0IRERER8cVwXoMxNhQRERH3wy0OoZ9da/3s5XL5Cx9t/6n1rS+E/t5xHD9wuVx+7jiOH1hr/byd6DiOq0ydZR0tu2j5P8vtWvbR3CKWqz3DjDG3mfGdOmssz20ZXsLzsa755R7rwjLLvL7V5TQHbB4YY3o9Ps/zfj7LXeZ1rU/mTqfP3jLBlr23fmBeFbY1lm+aUeZ+vtRYfdn9Weabz89+6XguP8vCz1pdWl2wXU/buWWYzfPG+7O8urXFadsl1vbMq2OOJLYVczfsrj29l+m2jYlWN9N++7r59fhyOT9XujemvglzgdjcYf6IqeNrN56xr7Ivc+w07yOPt/Xs1CPz9OnTq23rr/ZszFlm44W5Tmx84fEvX77cft7mDnM67RxFfJbsB1Onp9U914dWN7ZNrK4Mnp/rFLu+OZfYlqe+LJu3OQ6c91u7sfWi9bOpO4xtyd57DWuLZNpv7d3M5gwyHRfNO7fzddmYzbrje+3U4Wn9cOrQtDFx6qH7+LyvddQDXC6Xv7vW+tvHcfwTH/3Tj6y1/spa68+stX7XR//2u9Zaf/qzXiMiIiIiIiIiIj5/bv0rY//WWuuPf/QXxv76Wut3r299yfRTx3H8nrXW31xr/fYbrxEREREREREREZ8jN30hdLlc/s+11g8/sOtHbjlvRERERERERER8cdz6C6HPheM4rvKAzMeZE4g5Ve637KdlHS27adlF7j/nFS2DzBwqj7ecJ/dPHULTzK65A5jFZF1bdtJcHcRcBFZfrG8ydaGcn72VnXXLnCvLbmW1fmXuD8s0T9uW5XAtJ2u5WMuAT90IrF9zCO3qz7ws5q0g3G9j5K3eiem4MPVB2bho55u6yLif4+40H7/zEUzLTqwfm1Nv6gCyMdLGqRxCj5/jOK7GDLZvuj6sf5nTh+MR93Ob4wGvZ9scm8/92/qfOWh4bu43R8/Ug8LzcU1lLkBiaxh+3saTnTdxrU/eL//C3dQXx/PZPLxzmVi7tDUFxz4+qxcvXmzLYudnu7ZnZ94W3h/brs0NNi/bvGquMnMqme+L93cu39SVZe8q3N75ix4qO+vCrk9udSDZe66tQcl03rc5w9Yl1pfO9WPHWtk4H5mn2K5nz4LH2/bUS/fxdT/TpyIiIiIiIiIi4juWvhCKiIiIiIiIiLgz+kIoIiIiIiIiIuLOeBQOoSdPnlxl1M3xw8y0OYPM/2BukV0WcS3Pz+88QMy8cttyoby23ZttW3aRx9MtQFiXfHa3ukEs38+s5zSLadlQy93unE+WhbeM8jTPbrlWyzhbPtxyrJZrZXnfeOON7fW4n8/KMtyWC+Y2+7E9n13+3urO6t48LoRlYV0RuxfLh1vbmjp5pr6qWz1who0bdr0z5meyLP00rz7J2j+0PfVe7D4bj4MnT56s58+ff7xtbg/zspizi+fnWG79l3MFvTHmZDsf/+rVq+2x1matf9nYyzWRrdFYd+ZlNMeYja3m+rM1KedN1jfrw1wu5lI07895PLOx13xQvBdiPidbM/Fe7fPmgbR+a3VnbZ39mG2T7wt2ftb/dP2/qy9r19MxjucjHKNYN9M1iD1bMnVu2vqd2/auZA4jc6tx29Ylu3XI1C9qY5y9h9sYO/VGTrxoa32yrj71Oq91VEREREREREREfNfQF0IREREREREREXdGXwhFRERERERERNwZj8IhdBzHVfZ0mkW0zLNlui0TbZllc41w/zl3zGMtR2rZQfOo8F6Z+eXx5rlhnt2yjuYmMPcA4bPZOXvW8kzy+++/f7X9RTovWDY+O7YNwn7AuiPWzm2/OYYsh8u6tFyrtWXCtsDPW16ebcc8Opbf3zmMrB3atdg2bnWH8fxT94F5H6wfmzfH8u827ti4avdj46C5z87nN4eHZe2JlfVWf9LUPbbzQlhWPr4cnjx5cuW0sLHWvDSGreFsbuDYzHmbvo633377U4+fuvDMp8a6ozfl7Gp66Pw2j5gbz9wbvF+bG8zVZz4nwvNzLmP98vzmULP95+drazB6XugMevbs2dX2L/zCL2zPx3uxtmP9yuYdmxfNy2JOId6frbm4zfcBW/NZ2+D5du9XNk+ynVpZ+HlrK7bmmDqGbP3P89vxNq5MxyWrP/NBcXu6BjvDdmZ+VPvOwByeth42d6+9F1vdnu93tx7sF0IREREREREREXdGXwhFRERERERERNwZfSEUEREREREREXFnPBqH0DkDN/XSWPaTmF+CWUXL81nWkTnX8+enviLzK3HbMr7MqVoW0a5nuVMrP++fWB6e+XWWn64BO795cgjLv8sZs66ZdzavjGV6zS1iOVnLBFvudZqBnrYdlpfXs4w3sZwu91uGeecymDozpmMgvQ/meTGvxNRlYK4YO5+1dXuWVh4bs4mNe8b5eGuXdm9k6k+yurdxZ3r+8zjyRTrZ4rNzHMfV+Gpjv7UJYm3Uxh9bF3He/8Y3vnG1zXn/3N/N5cG64DxkazTOS9ye+tGM6bMjXH/b3GHrBlsvT31vtgbcrbd5vZ3v7CHs+Kmvie3W3F3E3pWs7s3fZPdn7w/cb23L2qZ5ZPh53v/5+vae9+abb27PxbLTGWROm6mXhs+K5aED01x/xNqSjYP8PDHnJtsKn4+9r9ziYrT1oI1x0/X5rWPe1I17/vxuru4XQhERERERERERd0ZfCEVERERERERE3Bl9IRQRERERERERcWc8CofQkydPrvKDls+zDLNtE8u/E2aUmQNmXp37z+W3XKRlB5mTtJwn3SKW8Z1eb+dNWcuzkry+OY2Ym7Xr8f4tQz1tG5O2Z+4Atg17tiwr26nlWu3Z8V4sM8zymqfF8uhse9a2LYPM6/H8lhkn1va22V3JGJsvifdO2E+IuQ+sX1h5iY3pdj4eb+OSjTuWGTd3hLkQdtc31wCxMcaejW2zLUzz7lOfVHz5HMexnY9sfJj61Ij5PDhPv/3221fbL1++vNrmeMc12LlN2hrGvCich3g850mb58y1MXVlTOf5W5+1nW86FnPbng+f/W78srJz/7Nnz6622S7t3rgm4/mmDkvzzvDe6bnhs2X5bY1HuA4xl+HU60jMK8n95+tbu7Y5ntvPnz+/2uazMz/U1EtI+Kys7qbv0Sy/tT2DbXO6RuX1+PndOGrvQuZjtTHY6tbavY0j1nbJ67ob+4VQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/CIcT8uuXziDkYzGnAPB5zvpZHf++997bH765nZbU8tuXbnz59erXNzLI5g8zjYu4MYr4Ky2Ka14bwfLxfZowth2pM3CHmJZk6euzZsWzMv089LJa3Nx/W1PfE+rCMtj2Lqa/LMtYcR1i+iUPIfFG8NsckaxssK8tm/Zr9xPLm5giaemzMlcC2beMmYX0S8/bsxvXP23Fn/hWrO7YNc33Z53deinxC35nYeGH93do82xzXYOaD4/Hm3zuXj+2ZaySuqWzbPIjmILJ5w+ZRG4vNxTcdq6fjD/fbeENsnuX2zh/FZ2FOHdYd2x3rkkzdIFM/lK2n7VnZ+Wz9zDUb3z+IzX1WX7ZO2fUdu1frt7Y+5DjCtmLXm65fzTfFZ8XyTP2p5gC69V2RLl7zTto4ci7v1OnD4+07CXuPte8krG3xfOaL2l3r6rqvfZaIiIiIiIiIiPiuoC+EIiIiIiIiIiLujL4QioiIiIiIiIi4Mx6FQ2it6zzl1OVhOVvm7ZgpNmcQs4wvX77c7re8/Q7mSLnNjC5zrdzPbeZImQm2DLJlJy0Lae4By3Ja7nfqnzK3wdTvwWe/O5/lWNluWVd8dlM/x/Pnz7dlZd0SyzSz7i0fb22F22zb7Nc8P48nlv8nNi7txgF+1hxBzKPb8dYOOeYRcyeYo8PKM3VzWQbbnoU5Rni8eTWm7oNzX7I8+HQMnLqxrN8R86rZGL4b8+JxYv3LfBBsc+b8efHixdX2q1evttv0NnJNZv373GY5L7z11ltX25wnp15FjiXcvtUTM10fE/NP2DplsuZZy8tv1zcnkW2fz0fvCJ+Nlc28MoROHZsHra5tHcDtr33ta9vzmTeH6xDzvBCej9ej+8+evfW93bpkOqaxrmxeNd+SrTk479qa0dZotgYibJt8NrZGsjWfOUDZV8yZZOPMxCHEstsYPHViWj/hs5r6knZzym4N1i+EIiIiIiIiIiLujL4QioiIiIiIiIi4M/pCKCIiIiIiIiLizngUDqEnT55c5S0tv255PcsDMn/HrCazksyv8/O8PvN/u/y6+R+YQzVHkDmDmGW0bcvw2vFTD43ldIk921vy5Wt51tPKu8s8m/Nn6mMiVnfWNqyfTbHPWx6fz8raojmBzGdFph6enevAsvrmZeE2xzDWheW1zT1g9zr1UPB8Vt5p27e+Y/D46fPYuRBuLQuxMZrbbCtWV+xHfDbc3jlLbh1D4tuDte9pG+bYTfcI11h0AnE/+5d5Gzm+nP0Ub7755tU+btMhZGsec5NwTWeuEh7PbVvT8PrTsdPWy7d6GKfHT9f35LzffKNspzye84KtocwjY5+3edzaJt8PbnWBmdI/4NcAACAASURBVNuQ5+c6h/dnbYH9nljbPq87zL3F/RwXrO3YvMoxbecdXOuT/Z7PgnXD69v1rO74bLmGM8fp1FVoz8McTrtxcTrG2Jhj71rEHEWsO/pRbYzj/Z3rduct6xdCERERERERERF3Rl8IRURERERERETcGX0hFBERERERERFxZzwKh9Ba1/k/5t8sW8nsI/N5li9ndpF5PcvvWU53V37Ll3ObOc7p9jTPbVgulDlT8y0xY2wZZsscW77e2oq1He63XO65fOa4Mc8K4X7LSFvbmPqWzN80LR8/b04h9iWWx9wNzGDzfNyeOo7O22eHxVruECI8N89n7dQ8Mzv/0UP7WddTd5iN6dO2ZuPSznPzOp8nEy+HZeOtn5Fb99v1JmPaQ8dP6zK+HM590Bxju8+u5fMi90/XYJwbzfnAsf/sBeLYeat30eYNO//UE8P9Njabc8zG1unx07He1gXEnj3r/3z96dhoLjsez3Znbitbw1jdcNscQSyfXW86T5vXhbA89N5M28rufYFrLl7b1ntTxw3HMF5/sn586Pq8V/qv3nvvvbWD5eP1rL7oWLJ1xc6t+9D1p+Parn5s/Td9r7R3G8PekwnnLPOove6Y10otIiIiIiIiIuLO6AuhiIiIiIiIiIg7oy+EIiIiIiIiIiLujEfhELpcLlcZOsvjmXPB8nTMSr58+fJqmy4Rfp55d8t27nLDzGUyG2g5Vts/cVusNfc/sK4tP2/eF8soEzuez84cQVNvjuWId1hmmPdG94D1C3MdWGaXx0/y2Wt53dn92bM1zwU/z37N65nfyp7t2Uux1r7tsd9bWSyzPM32cwyzfm9j6tQpZNebOn6m3h3eD/Pw9uxvyZDbszL/k7VzmwMsu2/z19RvxbYQj48nT55c9YGpY2G6JuNYzP42bTPmLmH/Prdh851Z/7Gxazpvs6w21vPzNjYTfp7l5bPhNp8t95s3huMJ28Z0vOT1WJ7d3GDeRhsbbWwl1nZs295tuJ/vFzye5eW29cupa2XqrZyuc3bPy56VzaPmFDLvmdUlxwFbk7HfvHjx4mqbTiEezzUpsTHdvJa2BrzVh2Xvluc1r40h9l5t71pkuv41nynr3uaQ151P+4VQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/CIbTWdSZu6jox9wf3M5/36tWrq236NSwryTyg5XDPeUDLm1uulWVj2S2/bnl0yzra8ZbrnGK5X8uZ2vkst2r1Yfc/uZbVteXLiZ3Ptoll8y23anXHTLM9K/YFwv1s++Y+mD6fZ8+efer1zUNhnjKWZeraYt2ap8YcRNYWOA6xbViefDKmPsQbb7xxtT11P0zLt5szrJ1ZVn/iK+K1X2d72rbMpXA+n43H8eVxfk50EBDrD+bqs/HDnAk297FN7tZR03mP17KxyNZwtqaY1u3UL8frc31svoqpl3HqCLXxx+aS3fnt2lNvIp/11O9Gps5K67d8tuY4srbFfkxvDsvD89m8znXD1BW4c5TasyLmGeTnebyVlXXHuiXmR6UzyPo9z8e2RdcuP8/r8Xx8llyDco1mnktbc+7GIfOUTduVucXMwWnnt/d6wro5t/XdvfULoYiIiIiIiIiIO6MvhCIiIiIiIiIi7oy+EIqIiIiIiIiIuDMejUPonFc0H4T5HywTzawm83bcb7lhyx2zvOf8H3OT03y1+Se4zbLY+c3nZM/KvC5Wl+a7sPw6sfqwtsX6mObXz+ebOnDMOcTM8tTnZO4B3qu5vcxfxfLe2pYsX87ym8tl6ufi+ci5Pqf9euromeaxzQvBZ8UxlflxwvOb581cBVY+cx5Zfdu4YH1j54bYecUewuYXG4PYNmyMs7ZmrgXzXsTjx9wmxDyLNjdMPZBT94v5M3bHmv9h6iLhfpuneT1bB/B4js3mW7Ly8fzmu+O2jeV2v+bfILu2M13/EXOPcL/5laZOS+7nPGieRO63NZs5iPhszdVi71ZTdx/Lv1snTb2DxJ4V2ymdOdYvbUyk0+edd97ZHm9rpqlbkPdn+/leTccmXb5Pnz692mZbYvnYFmx9f8bGPHPWTf2m5sjj9c1fxXtnWzsfv5vb+4VQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/CIXQcx1UGjhk3yxQT5vOYtZzmeB8q7xnmVpnn2+VczSE0zRRbFpJlsfy45Vr5bOz6hOWZOoGYS2V5zG1g3hhuTx1Hu/oyPxO3LV9tmV9rt3Zvlnu1tsLz8/r8PPczc23eFvO+WNuYYn31/DzNYWP9kJ83P9I0u295cCufPRsrP7G2Zm2b9cH9HEcsX28Z7t24YXVh8w+Pp7fB3F42pk49deZvmjqT4tvPcRzbdjdx8Kzl/Z9rMrbhqWOMbdrmznP56KaY+t3MNWfzKuHxNq+aD45jna2RzBlkx9v98dlym9ez+2FboD9j11btXmz9PH03ma637fN8f7DPk+ma0daAVj92fXse9M7wWXObnOub85a1G/Mn3bre5LPlepfjyosXL7bltXcXPls6e9gvWfesD5bv+fPn2/LRGcS2bI4iGzd2bjNbf1o/5blv9Z9a22Bd25zwWd9t+oVQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/SIWT+C2aImeczl4flBS2Tba4SZkuZTX3zzTc//m/mWKduEctT7zwmD21bpnmaUZ76KAj381kzW2meGcMyzIbd7/l5WO6T23zW5uBhJpfP2vLg5nGw/DlhWze3ATPSlmcnbMvWFqyfW1/i9Xi+3bktY2xjjDmGzJNm927OIY5xdPIQlt/y6NP74edZPl6P+63tE9a/zSE77Nip98HcBnb+W+fTyb3Hl8e5XfAZm0Nh6l+zdYzN+7YuIrt1D+clc+iYw8ecO7wXYg4uu1dez9aEU6eY1QfHfnt2NvdMzzeZu6bOH5s3p55GezY8nm2V21Ovo7Ut89zYPGx9g/OsrfFs28atM1N/kcF2Oa1LOnVsnuX1rJ/QAWTz9Fe/+tWrbdYPy8M11LR+rS+YU8jWgOe2Nn3ntzHXnEI2zti92fXIbkzczdWt1CIiIiIiIiIi7oy+EIqIiIiIiIiIuDP6QigiIiIiIiIi4s54FA6hta7zhZbfY75ulxVcy50IhHlzy93yfMz1Mlt5Lr9lfC1rz7Ky7rjfMsqE5WHdmgfG6p77LSfL7ZcvX15tm4tk2jYsB0zYNnb5fLuWOWm43/Ll5rxh3ZvvieWx3Cv7gWFuALt/lseepXlypv6AXVu3Z89zW9n47MxdYBln9iPLUE/bgnlw6AKzMZj9/OnTp1fb5slg/ZmbwdqytY3dPl7Lxmjup0vAXGNTp940D29tJ758njx5ctVOpk6gqX/D5jJu8/xs45zb3nrrravtnYONfZ3YWEs4NrDsNo+ZS9DWfFzjWP+1NZ6NteYusTWXjc1k6r3ZbZunkdi8aPOCuf9sXcB5beoqsX5pz4bltzWivd/csoZ6aNuOv8VnZ+4qYj4ke1eyfmTvStxv76nc5vXYtq0+OC7QaWrjkvVNfp59c/cuZmus6XcE7Kd2b/Yez/1sG+abIue2tzu2XwhFRERERERERNwZfSEUEREREREREXFn9IVQRERERERERMSd8SgcQsdxXGXoLKNtGedpTtQyyG+//fbVtuWEd3l1lo/7LPdpWUFzeUxdH2TqXeH5pw6fqWPIHELcnubXp21rl7FmO2JulHlxtpWpM4hltzy2weNZfvYrloeYf4nln25P75dtxfLz5sE4t+2pl8w8MLxX9hNzF/D65lGbjhvmGOH9Pnv27GqbefSpG8Ay4pbZtr5k4/S5vnjv5u4y54hl/bn/8+4nNubbnBWPg/NzMjeIPVP29xcvXmzPZ33C+rv5JeivOPchGzt24/hD1zLXnM0TxD5v8xDheMBnZe4S+iumz9bW7+S999672qZfztZ0Ow+O1ZXNA+YttHnaxk4bS6ceyqnPili/JLZusHWB9S1zL+7ed2weZdnY7m39++abb15t03dqz8rcXubcMScRy2t1O2075v5lXzJH0tSPtXt/sHZ563cM9p5sjk+OcVMHKM9/fi/e9dl+IRQRERERERERcWf0hVBERERERERExJ3RF0IREREREREREXfGo3AIrXWdN7RMMfNz5hxivo7ZRctMT69Hjw2Pf/78+cf/PXX+2L1Y3p1YTtPOP3UBWAaaz4Kftxyr5cdvdRiZH8A8OefzMefJPDrr2o63Z2XeGqtbyxhP9zMna23F+orV/fTZmh+L+y0ff8b6tWXjLevPujBPhPmfeG88nuy8DWt5vp39lNizYNv6vu/7vqtte1a8f47p9nx25Z06daydcnvaj4jl6a2udm0zn9Dj5fzc7BmbZ4qfZ/+hF8baGNu0OdPoDNq5RcyhZf4I874Qm1dsbDWPi3lZWPeE5eGz4bO0Z8WxnT44W4eYF9Lm0t1+m+esbdzq+rOxc+pJ5Dxr8645NK2tmctkOhdN3x9sHNrNRfZuQWcQ99PVx7pgOye2n1hdW9uwfso1k/mbWB6+j5hrbfde/NDnWT6O8WTnlbN+y/28Nx5vYy4/b+/N9p2DjRP2HcWn0S+EIiIiIiIiIiLujL4QioiIiIiIiIi4M/pCKCIiIiIiIiLizng0DqGdN8iykcznMWNsGW/LpRJ+furDOB/P+7YMrGWQzR1CLBPMeyG3ZiOZo7X7s1yr5VbNT2WZZu4359Eu423tcJovJ1ZX5luytsi2NnWVmKvB7t/6ofVje9bT+7Uc8fl4ZpC5bW4A6xfTezdXwNSrYXlzczPw/jiG2rPhfhsH7dmas4n1t8uA271OXVdWVrt3c5VZW7Ex9lx+a5fx5XAcx9atQsy/YZ4c+h/Mu2Ntztwtuz7z4sWLq310g9g8tHNVrOVjqXkNzXdm8xbXWDa+0NnD8tm6gtczjyPPZ2tO3h9dLzY+np/XdE1BbnXa2Bpp6hAyz+TU9XWrY2i6jiBWP7be3tWP9UPrV9NnzXZt7d4cPXzWT58+vdq2fsnr8dlzTcRxkeUxeDzPx/LYGs/GIc4x5/ObM2jqAGI7472x7p89e7a9PmF56VOyujtv767VL4QiIiIiIiIiIu6Mm74QOo7j3zmO4y8fx/GXjuP4E8dxfOU4jl91HMdfOI7jZ47j+K+P49j/yYSIiIiIiIiIiPi28pkjY8dx/OBa699ea/26y+Xy/nEcP7XW+h1rrd+81vpPLpfLnzyO479Ya/2etdYfknON/uy8xRHsT7Txz9vZn8Pjz8HsJ778PP/M5/l8/HPI9hNQMo0F2U/T+FM0+5me/Uxy+nNkq0uLTxAeb89y8ieUX2f/rm3ZzxSt7BbTsWc/iXo89PlpbIfn5086+XmL41kMyf6crWGxLl7foj+TP3fLbRsTWTf2c15rK/ZnK63f209y7Xj7M9T2034bh6y+LArAnwDbOLkr263znf203Y63+dLGWDv+s/4J1PjymM5NxCJeFl3htsWyrA1yLjlfn32X2Lw7jcnbWMvzc01GLGJlMX1bYzGSZX+u2qI0dv+2prU1m61bzs/bnr3F3ayfkM87gkZszTONdrNuuX638kxjVnweti7ivG3KiTM2L9mcbn823p6F1YXdC99jWV7en40z1natr7A8PL+t16drXmJt6bzfIsqMfJmKhXXLfjL9jsLq2sa43Zi767O3Rsa+Z631xnEc37PWerrW+rm11r+w1vpTH+3/ybXWb73xGhERERERERER8Tnymb8Qulwuf2et9R+vtf7W+tYXQe+stf63tdbbl8vll77++tm11g/eWsiIiIiIiIiIiPj8+MxfCB3H8X1rrd+y1vpVa61fsdZ6ttb6TYPP/+hxHF8/juPr77777mctRkREREQMOK/B3nnnnS+7OBEREfElccufnf8X11p/43K5/P211jqO479Za/1za62vHcfxPR/9SuiH1lp/56EPXy6Xn1hr/cRaa/3qX/2rL+dcm3lZLG9O+CfeLAfMXOrUr8HzM2t6Pj+zhvzTgTyXZWZ5Pivb1JVhmWD7k8f2Zy2nvgk7fuouMBeB/bnByZ/dtD+pOf3z0uZJsSy+9SNrO5Z5tv3mKqErgeWxZ0Gm7gH7k667zPJa1/U7dV/ZtuWv7VkRG2Pt8/Znnyd/Fnotd4gQcxGYF4SYs4Ts7oftlli2nlg/4hjOOYb3Znl5ls8cJmf6s/OPh/Ma7Nf8ml9zOc8X1kbNE0UHg61LzLkw9TjunEFrXY8n5qazP5Vt2+w/LJttT8de+7Ps3E8HEK9nf+LY/uQxxx+rLz5bc7nw+jx+N57yWdufBue5bZvYPGceGWLr5+m8Ny2PuQin4wixNdZ0DXkun/la7dnbuwXbvTl7zN9ErJ2zLZhTdPruyHUE4RzA93B7P7B3M2vLOy+POSltLW/Xtn7IstGnSjgGm4eSbe/8rL8oh9DfWmv9M8dxPD2+dbUfWWv9lbXW/7zW+m0fHfO71lp/+oZrRERERERERETE58wtDqG/sL4lj/7f11r/10fn+om11o+ttf7d4zh+Zq31j661/vDnUM6IiIiIiIiIiPicuCUyti6Xy4+vtX4c//zX11q/4ZbzRkRERERERETEF8dNXwh9npwzcJaPm5xrrU9m5pjXY3aSOWLL/VpOdudk4GfNiTP1xjCDO3UE7bKIDx1PmNW087E+pn4Mbk/r01whU1cLjz/fH++VmVtz2rCdWtnMEWQ+qKkvyXK67IdTfxTPb34Sc7NYBpl9idvm6dmVz7L7LIv1O6sLa6dTt5fdq5XXPDnmqTCnko3hnAPoE7DPT/qG5dFtvrO2YnVhz4bOESuPPYvztjke4stj10bNo2Pztvk36JdgG6RDwdYhHK94/fN+Wx/avGneGP7RFHMCTR1jNu+aX4JjK+ve1pjm/rCxk9j9mbeG6yi2jZ3zzNr5dJ42d4j52qzuzcc0Xd9ym3XJtjn1XdkajPXBeXi6jpisufhZcwqZX5T3Yt4zw9oer8fj6fwxfy1hffD4W72MU8+l+XVtHNqVZeqIZN1Mx3TOb/YuyHGA5+cYfm4bX5RDKCIiIiIiIiIivgPpC6GIiIiIiIiIiDujL4QiIiIiIiIiIu6MR+EQulwuV3k/yxibg8G2LQNt/gnm+8x/scv7McdpGWTzsvBemBdnVpJlM+cPz8+64fn4rJh15P1bDpblY4aZ52M2c4r5MSyfvsspTzOz1m4to2weFqv76bPnfmZ8p/3OfFPmGrC8ut0/c8JTn9S5vs3RYXVvnghiY6KNqeYAMQ+OlddcYeY6MHeAzSn0hrB8lhEnu+dh/Y5Yv5rOdzamT90G1q8tPx+Pg/Nz4xrH3H/W/823ZuOPOR1sP9vgbh6e+sTseO4394i5BTmP8/O2JrQ1IMvPZ8P7IdP1OTF/BuvHPDzkXD+8d3PoWF2bv9TGyum8b04hux9i/Yjl37m51nKfnTmCbplnH+J8Pltf2jxt7Y7tdDrP2r3txrS1vC6tbomtodg2bByzvmJ9h/dn/f5cfqtb80HZ+tLmNzJ997J3O65nd/Pd1XW2pYyIiIiIiIiIiO86+kIoIiIiIiIiIuLO6AuhiIiIiIiIiIg741E4hI7j2ObaLNtpbhFifgxmMZnHY4Z66vc47+e5LHPLsjJXyc+zLPy85VxfvXp1tW3ZSF6PDh/LTNv5WF5zlTBbyfNbVtSeJZ+fZdLP+y0Ty/3mqiIsO7enjiDDfEu8/tRrY26EqVvFvDTWVsztQnb5fMtPE7uW3fs042yfvzVDPb0ftiXzXlje3PxchOczf9Tu3LbNMc88ctN2aS4vwjGV5Zu60eJxcO4z1l/ZRuiHsHnaPDrmbOC8a212NxfavMT9LIt5GM3dx/28l3fffXd7fY59PN7WiDaW2fE23pgDyNrC1C9lHpzz56drdxvL7F1l6pGZtj0rD5k6e+xZcByYzi1Th5K1hd35zANjbi3z0Eydk+aFMUeOrdF4fSuP+bJY1xyT2RasrU5dvFPX8Pnz1q+s3U7fHcy1RWyOIM+ePdvuP18vh1BERERERERERHxMXwhFRERERERERNwZfSEUEREREREREXFnPAqH0Fr7XK9ljAn3M7PM/B6PZ+ba8vB2/V15Pvjgg9G5LJ9ubgxmGS3Hatl/y5Hy/pirtUywuT14PfPmmEfHnp35oyxfv9tnvifLxk9dI2zn5tKa+p7M68L7txyv7bfcr33esPs3j865LU39TOYYmvZbu77182n5zHNDzAVg+Xe25Wne3Jg6h87XN1cBz8V+anMAYV0y2094vqk3Y+JTisfBhx9+eDVX29zDNjv1UXAeNWcQr8c2bG2Obfjcxs1NYe4Qbtuajufj8bb+Zd3R08jysD/b2G5jta0h6Z00X4f5ocy/Yeua3XjGun7nnXeutukPNY8L6/Lp06fbz9saxpxGtoZiefhsWHfmvTGXirUtW0fw+jyftSVryzt3mJ2L+63fst2S6bMn0/Pf6meaugjZD9mXbBywNZyt2fg+tFsnmefM3vMIy2r3wjGcYyBhXZpf9dxWcwhFRERERERERMTH9IVQRERERERERMSd0RdCERERERERERF3xqNwCB3H8YkM3BnLOjLvZ9lEHm+OIHMomC9jl5+3stq1zbPCumL20erWcpv8vOU2mbu1nC7rjhljZqjND2WZa2LPlvXDjDbZZU8tL36rB4U5Vubbd33wofPbs7NMsvVj5mQtA235dmIOIHOFWV/abVs/Mg+aOXWmz8Kwe7O2Z3l483KYq4DbNqZbWzIXgo3b5Hw+K8vUFzWtW2vnPD/HcGtr8Z3J+TnaMzW3ofVXOoDefvvtq21b03EdMfXPnc9vviK7F967eQbNuUPM2TN1EHENxft7+fLl1bbVj90/j7e5jFh9Wv1yP30dZ3gvPNYcllyz8PPm5LG5wJyZxNbjPD/bBvvps2fPtsezrZp7xdqm9bWpK3E3rrFs5uoz583U78Rtns98qPauQqZOnun62vq1fX7az21O2t2fOSiJzU/TNZ21BftOgW1z5wDMIRQRERERERERER/TF0IREREREREREXdGXwhFRERERERERNwZj8Ih9EUzdTYQy4Kak4G52HPejzlHy4NP3Ro8P8ti/gi7vvkmpi4C87bY9c0hZOczXwbPb8+e5zvXP89tmVnmVi3Ta/lsu/60rnl93ruVn+ejW4G5WHOZmEtg6mrh+ez6u21rN+awIVPPmWXtb3Xu3OoYMT8TMVfY1Hlk57d8/G7csTHa+pn16+mYauW51TNnbTe+fC6Xy1U7m3qmpm2W65zpOohzwbQ857HbxkJz3Vl/s7HQ+pvNo+z/rDvuN0+O+evsflheO97GUrsfW8fsfFPm4CF0+BDzQdHJY2O3ja3TtmvPhvfH89E7afO2OYHMoWTrBnOr7Matab+crqenXkQbY3demIfKZ+WxNd10DWZM13zmSrR1066vTP1GUx+pvUvx+uaLmo6h7Ofn8u/Wvv1CKCIiIiIiIiLizugLoYiIiIiIiIiIO6MvhCIiIiIiIiIi7oxH4RC6XC5XGTnL3xHm58z5Y64Wc6Mw72fsnArMAu6y7mt5xtZyqjw/M8OW87RnY24RK79h2c9pztXKxyym5WzNk7PzyPCzL1682J7LMsjke7/3e7fHs11bv+A268raFu+XWG731n5qbWXqtbG2dN62urUxa3Kth7Z5L5bVn/qlbnUiWVuzPL15byaugYfOZ96K3Tg19T2ZY8TGREKnhrkHpr4lm0Pi8fHhhx9euWSm/dfWKeZWMd+FuVDYRs1dsrvWdOzgPGZrJpaN24TnN1ff1BM5dXEYNrfYGpDb5hq0eZ/rnvPzePfdd6/2sZ2aS2TqoaGviU4hW29Ox1J717F5jnXJtmjHT+fp6buf7Se7dy9zBFm/MbfXrh0+dH7WFT9P7D3Y+sl0DWc+KzIdV+x80/Kcx5Hpe66N8cTmDJuf7D3UHH3mY/o0+oVQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/CIWRYdvOWHOlankWcYhnp8/5plpFMc5jmjTF3h2UVLTPMPLjlcO3+uH/qIrB8vUEfB58XM+PPnj371HPx3l++fHm1zbp/+vTp1bZloplBtrw3t62fWX6e92e5WvNEmM9p6vwhbEsTL8VD+3m+3T7WFfuhZe8ti29lv/V465c25to4Ym3dzn+r84if5/Mh5+OnXgdicwbbDl1e5tYyn5Rl9a1u43Fyfo7T/s6xkf3B3Ibc/8Ybb1xtcx4lHF84d+zG3qkn8VaPi2H939YwrKtb+6+tv8m07XDbfHq2ZuWz57rnfD46fLies3s1fxOfBdshnUXTtjZ1hVg/NX+WufRs3py+a9m7lM2lu+dn67npuxCZeginPifzw9r1zEVGps/O1kS2PrdxaromPNeftZPpGszWOBxnJu30ofLYHLXzR+3urV8IRURERERERETcGX0hFBERERERERFxZ/SFUERERERERETEnfEoHUJTJ4Hlbi2raR4cc6FYXn6XIbey2Ll2Ocm1PnnvE9fFWp6ttFyq5V6nPgzLlVrO15xKllM1Vwoz4zsPD++NdW11Z22Bz8by7MxUWz/gvduzsrqzZ8d8P3O5dn66BHZeiYcwtwLra+emsH5tLizL7hPrV8bUbWB+J/NqWKZ62i+nLjFime2J02nqCLExy57NW2+9dbVt85d5GqbeivP5b/XzxRfDcRyfeK5nbK7h2Ey++tWvXm2/ePFi+3nrX+aNsXn8fD6O25wnbF7j8VNvIo/nPM3rm7dm6r3h/dtYN3V62vrbnrWNV8TGqzM21tn62taf5lVk2ejOYr9g3XENZG3NvDnWls1bY/2O90dsnp6u38m5fmxNYu9OU9+rYc9u6luydzFzFNE9yLZoLkJzELFePAAAIABJREFUgFpfYX1Mx6Xd87RnP/Wk2buXOX7s/OZz5fZ0ff9L9AuhiIiIiIiIiIg7oy+EIiIiIiIiIiLujL4QioiIiIiIiIi4Mx6lQ8iyhebssW3LeFv20TAfxS7narlJc4dYlpFYbpN1xRzp9PrmALIMt2W+LZ9OLHdKLNfLjPQuA22ZWvM1MeNrmV6ez5xBhLlUtgXLu1vem9uvXr3alof19ezZs+35zC1gPiti59/lhqduL3MXWD+x7L21+6m35tby2hhNzCHC8vJ4y6NbPt/ayuRYc+hN27W5yuzZ8PNTl9mkbuLL4/ycbF4lnEvYn16+fLn9/NThYE4FjqfcPrtSbE3Fa01dHtM1is37dj4eby5AG/utv3O/PTvC8cWeLa/HsZzbfPZPnz791LLsfENr+VhHuEbjs7E1k61PrW5sTfe1r33taptOIvqsbF41tx+f9XTdYS4xbu/eZ6bvaVMfqr3HTscRYv7ZqcfG2h7bhr2XE3vWtiaceiVtHbX7rLUNa2fmg7Jna/dq+3fjxK4e+oVQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/GIbTLxFnm2vJ+dq2pZ4Y52Gn5zvlCc/ZMfRGWA53m0XmvdAWY48jcIJajtZys1Y/leKc+D8Jc7YsXL7afP9+P+ZVYVmaY+WyI5a/NCcTP8/p0/JgLgc+C52PenuW1bZ7f+gqfnbWtad8h5+c5dVdZO7fjbRywstuYZs4g81awPGyb5i4wx4+NO2w7Vt9W/xOX2ef97G0OmHoqpg6UW/xK8eVwHMdVH7RnbvttPLG5y3wb0zY8dZLtjp2ORcQ8NJyXOC/zXjhvTtdYt64pbbwwb4w5zzg2835Z/1yj8nznz9s8ZnVnviY73tYs1tb4+TfffPNq2zyR1o/Prq2HPs/z27zPtjz1UfH85hDatQ1bH1u/t2dl5zM34K2emak30tZo1q9svWxt19YN5jyarKOsXU3Xe3a8vRuR6Xra1vvn43f30i+EIiIiIiIiIiLujL4QioiIiIiIiIi4M/pCKCIiIiIiIiLizngUDqHL5bJ1LJjjx7KJ3GaOdeL+eOh8ZJKntwytZRunPgdmEy1zy+N5vmlOlkzLP/XGEMti2vWZqTZH0s69Ms2tTu/d2grL/ou/+Ivbsr98+XJ7/NQ7wQwy65aZZXtWtp/uAV6f7oZpPp7s2q49C8sg27Wn3ghzdnzeLjO732n5ibU9cznYuHTr/U2wZ2HXtvmQY7x57czxYd63eJyc26j5Mab9Y3ettdzhNW3ztu45b7O9T90hth40t4bNu+YU4vnZP81HMd1vz9rcJ5xX7f5Z30+fPr3atvX8zktjTkyWzVx1tn6cuksIj3/27NnVtjk87dnaetuerb2L8dnZXMTy29w28cOaD8l8UjZGTZ1BVtf2rjZ9d+L57d2GfcF8U/z8u+++u27B7mcyLk3XYzwX28rUoWfeOZvvpu7b13Xm9QuhiIiIiIiIiIg7oy+EIiIiIiIiIiLujL4QioiIiIiIiIi4Mx6FQ2it60zd1IlgGWTLodItYs4hyy6SXfZx6hCa5retLollkA3LsZp7wFwm5gaZuj7Ml2HuEbvezlVi+e7ps7V8u+Vmp3nsqceF/fTNN9/cft4y0paJtpyutW3L9VrGe/fsp04e81SQqSPIsMyy9UtzBNkYa/Vl7jPuZ96d2Jg+dYedy8trW7udzjc2Zk39MDYu2Jh+iz8pvn1M/HbTNmAeK8L99OLY2D5xiO38QmvN54mpX8lcI5w3re5tzWX9n5+f+jKm9cFnzevZ+pzH21xy9u6Y641jtfnSzIkzdX+wPDyfzQ32+encQ2cR+yXrg/tfvXq1LQ89M7aO4PlY/7u+YM+CZSf2rkZu9cxMvWy25rJnxfu71btoTlBe395Fpx7N3fOZrsft3cjcZOapY9nNZ8U5gljb/Licr3VURERERERERER819AXQhERERERERERd0ZfCEVERERERERE3BmPxiF0hllCZhWZ1+Px5hxixtjOb9lFy+/tsqZTLwyZ+iQsc8x7M/8E687Ob9tkmrlmXVvG2erH8vjcz6znrn5475YrJSwrc6tTh9DULTB1DjF/bhlp88JYfU3z88TKw8+ba+F8ffNIWNtg3bMsU9eV5c0trz313nAMtoy03S+vZ/3ent2t7jSya2tTZx774RRzCUydI+Zhi8fP5XK5andTD4751GzNxPFg6ouwuWrXxm2ssXu3sYbYWEZnznQeY3k4thHWLY+3/m4eHmJtg9f7yle+crXNtsPzPX36dHv98/PksfZuYW46W09O13i8HtdQE2fOQ8fb9Vj+999//2rbym+uPbZ1OoF4v1Of7G59bvOurUlszWRrlGk/INbWiL1r2vlvfVcz95k5Pm2Mt3fZ3Xu3PUvrN7f2O/OTkmlb29371ee2V42IiIiIiIiIiO86+kIoIiIiIiIiIuLO6AuhiIiIiIiIiIg741E4hJhfnzp7mB3kfuZULd9nTiLuJ9Ps5xnLq099D4RlZ1ktI225TnMPWF2ap8Vyo5arNc+NZTHteizfLl+/c0ut5RnfqUPHPDS83vd+7/debZuTyPLdUzfX1GVg908XgXl2zFthbWP3PO3eeO6pp8z66bTdmydi6vCZPjveH+vDyjftWxMHyUPY/jPWzmxMJZbF5+etX0/bym4OsbkxvjzO7Wzq8LE2aq6QqYeHWBve9QnzOxAeb5+fujqmHkarO/PgcCw2P8V0DTQde6dzx9SdwnXNmcn6ba1PzkNTT6PVnXkJzQ9l3kebh6dzh7kP6SDi/bA+7Vnb9cjuPdPWWFMHpb3L2bvW9F2I55uu2dgvrD6mziXbtrZFpp67nb/VHD7TfjEdw8jU4WnltXHp4+Ne66iIiIiIiIiIiPiuoS+EIiIiIiIiIiLujL4QioiIiIiIiIi4Mx6NQ+icMWcezzLbzKczp2q+CebtzGtj+b8JU2fQ1NljuVHLpVpm17BnOc2VWvmJZSstT8+MtmWyLVv6wQcffPzf9mxZNsuXT7P10+y/ZXqJtVXLIBPLTJsrYNoXrC2yvjlu7OqLY5I9a/qPzu3oIViWaWZ56gpgXbJfWFsyt4F53og9S/NRmUPJxp3d9XguGwOtXbGs03GFWD/5PP1J8Ti4XC5X7WzqMDAfx8uXL7fHm9dx6mW08eV8PRv7zC1n/df6q827toa61d1h9zMdK4mNxTYPm+/j6dOno+vv5g62Wx47XTOYW491yWdpHkhbA9n1Xrx4cbVt7jyuQ6bz9vRdi+Xj9W0dsnMSsd1zTcX3yq9+9atX27Z+nfqmrO5t/T1dg3Hb/E7WD/kszVdr6wp797Tjd/VpY4z1eyu71f3UYURszJ2uVz8+72sdFRERERERERER3zX0hVBERERERERExJ3RF0IREREREREREXfGo3AIffjhh1d5TebxmL3kNrOe5hwyR8M0483sqHlkztuft4/B/BCWB2dd2L0Qq3tjmjOd5v9vdTRN3Si787PuuW3OIPMbTTPJ1s75LNjWLN/NfkvHD9ua5XbNs2NuBTJxAD10/MSlMO1H1m8tMzw9nzmObIw2LP/NMd2evfVza1tTV9kt3g7Lc/NZmbvK8uc2Rlk/t/y7+WOm7rH4cji3UZvHzell+9m/6Rgyh5nNlcSuv7v2dI1gY4uN1da/OfbYPDt1gPH8rCsbS7nfPDFW3+ZKNKfRbm5iO6VHZupve11Xx6dhz9Lmae5nXb777rtX2+bFmXooeT22HXuX4/WtX9tct3M4sa7pomJd8l7eeOON7bV5L1zv2rPi53m8jSuEdc3rTT2K1o+t7di7oTlBjYk7jO3Mxmire3N/Tf1Jdi82R+38gFfX/dQ9ERERERERERHxXUlfCEVERERERERE3Bl9IRQRERERERERcWc8CofQ5XK5yu4yW2iZZHMKma/DPDTMFzLLaK4Tnu98f+aeuDWbOM04Wz7esormGpj6KqbZzWn+f1ofVh77/NlzY14EcwvYs55u2/XMYWQurWfPnl1ts9+yrfB85kzi8eZQsgw1y8Pr27ix60tTNxf3m69pmomeejJY16wry0hb3doYam2V929zCtvidFwwzvc/bZdk6gazMeuWe3nofITPJh4fl8vlat3ENZY5eNgGePz7779/tW0+C1vn2LrDyn8+v40107HR1jh2fvM4EnOX8FmZk4h1aa4T3h8/z2dt44W5AKdukt34ZmsQ1qV5XWwesrq71dlp13/+/Pn2fLYmtDWTPWvbtvo1V6HNTed1xuQ9ba213nvvve21zRFk86A5eKZuMtYV+xWfLf1ZPJ+tOYmtM6a+WPs8y7v7/NS7aOtVu1f7DsPW69P3eHs2n0a/EIqIiIiIiIiIuDP0C6HjOP7IcRw/fxzHXzr92/cfx/HTx3H8tY/+9/s++vfjOI7/7DiOnzmO4y8ex/Hrv8jCR0RERERERETEnNf5hdAfXWv9Jvzb71tr/dnL5fJr11p/9qPttdb6l9dav/aj//vRtdYf+nyKGRERERERERERnxca7r9cLn/uOI5/DP/8W9Zav/Gj//7Jtdb/stb6sY/+/Y9dvhXe+/PHcXztOI4fuFwuP7e7xocffniVc7YcKvdbXnzqeWEW1Fwvln3c5YLNF2FZQbuWZZaJZajtfNN8vWF5e2LXMy/NNNNtWdNdeaftylwgljnm9ez8UweR1S37LZ1Cu7z3Wp8sP7etPixzzfJzHJn6sHj+8+ftXrnf/EzmeeD+nVNjrU/WrWGfp8fCvBOs26mLwTLcNq5Z27CM967t2RhGptl7awvkFhdWfHfw4YcfXo3PdP6w/5or0HwWPN7mJhvvdmPtQ5zbsDm4eC7zoOyutZbfm60nbZ4zXxuxsZFjn81d5jCy+rA1qpWHbe3Vq1dX2+e5z3xNNg/ZGsU+T3h9lt1cJjz/06dPr7bNxcVtWx9b27J5meW1dzdbZ1hfOsN2NHVmvvHGG1fbbOcsG5+lOTinbkGbt7nf3jfIdL07HYfsetNxd9fX7F45hpjLyjxvth62fmG+VPPQne93144+q0Pol5++5Pm7a61f/tF//+Ba62+fjvvZj/4tIiIiIiIiIiIeCTdLpT/6NdD4/2V4HMePHsfx9eM4vs5vTiMiIiLii+G8Bnv58uWXXZyIiIj4kvisXwj9veM4fmCttT7635//6N//zlrrV56O+6GP/u0TXC6Xn7hcLj98uVx+mD9rjIiIiIgvhvMajPHdiIiIuB/UIfQp/Jm11u9aa/3Bj/73T5/+/d88juNPrrX+6bXWO+YPWutbWcD33nvv423LgzMDxzyfeWS4n1lQZjstd2t59omDYepzMMfO1NnDbKI5crhtXhnzuth+qw9zEZhvg9j1rf53niBee1oWe9Z2fvPWEOZSLb/O8lkefZpnN6+L9SViuWAbB4xzX7HMr2WW6TljJtkyxtYvrO4s08xxgV/601Fi44Z5LXg989BNx0lzFVjbP2+bH4pl+cpXvrItm42JNobb8eYeMNfBtJ/Et59vfvOb65133vl4m/3TPDPTedLWBeYq+eCDD662ze3C8fXcJqdOLptnbE3Ce+fYaO4NW+/y3lkec/qYq4R1Sfisbc1o3p2pQ9Sud75/W69NHZS2/pz63KZtjXXFfsy5xNZk3ObnbV3AZ8XyMh1i6xZzA/LzZOdSYdn5Xsj9rFv7YYO1hemztTWSvXdO3WbsRzYuTtvu1Hc7fffafdbenWzNZH4ocwyx7Byj7d3Q+sXrOkH1C6HjOP7E+pZA+pcdx/Gza60fX9/6IuinjuP4PWutv7nW+u0fHf7frbV+81rrZ9Zar9Zav/u1ShEREREREREREd82XuevjP3OT9n1Iw8ce1lr/d5bCxUREREREREREV8cN0ulIyIiIiIiIiLiO4vP6hD6XPnwww+vMnjmPLCsILOHzLEyG8pcrLlWiLladtlJyxRbFtAcPJZdtKy+ZS2nOVZzBJnPwnwU5olh/dm25WDNpbAr/9TjQizvzvPZ8ZbxtfJZnpv9wvLiUzfDtHyWkSYcR3h9en14vvM4Q++B3bvVFTG3l2WSp14J65fmSLK2yTGYGWt+no4Ry4STqS9q4iiyc9m4YO12OoZO3QLWD23MjMfHN7/5zfWNb3zj4+2pu8+YugHJ1PFlbXbnp5iuWYj1X3NjEHN4WV1OvY429tl4ZI4he5Y219lYy7mBx5/nEnsW5hYxr+HU+WluPK4x7F3jrbfeutp+/vz59nrWj3h9exfjmonPhvXDedvWMebuo9fn/Lz4Hsh7IzYvWru3dj3tN7Zt78G2XrX1Or05HAfs3ZJ9x9Zk0/vftR1bj9qYY/MLP//uu+9ur2d1Mx03pv7UX6JfCEVERERERERE3Bl9IRQRERERERERcWf0hVBERERERERExJ3xaBxC5+yo5Vgnjp611nr27NnVNrOTvB4xZwKzqOZkOGctLcNs92aOoGk2kjCbyOPNaWR+C8sAT51Ddj/ms2C2lOWfOpl2finLn996L7z2tC5vdSHw2Zq3hkw9NxNPxEPH27Pm9TiuvG5O97OUZfpsLHPMdm790MYdc4dNHUPEymcuA7oLiHl5rH7No3Nuyzw3+72VzbwO5tyz69uzt7ohOYQeP5fL5aqd8RnbGmk6/kzXdDwfvY+cSzme8Phdm+W1Wbbpmms6L+3GjofKZz6LqTPMjifmnSQ2ltNNYm2PbpTJusCeDcdGW7OZk4ftknVlfidzXnIu4BqF5+e7i92fPTvC8phjyeZtG5d4P7s1pLm4bu0X5p6181k/MreYrQPef//97edZ9+bmsjXl1DvH49nPic1B5/0s23S9bWPUP/gH/+Bq297tzFE09ctO37U+vs5n+lRERERERERERHzH0hdCERERERERERF3Rl8IRURERERERETcGY/CIbTWdYbu+fPnn7pvrXnO1jLZ3DbHEK/H3Kplms/3Z1lA8z9YLtOy+iwr909zoSwvz28uEnNzWC7W8uZ2vanvgp9nbnjXlix/bhlmPptpBplY3Vt+3LwyrAu2zamHZupAIpbjZds1dwqf/e5+phnm6bO0bL7lsS1fb94IczHY/fL4s2PudT5vbcvg/ZhPipjXY3ctHmuOOpaN44LNEXZ9qzurm/P58wk9Ti6Xy9WYYOOPtW+OH+YisXn46dOnV9v0Wdj1bI14xtq73SuvNfUomntvuma0+7E1k42FxDw55nOz+7X6tPHrvN+ehTl3Js7Ih2DdcI3ENQW3eX6Wjw4h+7ytaYi5CVm/tkaa9iUez3Fih13bxpCXL19uj586h6aexakT1Dxr9i7D+3vvvfeutu3dz/xQ9m5p6wx7Tz/fH8tm7dZcXbwX68cvXry42qbjzr7DsDF+9965a2f9QigiIiIiIiIi4s7oC6GIiIiIiIiIiDujL4QiIiIiIiIiIu6MR+MQOmfcmOWcZrbNt2HZUF6PuVRe3zLYzPudr2c5TmIZXMtCmpeG5+e9m+fFMr/mIrHjWfd2/DR7yWypeWZYHmZBd5lzyyxb3Vq7tUwx4bM3l8Eks7vWJ9sm64bY/Zt3YuqHsuPNS2OZ6XNbNbcWnwUzymwrPN7qwp4tmfZ77qcDyPLixDwR07ZhTpGpQ8U416+V1XxL1o7N+0asbqaugmndxONg166mrg/zwNhc9+rVq21ZOXeYu283NrOsHKsMXtsck4Rju40Htj4m5hqcuvpsLuB4RcyFaI4hm2vMR3euP3u3MNcVsboyRxHXj/buMfWf2prTzm/rbevX5snk+W0u5H57Pjt/lM17fJb2bGzNYE5Kqyti/ZrYuw3Lx7ZJuJ/3M/UH8vpT79xuHWTv3Zwv6DXmmG9jEDFn0PQ7iskab/ccWrlFRERERERERNwZfSEUEREREREREXFn9IVQRERERERERMSd8SgcQk+ePLnKWVsm2/JzlvUkO8/LQ+dnVpTlZf5vh2VgWRZmH82RY1lGy0zz88w+mivAMsLMar7//vvb/fbsLc9ueXXLfPNZs+2wfnZtY+qisry75Vjt/NauLWNtGWjWlfVbux/zaU37hnl0zI3C87948WJ7/A5zVVke2/qB5dO5zbqeuhIsT26eC3N/Ee6fenFYfmurdr3z561uCa9tYxiPN0eQ1TWxuorvPC6Xy9Vz5TPlGoqeHVsXsM1Zm//+7//+7fls7rP+fr4/86jYWGXHs2zsrzZv2r2YP87221hqrhMbT9hWuG3OoKlfjve7ez4sOx08dq7pu4KtIWzNRWyemjpBWV62dWLrCHMQWf3asycsL98fzvfLc/Pdg/diaxobF2zetHtl2zSXH7dtjUJYflufW1uxcYvnZ1uxMZ7jys4nZe2SziBrl+bumq7BzPVFbJx5XX9TvxCKiIiIiIiIiLgz+kIoIiIiIiIiIuLO6AuhiIiIiIiIiIg749E4hJhBP2P5Pe63HLB5aXZleeh6xDLN52wjy2o+B8t1suyvXr361Guv9cncJ7OP9L6wPMw28t4tf2+51mmmmVlQcxZx2/L2Vj7WB5/HuT4t82vt0lwF5gqwdsq2aRleft5ytGR6PPezbdv+af7eysMMOq/HtnjGXGJ2L+ZLsjw7setPfUrE8vfm0TA3mXkxbA6xOcccQmw7Z08G7838S8zG2/GWvTfHyK6dPsTEG/e6Wfb49nIcx1U7Mr+F9U/z8tDRYHAuMo+N9ddz+aYuOfO7cR6wsevly5fb69kaz+6dTP1t5rUkfFYcT2yNyfHOYFubuA/N/2nvFub2mHoXbR6yfmbvPjZ32H4bB+iDsnWG3b+5/myu262LrB2bm88cPnbvxNa/U0/M1BnEcWDqCiP2vmDjgo3D03fJ87jMYzkfsSwcc219PHV43uqTvbXt/RL9QigiIiIiIiIi4s7oC6GIiIiIiIiIiDujL4QiIiIiIiIiIu6MR+EQulwuV5k35ufMU2PHM083zQFbjtb8HNx/zhOah4U5Vp6LjiDy1ltvXW0zr/706dOrbZaH+W5mLy37yPKb28N8GKxb5vV5ftYPnUGW1WR2lFlT7uc229q5viyja+2emMvDXADTfmWZZrZlYv2E5zNPjtWP3Z/B67MtkZ2Hhu3asvSWDzc/lOXfrS55vGWULR9un+f98HxWH1Z/1van25bPP9+vjYHMxu/mj4fOZ8+e5+f21B9l/c7qJr586HG0sZz7uY6wucJce9aGzX/HPsE2fr4enTUsi80TU38R10SsO1vz2L1N/UrmZZnOszwfnzXnTV6P9b97dmt98v74vLgGO3+eZbNtnnta1+Ysmq5xeD5uT10jVpfmHJpej583z6O1fWtbuzWblX3ngn3oeGsbNqZaWyBWfn7eni3HKXt3Yr80F6E968l79Fre987vbvysuWpZF2TqliX2rsU5ivunjtFPo5VaRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY/CIXQcx1WGjtk/ZheZz2NG2LKclgm3PJ/lWifnZ1nNf8RtOm3Mc8K6IpZrZd1PM8TTfDzLb04i7rfcLOub90+HErd5/M4ZxONZtqlXwTwqU0+MOXysXU+dR1OHD8szdSYRqw/z2FiGfHe+WzO/VnfWVux4y/5Px1jzSljWn+U3RxAxx4i1VYP3v8u3s+zmPTCXgTmFLFtvWXyrW3vW5zExn9B3BnymbAN0E7I/c13C/W+++ebVtvkz6Nkh03n+vG3H8tz0S5jfiMfzfPRDcL85fcyHYS4SWzMZvD637fw2F5p3h/fLtsV11Pn4STtZy70wUwePtfvd+nEtX+PYPGlj/61eG7Z9W6OaG5Hl4dxJpyj71rk80zWDOW/s2fLdwNZU0zWPvU+Y09Pags0Jtm4hPL/Vr42T9m52Hoc4P3GMmvqezDlk31FY27nVY3euq91zaXUWEREREREREXFn9IVQRERERERERMSd0RdCERERERERERF3xqNwCK21z7WZp8Uy39McsGWUp9nOnWOInyXmK7J7Ya6TOUvLw5uvgjAHarlSHs9M8C4D/DrbrC+2Hd6ftTXbz7w628L5+Vh+3dqG5bvNPWDnM0+MZYQtbz51lUzz7FMnkLlc2LaI5dl3mNPG2sLUy2DPwtxlNubas2Hd2LhB7P7MVcP6NB+AeS2s/s71wXw5efbs2dW2tUvWtbm8pv3M3AFTl1Y8Pp48eXLlXmEbNb8d2yw9LoROIPZHzvs2r3JsNkfD7tw2dtg8w2uZx3HqFLOxnp+fOo2mjsxpfdj5bE3LsZdtjW2RvpBzffJc5lHk/qlrz+rGfElTB9t0HUCmbYfnf/Xq1dW2+abYdqeOUZaP+3duRmunPDefzfRdwLZtjcLymCPInt2t77XWNq1fm5OI98dna/d/vh/WhfXz6frSfFG2vrQ13XQN9rpeuH4hFBERERERERFxZ/SFUERERERERETEndEXQhERERERERERd8ajcAgdx3GVybOsIfN3zOtZTtVywsTyg5Y9JefPm+fE8t3MFrKsb7311qhszL0ya8m6tOzj1BVi7hJzBpnnhvXFfDnbEvPpzKnyeVjbOl/fnEHTTK55YSzza86gaT8hdn7rZ5abtbbFbXoy6K0wnxXr0/rCeZvtlu2OmLOG7ZB1bf1s6o0x59HO2fHQ5y0DbV4M81C8bob60463+rK+dS6ftdv33nvvavtrX/va1Tbboc1/xLwW021zgpzLM30O8e2BazCuG9jG6Gmhm5BzmTkZ2KanTjNi49X587w38yqaT4xwnmf/tHtlXdPLYm48W59OXSHEym9jKWHbs/1co3E/17QTj4zNg7bmMj8pt82Fdas7b+qL4vqd5zdvIvs1t3k812RW3/Z+snvWVpcc06ZrNFuj2JrN5llivlRzlU0dQ8TKP22b1lbt3WvnYps6dnhuW+/ZepXtnO2WY5Y5PK1tvC79QigiIiIiIiJfs6c2AAAgAElEQVQi4s7oC6GIiIiIiIiIiDvj0UTGzj+348+h7Kd7/DkW99uff7b4g/3UzWJLu/iCxdnsz0/bn5mfRqjsT/9x/8uXL6+2+VM47ufn7adzVh7+lM7+1KP9OUCL5vDzFuEju8jY9OfIrAvrB/ZTbm5b+citEbTpT8vtz1nbT99Zf/w5s8UT+bNQtuVdfVt8jfsZbSTTnyMTi6qSafzP4nus21t/Amtt2yIo1pYtVrL70/L8k9sWH+T8Nf25s/1ZaGJ/ItXicvz8ec7qT9A/To7juFpn2c/1OU9y3uXcwePtTziTXfx2LY8r7GJU9vN++7Pn9ieCX7x4sd3/7rvvXm1PY/A83qLL0z8fbXOTxXstUmrPyv6sPNsen8/uT8tP25VFm60dW3SSn7fY0TRiZu8Ttsax8nObn2fcke8H1vZtDWtz0W6utIiWrWFszTJ9FtNYv92r6TuI9YVpfHH6p9Ht2XLbInDn58Uxw947CfdbjNfmP4v5W93Y9wjn43f31i+EIiIiIiIiIiLujL4QioiIiIiIiIi4M/pCKCIiIiIiIiLizngUDqEnT55c5dfNk2MOIWbkmDmmK2TqGrFMtP0px/N++7Pxlh+3HKhljqd/Rp5ZyXfeeWd7fvtzspa9ZC7U3AaE2U3WN9sS97PtWNtj2905naY5UMsI8/ipR4ZYft0yvdM/ZW7lu/XPYxMrv2XGrf53fc2ePb0N9meczcljvqapH8qy+tzP+7F+bd42G/PN8WNYfdicsHs+LJv92WcydQcQcwTZ8ea9mD7LeHywTdqfleczpieL/d38drYusT4wcapZezVPC9u/jW1c87Cufu7nfm57fpsrbG6xsZPXs7Hb1sM2frAt0ZfHNZg9W/M8nu//1rLaet2coLy+tXtb79vcYv3W+sLU6UlvDZ1BfD+wucXWoOZwOtePeVvMeTOdN6c+Kvsz9uZdNAfme++9d7Vt71bcb+9WVr9k2jZtzcdx61xeW3PZuwGxdskx39bndr3pmux1nZyt1CIiIiIiIiIi7oy+EIqIiIiIiIiIuDP6QigiIiIiIiIi4s54FA6h4ziucr9f+cpXrvZbft1cIcw8E8sm2ra5U5jvO9/f1IPC3CbPzdykeWRYNy9evLjatlymZSPtesTq2vLjhPXFtmX7rXyWe2WWc+cQMtfG1KsydQ7tfEcPnc/8VoaVb1o/5k6ZXt/u1xxIO/cZ+wG3zalBLD9ubgN71tO64n66BHi/ls+3Md/cCjZGT88/9VWdxwGei3XBsnF76gQx9xWZ+qImefxpH41vD0+ePLma+zgv0uNi86i1GVtDsU/Qs2Nt2vrIeZtl49hpviNz7LBuuMZ6++23r7atv/N6Vn7bb24Qjt327Hh+tp3dmmitT7Ytc4jy+nzWrL/z3GLjvo3FNvaaX8k8heYAndYN26Z5J6duFZ7f2pp5Z1ge81JOHEJ8d7H1sa2Z2BZYN7aetX5kYxyP/8Y3vrE9fupp4xjM8pgnjth6397FeL+Enz+fn2Oa1b35nPh5jvG2nufnWXf2rsZ2z7Z9vv5urdwvhCIiIiIiIiIi7oy+EIqIiIiIiIiIuDP6QigiIiIiIiIi4s54NA6hc0Zu6ggynwOzkMwWMi9oeUKWj/sth7vLPlq+elrWd95551Ov9dDxH3zwwdU2s5aEx1sO13Kq5kqxDDFzrvRPMc9ubcOeB/eb0+hcP5ZXN9/G1PNi7ZL3Yvlxy9Wak2jq5pr6Ryx3az6qadu0tnDua1Y3UzeB1Y1lngnHBcubm1OI3gu7PjPUdn/Wtm1cmp7f5pxd/XCMYl2wHZlzyPrd1C023Tb/Fes6Hh9Pnjy5miv5zNgfOV6xDbCNsM1PfRUsj3ljyM6bYP2F1+ZYZmO5eRlZV1xT2bxj/ZHztK0p6Z+wNQ3bBq9n2xzv+Oyt/FzjmS/ufD/TNQrrwtxyxLyENo/Zuw3rytr21PU1/bw5gKyvkanjaPc87Vq8l+may+ZNe5ezccD8Tfy8ld/eR3g+68f2fmFY37I5gZz323rO2pG99xI+a3v2Ns5Yv9s9611Z+4VQRERERERERMSd0RdCERERERERERF3Rl8IRURERERERETcGY8i3H8cx1UOmdlEZpSnfo2J12WtT+YFeb5JVvGh853zfnYuc/DQ8cP8N/PuhPt5vml20c5v7g7z0nCbuVQ6gJgvZ1uaukEsE23P65zz5b1appdYZpdYv7HcqvUz3jvPd6sTiJi3hlhe37YtszzxCZj/ie2Y7dbq2vLx9iyZR+f21Alk98t8vHkwzJ1ArF+zbUw/b9tnrK5YF3yWVtZb2yn3s26tn+2e7a19Pr4YjuO4mk/MDzF1aE39FcT8GTa2k51PgmXnmor+BxsLOZbxfC9fvtx+nufnvfJZTb2N5mXhXGRrIDqAbI1nY7ftt/U6r3+u/6nXhEzXILwe2xLnefNHTd9NzJVia0Q7n/X7qWfyVo/Mri9a2cwnSuzZT8cN8zhyHKEv1ty35oXj/dCbw/oxP+t0DWb1OXWwnvezrlmXPDfHcPM8kun8ZOfjvdma8TyG5hCKiIiIiIiIiIiP6QuhiIiIiIiIiIg7oy+EIiIiIiIiIiLujEfhEHry5MlVHpH5N/POMN/HPB2zlITHW9bRcrpkdz7LIFsmlrlOy/QyK8l7Z57dMr/EcqMG8+KWvaQjyNwiPJ4wB2u+Dj4/c7Ocz8ccKp+ltXvL0HLbngWvz7ZjWX07v9Wlnd8ywuZOsPy5uQrMucS+sfN3sSzm7LBnaffKbY6J3Gbd271xv/mneDy3mW8n5rXhNp/1xPnzOsdP+qL1U6srG2Ot7m91jdmcwG3rV/HlcxzHVR8xX4Y9c67Jpv2LfgpbJ009kedtm7M51vHeePx77713tf3uu+9ut+18HLvMHWiOH9vP/konEK9v8y7XXOaHM++jzWW8PvefP8+y2DwxXZPYGoHnZ1szh6Xdqzkuef9Tj4u1TT5bGxesvDY3kl3fsncNGwPN7Td1zXLb/LB8V+O9si3Yu8+k3zy035xF1pbNBzV9L9+1DWt31o/s2U2dlLbeJzYfTl1gH1/3tY6KiIiIiIiIiIjvGvpCKCIiIiIiIiLizugLoYiIiIiIiIiIO+NROITWus68MQ/HXC3zeubdMacCmToYLI+4O//UQcNcqeU4X7x4cbVNT4zdi+237CJzotzm+Vl3dAkwT85t5lCZf+d+YuUjdr87n4ZlYq3umTtlhpiZYT57y+iy7u3zZNIP1nIPxBTzVpiPittsO8xwW579XB7WrXkept6Yqf/JHCAcd7jfxkzzuE0dSrfWl2WweX1rSzaO7XwE9uymY7Sd3zCPhLW9XT943Sx7fLmwv5qX0dowxwvC8ZDnZ/8xv8dkTWjuDLZZzoM2NtL9QceQOYOmvjOOfbaGMg8MvYrmTjEXyNe+9rWHiv2p5WPbs/qysf28n/s4p9s4zrplWfgs2HY47/He+Hm2LdaVzevmc7P1LPfb/XANZa4VW6dYeXg82+752bMueSwx9xaxfsu2x7rkNstrzk9i7l1bZ9i7k61b+GzYVswPNXWE7tqSrUfNEWRlt/WovTtN1/d2/PlZ79Zg/UIoIiIiIiIiIuLO6AuhiIiIiIiIiIg7oy+EIiIiIiIiIiLujEfhEDqO4ypjZ46EqU+Dx1s+/qHy7coz9cycy8ecozmBWFb6lbjNzzM3OsldsuwPHW+eG8tWWh7dMtzMdlrW03Kxlme3/PouZ2x1ScwdwHtlWa3dmjfG6opYhpiwvOYUsvObI4nP1twDltFm/ez8AOaomXoiuG1ltbq1fsny27jE85mHglj92Lhh/dLu157H1CW2O3bqFDIPg7kB7N7M+2Dz43lcmfqM4tvHZA3GsY3rDHpzzAVibkPOTXQDmlOI/e/cDnlu80twbOPn6aHhPGr90a5PONZx+80337zatjUSn415Z+iJ4f3w8zZemYfH/HW8/s6VYs4dloX72W65Hp269WwNZ/2O5+cah8fbet+cRTaXsLz8vM1V5o2xNe2ubU3rwhxDZOrQZHnsXY51w7Zo717WNmwOsH46PZ5Y/Rm2rtlh/d7qysYk22bbm76b7M6fQygiIiIiIiIiIj6mL4QiIiIiIiIiIu4M/ULoOI4/chzHzx/H8ZdO//YfHcfxV4/j+IvHcfy3x3F87bTv9x/H8TPHcfw/x3H8S19UwSMiIiIiIiIi4rPxOlKQP7rW+s/XWn/s9G8/vdb6/ZfL5R8ex/EfrrV+/1rrx47j+HVrrd+x1von11q/Yq31Px3H8Y9fLpe9PARMnQjM9zF/xyymeQy43zw6xHKsu89bdp7bdq88nlj20fLulhG2fLo9a2aymYudblvm2rKb5s+YOJksF8qyW/7cvDQGzz+ty2n5LaNsmW7zthiWabb7M2fQri9av+C9mV9p6sCxujNniPmg6PiYXs/GWPNOmO+K+6f92LxxO4+d3auVxbwJ088Tc4/ZfLZzIt2S648vjsvlcuWgMH+DrVPMzTJtY3Y96+9so+frm6fR1lh0BJnX0dYcnDdtruCai64Tft78S9P1t3lhpmtKwvPxfuhOsXn5fH0rK/fvXFQ89+uU3Twy1g+5Pmb5Ju8ea80cmGv5usXWJdM1rdWPOVnP4wbPTRcW+y3rmp407rd513yr3GY7t3WDvbdO3bnmHuM4wPJP12TmgSS2Ztsdy/lk6qS0fjyd/+xdxsZMXn/33nlGfyF0uVz+3FrrG/i3//FyufzSHf35tdYPffTfv2Wt9Scvl8v/e7lc/sZa62fWWr/BrhEREREREREREd8+Pg+H0L++1vrvP/rvH1xr/e3Tvp/96N8+wXEcP3ocx9eP4/j6u++++zkUIyIiIiKM8xrsxYsXX3ZxIiIi4kvipi+EjuP4D9Za/3Ct9cenn71cLj9xuVx++HK5/PBbb711SzEiIiIi4jU5r8GeP3/+ZRcnIiIiviRexyH0IMdx/GtrrX9lrfUjl/8/lPZ31lq/8nTYD330b8o512aeF3P87HKjD33ezmefn3p4zuc3F4dlEc01wvNPHT7MTnI/c7fM0U4zxDzecqt2PZ6f+XlrC5ahttzqri1zn+Wxrd0Ra4fmArB+Mc3VEntWltdnRtlcJnY9823xfs03xfpkWz1j2XvLHPNaO4/LQ1jbIzbuWL/i561u2a9sXLH7mebTp5nu3ThnzhBiLqsp5pXg9azurF+dXQxWz/HlcLlcrvqwOcjMP2Ftim3G1nBT59DEUTZdH/J4ukaIjVVTRxDnEW5zjcNtHm/P2sbSqRuF+1l/rA9i5eP1dv4OXmvq8DF/kvmibCyduvbM3TVd/9v9WFvm9a2+3nvvvattaztsy/Y+cT6ftVv2m6mnkc/W1qfm3OG7Ft1l1tamnkR71+SzNgfS1BlqbW/quzpf356d9SM+S/qkeDx/gWvre/Mp2XsyOc9ZNzmEHuI4jt+01vr31lr/6uVyOdfEn1lr/Y7jOL73OI5ftdb6tWut//WzXCMiIiIiIiIiIr4Y9BdCx3H8ibXWb1xr/bLjOH52rfXj61t/Vex711o//dE3a3/+crn8G5fL5S8fx/FTa62/sr4VJfu9078wFhERERERERERXyz6hdDlcvmdD/zzH94c/wfWWn/glkJFRERERERERMQXx2d2CH2eXC6Xq8yeZbiZnfzggw8+cb4zzOeZQ8E8PMyQW55+5xqxDLDtt8zsdNuyi8yFMmfL4w1zHFme3p4l97PtMHtpuVXDvD27fdZuLQNsPipiz8r6odXN1K9kbhTLYJv3hsfTXcDzs61ZW2VfIOfPm+OH/WxaFvNP8VlYFt/y4hyDzfnB/Ty/eSLM6WN5cpZn2hfMrbZzJk08Yw+dy9wA5sGYeiGm7gF7dvE4OT+36ZqI+81DNfVYvXz5cnu8+UDIeb/5IXivtj118HBst+0333zzavvZs2dX23SN2BqHYznrfnp/xPxx5r+wNa+NT7vrmc/NXH3ctncBYl6V6fmsbng+zvt8t7G5gvU1de1xm23b1v/Ttrl795p6Drlt63WWhXVv71r0K7EuuJ7luw7HCZbX3u3YT81rY/dj4xD3T99vbA7YYWsiW7Nxv3mN7V7t3WTaNj+Nz+PPzkdERERERERExHcQfSEUEREREREREXFn9IVQRERERERERMSd8SgcQt/85jfXixcvPt5mXo5+CubvuJ95PcIspOXlCa/P7KZxzhtaNt88M+bWYC6T28waWr6b+fTnz59vP29ZSGYnLQtpPgxzh9j9sv7sfJaJ5vXO92/tjs/KXCFWFnP+8HrEnh2xPPvUjWKft/w6+y2fDa/P/cx8M8dr+f/z9e3eeC3rR8ScN1MXmY1DbDscE3m8OZCsPDZu2Lhg52P5Jo6gtWYuCJ7LHHVTX4qNI8TaJjGHkLXV+PL58MMPP+GcOMM2aT4HGx/MsWDjk60JyW5etrHJ5jlzZ3ANxf7M4+lRocvDts27YmOtjWW2xrN5fOoOIVPn027NZ24OtkvWnb0r8Nrmh5v6kczxY5+3upyuA3h+W1MSHm/P59WrV9vr8/mdz2/eMvZba1c2D5L/r737DZWsruM4/vmyZpu7kmuGlGtpJIVJpUgYRYgFaYnbgyjDyKyIIMiiCDcfRA96EEX/qIRQWwPRwqyWwEhMqCdammCmWaL5D/9V67buhTW93x7McZ379d753N+ZuzvnNu8XyO65M3fmN7/5nXO+c5zvZ93acPuRe+0uO6x+lnM1U308lynkPnu6tdu67Wq08ZrUvVb32aruN7Xerdsuc8jlzLn9wu3Hq63B+IYQAAAAAADAnOGCEAAAAAAAwJzhghAAAAAAAMCcGUSG0OLi4pIMIZfRU7drr6Xrp3N5Gq4/vrU/z2W3jGvNpHF9o7X30GUCub7Qev+adeKyM6bNv6jjcxlKrme7cn2pbrzu8SdlF7Ssk+W0ZvTUuWzNkXGZR+75W3uAXUaQe29aM5Tce1/3jXrcqs83Pt/1NteP3prz4HIY3Hszbf/3pOys5e5ftWaluZ5wtzZcRpNbey35V3WduMwgNzZ3TnB5Lq05aW4/nnQ+nvYYhwNjcXFRe/fu3b/tsjhcjVW5NVxvr/uEy9lyGUKTMsLc+q9cNkfdv2rNVI8l9fE2bdo08fb6eHXbHevqe+eOpe5Y6OoEd7xx5wo33sqdi8Zvd5mV7tjqjoWVq4drPV3Vzz7uXFCfr2bwuBrOHdvdZ6W6dt1xpW7XtV33c1eHuLU8zmWBta5Tt3ZcjVIz3WpWmDvO1Jw1lyXo8mer1vsf6AzSOv+TPhu781cdW53Luu0yMet2Xecuk9Ll2rnHWy2+IQQAAAAAADBnuCAEAAAAAAAwZ7ggBAAAAAAAMGcGkSH07LPPavfu3fu3az+cywxyvZ1V7R90/eqV6xN2vaGTfr/2YdbX5vpcXV9n7U+vfae1x9j10bp+7tYeX5eJ1JqXUbVmr7i+1cpln4w/nssecL390+bIVK5P1b32en/XN+uyDup7XbnxurVbjwN1rbl8gDofrl9+0u+6HIb62ly/tOvtb10b7vdbc2hcflbr49X5a80scvlXdb4rtzbGf7/uB615SpXLZ3G5dG7tta7FSXkzZAgN0+LiohYWFvZvT8pdkbTkvsvd7vLUXJZKXZOtmUXuXDm+7WqqzZs3L9muWR4us6bev54nas3lap7WGmzaLL/WY7E7ltbnq8cLd+5ydUNLzmPrsW5SPSf5Y7ubW5fR0/reuRqs7qd1rbr3oqrP1/pe1rXj8vRc9uJ4Nm19PrcfteYKuvrP1UD18d1nsZpj43JrqtbPG9WkjEyp/fW6fcuZJq/WZdK5rKua9+SOedN+jnWZlZOO+ZMem28IAQAAAAAAzBkuCAEAAAAAAMwZLggBAAAAAADMmcFkCO3atWv/tuvrdb2Brbe7HmvX9+v63yf1D7o+zdpHWrfr2GtfaM0IqhlCte+zbrs+0zq37vW4fAuXq9P6XrisAden6no1W/tsJ/VUt2YDuOdqzQ6Ydj+oWnNyKtcX63Jz3Hvrnr/2Cdfnd5lFk+7vMjJcflI17Xvtnq/O5caNG5ds17ly/eSt2QJurbkshsqdI1zOjsuKmPR+uDyU1uyByu03NXvAZZRUbq3Vfvq9e/eu+LsYhsxcsi7re1jf45qxUNdcXUNuf3G31+1ax9Tfrybt73Ws9bG3bNmyZLvmrLjzYq3BXI3l5tLlT7jzYGsuzLTnUZdrU8dTj48uy9Dlx7WMrSV3ZLnb3Vjdunfn5brd+l67nJV6bqhrs2rNeXHjc/Wy+/16XKrGH7/et+6nk7LwlhtL61ppnbu6zutnwapmn7m1UB+vdb92eU51bbu6x9VULudx0jnEZUK6Gscdg9xxxX22at0Pqjq+8bU+6fjIN4QAAAAAAADmDBeEAAAAAAAA5gwXhAAAAAAAAObMIDKEMnNJj5zrj6t9rbXX02Wv1MevPd2tvZOuR3pSr6TLa3CZOjXLo76WenvtE633d72Qbm7r/Vv7210faWtGkOsVdZlIjnv+SWvJZX1Ubm5be55dD7EbT2s+k+und49fX7/rGXZrte57rdkF7vfrvrVv375VP3ZrZse0WQWt/eP19pqrMf5aJd9vXsfnjrGtPdSuR9tlFFX19+v705IdVo/R7r1rzRBxvfdu7bm5rq99YWFhyfb4WnDrCrORmUvep7r/1mNbrUPqGnrqqacmPl99vHr8cMfaus/U213dMH5uqOeJ+tguR9HlMNbt1swul49WH7/ev+6fLofGPb8777rjkzv+OGuZmdSaSdmaT1q587DLWXT5S/W9du+dywhyr9+dN+v4XJ3hPn/U1+fmb1IGaX1ut65dbmIdW32trftFfS179uyZ+PuHH374km2XFehqqnp/97m7cp/t3Hw4rsaaNJ/1uWtmnltnlTv/tGZcurynen6uXE23Er4hBAAAAAAAMGe4IAQAAAAAADBnuCAEAAAAAAAwZ2IIPf0R8YSk+yUdJemfMx7OesXcTYf564+564+564+5m84s5u/Vmfnyg/ycMKjB1gRz1x9zNx3mrz/mrj/mrr9Zzd2KNdggLgg9JyJuycxTZz2O9Yi5mw7z1x9z1x9z1x9zNx3mDxVroj/mrj/mbjrMX3/MXX/MXX9DnDtaxgAAAAAAAOYMF4QAAAAAAADmzNAuCP1w1gNYx5i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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 22, + "id": "4f6e3663", + "metadata": { + "execution": {}, + "id": "4f6e3663", + "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "test: 100%|██████████| 216/216 [00:12<00:00, 17.76it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "final test accuracy: 0.8054750869061413\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + } + ], + "source": [ + "accuracy = test(test_dataset, device=device)\n", + "print(f\"final test accuracy: {accuracy}\")" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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" + "cell_type": "markdown", + "id": "3f43bba5", + "metadata": { + "execution": {}, + "id": "3f43bba5" + }, + "source": [ + "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", + "\n", + "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n", + "\n", + "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types." ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "markdown", + "id": "72b5240c", + "metadata": { + "execution": {}, + "id": "72b5240c" + }, + "source": [ + "---\n", + "# Train a GAN to Translate Images\n", + "\n", + "We will train a so-called CycleGAN to translate images from one class to another." ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" }, { - "data": { - "image/png": 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- "text/plain": [ - "
" + "cell_type": "code", + "execution_count": 23, + "id": "41c9e63b", + "metadata": { + "cellView": "form", + "execution": {}, + "id": "41c9e63b" + }, + "outputs": [], + "source": [ + "# @title Get the CycleGAN code and dependencies\n", + "\n", + "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n", + "\n", + "import requests, zipfile, io\n", + "\n", + "url = 'https://osf.io/vutn5/download'\n", + "r = requests.get(url)\n", + "z = zipfile.ZipFile(io.BytesIO(r.content))\n", + "z.extractall()\n", + "\n", + "!pip install dominate --quiet" ] - }, - "metadata": { - "needs_background": "light", - "tags": [] - }, - "output_type": "display_data" + }, + { + "cell_type": "markdown", + "id": "e5da5c01", + "metadata": { + "execution": {}, + "id": "e5da5c01" + }, + "source": [ + "In this example, we will translate between GABAergic and glutamatergic synapses.\n", + "\n", + "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "2b2519c4", + "metadata": { + "execution": {}, + "id": "2b2519c4", + "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|██████████| 22648/22648 [00:03<00:00, 6536.44it/s]\n", + "100%|██████████| 5085/5085 [00:01<00:00, 3398.25it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] + } + ], + "source": [ + "import cycle_gan\n", + "\n", + "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n", + "\n", + "## Uncomment if you want to enable the training procedure\n", + "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)" + ] + }, + { + "cell_type": "markdown", + "id": "0d328904", + "metadata": { + "execution": {}, + "id": "0d328904" + }, + "source": [ + "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", + "\n", + "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "a182c3bc", + "metadata": { + "execution": {}, + "id": "a182c3bc", + "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "----------------- Options ---------------\n", + " aspect_ratio: 1.0 \n", + " aux_checkpoint: checkpoints/synapses/classifier/vgg_checkpoint\t[default: None]\n", + " aux_downsample_factors: [(2, 2), (2, 2), (2, 2), (2, 2)]\n", + " aux_input_nc: 1 \n", + " aux_input_size: 128 \n", + " aux_net: vgg2d \n", + " aux_output_classes: 6 \n", + " batch_size: 1 \n", + " checkpoints_dir: checkpoints/synapses/cycle_gan/gaba_glutamate\t[default: ./checkpoints]\n", + " crop_size: 128 \n", + " dataroot: data/raw/synapses/cycle_gan/0_gaba_2_glutamate\t[default: None]\n", + " dataset_mode: single \n", + " direction: AtoB \n", + " display_winsize: 256 \n", + " epoch: latest \n", + " eval: False \n", + " gpu_ids: 0 \n", + " init_gain: 0.02 \n", + " init_type: normal \n", + " input_nc: 1 \n", + " isTrain: False \t[default: None]\n", + " load_iter: 0 \t[default: 0]\n", + " load_size: 128 \n", + " max_dataset_size: inf \n", + " model: test \n", + " model_suffix: _A \t[default: ]\n", + " n_layers_D: 3 \n", + " name: \t[default: experiment_name]\n", + " ndf: 64 \n", + " netD: basic \n", + " netG: resnet_9blocks \n", + " ngf: 64 \n", + " no_dropout: True \t[default: False]\n", + " no_flip: True \n", + " norm: instance \n", + " ntest: inf \n", + " num_test: 500 \t[default: 50]\n", + " num_threads: 1 \t[default: 4]\n", + " output_nc: 1 \n", + " phase: test \n", + " preprocess: none \n", + " results_dir: data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results\t[default: ./results/]\n", + " serial_batches: False \n", + " suffix: \n", + " verbose: True \t[default: False]\n", + "----------------- End -------------------\n", + "dataset [SingleDataset] was created\n", + "initialize network with normal\n", + "model [TestModel] was created\n", + "loading the model from checkpoints/synapses/cycle_gan/gaba_glutamate/latest_net_G_A.pth\n", + "---------- Networks initialized -------------\n", + "DataParallel(\n", + " (module): ResnetGenerator(\n", + " (model): Sequential(\n", + " (0): ReflectionPad2d((3, 3, 3, 3))\n", + " (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1))\n", + " (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", + " (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (6): ReLU(inplace=True)\n", + " (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", + " (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (9): ReLU(inplace=True)\n", + " (10): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (11): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (12): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (13): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (14): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (15): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (16): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (17): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (18): ResnetBlock(\n", + " (conv_block): Sequential(\n", + " (0): ReflectionPad2d((1, 1, 1, 1))\n", + " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (3): ReLU(inplace=True)\n", + " (4): ReflectionPad2d((1, 1, 1, 1))\n", + " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", + " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " )\n", + " )\n", + " (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", + " (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (21): ReLU(inplace=True)\n", + " (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", + " (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", + " (24): ReLU(inplace=True)\n", + " (25): ReflectionPad2d((3, 3, 3, 3))\n", + " (26): Conv2d(64, 1, kernel_size=(7, 7), stride=(1, 1))\n", + " (27): Tanh()\n", + " )\n", + " )\n", + ")\n", + "[Network G_A] Total number of parameters : 11.366 M\n", + "-----------------------------------------------\n", + "creating web directory data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest\n", + "processing (0000)-th image... 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['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10284_train.png']\n", + "processing (0320)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10289_train.png']\n", + "processing (0325)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10293_train.png']\n", + "processing (0330)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10298_train.png']\n", + "processing (0335)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10301_train.png']\n", + "processing (0340)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10306_train.png']\n", + "processing (0345)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10310_train.png']\n", + "processing (0350)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10315_train.png']\n", + "processing (0355)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1031_train.png']\n", + "processing (0360)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10324_train.png']\n", + "processing (0365)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10329_train.png']\n", + "processing (0370)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10333_train.png']\n", + "processing (0375)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10338_train.png']\n", + "processing (0380)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10342_train.png']\n", + "processing (0385)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10347_train.png']\n", + "processing (0390)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10351_train.png']\n", + "processing (0395)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10356_train.png']\n", + "processing (0400)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10360_train.png']\n", + "processing (0405)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10365_train.png']\n", + "processing (0410)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1036_train.png']\n", + "processing (0415)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10374_train.png']\n", + "processing (0420)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10379_train.png']\n", + "processing (0425)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10383_train.png']\n", + "processing (0430)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10388_train.png']\n", + "processing (0435)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10392_train.png']\n", + "processing (0440)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10397_train.png']\n", + "processing (0445)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10400_train.png']\n", + "processing (0450)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10405_train.png']\n", + "processing (0455)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1040_train.png']\n", + "processing (0460)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10414_train.png']\n", + "processing (0465)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10419_train.png']\n", + "processing (0470)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10423_train.png']\n", + "processing (0475)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10428_train.png']\n", + "processing (0480)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10432_train.png']\n", + "processing (0485)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10437_train.png']\n", + "processing (0490)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10441_train.png']\n", + "processing (0495)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10446_train.png']\n" + ] + } + ], + "source": [ + "# translate images from class A to B, and classify each with the VGG network trained above\n", + "cycle_gan.test(\n", + " data_dir='data/raw/synapses/',\n", + " class_A='0_gaba',\n", + " class_B='2_glutamate',\n", + " img_size=128,\n", + " checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n", + " vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "17fc1703", + "metadata": { + "execution": {}, + "id": "17fc1703" + }, + "source": [ + "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2a582ba6", + "metadata": { + "execution": {}, + "id": "2a582ba6" + }, + "outputs": [], + "source": [ + "class_A_index = 0\n", + "class_B_index = 2\n", + "\n", + "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n", + "classification_results = []\n", + "for f in glob.glob(result_dir + '/*.json'):\n", + " result = json.load(open(f))\n", + " result['basename'] = f.replace('_aux.json', '')\n", + " classification_results.append(result)\n", + "classification_results.sort(\n", + " key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n", + " reverse=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2cc0d486", + "metadata": { + "execution": {}, + "id": "2cc0d486" + }, + "source": [ + "Show the top real and fake images that make the classifier change its mind:" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "1567b00e", + "metadata": { + "execution": {}, + "id": "1567b00e", + "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ], + "source": [ + "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", + " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", + " axs[0].imshow(a, cmap='gray')\n", + " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", + " axs[1].imshow(b, cmap='gray')\n", + " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", + " plt.show()\n", + "\n", + "\n", + "# show the top successful translations (according to our VGG classifier)\n", + "for i in range(10):\n", + " basename = classification_results[i]['basename']\n", + " score_A = classification_results[i]['aux_real'][class_A_index]\n", + " score_B = classification_results[i]['aux_fake'][class_B_index]\n", + " real_A = imread(basename + '_real.png')\n", + " fake_B = imread(basename + '_fake.png')\n", + " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "m7XsnW_R7wBN" + }, + "id": "m7XsnW_R7wBN", + "execution_count": 27, + "outputs": [] } - ], - "source": [ - "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", - " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", - " axs[0].imshow(a, cmap='gray')\n", - " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", - " axs[1].imshow(b, cmap='gray')\n", - " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", - " plt.show()\n", - "\n", - "\n", - "# show the top successful translations (according to our VGG classifier)\n", - "for i in range(10):\n", - " basename = classification_results[i]['basename']\n", - " score_A = classification_results[i]['aux_real'][class_A_index]\n", - " score_B = classification_results[i]['aux_fake'][class_B_index]\n", - " real_A = imread(basename + '_real.png')\n", - " fake_B = imread(basename + '_fake.png')\n", - " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "machine_shape": "hm", - "name": "em_synapses", - "provenance": [], - "toc_visible": true - }, - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.10" + }, + "accelerator": "GPU" }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} + "nbformat": 4, + "nbformat_minor": 5 +} \ No newline at end of file From 4ae5cf7ae8e67d141395f44727ed9a981fb12a2c Mon Sep 17 00:00:00 2001 From: Mobin Nesari Date: Tue, 9 Jul 2024 18:33:11 +0330 Subject: [PATCH 15/25] Removing extra files --- .DS_Store | Bin 6148 -> 0 bytes projects/{ => ComputerVision}/.DS_Store | Bin 6148 -> 6148 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 .DS_Store rename projects/{ => ComputerVision}/.DS_Store (83%) diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 988d1b44537a728310113a45890b2741a8e33794..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeHKO>fgM7=GP#kZeNq0Maf+&_E3`+!3iPcH?kjpypJ6}cI=plL}xN=5!HyuL13(IAzEWx&tb(X zreqr^xQ*jPo`#7Gk>>`jSp}>D|F#1B?N%uyiRY~J{kBop_^wP1Vv~;OA+_lN^-7+f 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--- projects/ComputerVision/.DS_Store | Bin 6148 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 projects/ComputerVision/.DS_Store diff --git a/projects/ComputerVision/.DS_Store b/projects/ComputerVision/.DS_Store deleted file mode 100644 index c066fc6ec5b7614ab48c8ada8b5ed7b8ce435912..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~&rTaL5XQ$e2U2=~Lj^*rv`-MJZ?K?n;J~Fk0BjP7A|)G?KdN%uJMYjpsCd4f z%5TP2l3<#O;-`5uP9HHPo>H6W)TOGi}lbhjR(vMr2nB7^O*gdYv2YsONr zBR#-yA!C?_@ag`f^V>PA@LXeR%P+Ztp({6F2Znmz|7scE7-L^%^z?h*><@;uyf)D~ zPE|lvYxHD_|JhLdoVX{V`^;#@*BVk5ra8^WC`i0QOvDK1Y~MU3eZ(&Zzho Date: Tue, 9 Jul 2024 19:47:19 +0330 Subject: [PATCH 17/25] Fixing Lunar Lander notebook problem --- .../ReinforcementLearning/lunar_lander.ipynb | 2453 +++++++++-------- 1 file changed, 1264 insertions(+), 1189 deletions(-) diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 30d72f76f..80b6eda54 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -1,1225 +1,1300 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "execution": {}, - "id": "view-in-github" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", - "\n", - "__Content editors:__ Shaonan Wang, Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [], - "source": [ - "# @title Update/Upgrade the system and install libs\n", - "!apt-get update > /dev/null 2>&1\n", - "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", - "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", - "!apt-get install x11-utils > /dev/null 2>&1\n", - "!apt-get install xvfb > /dev/null 2>&1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [ + "cells": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "# @title Install dependencies\n", - "!pip install rarfile --quiet\n", - "!pip install stable-baselines3[extra] --quiet\n", - "!pip install ale-py --quiet\n", - "!pip install gym[box2d] --quiet\n", - "!pip install pyvirtualdisplay --quiet\n", - "!pip install pyglet --quiet\n", - "!pip install pygame --quiet\n", - "!pip install minigrid --quiet\n", - "!pip install -q swig --quiet\n", - "!pip install -q gymnasium[box2d] --quiet\n", - "!pip install 'minigrid<=2.1.1' --quiet\n", - "!pip3 install box2d-py --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "sgIXqXYCDBuR" + }, + "source": [ + "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", + "\n", + "__Content editors:__ Shaonan Wang, Spiros Chavlis" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/tensorflow/python/framework/dtypes.py:35: DeprecationWarning: ml_dtypes.float8_e4m3b11 is deprecated. Use ml_dtypes.float8_e4m3b11fnuz\n", - " from tensorflow.tsl.python.lib.core import pywrap_ml_dtypes\n" - ] - } - ], - "source": [ - "# Imports\n", - "import io\n", - "import os\n", - "import glob\n", - "import torch\n", - "import base64\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import sys\n", - "import gymnasium\n", - "sys.modules[\"gym\"] = gymnasium\n", - "\n", - "import stable_baselines3\n", - "from stable_baselines3 import DQN\n", - "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", - "from stable_baselines3.common.callbacks import EvalCallback\n", - "from stable_baselines3.common.env_util import make_atari_env\n", - "\n", - "import gymnasium as gym\n", - "from gym import spaces\n", - "from gym.envs.box2d.lunar_lander import *\n", - "from gym.wrappers.monitoring.video_recorder import VideoRecorder" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "AKlrTCmFDBuS" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "# @title Play Video function\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "from pyvirtualdisplay import Display\n", - "\n", - "# create the directory to store the video(s)\n", - "os.makedirs(\"./video\", exist_ok=True)\n", - "\n", - "display = Display(visible=False, size=(1400, 900))\n", - "_ = display.start()\n", - "\n", - "\"\"\"\n", - "Utility functions to enable video recording of gym environment\n", - "and displaying it.\n", - "To enable video, just do \"env = wrap_env(env)\"\"\n", - "\"\"\"\n", - "def render_mp4(videopath: str) -> str:\n", - " \"\"\"\n", - " Gets a string containing a b4-encoded version of the MP4 video\n", - " at the specified path.\n", - " \"\"\"\n", - " mp4 = open(videopath, 'rb').read()\n", - " base64_encoded_mp4 = b64encode(mp4).decode()\n", - " return f''" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Introduction\n", - "\n", - "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", - "\n", - "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", - "\n", - "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", - "\n", - "In this project, you should investigate the following question:\n", - "\n", - "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", - "\n", - "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", - "\n", - "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", - "\n", - "The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "The actions of the agents are:\n", - " 1. Do Nothing\n", - " 2. Fire Main Engine\n", - " 3. Fire Left Engine\n", - " 4. Fire Right Engine\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Basic DQN Implementation\n", - "\n", - "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "x5XKBDyYDBuS" + }, + "source": [ + "---\n", + "# Setup" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", - " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", - "\n", - "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", - " # Tip: Use smaller step-sizes for larger networks." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now, let us setup our model and the DQN algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "log_dir = \"/tmp/gym/\"\n", - "os.makedirs(log_dir, exist_ok=True)\n", - "\n", - "# Create environment\n", - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", - "# Refer to https://gym.openai.com/docs/ for descriptions.\n", - "\n", - "# For example, if you would like to load Cartpole,\n", - "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", - "\n", - "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", - "\n", - "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", - "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", - " net_arch=nn_layers)\n", - "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", - " learning_rate=learning_rate,\n", - " batch_size=1, # for simplicity, we are not doing batch update.\n", - " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", - " learning_starts=1, # learning starts immediately!\n", - " gamma=0.99, # discount facto. range is between 0 and 1.\n", - " tau = 1, # the soft update coefficient for updating the target network\n", - " target_update_interval=1, # update the target network immediately.\n", - " train_freq=(1,\"step\"), # train the network at every step.\n", - " max_grad_norm = 10, # the maximum value for the gradient clipping\n", - " exploration_initial_eps = 1, # initial value of random action probability\n", - " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", - " gradient_steps = 1, # number of gradient steps\n", - " seed = 1, # seed for the pseudo random generators\n", - " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", - "\n", - "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", - "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", - "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", - "\n", - "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "execution": {}, + "id": "WsHayfTHDBuS" + }, + "outputs": [], + "source": [ + "# @title Update/Upgrade the system and install libs\n", + "!apt-get update > /dev/null 2>&1\n", + "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", + "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", + "!apt-get install x11-utils > /dev/null 2>&1\n", + "!apt-get install xvfb > /dev/null 2>&1" + ] + }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "State shape: (8,)\n", - "Number of actions: 4\n" - ] + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": {}, + "id": "6fooEJQSDBuT", + "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: swig in /usr/local/lib/python3.10/dist-packages (4.2.1)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install rarfile --quiet\n", + "!pip install stable-baselines3[extra] --quiet\n", + "!pip install ale-py --quiet\n", + "!pip install swig\n", + "!pip install gym[box2d] --quiet\n", + "!pip install pyvirtualdisplay --quiet\n", + "!pip install pyglet --quiet\n", + "!pip install pygame --quiet\n", + "!pip install minigrid --quiet\n", + "!pip install -q swig --quiet\n", + "!pip install -q gymnasium[box2d] --quiet\n", + "!pip install 'minigrid<=2.1.1' --quiet\n", + "!pip3 install box2d-py --quiet" + ] }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "print('State shape: ', env.observation_space.shape)\n", - "print('Number of actions: ', env.action_space.n)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "code", + "execution_count": 3, + "metadata": { + "execution": {}, + "id": "nA2Y9HGUDBuT" + }, + "outputs": [], + "source": [ + "# Imports\n", + "import io\n", + "import os\n", + "import glob\n", + "import torch\n", + "import base64\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import sys\n", + "import gymnasium\n", + "sys.modules[\"gym\"] = gymnasium\n", + "\n", + "import stable_baselines3\n", + "from stable_baselines3 import DQN\n", + "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", + "from stable_baselines3.common.callbacks import EvalCallback\n", + "from stable_baselines3.common.env_util import make_atari_env\n", + "\n", + "import gymnasium as gym\n", + "from gym import spaces\n", + "from gym.envs.box2d.lunar_lander import *\n", + "from gym.wrappers.monitoring.video_recorder import VideoRecorder" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n" - ] + "cell_type": "code", + "execution_count": 4, + "metadata": { + "cellView": "form", + "execution": {}, + "id": "_M-76WwDDBuT", + "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + } + ], + "source": [ + "# @title Play Video function\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "from pyvirtualdisplay import Display\n", + "\n", + "# create the directory to store the video(s)\n", + "os.makedirs(\"./video\", exist_ok=True)\n", + "\n", + "display = Display(visible=False, size=(1400, 900))\n", + "_ = display.start()\n", + "\n", + "\"\"\"\n", + "Utility functions to enable video recording of gym environment\n", + "and displaying it.\n", + "To enable video, just do \"env = wrap_env(env)\"\"\n", + "\"\"\"\n", + "def render_mp4(videopath: str) -> str:\n", + " \"\"\"\n", + " Gets a string containing a b4-encoded version of the MP4 video\n", + " at the specified path.\n", + " \"\"\"\n", + " mp4 = open(videopath, 'rb').read()\n", + " base64_encoded_mp4 = b64encode(mp4).decode()\n", + " return f''" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Total reward: -597.0358279244006\n" - ] + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UJnBSc5KDBuU" + }, + "source": [ + "---\n", + "# Introduction\n", + "\n", + "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", + "\n", + "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", + "\n", + "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", + "\n", + "In this project, you should investigate the following question:\n", + "\n", + "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", + "\n", + "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", + "\n", + "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", + "\n", + "The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "The actions of the agents are:\n", + " 1. Do Nothing\n", + " 2. Fire Main Engine\n", + " 3. Fire Left Engine\n", + " 4. Fire Right Engine\n", + "\n", + "\n", + "" + ] }, { - "data": { - "text/html": [ - "" + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "XuSpVWuGDBuU" + }, + "source": [ + "---\n", + "# Basic DQN Implementation\n", + "\n", + "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "dYwJRvx-DBuV" + }, + "source": [ + "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": {}, + "id": "MvWRAJiSDBuV", + "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + } ], - "text/plain": [ - "" + "source": [ + "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", + " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", + "\n", + "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", + " # Tip: Use smaller step-sizes for larger networks." ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", - "HTML(html)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "From the video above, we see that the lander has crashed!\n", - "It is now the time for training!\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EHLx2d5xDBuV" + }, + "source": [ + "Now, let us setup our model and the DQN algorithm." + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": {}, + "id": "4PzeDS2dDBuV" + }, + "outputs": [], + "source": [ + "log_dir = \"/tmp/gym/\"\n", + "os.makedirs(log_dir, exist_ok=True)\n", + "\n", + "# Create environment\n", + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", + "# Refer to https://gym.openai.com/docs/ for descriptions.\n", + "\n", + "# For example, if you would like to load Cartpole,\n", + "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", + "\n", + "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", + "\n", + "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", + "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", + " net_arch=nn_layers)\n", + "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", + " learning_rate=learning_rate,\n", + " batch_size=1, # for simplicity, we are not doing batch update.\n", + " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", + " learning_starts=1, # learning starts immediately!\n", + " gamma=0.99, # discount facto. range is between 0 and 1.\n", + " tau = 1, # the soft update coefficient for updating the target network\n", + " target_update_interval=1, # update the target network immediately.\n", + " train_freq=(1,\"step\"), # train the network at every step.\n", + " max_grad_norm = 10, # the maximum value for the gradient clipping\n", + " exploration_initial_eps = 1, # initial value of random action probability\n", + " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", + " gradient_steps = 1, # number of gradient steps\n", + " seed = 1, # seed for the pseudo random generators\n", + " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", + "\n", + "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", + "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", + "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n", - "Episode length: 151.80 +/- 30.46\n", - "New best mean reward!\n", - "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n", - "Episode length: 878.80 +/- 72.71\n", - "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n", - "Episode length: 240.00 +/- 51.61\n", - "New best mean reward!\n", - "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n", - "Episode length: 338.20 +/- 107.08\n", - "New best mean reward!\n", - "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n", - "Episode length: 241.20 +/- 55.82\n", - "New best mean reward!\n", - "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n", - "Episode length: 646.80 +/- 65.03\n", - "New best mean reward!\n", - "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n", - "Episode length: 139.80 +/- 35.46\n", - "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n", - "Episode length: 321.60 +/- 31.12\n", - "New best mean reward!\n", - "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n", - "Episode length: 536.00 +/- 257.21\n", - "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n", - "Episode length: 339.80 +/- 19.18\n" - ] + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1FIshtazDBuW" + }, + "source": [ + "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", + "\n", + "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." + ] }, { - "data": { - "text/plain": [ - "" + "cell_type": "code", + "execution_count": 7, + "metadata": { + "execution": {}, + "id": "SyD6VwDhDBuW", + "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "State shape: (8,)\n", + "Number of actions: 4\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + } + ], + "source": [ + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "print('State shape: ', env.observation_space.shape)\n", + "print('Number of actions: ', env.action_space.n)" ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", - "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", - "# view the performance at every training episode." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", - "\n", - "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", - "\n", - "```\n", - "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", - "Episode length: 259.80 +/- 47.47\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Now, let us look at the visual performance of the lander.\n", - "\n", - "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n" - ] + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": {}, + "id": "68D-3iePDBuW", + "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", + " logger.deprecation(\n", + "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = _posixsubprocess.fork_exec(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Total reward: -449.2162305654916\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 8 + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", + "observation = env.reset()[0]\n", + "\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", + "HTML(html)" + ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Total reward: 252.88935234615718\n" - ] + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "fhtq8GDLDBuW" + }, + "source": [ + "From the video above, we see that the lander has crashed!\n", + "It is now the time for training!\n" + ] }, { - "data": { - "text/html": [ - "" + "cell_type": "code", + "execution_count": 9, + "metadata": { + "execution": {}, + "id": "Xhl3ojMwDBuW", + "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n", + "Episode length: 151.80 +/- 30.46\n", + "New best mean reward!\n", + "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n", + "Episode length: 878.80 +/- 72.71\n", + "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n", + "Episode length: 240.00 +/- 51.61\n", + "New best mean reward!\n", + "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n", + "Episode length: 338.20 +/- 107.08\n", + "New best mean reward!\n", + "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n", + "Episode length: 241.20 +/- 55.82\n", + "New best mean reward!\n", + "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n", + "Episode length: 646.80 +/- 65.03\n", + "New best mean reward!\n", + "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n", + "Episode length: 139.80 +/- 35.46\n", + "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n", + "Episode length: 321.60 +/- 31.12\n", + "New best mean reward!\n", + "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n", + "Episode length: 536.00 +/- 257.21\n", + "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n", + "Episode length: 339.80 +/- 19.18\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 9 + } ], - "text/plain": [ - "" + "source": [ + "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", + "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", + "# view the performance at every training episode." ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", - "HTML(html)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "The lander has landed safely!!\n", - "\n", - "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "IYynM83tDBuX" + }, + "source": [ + "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", + "\n", + "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", + "\n", + "```\n", + "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", + "Episode length: 259.80 +/- 47.47\n", + "```" + ] }, { - "data": { - "image/png": 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\n", 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" + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UFNQVKokDBuX" + }, + "source": [ + "Now, let us look at the visual performance of the lander.\n", + "\n", + "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", - "plt.plot(x, y)\n", - "plt.ylim([-300, 300])\n", - "plt.xlabel('Timesteps')\n", - "plt.ylabel('Episode Rewards')\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", - "\n", - "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# Additional Project Ideas" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 1 Play with exploration-exploitation trade-off\n", - "\n", - "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", - "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", - "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 2 Reward Shaping\n", - "\n", - "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", - "class CustomEnv(gym.Env):\n", - " \"\"\"Custom Environment that follows gym interface\"\"\"\n", - " metadata = {'render.modes': ['human']}\n", - "\n", - " def __init__(self, arg1, arg2):\n", - " super(CustomEnv, self).__init__()\n", - " # Define action and observation space\n", - " # They must be gym.spaces objects\n", - " # Example when using discrete actions:\n", - " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", - " # Example for using image as input (channel-first; channel-last also works):\n", - " self.observation_space = spaces.Box(low=0, high=255,\n", - " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", - "\n", - " def step(self, action):\n", - " ...\n", - " return observation, reward, done, info\n", - " def reset(self):\n", - " ...\n", - " return observation # reward, done, info can't be included\n", - " def render(self, mode='human'):\n", - " ...\n", - " def close (self):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "class Custom_LunarLander(LunarLander):\n", - "\n", - " def step(self, action):\n", - " assert self.lander is not None\n", - "\n", - " # Update wind\n", - " assert self.lander is not None, \"You forgot to call reset()\"\n", - " if self.enable_wind and not (\n", - " self.legs[0].ground_contact or self.legs[1].ground_contact\n", - " ):\n", - " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " wind_mag = (\n", - " math.tanh(\n", - " math.sin(0.02 * self.wind_idx)\n", - " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", - " )\n", - " * self.wind_power\n", - " )\n", - " self.wind_idx += 1\n", - " self.lander.ApplyForceToCenter(\n", - " (wind_mag, 0.0),\n", - " True,\n", - " )\n", - "\n", - " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " torque_mag = math.tanh(\n", - " math.sin(0.02 * self.torque_idx)\n", - " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", - " ) * (self.turbulence_power)\n", - " self.torque_idx += 1\n", - " self.lander.ApplyTorque(\n", - " (torque_mag),\n", - " True,\n", - " )\n", - "\n", - " if self.continuous:\n", - " action = np.clip(action, -1, +1).astype(np.float32)\n", - " else:\n", - " assert self.action_space.contains(\n", - " action\n", - " ), f\"{action!r} ({type(action)}) invalid \"\n", - "\n", - " # Engines\n", - " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", - " side = (-tip[1], tip[0])\n", - " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", - "\n", - " m_power = 0.0\n", - " if (self.continuous and action[0] > 0.0) or (\n", - " not self.continuous and action == 2\n", - " ):\n", - " # Main engine\n", - " if self.continuous:\n", - " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", - " assert m_power >= 0.5 and m_power <= 1.0\n", - " else:\n", - " m_power = 1.0\n", - " # 4 is move a bit downwards, +-2 for randomness\n", - " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", - " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", - " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", - " p = self._create_particle(\n", - " 3.5, # 3.5 is here to make particle speed adequate\n", - " impulse_pos[0],\n", - " impulse_pos[1],\n", - " m_power,\n", - " ) # particles are just a decoration\n", - " p.ApplyLinearImpulse(\n", - " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " s_power = 0.0\n", - " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", - " not self.continuous and action in [1, 3]\n", - " ):\n", - " # Orientation engines\n", - " if self.continuous:\n", - " direction = np.sign(action[1])\n", - " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", - " assert s_power >= 0.5 and s_power <= 1.0\n", - " else:\n", - " direction = action - 2\n", - " s_power = 1.0\n", - " ox = tip[0] * dispersion[0] + side[0] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " oy = -tip[1] * dispersion[0] - side[1] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " impulse_pos = (\n", - " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", - " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", - " )\n", - " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", - " p.ApplyLinearImpulse(\n", - " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", - "\n", - " pos = self.lander.position\n", - " vel = self.lander.linearVelocity\n", - " state = [\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]\n", - " assert len(state) == 8\n", - "\n", - " # Compare with / without shaping, referring the state description below\n", - " '''\n", - " state[0]: the horizontal coordinate\n", - " state[1]: the vertical coordinate\n", - " state[2]: the horizontal speed\n", - " state[3]: the vertical speed\n", - " state[4]: the angle\n", - " state[5]: the angular speed\n", - " state[6]: first leg contact\n", - " state[7]: second leg contact\n", - " '''\n", - " reward = 0\n", - " shaping = (\n", - " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", - " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", - " - 100 * abs(state[4])\n", - " + 10 * state[6]\n", - " + 10 * state[7]\n", - " ) # And ten points for legs contact, the idea is if you\n", - " # lose contact again after landing, you get negative reward\n", - " if self.prev_shaping is not None:\n", - " reward = shaping - self.prev_shaping\n", - " self.prev_shaping = shaping\n", - "\n", - " reward -= (\n", - " m_power * 0.30\n", - " ) # less fuel spent is better, about -30 for heuristic landing\n", - " reward -= s_power * 0.03\n", - "\n", - " terminated = False\n", - " if self.game_over or abs(state[0]) >= 1.0:\n", - " terminated = True\n", - " reward = -100\n", - " if not self.lander.awake:\n", - " terminated = True\n", - " reward = +100\n", - "\n", - " if self.render_mode == \"human\":\n", - " self.render()\n", - " return np.array(state, dtype=np.float32), reward, terminated, False, {}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Once you have cutomized your own environment, you can execute that environment by just calling:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "## Enter the name of the custome environment you created and uncomment the line below.\n", - "# env = Custom_LunarLander()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 3 Identify the state information crucial to its performance.\n", - "\n", - "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "def step(self, actions):\n", - " ...\n", - " ...\n", - " ...\n", - " state = [ # Remove one component at a time to investigate the effect on performance!\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 4 Extension to Atari Games\n", - "\n", - "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", - "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", - "\n", - "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", - "# env = VecFrameStack(env, n_stack=4)\n", - "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", - "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 5 Obstacle Avoidance and Transfer Learning\n", - "\n", - "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", - "\n", - "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", - "\n", - "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "## Specify the load path and uncomment below:\n", - "\n", - "# model = load(load_path,\n", - "# env=gym.make('LunarLander-v2'),\n", - "# custom_objects=None, **kwargs)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Following are some of the resources on transfer learning that you would want to start with.\n", - "\n", - "**Research Papers**\n", - "\n", - "Surveys:\n", - "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", - " - Long, Old, Highly cited\n", - "\n", - "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", - " - Medium, Old, Good for a quick read\n", - "\n", - "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", - " - Medium, Recent, Good for a quick read\n", - "\n", - "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", - " - Specific example" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 5(b) Transfer Learning in minigrid environment\n", - "\n", - "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], - "source": [ - "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ + "cell_type": "code", + "execution_count": 10, + "metadata": { + "execution": {}, + "id": "hc0xXn5aDBuX", + "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", + " logger.deprecation(\n", + "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", + " self.pid = _posixsubprocess.fork_exec(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Total reward: 249.47508187060953\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "" + ] + }, + "metadata": {}, + "execution_count": 10 + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", + "observation = env.reset()[0]\n", + "\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", + "HTML(html)" + ] + }, { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n", - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n" - ] + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "cVCcx8GUDBuX" + }, + "source": [ + "The lander has landed safely!!\n", + "\n", + "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "execution": {}, + "id": "_8ibUiTmDBuX", + "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ], + "source": [ + "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", + "plt.plot(x, y)\n", + "plt.ylim([-300, 300])\n", + "plt.xlabel('Timesteps')\n", + "plt.ylabel('Episode Rewards')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "2Zo8kpDUDBuX" + }, + "source": [ + "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", + "\n", + "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "D7JAEDEzDBuX" + }, + "source": [ + "---\n", + "# Additional Project Ideas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1m6YBf5nDBuX" + }, + "source": [ + "## 1 Play with exploration-exploitation trade-off\n", + "\n", + "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": {}, + "id": "tnbb16KUDBuY", + "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", + " and should_run_async(code)\n" + ] + } + ], + "source": [ + "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", + "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", + "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "794AyGDPDBuY" + }, + "source": [ + "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "Ljf9XG5BDBuY" + }, + "source": [ + "## 2 Reward Shaping\n", + "\n", + "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "execution": {}, + "id": "zAAhdiflDBuY" + }, + "outputs": [], + "source": [ + "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", + "class CustomEnv(gym.Env):\n", + " \"\"\"Custom Environment that follows gym interface\"\"\"\n", + " metadata = {'render.modes': ['human']}\n", + "\n", + " def __init__(self, arg1, arg2):\n", + " super(CustomEnv, self).__init__()\n", + " # Define action and observation space\n", + " # They must be gym.spaces objects\n", + " # Example when using discrete actions:\n", + " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", + " # Example for using image as input (channel-first; channel-last also works):\n", + " self.observation_space = spaces.Box(low=0, high=255,\n", + " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", + "\n", + " def step(self, action):\n", + " ...\n", + " return observation, reward, done, info\n", + " def reset(self):\n", + " ...\n", + " return observation # reward, done, info can't be included\n", + " def render(self, mode='human'):\n", + " ...\n", + " def close (self):\n", + " ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "n7u1oEO2DBuY" + }, + "source": [ + "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": {}, + "id": "463GUtbuDBuY" + }, + "outputs": [], + "source": [ + "class Custom_LunarLander(LunarLander):\n", + "\n", + " def step(self, action):\n", + " assert self.lander is not None\n", + "\n", + " # Update wind\n", + " assert self.lander is not None, \"You forgot to call reset()\"\n", + " if self.enable_wind and not (\n", + " self.legs[0].ground_contact or self.legs[1].ground_contact\n", + " ):\n", + " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " wind_mag = (\n", + " math.tanh(\n", + " math.sin(0.02 * self.wind_idx)\n", + " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", + " )\n", + " * self.wind_power\n", + " )\n", + " self.wind_idx += 1\n", + " self.lander.ApplyForceToCenter(\n", + " (wind_mag, 0.0),\n", + " True,\n", + " )\n", + "\n", + " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " torque_mag = math.tanh(\n", + " math.sin(0.02 * self.torque_idx)\n", + " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", + " ) * (self.turbulence_power)\n", + " self.torque_idx += 1\n", + " self.lander.ApplyTorque(\n", + " (torque_mag),\n", + " True,\n", + " )\n", + "\n", + " if self.continuous:\n", + " action = np.clip(action, -1, +1).astype(np.float32)\n", + " else:\n", + " assert self.action_space.contains(\n", + " action\n", + " ), f\"{action!r} ({type(action)}) invalid \"\n", + "\n", + " # Engines\n", + " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", + " side = (-tip[1], tip[0])\n", + " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", + "\n", + " m_power = 0.0\n", + " if (self.continuous and action[0] > 0.0) or (\n", + " not self.continuous and action == 2\n", + " ):\n", + " # Main engine\n", + " if self.continuous:\n", + " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", + " assert m_power >= 0.5 and m_power <= 1.0\n", + " else:\n", + " m_power = 1.0\n", + " # 4 is move a bit downwards, +-2 for randomness\n", + " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", + " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", + " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", + " p = self._create_particle(\n", + " 3.5, # 3.5 is here to make particle speed adequate\n", + " impulse_pos[0],\n", + " impulse_pos[1],\n", + " m_power,\n", + " ) # particles are just a decoration\n", + " p.ApplyLinearImpulse(\n", + " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " s_power = 0.0\n", + " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", + " not self.continuous and action in [1, 3]\n", + " ):\n", + " # Orientation engines\n", + " if self.continuous:\n", + " direction = np.sign(action[1])\n", + " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", + " assert s_power >= 0.5 and s_power <= 1.0\n", + " else:\n", + " direction = action - 2\n", + " s_power = 1.0\n", + " ox = tip[0] * dispersion[0] + side[0] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " oy = -tip[1] * dispersion[0] - side[1] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " impulse_pos = (\n", + " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", + " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", + " )\n", + " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", + " p.ApplyLinearImpulse(\n", + " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", + "\n", + " pos = self.lander.position\n", + " vel = self.lander.linearVelocity\n", + " state = [\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]\n", + " assert len(state) == 8\n", + "\n", + " # Compare with / without shaping, referring the state description below\n", + " '''\n", + " state[0]: the horizontal coordinate\n", + " state[1]: the vertical coordinate\n", + " state[2]: the horizontal speed\n", + " state[3]: the vertical speed\n", + " state[4]: the angle\n", + " state[5]: the angular speed\n", + " state[6]: first leg contact\n", + " state[7]: second leg contact\n", + " '''\n", + " reward = 0\n", + " shaping = (\n", + " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", + " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", + " - 100 * abs(state[4])\n", + " + 10 * state[6]\n", + " + 10 * state[7]\n", + " ) # And ten points for legs contact, the idea is if you\n", + " # lose contact again after landing, you get negative reward\n", + " if self.prev_shaping is not None:\n", + " reward = shaping - self.prev_shaping\n", + " self.prev_shaping = shaping\n", + "\n", + " reward -= (\n", + " m_power * 0.30\n", + " ) # less fuel spent is better, about -30 for heuristic landing\n", + " reward -= s_power * 0.03\n", + "\n", + " terminated = False\n", + " if self.game_over or abs(state[0]) >= 1.0:\n", + " terminated = True\n", + " reward = -100\n", + " if not self.lander.awake:\n", + " terminated = True\n", + " reward = +100\n", + "\n", + " if self.render_mode == \"human\":\n", + " self.render()\n", + " return np.array(state, dtype=np.float32), reward, terminated, False, {}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "V-qZ4WYxDBuZ" + }, + "source": [ + "Once you have cutomized your own environment, you can execute that environment by just calling:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": {}, + "id": "yq4902DQDBuZ" + }, + "outputs": [], + "source": [ + "## Enter the name of the custome environment you created and uncomment the line below.\n", + "# env = Custom_LunarLander()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EAP-DUd6DBuZ" + }, + "source": [ + "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QTq0hmHCDBuZ" + }, + "source": [ + "## 3 Identify the state information crucial to its performance.\n", + "\n", + "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": {}, + "id": "sz45kgEaDBuZ" + }, + "outputs": [], + "source": [ + "def step(self, actions):\n", + " ...\n", + " ...\n", + " ...\n", + " state = [ # Remove one component at a time to investigate the effect on performance!\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "DXy9s2ymDBuZ" + }, + "source": [ + "## 4 Extension to Atari Games\n", + "\n", + "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": {}, + "id": "4RjAt0W-DBuZ" + }, + "outputs": [], + "source": [ + "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", + "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", + "\n", + "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", + "# env = VecFrameStack(env, n_stack=4)\n", + "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", + "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "E6_fFAYhDBue" + }, + "source": [ + "## 5 Obstacle Avoidance and Transfer Learning\n", + "\n", + "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", + "\n", + "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", + "\n", + "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": {}, + "id": "h6knZ3U8DBue" + }, + "outputs": [], + "source": [ + "## Specify the load path and uncomment below:\n", + "\n", + "# model = load(load_path,\n", + "# env=gym.make('LunarLander-v2'),\n", + "# custom_objects=None, **kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "GXBIbO25DBue" + }, + "source": [ + "Following are some of the resources on transfer learning that you would want to start with.\n", + "\n", + "**Research Papers**\n", + "\n", + "Surveys:\n", + "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", + " - Long, Old, Highly cited\n", + "\n", + "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", + " - Medium, Old, Good for a quick read\n", + "\n", + "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", + " - Medium, Recent, Good for a quick read\n", + "\n", + "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", + " - Specific example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "MlOksW3ODBue" + }, + "source": [ + "## 5(b) Transfer Learning in minigrid environment\n", + "\n", + "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": {}, + "id": "v7rB2JQlDBue" + }, + "outputs": [], + "source": [ + "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "OPcds7ZtDBue" + }, + "source": [ + "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": {}, + "id": "d0NiSkyeDBue", + "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n", + "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n" + ] + } + ], + "source": [ + "import minigrid\n", + "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "7J92iMqqDBue" + }, + "source": [ + "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", + "\n", + "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "HL0W5M4uDBue" + }, + "source": [ + "## 6 Preference-Based RL (PBRL)\n", + "\n", + "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", + "\n", + "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", + "\n", + "**Research papers:**\n", + "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", + "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", + "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", + "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", + "\n", + "**Code Bases:**\n", + "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", + "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QdW-XKCMDBue" + }, + "source": [ + "---\n", + "# References\n", + "\n", + "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", + "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", + "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", + "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "lunar_lander", + "provenance": [], + "toc_visible": true + }, + "gpuClass": "standard", + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ], - "source": [ - "import minigrid\n", - "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", - "\n", - "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "## 6 Preference-Based RL (PBRL)\n", - "\n", - "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", - "\n", - "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", - "\n", - "**Research papers:**\n", - "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", - "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", - "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", - "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", - "\n", - "**Code Bases:**\n", - "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", - "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {} - }, - "source": [ - "---\n", - "# References\n", - "\n", - "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", - "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", - "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", - "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "collapsed_sections": [], - "include_colab_link": true, - "name": "lunar_lander", - "provenance": [], - "toc_visible": true - }, - "gpuClass": "standard", - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" }, - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 73af67896ce2affe7c827f9dd2ac50cecc4133e4 Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 9 Jul 2024 13:44:05 -0400 Subject: [PATCH 18/25] add comments to workflow ci:execute --- .github/workflows/notebook-pr.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index 5cbdfd03d..565b14256 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -63,6 +63,7 @@ jobs: - name: Install dependencies if: "!contains(env.COMMIT_MESSAGE, 'skip ci') && contains(env.COMMIT_MESSAGE, 'ci:execute')" run: | + # Install deps for GH execution sudo apt-get update && sudo apt install xvfb -y python -m pip install --upgrade pip wheel pip install -r requirements.txt From 94bcfb08be0b29472d2231e7214248f69a69be05 Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 9 Jul 2024 16:15:12 -0400 Subject: [PATCH 19/25] remove comments from workflow --- .github/workflows/notebook-pr.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index 565b14256..5cbdfd03d 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -63,7 +63,6 @@ jobs: - name: Install dependencies if: "!contains(env.COMMIT_MESSAGE, 'skip ci') && contains(env.COMMIT_MESSAGE, 'ci:execute')" run: | - # Install deps for GH execution sudo apt-get update && sudo apt install xvfb -y python -m pip install --upgrade pip wheel pip install -r requirements.txt From 49063b80cc1372eddfe8708c4979da7cf9e36d2e Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 9 Jul 2024 16:17:20 -0400 Subject: [PATCH 20/25] install nvidia toolkit --- .github/workflows/notebook-pr.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index 5cbdfd03d..81e78eb2a 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -63,6 +63,7 @@ jobs: - name: Install dependencies if: "!contains(env.COMMIT_MESSAGE, 'skip ci') && contains(env.COMMIT_MESSAGE, 'ci:execute')" run: | + sudo apt install nvidia-cuda-toolkit -y sudo apt-get update && sudo apt install xvfb -y python -m pip install --upgrade pip wheel pip install -r requirements.txt From 685f181d42777a0398b7c15a2ea41cafb02f0c71 Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 9 Jul 2024 16:17:56 -0400 Subject: [PATCH 21/25] install nvidia toolkit ci:execute --- .github/workflows/notebook-pr.yaml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/notebook-pr.yaml b/.github/workflows/notebook-pr.yaml index 81e78eb2a..c590cc52b 100644 --- a/.github/workflows/notebook-pr.yaml +++ b/.github/workflows/notebook-pr.yaml @@ -63,6 +63,7 @@ jobs: - name: Install dependencies if: "!contains(env.COMMIT_MESSAGE, 'skip ci') && contains(env.COMMIT_MESSAGE, 'ci:execute')" run: | + # Install dependencies for github processing sudo apt install nvidia-cuda-toolkit -y sudo apt-get update && sudo apt install xvfb -y python -m pip install --upgrade pip wheel From 53b24c09b88a59169de026d729081b3cd2d38e3e Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 16 Jul 2024 14:46:34 -0400 Subject: [PATCH 22/25] update projects for 2024 --- projects/ComputerVision/em_synapses.ipynb | 2542 +++++++---------- projects/ReinforcementLearning/human_rl.ipynb | 543 +--- .../ReinforcementLearning/lunar_lander.ipynb | 2514 ++++++++-------- requirements.txt | 10 +- 4 files changed, 2472 insertions(+), 3137 deletions(-) diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb index 32441053b..3914df0e3 100644 --- a/projects/ComputerVision/em_synapses.ipynb +++ b/projects/ComputerVision/em_synapses.ipynb @@ -1,1487 +1,1107 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "2d9f0b20", - "metadata": { - "execution": {}, - "id": "2d9f0b20" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "id": "renayVUI7b9x", - "metadata": { - "execution": {}, - "id": "renayVUI7b9x" - }, - "source": [ - "# Knowledge Extraction from a Convolutional Neural Network\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Jan Funke\n", - "\n", - "__Production editors:__ Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "id": "U6wofKujWp6X", - "metadata": { - "execution": {}, - "id": "U6wofKujWp6X" - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", - "\n", - "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", - "\n", - "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." - ] - }, - { - "cell_type": "markdown", - "id": "zO4YN6W8W0Cp", - "metadata": { - "execution": {}, - "id": "zO4YN6W8W0Cp" - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fO1IZwvkW9Me", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "fO1IZwvkW9Me" - }, - "outputs": [], - "source": [ - "# @title Install dependencies\n", - "!pip install scikit-image --quiet\n", - "!pip install pillow --quiet\n", - "!pip install scikit-image --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "gKkHjjTGWzUk", - "metadata": { - "execution": {}, - "id": "gKkHjjTGWzUk" - }, - "outputs": [], - "source": [ - "import glob\n", - "import json\n", - "import torch\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "\n", - "from skimage.io import imread\n", - "from torchvision.datasets import ImageFolder\n", - "from torch.utils.data import DataLoader, random_split\n", - "from torch.utils.data.sampler import WeightedRandomSampler\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "bd7d427d", - "metadata": { - "execution": {}, - "id": "bd7d427d" - }, - "source": [ - "---\n", - "# Project Ideas\n", - "\n", - "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n", - " * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n", - " * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n", - " * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n", - " * (medium) Other networks: Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n", - " * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n", - " \n", - " \n", - "2. Explore the CycleGAN.\n", - " * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n", - " * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n", - " \n", - "\n", - "3. Try on your own data!\n", - " * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n", - "\n", - "### Acknowledgments\n", - "\n", - "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "5642d709", - "metadata": { - "execution": {}, - "id": "5642d709" - }, - "source": [ - "---\n", - "# Train an Image Classifier\n", - "\n", - "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine." - ] - }, - { - "cell_type": "markdown", - "id": "c61a11c6", - "metadata": { - "execution": {}, - "id": "c61a11c6" - }, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "821dc497", - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "821dc497", - "outputId": "9871a113-d169-4d6a-b927-b00706fabd22" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Data downloading...\n", - "Downloaded: ./part1\n", - "Downloaded: ./part2\n", - "Downloaded: ./part3\n", - "Download is completed.\n", - "Reassembling Files...\n", - "Downloaded files have been removed!\n", - "Reassembled 3 parts into resources.zip\n", - "Extracting all the files now...\n", - "Done!\n", - "\n", - "Archive: data.zip\n", - "\tExtracting data...\n", - "\n", - "Archive: checkpoints.zip\n", - "\tExtracting data...\n", - "Done!\n" - ] - } - ], - "source": [ - "# @title Download the data\n", - "import requests, os\n", - "from zipfile import ZipFile\n", - "\n", - "def download_file_parts(urls, output_directory='.'):\n", - " \"\"\"\n", - " Download file parts from given URLs and save them in the specified directory.\n", - "\n", - " :param urls: List of URLs to download\n", - " :param output_directory: Directory to save the downloaded parts (default is current directory)\n", - " :return: List of downloaded file paths\n", - " \"\"\"\n", - " if not os.path.exists(output_directory):\n", - " os.makedirs(output_directory)\n", - "\n", - " downloaded_files = []\n", - "\n", - " for i, url in enumerate(urls, 1):\n", - " try:\n", - " response = requests.get(url, stream=True)\n", - " response.raise_for_status() # Raises an HTTPError for bad requests\n", - "\n", - " file_name = f\"part{i}\"\n", - " file_path = os.path.join(output_directory, file_name)\n", - "\n", - " with open(file_path, 'wb') as file:\n", - " for chunk in response.iter_content(chunk_size=32768):\n", - " file.write(chunk)\n", - "\n", - " downloaded_files.append(file_path)\n", - " print(f\"Downloaded: {file_path}\")\n", - "\n", - "\n", - " except requests.RequestException as e:\n", - " print(f\"Error downloading {url}: {e}\")\n", - "\n", - " return downloaded_files\n", - "\n", - "def reassemble_file(output_file):\n", - " chunk_number = 1\n", - " with open(output_file, 'wb') as outfile:\n", - " while True:\n", - " chunk_name = f'part{chunk_number}'\n", - " if not os.path.exists(chunk_name):\n", - " break\n", - " with open(chunk_name, 'rb') as infile:\n", - " outfile.write(infile.read())\n", - " chunk_number += 1\n", - " for i in ['part1', 'part2', 'part3']:\n", - " if os.path.exists(i):\n", - " os.remove(i)\n", - " print(f\"Downloaded files have been removed!\")\n", - " print(f\"Reassembled {chunk_number-1} parts into {output_file}\")\n", - "\n", - "\n", - "\n", - "# @markdown Download the resources for this tutorial (one zip file)\n", - "fname = 'resources.zip'\n", - "urls = [\n", - " \"https://osf.io/download/4x7p3/\",\n", - " \"https://osf.io/download/fzwea/\",\n", - " \"https://osf.io/download/qpbcv/\"\n", - "]\n", - "\n", - "if not os.path.exists('data/'):\n", - " print('Data downloading...')\n", - " output_dir = \".\"\n", - " downloaded_parts = download_file_parts(urls, output_dir)\n", - " print('Download is completed.')\n", - "\n", - " print('Reassembling Files...')\n", - " base_name = ''\n", - " reassemble_file(fname)\n", - "\n", - " # @markdown Unzip the file\n", - " with ZipFile(fname, 'r') as zf:\n", - " # extracting all the files\n", - " print('Extracting all the files now...')\n", - " zf.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # # @markdown Extract the data\n", - " fnames = ['data.zip', 'checkpoints.zip']\n", - "\n", - " for fname in fnames:\n", - " with ZipFile(fname, 'r') as zh:\n", - " # extracting all the files\n", - " print(f\"\\nArchive: {fname}\")\n", - " print(f\"\\tExtracting data...\")\n", - " zh.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # @markdown Make sure the order of classes matches the pretrained model\n", - " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", - " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", - " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", - " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", - " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", - " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", - "\n", - " # @markdown Remove the archives\n", - " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", - " if os.path.exists(i):\n", - " os.remove(i)\n", - "\n", - "else:\n", - " print('Data are already downloaded.')" - ] - }, - { - "cell_type": "markdown", - "id": "0b84ec7b", - "metadata": { - "execution": {}, - "id": "0b84ec7b" - }, - "source": [ - "## Classifier Training" - ] - }, - { - "cell_type": "markdown", - "id": "a79ab567", - "metadata": { - "execution": {}, - "id": "a79ab567" - }, - "source": [ - "### Create and Inspect Datasets\n", - "\n", - "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ae50b16a", - "metadata": { - "execution": {}, - "id": "ae50b16a", - "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Number of images per class:\n", - "\t0_gaba:\tn=15855\tweight=6.30715862503942e-05\n", - "\t1_acetylcholine:\tn=4911\tweight=0.00020362451639177357\n", - "\t2_glutamate:\tn=3550\tweight=0.00028169014084507044\n", - "\t3_serotonin:\tn=2297\tweight=0.00043535045711797995\n", - "\t4_octopamine:\tn=951\tweight=0.0010515247108307045\n", - "\t5_dopamine:\tn=4649\tweight=0.00021510002151000216\n" - ] - } - ], - "source": [ - "def load_image(filename):\n", - "\n", - " image = imread(filename)\n", - "\n", - " # images are grescale, we only need one of the RGB channels\n", - " image = image[:, :, 0]\n", - " # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n", - " image = image.astype(np.float32)/255.0\n", - " image = (image - 0.5)/0.5\n", - "\n", - " return image\n", - "\n", - "\n", - "# create a dataset for all images of all classes\n", - "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n", - "\n", - "# randomly split the dataset into train, validation, and test\n", - "num_images = len(full_dataset)\n", - "# ~70% for training\n", - "num_training = int(0.7 * num_images)\n", - "# ~15% for validation\n", - "num_validation = int(0.15 * num_images)\n", - "# ~15% for testing\n", - "num_test = num_images - (num_training + num_validation)\n", - "# split the data randomly (but with a fixed random seed)\n", - "train_dataset, validation_dataset, test_dataset = random_split(\n", - " full_dataset,\n", - " [num_training, num_validation, num_test],\n", - " generator=torch.Generator().manual_seed(23061912))\n", - "\n", - "# compute class weights in training dataset for uniform sampling\n", - "ys = np.array([y for _, y in train_dataset])\n", - "counts = np.bincount(ys)\n", - "label_weights = 1.0 / counts\n", - "weights = label_weights[ys]\n", - "\n", - "print(\"Number of images per class:\")\n", - "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n", - " print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n", - "\n", - "# create a data loader with uniform sampling\n", - "sampler = WeightedRandomSampler(weights, len(weights))\n", - "# this data loader will serve 8 images in a \"mini-batch\" at a time\n", - "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)" - ] - }, - { - "cell_type": "markdown", - "id": "e9010bdc", - "metadata": { - "execution": {}, - "id": "e9010bdc" - }, - "source": [ - "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3d8c6f3a", - "metadata": { - "execution": {}, - "id": "3d8c6f3a", - "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 117 - } - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "def show_batch(x, y):\n", - " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", - " for i in range(x.shape[0]):\n", - " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", - " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", - " plt.show()\n", - "\n", - "# show a random batch from the data loader\n", - "# (run this cell repeatedly to see different batches)\n", - "for x, y in dataloader:\n", - " show_batch(x, y)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "f882416f", - "metadata": { - "execution": {}, - "id": "f882416f" - }, - "source": [ - "### Create a Model, Loss, and Optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "54f177cc", - "metadata": { - "execution": {}, - "id": "54f177cc" - }, - "outputs": [], - "source": [ - "class Vgg2D(torch.nn.Module):\n", - "\n", - " def __init__(\n", - " self,\n", - " input_size,\n", - " fmaps=12,\n", - " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", - " output_classes=6):\n", - "\n", - " super(Vgg2D, self).__init__()\n", - "\n", - " self.input_size = input_size\n", - "\n", - " current_fmaps = 1\n", - " current_size = tuple(input_size)\n", - "\n", - " features = []\n", - " for i in range(len(downsample_factors)):\n", - "\n", - " features += [\n", - " torch.nn.Conv2d(\n", - " current_fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Conv2d(\n", - " fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.MaxPool2d(downsample_factors[i])\n", - " ]\n", - "\n", - " current_fmaps = fmaps\n", - " fmaps *= 2\n", - "\n", - " size = tuple(\n", - " int(c/d)\n", - " for c, d in zip(current_size, downsample_factors[i]))\n", - " check = (\n", - " s*d == c\n", - " for s, d, c in zip(size, downsample_factors[i], current_size))\n", - " assert all(check), \\\n", - " \"Can not downsample %s by chosen downsample factor\" % \\\n", - " (current_size,)\n", - " current_size = size\n", - "\n", - " self.features = torch.nn.Sequential(*features)\n", - "\n", - " classifier = [\n", - " torch.nn.Linear(\n", - " current_size[0] *\n", - " current_size[1] *\n", - " current_fmaps,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " output_classes)\n", - " ]\n", - "\n", - " self.classifier = torch.nn.Sequential(*classifier)\n", - "\n", - " def forward(self, raw):\n", - "\n", - " # add a channel dimension to raw\n", - " shape = tuple(raw.shape)\n", - " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", - "\n", - " # compute features\n", - " f = self.features(raw)\n", - " f = f.view(f.size(0), -1)\n", - "\n", - " # classify\n", - " y = self.classifier(f)\n", - "\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5da43245", - "metadata": { - "execution": {}, - "id": "5da43245" - }, - "outputs": [], - "source": [ - "# get the size of our images\n", - "for x, y in train_dataset:\n", - " input_size = x.shape\n", - " break\n", - "\n", - "# create the model to train\n", - "model = Vgg2D(input_size)\n", - "\n", - "# create a loss\n", - "loss = torch.nn.CrossEntropyLoss()\n", - "\n", - "# create an optimzer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" - ] - }, - { - "cell_type": "markdown", - "id": "01688095", - "metadata": { - "execution": {}, - "id": "01688095" - }, - "source": [ - "### Train the Model" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "fa65090d", - "metadata": { - "execution": {}, - "id": "fa65090d", - "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Will use device cuda for training\n" - ] - } - ], - "source": [ - "# use a GPU, if it is available\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "model.to(device)\n", - "print(f\"Will use device {device} for training\")" - ] - }, - { - "cell_type": "markdown", - "id": "ecbab4f7", - "metadata": { - "execution": {}, - "id": "ecbab4f7" - }, - "source": [ - "The next cell merely defines some convenience functions for training, validation, and testing:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1a8c7fe9", - "metadata": { - "execution": {}, - "id": "1a8c7fe9" - }, - "outputs": [], - "source": [ - "def train(dataloader, optimizer, loss, device):\n", - " '''Train the model for one epoch.'''\n", - "\n", - " # set the model into train mode\n", - " model.train()\n", - "\n", - " epoch_loss, num_batches = 0, 0\n", - " for x, y in tqdm(dataloader, 'train'):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - " optimizer.zero_grad()\n", - "\n", - " y_pred = model(x)\n", - " l = loss(y_pred, y)\n", - " l.backward()\n", - "\n", - " optimizer.step()\n", - "\n", - " epoch_loss += l\n", - " num_batches += 1\n", - "\n", - " return epoch_loss/num_batches\n", - "\n", - "\n", - "def evaluate(dataloader, name, device):\n", - "\n", - " correct = 0\n", - " total = 0\n", - " for x, y in tqdm(dataloader, name):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - "\n", - " logits = model(x)\n", - " probs = torch.nn.Softmax(dim=1)(logits)\n", - " predictions = torch.argmax(probs, dim=1)\n", - "\n", - " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", - " total += len(y)\n", - "\n", - " accuracy = correct/total\n", - "\n", - " return accuracy\n", - "\n", - "\n", - "def validate(validation_dataset, device):\n", - " '''Evaluate prediction accuracy on the validation dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(validation_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'validate', device)\n", - "\n", - "\n", - "def test(test_dataset, device):\n", - " '''Evaluate prediction accuracy on the test dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(test_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'test', device)" - ] - }, - { - "cell_type": "markdown", - "id": "68bcfbbf", - "metadata": { - "execution": {}, - "id": "68bcfbbf" - }, - "source": [ - "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d0af7638", - "metadata": { - "execution": {}, - "id": "d0af7638" - }, - "outputs": [], - "source": [ - "def train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device):\n", - "\n", - " for epoch in range(num_epochs):\n", - " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", - " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", - "\n", - " accuracy = validate(validation_dataset, device=device)\n", - " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "id": "45e31b87", - "metadata": { - "execution": {}, - "id": "45e31b87" - }, - "source": [ - "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", - "\n", - "Unceck the box below and run the cell to train a model." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "W5KA7zDIa3Lw", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "W5KA7zDIa3Lw" - }, - "outputs": [], - "source": [ - "# @markdown\n", - "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "53fb8dda", - "metadata": { - "execution": {}, - "id": "53fb8dda" - }, - "outputs": [], - "source": [ - "# Load a pretrained model or train the model from scratch\n", - "\n", - "# set this to True and run this cell if you want a shortcut\n", - "\n", - "if yes_I_want_the_pretrained_model:\n", - " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", - " map_location=device)\n", - " model.load_state_dict(checkpoint['model_state_dict'])\n", - "else:\n", - " train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4f6e3663", - "metadata": { - "execution": {}, - "id": "4f6e3663", - "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "test: 100%|██████████| 216/216 [00:12<00:00, 17.76it/s]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "final test accuracy: 0.8054750869061413\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ], - "source": [ - "accuracy = test(test_dataset, device=device)\n", - "print(f\"final test accuracy: {accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "id": "3f43bba5", - "metadata": { - "execution": {}, - "id": "3f43bba5" - }, - "source": [ - "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", - "\n", - "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n", - "\n", - "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "2d9f0b20", + "metadata": { + "execution": {}, + "id": "2d9f0b20" + }, + "source": [ + "\"Open   \"Open" + ] + }, + { + "cell_type": "markdown", + "id": "renayVUI7b9x", + "metadata": { + "execution": {}, + "id": "renayVUI7b9x" + }, + "source": [ + "# Knowledge Extraction from a Convolutional Neural Network\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Jan Funke\n", + "\n", + "__Production editors:__ Spiros Chavlis, Konstantine Tsafatinos" + ] + }, + { + "cell_type": "markdown", + "id": "U6wofKujWp6X", + "metadata": { + "execution": {}, + "id": "U6wofKujWp6X" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", + "\n", + "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", + "\n", + "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." + ] + }, + { + "cell_type": "markdown", + "id": "zO4YN6W8W0Cp", + "metadata": { + "execution": {}, + "id": "zO4YN6W8W0Cp" + }, + "source": [ + "---\n", + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fO1IZwvkW9Me", + "metadata": { + "cellView": "form", + "id": "fO1IZwvkW9Me" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "72b5240c", - "metadata": { - "execution": {}, - "id": "72b5240c" - }, - "source": [ - "---\n", - "# Train a GAN to Translate Images\n", - "\n", - "We will train a so-called CycleGAN to translate images from one class to another." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install scikit-image --quiet\n", + "!pip install pillow --quiet\n", + "!pip install scikit-image --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "gKkHjjTGWzUk", + "metadata": { + "id": "gKkHjjTGWzUk" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "id": "41c9e63b", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "41c9e63b" - }, - "outputs": [], - "source": [ - "# @title Get the CycleGAN code and dependencies\n", - "\n", - "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n", - "\n", - "import requests, zipfile, io\n", - "\n", - "url = 'https://osf.io/vutn5/download'\n", - "r = requests.get(url)\n", - "z = zipfile.ZipFile(io.BytesIO(r.content))\n", - "z.extractall()\n", - "\n", - "!pip install dominate --quiet" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], + "source": [ + "import glob\n", + "import json\n", + "import torch\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "\n", + "from skimage.io import imread\n", + "from torchvision.datasets import ImageFolder\n", + "from torch.utils.data import DataLoader, random_split\n", + "from torch.utils.data.sampler import WeightedRandomSampler\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "bd7d427d", + "metadata": { + "execution": {}, + "id": "bd7d427d" + }, + "source": [ + "---\n", + "# Project Ideas\n", + "\n", + "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n", + " * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n", + " * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n", + " * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n", + " * (medium) Other networks: Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n", + " * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n", + " \n", + " \n", + "2. Explore the CycleGAN.\n", + " * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n", + " * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n", + " \n", + "\n", + "3. Try on your own data!\n", + " * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n", + "\n", + "### Acknowledgments\n", + "\n", + "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5642d709", + "metadata": { + "execution": {}, + "id": "5642d709" + }, + "source": [ + "---\n", + "# Train an Image Classifier\n", + "\n", + "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine." + ] + }, + { + "cell_type": "markdown", + "id": "c61a11c6", + "metadata": { + "execution": {}, + "id": "c61a11c6" + }, + "source": [ + "## Data Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "821dc497", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "821dc497", + "outputId": "9871a113-d169-4d6a-b927-b00706fabd22" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "e5da5c01", - "metadata": { - "execution": {}, - "id": "e5da5c01" - }, - "source": [ - "In this example, we will translate between GABAergic and glutamatergic synapses.\n", - "\n", - "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Data are already downloaded.\n" + ] + } + ], + "source": [ + "# @title Download the data\n", + "import requests, os\n", + "from zipfile import ZipFile\n", + "\n", + "def download_file_parts(urls, output_directory='.'):\n", + " \"\"\"\n", + " Download file parts from given URLs and save them in the specified directory.\n", + "\n", + " :param urls: List of URLs to download\n", + " :param output_directory: Directory to save the downloaded parts (default is current directory)\n", + " :return: List of downloaded file paths\n", + " \"\"\"\n", + " if not os.path.exists(output_directory):\n", + " os.makedirs(output_directory)\n", + "\n", + " downloaded_files = []\n", + "\n", + " for i, url in enumerate(urls, 1):\n", + " try:\n", + " response = requests.get(url, stream=True)\n", + " response.raise_for_status() # Raises an HTTPError for bad requests\n", + "\n", + " file_name = f\"part{i}\"\n", + " file_path = os.path.join(output_directory, file_name)\n", + "\n", + " with open(file_path, 'wb') as file:\n", + " for chunk in response.iter_content(chunk_size=32768):\n", + " file.write(chunk)\n", + "\n", + " downloaded_files.append(file_path)\n", + " print(f\"Downloaded: {file_path}\")\n", + "\n", + "\n", + " except requests.RequestException as e:\n", + " print(f\"Error downloading {url}: {e}\")\n", + "\n", + " return downloaded_files\n", + "\n", + "def reassemble_file(output_file):\n", + " chunk_number = 1\n", + " with open(output_file, 'wb') as outfile:\n", + " while True:\n", + " chunk_name = f'part{chunk_number}'\n", + " if not os.path.exists(chunk_name):\n", + " break\n", + " with open(chunk_name, 'rb') as infile:\n", + " outfile.write(infile.read())\n", + " chunk_number += 1\n", + " for i in ['part1', 'part2', 'part3']:\n", + " if os.path.exists(i):\n", + " os.remove(i)\n", + " print(f\"Downloaded files have been removed!\")\n", + " print(f\"Reassembled {chunk_number-1} parts into {output_file}\")\n", + "\n", + "\n", + "\n", + "# @markdown Download the resources for this tutorial (one zip file)\n", + "fname = 'resources.zip'\n", + "urls = [\n", + " \"https://osf.io/download/4x7p3/\",\n", + " \"https://osf.io/download/fzwea/\",\n", + " \"https://osf.io/download/qpbcv/\"\n", + "]\n", + "\n", + "if not os.path.exists('data/'):\n", + " print('Data downloading...')\n", + " output_dir = \".\"\n", + " downloaded_parts = download_file_parts(urls, output_dir)\n", + " print('Download is completed.')\n", + "\n", + " print('Reassembling Files...')\n", + " base_name = ''\n", + " reassemble_file(fname)\n", + "\n", + " # @markdown Unzip the file\n", + " with ZipFile(fname, 'r') as zf:\n", + " # extracting all the files\n", + " print('Extracting all the files now...')\n", + " zf.extractall(path='.')\n", + " print('Done!')\n", + "\n", + " # # @markdown Extract the data\n", + " fnames = ['data.zip', 'checkpoints.zip']\n", + "\n", + " for fname in fnames:\n", + " with ZipFile(fname, 'r') as zh:\n", + " # extracting all the files\n", + " print(f\"\\nArchive: {fname}\")\n", + " print(f\"\\tExtracting data...\")\n", + " zh.extractall(path='.')\n", + " print('Done!')\n", + "\n", + " # @markdown Make sure the order of classes matches the pretrained model\n", + " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", + " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", + " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", + " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", + " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", + " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", + "\n", + " # @markdown Remove the archives\n", + " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", + " if os.path.exists(i):\n", + " os.remove(i)\n", + "\n", + "else:\n", + " print('Data are already downloaded.')" + ] + }, + { + "cell_type": "markdown", + "id": "0b84ec7b", + "metadata": { + "execution": {}, + "id": "0b84ec7b" + }, + "source": [ + "## Classifier Training" + ] + }, + { + "cell_type": "markdown", + "id": "a79ab567", + "metadata": { + "execution": {}, + "id": "a79ab567" + }, + "source": [ + "### Create and Inspect Datasets\n", + "\n", + "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae50b16a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ae50b16a", + "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "id": "2b2519c4", - "metadata": { - "execution": {}, - "id": "2b2519c4", - "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 22648/22648 [00:03<00:00, 6536.44it/s]\n", - "100%|██████████| 5085/5085 [00:01<00:00, 3398.25it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n" - ] - } - ], - "source": [ - "import cycle_gan\n", - "\n", - "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n", - "\n", - "## Uncomment if you want to enable the training procedure\n", - "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of images per class:\n", + "\t0_gaba:\tn=15945\tweight=6.271558482282847e-05\n", + "\t1_acetylcholine:\tn=4852\tweight=0.00020610057708161583\n", + "\t2_glutamate:\tn=3556\tweight=0.00028121484814398203\n", + "\t3_serotonin:\tn=2316\tweight=0.0004317789291882556\n", + "\t4_octopamine:\tn=934\tweight=0.0010706638115631692\n", + "\t5_dopamine:\tn=4640\tweight=0.00021551724137931034\n", + "\tcycle_gan:\tn=19383\tweight=5.159160088737554e-05\n" + ] + } + ], + "source": [ + "def load_image(filename):\n", + "\n", + " image = imread(filename)\n", + "\n", + " # images are grescale, we only need one of the RGB channels\n", + " image = image[:, :, 0]\n", + " # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n", + " image = image.astype(np.float32)/255.0\n", + " image = (image - 0.5)/0.5\n", + "\n", + " return image\n", + "\n", + "\n", + "# create a dataset for all images of all classes\n", + "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n", + "\n", + "# randomly split the dataset into train, validation, and test\n", + "num_images = len(full_dataset)\n", + "# ~70% for training\n", + "num_training = int(0.7 * num_images)\n", + "# ~15% for validation\n", + "num_validation = int(0.15 * num_images)\n", + "# ~15% for testing\n", + "num_test = num_images - (num_training + num_validation)\n", + "# split the data randomly (but with a fixed random seed)\n", + "train_dataset, validation_dataset, test_dataset = random_split(\n", + " full_dataset,\n", + " [num_training, num_validation, num_test],\n", + " generator=torch.Generator().manual_seed(23061912))\n", + "\n", + "# compute class weights in training dataset for uniform sampling\n", + "ys = np.array([y for _, y in train_dataset])\n", + "counts = np.bincount(ys)\n", + "label_weights = 1.0 / counts\n", + "weights = label_weights[ys]\n", + "\n", + "print(\"Number of images per class:\")\n", + "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n", + " print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n", + "\n", + "# create a data loader with uniform sampling\n", + "sampler = WeightedRandomSampler(weights, len(weights))\n", + "# this data loader will serve 8 images in a \"mini-batch\" at a time\n", + "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)" + ] + }, + { + "cell_type": "markdown", + "id": "e9010bdc", + "metadata": { + "execution": {}, + "id": "e9010bdc" + }, + "source": [ + "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d8c6f3a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 117 }, + "id": "3d8c6f3a", + "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "0d328904", - "metadata": { - "execution": {}, - "id": "0d328904" - }, - "source": [ - "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", - "\n", - "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def show_batch(x, y):\n", + " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", + " for i in range(x.shape[0]):\n", + " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", + " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", + " plt.show()\n", + "\n", + "# show a random batch from the data loader\n", + "# (run this cell repeatedly to see different batches)\n", + "for x, y in dataloader:\n", + " show_batch(x, y)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "f882416f", + "metadata": { + "execution": {}, + "id": "f882416f" + }, + "source": [ + "### Create a Model, Loss, and Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "54f177cc", + "metadata": { + "id": "54f177cc" + }, + "outputs": [], + "source": [ + "class Vgg2D(torch.nn.Module):\n", + "\n", + " def __init__(\n", + " self,\n", + " input_size,\n", + " fmaps=12,\n", + " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", + " output_classes=6):\n", + "\n", + " super(Vgg2D, self).__init__()\n", + "\n", + " self.input_size = input_size\n", + "\n", + " current_fmaps = 1\n", + " current_size = tuple(input_size)\n", + "\n", + " features = []\n", + " for i in range(len(downsample_factors)):\n", + "\n", + " features += [\n", + " torch.nn.Conv2d(\n", + " current_fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Conv2d(\n", + " fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.MaxPool2d(downsample_factors[i])\n", + " ]\n", + "\n", + " current_fmaps = fmaps\n", + " fmaps *= 2\n", + "\n", + " size = tuple(\n", + " int(c/d)\n", + " for c, d in zip(current_size, downsample_factors[i]))\n", + " check = (\n", + " s*d == c\n", + " for s, d, c in zip(size, downsample_factors[i], current_size))\n", + " assert all(check), \\\n", + " \"Can not downsample %s by chosen downsample factor\" % \\\n", + " (current_size,)\n", + " current_size = size\n", + "\n", + " self.features = torch.nn.Sequential(*features)\n", + "\n", + " classifier = [\n", + " torch.nn.Linear(\n", + " current_size[0] *\n", + " current_size[1] *\n", + " current_fmaps,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " output_classes)\n", + " ]\n", + "\n", + " self.classifier = torch.nn.Sequential(*classifier)\n", + "\n", + " def forward(self, raw):\n", + "\n", + " # add a channel dimension to raw\n", + " shape = tuple(raw.shape)\n", + " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", + "\n", + " # compute features\n", + " f = self.features(raw)\n", + " f = f.view(f.size(0), -1)\n", + "\n", + " # classify\n", + " y = self.classifier(f)\n", + "\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5da43245", + "metadata": { + "id": "5da43245" + }, + "outputs": [], + "source": [ + "# get the size of our images\n", + "for x, y in train_dataset:\n", + " input_size = x.shape\n", + " break\n", + "\n", + "# create the model to train\n", + "model = Vgg2D(input_size)\n", + "\n", + "# create a loss\n", + "loss = torch.nn.CrossEntropyLoss()\n", + "\n", + "# create an optimzer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" + ] + }, + { + "cell_type": "markdown", + "id": "01688095", + "metadata": { + "execution": {}, + "id": "01688095" + }, + "source": [ + "### Train the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fa65090d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "fa65090d", + "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "id": "a182c3bc", - "metadata": { - "execution": {}, - "id": "a182c3bc", - "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "----------------- Options ---------------\n", - " aspect_ratio: 1.0 \n", - " aux_checkpoint: checkpoints/synapses/classifier/vgg_checkpoint\t[default: None]\n", - " aux_downsample_factors: [(2, 2), (2, 2), (2, 2), (2, 2)]\n", - " aux_input_nc: 1 \n", - " aux_input_size: 128 \n", - " aux_net: vgg2d \n", - " aux_output_classes: 6 \n", - " batch_size: 1 \n", - " checkpoints_dir: checkpoints/synapses/cycle_gan/gaba_glutamate\t[default: ./checkpoints]\n", - " crop_size: 128 \n", - " dataroot: data/raw/synapses/cycle_gan/0_gaba_2_glutamate\t[default: None]\n", - " dataset_mode: single \n", - " direction: AtoB \n", - " display_winsize: 256 \n", - " epoch: latest \n", - " eval: False \n", - " gpu_ids: 0 \n", - " init_gain: 0.02 \n", - " init_type: normal \n", - " input_nc: 1 \n", - " isTrain: False \t[default: None]\n", - " load_iter: 0 \t[default: 0]\n", - " load_size: 128 \n", - " max_dataset_size: inf \n", - " model: test \n", - " model_suffix: _A \t[default: ]\n", - " n_layers_D: 3 \n", - " name: \t[default: experiment_name]\n", - " ndf: 64 \n", - " netD: basic \n", - " netG: resnet_9blocks \n", - " ngf: 64 \n", - " no_dropout: True \t[default: False]\n", - " no_flip: True \n", - " norm: instance \n", - " ntest: inf \n", - " num_test: 500 \t[default: 50]\n", - " num_threads: 1 \t[default: 4]\n", - " output_nc: 1 \n", - " phase: test \n", - " preprocess: none \n", - " results_dir: data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results\t[default: ./results/]\n", - " serial_batches: False \n", - " suffix: \n", - " verbose: True \t[default: False]\n", - "----------------- End -------------------\n", - "dataset [SingleDataset] was created\n", - "initialize network with normal\n", - "model [TestModel] was created\n", - "loading the model from checkpoints/synapses/cycle_gan/gaba_glutamate/latest_net_G_A.pth\n", - "---------- Networks initialized -------------\n", - "DataParallel(\n", - " (module): ResnetGenerator(\n", - " (model): Sequential(\n", - " (0): ReflectionPad2d((3, 3, 3, 3))\n", - " (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1))\n", - " (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", - " (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (6): ReLU(inplace=True)\n", - " (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", - " (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (9): ReLU(inplace=True)\n", - " (10): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (11): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (12): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (13): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (14): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (15): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (16): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (17): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (18): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", - " (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (21): ReLU(inplace=True)\n", - " (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", - " (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (24): ReLU(inplace=True)\n", - " (25): ReflectionPad2d((3, 3, 3, 3))\n", - " (26): Conv2d(64, 1, kernel_size=(7, 7), stride=(1, 1))\n", - " (27): Tanh()\n", - " )\n", - " )\n", - ")\n", - "[Network G_A] Total number of parameters : 11.366 M\n", - "-----------------------------------------------\n", - "creating web directory data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest\n", - "processing (0000)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/0_train.png']\n", - "processing (0005)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10004_train.png']\n", - "processing (0010)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10009_train.png']\n", - "processing (0015)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10013_train.png']\n", - "processing (0020)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10018_train.png']\n", - "processing (0025)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10022_train.png']\n", - "processing (0030)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10027_train.png']\n", - "processing (0035)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10031_train.png']\n", - "processing (0040)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10036_train.png']\n", - "processing (0045)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10040_train.png']\n", - "processing (0050)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10045_train.png']\n", - "processing (0055)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1004_train.png']\n", - "processing (0060)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10054_train.png']\n", - "processing (0065)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10059_train.png']\n", - "processing (0070)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10063_train.png']\n", - "processing (0075)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10068_train.png']\n", - "processing (0080)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10072_train.png']\n", - "processing (0085)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10077_train.png']\n", - "processing (0090)-th image... 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['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10365_train.png']\n", - "processing (0410)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1036_train.png']\n", - "processing (0415)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10374_train.png']\n", - "processing (0420)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10379_train.png']\n", - "processing (0425)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10383_train.png']\n", - "processing (0430)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10388_train.png']\n", - "processing (0435)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10392_train.png']\n", - "processing (0440)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10397_train.png']\n", - "processing (0445)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10400_train.png']\n", - "processing (0450)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10405_train.png']\n", - "processing (0455)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1040_train.png']\n", - "processing (0460)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10414_train.png']\n", - "processing (0465)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10419_train.png']\n", - "processing (0470)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10423_train.png']\n", - "processing (0475)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10428_train.png']\n", - "processing (0480)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10432_train.png']\n", - "processing (0485)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10437_train.png']\n", - "processing (0490)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10441_train.png']\n", - "processing (0495)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10446_train.png']\n" - ] - } - ], - "source": [ - "# translate images from class A to B, and classify each with the VGG network trained above\n", - "cycle_gan.test(\n", - " data_dir='data/raw/synapses/',\n", - " class_A='0_gaba',\n", - " class_B='2_glutamate',\n", - " img_size=128,\n", - " checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n", - " vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Will use device cpu for training\n" + ] + } + ], + "source": [ + "# use a GPU, if it is available\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "model.to(device)\n", + "print(f\"Will use device {device} for training\")" + ] + }, + { + "cell_type": "markdown", + "id": "ecbab4f7", + "metadata": { + "execution": {}, + "id": "ecbab4f7" + }, + "source": [ + "The next cell merely defines some convenience functions for training, validation, and testing:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a8c7fe9", + "metadata": { + "id": "1a8c7fe9" + }, + "outputs": [], + "source": [ + "def train(dataloader, optimizer, loss, device):\n", + " '''Train the model for one epoch.'''\n", + "\n", + " # set the model into train mode\n", + " model.train()\n", + "\n", + " epoch_loss, num_batches = 0, 0\n", + " for x, y in tqdm(dataloader, 'train'):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + " optimizer.zero_grad()\n", + "\n", + " y_pred = model(x)\n", + " l = loss(y_pred, y)\n", + " l.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " epoch_loss += l\n", + " num_batches += 1\n", + "\n", + " return epoch_loss/num_batches\n", + "\n", + "\n", + "def evaluate(dataloader, name, device):\n", + "\n", + " correct = 0\n", + " total = 0\n", + " for x, y in tqdm(dataloader, name):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + "\n", + " logits = model(x)\n", + " probs = torch.nn.Softmax(dim=1)(logits)\n", + " predictions = torch.argmax(probs, dim=1)\n", + "\n", + " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", + " total += len(y)\n", + "\n", + " accuracy = correct/total\n", + "\n", + " return accuracy\n", + "\n", + "\n", + "def validate(validation_dataset, device):\n", + " '''Evaluate prediction accuracy on the validation dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(validation_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'validate', device)\n", + "\n", + "\n", + "def test(test_dataset, device):\n", + " '''Evaluate prediction accuracy on the test dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(test_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'test', device)" + ] + }, + { + "cell_type": "markdown", + "id": "68bcfbbf", + "metadata": { + "execution": {}, + "id": "68bcfbbf" + }, + "source": [ + "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d0af7638", + "metadata": { + "id": "d0af7638" + }, + "outputs": [], + "source": [ + "def train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device):\n", + "\n", + " for epoch in range(num_epochs):\n", + " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", + " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", + "\n", + " accuracy = validate(validation_dataset, device=device)\n", + " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "45e31b87", + "metadata": { + "execution": {}, + "id": "45e31b87" + }, + "source": [ + "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", + "\n", + "Unceck the box below and run the cell to train a model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "W5KA7zDIa3Lw", + "metadata": { + "cellView": "form", + "id": "W5KA7zDIa3Lw" + }, + "outputs": [], + "source": [ + "# @markdown\n", + "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "53fb8dda", + "metadata": { + "id": "53fb8dda" + }, + "outputs": [], + "source": [ + "# Load a pretrained model or train the model from scratch\n", + "\n", + "# set this to True and run this cell if you want a shortcut\n", + "\n", + "if yes_I_want_the_pretrained_model:\n", + " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", + " map_location=device)\n", + " model.load_state_dict(checkpoint['model_state_dict'])\n", + "else:\n", + " train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4f6e3663", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "4f6e3663", + "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "17fc1703", - "metadata": { - "execution": {}, - "id": "17fc1703" - }, - "source": [ - "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "test: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 346/346 [00:39<00:00, 8.72it/s]" + ] }, { - "cell_type": "code", - "execution_count": 26, - "id": "2a582ba6", - "metadata": { - "execution": {}, - "id": "2a582ba6" - }, - "outputs": [], - "source": [ - "class_A_index = 0\n", - "class_B_index = 2\n", - "\n", - "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n", - "classification_results = []\n", - "for f in glob.glob(result_dir + '/*.json'):\n", - " result = json.load(open(f))\n", - " result['basename'] = f.replace('_aux.json', '')\n", - " classification_results.append(result)\n", - "classification_results.sort(\n", - " key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n", - " reverse=True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "final test accuracy: 0.49737888647866957\n" + ] }, { - "cell_type": "markdown", - "id": "2cc0d486", - "metadata": { - "execution": {}, - "id": "2cc0d486" - }, - "source": [ - "Show the top real and fake images that make the classifier change its mind:" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "accuracy = test(test_dataset, device=device)\n", + "print(f\"final test accuracy: {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f43bba5", + "metadata": { + "execution": {}, + "id": "3f43bba5" + }, + "source": [ + "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", + "\n", + "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n", + "\n", + "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types." + ] + }, + { + "cell_type": "markdown", + "id": "72b5240c", + "metadata": { + "execution": {}, + "id": "72b5240c" + }, + "source": [ + "---\n", + "# Train a GAN to Translate Images\n", + "\n", + "We will train a so-called CycleGAN to translate images from one class to another." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41c9e63b", + "metadata": { + "cellView": "form", + "id": "41c9e63b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "id": "1567b00e", - "metadata": { - "execution": {}, - "id": "1567b00e", - "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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9YGAgbrrppnjDG94Q/f39M+lvfOMbo7e3N66++upZdVq6dGl86EMfiubm5hgaGorp6elSmbfffntEROk3ek444YQoisL+KeDVWlpaYptttpl1t2tvb48DDjiglFfdQwcGBiIi35+rbb755tHd3T3r8zN3xqrjDkA1vBgBsE7HH3/8zN9EPeqoo+KTn/zkrC/yI37/N0Nf/OIXl37229/+drz2ta+Ntra2uOCCC+LVr351nHTSSXHnnXeW8l544YWx5557xgc+8IE4//zzo7W1NY477rj49re/Xcp76623xjve8Y54wxveEB/4wAfimWeeiSOPPHLW3+r82c9+Fj/60Y/ihBNOiE9+8pNxyimnxC233BIHH3xwDA8Pl8rca6+94kc/+tF622NsbCyefvrpSv9bn1/84hex0047zTokRkS85CUviYhYZ8yG1X82SR1+urq64he/+MXMQXJsbEzm6+7ujoiY6Y9MmauNj4/H008/HY888khcd9118dGPfjS222672HHHHW3dI37/ImFwcDAWLly4znwRMfP3dFXe8847L3p7e6OzszP22Wef+P73v2/LeeaZZ+LJJ5+Mn//853HiiSdGRMShhx5aylflmW6++ebYeeed48Ybb4ytt946+vr6YsGCBXHuuefOaqNf/OIXERGx9957z/qMrbbaKrbeeuuZ/6489dRTcdNNN8WrXvUq+avpn/jEJ2L58uXx3ve+15Zx8MEHx8DAQJx88slx7733xsMPPxyXXnpp/Nu//Vucc8459udWU22/rvE0Pj5O8EwAAPBH69m62/yhLr/88ujo6IiXvexlcfnll8fll18eJ598ckTk7zdnnHFG3H///XHeeefF0UcfHZ/97Gfj3HPPjb/4i7+IqampOP/88+OlL31pfOQjH4nLL7981s9WuZutq65Lly6N/fbbL26++eY4/fTT48ILL4wdd9wxTjrpJPkl+l577RX33HPPzJfOzsTEROW7mPpSfU2/+MUvoqenJ174whfOSl99F1vXOT2i+jl7hx12iK233jr+5V/+Jb71rW/Fo48+Gj/96U/jlFNOie23317+ya4//dM/jf7+/uju7o6jjz467r///nXWJWNiYiKuvvrqOOCAA+TLuv/93/+Nnp6e6Ovri0WLFsW5554r4y1OTU3FGWecEW9961vjRS96kfysu+++OyYnJ0t3ofb29thjjz1mtfHNN98c/f398dhjj8ULXvCC6O3tjf7+/vjrv/7rWS8v3Z117bvtmoaGhuLpp5+O3/72t/Hxj388vvOd78h74NrUXWjvvfeOnp6eOPfcc+MHP/hBPPbYY3HrrbfGu971rthnn31Kf7Ys4vf/wO6pp56Ku+++O9761rfGwMDArM/P3Blrvd8BWMtG/X0VAH+03ve+9xURURx99NGz0k899dQiImZ+RXViYqJoamoq3vnOd5bKeNGLXlRsvfXWxapVq2bS/v3f/72IiGK77bablXd4eHjW/x8fHy923XXX4pBDDpmVHhFFRBQ///nPZ9IefvjhorOzszjmmGNseUVRFHfccUcREcW//uu/lv7b+eefX0REsXTp0tJ/W9PqX2+u8r/1Wbx4cen5iqIo/vu//7uIiOLSSy+1P/vUU08VTU1NxUknnTQr/b777pv5/NV/6uiMM84ompubS38q6YQTTigiojj99NPTZa721a9+ddYz77333sWvfvWr9T77P/7jP5b+vJcyNjZW7LLLLsX2228/69eKH3744eLwww8vLrnkkuKb3/xm8YlPfKLYdttti+bm5uKGG26QZXV0dMzUc8GCBfZPg1V5pv7+/mLevHlFR0dHce655xZf//rXi9e97nVFRBR/93d/N5PvIx/5SBERxe9+97vS5+yzzz7FfvvtZ5/9U5/6VBERxY033lj6b0888UTR19dXfOYznymKwv/a/eTkZHH66acXbW1tM8/T0tJSXHLJJfZzV3vmmWeKzTffvHjZy142K/1FL3pRsdNOO8368wJjY2PFtttuW0RE8fWvf329ZQMAADybnu27Taz1p7Te9KY3lfKsWa81uT9PVfV+s/pceMQRR8z687j7779/0dTUVJxyyikzaZOTk8XWW29d+jNMVe9mrq4nnXRSseWWW5buDieccEIxZ86cUvlXXnllERHFT37yk1JZa/rhD39Y+S724IMPrrOsl7/85cXznve8UvrQ0FDpTK9kztk/+clPih122GFW/fbaa69Zf6q3KIria1/7WvHmN7+5+PKXv1xcd911xXvf+96iu7u7WLhwobxPrJb5U1qr/0zyxRdfXPpvb3nLW4r3v//9xbXXXlv867/+a3H00UcXEVEcf/zxpbyf/vSnizlz5hRPPvlkURT6z3Bdc801RUQUt912W+nnjzvuuGLRokUz/3+33XYruru7i+7u7uKMM84orr322uKMM84oIqI44YQTZvJde+21RUQUl19++azyLr300iIiil133bX0WSeffPJMuzc3NxfHHntssWzZsvW0VFEcdthhRX9/f7F8+fJZ6TfccEOx5ZZbzurPI444YtbasKYXvOAFM/l6e3uL9773vbP+XFjmzljL/Q5AGcHXAazTaaedNuv/n3HGGXHxxRfHjTfeGLvttlssW7YsiqKIefPmzcr3+OOPx9133x1///d/H729vTPpBx10ULzoRS8q/WugNf/Fx/Lly2Nqaipe9rKXxVe/+tVSnfbff//Ya6+9Zv7/tttuG6985SvjW9/6VkxNTUVLS8us8iYmJmJgYCB23HHHmDt3btx1113xV3/1V7PKXF3/p59+OjbffHPbHkcccUTcdNNN9r9njIyMREdHRyl9zQDnzsKFC+P444+PL3/5y/HCF74wjjnmmHjsscfijDPOiLa2tpiYmJj5+be+9a1x6aWXxvHHHx8f//jHY4sttoirr746rrvuulmfkylztT/90z+Nm266KVasWBG33HJL/Nd//VcMDQ2t87lvu+22OO+88+L444+f9ee9lNNPPz1+/etfx7e//e1Zv1a87bbbxve+971Zef/qr/4qdtlll3jnO98ZL3/5y0tlfec734nR0dG499574ytf+YqtZ5VnGhwcjOnp6fjwhz8c7373uyMi4jWveU0sW7YsLrzwwvj7v//76Ovrm2kv18/r+ldxV155ZWy22WbxZ3/2Z6X/9u53vzue97znlYK1r62lpSV22GGHOOKII+K4446Lzs7O+OpXvxpnnHFGLFq0yAbKnJ6ejte//vWxYsWK+NSnPjXrv5166qnx13/913HSSSfFu971rpieno4PfvCD8cQTT0TEusctAADAxvRs3W02hOz95qSTTpr1Z7r23XffuOOOO+Kkk06aSWtpaYm999679K/sM3eztRVFEddee20cf/zxURTFrN+kP+KII+Kqq66Ku+66K/7kT/5kJn3Nu9i67L777pXvYosWLVrnf6/lLhaRO2fPmzcv9thjjzjuuONiv/32i9/85jdxwQUXxHHHHRc33XTTzGcef/zxcfzxx8/83Kte9ao44ogj4sADD4wPfehDcemll1Z69nW58soro62tbdbnrPaFL3xh1v//q7/6q3j7298en/vc5+Lss8+O/fbbLyJ+/5v4//AP/xDnnntubLbZZvaz1ncXWrONBwcHY3h4OE455ZT45Cc/GRERr371q2N8fDw+85nPxAc+8IF4/vOfH0cddVRst9128bd/+7fR3d0de+21V/zkJz+J97znPdHa2ir77R3veEcce+yx8fjjj8fVV18dU1NT6/1TzOeff37cfPPNcfHFF5f+bNVmm20We+65Z5x++umxePHi+OUvfxn//M//HCeeeGJcc801pbIuu+yyGBgYiAceeCAuu+yyGBkZiampqZk/hZa5M/6h9zsAxsZ9LwPgj9Xqf730wAMPzEofHx8vmpubZ4KFLV26tIiI4h//8R9n5fvRj35URETxxS9+sVT2McccU/oXU9/61reKfffdd9a/6o+IoqmpaVa+iCje+MY3lso899xzZwXIHh4eLs4999xi6623LpqammaVeeKJJ5Z+/uKLLy4iovj1r3+9/sapk1p+Y6QoimLFihUz/4pn9f/e8IY3FK9+9auLiJj1L1uuueaaYsGCBTP5Fi1aVFxyySVFRBRnnXXWH1Sm8qEPfajo7e0t/eun1e69995i/vz5xR577FEMDAyss6zVQePXHlvr8nd/93dFRBSPPPLIOvP95je/KTo7O4tPfepT6y1TPVNPT08REcXDDz88K++Xv/zlIiKKW2+9tSiKP/w3Rn7729/O+m2eNd1xxx1FU1NT8YMf/GAmzf3GyAUXXFAsWrSo9K+XDj744GKrrbaa9Vs4a1r9ryfVb1cVRVH8/d///ax/pbT33nsX73nPe4qIKK677jr5MwAAABvLs323iQ30GyNV7jerz4U//vGP5WctWbJkVvqb3vSmore3d1Za1buZquvqNlzX/9YOUH3jjTfa35TeUGr9jZGq5+wVK1YUW2yxRfHRj350Vr7Vv22kfnNjbfvtt1+xww472P9e9TdGVq1aVXR3dxeveMUr1pt3tdV/PWDNOXHKKacUO+64YzE2NjaTVutvjCxevHjWPWq1W2+9tYiI4stf/vJM2j333FPssssuM+Opo6OjuPDCC4vNN9+82H333df7TH/2Z39W7LPPPrN+o2pNV111lfxLCkXx+3tad3d36bfkv/SlL1Uaw8uWLSu22GKLWb+Vlrkz/qH3OwAaMUYApKz5r44iIubPnx9NTU2xfPnyP7jM22+/PY4++ujo7Oyc+RdbN910U7zuda+TAa2rOOOMM+JDH/pQHH/88XH11VfH97///bjppptiwYIF8u/Nrq7/+mJejIyMxJIlSyr9b3223HLLmX9lv6bVaVtttdU6f37OnDlx/fXXx8MPPxy33nprPPTQQ3H55ZfHE088MRNkcbXV/0Lmpz/9adxxxx3x8MMPx/Oe97yIiNhpp53+oDKVY48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- }, - "metadata": {} - } - ], - "source": [ - "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", - " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", - " axs[0].imshow(a, cmap='gray')\n", - " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", - " axs[1].imshow(b, cmap='gray')\n", - " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", - " plt.show()\n", - "\n", - "\n", - "# show the top successful translations (according to our VGG classifier)\n", - "for i in range(10):\n", - " basename = classification_results[i]['basename']\n", - " score_A = classification_results[i]['aux_real'][class_A_index]\n", - " score_B = classification_results[i]['aux_fake'][class_B_index]\n", - " real_A = imread(basename + '_real.png')\n", - " fake_B = imread(basename + '_fake.png')\n", - " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Get the CycleGAN code and dependencies\n", + "\n", + "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n", + "\n", + "import requests, zipfile, io\n", + "\n", + "url = 'https://osf.io/vutn5/download'\n", + "r = requests.get(url)\n", + "z = zipfile.ZipFile(io.BytesIO(r.content))\n", + "z.extractall()\n", + "\n", + "!pip install dominate --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "e5da5c01", + "metadata": { + "execution": {}, + "id": "e5da5c01" + }, + "source": [ + "In this example, we will translate between GABAergic and glutamatergic synapses.\n", + "\n", + "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2b2519c4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2b2519c4", + "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c" + }, + "outputs": [ { - "cell_type": "code", - "source": [], - "metadata": { - "id": "m7XsnW_R7wBN" - }, - "id": "m7XsnW_R7wBN", - "execution_count": 27, - "outputs": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22648/22648 [00:01<00:00, 20146.29it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5085/5085 [00:00<00:00, 20709.24it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] } - ], - "metadata": { + ], + "source": [ + "import cycle_gan\n", + "\n", + "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n", + "\n", + "## Uncomment if you want to enable the training procedure\n", + "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)" + ] + }, + { + "cell_type": "markdown", + "id": "0d328904", + "metadata": { + "execution": {}, + "id": "0d328904" + }, + "source": [ + "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", + "\n", + "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a182c3bc", + "metadata": { "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "base_uri": "https://localhost:8080/" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" + "id": "a182c3bc", + "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129" + }, + "outputs": [], + "source": [ + "# translate images from class A to B, and classify each with the VGG network trained above\n", + "# Note: cycle_gan requires CUDA devices\n", + "if device == \"cuda\":\n", + " cycle_gan.test(\n", + " data_dir='data/raw/synapses/',\n", + " class_A='0_gaba',\n", + " class_B='2_glutamate',\n", + " img_size=128,\n", + " checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n", + " vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "17fc1703", + "metadata": { + "execution": {}, + "id": "17fc1703" + }, + "source": [ + "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2a582ba6", + "metadata": { + "id": "2a582ba6" + }, + "outputs": [], + "source": [ + "class_A_index = 0\n", + "class_B_index = 2\n", + "\n", + "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n", + "classification_results = []\n", + "for f in glob.glob(result_dir + '/*.json'):\n", + " result = json.load(open(f))\n", + " result['basename'] = f.replace('_aux.json', '')\n", + " classification_results.append(result)\n", + "classification_results.sort(\n", + " key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n", + " reverse=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2cc0d486", + "metadata": { + "execution": {}, + "id": "2cc0d486" + }, + "source": [ + "Show the top real and fake images that make the classifier change its mind:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1567b00e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, - "accelerator": "GPU" + "id": "1567b00e", + "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c" + }, + "outputs": [], + "source": [ + "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", + " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", + " axs[0].imshow(a, cmap='gray')\n", + " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", + " axs[1].imshow(b, cmap='gray')\n", + " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", + " plt.show()\n", + "\n", + "\n", + "# show the top successful translations (according to our VGG classifier)\n", + "# Note: only run if cycle_gan ran successfully\n", + "if classification_results:\n", + " for i in range(10):\n", + " basename = classification_results[i]['basename']\n", + " score_A = classification_results[i]['aux_real'][class_A_index]\n", + " score_B = classification_results[i]['aux_fake'][class_B_index]\n", + " real_A = imread(basename + '_real.png')\n", + " fake_B = imread(basename + '_fake.png')\n", + " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "m7XsnW_R7wBN", + "metadata": { + "id": "m7XsnW_R7wBN" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/projects/ReinforcementLearning/human_rl.ipynb b/projects/ReinforcementLearning/human_rl.ipynb index e72d4367f..954a4bbba 100644 --- a/projects/ReinforcementLearning/human_rl.ipynb +++ b/projects/ReinforcementLearning/human_rl.ipynb @@ -54,21 +54,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.1 which is incompatible.\u001B[0m\u001B[31m\n", - "\u001B[0m\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "chex 0.1.81 requires numpy>=1.25.0, but you have numpy 1.23.3 which is incompatible.\u001B[0m\u001B[31m\n", - "\u001B[0m" + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\u001b[33mWARNING: Skipping seaborn as it is not installed.\u001b[0m\u001b[33m\n", + "\u001b[0m" ] } ], @@ -76,29 +75,25 @@ "# @title Install dependencies\n", "!pip install jedi --quiet --root-user-action=ignore\n", "!pip install --upgrade pip setuptools wheel --quiet --root-user-action=ignore\n", - "!pip install dm-acme[jax] --quiet --root-user-action=ignore\n", + "!pip install 'dm-acme[jax]' --quiet --root-user-action=ignore\n", "!pip install dm-sonnet --quiet --root-user-action=ignore\n", "!pip install trfl --quiet --root-user-action=ignore\n", - "!pip install numpy==1.23.3 --quiet --ignore-installed --root-user-action=ignore\n", + "!pip install numpy==1.24.1 --quiet --ignore-installed --root-user-action=ignore\n", "!pip uninstall seaborn -y --quiet --root-user-action=ignore\n", "!pip install seaborn --quiet --root-user-action=ignore" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/reverb/platform/default/ensure_tf_install.py:53: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " if (distutils.version.LooseVersion(version) <\n", - "/usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/__init__.py:57: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " if (distutils.version.LooseVersion(tf.__version__) <\n" + "2024-07-16 14:41:51.924400: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.0/lib64/:/usr/local/cuda-11.0/lib64/:/usr/local/cuda-11.0/lib64/\n", + "2024-07-16 14:41:51.924418: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" ] } ], @@ -123,21 +118,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], + "outputs": [], "source": [ "# @title Figure settings\n", "from IPython.display import clear_output, display, HTML\n", @@ -235,14 +220,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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3tbXVmr5ixQpYWVnhww8/1Fl+8uTJaNSokd6+MbaOHTvq9If6SIC9vb3OJ7OBAwcCgNZpqvv372PLli3w9vbGq6++qrV8vXr18NZbb0EQhEo9ntTUVGRnZ6Nv377w8fHRmqd+XqtC33NhY2OjOWpYFWFhYQZd3VHZ12hNuXXrFvbv348WLVpofaoFgICAAPTp0wfZ2dnYsWOHzrq9e/dGSEiI1rShQ4cC0P9e0GfDhg0AgOnTp2sdBbS1tcVbb70FAFi/fn3lH1A1TZkyRetoiJWVFQYPHgwAWqeddu/ejWvXrmHkyJE6R8iaNWuG8ePH486dOzh06FClt92yZUud95j6/VdUVIS3335ba7/Xr18/WFlZ4dy5c5ppRUVF2LJlC2xtbfHmm29CIpFo5rm6uiIqKgrFxcX473//q5muPjU0c+ZMNGvWTKeu5s2b66333LlzeOmll5CVlYWkpCSdfUdVREVFoWXLlli1apXmlHF1yGQyzJgxQ6u/XFxcNI+/7CkaGxsbvY+xYcOGePHFF5GTk6P1et61axcyMzMRGhqKfv366axXXn+pT+OtXLkSUVFR+Oqrr1CvXr1KPR5D94WtW7fWmWZjY4OXX34ZJSUlVXp9AqV9WPY1pfbKK68AKB2C8ih9+12pVApHR0fN72vWrEFxcTHee+89nddgSEgIQkNDsWvXLuTl5VWp3ooYdIqqXbt2egeKnThxAtbW1khJSUFKSorO/OLiYty7dw/379+Hk5MTevfujRUrVmDKlCkIDw/HM888g4CAAL1PmJq+Q/ENGzaEp6cnjh49isuXL8PT0xNnz54FAAQFBeltJzg4GGlpaTh79iwCAwO15j364gKAJ598EkDpoU610NBQfP7555g7dy7279+Prl27IiAgAG3bttV6gRQWFuL8+fNwcnLC8uXL9dZjY2NT5XENhih7uFBNPZDT09NTc+hRTf1CvHXrlmbaqVOnoFQqIZFI9F5qXVJSAgBIT09/bD3q5+nR5wDQfl4fp3PnzmjWrBmWLFmCM2fO4Nlnn0VAQIDex1RZ+k5fVkZlX6M1Rd3HHTt21DnlC5S+F7Zs2YKzZ89qAq2avteLvvfC47YvlUr19ktgYCBkMpnWH3BTq+xjOnHiBADgxo0bel/n6nF8ly9frvRpKn2vR/X7z9XVVSeIy2QyNG7cWGt8wt9//43CwkIEBAToPe0RHByMxMRErT49ceIEJBLJYy+1LystLQ3Lli2DnZ0dVq1ahXbt2lV6XX1sbGzw5ptvYvr06fjss8/w1Vdf6V1OoVDo3U+OHj1a64Pzk08+CRcXF53l1K8z9ete7eLFi/j2229x7Ngx3LlzR2c8Sdk+Vj/3VfmA8+DBA4wePRonTpzAjBkzdD78PY6h+8IbN24gKSkJhw4dws2bN/HgwQOt+frGjlakoKAAK1aswI4dO3DlyhXk5+dDEATNfPUFAADQtm1beHp6YuvWrcjMzERYWBg6duwIb29vnYyg7tOjR4/q/XB09+5dKJVKXLlyRe971BAGBZwnnnhC7/Ts7GyUlJRoBvuVp6CgAE5OTvD19cWqVavw9ddf45dfftEkbjc3N0RHR6Nv37466zZu3LjCmtQDBdX/l3cVRpMmTbSWK0tfUlbvlMoOrGvZsiV++uknxMXFYd++ffj1118BlL7xxo4dqxnMqlAoIAgC7t2799i+MbWKHpu+eeoxQerQApQ+z0Bp0KnoU3x+fv5j61H3f3mvqfKmP8re3h7r1q3DokWLsHPnTuzfvx8A4OTkhBEjRmDSpEl6/7hXpLLbflRlX6M1Rb099Wv+URW9F/SNe1G/Jio7yDQ3NxeOjo56PxRZWVnByckJd+/erVRbxlDZ97f6da7vw1pZBQUF1dq2uj/L+4RuZWWl9f6r7PNZ9qIO9XOg75N2ec6dO4f8/Hz4+/ujTZs2lV6vIn369MH333+PlJQUnDhxAn5+fjrLKBQKvfvJQYMGab0eH7fPKPt6PnHiBEaPHg2lUong4GCEhobC3t4eUqkU586dQ2pqqtaRVfW6+o52lSc/Px9nz56Fvb19lYLko9usyr4wIyMDQ4YMgUKhQKdOndC1a1fY29tDJpMhMzMTmzZtqtIR4+LiYowePRonT56EXC5H79694ezsrHmNxsfHa7Unk8mwfPlyJCQk4JdffsFnn30GALCzs8OgQYPw5ptvao7aqt9P3377bYU1VOX99DgGBRx9h6+A0j8ygiBU6hO3mr+/P7755hsUFRXh9OnT2LdvH1auXInp06fD2dlZZ+BbeTvCf/75B8C/Own1/+VdXaWebsipi7Lc3d3x5ZdfoqSkBOfPn8fBgwexcuVKzJ8/Hw0aNEBkZKRmG+3bt9dcAVWXqfv2lVdewaxZs4zSlvr5e1R50/Vp3rw5PvzwQwiCgEuXLuHw4cNYtWoVEhISoFKpNIPtKqu81/njVPY1qt5G2asFy9J31aEhHtfHxnovVLT9nJwcFBcX64TMkpIS3L9/32Tbrg51vy1evBhhYWFmruZflX0+y77OGjZsiOzsbDx48KDSIefll1/G3bt38eOPP2LSpElISEioUkDSRyKR4J133sHIkSPx8ccfY82aNTrLtGrVCn/99ddj23rcPqPs409MTMSDBw+wYsUKnaP633zzDVJTU7WmqdetytGPxo0bY/78+Zg0aRJGjRqFb7/9Vu/ZgPIYsi9ctmwZsrOzsWDBAs1pVrWtW7dW+e9NamoqTp48icGDB2PBggVa827fvq03eDo6OuLdd9/Fu+++i6tXr+Lo0aNYu3YtVq5cCYVCgU8//RTAv/uXtLS0Gnu/G/VOxn5+fsjJycHFixervK6NjQ0CAgLwxhtv4L333gMAnRcdAL3hKTc3F+fOnUO9evU09wBRnwIoL2ypL7318vKqcq36WFlZwdvbGxMmTMDnn3+uVb+dnR2efvppXLx4UZNiq0N9zrmyn6CNzdfXF1KpFL///nu121KPVTl27JjOPPXzWlUSiQRPP/00oqKisGzZMgDaryX1p3WlUmlIyY9V2dcoULpzuHv3rt6Qo+8SS6D0+a9K7eo+TktL0zoSoGbs98KjPD09oVKp9L5ejh07BqVSqTNmyRDGfl906NABAIzyOjcmNzc3NGjQAOfPn9cbgtXPZ9k+9fPzgyAIesdPlEcikWDOnDkYPXo09u/fjwkTJhjl03VgYCDCwsLwxx9/4JdffjG4nZs3b+q99Fr9/iv7+K9evYpGjRrpHbKg7/2qPrK0d+/eKtUUEhKCpUuXoqSkBGPGjMHx48crva4h+8KrV68CAF544QWdeeX97VO/T/TtQ65duwYAeP7553Xm6avrUU899RQiIyOxcuVK2Nraau131X1alfdTRbVWan2D1iqHehBSbGys3uRbUFCgOQ8HlA4EfvR8IfDvJ2B9nxbUYwXKiouLQ25uLvr06aM5DN6xY0e4ubkhLS1N5xBzSkoKfv/9d7i6uqJjx45VeoxlnT59Wu9hfXXSLlv/K6+8guLiYrz77rt6d0o5OTk4c+ZMpbarPu9e2cuWja1x48bo168fTp8+jYSEhHLfKJUZSBgWFgZHR0ds3bpV53SX+nmtjIsXL+r9hKPvuVD3n/reP8ZW2dcoUDreq6SkRHPfF7WNGzfijz/+0Nt+o0aNtMZEPU7z5s3RpUsXZGZm6oxt+PPPP7F161Y4OjpqLqE3thdffBEAsHDhQq07bBcWFmouDx8yZEi1t2Ps90VYWBhat26N1atXl3uvlOPHj9f4XcNtbGzQr18/5Ofn64xjuXbtGn744QdYW1tjwIABmukjR44EAHz00Ud6980VHal499138dprr+HIkSMYN26cUQaBzpgxA1ZWVprn3xBKpRKfffaZVqDNyMjADz/8ACsrK60B0S1btkR2drbOPb3Wr1+vOZ1dVo8ePdCyZUvs3LlT7329Knr/derUCcuWLYNEIsHYsWMrfUbDkH2h+pLpR7exb98+/PTTT3q3U9H7pLz2MjIyNKefHp2ubz+vPmJbdr/78ssvw9raGgsWLMDff/+ts05RUZFO+Knue9qodzIOCQnB9OnT8fnnnyM8PBzdu3dHq1atUFBQgBs3buDYsWMICAjQnINbunQpDh8+jE6dOqFVq1awtbXFpUuXsHfvXjg6OuKll17S2Ua3bt0wfPhwREREaO4RkJaWhpYtW2LGjBma5SQSCT7++GOMGTMGMTEx2Lp1K9q0aYO///5bc0+XTz75RO+VWpW1efNmrF27Fh07doSLiwscHR1x7do17Nq1CzY2Nhg9erRm2SFDhuDMmTNYvXo1nn/+eXTt2hVPPvkkcnJycP36dRw7dgyDBw/G3LlzK9XP3377LWJjY/HCCy/Azs4ODg4Omp1YTZg9ezauXr2KRYsWYcuWLQgICMATTzyB27dv4/Llyzh16hQ+//xzvYMAy7Kzs8PcuXMRExODl19+WeveDxcvXkRgYGClPjkcOHAAn376Kfz8/ODq6orGjRvj1q1bSE1NhVQqxbhx4zTLurm5oVmzZti2bRusrKzQokULSCQSDBgwoGr3WChHZV+jQOmVJRs3bsQHH3yAQ4cO4cknn8S5c+dw4sQJ9OjRA7t27dJpPyQkBNu2bcPEiRPRvn17WFlZITAwUO/gRLU5c+Zg+PDh+OSTT3DgwAF4e3tr7oMjlUrx4Ycfmuywcb9+/ZCamort27ejT58+6Nmzp+Y+ONevX0fv3r2rdXWOWnBwMKRSKT7//HNcvHhRM15j8uTJBrVnbW2NuLg4jB8/HhMmTIC/vz88PT1Rv3593Lp1C6dOnUJGRgb2799f41/bMX36dPz+++9YuXIlTp06haCgIM19cPLz8xEbG6v13uvatSsmTZqExMREREREaO6D888//yAtLQ1+fn746KOPyt3em2++CRsbG8TFxWHMmDFYunSp1hUyVdWmTRsMHToUq1evNrgNDw8PzemULl26aO6Do1Ao8NZbb2ldrKI+CjVixAhERESgYcOGOH36NNLS0hAeHq5zJMnGxgZfffUVxo0bh+nTp2Pt2rXo0KEDHj58iPT0dBw6dEjnQ0xZHTp0wPLlyzFmzBhMmDABCQkJ6NKlS4WPx5B94YgRI7Bx40a88cYbCA8PR9OmTXHx4kXs27cPERERSE5O1tlOSEgIUlJSMHXqVDz77LOoV68eWrRogYEDB6JHjx546qmnsGzZMly4cAGenp64efMmdu3aheeee04naPz111+Ijo6Gj48P3N3d0bRpU9y7dw+pqakoLi7WGmjt7u6O+fPn47333kPfvn3RrVs3uLq6oqSkBDdu3EBaWhqcnJy0DkhUVGtlGP2rGiZMmICAgAD88MMPSEtLw86dO2Fvb49mzZph6NChWgOHR4wYAUdHR/z5559IS0uDUqlEs2bNMGLECIwZM0bvH5tXXnkFzz//PJYvX47k5GTY2tpi8ODBiImJ0Rnc2aFDB/z0009ITEzEoUOHsGvXLjg5OaFPnz6YPHlytQfO9e3bF0VFRTh+/DjOnDmDBw8eoFmzZujTpw/GjBkDuVyutfz777+P7t2748cff8TBgwc1A/+efPJJjBs3rtI7+W7dumHmzJlYt24dli9fjuLiYrRs2bJGA469vT1++OEHrFu3Dlu3bsWvv/6Khw8f4oknnsBTTz2FWbNmVXjjsLJ69eqFhg0bIj4+Htu3b4eNjQ06deqEH3/8EUlJSZUKON26dcPNmzdx7NgxpKamIi8vD02bNkWXLl3wyiuvaH2fkUwmQ3x8PBYuXIiUlBTNVQIdO3Y0SsCpymu0bdu2WLZsGb744gvs2rULMplM89h37NihN+C89957kEgkOHToEPbs2QOVSoXo6OgKA46Liws2bNiAxYsXY+/evTh69Cjs7OzQrVs3TJw40eArxirr888/R2BgIDZs2IC1a9cCKN3hjR07FsOHDzfKNtzd3fHRRx/hu+++w+rVqzVXyRgacIDSK0Y3b96MZcuWYffu3di4cSOkUimaNGmC9u3bY+rUqVo386wpjRo1wtq1a/HNN99gx44dWLZsGerXrw9fX1+MGzcOXbt21Vln2rRp8Pf3x4oVK7B7924UFBSgcePG8Pb21jraU57o6GjUr18fn376KV555RV8++23Bt+0Eii99HnLli0GHxFydHREUlISPv30U2zcuBF5eXlo27Ytxo4dq3Npd/fu3fH1118jMTERycnJkMlk8PX1xYoVK5CRkaH3VJmPjw/++9//YsmSJdi7dy+OHz8OOzs7tG7dGq+//vpj62vfvj1++OEHjBkzBhMnTkRcXByee+65Ctep6r6wXbt2WLFiBb788kvs2bMHJSUlaNeuHeLj49GwYUO9AScyMhI3btzAtm3bNKfTOnfujIEDB8LW1hbLly/HZ599hqNHj+L333+Hi4sLJk+ejDFjxui0px6WcfToUezbtw85OTlwdnaGl5cXoqKidK4uHDBgANq1a4dly5bhyJEj2L9/P2xtbdG0aVOEh4cjIiKi0rVWhkQoe/1XLRYXF4f4+Hi9g8SIiIiIyjLqGBwiIiKi2oABh4iIiESHAYeIiIhEp86MwSEiIiKqLB7BISIiItFhwCEiIiLRMfp9cMi0lEoV7t17/JdYPo5UKoGzsx3u3cuHSmWZZynZB6XYD+wDNfaDZfdBkyb6v/C1ruIRHAsllUogkUgglRr2hZJiwD4oxX5gH6ixH9gHYsKAQ0RERKLDgENERESiw4BDREREosOAQ0RERKLDgENERESiw4BDREREosOAQ0RERKLDgENERESiw4BDREREosOAQ0RERKLDgENERESiw4BDREREosOAQ0RERKLDgENERESiw4BDREREosOAQ0RERKLDgENERESiY2XuAsi8ZDLTZFyVSoBKJZikbSIiosdhwLFQEokEKpUAB4cGJmlfqVIh+34BQw4REZkFA46FkkolkEolWPPLeWTdzTdq202dbTGilyekUgkDDhERmQUDjoW7fa8AmXfyzF0GERGRUXGQMREREYkOAw4RERGJDgMOERERiQ4DDhEREYkOAw4RERGJDgMOERERiQ4DDhEREYkOAw4RERGJDgMOERERiQ4DDhEREYkOAw4RERGJDgMOERERiQ4DDhEREYkOAw4RERGJDgMOERERiQ4DjoFSU1MRGRkJf39/dO3aFW+88QYyMjJ0llu/fj3Cw8Ph4+OD/v37Y9euXWaoloiIyLIw4BjgyJEjiI6ORtu2bZGQkIB3330X58+fx9ixY/HgwQPNctu2bUNsbCwiIiKQlJQEPz8/REdH48SJE+YrnoiIyAJYmbuAumjbtm1o0aIFPvzwQ0gkEgCAs7MzRo8ejdOnT6NTp04AgEWLFqFPnz6YNm0aACA4OBgXLlxAQkICkpKSzFU+ERGR6PEIjgFKSkpgZ2enCTcA0LBhQwCAIAgAgIyMDFy5cgURERFa6/bu3RuHDh1CUVFRzRVMRERkYXgExwCDBw/G5s2bsWrVKvTv3x/Z2dn4/PPP0b59ewQEBAAA0tPTAQBubm5a67q7u6O4uBgZGRlwd3c3aPtWVtXPpVLp/8KZBFpBzRjU7clktTs/q+ur7XWaGvuBfaDGfmAfiAkDjgE6deqE+Ph4TJ8+HXPnzgUAeHp6YunSpZDJZACAnJwcAICDg4PWuurf1fOrSiqVwMnJztDSdcikUlhZyYzWHvDvjsHBoYFR2zWVulKnqbEf2Adq7Af2gRgw4Bjgjz/+wNtvv42hQ4fiueeeQ3Z2NhYvXowJEyZg9erVqF+/vsm2rVIJUCgKqt2OtbUM9vb1oVSpUFKiNEJl/1IqVQAAhaJQ83NtJJNJ4eDQoNbXaWrsB/aBGvvBsvvAmB+eawMGHAPMmzcPwcHBmDlzpmaan58fnnvuOWzevBkvvfQSHB0dAQC5ublo0qSJZjmFQgEAmvmGKCmp/ptOc/hV+HfckLGo21MqVUap1dTqSp2mxn5gH6ixH9gHYsCTjAa4fPky2rVrpzWtefPmcHJywrVr1wAAbdq0AfDvWBy19PR0WFtbw8XFpWaKJSIiskAMOAZo0aIFzp49qzUtMzMT9+/fR8uWLQEALi4ucHV1RUpKitZyycnJCAkJgY2NTY3VS0REZGl4isoAw4YNw4cffoh58+YhNDQU2dnZSExMROPGjbUuC586dSpmzJiB1q1bIygoCMnJyTh58iRWrlxpxuqJiIjEjwHHAKNGjYKNjQ3WrFmDDRs2wM7ODn5+fvjyyy/h5OSkWa5v374oLCxEUlISlixZAjc3N8THx8Pf39+M1RMREYkfA44BJBIJhg8fjuHDhz922cjISERGRtZAVURERKTGMThEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDgGiIqKgoeHh95/27Zt0yy3fv16hIeHw8fHB/3798euXbvMWDUREZHlsDJ3AXXR+++/j7y8PK1py5cvx6+//oqQkBAAwLZt2xAbG4uJEyciODgYycnJiI6OxqpVq+Dn52eGqomIiCwHA44B2rZtqzNt+vTp6NKlC5ydnQEAixYtQp8+fTBt2jQAQHBwMC5cuICEhAQkJSXVZLlEREQWh6eojOCPP/7A9evX0a9fPwBARkYGrly5goiICK3levfujUOHDqGoqMgcZRIREVkMHsExgq1bt8LW1hZhYWEAgPT0dACAm5ub1nLu7u4oLi5GRkYG3N3dDd6elVX1c6lUKin9QQJIJJJqt1eWuj2ZrHbnZ3V9tb1OU2M/sA/U2A/sAzFhwKmmkpISbN++HaGhobC1tQUA5OTkAAAcHBy0llX/rp5vCKlUAicnO4PXf5RMKoWVlcxo7QH/7hgcHBoYtV1TqSt1mhr7gX2gxn5gH4gBA041HThwAPfu3UPfvn1rZHsqlQCFoqDa7Vhby2BvXx9KlQolJUojVPYvpVIFAFAoCjU/10YymRQODg1qfZ2mxn5gH6ixHyy7D4z54bk2YMCppq1bt6JRo0bo2rWrZpqjoyMAIDc3F02aNNFMVygUWvMNVVJS/Ted5vCrAAiCUO32ylK3p1SqjFKrqdWVOk2N/cA+UGM/sA/EgCcZq+HBgwf47bff0KtXL1hbW2umt2nTBsC/Y3HU0tPTYW1tDRcXlxqtk4iIyNIw4FTDzp07UVBQoLl6Ss3FxQWurq5ISUnRmp6cnIyQkBDY2NjUZJlEREQWh6eoquHnn39GixYt0LFjR515U6dOxYwZM9C6dWsEBQUhOTkZJ0+exMqVK81QKRERkWVhwDFQTk4O9u3bh9GjR+u9zLpv374oLCxEUlISlixZAjc3N8THx8Pf398M1RIREVkWBhwDOTo64vTp0xUuExkZicjIyBqqiIiIiNQ4BoeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAacaNm3ahIEDB8LHxwdBQUEYP348Hjx4oJm/c+dO9O/fHz4+PggPD8eGDRvMWC0REZHlsDJ3AXVVYmIikpKSMHHiRPj5+eH+/fs4dOgQlEolAOD3339HdHQ0hgwZgnfffReHDx/Ge++9Bzs7O/Tq1cvM1RMREYkbA44B0tPTER8fj8WLF+PZZ5/VTA8PD9f8nJiYCF9fX8ydOxcAEBwcjIyMDCxatIgBh4iIyMR4isoAGzduRKtWrbTCTVlFRUU4cuSITpDp3bs3Ll++jOvXr9dEmURERBaLR3AM8Oeff0Iul2Px4sX44YcfkJubC29vb8yaNQsdOnTAtWvXUFxcjDZt2mit5+7uDqD0CFCrVq0M3r6VVfVzqVQqKf1BAkgkkmq3V5a6PZmsdudndX21vU5TYz+wD9TYD+wDMWHAMcCdO3dw+vRpXLhwAe+//z4aNGiAr7/+GmPHjsWvv/6KnJwcAICDg4PWeurf1fMNIZVK4ORkZ3jxj5BJpbCykhmtPeDfHYODQwOjtmsqdaVOU2M/sA/U2A/sAzFgwDGAIAgoKCjAV199hXbt2gEAOnTogNDQUKxcuRJdu3Y12bZVKgEKRUG127G2lsHevj6UKhVKSpRGqOxfSqUKAKBQFGp+ro1kMikcHBrU+jpNjf3APlBjP1h2Hxjzw3NtwIBjAAcHBzRq1EgTbgCgUaNGaN++PS5duoQ+ffoAAHJzc7XWUygUAABHR8dqbb+kpPpvOs3hV6E0sBmTuj2lUmWUWk2trtRpauwH9oEa+4F9IAY8yWiAtm3bljvv4cOHaN26NaytrZGenq41T/37o2NziIiIyLgYcAzQo0cPZGdn49y5c5pp9+/fx5kzZ+Dl5QUbGxsEBQXhl19+0VovOTkZ7u7u1RpgTERERI/HU1QG6NmzJ3x8fPD6668jJiYG9erVw5IlS2BjY4MRI0YAACZNmoRRo0bhgw8+QEREBI4cOYKtW7fiiy++MHP1RERE4scjOAaQSqVYsmQJ/Pz8MHv2bLz55puwt7fHqlWr0KRJEwBAp06dEBcXh7S0NIwbNw5bt27FvHnzEBERYebqiYiIxI9HcAzk7OyMTz/9tMJlwsLCEBYWVkMVERERkRqP4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4Bhg48aN8PDw0Pn32WefaS23fv16hIeHw8fHB/3798euXbvMVDEREZFlsTJ3AXXZ0qVL0bBhQ83vzZo10/y8bds2xMbGYuLEiQgODkZycjKio6OxatUq+Pn5maFaIiIiy8GAUw1eXl5wdnbWO2/RokXo06cPpk2bBgAIDg7GhQsXkJCQgKSkpBqskoiIyPLwFJUJZGRk4MqVK4iIiNCa3rt3bxw6dAhFRUVmqoyIiMgy8AhONfTt2xf3799HixYtMHToUIwfPx4ymQzp6ekAADc3N63l3d3dUVxcjIyMDLi7uxu8XSur6udSqVRS+oMEkEgk1W6vLHV7Mlntzs/q+mp7nabGfmAfqLEf2AdiwoBjgCZNmmDq1Kno0KEDJBIJdu7ciS+//BJZWVmYPXs2cnJyAAAODg5a66l/V883hFQqgZOTneHFP0ImlcLKSma09oB/dwwODg2M2q6p1JU6TY39wD5QYz+wD8SAAccA3bp1Q7du3TS/d+3aFfXq1cPy5csxceJEk25bpRKgUBRUux1raxns7etDqVKhpERphMr+pVSqAAAKRaHm59pIJpPCwaFBra/T1NgP7AM19oNl94ExPzzXBgw4RhIREYHvvvsO586dg6OjIwAgNzcXTZo00SyjUCgAQDPfUCUl1X/TaQ6/CoAgCNVuryx1e0qlyii1mlpdqdPU2A/sAzX2A/tADHiS0QTatGkDAJqxOGrp6emwtraGi4uLOcoiIiKyGKIPOKNGjcKhQ4fKnX/48GGMGjWq2ttJTk6GTCZD+/bt4eLiAldXV6SkpOgsExISAhsbm2pvry6QyaSwsjL+P80AaSIionKI/hTV0aNHERkZWe78e/fu4dixY1Vqc9y4cQgKCoKHhwcAIDU1FevWrcOoUaM0p6SmTp2KGTNmoHXr1ggKCkJycjJOnjyJlStXGv5g6oiGttZQqQSTDdJTqlTIvl8Alcq4p9aIiEg8RB9wgIovg7569Srs7Ko2sMrNzQ0bNmzArVu3oFKp4OrqinfffRdRUVGaZfr27YvCwkIkJSVhyZIlcHNzQ3x8PPz9/Q1+HHVF/XpWkEolWPPLeWTdzTdq202dbTGilyekUgkDDhERlUuUAWfTpk3YtGmT5vfExESsW7dOZ7nc3Fz89ddf6N69e5Xa/7//+79KLRcZGVnh0SOxu32vAJl38sxdBhERWSBRBpzCwkLcv39f83t+fj6kUt3hRra2thg2bBimTJlSk+URERGRiYky4IwYMQIjRowAAISGhuK9995DWFiYmasiIiKimiLKgFPWzp07zV0CERER1TDRBxy1vLw83LhxAwqFQu+N7QIDA81QFREREZmC6APOvXv3MG/ePPz6669QKnW/kkAQBEgkEpw7d84M1REREZEpiD7gzJ49G7t27UJUVBQ6deqk8wWYREREJD6iDzgHDhzA6NGj8fbbb5u7FCIiIqohov+qhvr166Nly5bmLoOIiIhqkOgDTv/+/fHbb7+ZuwwiIiKqQaI/RRUeHo5jx45h3LhxeOmll9C8eXPIZDKd5by8vMxQHREREZmC6AOO+oZ/AHDw4EGd+byKioiISHxEH3AWLFhg7hKIiIiohok+4AwaNMjcJRAREVENE/0gYyIiIrI8oj+CM2vWrMcuI5FI8OGHH9ZANWQsMln1s7m6jbJtqVQCVCrdr/IgIqK6RfQB58iRIzrTVCoV7ty5A6VSCWdnZzRo0MAMlZEhGtpaQ6US4OBgvOesbFtKlQrZ9wsYcoiI6jjRB5zyvk28uLgYa9euxfLly/Hdd9/VcFVkqPr1rCCVSrDml/PIuptfrbYkEglkMimUShUEQUBTZ1uM6OUJqVTCgENEVMeJPuCUx9raGiNHjsSlS5fwn//8B0uWLDF3SVQFt+8VIPNOXrXakEgksLKSoaREqfcb5omIqO6y+EHG7dq1w7Fjx8xdBhERERmRxQecgwcPcgwOERGRyIj+FFV8fLze6bm5uTh27BjOnj2LCRMm1HBVREREZEoWG3AcHR3h4uKCOXPmYOjQoTVcFREREZmS6APO+fPnzV0CERER1TCLH4NDRERE4iP6IzhqR48exe7du3Hjxg0AQIsWLfDcc8+hc+fOZq6MiIiIjE30AaeoqAjTp0/Hb7/9BkEQ4ODgAABQKBRYtmwZnn/+eSxcuBDW1tZmrpSIiIiMRfSnqBISErBjxw6MGTMG+/fvx9GjR3H06FEcOHAAY8eOxa+//oqEhARzl0lERERGJPqA8/PPP2PQoEF4++238cQTT2imN27cGG+99RYGDhyILVu2mLFCIiIiMjbRB5w7d+7A19e33Pm+vr64c+dODVZEREREpib6gNO8eXMcPXq03PnHjh1D8+bNa7AiIiIiMjXRB5yBAwdi+/btmD17NtLT06FUKqFSqZCeno73338fKSkpGDRokLnLJCIiIiMS/VVUEydOREZGBtatW4f169dDKi3NdCqVCoIgYNCgQZg4caKZqyQiIiJjEn3Akclk+Oijj/DKK69g7969yMzMBAC0bNkS3bt3R7t27cxcIRERERmbKAPOw4cPMX/+fDz99NOIiooCALRr104nzKxYsQI//vgj3nvvvWrdByc/Px8RERHIysrCTz/9BB8fH8289evXY+nSpbhx4wbc3NwQExODHj16GLwtIiIiejxRjsFZu3YtNm3ahOeee67C5Z577jls2LAB69evr9b2Fi9eDKVSqTN927ZtiI2NRUREBJKSkuDn54fo6GicOHGiWtsjIiKiioky4Gzfvh0vvPACXFxcKlyudevW6NWrF7Zt22bwti5fvozVq1dj6tSpOvMWLVqEPn36YNq0aQgODsbcuXPh4+PDGwsSERGZmCgDzoULF9CxY8dKLevv74+//vrL4G3NmzcPw4YNg5ubm9b0jIwMXLlyBREREVrTe/fujUOHDqGoqMjgbRIREVHFRDkGp7i4uNJjaqytrQ0OGykpKbhw4QLi4uJw5swZrXnp6ekAoBN83N3dUVxcjIyMDLi7uxu0XSur6udSqVRS+oMEkEgk1W6vLE17tb1tyb//SyDRtCeTiTL3l0v9eC3tcZfFPijFfmAfiIkoA07Tpk1x8eLFSi178eJFNG3atMrbKCwsxEcffYSYmBjY29vrzM/JyQEAzZd7qql/V8+vKqlUAicnO4PW1UcmlcLKSma09tRt1qW2rWSl7ah3aA4ODYzSbl1jqY+7LPZBKfYD+0AMRBlwnnnmGWzevBmvvfYaGjduXO5yd+/exebNmxEeHl7lbSQmJqJx48Z48cUXq1NqlalUAhSKgmq3Y20tg719fShVKpSU6A6Qrg6lSqX5v1a3LSkNNyVKJSAASmVp2wpFoeZnSyCTSeHg0MDiHndZ7INS7AfL7gNjfniuDUQZcF599VVs2bIFo0ePxvz589GhQwedZf7880/83//9Hx4+fIjx48dXqf3MzEx89913SEhIQG5uLgCgoKBA839+fj4cHR0BALm5uWjSpIlmXYVCAQCa+YYoKan+m05z+FUABEGodntladqr5W1L1Oeo/teWuj2lUmWUPq5rLPVxl8U+KMV+YB+IgSgDjouLC7788ku8+eabGDZsGFxcXCCXy2FnZ4f8/HxcvHgR165dQ/369fH555+jdevWVWr/+vXrKC4uxoQJE3TmjRo1Ch06dMDChQsBlI7FadOmjWZ+eno6rK2tH3uFFxERERlOlAEHKL3HzZYtW5CUlITdu3fjt99+08xr2rQpIiMj8eqrrxoUNDw9PbFixQqtaefOncOCBQswZ84c+Pj4wMXFBa6urkhJSUHPnj01yyUnJyMkJAQ2NjaGPzgiIiKqkGgDDgC0atUKc+bMAQDk5eUhPz8fdnZ2egcFV4WDgwOCgoL0zvPy8oKXlxcAYOrUqZgxYwZat26NoKAgJCcn4+TJk1i5cmW1tk9EREQVE3XAKcve3r7awaaq+vbti8LCQiQlJWHJkiVwc3NDfHw8/P39a7QOIiIiS2MxAcfUgoKC9N4wMDIyEpGRkWaoiIiIyHLxTkZEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4BtizZw9GjhyJ4OBgeHt7IywsDAsWLEBubq7Wcjt37kT//v3h4+OD8PBwbNiwwUwVExERWRYrcxdQF2VnZ8PX1xdRUVFo1KgRLl68iLi4OFy8eBHfffcdAOD3339HdHQ0hgwZgnfffReHDx/Ge++9Bzs7O/Tq1cvMj4CIiEjcGHAMMGDAAK3fg4KCYGNjg9jYWGRlZaFZs2ZITEyEr68v5s6dCwAIDg5GRkYGFi1axIBDRERkYjxFZSSNGjUCABQXF6OoqAhHjhzRCTK9e/fG5cuXcf36dTNUSEREZDl4BKcalEolSkpKcOnSJSQkJCA0NBStWrXCpUuXUFxcjDZt2mgt7+7uDgBIT09Hq1atDN6ulVX1c6lUKin9QQJIJJJqt1eWpr3a3rbk3/8lkGjak8lMk/slEsm//W4CKpUAQRCqvJ768ZrqcdcF7INS7Af2gZgw4FRDjx49kJWVBQDo1q0bFi5cCADIyckBADg4OGgtr/5dPd8QUqkETk52Bq//KJlUCisrmdHaU7dZl9q2kpW2o96hOTg0MEq7j1KpBJMHnOq0b6rHXZewD0qxH9gHYsCAUw1LlixBYWEhLl26hMTEREycOBHLli0z6TZVKgEKRUG127G2lsHevj6UKhVKSpRGqOxfSpVK83+tbltSGm5KlEpAAJTK0rYVikLNz8Yik0nh4NAAa345j9v3qv/8Paqpsy2Gh7czqHZ1baZ43HUF+6AU+8Gy+8CYH55rAwacamjXrh0AwN/fHz4+PhgwYAB27NiBtm3bAoDOZeMKhQIA4OjoWK3tlpRU/02nOfwqwKDTGhXRtFfL25aoz1H9ry11e0qlyih9rE/W3Xxk3skzervGqN2Uj7uuYB+UYj+wD8SAJxmNxMPDA9bW1rh27Rpat24Na2trpKenay2j/v3RsTlUu8hkUlhZGfcfz+cTEdUsHsExkj///BPFxcVo1aoVbGxsEBQUhF9++QWjR4/WLJOcnAx3d/dqDTAm02loaw2VSuC5dyIiEWDAMUB0dDS8vb3h4eGB+vXr4/z58/j222/h4eGBnj17AgAmTZqEUaNG4YMPPkBERASOHDmCrVu34osvvjBz9VSe+vWsIJVKsOaX88i6m2/Utj1cnRHxjJvRryojIiL9GHAM4Ovri+TkZCxZsgSCIKBly5aIjIzEuHHjYGNjAwDo1KkT4uLi8OWXX+Knn35CixYtMG/ePERERJi5enqc2/cKjD5OpokTjwoREdUkBhwDTJgwARMmTHjscmFhYQgLC6uBioiIiKgsjnwkIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAhIiIi0WHAISIiItFhwCEiIiLRYcAxwPbt2zFp0iR0794dfn5+GDBgAH766ScIgqC13Pr16xEeHg4fHx/0798fu3btMlPFREREloUBxwDff/89GjRogJkzZyIxMRHdu3dHbGwsEhISNMts27YNsbGxiIiIQFJSEvz8/BAdHY0TJ06Yr3AiIiILYWXuAuqixMREODs7a34PCQlBdnY2li1bhsmTJ0MqlWLRokXo06cPpk2bBgAIDg7GhQsXkJCQgKSkJDNVTkREZBl4BMcAZcONmqenJ/Ly8lBQUICMjAxcuXIFERERWsv07t0bhw4dQlFRUU2VSkREZJF4BMdI0tLS0KxZM9jb2yMtLQ0A4ObmprWMu7s7iouLkZGRAXd3d4O3ZWVV/VwqlUpKf5AAEomk2u2VpWmvtrct+fd/CSR1p+4K2pfJqv7aUK9jyLpiwT4oxX5gH4gJA44R/P7770hOTsY777wDAMjJyQEAODg4aC2n/l093xBSqQROTnYGr/8omVQKKyuZ0dpTt1mX2raSyUzWtpop2wb+3Rk7ODQwuI3qrCsW7INS7Af2gRgw4FTTrVu3EBMTg6CgIIwaNcrk21OpBCgUBdVux9paBnv7+lCqVCgpURqhsn8pVSrN/7W6bUlpuClRKgGhDtWtr31lafsKRaHm58qSyaRwcGhg0LpiwT4oxX6w7D4w5ofn2oABpxoUCgVeffVVNGrUCHFxcZD+71O6o6MjACA3NxdNmjTRWr7sfEOVlFT/Tac5/CpA5/L26tK0V8vblqjPUf2vrbpSd0XtK5Uqg18f1VlXLNgHpdgP7AMx4ElGAz148ACvvfYacnNzsXTpUjRs2FAzr02bNgCA9PR0rXXS09NhbW0NFxeXGq2ViIjI0jDgGKCkpATTpk1Deno6li5dimbNmmnNd3FxgaurK1JSUrSmJycnIyQkBDY2NjVZLhERkcXhKSoDzJkzB7t27cLMmTORl5endfO+9u3bw8bGBlOnTsWMGTPQunVrBAUFITk5GSdPnsTKlSvNVzgREZGFYMAxwIEDBwAAH330kc681NRUtGrVCn379kVhYSGSkpKwZMkSuLm5IT4+Hv7+/jVdLhERkcVhwDHAzp07K7VcZGQkIiMjTVwNERERPYpjcIiIiEh0GHCIiIhIdBhwiIiISHQYcIiIiEh0GHCIiIhIdBhwiIiISHQYcIiIiEh0GHCIiIhIdBhwiIiISHR4J2MiMiupVAKpVGKStlUqASqVYJK2iah2Y8AhIrORSiVo5GQLmdQ0B5OVKhWy7xcw5BBZIAYcIjIbqVQCmVSK1SnncPtegVHbbupsixG9PCGVShhwiCwQAw4Rmd3tewXIvJNn7jKISEQ4yJiIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIdXURHRY5nqZnwyGT9jEZFpMOAQUYVMfTM+IiJTYMAhogqZ8mZ8Hq7OiHjGDRKJab6qgYgsFwMOEVWKKW7G18SpgVHbIyJS4zFnIiIiEh0ewSESEUMG7arXKW9dDgQmorqIAYdIBBraWkOlEuDgYPgpn+qsS0RU2zDgEIlA/XpWkEolWPPLeWTdza/SuhKJBDKZFEqlCoKg+63bHAhMRHURAw6RiBgyEFgikcDKSoaSEqXegMOBwERUF/HkOhEREYkOAw4RERGJDgMOERERiQ4DDhEREYkOAw4RERGJDgMOERERiQ4DjgGuXr2K2bNnY8CAAWjfvj369u2rd7n169cjPDwcPj4+6N+/P3bt2lXDlRIREVkmBhwDXLx4EXv27MFTTz0Fd3d3vcts27YNsbGxiIiIQFJSEvz8/BAdHY0TJ07UbLFEREQWiDf6M0BoaCh69uwJAJg5cyZOnz6ts8yiRYvQp08fTJs2DQAQHByMCxcuICEhAUlJSTVZLhERkcXhERwDSKUVd1tGRgauXLmCiIgIrem9e/fGoUOHUFRUZMryiIiILB6P4JhAeno6AMDNzU1ruru7O4qLi5GRkVHuqa3KsLKqfi6VSv/3vUISGP07hjTt1fa2Jf/+L4Gk7tRt7PYf6Qejtv24TddA25X5NvTHfaO6pWA/sA/EhAHHBHJycgAADg4OWtPVv6vnG0IqlcDJyc7w4h4hk0phZSUzWnvqNutS21YymcnaVjNl28ZqX90Ppmi7PCZt+39/oKryLen8RvVS7Af2gRgw4NQxKpUAhaKg2u1YW8tgb18fSpUKJSVKI1T2L6VKpfm/VrctKf2jXqJUAkIdqtvY7T/SD0Zt+zFM2raytG2FolDzc3lkMikcHBpUalkxYz9Ydh8Y88NzbcCAYwKOjo4AgNzcXDRp0kQzXaFQaM03VElJ9d90msOvAvR+g3R1aNqr5W1rTsf8r626Urex23+0H4zZ9uPURNtKparS75mqLCtm7Af2gRjwJKMJtGnTBsC/Y3HU0tPTYW1tDRcXF3OURUREZDEYcEzAxcUFrq6uSElJ0ZqenJyMkJAQ2NjYmKkyIiIiy8BTVAYoLCzEnj17AACZmZnIy8vThJnOnTvD2dkZU6dOxYwZM9C6dWsEBQUhOTkZJ0+exMqVK81ZOhERkUVgwDHA3bt38cYbb2hNU/++YsUKBAUFoW/fvigsLERSUhKWLFkCNzc3xMfHw9/f3xwlExERWRQGHAO0atUKf/3112OXi4yMRGRkZA1URERERGUx4BARGUAqlfx7w0wTUKkEqFTGv+KOyFIw4BARVZFUKkEjJ1vNjQpNQalSIft+AUMOkYEYcIiIqkgqlUAmlWJ1yjncvlf9G28+qqmzLUb08oRUKmHAITIQAw4RkYFu3ytA5p08c5dBRHrwPjhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6DDhEREQkOgw4REREJDoMOERERCQ6VuYugIjIlGSyx3+OUy9TmWWrshwRmQ8DDhGJUkNba6hUAhwcGlR6naosS0S1GwMOEYlS/XpWkEolWPPLeWTdza9wWYlEAplMCqVSBUEQHtu2h6szIp5xg0QiMVa5RGRkDDhEJGq37xUg805ehctIJBJYWclQUqKsVMBp4sQjPUS1HU8kExERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHo8CoqIqJaqqZvKFjVGx7qo1IJUKkefyUakakx4BAR1TKG3KSwKlQqAVJp+ffwqc52lSoVsu8XMOSQ2THgEBHVMlW5SWFVqW9SqK/tqt7w8FFNnW0xopcnpFIJAw6ZHQMOEVEtVZmbFFaV+iaF+tqu6g0PiWozBhwiIqoTpFJJhafWjMEY45BqGsc96ceAY0KXL1/GvHnzcPz4cdjZ2WHAgAGYNm0abGxszF0aEVGdIpVK0MjJFjKpaYLHo+OSjDn+6XFjnqqL4570Y8AxkZycHIwePRqurq6Ii4tDVlYWPvroIzx48ACzZ882d3lERHWKVCqBTCrF6pRzuH2vwKhtlx2XdPteQbXGIVXUtrHHUwEc91QRBhwT+fHHH5Gfn4/4+Hg0atQIAKBUKjFnzhy89tpraNasmXkLJCKqg2piXJIxxyFVNOaJTKvunGSsY/bu3YuQkBBNuAGAiIgIqFQqHDhwwHyFERERWQCJwKHyJhESEoIXX3wRM2bM0JrerVs3DBgwQGd6ZQmCcQaTSSSAVCpFXkERlEY+rGltJYVtfWu2XUNtm7p9tl2zbZu6fVO2LZNKYG9rA5VKZdR21bjP0lW2z6v717wuDayuDJ6iMhGFQgEHBwed6Y6OjsjJyTG43dL7VBhvsJq9rekGPLPtmm3b1O2z7Zpt29Ttm7JtqYkGAgN1t09M/VoxZZ/XVewRIiIiEh0GHBNxcHBAbm6uzvScnBw4OjqaoSIiIiLLwYBjIm3atEF6errWtNzcXNy5cwdt2rQxU1VERESWgQHHRLp3746DBw9CoVBopqWkpEAqlaJLly5mrIyIiEj8eBWVieTk5KBPnz5wc3PDa6+9prnRX79+/XijPyIiIhNjwDGhy5cv4z//+Y/WVzXExMTwqxqIiIhMjAGHiIiIRIdjcIiIiEh0GHCIiIhIdBhwiIiISHQYcIiIiEh0GHCIiIhIdBhwiIiISHQYcCzM5cuXMWbMGPj5+aFLly745JNPUFRUZO6yatTVq1cxe/ZsDBgwAO3bt0ffvn3NXVKN2759OyZNmoTu3bvDz88PAwYMwE8//QRLu2vEnj17MHLkSAQHB8Pb2xthYWFYsGCB3u+RsxT5+fno3r07PDw8cOrUKXOXU2M2btwIDw8PnX+fffaZuUsjA1mZuwCqOTk5ORg9ejRcXV0RFxenubvygwcPLOruyhcvXsSePXvQoUMHqFQqi/ujDgDff/89WrZsiZkzZ8LJyQkHDx5EbGwsbt26hejoaHOXV2Oys7Ph6+uLqKgoNGrUCBcvXkRcXBwuXryI7777ztzlmcXixYuhVCrNXYbZLF26FA0bNtT83qxZMzNWQ9XBgGNBfvzxR+Tn5yM+Ph6NGjUCACiVSsyZMwevvfaaxbyRQ0ND0bNnTwDAzJkzcfr0aTNXVPMSExPh7Oys+T0kJATZ2dlYtmwZJk+eDKnUMg7uDhgwQOv3oKAg2NjYIDY2FllZWRbznlC7fPkyVq9ejXfeeQfvv/++ucsxCy8vL633BtVdlrEXIwDA3r17ERISogk3ABAREQGVSoUDBw6Yr7AaZil/vCuibwfu6emJvLw8FBQUmKGi2kP9/iguLjZvIWYwb948DBs2DG5ubuYuhajauKe3IOnp6WjTpo3WNAcHBzRp0gTp6elmqopqi7S0NDRr1gz29vbmLqXGKZVKPHz4EGfOnEFCQgJCQ0PRqlUrc5dVo1JSUnDhwgVMmTLF3KWYVd++feHp6YmwsDB88803Fn26rq7jKSoLolAo4ODgoDPd0dEROTk5ZqiIaovff/8dycnJeOedd8xdiln06NEDWVlZAIBu3bph4cKFZq6oZhUWFuKjjz5CTEyMRQZcAGjSpAmmTp2KDh06QCKRYOfOnfjyyy+RlZVlUWMUxYQBh8jC3bp1CzExMQgKCsKoUaPMXY5ZLFmyBIWFhbh06RISExMxceJELFu2DDKZzNyl1YjExEQ0btwYL774orlLMZtu3bqhW7dumt+7du2KevXqYfny5Zg4cSKaNm1qxurIEDxFZUEcHBz0Xv6ak5MDR0dHM1RE5qZQKPDqq6+iUaNGiIuLs9jxSe3atYO/vz8iIyOxePFiHDlyBDt27DB3WTUiMzMT3333HV5//XXk5uZCoVBoxmEVFBQgPz/fzBWaT0REBJRKJc6dO2fuUsgAPIJjQdq0aaMz1iY3Nxd37tzRGZtD4vfgwQO89tpryM3Nxdq1a7UujbVkHh4esLa2xrVr18xdSo24fv06iouLMWHCBJ15o0aNQocOHbBu3TozVEZUPQw4FqR79+74+uuvtcbipKSkQCqVokuXLmaujmpSSUkJpk2bhvT0dKxatcriLoeuyJ9//oni4mKLGWTs6emJFStWaE07d+4cFixYgDlz5sDHx8dMlZlfcnIyZDIZ2rdvb+5SyAAMOBZk2LBh+OGHHzBlyhS89tpryMrKwieffIJhw4ZZ1B+4wsJC7NmzB0Dp4fm8vDykpKQAADp37mwR98CYM2cOdu3ahZkzZyIvLw8nTpzQzGvfvj1sbGzMV1wNio6Ohre3Nzw8PFC/fn2cP38e3377LTw8PDT3ShI7BwcHBAUF6Z3n5eUFLy+vGq7IPMaNG4egoCB4eHgAAFJTU7Fu3TqMGjUKTZo0MXN1ZAiJYIm3cbVgly9fxn/+8x8cP34cdnZ2GDBgAGJiYizmDxpQekg+LCxM77wVK1aUu7MXk9DQUGRmZuqdl5qaajFHL5YsWYLk5GRcu3YNgiCgZcuWeP755zFu3DiLvZoIAI4cOYJRo0bhp59+spgjOPPmzcO+fftw69YtqFQquLq6IjIyElFRUZBIJOYujwzAgENERESiY5mXTBAREZGoMeAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBERkegw4BAREZHoMOAQERGR6DDgEBGZQGhoKGbOnGnuMogsFgMOEZGB/vjjD8TFxUGhUJi7FCJ6BL9sk4jIQMePH0d8fDwGDRoEBwcHrXkpKSn8DiMiM+IRHCJCQUGBuUsQHRsbG1hbW5u7DCKLxYBDZGHi4uLg4eGBS5cuYfr06QgMDMSIESMAAJs3b8bgwYPh6+uLzp07IyYmBjdv3tRa/8qVK5g6dSq6dOkCHx8fdO/eHTExMcjNzdUs4+Hhgblz52LLli0IDw+Hj48PBg8ejGPHjunUc/bsWYwfPx4BAQHw9/fH6NGjceLECa1lNm7cCA8PD6SlpWHBggUIDg6Gn58fpkyZgnv37mkte+rUKYwbNw5BQUHw9fVFaGgoZs2apbWMSqXC999/jz59+sDHxwfPPPMMZs+ejZycnCr14yeffAIACAsLg4eHBzw8PHD9+nUAumNw1I/h999/x7x58xAcHIxOnTph9uzZKCoqgkKhwNtvv43AwEAEBgbik08+waPfhWyMuoksBU9REVmoN954A0899RRiYmIgCAISExPx1VdfISIiAkOGDMG9e/ewcuVKvPzyy/jvf/8LBwcHFBUVYdy4cSgqKsLIkSPxxBNPICsrC7t374ZCoUDDhg017R87dgzJycmIioqCjY0N1qxZg/Hjx2P9+vWQy+UAgIsXL+Lll1+GnZ0dxo8fDysrK6xduxZRUVFYuXIlOnTooFXzvHnz4ODggOjoaGRmZmL58uWYO3cuvvzySwDA3bt3MW7cODg5OWHChAlwcHDA9evXsWPHDq12Zs+ejU2bNmHw4MGIiorC9evXsWrVKpw9exZr1qyp1JGX559/HleuXMHWrVsxa9YsODk5AQCcnZ0rXG/evHl44oknMHXqVPz5559Yu3YtGjZsiOPHj+PJJ59ETEwM9u7di2+//RZyuRwDBw40at1EFkMgIouyaNEiQS6XC2+++aZm2vXr1wVPT08hMTFRa9m//vpLaN++vWb62bNnBblcLmzfvr3CbcjlckEulwunTp3STMvMzBR8fHyEKVOmaKZNnjxZ8PLyEq5du6aZlpWVJfj7+wsvv/yyZtqGDRsEuVwuvPLKK4JKpdJM//DDDwVPT09BoVAIgiAIO3bsEORyuXDy5Mlyazt27Jggl8uFLVu2aE3fu3ev3ukVWbp0qSCXy4WMjAydeT169BDeeecdnccwduxYrcfw0ksvCR4eHsLs2bM100pKSoTu3bsLI0eONEndRJaAp6iILNSwYcM0P+/YsQMqlQoRERG4d++e5t8TTzyBp556CkeOHAEA2NvbAwD279+PwsLCCtv39/eHt7e35vcWLVogLCwM+/fvh1KphFKpxIEDB9CzZ0+4uLholmvatCn69u2LtLQ05OXlabU5dOhQrYG7nTp1glKpRGZmJgBojiDt3r0bxcXFeutKSUlBw4YN0aVLF63H6uXlBVtbW81jNZUhQ4ZoPQZfX18IgoAhQ4ZopslkMnh7eyMjI6PW1E1U1/AUFZGFatWqlebnK1euQBAEvPDCC3qXtbIq3VW4uLhgzJgxWLZsGX7++Wd06tQJoaGh6N+/v9bpKQB46qmndNpxdXVFYWGhZtxMYWEh3NzcdJZzd3eHSqXCzZs38fTTT2umt2jRQms59ZVL6su0O3fujPDwcMTHx+P7779H586d0bNnT/Tr1w82NjYAgKtXryI3NxchISF6H+vdu3f1TjeWRx+Dut+efPJJnellx9aYu26iuoYBh8hC1atXT/OzSqWCRCJBUlISZDKZzrK2traan2fOnIlBgwYhNTUVBw4cwLx58/DNN99g3bp1aN68uUlrlkr1H3QW/jcYVyKRYNGiRThx4gR27dqFffv24d1338WyZcuwdu1a2NnZQaVSoXHjxvjss8/0tvW4MTTVVd5jKG+6mrnrJqprGHCICK1bt4YgCGjVqpXeIyqPUl8xNHnyZPzxxx8YPnw41qxZg5iYGM0yV69e1VnvypUraNCggeaPcYMGDfD333/rLJeeng6pVKpzVKOy/Pz84Ofnh5iYGPz888+YMWMGkpOTERkZidatW+PQoUMICAhA/fr1DWpfrSbvc2PMuoksAcfgEBFeeOEFyGQyxMfH61yaLAgC7t+/DwDIy8tDSUmJ1ny5XA6pVIqioiKt6cePH8eZM2c0v9+8eROpqano0qULZDIZZDIZunTpgtTUVM2l1QDwzz//YOvWrejYsaNmzE9l5eTk6NTv6ekJAJr6IiIioFQqsXjxYp31S0pKqnRX4gYNGgCA1iXypmLMuoksAY/gEBFat26NadOmYeHChcjMzETPnj1hZ2eH69ev47fffsPQoUMxbtw4HD58GHPnzkWvXr3g6uoKpVKJzZs3QyaTITw8XKtNuVyOcePGaV0mDgBTp07VLDNt2jQcPHgQI0aMwIgRIyCTybB27VoUFRXhrbfeqvLj2LRpE9asWYOePXuidevWyM/Px7p162Bvb4/u3bsDKB2n89JLL+Gbb77BuXPn0KVLF1hbW+PKlStISUnBe++9h169elVqe15eXgCAL774Ar1794a1tTV69OihdUrPWIxZN5ElYMAhIgDAhAkT4Orqiu+//x4JCQkAgObNm6NLly4IDQ0FUHpqqmvXrti1axeysrLQoEEDeHh4ICkpCX5+flrtBQYGws/PDwkJCbhx4wbatm2LBQsWoF27dpplnn76aaxatQoLFy7EN998A0EQ4Ovri08//VTnHjiV0blzZ5w6dQrJycn4559/0LBhQ/j6+uKzzz7TulJr7ty58Pb2xo8//ogvvvgCMpkMLVu2RP/+/REQEFDp7fn6+uKNN97Ajz/+iH379kGlUiE1NdUkAceYdRNZAonw6PFcIqJq8vDwwMsvv4zZs2ebuxQislAcg0NERESiw1NURESPyM/Pf+wXkDo7O+u9pJ6IagcGHCKiR3z33XeIj4+vcJnU1FStmyUSUe3CMThERI/IyMjQ+poEfTp27Kh1s0Qiql0YcIiIiEh0OMiYiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIRIcBh4iIiESHAYeIiIhEhwGHiIiIROf/AdqoCqukh8+qAAAAAElFTkSuQmCC\n", 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", 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\n", 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", 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\n", + "image/png": 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" ], "text/plain": [ " participant_id trial_index stimulus response response_time \\\n", "0 sub-1 1 A None NaN \n", - "1 sub-1 2 D None NaN \n", - "2 sub-1 3 A non-match 1.697356 \n", - "3 sub-1 4 F non-match 0.149110 \n", - "4 sub-1 5 D non-match 0.277760 \n", + "1 sub-1 2 C None NaN \n", + "2 sub-1 3 C non-match 0.317768 \n", + "3 sub-1 4 C non-match 1.096391 \n", + "4 sub-1 5 F non-match 0.843995 \n", "\n", " expected_response is_correct \n", "0 None 1 \n", "1 None 1 \n", - "2 match 0 \n", - "3 non-match 1 \n", + "2 non-match 1 \n", + "3 match 0 \n", "4 non-match 1 " ] }, @@ -604,20 +441,9 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "class NBack(dm_env.Environment):\n", "\n", @@ -776,10 +602,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "class RandomAgent(acme.Actor):\n", @@ -817,10 +641,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -855,14 +677,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ], "text/plain": [ " episode_length episode_return steps_per_second episodes steps\n", - "995 32 -10.0 329.379165 996 31872\n", - "996 32 -11.0 326.324034 997 31904\n", - "997 32 -9.0 373.017676 998 31936\n", - "998 32 -11.0 309.737031 999 31968\n", - "999 32 -9.0 405.329983 1000 32000" + "995 32 -13.0 1049.248175 996 31872\n", + "996 32 -13.0 1113.221097 997 31904\n", + "997 32 -14.0 1053.158887 998 31936\n", + "998 32 -19.0 1213.915019 999 31968\n", + "999 32 -11.0 1209.724540 1000 32000" ] }, "execution_count": 11, @@ -1283,11 +978,23 @@ "name": "python3" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 80b6eda54..adf6b71b6 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -1,1300 +1,1306 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "sgIXqXYCDBuR" - }, - "source": [ - "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", - "\n", - "__Content editors:__ Shaonan Wang, Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "AKlrTCmFDBuS" - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "x5XKBDyYDBuS" - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "form", - "execution": {}, - "id": "WsHayfTHDBuS" - }, - "outputs": [], - "source": [ - "# @title Update/Upgrade the system and install libs\n", - "!apt-get update > /dev/null 2>&1\n", - "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", - "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", - "!apt-get install x11-utils > /dev/null 2>&1\n", - "!apt-get install xvfb > /dev/null 2>&1" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "execution": {}, - "id": "6fooEJQSDBuT", - "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: swig in /usr/local/lib/python3.10/dist-packages (4.2.1)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "# @title Install dependencies\n", - "!pip install rarfile --quiet\n", - "!pip install stable-baselines3[extra] --quiet\n", - "!pip install ale-py --quiet\n", - "!pip install swig\n", - "!pip install gym[box2d] --quiet\n", - "!pip install pyvirtualdisplay --quiet\n", - "!pip install pyglet --quiet\n", - "!pip install pygame --quiet\n", - "!pip install minigrid --quiet\n", - "!pip install -q swig --quiet\n", - "!pip install -q gymnasium[box2d] --quiet\n", - "!pip install 'minigrid<=2.1.1' --quiet\n", - "!pip3 install box2d-py --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": {}, - "id": "nA2Y9HGUDBuT" - }, - "outputs": [], - "source": [ - "# Imports\n", - "import io\n", - "import os\n", - "import glob\n", - "import torch\n", - "import base64\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import sys\n", - "import gymnasium\n", - "sys.modules[\"gym\"] = gymnasium\n", - "\n", - "import stable_baselines3\n", - "from stable_baselines3 import DQN\n", - "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", - "from stable_baselines3.common.callbacks import EvalCallback\n", - "from stable_baselines3.common.env_util import make_atari_env\n", - "\n", - "import gymnasium as gym\n", - "from gym import spaces\n", - "from gym.envs.box2d.lunar_lander import *\n", - "from gym.wrappers.monitoring.video_recorder import VideoRecorder" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "cellView": "form", - "execution": {}, - "id": "_M-76WwDDBuT", - "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "# @title Play Video function\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "from pyvirtualdisplay import Display\n", - "\n", - "# create the directory to store the video(s)\n", - "os.makedirs(\"./video\", exist_ok=True)\n", - "\n", - "display = Display(visible=False, size=(1400, 900))\n", - "_ = display.start()\n", - "\n", - "\"\"\"\n", - "Utility functions to enable video recording of gym environment\n", - "and displaying it.\n", - "To enable video, just do \"env = wrap_env(env)\"\"\n", - "\"\"\"\n", - "def render_mp4(videopath: str) -> str:\n", - " \"\"\"\n", - " Gets a string containing a b4-encoded version of the MP4 video\n", - " at the specified path.\n", - " \"\"\"\n", - " mp4 = open(videopath, 'rb').read()\n", - " base64_encoded_mp4 = b64encode(mp4).decode()\n", - " return f''" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "UJnBSc5KDBuU" - }, - "source": [ - "---\n", - "# Introduction\n", - "\n", - "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", - "\n", - "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", - "\n", - "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", - "\n", - "In this project, you should investigate the following question:\n", - "\n", - "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", - "\n", - "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", - "\n", - "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", - "\n", - "The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "The actions of the agents are:\n", - " 1. Do Nothing\n", - " 2. Fire Main Engine\n", - " 3. Fire Left Engine\n", - " 4. Fire Right Engine\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "XuSpVWuGDBuU" - }, - "source": [ - "---\n", - "# Basic DQN Implementation\n", - "\n", - "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "dYwJRvx-DBuV" - }, - "source": [ - "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": {}, - "id": "MvWRAJiSDBuV", - "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", - " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", - "\n", - "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", - " # Tip: Use smaller step-sizes for larger networks." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "EHLx2d5xDBuV" - }, - "source": [ - "Now, let us setup our model and the DQN algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": {}, - "id": "4PzeDS2dDBuV" - }, - "outputs": [], - "source": [ - "log_dir = \"/tmp/gym/\"\n", - "os.makedirs(log_dir, exist_ok=True)\n", - "\n", - "# Create environment\n", - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", - "# Refer to https://gym.openai.com/docs/ for descriptions.\n", - "\n", - "# For example, if you would like to load Cartpole,\n", - "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", - "\n", - "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", - "\n", - "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", - "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", - " net_arch=nn_layers)\n", - "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", - " learning_rate=learning_rate,\n", - " batch_size=1, # for simplicity, we are not doing batch update.\n", - " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", - " learning_starts=1, # learning starts immediately!\n", - " gamma=0.99, # discount facto. range is between 0 and 1.\n", - " tau = 1, # the soft update coefficient for updating the target network\n", - " target_update_interval=1, # update the target network immediately.\n", - " train_freq=(1,\"step\"), # train the network at every step.\n", - " max_grad_norm = 10, # the maximum value for the gradient clipping\n", - " exploration_initial_eps = 1, # initial value of random action probability\n", - " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", - " gradient_steps = 1, # number of gradient steps\n", - " seed = 1, # seed for the pseudo random generators\n", - " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", - "\n", - "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", - "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", - "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "1FIshtazDBuW" - }, - "source": [ - "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", - "\n", - "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": {}, - "id": "SyD6VwDhDBuW", - "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "State shape: (8,)\n", - "Number of actions: 4\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "print('State shape: ', env.observation_space.shape)\n", - "print('Number of actions: ', env.action_space.n)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": {}, - "id": "68D-3iePDBuW", - "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n", - "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = _posixsubprocess.fork_exec(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Total reward: -449.2162305654916\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", - "HTML(html)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "fhtq8GDLDBuW" - }, - "source": [ - "From the video above, we see that the lander has crashed!\n", - "It is now the time for training!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": {}, - "id": "Xhl3ojMwDBuW", - "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n", - "Episode length: 151.80 +/- 30.46\n", - "New best mean reward!\n", - "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n", - "Episode length: 878.80 +/- 72.71\n", - "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n", - "Episode length: 240.00 +/- 51.61\n", - "New best mean reward!\n", - "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n", - "Episode length: 338.20 +/- 107.08\n", - "New best mean reward!\n", - "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n", - "Episode length: 241.20 +/- 55.82\n", - "New best mean reward!\n", - "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n", - "Episode length: 646.80 +/- 65.03\n", - "New best mean reward!\n", - "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n", - "Episode length: 139.80 +/- 35.46\n", - "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n", - "Episode length: 321.60 +/- 31.12\n", - "New best mean reward!\n", - "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n", - "Episode length: 536.00 +/- 257.21\n", - "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n", - "Episode length: 339.80 +/- 19.18\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ], - "source": [ - "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", - "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", - "# view the performance at every training episode." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "sgIXqXYCDBuR" + }, + "source": [ + "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", + "\n", + "__Content editors:__ Shaonan Wang, Spiros Chavlis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "AKlrTCmFDBuS" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "x5XKBDyYDBuS" + }, + "source": [ + "---\n", + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "id": "WsHayfTHDBuS" + }, + "outputs": [], + "source": [ + "# @title Update/Upgrade the system and install libs\n", + "!apt-get update > /dev/null 2>&1\n", + "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", + "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", + "!apt-get install x11-utils > /dev/null 2>&1\n", + "!apt-get install xvfb > /dev/null 2>&1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "6fooEJQSDBuT", + "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "IYynM83tDBuX" - }, - "source": [ - "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", - "\n", - "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", - "\n", - "```\n", - "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", - "Episode length: 259.80 +/- 47.47\n", - "```" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: swig in /home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages (4.2.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install rarfile --quiet\n", + "!pip install 'stable-baselines3[extra]' --quiet\n", + "!pip install ale-py --quiet\n", + "!pip install swig\n", + "!pip install gym --quiet\n", + "!pip install pyvirtualdisplay --quiet\n", + "!pip install pyglet --quiet\n", + "!pip install pygame --quiet\n", + "!pip install minigrid --quiet\n", + "!pip install -q swig --quiet\n", + "!pip install -q gymnasium --quiet\n", + "!pip install 'minigrid<=2.1.1' --quiet\n", + "!pip3 install box2d-py --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "nA2Y9HGUDBuT" + }, + "outputs": [], + "source": [ + "# Imports\n", + "import io\n", + "import os\n", + "import glob\n", + "import torch\n", + "import base64\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import sys\n", + "import gymnasium\n", + "sys.modules[\"gym\"] = gymnasium\n", + "\n", + "import stable_baselines3\n", + "from stable_baselines3 import DQN\n", + "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", + "from stable_baselines3.common.callbacks import EvalCallback\n", + "from stable_baselines3.common.env_util import make_atari_env\n", + "\n", + "import gymnasium as gym\n", + "from gym import spaces\n", + "from gym.envs.box2d.lunar_lander import *\n", + "from gym.wrappers.monitoring.video_recorder import VideoRecorder" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "UFNQVKokDBuX" - }, - "source": [ - "Now, let us look at the visual performance of the lander.\n", - "\n", - "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" - ] + "id": "_M-76WwDDBuT", + "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2" + }, + "outputs": [], + "source": [ + "# @title Play Video function\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "from pyvirtualdisplay import Display\n", + "\n", + "# create the directory to store the video(s)\n", + "os.makedirs(\"./video\", exist_ok=True)\n", + "\n", + "display = Display(visible=False, size=(1400, 900))\n", + "_ = display.start()\n", + "\n", + "\"\"\"\n", + "Utility functions to enable video recording of gym environment\n", + "and displaying it.\n", + "To enable video, just do \"env = wrap_env(env)\"\"\n", + "\"\"\"\n", + "def render_mp4(videopath: str) -> str:\n", + " \"\"\"\n", + " Gets a string containing a b4-encoded version of the MP4 video\n", + " at the specified path.\n", + " \"\"\"\n", + " mp4 = open(videopath, 'rb').read()\n", + " base64_encoded_mp4 = b64encode(mp4).decode()\n", + " return f''" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UJnBSc5KDBuU" + }, + "source": [ + "---\n", + "# Introduction\n", + "\n", + "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", + "\n", + "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", + "\n", + "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", + "\n", + "In this project, you should investigate the following question:\n", + "\n", + "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", + "\n", + "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", + "\n", + "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", + "\n", + "The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "The actions of the agents are:\n", + " 1. Do Nothing\n", + " 2. Fire Main Engine\n", + " 3. Fire Left Engine\n", + " 4. Fire Right Engine\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "XuSpVWuGDBuU" + }, + "source": [ + "---\n", + "# Basic DQN Implementation\n", + "\n", + "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "dYwJRvx-DBuV" + }, + "source": [ + "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": {}, - "id": "hc0xXn5aDBuX", - "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n", - "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = _posixsubprocess.fork_exec(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Total reward: 249.47508187060953\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", - "HTML(html)" - ] + "id": "MvWRAJiSDBuV", + "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e" + }, + "outputs": [], + "source": [ + "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", + " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", + "\n", + "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", + " # Tip: Use smaller step-sizes for larger networks." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EHLx2d5xDBuV" + }, + "source": [ + "Now, let us setup our model and the DQN algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "4PzeDS2dDBuV" + }, + "outputs": [], + "source": [ + "log_dir = \"/tmp/gym/\"\n", + "os.makedirs(log_dir, exist_ok=True)\n", + "\n", + "# Create environment\n", + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", + "# Refer to https://gym.openai.com/docs/ for descriptions.\n", + "\n", + "# For example, if you would like to load Cartpole,\n", + "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", + "\n", + "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", + "\n", + "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", + "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", + " net_arch=nn_layers)\n", + "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", + " learning_rate=learning_rate,\n", + " batch_size=1, # for simplicity, we are not doing batch update.\n", + " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", + " learning_starts=1, # learning starts immediately!\n", + " gamma=0.99, # discount facto. range is between 0 and 1.\n", + " tau = 1, # the soft update coefficient for updating the target network\n", + " target_update_interval=1, # update the target network immediately.\n", + " train_freq=(1,\"step\"), # train the network at every step.\n", + " max_grad_norm = 10, # the maximum value for the gradient clipping\n", + " exploration_initial_eps = 1, # initial value of random action probability\n", + " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", + " gradient_steps = 1, # number of gradient steps\n", + " seed = 1, # seed for the pseudo random generators\n", + " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", + "\n", + "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", + "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", + "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1FIshtazDBuW" + }, + "source": [ + "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", + "\n", + "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "SyD6VwDhDBuW", + "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "cVCcx8GUDBuX" - }, - "source": [ - "The lander has landed safely!!\n", - "\n", - "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "State shape: (8,)\n", + "Number of actions: 4\n" + ] + } + ], + "source": [ + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "print('State shape: ', env.observation_space.shape)\n", + "print('Number of actions: ', env.action_space.n)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 }, + "id": "68D-3iePDBuW", + "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": {}, - "id": "_8ibUiTmDBuX", - "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 510 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", - "plt.plot(x, y)\n", - "plt.ylim([-300, 300])\n", - "plt.xlabel('Timesteps')\n", - "plt.ylabel('Episode Rewards')\n", - "plt.show()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Building video video/LunarLander-v2_pretraining.mp4.\n", + "Moviepy - Writing video video/LunarLander-v2_pretraining.mp4\n", + "\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "2Zo8kpDUDBuX" - }, - "source": [ - "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", - "\n", - "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " " + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "D7JAEDEzDBuX" - }, - "source": [ - "---\n", - "# Additional Project Ideas" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Done !\n", + "Moviepy - video ready video/LunarLander-v2_pretraining.mp4\n", + "\n", + "Total reward: -723.5946813053503\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "1m6YBf5nDBuX" - }, - "source": [ - "## 1 Play with exploration-exploitation trade-off\n", - "\n", - "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": {}, - "id": "tnbb16KUDBuY", - "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } + "data": { + "text/html": [ + "" ], - "source": [ - "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", - "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", - "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "794AyGDPDBuY" - }, - "source": [ - "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "Ljf9XG5BDBuY" - }, - "source": [ - "## 2 Reward Shaping\n", - "\n", - "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": {}, - "id": "zAAhdiflDBuY" - }, - "outputs": [], - "source": [ - "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", - "class CustomEnv(gym.Env):\n", - " \"\"\"Custom Environment that follows gym interface\"\"\"\n", - " metadata = {'render.modes': ['human']}\n", - "\n", - " def __init__(self, arg1, arg2):\n", - " super(CustomEnv, self).__init__()\n", - " # Define action and observation space\n", - " # They must be gym.spaces objects\n", - " # Example when using discrete actions:\n", - " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", - " # Example for using image as input (channel-first; channel-last also works):\n", - " self.observation_space = spaces.Box(low=0, high=255,\n", - " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", - "\n", - " def step(self, action):\n", - " ...\n", - " return observation, reward, done, info\n", - " def reset(self):\n", - " ...\n", - " return observation # reward, done, info can't be included\n", - " def render(self, mode='human'):\n", - " ...\n", - " def close (self):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "n7u1oEO2DBuY" - }, - "source": [ - "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": {}, - "id": "463GUtbuDBuY" - }, - "outputs": [], - "source": [ - "class Custom_LunarLander(LunarLander):\n", - "\n", - " def step(self, action):\n", - " assert self.lander is not None\n", - "\n", - " # Update wind\n", - " assert self.lander is not None, \"You forgot to call reset()\"\n", - " if self.enable_wind and not (\n", - " self.legs[0].ground_contact or self.legs[1].ground_contact\n", - " ):\n", - " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " wind_mag = (\n", - " math.tanh(\n", - " math.sin(0.02 * self.wind_idx)\n", - " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", - " )\n", - " * self.wind_power\n", - " )\n", - " self.wind_idx += 1\n", - " self.lander.ApplyForceToCenter(\n", - " (wind_mag, 0.0),\n", - " True,\n", - " )\n", - "\n", - " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " torque_mag = math.tanh(\n", - " math.sin(0.02 * self.torque_idx)\n", - " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", - " ) * (self.turbulence_power)\n", - " self.torque_idx += 1\n", - " self.lander.ApplyTorque(\n", - " (torque_mag),\n", - " True,\n", - " )\n", - "\n", - " if self.continuous:\n", - " action = np.clip(action, -1, +1).astype(np.float32)\n", - " else:\n", - " assert self.action_space.contains(\n", - " action\n", - " ), f\"{action!r} ({type(action)}) invalid \"\n", - "\n", - " # Engines\n", - " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", - " side = (-tip[1], tip[0])\n", - " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", - "\n", - " m_power = 0.0\n", - " if (self.continuous and action[0] > 0.0) or (\n", - " not self.continuous and action == 2\n", - " ):\n", - " # Main engine\n", - " if self.continuous:\n", - " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", - " assert m_power >= 0.5 and m_power <= 1.0\n", - " else:\n", - " m_power = 1.0\n", - " # 4 is move a bit downwards, +-2 for randomness\n", - " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", - " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", - " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", - " p = self._create_particle(\n", - " 3.5, # 3.5 is here to make particle speed adequate\n", - " impulse_pos[0],\n", - " impulse_pos[1],\n", - " m_power,\n", - " ) # particles are just a decoration\n", - " p.ApplyLinearImpulse(\n", - " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " s_power = 0.0\n", - " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", - " not self.continuous and action in [1, 3]\n", - " ):\n", - " # Orientation engines\n", - " if self.continuous:\n", - " direction = np.sign(action[1])\n", - " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", - " assert s_power >= 0.5 and s_power <= 1.0\n", - " else:\n", - " direction = action - 2\n", - " s_power = 1.0\n", - " ox = tip[0] * dispersion[0] + side[0] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " oy = -tip[1] * dispersion[0] - side[1] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " impulse_pos = (\n", - " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", - " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", - " )\n", - " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", - " p.ApplyLinearImpulse(\n", - " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", - "\n", - " pos = self.lander.position\n", - " vel = self.lander.linearVelocity\n", - " state = [\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]\n", - " assert len(state) == 8\n", - "\n", - " # Compare with / without shaping, referring the state description below\n", - " '''\n", - " state[0]: the horizontal coordinate\n", - " state[1]: the vertical coordinate\n", - " state[2]: the horizontal speed\n", - " state[3]: the vertical speed\n", - " state[4]: the angle\n", - " state[5]: the angular speed\n", - " state[6]: first leg contact\n", - " state[7]: second leg contact\n", - " '''\n", - " reward = 0\n", - " shaping = (\n", - " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", - " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", - " - 100 * abs(state[4])\n", - " + 10 * state[6]\n", - " + 10 * state[7]\n", - " ) # And ten points for legs contact, the idea is if you\n", - " # lose contact again after landing, you get negative reward\n", - " if self.prev_shaping is not None:\n", - " reward = shaping - self.prev_shaping\n", - " self.prev_shaping = shaping\n", - "\n", - " reward -= (\n", - " m_power * 0.30\n", - " ) # less fuel spent is better, about -30 for heuristic landing\n", - " reward -= s_power * 0.03\n", - "\n", - " terminated = False\n", - " if self.game_over or abs(state[0]) >= 1.0:\n", - " terminated = True\n", - " reward = -100\n", - " if not self.lander.awake:\n", - " terminated = True\n", - " reward = +100\n", - "\n", - " if self.render_mode == \"human\":\n", - " self.render()\n", - " return np.array(state, dtype=np.float32), reward, terminated, False, {}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "V-qZ4WYxDBuZ" - }, - "source": [ - "Once you have cutomized your own environment, you can execute that environment by just calling:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "execution": {}, - "id": "yq4902DQDBuZ" - }, - "outputs": [], - "source": [ - "## Enter the name of the custome environment you created and uncomment the line below.\n", - "# env = Custom_LunarLander()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "EAP-DUd6DBuZ" - }, - "source": [ - "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "QTq0hmHCDBuZ" - }, - "source": [ - "## 3 Identify the state information crucial to its performance.\n", - "\n", - "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": {}, - "id": "sz45kgEaDBuZ" - }, - "outputs": [], - "source": [ - "def step(self, actions):\n", - " ...\n", - " ...\n", - " ...\n", - " state = [ # Remove one component at a time to investigate the effect on performance!\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "DXy9s2ymDBuZ" - }, - "source": [ - "## 4 Extension to Atari Games\n", - "\n", - "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "execution": {}, - "id": "4RjAt0W-DBuZ" - }, - "outputs": [], - "source": [ - "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", - "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", - "\n", - "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", - "# env = VecFrameStack(env, n_stack=4)\n", - "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", - "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" + "text/plain": [ + "" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", + "\n", + "observation = env.reset()[0]\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", + "HTML(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "fhtq8GDLDBuW" + }, + "source": [ + "From the video above, we see that the lander has crashed!\n", + "It is now the time for training!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Xhl3ojMwDBuW", + "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "E6_fFAYhDBue" - }, - "source": [ - "## 5 Obstacle Avoidance and Transfer Learning\n", - "\n", - "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", - "\n", - "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", - "\n", - "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval num_timesteps=10000, episode_reward=-283.46 +/- 58.80\n", + "Episode length: 187.00 +/- 38.92\n", + "New best mean reward!\n", + "Eval num_timesteps=20000, episode_reward=-406.35 +/- 59.73\n", + "Episode length: 201.40 +/- 71.19\n", + "Eval num_timesteps=30000, episode_reward=-323.96 +/- 57.66\n", + "Episode length: 774.80 +/- 166.26\n", + "Eval num_timesteps=40000, episode_reward=69.12 +/- 117.61\n", + "Episode length: 418.00 +/- 31.63\n", + "New best mean reward!\n", + "Eval num_timesteps=50000, episode_reward=-170.94 +/- 98.78\n", + "Episode length: 145.40 +/- 73.48\n", + "Eval num_timesteps=60000, episode_reward=147.63 +/- 90.91\n", + "Episode length: 543.60 +/- 90.69\n", + "New best mean reward!\n", + "Eval num_timesteps=70000, episode_reward=-30.95 +/- 269.95\n", + "Episode length: 421.40 +/- 225.90\n", + "Eval num_timesteps=80000, episode_reward=117.20 +/- 86.79\n", + "Episode length: 766.40 +/- 125.80\n", + "Eval num_timesteps=90000, episode_reward=-26.99 +/- 20.29\n", + "Episode length: 639.80 +/- 441.17\n", + "Eval num_timesteps=100000, episode_reward=101.46 +/- 61.07\n", + "Episode length: 893.80 +/- 130.44\n" + ] }, { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "execution": {}, - "id": "h6knZ3U8DBue" - }, - "outputs": [], - "source": [ - "## Specify the load path and uncomment below:\n", - "\n", - "# model = load(load_path,\n", - "# env=gym.make('LunarLander-v2'),\n", - "# custom_objects=None, **kwargs)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", + "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", + "# view the performance at every training episode." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "IYynM83tDBuX" + }, + "source": [ + "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", + "\n", + "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", + "\n", + "```\n", + "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", + "Episode length: 259.80 +/- 47.47\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UFNQVKokDBuX" + }, + "source": [ + "Now, let us look at the visual performance of the lander.\n", + "\n", + "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 }, + "id": "hc0xXn5aDBuX", + "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "GXBIbO25DBue" - }, - "source": [ - "Following are some of the resources on transfer learning that you would want to start with.\n", - "\n", - "**Research Papers**\n", - "\n", - "Surveys:\n", - "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", - " - Long, Old, Highly cited\n", - "\n", - "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", - " - Medium, Old, Good for a quick read\n", - "\n", - "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", - " - Medium, Recent, Good for a quick read\n", - "\n", - "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", - " - Specific example" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Building video video/LunarLander-v2_learned.mp4.\n", + "Moviepy - Writing video video/LunarLander-v2_learned.mp4\n", + "\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "MlOksW3ODBue" - }, - "source": [ - "## 5(b) Transfer Learning in minigrid environment\n", - "\n", - "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " " + ] }, { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "execution": {}, - "id": "v7rB2JQlDBue" - }, - "outputs": [], - "source": [ - "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Done !\n", + "Moviepy - video ready video/LunarLander-v2_learned.mp4\n", + "\n", + "Total reward: 211.6755132885056\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "OPcds7ZtDBue" - }, - "source": [ - "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "execution": {}, - "id": "d0NiSkyeDBue", - "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n", - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n" - ] - } + "data": { + "text/html": [ + "" ], - "source": [ - "import minigrid\n", - "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "7J92iMqqDBue" - }, - "source": [ - "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", - "\n", - "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" + "text/plain": [ + "" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", + "observation = env.reset()[0]\n", + "\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", + "HTML(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "cVCcx8GUDBuX" + }, + "source": [ + "The lander has landed safely!!\n", + "\n", + "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 }, + "id": "_8ibUiTmDBuX", + "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "HL0W5M4uDBue" - }, - "source": [ - "## 6 Preference-Based RL (PBRL)\n", - "\n", - "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", - "\n", - "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", - "\n", - "**Research papers:**\n", - "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", - "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", - "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", - "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", - "\n", - "**Code Bases:**\n", - "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", - "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", + "plt.plot(x, y)\n", + "plt.ylim([-300, 300])\n", + "plt.xlabel('Timesteps')\n", + "plt.ylabel('Episode Rewards')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "2Zo8kpDUDBuX" + }, + "source": [ + "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", + "\n", + "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "D7JAEDEzDBuX" + }, + "source": [ + "---\n", + "# Additional Project Ideas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1m6YBf5nDBuX" + }, + "source": [ + "## 1 Play with exploration-exploitation trade-off\n", + "\n", + "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "tnbb16KUDBuY", + "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f" + }, + "outputs": [], + "source": [ + "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", + "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", + "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "794AyGDPDBuY" + }, + "source": [ + "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "Ljf9XG5BDBuY" + }, + "source": [ + "## 2 Reward Shaping\n", + "\n", + "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "zAAhdiflDBuY" + }, + "outputs": [], + "source": [ + "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", + "class CustomEnv(gym.Env):\n", + " \"\"\"Custom Environment that follows gym interface\"\"\"\n", + " metadata = {'render.modes': ['human']}\n", + "\n", + " def __init__(self, arg1, arg2):\n", + " super(CustomEnv, self).__init__()\n", + " # Define action and observation space\n", + " # They must be gym.spaces objects\n", + " # Example when using discrete actions:\n", + " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", + " # Example for using image as input (channel-first; channel-last also works):\n", + " self.observation_space = spaces.Box(low=0, high=255,\n", + " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", + "\n", + " def step(self, action):\n", + " ...\n", + " return observation, reward, done, info\n", + " def reset(self):\n", + " ...\n", + " return observation # reward, done, info can't be included\n", + " def render(self, mode='human'):\n", + " ...\n", + " def close (self):\n", + " ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "n7u1oEO2DBuY" + }, + "source": [ + "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "463GUtbuDBuY" + }, + "outputs": [], + "source": [ + "class Custom_LunarLander(LunarLander):\n", + "\n", + " def step(self, action):\n", + " assert self.lander is not None\n", + "\n", + " # Update wind\n", + " assert self.lander is not None, \"You forgot to call reset()\"\n", + " if self.enable_wind and not (\n", + " self.legs[0].ground_contact or self.legs[1].ground_contact\n", + " ):\n", + " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " wind_mag = (\n", + " math.tanh(\n", + " math.sin(0.02 * self.wind_idx)\n", + " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", + " )\n", + " * self.wind_power\n", + " )\n", + " self.wind_idx += 1\n", + " self.lander.ApplyForceToCenter(\n", + " (wind_mag, 0.0),\n", + " True,\n", + " )\n", + "\n", + " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " torque_mag = math.tanh(\n", + " math.sin(0.02 * self.torque_idx)\n", + " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", + " ) * (self.turbulence_power)\n", + " self.torque_idx += 1\n", + " self.lander.ApplyTorque(\n", + " (torque_mag),\n", + " True,\n", + " )\n", + "\n", + " if self.continuous:\n", + " action = np.clip(action, -1, +1).astype(np.float32)\n", + " else:\n", + " assert self.action_space.contains(\n", + " action\n", + " ), f\"{action!r} ({type(action)}) invalid \"\n", + "\n", + " # Engines\n", + " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", + " side = (-tip[1], tip[0])\n", + " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", + "\n", + " m_power = 0.0\n", + " if (self.continuous and action[0] > 0.0) or (\n", + " not self.continuous and action == 2\n", + " ):\n", + " # Main engine\n", + " if self.continuous:\n", + " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", + " assert m_power >= 0.5 and m_power <= 1.0\n", + " else:\n", + " m_power = 1.0\n", + " # 4 is move a bit downwards, +-2 for randomness\n", + " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", + " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", + " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", + " p = self._create_particle(\n", + " 3.5, # 3.5 is here to make particle speed adequate\n", + " impulse_pos[0],\n", + " impulse_pos[1],\n", + " m_power,\n", + " ) # particles are just a decoration\n", + " p.ApplyLinearImpulse(\n", + " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " s_power = 0.0\n", + " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", + " not self.continuous and action in [1, 3]\n", + " ):\n", + " # Orientation engines\n", + " if self.continuous:\n", + " direction = np.sign(action[1])\n", + " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", + " assert s_power >= 0.5 and s_power <= 1.0\n", + " else:\n", + " direction = action - 2\n", + " s_power = 1.0\n", + " ox = tip[0] * dispersion[0] + side[0] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " oy = -tip[1] * dispersion[0] - side[1] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " impulse_pos = (\n", + " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", + " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", + " )\n", + " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", + " p.ApplyLinearImpulse(\n", + " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", + "\n", + " pos = self.lander.position\n", + " vel = self.lander.linearVelocity\n", + " state = [\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]\n", + " assert len(state) == 8\n", + "\n", + " # Compare with / without shaping, referring the state description below\n", + " '''\n", + " state[0]: the horizontal coordinate\n", + " state[1]: the vertical coordinate\n", + " state[2]: the horizontal speed\n", + " state[3]: the vertical speed\n", + " state[4]: the angle\n", + " state[5]: the angular speed\n", + " state[6]: first leg contact\n", + " state[7]: second leg contact\n", + " '''\n", + " reward = 0\n", + " shaping = (\n", + " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", + " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", + " - 100 * abs(state[4])\n", + " + 10 * state[6]\n", + " + 10 * state[7]\n", + " ) # And ten points for legs contact, the idea is if you\n", + " # lose contact again after landing, you get negative reward\n", + " if self.prev_shaping is not None:\n", + " reward = shaping - self.prev_shaping\n", + " self.prev_shaping = shaping\n", + "\n", + " reward -= (\n", + " m_power * 0.30\n", + " ) # less fuel spent is better, about -30 for heuristic landing\n", + " reward -= s_power * 0.03\n", + "\n", + " terminated = False\n", + " if self.game_over or abs(state[0]) >= 1.0:\n", + " terminated = True\n", + " reward = -100\n", + " if not self.lander.awake:\n", + " terminated = True\n", + " reward = +100\n", + "\n", + " if self.render_mode == \"human\":\n", + " self.render()\n", + " return np.array(state, dtype=np.float32), reward, terminated, False, {}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "V-qZ4WYxDBuZ" + }, + "source": [ + "Once you have cutomized your own environment, you can execute that environment by just calling:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "yq4902DQDBuZ" + }, + "outputs": [], + "source": [ + "## Enter the name of the custome environment you created and uncomment the line below.\n", + "# env = Custom_LunarLander()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EAP-DUd6DBuZ" + }, + "source": [ + "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QTq0hmHCDBuZ" + }, + "source": [ + "## 3 Identify the state information crucial to its performance.\n", + "\n", + "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "sz45kgEaDBuZ" + }, + "outputs": [], + "source": [ + "def step(self, actions):\n", + " ...\n", + " ...\n", + " ...\n", + " state = [ # Remove one component at a time to investigate the effect on performance!\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "DXy9s2ymDBuZ" + }, + "source": [ + "## 4 Extension to Atari Games\n", + "\n", + "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "4RjAt0W-DBuZ" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "QdW-XKCMDBue" - }, - "source": [ - "---\n", - "# References\n", - "\n", - "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", - "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", - "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", - "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n", + "[Powered by Stella]\n" + ] } - ], - "metadata": { - "accelerator": "GPU", + ], + "source": [ + "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", + "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", + "\n", + "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", + "# env = VecFrameStack(env, n_stack=4)\n", + "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", + "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "E6_fFAYhDBue" + }, + "source": [ + "## 5 Obstacle Avoidance and Transfer Learning\n", + "\n", + "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", + "\n", + "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", + "\n", + "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "h6knZ3U8DBue" + }, + "outputs": [], + "source": [ + "## Specify the load path and uncomment below:\n", + "\n", + "# model = load(load_path,\n", + "# env=gym.make('LunarLander-v2'),\n", + "# custom_objects=None, **kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "GXBIbO25DBue" + }, + "source": [ + "Following are some of the resources on transfer learning that you would want to start with.\n", + "\n", + "**Research Papers**\n", + "\n", + "Surveys:\n", + "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", + " - Long, Old, Highly cited\n", + "\n", + "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", + " - Medium, Old, Good for a quick read\n", + "\n", + "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", + " - Medium, Recent, Good for a quick read\n", + "\n", + "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", + " - Specific example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "MlOksW3ODBue" + }, + "source": [ + "## 5(b) Transfer Learning in minigrid environment\n", + "\n", + "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "v7rB2JQlDBue" + }, + "outputs": [], + "source": [ + "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "OPcds7ZtDBue" + }, + "source": [ + "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { "colab": { - "name": "lunar_lander", - "provenance": [], - "toc_visible": true + "base_uri": "https://localhost:8080/" }, - "gpuClass": "standard", - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "id": "d0NiSkyeDBue", + "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n", + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n" + ] } + ], + "source": [ + "import minigrid\n", + "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "7J92iMqqDBue" + }, + "source": [ + "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", + "\n", + "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "HL0W5M4uDBue" + }, + "source": [ + "## 6 Preference-Based RL (PBRL)\n", + "\n", + "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", + "\n", + "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", + "\n", + "**Research papers:**\n", + "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", + "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", + "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", + "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", + "\n", + "**Code Bases:**\n", + "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", + "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QdW-XKCMDBue" + }, + "source": [ + "---\n", + "# References\n", + "\n", + "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", + "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", + "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", + "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "lunar_lander", + "provenance": [], + "toc_visible": true + }, + "gpuClass": "standard", + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt index b8d8390a9..356436466 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,17 @@ requests -numpy +numpy==1.26.4 scipy matplotlib scikit-learn -torch +torch==1.13.0 +torchvision==0.14.0 ipywidgets tqdm torchvision pathlib xkcd -decorator==5.0.9 +decorator==4.0.2 pyvirtualdisplay tensorboard -moviepy +moviepy==1.0.3 +imageio_ffmpeg From b8bd3d225f2a5b75e5787bd25e6bb5130bbd1177 Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 16 Jul 2024 15:17:13 -0400 Subject: [PATCH 23/25] update projects for 2024 again --- .../ComputerVision/spectrogram_analysis.ipynb | 195 +++++------ .../Neuroscience/cellular_segmentation.ipynb | 323 ++++++++---------- 2 files changed, 214 insertions(+), 304 deletions(-) diff --git a/projects/ComputerVision/spectrogram_analysis.ipynb b/projects/ComputerVision/spectrogram_analysis.ipynb index a6cae269e..67f485971 100644 --- a/projects/ComputerVision/spectrogram_analysis.ipynb +++ b/projects/ComputerVision/spectrogram_analysis.ipynb @@ -73,30 +73,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reading package lists...\n", - "Building dependency tree...\n", - "Reading state information...\n", - "ffmpeg is already the newest version (7:3.4.11-0ubuntu0.1).\n", - "The following package was automatically installed and is no longer required:\n", - " libnvidia-common-460\n", - "Use 'sudo apt autoremove' to remove it.\n", - "0 upgraded, 0 newly installed, 0 to remove and 49 not upgraded.\n" - ] - } - ], + "outputs": [], "source": [ "# @title Install dependencies\n", - "!sudo apt-get install -y ffmpeg --quiet\n", + "# !sudo apt-get install -y ffmpeg --quiet\n", "!pip install librosa --quiet\n", "!pip install imageio --quiet\n", "!pip install imageio-ffmpeg --quiet" @@ -104,20 +88,9 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/resampy/interpn.py:114: NumbaWarning: The TBB threading layer requires TBB version 2019.5 or later i.e., TBB_INTERFACE_VERSION >= 11005. Found TBB_INTERFACE_VERSION = 9107. The TBB threading layer is disabled.\n", - " _resample_loop_p(x, t_out, interp_win, interp_delta, num_table, scale, y)\n" - ] - } - ], + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "# Import necessary libraries.\n", "import os\n", @@ -139,10 +112,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "import requests\n", @@ -180,10 +151,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "from zipfile import ZipFile\n", @@ -206,16 +175,14 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", - "
" ], "text/plain": [ - " polarity ... text\n", - "0 0 ... @switchfoot http://twitpic.com/2y1zl - Awww, t...\n", - "1 0 ... is upset that he can't update his Facebook by ...\n", - "2 0 ... @Kenichan I dived many times for the ball. Man...\n", - "3 0 ... my whole body feels itchy and like its on fire \n", - "4 0 ... @nationwideclass no, it's not behaving at all....\n", - "\n", - "[5 rows x 6 columns]" + " polarity user date query \\\n", + "0 0 _TheSpecialOne_ Mon Apr 06 22:19:45 PDT 2009 NO_QUERY \n", + "1 0 scotthamilton Mon Apr 06 22:19:49 PDT 2009 NO_QUERY \n", + "2 0 mattycus Mon Apr 06 22:19:53 PDT 2009 NO_QUERY \n", + "3 0 ElleCTF Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", + "4 0 Karoli Mon Apr 06 22:19:57 PDT 2009 NO_QUERY \n", + "\n", + " user text \n", + "0 _TheSpecialOne_ @switchfoot http://twitpic.com/2y1zl - Awww, t... \n", + "1 scotthamilton is upset that he can't update his Facebook by ... \n", + "2 mattycus @Kenichan I dived many times for the ball. Man... \n", + "3 ElleCTF my whole body feels itchy and like its on fire \n", + "4 Karoli @nationwideclass no, it's not behaving at all.... " ] }, "execution_count": 4, - "metadata": { - "tags": [] - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -270,10 +360,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 5, + "metadata": {}, "outputs": [], "source": [ "X = df.text.values\n", @@ -297,10 +385,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 6, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -330,10 +416,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -353,56 +437,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "499e7fb54aa048afb3cba78dd8d6bb0e", + "model_id": "cd7802db570b408eb5f29af92ca95be1", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "HBox(children=(FloatProgress(value=0.0, max=1280000.0), HTML(value='')))" + " 0%| | 0/1280000 [00:00" + "
" ] }, - "metadata": { - "needs_background": "light", - "tags": [] - }, + "metadata": {}, "output_type": "display_data" } ], @@ -572,10 +627,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "vectorizer = CountVectorizer(binary=True)\n", @@ -585,10 +638,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 13, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -604,10 +655,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -640,25 +689,424 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
LogisticRegression(solver='saga')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], "text/plain": [ - "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n", - " intercept_scaling=1, l1_ratio=None, max_iter=100,\n", - " multi_class='auto', n_jobs=None, penalty='l2',\n", - " random_state=None, solver='saga', tol=0.0001, verbose=0,\n", - " warm_start=False)" + "LogisticRegression(solver='saga')" ] }, "execution_count": 15, - "metadata": { - "tags": [] - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -669,10 +1117,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 16, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -708,10 +1154,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -732,35 +1176,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "roni: -3.862597673594883\n", - "inaperfectworld: -3.5734362290886375\n", - "dontyouhate: -3.500197620227523\n", - "xbllygbsn: -3.412645372640648\n", - "anqju: -3.336405291553548\n", - "sad: -3.200522312464158\n", - "pakcricket: -3.1949158120163412\n", - "condolences: -3.132498019366488\n", - "heartbreaking: -3.066508733796654\n", - "saddest: -3.041999809733714\n", - "sadd: -3.029070563580306\n", - "heartbroken: -3.0287688233900174\n", - "boohoo: -3.022608649696793\n", - "sadface: -2.9918411285807234\n", - "rachelle_lefevr: -2.925057253107806\n", - "disappointing: -2.902524113779547\n", - "lvbu: -2.894705935001672\n", - "saddens: -2.8855127179984654\n", - "bummed: -2.83650014970307\n", - "neda: -2.792944556837498\n" + "roni: -3.8625743295204957\n", + "inaperfectworld: -3.5734332703128837\n", + "dontyouhate: -3.5001974848534263\n", + "xbllygbsn: -3.4126706055950495\n", + "anqju: -3.3363734662876445\n", + "sad: -3.200515320038219\n", + "pakcricket: -3.1949201520762647\n", + "condolences: -3.1325044251779404\n", + "heartbreaking: -3.066490736238656\n", + "saddest: -3.0420206470077464\n", + "sadd: -3.0290359193094782\n", + "heartbroken: -3.028758057376751\n", + "boohoo: -3.0225999274601483\n", + "sadface: -2.9918433622017107\n", + "rachelle_lefevr: -2.9250755659458325\n", + "disappointing: -2.9025305172931777\n", + "lvbu: -2.8947126358841744\n", + "saddens: -2.885505898962137\n", + "bummed: -2.8364963698917003\n", + "neda: -2.792965453263205\n" ] } ], @@ -771,35 +1213,33 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 19, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "iamsoannoyed: 2.8494314732277672\n", - "myfax: 2.797451563471618\n", - "jennamadison: 2.5667257393706113\n", - "yeyy: 2.478028598852801\n", - "tryout: 2.4383315790116677\n", - "goldymom: 2.4374026022205535\n", - "wooohooo: 2.40297322137544\n", - "thesupergirl: 2.3565118467330004\n", - "iammaxathotspot: 2.311648368632618\n", - "londicreations: 2.3074490293400993\n", - "smilin: 2.2991891636718216\n", - "worries: 2.2899429774914717\n", - "sinfulsignorita: 2.2798963640981817\n", - "finchensnail: 2.264302079155878\n", - "smackthis: 2.2376679263761083\n", - "kv: 2.2158393907798413\n", - "tojosan: 2.211784259253832\n", - "russmarshalek: 2.2095374025599384\n", - "traciknoppe: 2.1768297770350835\n", - "congratulations: 2.171590496227557\n" + "iamsoannoyed: 2.849404979800149\n", + "myfax: 2.7974231693128444\n", + "jennamadison: 2.566709062644627\n", + "yeyy: 2.4780376468589687\n", + "tryout: 2.4383326853311327\n", + "goldymom: 2.4374101525708456\n", + "wooohooo: 2.4029624310379774\n", + "thesupergirl: 2.3565547667359015\n", + "iammaxathotspot: 2.3116516945468017\n", + "londicreations: 2.3074597815514477\n", + "smilin: 2.2991641364971334\n", + "worries: 2.2899475148214545\n", + "sinfulsignorita: 2.2798843862743605\n", + "finchensnail: 2.264277870479377\n", + "smackthis: 2.237665085200092\n", + "kv: 2.215872609715896\n", + "tojosan: 2.2117820541821733\n", + "russmarshalek: 2.2095325084648705\n", + "traciknoppe: 2.1768517653116426\n", + "congratulations: 2.1715832459661644\n" ] } ], @@ -847,10 +1287,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 20, + "metadata": {}, "outputs": [], "source": [ "def set_device():\n", @@ -867,16 +1305,14 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "GPU is enabled in this notebook.\n" + "WARNING: For this notebook to perform best, if possible, in the menu under `Runtime` -> `Change runtime type.` select `GPU` \n" ] } ], @@ -896,10 +1332,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "data": { @@ -908,9 +1342,7 @@ ] }, "execution_count": 22, - "metadata": { - "tags": [] - }, + "metadata": {}, "output_type": "execute_result" } ], @@ -930,10 +1362,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 23, + "metadata": {}, "outputs": [], "source": [ "num_words_dict = 30000\n", @@ -952,10 +1382,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 24, + "metadata": {}, "outputs": [], "source": [ "# dictionary to go from words to idx\n", @@ -991,10 +1419,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 25, + "metadata": {}, "outputs": [], "source": [ "# A function to convert list of tokens to list of indexes\n", @@ -1013,10 +1439,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 26, + "metadata": {}, "outputs": [], "source": [ "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", @@ -1025,10 +1449,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 27, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1056,10 +1478,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 28, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1089,10 +1509,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 29, + "metadata": {}, "outputs": [], "source": [ " # We choose the max length\n", @@ -1116,10 +1534,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 30, + "metadata": {}, "outputs": [], "source": [ "# We convert our list of tokens into a numpy matrix\n", @@ -1134,10 +1550,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 31, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1167,10 +1581,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 32, + "metadata": {}, "outputs": [], "source": [ "# create Tensor datasets\n", @@ -1197,10 +1609,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 33, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1208,19 +1618,19 @@ "text": [ "Sample input size: torch.Size([100, 40])\n", "Sample input: \n", - " tensor([[ 0, 0, 0, ..., 4, 4, 4],\n", - " [ 0, 0, 0, ..., 7447, 14027, 2],\n", - " [ 0, 0, 0, ..., 100, 22241, 4],\n", + " tensor([[ 0, 0, 0, ..., 12, 4491, 2],\n", + " [ 0, 0, 0, ..., 0, 1, 383],\n", + " [ 0, 0, 0, ..., 6, 246, 2],\n", " ...,\n", - " [ 0, 0, 0, ..., 2702, 4409, 2],\n", - " [ 0, 0, 0, ..., 162, 17, 1],\n", - " [ 0, 0, 0, ..., 67, 12904, 49]])\n", + " [ 0, 0, 0, ..., 108, 14, 4],\n", + " [ 0, 0, 0, ..., 2434, 29, 1],\n", + " [ 0, 0, 0, ..., 1150, 20247, 2]])\n", "Sample input: \n", - " tensor([0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0,\n", - " 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,\n", - " 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0,\n", - " 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n", - " 0, 0, 1, 0])\n" + " tensor([1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1,\n", + " 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n", + " 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n", + " 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0,\n", + " 0, 0, 1, 1])\n" ] } ], @@ -1257,10 +1667,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 34, + "metadata": {}, "outputs": [], "source": [ "class SentimentRNN(nn.Module):\n", @@ -1337,10 +1745,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 35, + "metadata": {}, "outputs": [], "source": [ "# Parameters of our network\n", @@ -1366,10 +1772,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 36, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1396,10 +1800,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 37, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1427,11 +1829,18 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [], + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], "source": [ "# loss and optimization functions\n", "lr = 0.001\n", @@ -1459,39 +1868,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 39, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1\n", - "train_loss : 0.4367361353733577 val_loss : 0.39174133955966683\n", - "train_accuracy : 79.530625 val_accuracy : 82.3628125\n", - "Validation loss decreased (inf --> 0.391741). Saving model ...\n", + "train_loss : 0.4357354193425272 val_loss : 0.3897459434857592\n", + "train_accuracy : 79.552578125 val_accuracy : 82.34125\n", + "Validation loss decreased (inf --> 0.389746). Saving model ...\n", "==================================================\n", "Epoch 2\n", - "train_loss : 0.3765802335098851 val_loss : 0.3724124691961333\n", - "train_accuracy : 83.19140625 val_accuracy : 83.42031250000001\n", - "Validation loss decreased (0.391741 --> 0.372412). Saving model ...\n", + "train_loss : 0.3756973787629977 val_loss : 0.37125814022496345\n", + "train_accuracy : 83.20953125 val_accuracy : 83.41875\n", + "Validation loss decreased (0.389746 --> 0.371258). Saving model ...\n", "==================================================\n", "Epoch 3\n", - "train_loss : 0.35746844720793886 val_loss : 0.365050206175074\n", - "train_accuracy : 84.16882812499999 val_accuracy : 83.7440625\n", - "Validation loss decreased (0.372412 --> 0.365050). Saving model ...\n", + "train_loss : 0.35649536208133215 val_loss : 0.36528081766329706\n", + "train_accuracy : 84.24179687499999 val_accuracy : 83.76593749999999\n", + "Validation loss decreased (0.371258 --> 0.365281). Saving model ...\n", "==================================================\n", "Epoch 4\n", - "train_loss : 0.34491546426317654 val_loss : 0.36467386982403693\n", - "train_accuracy : 84.879140625 val_accuracy : 83.77\n", - "Validation loss decreased (0.365050 --> 0.364674). Saving model ...\n", + "train_loss : 0.3434784019400831 val_loss : 0.361129659358412\n", + "train_accuracy : 84.91164062499999 val_accuracy : 83.9671875\n", + "Validation loss decreased (0.365281 --> 0.361130). Saving model ...\n", "==================================================\n", "Epoch 5\n", - "train_loss : 0.33429012800217606 val_loss : 0.36189084346871825\n", - "train_accuracy : 85.44296875 val_accuracy : 84.0221875\n", - "Validation loss decreased (0.364674 --> 0.361891). Saving model ...\n", + "train_loss : 0.33264520978555084 val_loss : 0.3602768037747592\n", + "train_accuracy : 85.53132812499999 val_accuracy : 84.0209375\n", + "Validation loss decreased (0.361130 --> 0.360277). Saving model ...\n", "==================================================\n" ] } @@ -1590,16 +1997,14 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 40, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ - "
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" ] }, "metadata": {}, @@ -1659,13 +2064,23 @@ "name": "python3" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/projects/Neuroscience/blurry_vision.ipynb b/projects/Neuroscience/blurry_vision.ipynb index ee68c5f6d..b3e073352 100644 --- a/projects/Neuroscience/blurry_vision.ipynb +++ b/projects/Neuroscience/blurry_vision.ipynb @@ -50,20 +50,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " Building wheel for torch-intermediate-layer-getter (setup.py) ... \u001B[?25l\u001B[?25hdone\n" - ] - } - ], + "outputs": [], "source": [ "# @title Install dependencies\n", "!pip install torch_intermediate_layer_getter --quiet\n", @@ -72,10 +63,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ "# Imports\n", @@ -107,10 +96,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -149,17 +137,15 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Random seed 42 has been set.\n", - "GPU is enabled in this notebook.\n" + "WARNING: For this notebook to perform best, if possible, in the menu under `Runtime` -> `Change runtime type.` select `GPU` \n" ] } ], @@ -187,10 +173,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -247,10 +232,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "# Define Preprocessing Filters\n", @@ -322,10 +305,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "# Get an example of a clear and noisy versions of cat and dog image\n", @@ -346,31 +327,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n", - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-1.0..1.0].\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.44313723..1.0].\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n", + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -428,10 +405,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "# Define AlexNet with different modules representing different brain areas\n", @@ -513,10 +488,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 10, + "metadata": {}, "outputs": [], "source": [ "# Create an neural network object and visualise it with an example image\n", @@ -528,29 +501,30 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 11, + "metadata": {}, "outputs": [ { "data": { - "application/javascript": [ + "text/html": [ "\n", - " (async () => {\n", - " const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n", - " url.searchParams.set('tensorboardColab', 'true');\n", - " const iframe = document.createElement('iframe');\n", - " iframe.src = url;\n", - " iframe.setAttribute('width', '100%');\n", - " iframe.setAttribute('height', '800');\n", - " iframe.setAttribute('frameborder', 0);\n", - " document.body.appendChild(iframe);\n", + " \n", + " \n", " " ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -572,10 +546,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 12, + "metadata": {}, "outputs": [], "source": [ "# define function for running some epochs of training\n", @@ -620,10 +592,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 13, + "metadata": {}, "outputs": [], "source": [ "# define function to calculate current accuracy with a given dataloader\n", @@ -684,15 +654,13 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 14, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "ac2074d4eba742ea8b69d976e2c75935", + "model_id": "392a6a9408c14847a3611bf0ae7a4eee", "version_major": 2, "version_minor": 0 }, @@ -707,28 +675,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 32 bytes but only got 0. Skipping tag 270\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 5 bytes but only got 0. Skipping tag 271\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 272\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 282\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 283\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 20 bytes but only got 0. Skipping tag 306\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 48 bytes but only got 0. Skipping tag 532\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 2 bytes but only got 0. \n", + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/PIL/TiffImagePlugin.py:900: UserWarning: Truncated File Read\n", " warnings.warn(str(msg))\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9f8ee2b3d9b34c91a426a614f243a9ea", + "model_id": "6eb2c853b47e4290ba30efdd0e1edbb1", "version_major": 2, "version_minor": 0 }, @@ -750,7 +704,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "a17279272eb6494f84136b77a617fcae", + "model_id": "7bf39b3fd12241b6a9721fa437147be0", "version_major": 2, "version_minor": 0 }, @@ -764,7 +718,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e081de51b9904b5f936d9bd0ee2660df", + "model_id": "a0acb0f29f0e4c6ab2248c021ce251f1", "version_major": 2, "version_minor": 0 }, @@ -786,7 +740,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "22ca6448031d4ce8b53dc202a64f9719", + "model_id": "5c8c0aacb4754e55a31f86ce2abbd9f3", "version_major": 2, "version_minor": 0 }, @@ -800,7 +754,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b8a17a98e9e4eadb9f2c96ef4ad6630", + "model_id": "962f8548de614a4182c11e2c43f35cf0", "version_major": 2, "version_minor": 0 }, @@ -814,7 +768,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b1454536906d4faa84a760528adbfd1d", + "model_id": "5e58c8594ba3462cbfd08e03fc066dc2", "version_major": 2, "version_minor": 0 }, @@ -829,14 +783,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 clear training samples after training: 64.09\n", - "Accuracy on the 2494 clear testing samples after training: 63.19\n" + "Accuracy on the 17953 clear training samples after training: 62.42\n", + "Accuracy on the 2494 clear testing samples after training: 62.35\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "62dbd641a41244af9ebdf0328de90dce", + "model_id": "942fd3d11d4946b785eead22422670dc", "version_major": 2, "version_minor": 0 }, @@ -850,7 +804,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d12425601e5d4aa78193914e2b823f4c", + "model_id": "d792cfe2ff29424eb84485da1301989c", "version_major": 2, "version_minor": 0 }, @@ -865,20 +819,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 blurry training samples after training: 73.98\n", - "Accuracy on the 2494 blurry testing samples after training: 71.97\n" + "Accuracy on the 17953 blurry training samples after training: 71.32\n", + "Accuracy on the 2494 blurry testing samples after training: 71.13\n" ] }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -943,15 +895,13 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 15, + "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1c3f2bd364ff418abea54cee28520382", + "model_id": "bf689e2de4ea4dd5ad625a6793625892", "version_major": 2, "version_minor": 0 }, @@ -962,32 +912,10 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 32 bytes but only got 0. Skipping tag 270\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 5 bytes but only got 0. Skipping tag 271\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 272\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 282\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 8 bytes but only got 0. Skipping tag 283\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 20 bytes but only got 0. Skipping tag 306\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:770: UserWarning: Possibly corrupt EXIF data. Expecting to read 48 bytes but only got 0. Skipping tag 532\n", - " \" Skipping tag %s\" % (size, len(data), tag)\n", - "/usr/local/lib/python3.7/dist-packages/PIL/TiffImagePlugin.py:788: UserWarning: Corrupt EXIF data. Expecting to read 2 bytes but only got 0. \n", - " warnings.warn(str(msg))\n" - ] - }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "50416897e5da43cd8cf7ee956bd00bb6", + "model_id": "18871e3700ad42e0a9826ea229cf1703", "version_major": 2, "version_minor": 0 }, @@ -1009,7 +937,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "02534dcb995f4de59d7cf43055a11753", + "model_id": "8ff4e4f2568b4147afab5154da08b944", "version_major": 2, "version_minor": 0 }, @@ -1023,7 +951,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e34fec1ee9354229879f7848888cea66", + "model_id": "d4ed04edb46445f8a6a5d5f18e6b86e6", "version_major": 2, "version_minor": 0 }, @@ -1045,7 +973,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "1ffd74459a1541b7b0c3badbd41e3675", + "model_id": "2a26a05b2f164f918f5e123891b40d4c", "version_major": 2, "version_minor": 0 }, @@ -1059,7 +987,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "37bef7a6d95f4cc48c26c8bbb908ad32", + "model_id": "30155460f3bf42eeb129da606fcf75c2", "version_major": 2, "version_minor": 0 }, @@ -1073,7 +1001,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2ba68c5d0f384d53b91205006cc69d85", + "model_id": "02009c120e9741afbcb5357f22941e25", "version_major": 2, "version_minor": 0 }, @@ -1088,14 +1016,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 clear training samples after pretraining: 76.43\n", - "Accuracy on the 2494 clear testing samples after pretraining: 75.74\n" + "Accuracy on the 17953 clear training samples after pretraining: 77.26\n", + "Accuracy on the 2494 clear testing samples after pretraining: 76.50\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "3fd54eb969b94ad686555d9e08ef9c8a", + "model_id": "5b6bdeeee35a49008b5e89e607d417bb", "version_major": 2, "version_minor": 0 }, @@ -1109,7 +1037,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "df9d9ed605c74840bbd0e0c912c291b2", + "model_id": "a301f6de7bb74d9a9afb05cf73703a96", "version_major": 2, "version_minor": 0 }, @@ -1124,14 +1052,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 blurry training samples after pretraining: 61.01\n", - "Accuracy on the 2494 blurry testing samples after pretraining: 60.06\n" + "Accuracy on the 17953 blurry training samples after pretraining: 58.27\n", + "Accuracy on the 2494 blurry testing samples after pretraining: 57.62\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "5ca2a99ef00d4b76871b6b5162c46f43", + "model_id": "550e7f421fdb4727bee5f99ba61053ad", "version_major": 2, "version_minor": 0 }, @@ -1145,7 +1073,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7a0e7f9f69f248828d437270299447b5", + "model_id": "00221bd901984f60a292641bc771749d", "version_major": 2, "version_minor": 0 }, @@ -1159,7 +1087,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4b5ca373beb0414fbf6d218d6c1133ac", + "model_id": "1bc6003fe88b49c48f071ff6dc1e8abf", "version_major": 2, "version_minor": 0 }, @@ -1174,14 +1102,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 clear training samples after training: 72.82\n", - "Accuracy on the 2494 clear testing samples after training: 72.29\n" + "Accuracy on the 17953 clear training samples after training: 76.25\n", + "Accuracy on the 2494 clear testing samples after training: 75.82\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d34b7c39753a46a3bca91717b7e1f0c4", + "model_id": "4270771b1bd44e08b9f89291ee943877", "version_major": 2, "version_minor": 0 }, @@ -1195,7 +1123,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cbe594f8af104a1eab5f3e53c2566297", + "model_id": "019c872eea0a44fda4e42adc8e5929f2", "version_major": 2, "version_minor": 0 }, @@ -1210,20 +1138,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "Accuracy on the 17953 blurry training samples after training: 77.60\n", - "Accuracy on the 2494 blurry testing samples after training: 77.71\n" + "Accuracy on the 17953 blurry training samples after training: 76.87\n", + "Accuracy on the 2494 blurry testing samples after training: 77.19\n" ] }, { "data": { - "image/png": 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\n", 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fxJI9p22+JoSIiEhxowSoGHF2sOPFjvVYHh5CoK8HV1MyGLNoH4Pm7+TM1eu2Dk9ERKTYUAJUDDWo4snyYSG8EFYXR3sjvxy5QIdpm/jv9hhMJvUGiYiI3I4SoEKUnJyMm5sbbm5uJCcn31VbDnZGwh+oxaoRrWlavRzJ6Vm8suwAj326nRMXkgooYhERkZJJCVAhS0lJISUlpcDaq1XJne+eDWZy10BcHe3YcfIynWZu5uNNf6i4qoiIyE0oASoBjEYDA0NqsGZUG1rXrkhapom3fjpMz4+2cuhsoq3DExERKXKUAJUgfuVd+fKpFrzbuyEezvZEnkmg26xfeX/tEdIyVVxVRMSa2rVrx6hRoyzf+/v7M2PGjFueYzAYWLZs2V2/d0G1U5ooASphDAYDjzbzY/2YtoTV9ybTZObDn4/z8Ae/sif2iq3DExHJG1MWRG+GyMXZ/zVZ74+4rl270rFjx1xf27x5MwaDgf379+e73Z07d/LMM8/cbXg5TJ48mcaNG9+w/9y5c3Tq1KlA3+uf5s+fT9myZa36HoVJCyGWUJU8nPnkyWasijzHxOUHOBafRK85WxnUqgZjw+rg6qj/9SJSRB1aAavHQeLZ/+3zqAwd34bAbgX+dk8//TS9evXi9OnTVK1aNcdr8+bNo1mzZjRs2DDf7Xp5FV79Rh8fn0J7r5JCPUAlXOcgX9aNbssj91bBbIbPt0QTNiOCLccv2jo0EZEbHVoBi/rnTH4AEs9l7z+0osDf8uGHH8bLy4v58+fn2J+UlMR3333H008/zaVLl3j88cepUqUKrq6uBAUF8c0339yy3X8+Ajt27Bht2rTB2dmZwMBA1q1bd8M548aNo06dOri6ulKzZk1eeeUVMjIygOwemClTprBv3z4MBgMGg8ES8z8fgUVGRvLggw/i4uJChQoVeOaZZ0hK+t8M4YEDB9KjRw/ee+89fH19qVChAuHh4Zb3uhOxsbF0794dd3d3PDw86NOnD+fPn7e8vm/fPh544AHKlCmDh4cHTZs2ZdeuXUB2TbOuXbtSrlw53NzcqF+/PqtWrbrjWPJC3QCFyGg00rZtW8vXhaWcmyPT+jSmW6PKTFh6gFOXr9PvP7/xWHM/xncOwNNFxVVFxErMZsjI48xXUxb89CKQ23pmZsCQ3TNUsx0Y81BOyMEVDLevIG9vb0///v2ZP38+EyZMwPDnOd999x1ZWVk8/vjjJCUl0bRpU8aNG4eHhwcrV67kySef5J577qFFixa3/2gmE4888gje3t789ttvJCQk5Bgv9JcyZcowf/58KleuTGRkJEOGDKFMmTK8+OKL9O3blwMHDrB69WrWr18PgKen5w1tJCcnExYWRnBwMDt37iQ+Pp7BgwczbNiwHEnexo0b8fX1ZePGjRw/fpy+ffvSuHFjhgwZctvPk9vn+yv52bRpE5mZmYSHh9O3b19++eUXAPr160eTJk2YM2cOdnZ27N27FweH7PtPeHg46enpRERE4ObmxqFDh3B3d893HPmhBKgQubi4WH4QbKFd3UqsGd2Gd1Yf5sttMSzceYqNR+J5vUcQ7QO9bRaXiJRgGSnwZuUCasyc3TP0ll/eDv+/s+DolqdDn3rqKd599102bdpEu3btgOzHX7169cLT0xNPT0/Gjh1rOX748OGsWbOGRYsW5SkBWr9+PYcPH2bNmjVUrpx9Pd58880bxu28/PLLlq/9/f0ZO3YsCxcu5MUXX8TFxQV3d3fs7e1v+cjr66+/JjU1lS+//BI3t+zPP2vWLLp27crbb7+Nt3f27/ty5coxa9Ys7OzsqFevHl26dGHDhg13lABt2LCByMhIoqOj8fPL/v/z5ZdfUr9+fXbu3Enz5s2JjY3lhRdeoF69egDUrl3bcn5sbCy9evUiKCgIgJo1a+Y7hvzSI7BSxt3Jnle7N2DRs8HUqOjG+cQ0hny5i2Ff7+FiUpqtwxMRsYl69erRqlUrPv/8cwCOHz/O5s2befrppwHIysritddeIygoiPLly+Pu7s6aNWuIjY3NU/tRUVH4+flZkh+A4ODgG4779ttvCQkJwcfHB3d3d15++eU8v8ff36tRo0aW5AcgJCQEk8nEkSNHLPvq16+fozC3r68v8fHx+Xqvv7+nn5+fJfkBCAwMpGzZskRFRQEwZswYBg8eTGhoKG+99RZ//PGH5dgRI0bw+uuvExISwqRJk+5o0Hl+qQeolGpRozw/jWzNjPXHmLv5BD/uP8eW4xeZ1LU+3RtXtnQBi4jcFQfX7J6YvIjZCgt63/64fouhequ8vXc+PP300wwfPpzZs2czb9487rnnHsuwhXfffZeZM2cyY8YMgoKCcHNzY9SoUaSnp+frPW5l27Zt9OvXjylTphAWFoanpycLFy7k/fffL7D3+Lu/Hj/9xWAwYDJZbwHdyZMn88QTT7By5Up++uknJk2axMKFC+nZsyeDBw8mLCyMlStXsnbtWqZOncr777/P8OHDrRaPeoAKUXJyMl5eXnh5ed11KYyC4Oxgx0ud6rHs+RACfD24kpLBqG/38vQXuzir4qoiUhAMhuzHUHnZ7nkwe7YXN/sDzAAeVbKPy0t7+fxDrk+fPhiNRr7++mu+/PJLnnrqKcsfg1u2bKF79+7861//olGjRtSsWZOjR4/mue2AgABOnTrFuXPnLPu2b9+e45itW7dSvXp1JkyYQLNmzahduzYxMTE5jnF0dCQr69ZLAgQEBLBv374c95ktW7ZgNBqpW7dunmPOj78+36lTpyz7Dh06xNWrVwkMDLTsq1OnDqNHj2bt2rU88sgjzJs3z/Kan58fQ4cOZcmSJfz73/9m7ty5Von1L0qACtnFixe5eLFozcAKqurJimEhjO1QB0c7Iz8fjqfD9AgW/KbiqiJSiIx22VPdgRuToD+/7/hW3gZA3wF3d3f69u3L+PHjOXfuHAMHDrS8Vrt2bdatW8fWrVuJiori2WefzTHD6XZCQ0OpU6cOAwYMYN++fWzevJkJEybkOKZ27drExsaycOFC/vjjDz744AOWLl2a4xh/f3+io6PZu3cvFy9eJC3txqEL/fr1w9nZmQEDBnDgwAE2btzI8OHDefLJJy3jf+5UVlYWe/fuzbFFRUURGhpKUFAQ/fr1Y8+ePezYsYP+/fvTtm1bmjVrxvXr1xk2bBi//PILMTExbNmyhZ07dxIQEADAqFGjWLNmDdHR0ezZs4eNGzdaXrMWJUACZBdXHfZgbVaOuJ8m1cqSlJbJhKUHeHzudk5etH1vlYiUEoHdoM+X4OGbc79H5ez9VlgH6O+efvpprly5QlhYWI7xOi+//DL33nsvYWFhtGvXDh8fH3r06JHndo1GI0uXLuX69eu0aNGCwYMH88Ybb+Q4plu3bowePZphw4bRuHFjtm7dyiuvvJLjmF69etGxY0ceeOABvLy8cp2K7+rqypo1a7h8+TLNmzend+/ePPTQQ8yaNSt/FyMXSUlJNGnSJMfWtWtXDAYDy5cvp1y5crRp04bQ0FBq1qzJt99+C4CdnR2XLl2if//+1KlThz59+tCpUyemTJkCZCdW4eHhBAQE0LFjR+rUqcNHH3101/HeisFsNutP/H9ITEzE09OThIQEPDw8Cqzd5ORky7S+pKSkHAPUipIsk5kvtp7k3TVHuJ6RhZO9kX93qMNTITWwt1POLCI3l5qaSnR0NDVq1MDZ2fnOGzJlZY8JSjoP7t7ZY36s1PMjxcutfsbyc//W3UxuYGc08NT9NVg7ug0htSqQlmnizVWH6TVnK4fjVFxVRAqB0Q5qtIag3tn/VfIjBUwJkNyUX3lXvnq6Je/0akgZZ3v2nU6g64e/Mn3dUdIzrTdTQERExNqUAMktGQwG+jTPLq7aPtCbjCwzMzcc4+EPN7P31FVbhyciInJHlAAVIqPRSLNmzWjWrFmhlsIoCN4eznz6ZFNmPdGECm6OHD2fxCMfbeH1Hw9xPd16VZpFRESsoXjdhYs5FxcXdu7cyc6dO3FxcbF1OPlmMBh4uGFl1o9pS88mVTCZ4T+/ZhdX3fpH0ZraLyK2pfk1Yi0F9bOlBEjyrZybI9P7NmbewOb4ejoTezmFJ+b+xvglkSSm3nklYREp/v4qrVCQKySL/F1KSnZx3X+uZJ1fmgafC2tNgy+JrqVm8Pbqw3y1PbtWjY+HM2/0bMBDASquKlIamc1mYmNjycjIoHLlysXucb8UXWazmZSUFOLj4ylbtiy+vr43HJOf+7cSoFxYKwFKSUmxLAl+6NAhXF3zV6emKNt+4hIvfb+fk5eyM/NujSozqWsgFdydbByZiBS29PR0oqOjrVpXSkqvsmXL4uPjk2vNSiVAd6m0L4R4p1Izspi+7ihzN5/AZIbybo5M6hpIt0YqripS2phMJj0GkwLn4OCQo4L9PykBuktKgO7O/tNXeXHxfg7HXQMgNKASr/cIwsfzLlaFFRERuQ2tBC021bBqWVYMu5/RoXVwsDOwPiqe9tM28c2OWM0MERGRIkEJkFiFo72RkaG1WTmiNY39ynItLZPxSyJ5Yu5vxFxScVUREbEtJUBiVXW8y/D9c614uUsAzg5Gtp24RNiMCP6z+QRZJvUGiYiIbSgBEquzMxoY3Loma0e1pdU9FUjNMPH6yigembOVo+ev2To8EREphWyeAM2ePRt/f3+cnZ1p2bIlO3bsuOXxV69eJTw8HF9fX5ycnKhTpw6rVq26qzYLi8FgIDAwkMDAwFI5K6paBVcWDG7JW48EUcbJnn2nrtLlg83MXH9MxVVFRKRQ2TQB+vbbbxkzZgyTJk1iz549NGrUiLCwMOLj43M9Pj09nfbt23Py5EkWL17MkSNHmDt3LlWqVLnjNguTq6srBw8e5ODBgyVqDaD8MBgMPNaiGuvGtCU0oBIZWWamrz9Kt1m/sk/FVUVEpJDYdBp8y5Ytad68ObNmzQKy143w8/Nj+PDhvPTSSzcc//HHH/Puu+9y+PDhmy6Bnd82c6OVoAuH2Wzmh/3nmLziIJeT0zEaYEjrmowKrYOL483XeRAREclNsZgGn56ezu7duwkNDf1fMEYjoaGhbNu2LddzVqxYQXBwMOHh4Xh7e9OgQQPefPNNsrKy7rhNsR2DwUC3RtnFVbs3rozJDJ9EnKDTzAi2n7hk6/BERKQEs1kCdPHiRbKysvD2zlkzytvbm7i4uFzPOXHiBIsXLyYrK4tVq1bxyiuv8P777/P666/fcZsAaWlpJCYm5tisISUlhfr161O/fn1LMTfJXjF65mNN+GxAM3w8nDl5KYXHPt3OhKWRXFNxVRERsQKbD4LOD5PJRKVKlfj0009p2rQpffv2ZcKECXz88cd31e7UqVPx9PS0bH5+fgUUcU5ms5lDhw5x6NAhLQiYi4cCvFk7pg1PtKwGwILfYukwPYKNh20/fktEREoWmyVAFStWxM7OjvPnz+fYf/78eXx8fHI9x9fXlzp16uSoAxIQEEBcXBzp6el31CbA+PHjSUhIsGynTp26i08md8PD2YE3ewbx9ZCWVK/gyrmEVAbN38mohb9zOVl1hUREpGDYLAFydHSkadOmbNiwwbLPZDKxYcMGgoODcz0nJCSE48eP56gwfPToUXx9fXF0dLyjNgGcnJzw8PDIsYlttbqnIqtHtmFI6xoYDbBs71naT9vEj/vPqvdMRETumk0fgY0ZM4a5c+fyxRdfEBUVxXPPPUdycjKDBg0CoH///owfP95y/HPPPcfly5cZOXIkR48eZeXKlbz55puEh4fnuU0pPlwc7ZjQJZAlz4dQ17sMl5LTGfb17zzz392cT0y1dXgiIlKM2dvyzfv27cuFCxeYOHEicXFxNG7cmNWrV1sGMcfGxmI0/i9H8/PzY82aNYwePZqGDRtSpUoVRo4cybhx4/LcphQ/jf3K8sPw+5m98Tgf/XKcdYfOs/3EJV7uEkCfZn6lclFJERG5OzZdB6iostY6QMnJybi7uwOQlJSEm5tbgbVdWhyOS2Tc4v3sO50AQEitCkzt2ZBqFUrnwpIiIvI/xWIdoNLIYDBQvXp1qlevrl6LO1TPx4Mlz4cwoXMATvZGthzPLq762a/RKq4qIiJ5ph6gXGgl6OLh5MVkxn2/n9+iLwNwb7WyvN2rIbW9y9g4MhERsQX1AEmp4F/RjW+G3MebPYNwd7JnT+xVunzwKx9uOEZGloqriojIzSkBkmLNaDTwRMtqrBvThgfrVSI9y8T7647S9cNfifxznJCIiMg/KQEqRNevX6d58+Y0b96c69ev2zqcEsXX04XPBjRj5mONKefqwOG4a/T4aAtv/XSY1IwsW4cnIiJFjMYA5UKzwIq3i0lpTPnhED/sOwtAjYpuvN2rIS1qlLdxZCIiYk0aAySlWkV3Jz58vAlz+zfD28OJ6IvJ9PlkG68sO0BSWqatwxMRkSJACZCUWO0DvVk7ui2Pt8gubvvf7TF0mLaJjUdUXFVEpLRTAiQlmqeLA1MfacjXg1viV96FswmpDJq3kzHf7uWKiquKiJRaSoCkVGhVqyJrRrXh6ftrYDDAkt/P0H76JlZFnlNxVRGRUkgJkJQaro72vPJwIN8/14raldy5mJTO8wv2MPSr3cSruKqISKmiBKiQVaxYkYoVK9o6jFLt3mrl+HHE/Yx4sBb2RgNrDp4ndNomFu06pd4gEZFSQtPgc6FSGKVH1LlEXly8n8gz2Ysmtq5dkTd7BuFXXsVVRUSKG02DF8mjAF8Plj7fivGd6uFkb2TzsYuEzYhg3pZoTCquKiJSYikBklLP3s7Is23v4aeRrWlRozwp6VlM+eEQj36yjePx12wdnoiIWIESoEJ0/fp12rVrR7t27VQKowiq6eXOwiH38VqPBrg52rE75gqdZ/7K7I3HVVxVRKSE0RigXKgUhpy5ep0JSyP55cgFAAJ9PXind0MaVPG0cWQiInIzGgMkcpeqlHVh3sDmTO/biLKuDhw6l0j32Vt4e7WKq4qIlARKgERuwmAw0LNJVdaPaUuXhr5kmczM+eUPOs/czM6Tl20dnoiI3AUlQCK3UdHdidlP3MsnTzbFq4wTJ/4srjppuYqriogUV0qARPIorL4P60e3pU+zqpjN8MW2GMKmRxBx9IKtQxMRkXxSAiSSD56uDrzTuxFfPd2SquVcOHP1Ov0/38HY7/ZxNUXFVUVEigslQIXM1dUVV1etMlzc3V87u7jqoBB/DAZYvPs0odMi+CnynK1DExGRPNA0+FyoFIbkx+6Yy7y4eD9/XEgGoFMDH6Z0r0+lMs42jkxEpHTRNHiRQtS0enlWjmjNsAeyi6v+dCCO9tMiWLz7tIqriogUUUqARAqAs4MdY8PqsnxYCA2qeJBwPYOx3+1jwLydnL6SYuvwRETkH5QAFaLU1FS6dOlCly5dSE1NtXU4YgX1K3uy7PkQxnWsh6O9kYijFwibHsGX206quKqISBGiMUC5UCkMKQh/XEjipe/3s/PkFQCa+5fjrV4NucfL3caRiYiUTBoDJFIE3OPlzrfPBPNq9/q4Odqx8+QVOs3czEe/HCdTxVVFRGxKCZCIFRmNBvoH+7NmdBva1PEiPdPEO6uP0OOjLRw8m2Dr8ERESi0lQCKFoGo5V74Y1Jz3H22Ep4sDB84k0n3WFt5bc0TFVUVEbEAJkEghMRgM9GpalXVj2tA5yIdMk5lZG4/T5YPN7I5RcVURkcKkBEikkFUq48xH/Zry8b/upaK7E39cSKb3x9uYvOIgySquKiJSKJQAidhIxwa+bBjTlt5Ns4urzt96krAZEWw+puKqIiLWpmnwuVApDClsEUcvMH5JJGeuXgegT7OqTOgciKerg40jExEpPjQNXqSYaVPHi7Wj2zCwVXZx1UW7ThM6fRNrDsbZOjQRkRJJCZBIEeHmZM/kbvVZ9GwwNb3cuHAtjWf/u5vwBXu4cC3N1uGJiJQoSoAKUWpqKo8++iiPPvqoSmHITTX3L8+qEa15vt092BkNrIw8R/vpm1iyR8VVRUQKisYA5UKlMKSoOHAmgRcX7+fQuUQA2tX14o2eQVQp62LjyEREih6NARIpIRpU8WT5sBBeCKuLo72RX45coMO0Tfx3e4yKq4qI3AUlQCJFnIOdkfAHarFqRGuaVi9HcnoWryw7wGNztxN9MdnW4YmIFEtKgESKiVqV3Pnu2WAmdw3E1dGOHdGX6Tgjgo83/aHiqiIi+aQESKQYMRoNDAypwZpRbWhduyJpmSbe+ukwPT/ayqGzibYOT0Sk2FACJFIM+ZV35cunWvBu74Z4ONsTeSaBbrN+5f21R0jLVHFVEZHbUQIkUkwZDAYebebH+jFtCavvTabJzIc/H+fhD35lT+wVW4cnIlKkaRp8Lqw1Dd5sNpOSkgKAq6srBoOhwNoWWRV5jonLD3AxKR2DAQa1qsHYsDq4OtrbOjQRkUKhafBFlMFgwM3NDTc3NyU/UuA6B/mybnRbHrm3CmYzfL4lmrAZEWw5ftHWoYmIFDlKgERKkHJujkzr05j5g5pTpawLpy5fp99/fuOl7/eTcD3D1uGJiBQZSoAKUVpaGgMHDmTgwIGkpam2k1hPu7qVWDO6Df2DqwOwcOcpOkzfxLpD520cmYhI0aAxQLlQKQwpSXZEX2bc9/stiyY+3NCXyd3qU9HdycaRiYgULKuOAbp+/bplIC9ATEwMM2bMYO3atfmPVESsrkWN8vw0sjVD22YXV/1x/znaT9vEst/PqLiqiJRa+U6AunfvzpdffgnA1atXadmyJe+//z7du3dnzpw5BR6giNw9Zwc7XupUj2XPhxDg68GVlAxGfbuXp7/Yxdmr120dnohIoct3ArRnzx5at24NwOLFi/H29iYmJoYvv/ySDz74oMADFJGCE1TVkxXDQhjboQ6OdkZ+PhxPh+kRLPhNxVVFpHTJdwKUkpJCmTJlAFi7di2PPPIIRqOR++67j5iYmHwHMHv2bPz9/XF2dqZly5bs2LHjpsfOnz8fg8GQY3N2ds5xTFJSEsOGDaNq1aq4uLgQGBjIxx9/nO+4REoqBzsjwx6szcoR99OkWlmS0jKZsPQAT/xnOydVXFVESol8J0C1atVi2bJlnDp1ijVr1tChQwcA4uPj8z1g+Ntvv2XMmDFMmjSJPXv20KhRI8LCwoiPj7/pOR4eHpw7d86y/TPpGjNmDKtXr+arr74iKiqKUaNGMWzYMFasWJHfjypSotX2LsPioa2Y+HAgLg52bD9xmbAZEXwaoeKqIlLy5TsBmjhxImPHjsXf35+WLVsSHBwMZPcGNWnSJF9tTZs2jSFDhjBo0CBLT42rqyuff/75Tc8xGAz4+PhYNm9v7xyvb926lQEDBtCuXTv8/f155plnaNSo0S17lkRKKzujgafur8Ha0W0IqVWBtEwTb646TK85Wzkcp+KqIlJy5TsB6t27N7GxsezatYvVq1db9j/00ENMnz49z+2kp6eze/duQkND/xeM0UhoaCjbtm276XlJSUlUr14dPz8/unfvzsGDB3O83qpVK1asWMGZM9kzXDZu3MjRo0ctPVW25OrqSnx8PPHx8bi6uto6HBELv/KufPV0S97p1ZAyzvbsO51A1w9/Zfq6o6RnqjdIREqeO1oI0cfHhyZNmmA0GklMTGTZsmWUKVOGevXq5bmNixcvkpWVdUMPjre3N3FxcbmeU7duXT7//HOWL1/OV199hclkolWrVpw+fdpyzIcffkhgYCBVq1bF0dGRjh07Mnv2bNq0aXPTWNLS0khMTMyxWYPBYMDLywsvLy+VwpAix2Aw0Kd5dnHV9oHeZGSZmbnhGA9/uJm9p67aOjwRkQKV7wSoT58+zJo1C8heE6hZs2b06dOHhg0b8v333xd4gH8XHBxM//79ady4MW3btmXJkiV4eXnxySefWI758MMP2b59OytWrGD37t28//77hIeHs379+pu2O3XqVDw9PS2bn5+fVT+HSFHm7eHMp082ZdYTTajg5sjR80k88tEW3lh5iOvpWbYOT0SkQOQ7AYqIiLBMg1+6dClms5mrV6/ywQcf8Prrr+e5nYoVK2JnZ8f58zmX5j9//jw+Pj55asPBwYEmTZpw/PhxIDsh+7//+z+mTZtG165dadiwIcOGDaNv37689957N21n/PjxJCQkWLZTp07l+XPkR1paGuHh4YSHh6sUhhRpBoOBhxtWZv2YtvRsUgWTGeZuzi6uuvUPFVcVkeIv3wlQQkIC5cuXB2D16tX06tULV1dXunTpwrFjx/LcjqOjI02bNmXDhg2WfSaTiQ0bNlgGVt9OVlYWkZGR+Pr6ApCRkUFGRgZGY86PZWdnh8l083EMTk5OeHh45NisITMzk48++oiPPvqIzMxMq7yHSEEq5+bI9L6NmTewOb6ezsReTuGJub8xfkkkiakqrioixVe+EyA/Pz+2bdtGcnIyq1evtgwuvnLlyg1r8tzOmDFjmDt3Ll988QVRUVE899xzJCcnM2jQIAD69+/P+PHjLce/+uqrrF27lhMnTrBnzx7+9a9/ERMTw+DBg4HsKfJt27blhRde4JdffiE6Opr58+fz5Zdf0rNnz/x+VBH50wP1KrF2dBv+dV81AL7ZEUuHaRFsiFJxVREpnuzze8KoUaPo168f7u7uVK9enXbt2gHZj8aCgoLy1Vbfvn25cOECEydOJC4ujsaNG7N69WrLwOjY2NgcvTlXrlxhyJAhxMXFUa5cOZo2bcrWrVsJDAy0HLNw4ULGjx9Pv379uHz5MtWrV+eNN95g6NCh+f2oIvI3ZZwdeL1HEA83rMxL3+/n5KUUnv5iF90aVWZS10AqqLiqiBQjd1QNfteuXZw6dYr27dtbqpuvXLmSsmXLEhISUuBBFjZVgxe5tdSMLKavO8rczScwmaG8myOTu9Wna0NfzXAUEZvJz/37jhKgv/x1akn7hacESCRv9p++youL93M47hoAoQGVeL1HED6e+XscLiJSEPJz/76jdYC+/PJLgoKCcHFxwcXFhYYNG/Lf//73joIVkeKrYdWyrBh2P6ND6+BgZ2B9VDztp23imx2x3MXfViIiVpfvBGjatGk899xzdO7cmUWLFrFo0SI6duzI0KFD87UStIiUDI72RkaG1mbliNY09ivLtbRMxi+J5Im5vxFzScVVRaRoyvcjsBo1ajBlyhT69++fY/8XX3zB5MmTiY6OLtAAbcFaj8BMJhOxsbEAVKtW7Ybp+iLFXZbJzLwt0by39gipGSacHYyM7VCXQSE1sDOWrEflIlL0WHUMkLOzMwcOHKBWrVo59h87doygoCBSU1PzH3ERY60ESKS0iL2UwktL9rP1j0sANPYryzu9G1LHu4yNIxORksyqY4Bq1arFokWLbtj/7bffUrt27fw2JyIlULUKriwY3JK3HgmijJM9e09dpcsHm5m5/piKq4pIkZDvHqDvv/+evn37EhoaapnyvmXLFjZs2MCiRYtKxIKD1uoBSk9PZ8KECQC88cYbODo6FljbIkVVXEIqLy+LZH1UPAD1fMrwdq+GNPIra9vARKTEsfo0+N27dzN9+nSioqIACAgI4N///jdNmjS5s4iLGE2DFylYZrOZH/afY/KKg1xOTsdogCGtazIqtA4ujna2Dk9ESohCWwfo7+Lj4/nPf/7D//3f/xVEczalBEjEOi4npzPlh4Ms33sWAP8KrrzVqyH31axg48hEpCSw+jpAuTl37hyvvPJKQTUnIiVQeTdHZj7WhM8GNMPHw5mTl1J47NPtTFgayTUVVxWRQqR52CJS6B4K8GbtmDY80TK7uOqC32LpMD2CjYfjbRyZiJQWSoBExCY8nB14s2cQXw9pSfUKrpxLSGXQ/J2MWvg7l5PTbR2eiJRwSoBExKZa3VOR1SPbMKR1DYwGWLb3LO2nbeLH/WdVTkNErMY+rweOGTPmlq9fuHDhroMRkdLJxdGOCV0C6dKwMuMW7+fI+WsM+/p3lgee5fUeDfD2UHFVESlYeZ4F9sADD+SpwY0bN95VQEWBNUth/H3pAJXCELlReqaJ2RuP89Evx8nIMlPG2Z6XuwTQp5kfBoPKaYjIzdlkGnxJolIYIrZ3OC6RcYv3s+90AgAhtSowtWdDqlVwtXFkIlJU2WQavIhIQarn48GS50OY0DkAJ3sjW45fImxGBJ/9Gk2WSX+3icjdUQJUiNLT05k8eTKTJ08mPV2zXERux85oYEibmqwZ1YaWNcpzPSOL1348xKMfb+XY+Wu2Dk9EijE9AsuFVoIWKXpMJjMLd57izVVRJKVl4mhnZPiDtRja7h4c7PS3nIjoEZiIlEBGo4EnWlZj3Zg2PFivEulZJt5fd5SuH/5K5J/jhERE8koJkIgUK76eLnw2oBkzH2tMOVcHDsddo8dHW3jrp8OkZmTZOjwRKSbynQD5+/vz6quvEhsba414RERuy2Aw0L1xFdaNaUvXRpXJMpn5eNMfdJq5mR3Rl20dnogUA/lOgEaNGsWSJUuoWbMm7du3Z+HChaSlpVkjNhGRW6ro7sSHjzdhbv9meHs4EX0xmT6fbOOVZQdISsu0dXgiUoTdUQK0d+9eduzYQUBAAMOHD8fX15dhw4axZ88ea8QoInJL7QO9WTu6LY+38APgv9tj6DBtE78cUXFVEcndXc8Cy8jI4KOPPmLcuHFkZGQQFBTEiBEjGDRoULFdtVWzwESKr63HLzJuyX5OXb4OwCP3VuGVLoGUc3O0cWQiYm2FshJ0RkYGS5cuZd68eaxbt4777ruPp59+mtOnTzN79mwefPBBvv766zv6ALZmrQQoKyvL0kt27733YmdnV2Bti8j/pKRn8v7ao3y+JRqzGSq6O/Jq9wZ0auBTbP8wE5Hbs2oCtGfPHubNm8c333yD0Wikf//+DB48mHr16lmOOXDgAM2bN+f69et39glsTKUwREqGPbFXGLd4P8fikwAIq+/Na90bUEnFVUVKJKuuA9S8eXOOHTvGnDlzOHPmDO+9916O5AegRo0aPPbYY/ltWkSkQN1brRw/jrifEQ/Wwt5oYM3B84RO28SiXafQGrAipVu+e4BiYmKoXr26teIpEqzVA5Sens7MmTMBGDlyJI6OGpMgUliiziXy4uL9RJ7JXjSxde2KvNkzCL/yKq4qUlIUyhigXbt2ERUVBUBAQADNmjW7k2aKJA2CFimZMrNMfPZrNNPWHSUt04Srox0vhtWlf7A/RqPGBokUd1ZNgE6fPs3jjz/Oli1bKFu2LABXr16lVatWLFy4kKpVq95x4EWFEiCRku3EhSReWhJpWTSxafVyvN0riFqVytg4MhG5G1YdAzR48GAyMjKIiori8uXLXL58maioKEwmE4MHD77joEVECktNL3cWDrmP13o0wM3Rjt0xV+g881dmbzxORpbJ1uGJSCHIdw+Qi4sLW7dupUmTJjn27969m9atW5OSklKgAdqCeoBESo8zV68zYWkkvxy5AECgrwfv9G5IgyqeNo5MRPLLqj1Afn5+ZGRk3LA/KyuLypUr57c5ERGbqlLWhXkDmzO9byPKujpw6Fwi3Wdv4Z3VKq4qUpLlOwF69913GT58OLt27bLs27VrFyNHjuS9994r0OBERAqDwWCgZ5OqrB/Tli4Nfckymfnolz/o/MFmdp1UcVWRkijfj8DKlStHSkoKmZmZ2NvbA1i+/ucjncuXi+cvDj0CEynd1hyM4+VlB7hwLQ2DAfrfV50XOtbD3cne1qGJyC3k5/6d73/NM2bMuNO4Sj1nZ2c2btxo+VpEiqaw+j7cV6MCb6w6xKJdp/liWwzro+KZ+kgQbep42To8ESkAd10MtSRSKQwR+cuvxy7y0pL9nL6SXdqnd9OqvNwlgLKuWshUpKix+kKIWVlZLFu2zLIQYv369enWrVuJKe6pBEhE/i45LZP31h5h/taTfxZXdeL1HvXp2MDX1qGJyN9YNQE6fvw4nTt35syZM9StWxeAI0eO4Ofnx8qVK7nnnnvuPPIiwloJUEZGBp9++ikAzzzzDA4ODgXWtohY3+6Yy7y4eD9/XEgGoFMDH6Z0r0+lMnqkLVIUWDUB6ty5M2azmQULFlC+fHkALl26xL/+9S+MRiMrV66888iLCA2CFpGbSc3IYtbPx/l40x9kmsx4ujjwysOB9Lq3CgaDymmI2JJVEyA3Nze2b99OUFBQjv379u0jJCSEpKSk/EdcxCgBEpHbOXg2gXHf7+fAmUQA2tTx4s2eDahaTsVVRWzFqgshOjk5ce3atRv2JyUlqbq5iJQa9St7suz5EMZ1rIejvZGIoxcImx7Bl9tOYjJpbolIUZfvBOjhhx/mmWee4bfffsNsNmM2m9m+fTtDhw6lW7du1ohRRKRIsrcz8ly7e/hpZGua+5cjOT2LicsP0vfTbfxxofj3houUZPlOgD744APuuecegoODcXZ2xtnZmZCQEGrVqsXMmTOtEaOISJF2j5c73z4TzKvd6+PmaMfOk1foNHMzH/1ynEwVVxUpkvI1BshsNnPq1Cm8vLw4c+aMZRp8QEAAtWrVslqQhU1jgETkTp2+ksL/LT1AxNHs4qoNqnjwdq+G1K+s4qoi1ma1QdAmkwlnZ2cOHjxI7dq17zrQokoJkIjcDbPZzJI9Z3j1x0MkXM/A3mhgaNt7GPZgLZwdSsZ6aSJFkdUGQRuNRmrXrs2lS5fuKsDSysnJiR9//JEff/wRJycnW4cjIlZiMBjo1bQq68a0oXOQD5kmM7M2HqfLB5vZHVM8aySKlDT5ngb/ww8/8M477zBnzhwaNGhgrbhsSitBi0hBWn3gHC8vO8jFpOziqgOC/XkhrC5uKq4qUqCsug7Q36vBOzo64uLikuP14loB/u+UAIlIQUtIyeC1lYdYvPs0AFXLuTD1kSBa11ZxVZGCYtVq8NOnT9dqp3coIyODBQsWANCvXz+VwhApRTxdHXjv0UZ0a1SZ8UsiOX3lOk9+toM+zaoyoXMgnq76fSBSmFQNPhcaBC0i1pSclsm7a47wxbbs4qpeZZx4vUcDwur72Do0kWLNqitB29nZER8ff8P+S5culZhq8CIi1uTmZM/kbvVZ9GwwNb3cuHAtjWf/u5vwBXu4cC3N1uGJlAr5ToBu1mGUlpZ2R6UwZs+ejb+/P87OzrRs2ZIdO3bc9Nj58+djMBhybM7ON1ZhjoqKolu3bnh6euLm5kbz5s2JjY3Nd2wiItbU3L88q0a05vl292BnNLAy8hztp29iyZ7TN/1dKyIFI89jgD744AMge3rnf/7zH8ujHICsrCwiIiKoV69evt7822+/ZcyYMXz88ce0bNmSGTNmEBYWxpEjR6hUqVKu53h4eHDkyBHL9/8cj/THH39w//338/TTTzNlyhQ8PDw4ePBgromSiIitOTvY8WLHenQO8uXFxfs5dC6RMYv2sWLfWd7oGUSVsi63b0RE8i3PY4Bq1KgBQExMDFWrVs3xuMvR0RF/f39effVVWrZsmec3b9myJc2bN2fWrFlA9kKLfn5+DB8+nJdeeumG4+fPn8+oUaO4evXqTdt87LHHcHBw4L///W+e4/gnjQESEVvIyDLxacQJZm44RnqmCTdHO17qHEC/FtUwGjX5ROR2rDIGKDo6mujoaNq2bcu+ffss30dHR3PkyBHWrFmTr+QnPT2d3bt3Exoa+r9gjEZCQ0PZtm3bTc9LSkqievXq+Pn50b17dw4ePGh5zWQysXLlSurUqUNYWBiVKlWiZcuWLFu2LM9xiYjYioOdkfAHarFqRGuaVs8urvrKsgM8Nnc70ReTbR2eSImS7zFAGzdupFy5cnf9xhcvXiQrKwtvb+8c+729vYmLi8v1nLp16/L555+zfPlyvvrqK0wmE61ateL06ex1NeLj40lKSuKtt96iY8eOrF27lp49e/LII4+wadOmm8aSlpZGYmJijk1ExFZqVXLnu2eDmdw1EFdHO3ZEX6bjjAg+3vSHiquKFJB8rwOUlZXF/Pnz2bBhA/Hx8ZhMOf8x/vzzzwUW3D8FBwcTHBxs+b5Vq1YEBATwySef8Nprr1li6d69O6NHjwagcePGbN26lY8//pi2bdvm2u7UqVOZMmWK1eL+i5OTE4sWLbJ8LSJyM0ajgYEhNXgowJv/WxrJ5mMXeeunw6zcf453ejckwFeLtIrcjXz3AI0cOZKRI0eSlZVFgwYNaNSoUY4trypWrIidnR3nz5/Psf/8+fP4+ORtLQwHBweaNGnC8ePHLW3a29sTGBiY47iAgIBbzgIbP348CQkJlu3UqVN5/hz5YW9vz6OPPsqjjz6Kvb2WwBeR2/Mr78qXT7Xg3d4N8XC2J/JMAl0//JVpa4+Qlpll6/BEiq1834UXLlzIokWL6Ny58129saOjI02bNmXDhg306NEDyB7Ds2HDBoYNG5anNrKysoiMjLTE4ujoSPPmzXPMEgM4evQo1atXv2k7Tk5O6pERkSLLYDDwaDM/2tbx4pXlB1hz8Dwf/Hycnw7E8Xbvhtxb7e6HJYiUNvlOgBwdHalVq1aBvPmYMWMYMGAAzZo1o0WLFsyYMYPk5GQGDRoEQP/+/alSpQpTp04F4NVXX+W+++6jVq1aXL16lXfffZeYmBgGDx5safOFF16gb9++tGnThgceeIDVq1fzww8/8MsvvxRIzHcjMzOTpUuXAtCzZ0/1AolIvlTycOaTJ5uxKvIcE5cf4Fh8Er3mbOWpkBr8u0MdXB31O0Ukr/L9r+Xf//43M2fOZNasWXddE6xv375cuHCBiRMnEhcXR+PGjVm9erVlYHRsbCxG4/+e0l25coUhQ4YQFxdHuXLlaNq0KVu3bs3xyKtnz558/PHHTJ06lREjRlC3bl2+//577r///ruKtSCkpaXRp08fIHs2mxIgEbkTnYN8Ca5ZgddWHmLJnjN89ms0aw/F8dYjDQmpVdHW4YkUC/muBdazZ082btxI+fLlqV+//g0FPZcsWVKgAdqC1gESkeLilyPxTFh6gDNXrwPwWHM/xncOwNNFxVWl9LFqNfiyZcvSs2fPOw5OREQKTru6lVgzug3vrD7Ml9tiWLjzFBuPxPN6jyDaB3rfvgGRUkrV4HOhHiARKY52RF9m3Pf7LYsmPtzQlynd6lPBXZM8pHSwykrQuVWA/7vMzMxbFjIVERHralGjPD+NbM3QttnFVX/cf47QaZtYvveMiquK/EOeEyBfX98cSVBQUFCO9XIuXbqUY5FCEREpfM4OdrzUqR7Lng8hwNeDKykZjFy4l6e/2MXZP8cJiUg+EqB//vVw8uRJMjIybnmMiIjYRlBVT1YMC2Fshzo42hn5+XA8HaZHsOC3GEwm/a4WKdB52Hc7Lb6kc3R0ZN68eZavRUSsycHOyLAHaxNW34cXv9/P77FXmbD0AD/sO8tbjzTEv6LGIUrple9SGHLnHBwcGDhwIAMHDrxh+QAREWup7V2GxUNbMfHhQFwc7Nh+4jJhMyL4NOIPstQbJKVUnhMgg8HAtWvXSExMJCEhAYPBQFJSkiqoi4gUA3ZGA0/dX4O1o9sQUqsCaZkm3lx1mEc+2sKRuGu2Dk+k0OV5GrzRaMzxiMtsNuf6fVZW8S/OZ61p8JmZmaxZswaAsLAwrQQtIjZhNpv5btdpXlt5iGupmTjYGXi+XS3CH6iFo70eDEjxlZ/7d54ToE2bNuXpzdu2bZun44oyrQMkIqXB+cRUXl52gHWHzgNQx9udd3o3orFfWdsGJnKHrJIAlSZKgESktDCbzayMPMek5Qe5lJyO0QBP31+DMe3r4uJoZ+vwRPLFKgshiohIyWMwGHi4YWXWj2lLzyZVMJlh7uZoOs6MYNsfl2wdnojVKAESERHKuTkyvW9j5g1sjq+nMzGXUnh87nbGL4kkMTXj9g2IFDNKgERExOKBepVYO7oN/7qvGgDf7Iilw7QINkSdt3FkIgVLCZCIiORQxtmB13sEsfCZ+/Cv4EpcYipPf7GLEd/8zqWkNFuHJ1Ig7joBSkxMZNmyZURFRRVEPCIiUkTcV7MCq0e14dk2NTEaYMW+s7SfHsGKfWdV+kiKvXwnQH369GHWrFkAXL9+nWbNmtGnTx8aNmzI999/X+ABliSOjo7MmjWLWbNmqRSGiBQLzg52jO8cwLLwEOr5lOFycjojvvmdIV/uIi4h1dbhidyxfCdAERERtG7dGoClS5diNpu5evUqH3zwAa+//nqBB1iSODg4EB4eTnh4uEphiEix0rBqWVYMu5/RoXVwsDOwPiqe9tM28c2OWPUGSbGU7wQoISGB8uXLA7B69Wp69eqFq6srXbp04dixYwUeoIiIFA2O9kZGhtZm5YjWNPYry7W0TMYvieSJub8RcynZ1uGJ5Eu+EyA/Pz+2bdtGcnIyq1evpkOHDgBcuXIFZ2fnAg+wJMnKyuKXX37hl19+KRElQ0SkdKrjXYbvn2vFy10CcHYwsu3EJcJmRPCfzSdUXFWKjXwnQKNGjaJfv35UrVqVypUr065dOyD70VhQUFBBx1eipKam8sADD/DAAw+Qmqpn5yJSfNkZDQxuXZO1o9rS6p4KpGaYeH1lFL3mbOXoeRVXlaLvjkph7Nq1i1OnTtG+fXtLaYeVK1dStmxZQkJCCjzIwqZSGCIieWc2m/l25yneWBnFtbTs4qrDHqjNc+3uUXFVKVSFWgssKyuLyMhIqlevTrly5e6mqSJDCZCISP7FJaTy8rJI1kfFA1DPpwxv92pIIxVXlUJi1Vpgo0aN4rPPPgOyk5+2bdty77334ufnxy+//HJHAYuISPHn4+nM3P7N+ODxJpR3c+Rw3DV6frSFqauiuJ6ucY9StOQ7AVq8eDGNGjUC4IcffiA6OprDhw8zevRoJkyYUOABiohI8WEwGOjWKLu4avfGlTGZ4ZOIE3SaGcH2EyquKkVHvhOgixcv4uPjA8CqVat49NFHqVOnDk899RSRkZEFHqCIiBQ/5d0cmflYEz4b0AwfD2dOXkrhsU+3M2FpJNdUXFWKgHwnQN7e3hw6dIisrCxWr15N+/btAUhJScHOzq7AAxQRkeLroQBv1o5pwxMts4urLvgtlg7TI9h4ON7GkUlpZ5/fEwYNGkSfPn3w9fXFYDAQGhoKwG+//Ua9evUKPMCSxMHBgXfeecfytYhIaeDh7MCbPYN4uKEv45dEEnMphUHzd9KzSRVeeTiQ8m4qDSSF745mgS1evJhTp07x6KOPUrVqVQC++OILypYtS/fu3Qs8yMJmrVlgIiKl3fX0LKatO8Jnv0ZjMkMFN0emdK9Pl6DsP6pF7kahToMviZQAiYhY195TVxm3eD9H/lw0sX2gN6/3aIC3hyoKyJ2z6jR4gE2bNtG1a1dq1apFrVq16NatG5s3b76jYEuTrKwsdu7cyc6dO1UKQ0RKtcZ+Zflh+P2MfKg2DnYG1h06T+i0TXy7U8VVpXDkOwH66quvCA0NxdXVlREjRjBixAhcXFx46KGH+Prrr60RY4mRmppKixYtaNGihUphiEip52hvZHT7Ovww/H4aVfXkWmom476P5F+f/capyym2Dk9KuHw/AgsICOCZZ55h9OjROfZPmzaNuXPnEhUVVaAB2oJWghYRKVxZJjOf/xrNe2uPkJZpwsXBjhfC6jKglT92Ro0Nkryx6iOwEydO0LVr1xv2d+vWjejo6Pw2JyIigp3RwJA2NVkzqg0ta5TnekYWr/54iEc/3soxFVcVK8h3AuTn58eGDRtu2L9+/Xr8/PwKJCgRESmd/Cu68c2Q+3izZxDuTvbsib1Klw9+5cMNx8jIMtk6PClB8r0O0L///W9GjBjB3r17adWqFQBbtmxh/vz5zJw5s8ADFBGR0sVoNPBEy2o8UM+LCUsP8PPheN5fd5SVked4t3cjgqp62jpEKQHuaBr80qVLef/99y3jfQICAnjhhRdKxBpAoDFAIiJFhdlsZsW+s0xecZArKRnZj8pa12RUaG2cHVR9QHKy2jpAmZmZvPnmmzz11FOWBRBLIiVAIiJFy8WkNKb8cIgf9p0FoGZFN97q1ZAWNcrbODIpSqy6EKK7uzsHDhzA39//bmIs0qyVAKWnp/Pmm28C8H//9384Omr5dxGR/Fh36DwvL4vkfGIaAE/eV51xnerh7pTvER1SAlk1AerevTuPPPIIAwYMuKsgizKtBC0iUnQlXM/grZ+i+GbHKQAqezrz5iNBtKtbycaRia3l5/6d75S5U6dOvPTSS0RGRtK0adMbHuN069Ytv02KiIjkmaeLA1MfaUjXhpUZt2Q/py5fZ+C8nTxybxVe6RJIORVXlTzIdw+Q0XjzmfMGg6FElHiwVg+QyWTKMXD8VtdSRERuLyU9k/fXHuXzLdGYzVDR3ZFXuzegc5CvrUMTG1Ax1LukQdAiIsXLntgrjFu8n2PxSQB0rO/Dq93rU0nFVUsVqxdDFRERKUrurVaOH0fcz4gHa2FvNLD6YByh0zaxaNcpFVeVXOU5Afr5558JDAwkMTHxhtcSEhKoX78+ERERBRqciIhIXjnZ2zGmQ11+GH4/QVU8SUzN5MXF++n/+Q4VV5Ub5DkBmjFjBkOGDMm1S8nT05Nnn32W6dOnF2hwIiIi+RXg68HS51sxvlM9nOyNbD52kbAZEczfEo3JpN4gyZbnBGjfvn107Njxpq936NCB3bt3F0hQIiIid8Pezsizbe/hp5GtaVGjPCnpWUz+4RCPfrKN43+OE5LSLc8J0Pnz53FwcLjp6/b29ly4cKFAghIRESkINb3cWTjkPl7r0QA3Rzt2x1yh88zNzN54XMVVS7k8J0BVqlThwIEDN319//79+Ppq2qGIiBQtRqOBJ++rztoxbWlX14v0LBPvrjlC91lbOHAmwdbhiY3kOQHq3Lkzr7zyCqmpqTe8dv36dSZNmsTDDz9coMGVNA4ODowdO5axY8fesjdNREQKXpWyLswb2JzpfRtR1tWBQ+cS6T57C++sPkxqRvFfw07yJ8/rAJ0/f557770XOzs7hg0bRt26dQE4fPgws2fPJisriz179uDt7W3VgAuDSmGIiJRsF5PSmLTiICv3nwOgppcb7/RqSDN/FVctzqy2EGJMTAzPPfcca9assayrYDAYCAsLY/bs2dSoUePuIi8ilACJiJQOaw7G8fKyA1y4lobBAP3vq86LHevhpuKqxZLVV4K+cuUKx48fx2w2U7t2bcqVK3fHwRZF1iyFERsbC0C1atVUCkNEpAhISMngjVWHWLTrNJD9qGzqI0G0qeNl48gkv1QK4y6pFIaISOnz67GLvLRkP6evXAegd9OqvNwlgLKuKq5aXBS7UhizZ8/G398fZ2dnWrZsyY4dO2567Pz58zEYDDk2Z+eb13oZOnQoBoOBGTNmWCFyEREpKe6vXZE1o9owKMQfgwEW7z5N6LQIVh84Z+vQxApsngB9++23jBkzhkmTJrFnzx4aNWpEWFgY8fHxNz3Hw8ODc+fOWbaYmJhcj1u6dCnbt2+ncuXK1gpfRERKEDcneyZ1rc/iocHc4+XGxaQ0hn61h+e+2k38tRtnQUvxZfMEaNq0aQwZMoRBgwYRGBjIxx9/jKurK59//vlNzzEYDPj4+Fi23GaenTlzhuHDh7NgwQJNORcRkXxpWr08K0e0ZtgD2cVVfzoQR/tpESzefVrFVUsImyZA6enp7N69m9DQUMs+o9FIaGgo27Ztu+l5SUlJVK9eHT8/P7p3787BgwdzvG4ymXjyySd54YUXqF+//m3jSEtLIzExMccmIiKlm7ODHWPD6rJ8WAgNqniQcD2Dsd/tY8C8nZy+ouKqxZ1NE6CLFy+SlZV1Qw+Ot7c3cXFxuZ5Tt25dPv/8c5YvX85XX32FyWSiVatWnD592nLM22+/jb29PSNGjMhTHFOnTsXT09Oy+fn53fmHEhGREqV+ZU+WPR/CuI71cLQ3EnH0AmHTI/hy20kVVy3GbP4ILL+Cg4Pp378/jRs3pm3btixZsgQvLy8++eQTAHbv3s3MmTMtg6XzYvz48SQkJFi2U6dOWfMjiIhIMWNvZ+S5dtnFVZv7lyM5PYuJyw/S99Nt/HFBxVWLI5smQBUrVsTOzo7z58/n2H/+/Hl8fHzy1IaDgwNNmjTh+PHjAGzevJn4+HiqVauGvb099vb2xMTE8O9//xt/f/9c23BycsLDwyPHZg329vY8//zzPP/889jba5EtEZHi5h4vd759JphXu9fHzdGOnSev0GnmZj765TiZKq5arNg0AXJ0dKRp06Zs2LDBss9kMrFhwwaCg4Pz1EZWVhaRkZGWQqxPPvkk+/fvZ+/evZatcuXKvPDCC6xZs8YqnyOvnJycmD17NrNnz8bJycmmsYiIyJ0xGg30D/Znzeg2tKnjRXqmiXdWH6HHR1s4eFbFVYsLm3dDjBkzhgEDBtCsWTNatGjBjBkzSE5OZtCgQQD079+fKlWqMHXqVABeffVV7rvvPmrVqsXVq1d59913iYmJYfDgwQBUqFCBChUq5HgPBwcHfHx8LPXLRERE7lbVcq58Mag5S/ac4dUfD3HgTCLdZ21haNt7GPZgLZwd7GwdotyCzROgvn37cuHCBSZOnEhcXByNGzdm9erVloHRsbGxOUpGXLlyhSFDhhAXF0e5cuVo2rQpW7duJTAw0FYfIc/MZjMXL14Esh//5XWMkoiIFE0Gg4FeTavSuk5FJq84yKrIOGZtPM5PB87xTu9GNK1eskpFlSQqhZELlcIQEZE7sfrAOV5edpCLSdnFVQe28mdsh7oqrlpIil0pDBERkZKgYwNfNoxpS++mVTGbYd6Wk4TNiGDzsQu2Dk3+QQmQiIhIAfJ0deC9Rxvx5VMtqFLWhdNXrvPkZzt4cfE+Eq5n2Do8+ZMSIBEREStoU8eLtaPbMLBVdnHVRbtO037aJtYczH2hXylcSoBERESsxM3Jnsnd6rPo2WBqerkRfy2NZ/+7m/AFe7hwLc3W4ZVqSoBERESsrLl/eVaNaM3z7e7BzmhgZeQ52k/fxNLfVVzVVpQAiYiIFAJnBzte7FiP5eEhBPp6cDUlg9Hf7mPQ/J2cuXrd1uGVOkqACpG9vT0DBgxgwIABKoUhIlJKNajiyfJhIbwQVhdHeyO/HLlAh2mb+O/2GBVXLURaBygX1loHSERE5O+Oxycx7vv97I65AkCLGuV5u1dDalTUOnF3QusAiYiIFAO1Krnz3bPBTO4aiKujHTuiL9NxRgSfbPpDxVWtTAlQITKbzSQnJ5OcnKxBbyIiAmQXVx0YUoM1o9rQunZF0jJNTP3pMI/M2UrUuURbh1diKQEqRCkpKbi7u+Pu7k5KSoqtwxERkSLEr7wrXz7Vgnd7N8TD2Z79pxPo+uGvTFt7hLTMLFuHV+IoARIRESkiDAYDjzbzY/2YtoTV9ybTZOaDn4/z8Ae/sif2iq3DK1GUAImIiBQxlTyc+eTJZnzU714qujtyLD6JXnO28tqPh0hJz7R1eCWCEiAREZEiqnOQL+tGt+WRe6tgNsNnv0bTccZmth6/aOvQij0lQCIiIkVYOTdHpvVpzPxBzalS1oXYyyk88Z/feOn7/SqueheUAImIiBQD7epWYs3oNvQPrg7Awp2n6DB9E+sOnbdxZMWTEiAREZFiwt3Jnle7N2DRs8HUqOjG+cQ0hny5i2Ff7+FSkoqr5ocSoEJkZ2dH79696d27N3Z2drYOR0REiqkWNcrz08jWDG2bXVz1x/3nCJ22ieV7z2iduTxSKYxcqBSGiIgUF5GnE3jx+/2WRRMfrFeJN3o2wNfTxcaRFT6VwhARESklgqp6smJYCGM71MHRzsjPh+NpPy2CBb+puOqtKAESEREp5hzsjAx7sDYrR9xPk2plSUrLZMLSAzzxn+2cvJhs6/CKJCVAhSg5ORmDwYDBYCA5WT+QIiJSsGp7l2Hx0FZMfDgQFwc7tp+4TNiMCD6N+IMs9QbloARIRESkBLEzGnjq/hqsHd2GkFoVSMs08eaqwzzy0RaOxF2zdXhFhhIgERGREsivvCtfPd2Sd3o1pIyzPftOJ/Dwh5uZvu4o6ZkmW4dnc0qARERESiiDwUCf5tnFVdsHepORZWbmhmN0/fBX9p66auvwbEoJkIiISAnn7eHMp082ZdYTTajg5siR89d45KMtvLHyENfTs2wdnk0oARIRESkFDAYDDzeszPoxbenZpAomM8zdHE3HmRFs++OSrcMrdEqARERESpFybo5M79uYeQOb4+vpTMylFB6fu53xSyJJTC09xVWVABUiOzs7OnfuTOfOnVUKQ0REbOqBepVYO7oN/7qvGgDf7Iilw7QINkSVjuKqKoWRC5XCEBGR0mT7iUu89P1+Tl5KAaB748pMfDiQCu5ONo4sf1QKQ0RERPLsvpoVWD2qDc+2qYnRAMv3nqX99AhW7DtbYourKgESERERnB3sGN85gGXhIdTzKcPl5HRGfPM7Q77cRVxCqq3DK3BKgApRcnIybm5uuLm5qRSGiIgUSQ2rlmXFsPsZHVoHBzsD66PiaT9tE9/siC1RvUFKgApZSkoKKSkptg5DRETkphztjYwMrc3KEa1p7FeWa2mZjF8SyRNzfyPmUsn4A14JkIiIiOSqjncZvn+uFS93CcDZwci2E5cImxHBfzafKPbFVZUAiYiIyE3ZGQ0Mbl2TtaPa0uqeCqRmmHh9ZRS95mzl6PniW1xVCZCIiIjcVrUKriwY3JK3HgmijJM9e09dpcsHm5m5/lixLK6qBEhERETyxGAw8FiLaqwb05bQgEpkZJmZvv4o3Wb9yr5iVlxVCZCIiIjki4+nM3P7N+ODx5tQ3s2Rw3HX6PnRFqauiiI1o3gUV1UCVIiMRiNt27albdu2GI269CIiUnwZDAa6Ncourtq9cWVMZvgk4gQdZ0Sw/UTRL66qUhi5UCkMERGR/NkQdZ4JSw8Ql5i9aGK/ltV4qVM9yjg7FFoMKoUhIiIiheqhAG/WjmnDEy2zi6su+C2WDtMj2Hg43nJMlsnMtj8usXzvGbb9ccmmU+nVA5QL9QCJiIjcua1/XGT8kkhi/iyu2rNJFVrdU4Fp645y7m9lNXw9nZnUNZCODXwL5H3zc/9WApQLayVAycnJ+Pv7A3Dy5Enc3NwKrG0REZGi5Hp6FtPWHeGzX6O5WUeP4c//zvnXvQWSBOkRWBF28eJFLl68aOswRERErMrF0Y4JXQL5bmgr7I2GXI/5Ky+a8sOhQn8cpgRIRERErCY900TmLZIbM3AuIZUd0ZcLLyiUAImIiIgVxV9Lvf1B+TiuoCgBEhEREaupVMa5QI8rKEqARERExGpa1CiPr6czuY8Cyh4I7evpTIsa5QszLCVAIiIiYj12RgOTugYC3JAE/fX9pK6B2N1koLS1KAEqREajkWbNmtGsWTOVwhARkVKjYwNf5vzrXnw8cz7m8vF0LrAp8PmldYByoYUQRURECl6WycyO6MvEX0ulUpnsx14F2fOTn/u3fYG9q4iIiMgt2BkNBN9TwdZhAEXkEdjs2bPx9/fH2dmZli1bsmPHjpseO3/+fAwGQ47N2fl/XWoZGRmMGzeOoKAg3NzcqFy5Mv379+fs2bOF8VFERESkGLB5AvTtt98yZswYJk2axJ49e2jUqBFhYWHEx8ff9BwPDw/OnTtn2WJiYiyvpaSksGfPHl555RX27NnDkiVLOHLkCN26dSuMj3NLKSkp+Pv74+/vT0pKiq3DERERKbVsPgaoZcuWNG/enFmzZgFgMpnw8/Nj+PDhvPTSSzccP3/+fEaNGsXVq1fz/B47d+6kRYsWxMTEUK1atdseb81aYO7u7gAkJSWpFpiIiEgBKja1wNLT09m9ezehoaGWfUajkdDQULZt23bT85KSkqhevTp+fn50796dgwcP3vJ9EhISMBgMlC1bNtfX09LSSExMzLGJiIhIyWXTBOjixYtkZWXh7e2dY7+3tzdxcXG5nlO3bl0+//xzli9fzldffYXJZKJVq1acPn061+NTU1MZN24cjz/++E2zwalTp+Lp6WnZ/Pz87u6DiYiISJFm8zFA+RUcHEz//v1p3Lgxbdu2ZcmSJXh5efHJJ5/ccGxGRgZ9+vTBbDYzZ86cm7Y5fvx4EhISLNupU6es+RFERETExmw6Db5ixYrY2dlx/vz5HPvPnz+Pj49PntpwcHCgSZMmHD9+PMf+v5KfmJgYfv7551s+C3RycsLJySn/H0BERESKJZv2ADk6OtK0aVM2bNhg2WcymdiwYQPBwcF5aiMrK4vIyEh8ff+3iuRfyc+xY8dYv349FSoUjTUHREREpGiw+UKIY8aMYcCAATRr1owWLVowY8YMkpOTGTRoEAD9+/enSpUqTJ06FYBXX32V++67j1q1anH16lXeffddYmJiGDx4MJCd/PTu3Zs9e/bw448/kpWVZRlPVL58eRwdHW3zQQGDwUBgYKDlaxEREbENmydAffv25cKFC0ycOJG4uDgaN27M6tWrLQOjY2Njc9TNunLlCkOGDCEuLo5y5crRtGlTtm7dakkszpw5w4oVKwBo3LhxjvfauHEj7dq1K5TPlRtXV9fbzlgTERER67P5OkBFkWqBiYiIFD/FZh0gEREREVtQAlSIUlJSqF+/PvXr11cpDBERERuy+Rig0sRsNnPo0CHL1yIiImIb6gESERGRUkcJkIiIiJQ6SoBERESk1FECJCIiIqWOEiAREREpdTQLrBAZDAaqV69u+VpERERsQwlQIXJ1deXkyZO2DkNERKTU0yMwERERKXWUAImIiEipo0dghej69eu0adMGgIiICFxcXGwckYiISCEyZUHMVkg6D+7eUL0VGO1sEooSoEJkMpnYtWuX5WsREZFS49AKWD0OEs/+b59HZej4NgR2K/Rw9AhMRERErOvQCljUP2fyA5B4Lnv/oRWFHpISIBEREbEeU1Z2zw+5FQH/c9/ql7KPK0R6BCYiIlIcmM3ZSYIpA0yZ2VvWn/+17MuCrIy/7cv687i/nWM5NyOf7d3hudev3Njzk/ODQeKZ7LFBNVoX2uVUAiQiIsWb2fyPG3Nmzptz1t8SAcu+/Nzo/7nPmknHLc41Zdr6SltX0vlCfTslQCIiJZXZnI+b+q2ShL/dnG/ZXl6Tjry0l49zzYX76KTIMdrfuNk5/Pm1HRj//Nru78c4ZL9mOe525/55vOXcf7b3z3P/tl08Chum3P5zuHtb/1r9jRKgQlaxYkVbhyAiJlMhPja428QhP+f+I0kwl/LZpje9Wf/z5p+fG/3tEoe7OfcOYynqpZVMWbBzbvaA51zHARmyZ4NVb1WoYSkBKkRuLs5c2LEku5svfo9N1z8QyZXlxnsXN/qCThwK5DHEP9631CcG/7zB/u2GbPePm+vN/qq36rn2/0gU7iBxMBiLfmJQWhjtsqe6L+oPGMiZBP35/6jjW4V+P1QCVFiK2PoHkg9mc/YNs9C6/m/W3h2MGcjvubn+dVZaGG684d7RzboAbvQFnTj8M1EQKWyB3aDPlze5D75lk/ugwWw2l+bfeLlKTEzE09OThIQEPDw87r7Bv9Y/uOHm8mfm2+fL4pkEWWYkFMMxA/k9tzQzGG/RBZ+f7nsrdP0XSHt/Ha9VQUSszsorQefn/q0eIGv72/oH1zPMdFqQAsBP/VxxcfjzmJVjwM0ru5fBGn/V3+rc2978b5GwmDJtdlmLBMM/ewiK0ZiBvLanxEBECpLRrlCnut+KEiBri9lq6e4zmWFTTJbla4vkCzCvow2CsxLDnT4OKMiu/9xu7Pa3ODefsRjslBiIiBRjSoCsLa/rGrh5gXPZPHbV52fMwN2ce4fjDTTwUEREijglQNaW13UNes8rMt2CIiIiJZ368K2teqvsUe7crFfEAB5VCn39AxERkdJMCZC1/bX+AXBjEmS79Q9ERERKMyVAheGv9Q/K+OTc71G5+E6BFxERKcY0BqiwBHYDv3a4vuadvX5Ov8UQ8JB6fkRERGxACVAhcivjQXLKdVuHISIiUurpEZiIiIiUOkqAREREpNRRAlSIUlNT6dKlC126dCE1NdXW4YiIiJRaGgNUiLKysli1apXlaxEREbEN9QCJiIhIqaMESEREREodJUAiIiJS6igBEhERkVJHCZCIiIiUOpoFlguz2QxAYmJigbabnJxs+ToxMVEzwURERArQX/ftv+7jt6IEKBfXrl0DwM/Pz2rvUblyZau1LSIiUppdu3YNT0/PWx5jMOclTSplTCYTZ8+epUyZMhgMhgJtOzExET8/P06dOoWHh0eBti3/o+tcOHSdC4euc+HQdS4c1rzOZrOZa9euUblyZYzGW4/yUQ9QLoxGI1WrVrXqe3h4eOgfWCHQdS4cus6FQ9e5cOg6Fw5rXefb9fz8RYOgRUREpNRRAiQiIiKljhKgQubk5MSkSZNwcnKydSglmq5z4dB1Lhy6zoVD17lwFJXrrEHQIiIiUuqoB0hERERKHSVAIiIiUuooARIREZFSRwmQiIiIlDpKgApQREQEXbt2pXLlyhgMBpYtW3bbc3755RfuvfdenJycqFWrFvPnz7d6nMVdfq/zkiVLaN++PV5eXnh4eBAcHMyaNWsKJ9hi7E5+nv+yZcsW7O3tady4sdXiK0nu5FqnpaUxYcIEqlevjpOTE/7+/nz++efWD7YYu5PrvGDBAho1aoSrqyu+vr489dRTXLp0yfrBFmNTp06lefPmlClThkqVKtGjRw+OHDly2/O+++476tWrh7OzM0FBQaxatcqqcSoBKkDJyck0atSI2bNn5+n46OhounTpwgMPPMDevXsZNWoUgwcP1s35NvJ7nSMiImjfvj2rVq1i9+7dPPDAA3Tt2pXff//dypEWb/m9zn+5evUq/fv356GHHrJSZCXPnVzrPn36sGHDBj777DOOHDnCN998Q926da0YZfGX3+u8ZcsW+vfvz9NPP83Bgwf57rvv2LFjB0OGDLFypMXbpk2bCA8PZ/v27axbt46MjAw6dOiQoyD4P23dupXHH3+cp59+mt9//50ePXrQo0cPDhw4YL1AzWIVgHnp0qW3PObFF180169fP8e+vn37msPCwqwYWcmSl+ucm8DAQPOUKVMKPqASKj/XuW/fvuaXX37ZPGnSJHOjRo2sGldJlJdr/dNPP5k9PT3Nly5dKpygSqC8XOd3333XXLNmzRz7PvjgA3OVKlWsGFnJEx8fbwbMmzZtuukxffr0MXfp0iXHvpYtW5qfffZZq8WlHiAb2rZtG6GhoTn2hYWFsW3bNhtFVDqYTCauXbtG+fLlbR1KiTNv3jxOnDjBpEmTbB1KibZixQqaNWvGO++8Q5UqVahTpw5jx47l+vXrtg6tRAkODubUqVOsWrUKs9nM+fPnWbx4MZ07d7Z1aMVKQkICwC1/59rifqhiqDYUFxeHt7d3jn3e3t4kJiZy/fp1XFxcbBRZyfbee++RlJREnz59bB1KiXLs2DFeeuklNm/ejL29frVY04kTJ/j1119xdnZm6dKlXLx4keeff55Lly4xb948W4dXYoSEhLBgwQL69u1LamoqmZmZdO3aNd+PhUszk8nEqFGjCAkJoUGDBjc97mb3w7i4OKvFph4gKVW+/vprpkyZwqJFi6hUqZKtwykxsrKyeOKJJ5gyZQp16tSxdTglnslkwmAwsGDBAlq0aEHnzp2ZNm0aX3zxhXqBCtChQ4cYOXIkEydOZPfu3axevZqTJ08ydOhQW4dWbISHh3PgwAEWLlxo61BuoD/TbMjHx4fz58/n2Hf+/Hk8PDzU+2MFCxcuZPDgwXz33Xc3dLXK3bl27Rq7du3i999/Z9iwYUD2TdpsNmNvb8/atWt58MEHbRxlyeHr60uVKlXw9PS07AsICMBsNnP69Glq165tw+hKjqlTpxISEsILL7wAQMOGDXFzc6N169a8/vrr+Pr62jjCom3YsGH8+OOPREREULVq1Vsee7P7oY+Pj9XiUw+QDQUHB7Nhw4Yc+9atW0dwcLCNIiq5vvnmGwYNGsQ333xDly5dbB1OiePh4UFkZCR79+61bEOHDqVu3brs3buXli1b2jrEEiUkJISzZ8+SlJRk2Xf06FGMRuNtbzSSdykpKRiNOW+TdnZ2AJhVRvOmzGYzw4YNY+nSpfz888/UqFHjtufY4n6oHqAClJSUxPHjxy3fR0dHs3fvXsqXL0+1atUYP348Z86c4csvvwRg6NChzJo1ixdffJGnnnqKn3/+mUWLFrFy5UpbfYRiIb/X+euvv2bAgAHMnDmTli1bWp4pu7i45PgLWnLKz3U2Go03PN+vVKkSzs7Ot3zuL9ny+zP9xBNP8NprrzFo0CCmTJnCxYsXeeGFF3jqqafUe3wL+b3OXbt2ZciQIcyZM4ewsDDOnTvHqFGjaNGiBZUrV7bVxyjywsPD+frrr1m+fDllypSx/M719PS0/Hz279+fKlWqMHXqVABGjhxJ27Ztef/99+nSpQsLFy5k165dfPrpp9YL1Grzy0qhjRs3moEbtgEDBpjNZrN5wIAB5rZt295wTuPGjc2Ojo7mmjVrmufNm1focRc3+b3Obdu2veXxkrs7+Xn+O02Dz7s7udZRUVHm0NBQs4uLi7lq1armMWPGmFNSUgo/+GLkTq7zBx98YA4MDDS7uLiYfX19zf369TOfPn268IMvRnK7xkCO+1vbtm1v+B28aNEic506dcyOjo7m+vXrm1euXGnVOA1/BisiIiJSamgMkIiIiJQ6SoBERESk1FECJCIiIqWOEiAREREpdZQAiYiISKmjBEhERERKHSVAIiIiUuooARIRuQmDwcCyZctsHYaIWIESIBEpkgYOHIjBYLhh69ixo61DE5ESQLXARKTI6tixI/Pmzcuxz8nJyUbRiEhJoh4gESmynJyc8PHxybGVK1cOyH48NWfOHDp16oSLiws1a9Zk8eLFOc6PjIzkwQcfxMXFhQoVKvDMM8/kqKAO8Pnnn1O/fn2cnJzw9fVl2LBhOV6/ePEiPXv2xNXVldq1a7NixQrLa1euXKFfv354eXnh4uJC7dq1b0jYRKRoUgIkIsXWK6+8Qq9evdi3bx/9+vXjscceIyoqCoDk5GTCwsIoV64cO3fu5LvvvmP9+vU5Epw5c+YQHh7OM888Q2RkJCtWrKBWrVo53mPKlCn06dOH/fv307lzZ/r168fly5ct73/o0CF++uknoqKimDNnDhUrViy8CyAid86qpVZFRO7QgAEDzHZ2dmY3N7cc2xtvvGE2m7MrTg8dOjTHOS1btjQ/99xzZrPZbP7000/N5cqVMyclJVleX7lypdloNJrj4uLMZrPZXLlyZfOECRNuGgNgfvnlly3fJyUlmQHzTz/9ZDabzeauXbuaBw0aVDAfWEQKlcYAiUiR9cADDzBnzpwc+8qXL2/5Ojg4OMdrwcHB7N27F4CoqCgaNWqEm5ub5fWQkBBMJhNHjhzBYDBw9uxZHnrooVvG0LBhQ8vXbm5ueHh4EB8fD8Bzzz1Hr1692LNnDx06dKBHjx60atXqjj6riBQuJUAiUmS5ubnd8EiqoLi4uOTpOAcHhxzfGwwGTCYTAJ06dSImJoZVq1axbt06HnroIcLDw3nvvfcKPF4RKVgaAyQixdb27dtv+D4gIACAgIAA9u3bR3JysuX1LVu2YDQaqVu3LmXKlMHf358NGzbcVQxeXl4MGDCAr776ihkzZvDpp5/eVXsiUjjUAyQiRVZaWhpxcXE59tnb21sGGn/33Xc0a9aM+++/nwULFrBjxw4+++wzAPr168ekSZMYMGAAkydP5sKFCwwfPpwnn3wSb29vACZPnszQoUOpVKkSnTp14tq1a2zZsoXhw4fnKb6JEyfStGlT6tevT1paGj/++KMlARORok0JkIgUWatXr8bX1zfHvrp163L48GEge4bWwoULef755/H19eWbb74hMDAQAFdXV9asWcPIkSNp3rw5rq6u9OrVi2nTplnaGjBgAKmpqUyfPp2xY8dSsWJFevfunef4HB0dGT9+PCdPnsTFxYXWrVuzcOHCAvjkImJtBrPZbLZ1ECIi+WUwGFi6dCk9evSwdSgiUgxpDJCIiIiUOkqAREREpNTRGCARKZb09F5E7oZ6gERERKTUUQIkIiIipY4SIBERESl1lACJiIhIqaMESEREREodJUAiIiJS6igBEhERkVJHCZCIiIiUOkqAREREpNT5f5UZ2oHQzGiaAAAAAElFTkSuQmCC", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1315,10 +1241,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -1355,33 +1280,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 17, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", + "image/png": 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", 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\n", 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", 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\n", 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", 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1487,10 +1398,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 19, + "metadata": {}, "outputs": [], "source": [ "# choose intermidiate layers from which to get the output\n", @@ -1504,10 +1413,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -1547,16 +1455,14 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { @@ -1570,40 +1476,36 @@ }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { @@ -1615,33 +1517,29 @@ }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n" ] }, { "data": { - "image/png": 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\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1682,16 +1580,14 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 22, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { @@ -1705,40 +1601,36 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n" ] }, { "data": { - "image/png": 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dr0yqrdxh5CnHNwOfHWP89dt9PLeKbGl6Eng7aar59cBLY4xvvq0HpvRGx66O3TuV7H74FlLe4GecYetqZHeCPybNHnwWyaXwvbP7wYnhRLoE3EBeDvxlXp5PSlP1jBCrmZeSHLkfJ5nlX6Fi9c5HRL6QNGX4S8+mP/iZu0l+W/uk3H5frH/w7xx07OrYvVMRkX9Kcp37F88msZp5H1LQ1AVScOGnnTSxCs9wC6uiKIqiKIpy5/NMt7AqiqIoiqIodzhHJedfICJqflWeNjHGW5GzdoUXfuVre41dX21u426wk8XlFzSbGwFbd+1vbPOx7/VHvfq6r+qX0/nXnnjB5kbAPcOLG9u87am7N7YBePSpfplh6guDXu36sPsHx/70Lfj9b/uyWz52P/zTvqXX2O31rep59NO9fraLJz583qudHG6+vqN7No9vAGtCr3bzut897fNnbfL4aGMbAIp+xyYT26udPdx8Hx76qX7X7T/+ztfc0rH7vl/b7zf3Wmhe2O9cr4Xv+uAf2dzoGtk2082NrqU/qW9ofwDf98RH3PA+f+43PmRzo2vk4X/45Vcdt2phVRRFURRFUU40KlgVRVEURVGUE40KVkVRFEVRFOVEo4JVURRFURRFOdGoYFUURVEURVFONCpYFUVRFEVRlBONClZFURRFURTlRHOsYC1u1VEoiqIoiqIoylU4VrAOUdGqKIqiKIqi3F6OLQ0yJBVKOQD61eZRlJNB3+pUfri5zfR8vwIuzVa/qjd791zq1e4ldz+ysc3f3PmTXn096J7o1e4fnf7zXu3+YL75Al8426OMGPDad31sr3b/LTynV7vmUrmxTdjc5LZh6n7jbba72aPryb/Wr9jR+7+0331/nutXgefhi6c3tnlw98lefX3I7sO92r2n3unVzrL5e/qz4cW9+urLbNbjh6YnT77/1g3rS1HuJI4VrAPAAgHYB7ROq6IoiqIoinKrOVawFoARaCLUwI2tlqsoiqIoiqIomzlesDpwBkINPsIMtbIqiqIoiqIot5bjBWsJUQALcQ6HHnq6Bt7xCMklYmRBIkwCHKKCXVEURVEU5Vaz0cIqDgoBY2FyAPMI/lYd3W1CgHMO7ts1bG856sbznsc975olK7OiKIqiKIpy6zhWsFoLxoEYMAZ2PEwncPlWHd1twABnC+H59+1yzz1nqAaW/f1LTJrHeM97vApWRVEURVGUW8yxgtW5JFRNkYTrKMB2DbMG5rfqCG8xpw284P7zPPDg/Zw+vYexYIrHGV+YUD1xkQPN76UoiqIoinJLOV6wFmCdYAqHE2HLeubec3gJ6vDM8+fcEXj+XTvc/8ADnL/3PsajEZGGufeMx49TuouakFZRFEVRFOUWc6xgNQNwzmGKgmgso8owZZ9x7dk/eGZpt5HA806PeN5zH+TcfXexd+YUg2KLZjpj6vbZHVRsOcEQe6SdVm43e3/abw6g3rIb21y+f3MbgMN7NidyB7jwWL/E37/y7hdubPPbdz3Qq6/Pf8Fv9Wr3V/5dvdq9efL8jW1+8uEP6tXXUw+f6tVu+O5+92H7qc2P0qf/6OQm6Zvu9TvPSw9tLgpg3rufA9efPXmmV7vJYb9iEC958C82tjlf9Tu2T97+/V7tXvvYR/Vq95Ktt29s8/0veUuvvt4y7ff9+9a3fHSvdo3bXFty/36tP6k8OzlWsMYIMUQi4IqCQgq2omc2O+BgFrnwDFGsJfDguOB5D9zLPXefZ+/MHoNRRYGlCZZRVbA9HnBqaBnsNxze7gNWFEVRFEV5FnGsYJ3NgMJTlREjgisKqjBgNJqzNZhzsJ8KCtzJlMB7jywvuP889911ntO7O1SFwRGxNEQ8lRVGpeXsjuPskw3v9KiVVVEURVFaboKP4HPP9Svfey28pNq/4X3umhtXehfg7fWND+/+s/1zN7zP4aP9ZhVvFMfu7amn4OAgMp/XxBix1jIaDRkNR4wGQtmvTPWJZQg8byC8z72nec7d5zi1vUVpINQzmvkEP58Q6ikSagpn2BlZ7hrC3u0+cEVRFEVRlGcRx1pYL03ARhgMA+Ij1iTROhw5RmPD4JLncH5nBl/tCDwwEh56zmnuu+8cu3sjTFEz95fwhxViJjgisZ7hJxdw84tsuYbzWzCfgPVwgTvfwqwoiqIoinLSOV6wAsUMtqeR2DRIjFgRisIwGArDCsz8ziokYIA9Aw+dMjx472nuvvs8u3vbVJUhyozGB2o/JcaI+BrrJ/jpAcwuMIgNpwqIAxgeJMH6JHARLSigKIqiKIpyszhWsE6AwwiTWaRpaiINYixF4SjLgsGgodyHyR1iYi2Au0p477sK3uue09xz5gw7O1uUlcNYj/dzmqZGgOhrwnxKbCaE2SE0+5QEtksw26lk6+kZnKvh8QB/RRL4anFVFEVRFEW5sRwrWAPJcjhvYN7UNM0MWwjWGsqypConFAYmd4CJdSTw4I7wwL0j7r37NGf2djk1HFCVgjENITYgNZga/IQQZvjmEOoJpqkpY8Q6sFtQlLC1DfM5TCZw6jKcn8BjrArXO0THK4qiKIqinGiOFayQhNe0htnM0zRTymAxrsI5S1kaChsQf7LF2a6D591V8NB7neHeu8+xuz1mWDp2DBiJ+FATQsAS8MHjbIOPNcHNCb4mmkh00BgohjCMEEISrOMZjIaw9wTsTOFUk9wELgD7pIpgc0729VEURVEURTnJ9BKs8wbqKZi5pxxEvA0UJlIUYB0nuk5raeD+82Meep8HePD+ezmzt0NVOgrA+Ybo55hmgoQpzs+J1lIETxBoIvgo1HFGtIFgwTiDFSGEyGwSmB5GHHDJwugSmAlszeCuBqYka+sTJAF7lHCVvBQk/9pAuuaaNktRFEVRFCWxUbB64CCmqe/ZrKGu61yqFUorVC4JrZPoFWCA+87u8byHHuLBB57H3XefZW88pnACoUHm+wQ/IzRTCFPwUyQ2SDPCzyfYekaophTNDIJHjMU4i8RIrGuKeIg0B/gyUM8gDJJAHpfQ1NB4OOXhHHApwgEwMxANOGMZ2ILRwDIcWkrnCMFzOJ/yxKUZj+7Dvpplrxs76yf56+3NVYXmO/32WVzul+fNNP0q1cQeBY+G9/fzmv71JzZXpgJ4k31ur3bv3N+c3K2w/e6BmfbL5Vde6tWMokeaRbd/cr3N3bTfddt6x+brNt3f7tXX9Fy/Hxvbs1jMm+rN4+iTX/TmXn192cOf2qvd65//y73a1XHzX6s3zfp9l59qxr3a+Sf6VQjrs9e+3wNFeaaxUbBCshRO5jCZeMbjmjJYCuMZuMiwBCfgT6C42ho47rv3Odx97/M5c/5B9k7tsT0aUjqDRE9oLoOfE/0MiTOin+H9DJoD/HxGU0+JzRy8xxAQBGLEzw6oDy7S2CmFREYWbAFlBVMLzSBZSI0Bb0EKIZSOxhbEaowdDKmqUwwGW1RVhTEG7z2Hh4c8cfGvGLznL5k+MmMyPZkPAoqiKIqiKLeSXoK1BiZNqnzVNJ6qqbGS3AJGBQwNzE6YsioEzp/Z5a577ubM3Xexd/YMu6d2GRQWZ4QQGqKvIHoMcyTMkTgnhjm+mRH8HN/MkeAxsYEQiM2EZnrANM5pJjUSDzExUkSwAqaAQQmNBSy4ymCHY4rhGDc+hYx2McM93GALZwcURQlACIHDw0MuXrzI4ewyxgjmDi/KoCiKoiiKcqPoJVgDKXXVdAZ1XeODYKxQFMKgigwcJy7walw5zpw5y+nTZ9jZ22J7d8Rw5Bg4ySmrZgQJGGtwdoA1Q8QIEAh+hsRUf1WCBz+jqQ9pDp9g1uwziYc08wtEX2N8Kq5gHAwLiHmxlaUc7sL4PNXWaaqtc7jRaWSwhSkHWBFMjNTeM53PmDUBzyUOG8+lecNBo9ZVRVEURVEU6ClYIbsFzGBWB0ahwZQlrjCUg8CgAjuDnu5NNx0BhsOKrZ1dRls7jIZjhlVFVThK8fh6Tl0fgN8HUyDVFmLHODci2oLSznHGYLxBmgZfX2Z6GOAwMqtnUO8T5oeEOdAkX9lYJdEqZVqKQUlRDfG7WwzGe1SjU7jRaWw1IDqD9YYYIUgNTaTBMYuWw2jYR5je5muoKIqiKIpyUugtWGvgsIbJFLZrT1k0FAaGFWyNYHwAF0+ISVAAZw3OOoqioKoqBlVFWRYYP6MOgbquqesZxngCBZUZUA0sbjDAVAWFsVgPTCfMGk+sD/Czy/jpAbFOARvGgatIIf4CpgQ7BCkdphhj3RBXlKkvBBsFm/MCRHEgQjSCuIAUFeIqXDGgLCtKV3NwcuNCFEVRFEVRbhm9BasHph5mc2jmkVDVWAuVTVHxWxYO/MmwskYghOSgYK3FFQ5XFLjCYWgwxhBCoGkaRCKYGls2hBgx1iKFIRqD93OIc3y9T6wvEiYXkfqQ6OdYF6EEDEgAkWRZpRRsNQQ7RuyAGMD4AE0gzmuCdRgcwRiisWAFKaAYNAy2dhjv7LG3s8fl7QmTqWd6kvwsFEVRFEVRbgO9BWsk+bFOptDMgGHECBQWhiVsDeDiPCXLvxUIV/eZFUiBS9ZgrcXmcrKDaoCYSKwr5mWJ9w7vIcZIjBFiBCLRFgQBYiDUU+rZZfz0Ms30EmE+wRAoS0c0DaEBiQYjASkEyhIpxmBHiBliTIFgMCESGw8+gAkEMWBKxELhKkY49oJw2My4PLnE9OCQ2eRJHrt8Mh4CFEVRFEVRbhe9BSukMq2zGdQ1xGxVLFxK5zSoYCApZ+vNNgpKZzkqY6EIWCdYC2ICxoBxjqIcEA2Y+Rwz2ML5BmkCthggpiRahxiHDYJIQwhToj+g8Yc0vgEi1lrKYkiwEOyMUKZosyjJKhtdhXEDbFEgztIYg5iImBpjPSZEbLAEEcRanLW4CMYWxGiYec9hPWM+r5lNZ8ybA56aaCEBRVEURVGevVyTYPXkqldzaAJZaIFzUJakIgL1zY9ujywFqyWJuVYkC8mvdrw9oKwKrE0ZDRAhIkRjEVdhyxFlE/Au4MqKcrBFWQ1xRYXFQ4hE7yHUgGBcSVEOMc2MgKcJJk3thzkxpvcxZqupcRgrGAexcEi28lpncbbEFgOoSmxVgnOEaCiS5y21wMTXzGcz5tND5vU78I/OuVSraFUURVEU5dnJNQnWAMwCzOfQNGkGXQSKIgnW0oG9BYIVkkA1kvKfEklT+AJlAXu7ju3tLYbDIYVzGJMqwnjvMUSMEYqiwA3GBB9x5YByuEVVjSlchZiG0HhCEGIwGHEYW0IxQKoRnoAEQ8ASgyWEBpqAD8nKaozBGIuxlkFRIK5EXEW0BVKWmKLCVRWmLBFbgLEEDME6fGmo8fh6SjM7oJ4dUE8fpXkichBOVuqwk0y8gYlsd/+s36NCtP32OTnbr53vURzn4DfO9errDwf92kXXb4Q1D2zOY2F7VrqK9/TLiVE/MezVbvjY5nNotvpVG7sd2Hm/e+Amm9tJ6Pk96Nls8Fi/hvVzNh/brzzyPr36qop+TlH/adKjNBxwEEYb21jpN3YvNv3G5PmHnujV7vG3nd3YZvudJyS6WVFuMdckWCPQxOQSUM+TO2ZhhKKIDAZQVVBNoefv7dMikgRzJLklmFxtalAJO7tjxuMtxuMR1WCA5EpSTVNjJRJCRESwdoBxFlcOKMoRpRviXEWMc3yYEbwQgiEEQzOPxCxScRWEkBYjmFhTGo8PQiM2+c3aJFiNGIy1UFh8URDLgjgssUWFdSXGlSnAC0PpHGNn8RF8M2c+vcxsconD/ctMDvdpDtB0V4qiKIqiPOu4JsEKycpaN+A9EEFEcC5SljAcwnAfDm9R0nsPSLbyuuxLOxpZtra22NoaMxgMKcsSZywxRrz3RDwheEBwtsK5gmo0oqiG2KJCTJGKI3gIPtLUkaYONHUgNIGYAv4JQUAMxuagKlNjQnJSMMZijMEagxGDGENwFluVyKBChhVFUWFthXElXgSi4IwnYBj5QL2zy/TUWSYHF7jw5GNcvnDIbBqYe3UNUBRFURTl2cU1C9YamASYBphFsETEJLeA0TgJ1uHlm5stoPVdtYA1pKAqk14XhaMsSwaDIVU1oDAFZbTYJiIWQozEAM6VEAukyr4MRSS6GSHOiCEQmlBLSKIAACAASURBVAi4lFzVVGAHhHKL2qQsCVbACRTiMZQgWzjX4EwAU+DcAGuH2KKEoiC4El+WxDK5FUQ7RFyRRC8gMSJOMAixMTTDHcZbZxnvXGR86izDC09QXDrEHD59wSqkYgdd319FURRFuV72/vTGm1Le4d7rhvf5gf/fq294n3v3XLqh/TXB3ND+APbfs3XD+7z/D29tDqNrFqwemIccfNUkAWhtcgcYjWFrK+Vqnc9gfhMOWEh5+h1ZrBbg2qVMwjW5tXpCqKmbGfNmRjOvaEzExgChxhjBGoPFYWL6osUIgUgUwFhMOUx+ruE0DYLUA2R+GTvbx4Ypjik2ThHviU2DNwZrPMYW2CKJVlPsEKsxUm1jqzGxGmDKAcaUOLf0o4sxYmPESEMoC/xgyGi0xXhrm/HWNqOtIYPBIe7w+tNcGbLvL71d1hRFURRFUW471yxYI8kdoGmSCycIxrDiFjCooMylWm/UM1drFTQky6oTsDb7rlpwNgVhRRrm9SHT6T77+5e4fHCRi8MhzgRiU1LGgDRzAin/qR1tEYlEY8AMEOtwBQiGUFjCoKTY2qacnqap92nm+zTTfWgOMP6AWB8Q5hP8bB9hBjaAG2DLXUyxA+4crhpBuQWDHWQwxpRDMA6R5VPUIhcsUJSRqoHhcMBwMGQ4GjIcDhkMDIUJTK/josra/7AUsOpioCiKoijKSea6fFibkASrb5LQMsbiXFiI1qqC0sLc3xgrqyOLVJIodZLKoopNCyTrqA/QeM/hZJ8nn3oc50qcNYj3hPkh9WDAMEZc01BiCGWJaTzOOnADXGkRN6QgUpUVkTHg8bGhaeb4+SHN7IBmehk/u0SYXaSZXsLPDomTA5owI5iAKSrKco+i3IXyDKYYIOUQKUdIMUJciTdmMR0fQkAkSUljDDGALyJlWVENKqqqoqoGlIWlsAETrl1ktmnAYCn8IfXTFmFoF0VRFEVRlJPE9QnWmARr41PwkYjBOaEsmyRWs1uo88nn9biKVMcJpNZXtSRbVA1Yl6b923ntKEms1j75ljZNpJ4f4psn8K2o9gHqGWE8ZiyGQYgMjRAHA6wrcOMtSh+IGKwtKJzDGkGMAQNNbChiwNdT/OyQenqJONsiTMfMJ2OaySHBTrBhRrQR4yqKchtXbOGHp3BFhSsrsBVRXFLdCKFz8q11NQWxWZwzFIWjKgdU5YiyGFO6gsLUi9yzfWmt093XrXi1nW2R5PKhVldFURRFUU4S1yVYfRaswUP0ABbnLEUhVFWdsgUMYFpDHZJo7dLRmwuBBKviVfLBFUBp8rS/gVhC6KTbayuqxib7ZxqYTSOTgwMmhx7fCMGTzMKn94jDIViLMUmUBicEI8TCQCFIYWhMQRCLSI7yl4iEQHQlDQURR6QihgITh1g7hZHHBY+IYMTgioKiLJkOzyBFiTiHiBBjJISAjQYJcWFdbS2sMYZUGct6itJQlkNGg7OMqnOMBu9mWB5SzdJ16ysqu+4A3WvfXvN1N4GQFy0JqyiKoijKSeCaBSsksVTXrZU1+V6KpGT8VeWpqsBgAOVhKjTQTXHVTk2bzutWqJq1dg4ociWtVrB6u9qwTYfqA4S4fO9MJMYJRfEUZTlgazRiPCgZlgPKqmQ4HOPGu5jhdpquty75dPqGgCEaj8EQfJqyjzFQ+xrf1ElkGotUY6yxmHJMEQRBMDYdXFEUuKLADUaIkTTVH5MgDSHgfZKFrVhtLawxkgsPGJxzDAYDRqMRW1vbjLZ3GG0/xWBaU9fJ3WLTFH5rpe5e965gPSr4qhWsbenb2FmvKIqiKIpyq7m+oCuSO0DTQAwsBKsRwVpZ+rEWUObKV60l1awtXVohZfIb2xGr1qTAKrHJLUAkC9VWDWfXgHQ86fjqBiazQ/anl9mfXGbiTzMzBX6wQxzvEYc7xHKLWAwJGLyPxHpOiBHJlZJCSAIzvfYE74mBZIF1A8RWSIyYsBSaANY5jHOURTpYIU33e+9p8BAbRFLfSczGvK+ItZayLGmahsFgwPb2NnunT7N39jwXLl/gYPIE84sx+exe5T51p/xbjb8uTrsPDe2HFv6scfm6vX93Enbazz5sZ5ur44ze1a9cg/h+kt4+d9yrnS8353KIPbOfuGk/7+RLD/brsHlscxku3zMVRTzdz9N9ttfTw/oOT4FRXuw3dov9PuOjZ0Uv6XffQ9+/GJc373ff9Luf99//aK92X/ymV/Zq97y7H9vYZq+c9Oqrsv3u1WNPbvdqN37X5vtQ7N+M/DuKcvK5ZsHa/kSG1o+1WVoFrZjsGuApipSb1RkowpUplbpWVkPOoZr9U8Uk31TTFazZwtpYiLmd98uMBbUHabKQbZZVsELwNL5mFhpmMdLYgjAYEcc7UG0RiiFBCkIQfNPgaYh+njrKLPxLY66NKqkwQBRDAIxEnI2pqpWRXEXLYZ3FUK30Y8QjNHgjhOAXBQ1aUSySFmstRVEwGo3Y29vjcHKO/YOL7B9eZl5PCWGfcDnlxF3Pp9r1We1mVlgcR6e9sDzVdt/pWIGwvGft59TKqiiKoijKreZYweroCFRWI8nbbAHzkLIBDKIBkVSS1AjWxiRaHTTz5fTyUULKmiRMC5f+d3nHxqRsACabCa1N2Qesy6I2Jj/aJueEnddpaWwKDDMWCmexCBabBJk1GGfBCb6INC5QGw9S40PANEJoksVYjEnnL3lKH/L0fsSYVMGK1mc1B2kZY/I1SP9b44ghErIF1RhwRiBIyt8aGyxCxKf0WjHSRA8SMc4h5RDGuzR7MyaTfQ4nl5jM9jmczzhoauaHqdpXa8HuugC011lWzKhgY64QlrcZ0rWO6+1srj4bUnvD0ne2j8X1Djd0KYqiKIpyQjhWsBYsp4VbwdoKlRBTQFUTwIeIz2opibXldH5RJH9X4qqA6WojyQKqtbIWRWtlXKavwrVCNUXRt9PoMUaaJqSlgOksWVvnHsiVrwpbUFhHYS1FkQLErBVMIWAjngYThRgiBrMIgJIYFyJ0IWDz+9XFYkyRLat20b7dHiRgYiQSMWKTsPfJKk3IV8IIBPA+1V6NRhBjaYxjUFRsjcacOnWayeQeZrND5s2M2j9KjIHpJN2PdSvrwpKa783CTSDvDrJgzUuUpQW1tbYulixcG9JynLX1KHcPRVEURVGU62WjhbWbXL614BlycJNPaaO8DzRNgyta0SY4FxfT+caA+Cstbm2/bbBUO70vksRuzOtzHFNen4K7rF1Ocqcp9YbGe1wZmddga/DRUDiXAqCcy0sKhiqcw+V+2qCndmkDodqlFZ7WuSsEazv9b0yxsq4b+d+Ka1jmXDXGJDeAAMYI0RuiBxFPCB7vIyJh4RowHm2x589Qz+fM6xnzekY9n+GbJxEfibNlCrFuQFX3ocC07gZdwdre06wyr/BzNVnUmuRyYTr+BOuFIVrr7roLgqIoiqIoytPhWMHaWspaEdQVJyFmP1YPIadnghx8ZVhZ4Oio9G70uQ/JErtIU5UXyZZDMWmKP3QOohWErRuCMZKm0q2AhSZYiqJcCFZr7WIx2RLqnDvCYroqSNt2YgwmC9zj2nfFavc4288trmFIqQ0kR6f46Alh6VbgDYvXZVUx8tvs7MyYTA+ZzSbM5zOapsaGS5gLqSRu3QmWan2ELUlo2pj8gKVdWutpzG4Da2bREFZcebE2WVpLv7x/7dLu57ggL0VRFEVRlOvhutJawTIXa0pvldI+LYScGKwJWLOcbu4K367lz0hnXY70b3LgZYxJIGHSdLQPYEIKUlr4luap+vb/0liMNQQTscFRuJKyLFZEawqOMtiOwDxOtC4Wa1csrCJdl4ClYO2WWU2HubS0LleCiWaRPyrkimHWWkJweA/GRKxtU1wVDKsxfrtOVbf8nPlsRj2fI35C8DVcBFOvum50g9yEpe+qkVyEId8IY/LnYufadwKwYljeIxGgWYrUrmC9WgYIRVEURVGU6+VYwboeZNUG3MxJAqVuCwjUIL7BxAYbI07iwqrXzXC/sMLJ0vraCqD2dZeQxZxtI9YDSEipp7w3GGuIWai2QVnGCTRQiiGKw9mCsqooy3LpStDZT7fC1LqFtF239EttnSTSItJdrswq0Pbb/t9tE0wS+UTA2mXUfjRYcRjmGJmlIC0X8BGciwwGY7a2auraU8+F2SzQhEPm/i/xoWF+AH6eo/xZzQbQWj+lcw/EJjfaaNL1NWuOqaFNytqOg2xVb/vroiJVURRFUZSbwbGCtdUuC19TlsLVkiLz66ZNb+WT5dPaXFrUr4jSLm1AVevjepRgbddFu+pekFJVtUcXlz4H5LRSVnAuRWhFqShsxaCqqLJoXfijHjOl3w2ckvWDhyvWtTlUj2rf9bVdcQcgLHKdrvTdsT8vLbgGa83Cn3UwGLC1tc10OuHUqdMczk4zPbxMmF9gIpH5IfhZujw+rvofw+o+F9e+9WHtCNbWJaAN3GrFb+tfHONasYeOD6y/0xK3KoqiKIpyYtloYW0NpF3RulgfoKmXbgEhhE5Kp2X+VGNSCddFH1kUGbNMY+XcUiB1NV+0yXdVruIU2U6/G2SRvsmKAXEEsTibAq2stSt+rN3pfZfLprZitWtdXfVFJWUKWPNTjYuUVYZ1S206z9X3ISTLaoyRGALRe2J3XVuIoWPdNcZmoVhQVRVNM2Rra4udnV0OpmeYHV4mzKZYP2GSo/l9zk3rw1KwHqG/l6K18779v+szHGX5sBFai3enn+4Dive5Tc9c7zea4tGLPVvubmxh6n7qW3q2Gz62Xqz4aCbnNydfN3W/C9y3wMD2I/0y7YZyc4d991nXm4sQQHJn6UWPdnZycgsPS9PvHkiPU/BV2auvZtirGfO9fsdWPLX55of9Qa++3j463avd7na/ZP9v+9P7Nrb52Be/tVdff3LxfK924bCn910Px39pbtOPqqLcZnp9i+La0q5rq0k1TZvE32OcywFQSwFjzarf6qLf3Fk3DVYrnlpCa/3LwtUZsnV0LYrfSQq2aiOKOt/pVcttKqEq3fdrPqxd0dmKRukkM123pK5nGDiqTbddCIGQS7RG7xeCVUIyW3YzFSzyuUYoxBBig/OOskyW1vF4i63RLgfjPWbjy4TpDKkDtUCd6x/YsPowEK/yoyitz3G+/q3gjD6degxLAdsK1tj5+9W9560/cuj3901RFEVRFOWqbLSwtv93xWqrJ31Iaa3apP3eB2xWKN3UViaXVqVrQT1CwbpOpatW9LRpSiUXEbA58b6YZCEFgxi7tMIaC9FgYkQIGAIxFxeNMfmNpoyoZMXc+rC2fqyCMauZABbR/ZKuRow5yevKSax6/KagpXSlWp/WFcEaklANISQraxvZRCRI61famoxTAQMbwMUCF0pc01ANRoxGW2xt7bI/3mEy2qKeXCb4Scpdm6f5Q8iBbPnah05e1jYIy5LFqlumv1oITpusOaEjUEMWr74TINf1SXY2X17Nb6UoiqIoytPkWMEqXDm7ZvKHLMspuiakZP1NCLgsvkRSAYCyhLLI9ej9qv+qs2lZlF51HQudW/pPtlbXVjM6E5DCEk2uKGBc3haTD0EUiggQsNREZgRmNH5GCA3B+2z5i1mYJROiSFfAXmkdRZJiS+ex6jaQxKhZLCJtpgCbgqtYFayxM2eegpkigeQUGp3gRdI1EyEUJvmXNiDB4YoBrokUpWcw3GM8usRovMdg9ASHByWmnqTza0gXPovT2F7P2MneIODygllatBcPDNlp2cRlcBYsBSuxY4nN48H7pQA+ygVBURRFURTlWtgoWNulaz9s4+RtFiMhp5xq87GmaexledbBMO2pnZbuBlw5OsK2XA0AukIsL6ass4XVOZAiCVZpLZQWos3T3indFSR3hbqu8d7jQyD4sBLN3yVkK3ErRFthuh79v5KmKtN1DzCmG6O/3B6OmScXEcTaVBkrZw9IZWEFEyPGL4OvqmrAaDRmOt5la2uXw9EOB4MR08lljAkreXCh4wrQprbK7hq2FajZEr4iWNtrXyxdAtqUV62v6vK6rbkAHBFwpyiKoiiKcq1srHTVIiyDrtr3XS0SsmBdCrZlUFVZknRkXLWmWpNEb+GutLCGbA3sWu4Wx9JNPZU/IBKRmM2EMSmllJc/WzWzn2jTNPgmTcX7PCVvrV0RkiZPo7eCdLEtq7R2392UWN0lrKu2jtV2PUdr23/ar1n4sLYCuX0ASFkIwDlHjHEhWsuyZFCNGA93GI12GFRjDlyBszN8xx81dtMEkPOw2mWWBiQZkEVSaqsVr42OL4htXQraMeKuFK3ep/5bi7qiKIryzGbrkekN77PY7xc0eC2Y+mYEVmzd0N4e/bAbf96DmxCrV+zPbnynx3CsYB2wmsezmyFAWE7nt/6OqZBAg7UG78MimKqq0o6uEKw2TUW3U8cLH8gsWv0RUeam9S21yXe1Vbhpejvbf4MlhJQ1wAPBe+Z1zWw2YzqdUjc1PqQ0XN77lcwAMUa8j0entToi/VU3QGqdZIlcU4pH0LXUxhCWvsNZuCZRbdL1jxHn3EJsl0USrFU1YlCNGVRjCldh7SxF85vsX9r6knYOJXYsqOTgKpun/tdPp00ZC3lbds+wdvlQ0X6m6y6gKIqiKIrydDlWsHbjZRbaoxWYBooKXJmmkqO0vqyBJgRqn8SQcVCQfSRZZgSwdmlhbTtfieYnBVeF1sxK+3mDdSaL3IhIwCw6iCn+P4sp7yMSszvAbE5T1zTzGl83hMYjPmJ8ShMiOcJIjMGLX0lTlY5NFrbSKKnC1koN2WtGELELS+rCWmtkWbI2H0NqE7AiRDEgEWctwaS0XbaocOUAVw4oygG2rBAxBBNSwBTZatrVzssYrzTV3wmGW7gSdOPJ8v+Sb07r7mvI5V5jsrxKTP7HsV5ayBVFURRFUZ4OmwVrZ+4/5EJPkg2bVQXlAGyR1jeBRW5An/Ou2mLZftFvp1iAiRxpf7Rir4iyBzDOYpxFEGwOJjIxRRGF7McaYkgizKdjCsypzZw6i1Xf1IS6SUK1iTlPrCeaSJCAz2bfrv/qwuJKzpLQEatdd4KutTWd41L5dd0Iuin3uy4EMTT4XOa2teAmwZotoGKIBJxYGgQrBmsdJj8FiHOYogArhI5rhTXLKldpn3lqv5P+IWQDdZSFV8UVdBIr0A4Pk18YUsxb6zZwhU+roiiKoijKdXC8D2u3ApJJglXsUoCWZQqYaos5hZCKCLTGxxVram7TiqBFlqjO1PHq7PtS6HWrRYk1KeE/nZRTmBS9LkIMkRAidR1omkgThECzmP5PKaXa1FJh4cvaFaehPem871URuvo/ACv+r91iA0K3bOuqYF3zXW3fi1mc70rAVwC/nMDvCGmLscsCA9bY7DbBSqJ1I6tVqbq+rV0raPLdXf7fOcXcgBWy220+ps49XLvfiqIoiqIo18vxgrW1qGaramjFaidIqg2UgqU1zS5dS1d8U7vJ/oEsVFcV0DJV1NIeuCICjSDWYLApXyrJHyFky2rwqepW0wTqOuAxiIu5wlXrgyopGCvEVE5WVqtctTPny7KodqVc63oFrNh14My05VRbK2tXrFprFwK3FaULt4DcfplpoA24kiuW9rissVjnlp/L11Zaq+dadoA2tdUiW0B7O0x77EcLzW4e1nb7egnWNguEyVba22VhjY8/2a/h3ZsrXfX1a6hP9ysXJKFff32qWLnDfhc42n5PDr6nC8fWOzY3nO3122cz6rfPvj7RzXDzfs305Fa6Kp446NXu8vturgDle8Zu7DzcbxyZnpdtvr25zexUv/Fx8O5+AS2TnX4V5Iqt+cY2b/idD+jVVxz2q27nLvSrdDU9s3mQR6dWAOXZybHfoqJNuNpG7mcLK2YpStuUSOsipytUU4ore4XQSw1XfyhbkdidwF75jE2ZAYykRSQlD00ircli1ecl4jHYoi1mkEqyGpOOpa021fqKdo+hLdt6VDDVUev6sv7ZbnnXo9oufFjbErdZxPpWKWaBa/NirOlUAssddXxX4+IflqLWtPtbdd9YKcsal5+Pa3/bYly93y0BtbAqiqIoivL0Od6Hta0ela2qIWeMMpL8FF32i2wXJykaXcjBN3laWQJE0wksWqSkEmq7VFIhxlxpylBgMVllhRiXbgM5bZWIyQn/I4iH6HF4ggnMY6QOkZn3YBziLTFKElakQC0xntqm18HGFO2ercgp48CaNfgIlhkCDHFxFZJXZ8glulIu1lVBDEsr6pU5WYWAzSnEBB9jeh/bILNVq7MREBosgUIMDocNDgkO2zSpZG7ehZdlmqo2zZVIssC2eVhba7qQfIDb8qsmZkts6+N6lXizhXsAaYxcrQysoiiKoihKXzYK1kVZ1CxmglyZcH5RNcl0ppeTTmsLlSK5AlY3XZRgkiBGiBIXZUIXGkc6nXWFz4pI6kQORSF4qGvPbNYwmwcQS5Q5TdOsBje1i6T9tK/XBVYva6p0j1ronkW3LGu3r3V/1k5ni//TZrlCFF7pR5vOJu1VFifWBp4Ff/Wp3o578CIDQPvGmPb6dHyOWQrVNqiqTVHbRatcKYqiKIpyo+htYV1YWjv5N9eDqrr6shU0vi0NGlLlJ2NidhMwmBiRkK2YkoSZiWByfqQQr3QXIBoEoZv/NMaYgpIaqOeBpoGmjsxngSg14ppFedRW7aZdHl2tqktXcK4m8V/3KV3N0bruF7suMrtZBdpz61bJusIafZVcrivn0FGUydVheQ+696QNjmqD45Z9rQZRrayXVRHa9t10fNra54HuQ4yiKIqiKMrTZXPQlZCEql2mtepaVGF1anjd8haylVV86+sacwUsjzUBJ8lfVLqpBSSkHKLdHKiLtFKr1suQLbe+CdQzmM8D9dzT1IEYDKYoFhWhiqJYBCdBGxhlV0TlkQFVrAZHtSw+k5WeMaui9ajiA93+2j6O6v+oIKtWLK8K42WGgRRwlYKuQogrUfvd+5L6X4rT9an9K/yRWaauWskEwOoYaLcZk9qrhVVRFEVRlBvB8aGLdhmIY13yY8WspkdqLXatFa/Vnd6npa09HxeCNQnhGCC6iJjWVSCJNUMktFP0LK2UELKoaiOIZJETNPhI3QRmTWDWNNQ+EsThBhXD4TZ7e2fZ2zvH7s5ptrd3GY3GVNWA0jqcGFy2jlpjU57TNYvoUnRm/4j8fzsJH9fsn91gKbhSpHZZF8ArVa9WzJ8gJgWcSQwY21b7ks7DwdI621YLa63gXUG58KAIyyCqhW9rWA2ik9bvteO32t3WWta7bgEbjNaKoiiKoijXxLGC1XfSU0XTETVtFidZWu5aIXO1CPPYpD6iSUnlTY6jCq4VWa3wCSntVIjZ8toGJ60KSDApkMoLTROZNQ2T0DAHonMUxZhysMPpvbu46+x93HXX3Zw5fYa93bMMh2MG1ZBxUeGMw5lkdRUEEw0+mwmvsHKaNq+XXfi9hpQR9sgJ+6N9VFmcR3fqv7v+yDKvgCcSJKZiAEbwRHyMRG+IIedq9Z4QG4yEhbWzKJaiMoT8wBHWBGgOkhPJvsmwSHm1ELksH0bae56O+UrLa+v7qiiKoiiK8nTplxwuszBwhpw9teMHuW5VW9FhrXDJKiiQRKsPS1eBlvQ6pqL2mW6wVFfQpcCuJG69j8RocEVBacYUgz22ts9y/ty93H3uXs6ePcfe3h47OzuMx2OqsqIQh8suAW3fxhj80xBaXTeF9dKufT+/6X2McVEEwXuPD/ViCWGe/o9ZsCbjdLJq54cL01pY45rf6ZpVdjHFn62s6z6uXb/YdbeCdQGrKIqiKIpyvfQSrItgHUgilaWFtesT2RUt3elhwzIn50IIZZHqO36V7dR1iCAETA6uOirICSCGlBUgpSQ1GLEU5Yiy3GW8e57TZ+7l/Ll7OH/mPLu7u2xvbzMej7M/q6M0JYVz+RzXfFPXfFGNWXUVSMe7mlu2Pc52WzdQ62q+rF0f1nZZCNFFZa7Vpd2WxGqDD3PqesJ8fsi8PqRpJvgcdNXeg/Y+GrNMvrVeOrUVqt2CD20A1dW0Z9cHdl3IKoqiKIqi3AiuycLasqhBL6vCppspYB2bK2a1Aiiy9G1tWUlAHyMSmuzb2q5fT+S/LN9qjKNyQ8rhNsPhHnt7Zzl96hyndk8vhGpVVQuR2RYPWPczhbWAo84b0xGy6wFWXY6zpq4XClh/3wrWdYF6lHANIRB8oK5n1M2Uupniwxzvm8W0ffCLS7Q4L5GlH/LquV71sBcPKutCdBFYt5YZousycDvw+/2qBdlLs41tpO5X3scWdnMjINp+Tr6m2XwB3aRfpZ1Q9tun9Eyc64vN7fpW5OlbPSn2O4VeFcLC4Lp++m4Jsbxxx7b17n7jY/DY5u8BwOTuQa92pt5870PP0ywu9rvxftavrFe9s/ma2GnP78tBv3ax6PdjKM3m69aMNEhAeXZyfJYAn3wbsUngtDq1a27rilWzltoKkkhtLaqLQB3TySLQdkxrWc0iq5NVYDEVbXzyazVmcRzBQBQD4jDFEFsOGG5tsb2zxe72iO3xgO1yyMiWFNFQBKGMabEdwdq1krqc2sBkzwRjDTYmm29rZT0qj+p6CdYrLMJH+Kx2119hPc1CdTXVVfLJiNREZvh4SGwOCfMJvpkT6gaagOSaCuuprLoW02uatu9YYhdZA0jFASBZytu8r77J91YtrYqiKIqi3ACOz8PaWuRIIse3Bs3YcUvt5GKVpcFzIUK708twpUASWTRdfKwbsR7J/cblfhdR6+1qEaIxGGuxhaOqKobDisGgpCoKCmsX2QCcGCwGK1cGVS0IWWym0gYpN2yUxf7Wp/bXLapHuQBcjXWxepQVtWt9TflkAyF6Gl9T1xPq+pB6fkhTTwm+QWJcFnLI124hHNvrdoTfaXs/1otvSadowOImddqvBFvlbU1zpcuBEbbhUQAAIABJREFUoiiKoijK9XCsYF1YRnNaq7YsZzcAvJsCyXQEa4xXbj9uP1e8bsWprC4AktNd+ZU+UvqrZc7VkrIoKMpikWu1dQdYf78y7X9UdoAjrKlXP5ej87muc5QLQBtMdawLQGuFbRrm8znT6ZTp7JDZfMJsfkjjZ0QaTC6na7LbRYzp3kWyiDxGsHZdB9o2y2sPRzm0HvUgooFXiqIoiqLcCDZ6EXVLrsZWsEp2FWBVjIbAFWJmXfC062ApiFpaH1iTo7TWLbPta5PNuiHENWtfDpDqCFJrLdbYhaBdt352LaStiGyzBrR9rm8/jquVYr1atH+bAaGbXWB9311Xglawzuuauq6ZTqdMpgdMZ/vU9SQJ1lzjthvVv3AmiCxL4K49XCz3e/SyPPbV1939tP8XhVpYFUVRFEW5MRzvEtAGSuUpf2y20q0J04Vva1wVKEay62MrWjt9yxHlO7uWWrFtDtClddMsGq/NcefE/UbM/9/e+zTJkSRZfj9Vc4/ITADVM80dLsmlzB5mZQ+88L5fnsIvQBFeZg5zoAwP2z1VQAHIfxHhbma6BzVzN48MZEY3MOzapj0Rr4iMcDf/l4V8/uzpU4YwsBtHV1fHHeMwuge1kNVvoQ3tl2a9DWFVJV+QDC/ZAurnLc4JaUtSz6f/676340FKmXmu6uqB5+dHHh+/cDg8cDodyWlCxAgl83aJBKjFVwKafVkK3y5M8V+ycLRuj0p4Q3jZ7rW1IXTC2tHR0fHXj+Ef/+WHjzne3f7wMaf/9O9//Jg/jT90vJtPP3Q4AE6/+/FjjvfTjx/0FbxKWJfCJq/zIYsTSZP65WoRqMpdOCM/S4vOXEhvgdrqX31BkGz1w76YatYMMpcCrREhEHQkhD23esNduOVW/f0NO3aMi8IK65R/VSp9n7L5PgXIUprAns2RX/S8spLL11q5VtT4q1ZlbbNmL6msvp2R08A8BY6Hmaenrzw9/8rh6QvH5wfi8RmbI5qdp6KlcAxItt7PWhy3FEWdHaLWBxVpllYJZ1XZ0wVyW78TezF0R0dHR0dHR8efjNc7XdVQ/1SLm8oXsqptOa+fa0N+qv+V1g5g27aeG0W14YWGk2N/bw1ptc2riBB0IAx7xt0tN/sbbvc33OxuuNntGcOAyjaCant+6WLhlXs9y4G3pHkhZKtPtf0Zvp0EcC3O1dZt8ZXnrs5x4nh85unpnofHrzw93XN4fuQ0PTPPpyUzN5QOVhslu5BJE5B8WV09X4JsH0TqpagPF/Wr6lfO2cfu9tWOjo6Ojo6OH4HXCWvGyWpRSq3tMX9h/Us07Xyq+EUofbNQIghyGajaCVqCXKt+VJ0RSwgMpbhqv9+7DWAclpzVSwVUsCqg500JzMwtD/o63fqW0vrimlwgr+12l8a5SFoteRerNBHjgWk68Hx44Plwz+H4yPH0xDQ9l3VZiHZtxhDCqrBKzWdl6yc+f5CA1b+sZwS0qqjnftaWsHL2fUdHR0dHR0fHn4PXi66aKeOWd+h5zFH93Nap40qWhmHbVOAlUdUNsXTClT2+ybb+yM2+ShcCK/FUKq1P1YmvXTkh/aL4SXVJIvgWId1mo/Iiz/Wt5gHnhLWO2WKTCpAjczyR0swcj0zzM6fpmWl6Yo4HYjqSbUaDk9FFTD73kNaCNlaP6bnKff5+sXWcX85iAWk/rkVYIu517h7Wjo6Ojo6Oju/FVb1GpFkUnyKuKuuGyNoaKF9J6jCs7T7XJgNru1NdKvd1zSPNCbOZVDsJtCoe1RtprMmovhglr4lCJL8x3b2MJeuSWYP5rTD1825U1SVQx87mlUwi4pX5y77km4VY5/hWesC5vzWnRIxzWWJZZmLy90ZGiiqsoRRcRV4+WdSfdSW1L0hqsXMIq52gHWZTWNf4ktsCrkv89rcIuYZRv6G2L6sd5qvWiz9d1y1oOLx9bPOH69oFnX66rjvOcLruruUruljFK+sl0s11+7z55bpz2N9f193ptwo5XFfIkHZv34N0ZYezcGWXKJ2uu1fH3799bLv7q4bi9pfr1vv6n677/zR8fbsjXbiu8Rd2XXM70u6666anK67blytbw3V0/JXh9RzWOn1vMKgT1QGwxNJm9YwXAquaeskOEIIroiEMhBCwweMApA6WEhaNMKubaBOLRcAPCgiGEYGA6AgkUp7I+UCyG3KeyTk2Cqv/4Xc1c/XXzmE9D1W3GZhKaVzw0osqGCTDUsJY1WHU/bBtp6tKNr+l0rbT/ecE9XKXK8EskHMkRe8mhY2o7JGwQ4cbdJxJ8zM6ZlIEGYrKaU0iQLEEtMprVVnBHwaqClsfBXJDWEv93bJc+nNYFfj/HghrR0dHR0dHx28fbzcOKO9ziULKF6bnL/pZ7WUOZwiyZKMOw4CGAIO3WlVVUkqYGeEsPmozfqlgp5JD3xuYkVKZQl9UyTZwP5UuUeHF8b+G1i5QVWa/Nu3xiVc4FdT0AVitAq+N/a1Yq23RVSrnsx5PPQ4nzgFV749rVmMC1rixVO5FyqCp+FjPiq7WNmOOupvz69U+pOT8cn1Ydt/R0dHR0dHR8d14PdbKtksqHZOEUn1+oYuVyKrY1W3auKNNp6khYGEdoCqWVgZWlULamoOSxvepleyG8rp6QVfF0ha1sqqgPl1fIq7OPLQtwXxZMPUtT+u2kOu1hgHt522c1WudraoFIOdESj4dJCoMgxP/oGE9d2xRtHM632dzT9J6b6q4Xe/Z+T1/YaWwC2SXM79qXtft6Ojo6Ojo6PgevEpYKwEx2BCQtkJ8E0dl6zaVyLbrnLdFDboS1tqWtOae1nXNIq1ImXUlqqIBUyWElXTWA8k5FaU1kXNYptkXcpq3yud5IdQlmGVPTmjW91QB90hsVFcRWgX15Vgv1dUY44Zcn7/WxSwvRWHuA9YlvstU0XrP1O0AKizHve5/6zn1g/Z16z1rfw/OT+ESaW3HayPOOjo6Ojo6Ojq+B28rrLh+uBDRMtcr7Tq2vm+3bclsGEDUioGyQASxUl1vheBZRk1JOpAVLEhRIcvkf4myQodGHYWAoZa9at3SkjaQbatWLt7SkkTgxG/rt73Es8wMsbzxcqacMZw8k1nU23ab85/Pv9skAbxCVnOGFAM5ebGXeZnYEiW1Fkk5kccyQW25dyprtJWJF2WJOaE9E7BXZ0CjjHNGTvMlS8FycqsS39HR0dHR0dHxvXiVsC5E6EJmau2WBFvSsp3+bywDAkguZDKSkn/YFiqRQZJAhpiFJIGs2wp8DSVZQEJ5FVQygYSmiORITjPZIpmZRCKfFTWpKpZLdBb6grCW8qoNcs6lVWzTkQqWwi7LW1vAOc7J67eaA6SUmhSAdUnRSFGLajyTs0dcpRQxfI5fSvGa5gELXhalOS0PDzGvhLxGTqXWNlCLs3BSW5XtRXEth99uk7fPH3+SP7ijo6Ojo6Oj4xq8mRJwKeC/Df9v0UYjbQhrs55lI1lGxJckealIrwVTqaijPubqLwUIwQjquau+HyuvmWxzWSZynkl5KukB4wtSKCpFCbyQBqCKNtmotdr/xfWpx2UehXUpFeDcz3oxsurMrzrPa3RVJbApGcnScl5zPDLPR2I6kbK3IgtBISsqhfyLevxWMbNKTQMoKQC5WVJyEruJEGtU8lZxbVMFOjo6Ojo6Ojr+rfE6YeXbCQDLtK9syWv7cwhlKd+lBELyFICcEc0kKyqjGZYz2YwsoBKWAqu2tWpgRqnZoFUPTBgJkYhZXEhrSidiPGHhblEzawW/qGCxjb5ayaWw7rfNYrUL1fmwcrxKRL/VDKAlqpeU1Xmel6W1CKyLE/B5PnjjgPmJGI/kErNVjymYk24xf0AQcatFTVjQwIawpgRRinJ6VixVc1lb1O5Zl6wg9QJKe2E6Ojo6Ojo6Or4Dr3tYX/m89bBesgFs1NiNEiubcSKZVEL7RUu+Z4ZRvAo+hIHQVPOPomXav9gILAFGLtPiRvQSeMneSMBq4VVeirpEhJS8P6lk2XzupM828VUhhOKJdZZWSegaXyWLD1RwsvjNa3qWtVpJdKus+vfnEVeZeT5yPD3x9PzA49MDx+MTp9ORGOeiohZyX48jG+atHhiG7IVSxUbcxlql0hlryXet977ufpvaBayk9VvQv3CHKwnXBWvZ8Hawuo1XhnRdec7p9rrxnv/u7aYA8fY6qfuakHmAOF/3lDG/u2K8Kx9Yxofrwu1v//W6Afcf305918frwvn/EpDTdceWx7fX0XjdNcvjdb8fn//zdY0q0v7tdR7/4boGD+Pn634/5Mrft+H+7fFO/3C8aiw7Xff/8vDrddft/f97xVhP1zUo6ej4a8PbKQGyDYd3JW/9eWkeYHiAfgApnZayQFKWrkoWAhYGbBgwVZLW8RQJXkAUBmFU4cactIoKIVQiCZIHLAuyKJU+jy0hkSVj6iTNCMCIMVJJcpuP6qZOLUVgPkcuYk6yiyrpca+lAxeQCrlsldRluLxKkvINJucFYFVJXqv+t15VyGkoY7tibBaJ6cjh9MTT01eeH79wuP9MfHqA4zNMz0hy0ipALk8IVhoHWJQy358IFP+xbnNyrSGn1R4gUvqGZb+fGyuI+n1eiq9yw09KMkFtrdvR0dHR0dHR8T14lbDG2fncebFVaIqtapU64FPBGXLJVdKl2IolL1VLpJWIFwiN0mxcFUIV9kBo7AAVlfSdB+2bJISMmi+SzYljM/W+6UKFQXBltJK1RUkt666ZrVs/69JIYKk6E18qvqGwbgqtbJsOsEz/m+ERWbVAbWaOJ47HJ56ePvPw9RP3Xz7ydP8rh+evTKcHcpqcuC9ydzVLeANbyrUW82uisJDqSkDPi+RSasimnBXflZSBxftqL1MD9MwP29HR0dHR0dHx5+JVwjpHz9Nc26rivLKoaa1v1ayocnnbLKBi8VeGsJBQL3zKC3lVrXmiwmC6eEkrkXzp6Wwq9rOnBGiOaJ4hTUhJDTBbSehCPgWf5y7HVT2krTWgHf/F/jbFWuJycjnejZLbYFNsZduWrK23VUrvVCMR88Rpeubp6QvPD594uv/I0/0vPD994nT4QoxHWmbYHr8Vo7GIeLpCtmIVqAVtKxlXNWLc2juWRhElEktbwtp4ldvirUpa6/TcK+6Ijo6Ojo6Ojo6r8Lqxpgl/P1fgWmJzjhcpAbKG3G+8ogiSs5NhK4pq832dym99n3amrC77tATpiM1H4umAzUdyPGF53hQ5LVP8KZMLQ6sKbiWb7XFeKpA637+fiS7H+i202yZLxCUBYPWz+qF4zmrKE9P0zOHwwOPjZx6//sLT/c8cHj8zH7+SpgcsRZKst3G5vipLFwBtCsGkMFBp1i1HR0t8672rKnoIzT2V4oVtfgfapW4j3/j96Ojo6Ojo6Oj4U/B6DqtRSnZcjFQtouTZ9HClllrTAQSG8hoEggqDiI+DK3wigmAIaSF7KooGKwRSl2n2SvIWFZJS9V7KvzzUHyRG5ulICE+k6Zk8PZHnO/Jwh+WAMfjsvQnkjM3RMwYMdHSvq0l0bdMSUjpYOcF0kmvJ9205N3FPCupNCJyxLSVbPrVPId3NtH/MmXhmB/BrEDACOZ1I08x0eOLw8IXHr7/y8PVnnh4/c3j+wun4QJ5PWC7RYIWE+uGUWC71pgiGX06/cX5MYqutoSYMBEnLTU+p3ILy0BIaQtpaPVKbwUtbUtfR0dHR0dHR8WPwKmHds5JQCeu0cCUt9bu6IM53BlwxHcoOvPwJNHlRkKouqt9Kc8QL/hHPD63kj9UrWguvTJrA/pICIHiFV44zOR6Zpyem6Z7dvGfY7TETVG5QhCDuodWUUYtIMi+aShmLCZshBSGJIBLIiMdtJStZpe5/UBGCKqYwB9yjK4WWWwAUGVZLwiZvNUEqXlzKNXGM5AhpnpgPE8eHBw5ff+Vw/4nnwxdOp3vSdPBIrpRLiH9aGzTg98Dbz3pBW30AsKK8WpNq0Jb6W2nSYLYR1xEDTWWav0ZElPftjH9mJbMm0D2sHR0dHR0dHT8CrxLWUKrJg3OvTSOBS00FasZnCK3nUWiLpqpKCmtTgBcoiQOXUNVKmtfyA5lIzjMxTZymJ8bjO8bbAzocGMaRUHyyZlrInbwI7R+Ggd0QEVxFtZyccFomJ3MmFgJSOm3Vk7choDqgMqA6umpsSrTLloJk6okBZxYCs5k5zhxP9zw8fubr/Ue+3n/i/uETT0/3HA9PTPFAznHpVlXR5uGubLHEfuVUis6cSZq9LN8XsY1EuiRDecDAJpv1EqqFwDemE9aOjo6O/59A3r/74WM+/u//yw8f89potj8Fp//hx/6xS7sf/8fzw7/8+PlPmeIPH/M1vHrnxtHXkLAqZjXLc6jT/2EtyuG8OKeSpyastQ3Wb4P563dO7hKq4SWZPStQqvDxXPU05kJaT6R84jQdCOORfbwhjyMpO7H24xgX0mhmxBiZponZnlCbsTiRpmcsnjw2SoQsAXTEwg4dbwi7GxhGGAZCGAlhh8qAyICIEtPa5rUlrjHLJse0nmvOM6f5kcPhM4/PH3l4+sjT80eeD585HB+Y5idSmhZLgpboKin2DA3FptEQfiOv1oBln+v9aGvHjJV0LgSV1bdac1vX417vc0toRemEtaOjo6Ojo+OH4PVOV7WoBhrS9dKn2K73p6LtYuXjlynpnLGFxOWlbeum2KkpGhISZqlkjObSvvSAnp4I4zO7044QlP0e1JxNiYUNYa2FXcP8BYnPxOMj6XBPPjxg8zMRsDDC7hYb79D9e8LtB8b9HQwjwzAw6A3DsEd1QHRg3m0v8eLDlWG5YpW8xxiZ5mceHj9x/+Vnfv38R758+ZmvD594Pnxlmg7EeCqZqr654A8Ul1rnvlBCF0IpmyKxRbHGsEsk8xvEsy2y8vvEGQm/vF1HR0dHR0dHx5+C13NYG79qxVJTftZPvinqX+DV4rZ0iMI8VslyXqbTRYNnhKoWYizkIvPJMk6rwiYsR6pvwEmrIsU76p2eIqQDebonD0qc9pxmJcwKYcB0h6oRJHrhUE7klIkpMk8zw/Ge+PyZ+PiF6fET8ekzdnwiWiSFEdm/Q25+Qm4+MNz9xLh/x7i7YRxvCLt3hP0tOt6gYYdO6p5cCZgIybzoSzQXS4Q/AURLTKcjh8Mj919+5vPnP/D54x/4+uWPPN7/wulwD/GIpOw2gDVy1ZsBmC/FveEqa70nRRWv7628X+KvaiOEUrzm7ca2CiuynfJfkgCU4j1ub/zVTZ/+zaB/87ur1ovvr2jJ84OZt105nIW3Vzz9/rrBpp+uk7uv6bQDnsP7FsKVDXnGX687ttvP13VG0vj2b59+/HzVWH8RXPn7Nn14e71rO6E9nrey+waOf3fdvYr/7u2bL3rdWJNe2Wluf92/OvKHt1uE7f7lin8XgNP/fN2U6HC47j7cfnr7d1zm6/4/6Oj4a8PrhBUnP1aI0MJJ80pI2mlg9eL7JZuzfl/9kh7b5FmsK5tqiqxgmZauuakt/Gf3XzrJ8i5VqlZeq40gIzZh6QmLgTjfMJ8Cx+Bz1UYgBGMgepRVTKSciHNpj3q85/j0K9Pjr5y+/sL88JH4fE9OMyYBG/aEm/fo7U8MN+8J+3eE3XvGm3eE/Xtkd0e4fc+4u2UXAlLUVtNAVm8FpqqejarOEOd44nB45OnhK58//pGvX/+Vx68fOT1/JR4f3JaQ82LtteaaD2wzUuuyuXasQmkuN6wlrDl7uK5QIqtapdSaZIA6Rvb1zjNY2887Ojo6Ojo6On4EXiWsNTi+WlDbjE3Oin1SKoRItmR13SajhUWt3tW1eGqJV2JV/Hzs1u+a3aRpnl/ahu57CL73khVVkERKEzEekHjPfPJwfFX3u+ZwCzYgQIyRnDNzjMzzhM1PnOKBU3z21/mJ0+HBn2wzXm0/fkGGkWF/i+7usP3fMNy8I+zfIft37G5/Ynf7jt3NB1QHGEYkjOiwQ4exmH290GmOR47HRx4eH3h++MTD5595uP/E4fFXpuMDcZrIKS3BCW3DhqAlQqyxAYiwIfs1UUGgBP77SrXobS1i88iwc4uH6uUaOEuQLhHWVBTdjo6Ojo6Ojo4fgFcJ63mfeTMYhqLulenhuk4ttKmfVRJbv1OFYXBSWb2i3vXqjLTa9ufzhgOplKzXoP22O5QXg7k9QDSDZnI+kuevpJCZ1cASlibCcEu2PRjM00xMkRQjqUiEpjcw3KC7O2S4g+FIjM8lisqQNGM2w9Mzol+Q3a/ouEf37m0d7z6wu/3A8O7vGHY3jPsbZNyjw44wjKQgmEBME9N85OnpnqfnB44PHzk9/sLz4z3z8Yk0n7yDFxBrbNWZX3UAVGR5IPBr3hZVNb7f8t+avXquYp8nAVSy2iqu9aGlelaXh5iOjo6Ojo6Ojn8DXKWwtoreQkLP1s0l+qiJT11Qf84JkhiqiZwDKSVEQtmHnm2zkikvyiqDWMCyR0ptyFipkPcCpMaISSTNj8w4SU4xEucTIjdIHiGb2wCin+wwDIThParv2e+Fne0Y88DAnsPuVw7HR+JpJkfzazGDWCIcnzB9wsavhP0du+MHptv3yCmxu71jd/OOsL9Bwh4dRmY1ZkvM84HD8ZHnp0dOp2dOj7+Qj5+YjyfSPEOM7i3VNS7M7b9+TVSEkNfCrRbnaQrLfbXmJn2DaUqj5qoUhbWsGuMSw+oKa+qEtaOjo6Ojo+PfDm8qrJfUNKGobrJWpENDTBuOpI3pcnEB5AwWIQuWPBc1myANDTYBofg9CQgwqCDD4BaADNkUMze9mwXMBkR2oONSzGUGFhOzHdGYSdMMPGAEYjKYEnaKSFI0jIy3t9y8OzLu7xj3e2z8PeO4Q2735Oc9+fiAPN2TDk/kYyRP5qcSYRYgJxIHkgZihmCfyOmZOR6QeAe7PVkHUjoyxyPzdOR0eOR0fGY+HsjTV4gPWDSstJHSkm87DjAM7gEOGkpKgCCBF0qpX5NVpW6J61BblS33OaNmHvzfRFR5XNnaLndpSRs8qCBTbAFpa/+ox9xTAjo6Ojo6Ojp+BN5UWGtleEtKa73UmaC3sQi82KYU7uTkxVwi0T2VxsXMVREpwf3OlJdiLdx7IGXRUkEqWgL7ZUR1IIRQyJqnEuSUSPGIpRMpGTnBKZ6wY0IPiSEHhv0NfPiASCSosrvZM4w3DPs9crdDD3fcHg5Mt/fMz/fMT1+Zj8/EaSamhGUjiZKGHVmUZJkhTcSpTMJbJM+BlIWUfLp/ng/E05E0HUnzCYsHJDsbrHmq4wghKOPo5+VWim0zhkuoJLUtrlpL55p0gHLDVBRTf6rYjNmYY608sdS81tYO0Oa5drLa0dHR0dHR8aPwKmFt/ah1yRlv+Zmd0IawXd8aT2v9rFaYn8dgiURyFlTzCz9lVfTqEooFQJpmA62/tZK4ltCt2a6QU8LmmXg8kabJEwHiCY6Z4WRggkw7Ek9MkglmjKaw/4CMgWF/x4ewI+0i0/5vme+emd8/cTw8MZ+eiPERTYnJjCiBqIEsgQnQnGE+IEzY5DFalo7EeSLFI6QZUkRtJltaSP/iUR2UYRgYR896PZ/6vzTt36qr7Xv/TjdT+C+zbauP+PL3NIVZLVltFdb2Pnd0dHR0dHR0fA/e7FHWpgTUuCptlLVKaGsmZy28qqhTw6EhOJX4zjOEEC+2aG3D9IdhYChWAETIxZtZiaqIuPe0Iap1SSmTzEgxkqcj6fhIPk3YlLEEOnleZMgG+UiSEyGfyKdHpsMzdvd7hvfv0L1iMhJ2O/ZDQPc36N0HdJ6Z5wNpfmSIM4c4cbBITDNTjEQThIik5F5UySCJPHsnLeKExQnJEcnZ7Ra6XrvVr6obUt4WVV1qfdsmKGzJK1QHcn0YOF+32j/cs2yI2NnDxNqKt7o4zpsGtOt1dHR0dHR0dHwPXiesZ7PC7eeVnKRUSKyshFUa/6JWchO26QGrYusFUm2nq/raFhItxEu1TEevRG1RWStRbSvkcybOiTjPMJ/I84TNGWYYMkgEdQ6JZYhiDPGeeDowHY/MT/eMh58Y726Iwx7GGzQMZFEsCMZACO8Iuz15PjLFE8zP5NmY8uznlxNiM0pCSH6+ecbyhCV/PQ/rr9e8nk89x1Y59nXWZIX2+sFL5fWydWBtF2u5sRi3lg5YQr7bYqxQllS2edGytQ1/7ejo6Ojo6Oj4M/EqYR2PgIIFloYBVlRW8UhSpNhKE6yV+k0xlmkhQQ1RbdW4NjN0VfgqOfLXGNduItJU81TSmnPGUvZoqxiJOS88Kc2RPCfsOJFPJ5iLP9ScsGYgmR9/yCBHOMxwmmbG07+ij58Z79+x2/8EtyO2H5DhBhtvyMMNFnaY7IhROSbhmI0ZY7JIFmNMXymXBjFZzm+WSNaMDU2b1eDkOeT1+imGiiGWyFkK6bcz0vqSwFbCX6/TeSOGXNpl1SYMNT9VcimoasmzgI7bQqrKhUvDsrKfhuzm9WHltww9vN2pxsYruwD9+9vr1vub6zr3HK/oYvX099d1vRm/XHcO8d11Ho5wfHsduTI64u6X67pMhMN1613VSew37FWxd9f9Hs1X3Cu58oFxOFy3Xh6vG3D3bnpznf/yH/+fq8b6v/74v1613n/43der1vun6e3xbv745uQjAOPH69Z791+vu243v77971G6fbtTV0fHXyOuy2GtSpviZO8bf/vM1kKtpTjrDEtU0rLooh4uVeivHVNRTX0sWUmrJqIZMs2biqA0J5giFidsntDkqirJ/zGviQdWA+8T5KISH4+ATgy7if3uHo47GEdkvIHdLYx35LDDZGBKyinNnGziaDMxHsmlu8LFPytWSu0JiGREM2JOAMfgmapOPMvqrFP4fq1XNhmCvlBaX1y3C2qrj7cN/MdWwrpk7Jb73t6/i6dkq9JaCetvmBd0dHR0dHR0/HeC11MCWEkOdJjeAAAW30lEQVRdW3hVOUirrtGs03RZ3ZC1NjXgnPRcCrBvp8CXblcNsW2XlGdsLi2WUkbmhGTDYibPhpgx5GIDKGTVynvKYnOJpzJXhilWhnmGeUrodIBwgOGBHAZs3GG6I4eRWQLRMhPGpGAqhJKT2naUWiwPjIjrpygBJYIZo/hNOe/2RSGYMcaSllCvj8+7V666NlKQF0urtFYyvFW6/Zq01f+5WBWqt7Yu9T7XfNhhWO0hfn6l21W3BHR0dHR0dHR8J15XWCmE1aM3N2i9jC8ir1g9jNqs961szrZBgP/sLKdW/Ndio5b0VWJWCVhMCWNGkqGzoRNoLIS0nEdVD6X6RbMT1DyzENYUSxC+4CetfpVyBJmdwJoaKczkMJPDE3lQ5kEwUVIYvaOVDCVHdiWM1b7g51E6coWRgYxYRCwTJBPMu3jVbl5mRm5MpfW8VzK/LYpq0xUuNQ/wz1+2VP0Wt6wWkJbIVmtHm8/b/m5k61y1o6Ojo6Oj48fgVcI643xNkyuOWpQ2kxIaL77UQqtaeLVBUSk1lHWK5zVbJcPOnLTGVamWpgS6IawLWY1Gnq34VDNUsmbJfawZQixLgmDuV5VGMbTkC8UCUElqjuV99GMkQCqpB0P5SLLP5idbe2mZZL8WIaFmGIJg5fgCmYwgKOrFS2YMBJTarcqK388YbCbkiZwiimE5YbkkHVhY7BApZ0wVU/XrXrRsoyHzlrGcXsqmhbgvS1VGbSX2dRH8vprXxrVD+PR/3Cqry22vDwidtXZ0dHR0dHR8J94krAEnMVoyV6UW1eCkrcpooZhBF78rbFMDdCWtUguxyn+MvPS2r1PoqsGbAkhlxEJKkTgnptNMTDNmzqBNimeyFiy1hMv8s+pRtUJMc3QvayVcdYnRSW6dHk9DKRwbyrjlvGwAGZ2MozAESCqgRib5h2L1SjhZphQ+SXkQaOwCACwWhwiiiCpihZSvl3pZt12sfNuqq5m0sU9s1NBGcc7l+iyqc3OPRcp+q4BrhfDbVmGFVUnPufw+2GJ97ejo6Ojo6Oj4s/EqYY044VBWEii2VpJL9gKlqrgpK0erOZ1WCF31u9bOWRuPpNNXxnHckNWWzKWUmOeZ0zQxnSIpslUBi0IYdC2kCrYek5Up/zivXtU4OUGtCmElrQEn12aFDA9l7GptGLxq3nZAVY5VUB2woMx16p61OKqt2L/URnW5CqYYYb3yEkCqn1UujpctbTysOWeyZVJKXqRWC7fOrns95yWSqonWqtP+SzxVo5zXbdquZn5MvoRy+NZjrTo6Ojo6Ojp+AN4krIF1hrhWUVVfaqrCIE5aFCeMWgqWLDjZ02IXgO3UcXEAFG9rW5TkS/Ve5pyZ55l5npnmzDS773QwX0KpSA/q70M5sdBYAciQJogn3zbOngJQVdV26jrS+G+rjWEE3cEwgu5BdpBHIAREpVwnT1lVVfJyPmlpHwusRVAbz+d67gkQlGwKpigZ1cFV1iZFYRNfZS8L0XLOpGQX+WIbTbUhrpHluCphDcXOQbn/9fNzD2wl+8u9LU0FekhAR0dHR0dHx/fi7aKr8r5Obuc6BV8Iq7RKaijT52WpRUtSclzrdPPSVODM77pGLRlGXNQ5J1+JeTLmGWJyxVcLWR3KFLTC0gBgbqa1wRXWePJlPsJ0glP0sVJZb1AIgyuoYecElZ2/DyMM+/WVXSCFgBR/rebZ7QbmU/Gp+nSLqfclcbtM5TLiCquUnNSiPreT67Ugq46jeAOGtUArY/l1slqD/yvRrKTTapEaTaZuLt7k5nrW15a0+sGV73/j+asdHR0dHT8ONr2dvfunYv/p9MPHfPeHH//H6fA//VhpxsKPn5r8/T/++PuT/+9/+uFjvoZXCWulSoarja7++XR42vkihajK4ARHpbxWZVKd2B5Y/bChjchq1LqqrhpGLPPSS/HO7GQqpKKeKuzFyaqmorLOJUu05IDOaa1uj7OT1ekEtX8ArLYHKL7UOz+vvIPdjftULYDt4VQI7G6vDENAg/o5kTBSKcoy1DKQCAhpGAgCQ1C0sjij6W26RlHlnMlipOApAqCuniKQBU1+u0wiOSe3AgChzr03BVFoyZe9dF9LrFdVpXMo2alt0VUhqqEW27Vm1FqwVu6JJbB4tq+mgURHR0dHR0dHx/fgVcJaU51EYByKulgUyCHAGPw1lGWoOZ3V97hAMBPMqRdW5VpY24Fa24nJVi8lLALjgO8/sNoBhmIHqMxzTqs/NU3lNcI0wxwhZi8mqxbYas8cdxDuYLgDvXFSHnZ+rlI8BnWauy1Ckm/4NIVV4WwvRT1H95ZuVdOcc+GCthRAaYliEJNSBGWb/ZlBjN94GlNe+mWt2AnUL2xQT0KoNg5yiaTKrjz7+OXhorVNnHl/N52xyknXAqy/CFTeXudKnP7u5qr10v66fU6/u2690+/ffsq+toPV/tfr9vnuD9fdsNuf56vWuwY6X7fP4et17ZgkXaFO3OyvGusvgby7rnvSzae3zzNd96u7fSB9Bbd/vO737cDdm+v8n7/8b1eN9bf/8fNV6/3TP/+Hq9Yb7t/uNHf8H9/uOAXwN/945b36fF1HuuPv3x7vd//1/qqxOjr+2vAmYQ3AqCsxHYp6GgbYDcU3GtbXWlm+VpMLWQQVzwxVjGB5JV41Tklwn6Y4oQpBURFXY3NGKZ2gzFVWNRjSWrlv+PS+RUhHV1OPB5hPTlJP+BLZktUdMN7A8B6GdxBuYbxdq/+t2h2qf5fVorBmw768dp544BdGVZcs2dVnmrGmkqkWY1kljInSylXJ2VwRXVrOFiW6Tsc3yVVtuL+Lrmsb17Zlqy/lXgYYR99vbrypbbHWkrlbdp8zpOzX/IUtoG6jXWHt6Ojo6Ojo+H68Slir6CbVL2o+PVwr8IOtpHYo61qj6Dn51EKOgo+joGJomZcO5NJzXhhCIAQlDIMvqj79jyHmCf8aJziBTetU9aKozk5W5wOcDnCcYDKfwZ6AIytZDfixDLcQ3sP4AXbvij81+DZWo7rCaotQahX92rTgEmMVEUQFkbVTV1vZb+aq8ksEHz/nosyu21R27xYKIzVE0c4UTtWVlLcw6glsq/u1eI4lbH2tMa5EtG3+YMkJ69yoq+3+rfyn57B2dHR0dHR0fC9eJax1ulyyK5eV0FhcC6tEa6YoiPgKgpT4VFcWBx0Yw8iIMSgMagQ8R2lnU7EQeCvTcRgYx5Fht/dtRXzsHIlxQqZ7Up6YJm+Zmo8QD+5NPR7h8AzTEU5pVVRpXmst2H4AeQe372H3HnZ3Xv1vY9UvvcBMykWoLWmX7yyTclxsDt5mtZEkyyKWkZxBdY2UMkipqqXrJuAdrbyMStbPzSvXUvEJmBk52XY6vhDXUG5cLSRbpGEvmfMxs2HZVlW0Eteyq3q+OflDQJwL+W2SEzDf98x6Tm3b3sX20QlrR0dHR0dHx3fiKgOOARSSSnAVkwxDIXOqMKKojkjYo800+DiO7IY9t3LDGGAMCZWIEhESmmbUEoqhZIYAQxBGDQQZfBodQAdUAqbPJCayefHU6RGmR5/6P53gUFTVE06mlqKxcrJBYH8D+3eQP8DNexhu3J9bGx+ks3OvPt5KXk1LWoJlghkiSmYgS/auVzl74L9BSNFJXvKoqmwwZ3nB41yxNSAiWNO3yorfNJAxJEXMFDNXdgU/5preUJeMP2i0rXLNxPdRbQS2VWizlQ5e5eDqNcNcTaX5jnJtYxtftdZ+Lcz+oojc0dHR0dHR0fEn4M0c1jp9bgY2u8KqCqPAXuEmCPtx4HZ/w7AbCVUhHQfGYWTcjYxhx05uGIMxaEZlRm1GiOg8kWPECnEVi0iMSD6AhsViYKXU3+aZNMHpGQ4PcPgKzw9eUJXMp/4nnKxSjh2cXO92Tk5375yw2rtCVoeVqKZmHn0JztftspneRwsRzEu1f8V5BNQiwG6m+V9Cmv+0/tOcXSnd1DaJF79lKQ8UrOQTQLUWdzWdruKqyG78p3UKv6izWsh5LZ6K51mrwFzSIKp/eXO+dEtAR0dHR0dHx/fjTcKaWD2qZBjNi7DuRnh/E7i7u+X29pabmxtCMEIwJ6qjL7vdjmHYMejeK/xtQiyT0wwpMwLJMilFJE1YmskpMjel+DXyKmcjzonDEzx/hcN9Ia2zE9TULLD6azV4IdVYUgB27/xViqqaxaO3qhpbC8dasjoMgaChEFVZVEXPPXWWV4msmRc6iSqijW8VDzRVkbINL0guXKjsr5+Ja66VBbYFTaF05jrvQuW5t2fdrrJ7UM8Jay2oatVkHdYmVy/ascrL69QmCZz7Wjs6Ojo6Ojo6/hy8SVhhJYADTkr2A9ztBz7cfuDDh5/Y7/eM40jQmaCTV/n7JDkDmWAe2qnJR7V0wtLJK3qmyefz5yNxOpGmxDSxFBSlMn2di2oYZzgdnayeHv1ncKKZyiviaQZSoql2Oxj3Hlc13IHcAjv3YhplGr0JLRh17dilKu6lHQZUBqTRD2tIv9Z+tQUisiYIEJDS+Yrm+FSUnPOSHLCiJACYWw4uqbDVT7pKrdpM/a/H0aqhLwZgSyiX9426Gka/NnpWV7YUYp0pz8PwkrD+xWKtOjo6Ojo6Ov5q8KaHtRZeLRFXBu8Uftopd/s9N7s9uzEQNBJyRNNcAvCFNE3EacLCgOrIrJDzhOUjOU1YiszxmTzP5Dlh0YhH70QVZydG81w6MDUFS3P075O4hzKbk6qxSsEjyN47VYURdsFfQ+laVQvFpKiEQba1QbUda/XhDsNACAOhabEKro56Xmz2FIQlLkqWdIRcLrGrl7l4daXEfVXCrKUcSjEylp16r7VbVc4cgAwyYCJkc9NGTS6AlwS0LotquhRjrZ8tZLT6TgVvq9sUTo1tsV392MpDTLmGQbdjmq2xYx0dHR0dHR0dfy5eJawl4YkBzyu9AfbZl9EiYjNGwsjuSY3P5OmZGJOrjyGQh8CggUEGkhgpz6Q0e5cmS1hKWCwCZSxh/zPYBHNZUloJZaYUBgXgDuSmHOPgfE7Lq4zlVc98qFZauabSWjbghKyqrYjHWYmiIaBhQIPHbGkzB1/zTFUFy4qavxctHa0KK1QJpFLVpLUQi9oHSzAJhbgqZuoKrhg5e1+xaoUwA5MAWRGG5TtyLokL9bheKqe1He7SlpWXcVew/XDx8vouPdKMEm9WvspWPi+L5fLLUklws8//r2EPj1etF/7w9gHe8O+uGuv5799dtd7Nr1eG8//y9jr2dga6j/XxuqD/23++YqeAfXo7zD3dXxdwrndvh8wDyG68br3b2zfXSR9/vWqsvwTCz9cF5d99fPv3LRyuC6wfrlzv4e+v60QQjm8HMN98vu5pdvw//vaq9f7h03W/49c07tDpumO7+fnrVevp43UtRvP7txtapH/656vG6uj4a8OrhPUOGMtKA15oNYorbWnKpOnIdHwszC8SpkfkdCTHEkBflDpTby8qYqRsXo1efJD1T7dQpv2BGFzJiwHmwYevBExLtFZt76qlKkwG31cY3M+pYa1W3+SDNgSumjMXW2pht6aKaEA1EMKA6oCWn9dx3EDgDQBsJayiqNYYKYg16r/kttYpfquBpq+gFnL5dngBWk0gyJ5GYCldVFfPp+LPf67X+/za1O/azwwnrXVhKOR0KSJbvbGS1tirjo6Ojo6Ojo4fgVcJ64fRCeGIV9kHgzEDk4fzy+6ADkJISpSJNE8wrzFJiFeWqxoBI8ZCbGQt8qm5nkU8JIov2Yp6tPMCoYUcBVdRV4+pb5vqdoUrajsFPq/krHLEJTe0jOH6gkdUiY6oDp6HagHLSk6eeSUiC4GsrRXa/NG14MkPOElayGpuWKMVw6gt6a3Nu6UbVrOdKDmnwrGLHzbnxeNbVdXwDcXtPNS/VWLblIDa0XS5TuUhIqtf46TUTrkLCbbs39XBqzjb0dHR0dHR0fEj8Cph3d0WdbVMo2t00mrRW5/mpwzhCduJZ5A2eZ5Lm1Ap0/1W2njaGrk0Bnhn7pU0K2SoKHg6OiHS7PuuJKj6S2seaipxVFlXImy2+m7fYk+XvhILVRrevFqWcm4lz7SuL+L+1sIEU149DDnkhXi2yNmVWc9PFVJLWEvr10pWU0quTptgJLcW5ETORk5+fc/VUj+uCydXr79tt1lsE2WbVqVt7Ribxd0Lm3atVZHt+asdHR0dHR0dPwqvE9bZCcxQFMSqqk3iPtPdBHp0JTBWvgaLykZeQ/erRSBXBa4ooqkQpTbEUwTSWOwCxT6wkFWAEhfl+3KGXIt7rBJgynbJSe8SAVDjmuq+yj5EFZHRK/qDYipLL1Zbjs/I5LWlausJXdid2x4qzZPkbLwqo9WTqpr8/FIEHT3rtLBrdRMtMRk5ZixGkplbFcr+JSdvdQubFq2XSKoVUrnkszbEElayugkeYJ3aN0r0V3lAqFaPevlNmve23uNqE+jo6Ojo6Ojo+B68Slin49rNaiGd4CQmQp4gPsHgnAvYKpYLESqEr52WF0ov+lIIJdl9qWorOW67ihq1gMrn8UXV95XL2paXWCYKMZNMkV/X6f/aXrQel6grnKoB0SaCarNvw6RM0+NRVtYwPufBa8D/CiO01U64sirqpWq1G5blVA5bEBMUv9iWMzklckzMKWEBstlS5LQQz8anezHKqpnqP+9WtclQLd9X1LEq18+wdNJaUJsVVIW1qr11u05YOzo6Ojo6Or4TrxLWJ/MYqyGvJK+qjZZdZa2V4xqLAtrws4UMhUIMbSWsqq58ivmragn5Lz7MNs+zjuXFUWt0lH9efoZNEVBtPyrJxwphJWahdGZCQYNiCFl1KboSqQqhrUVSF/JQ6zS/wtKR6yWkFGHV8yhdsayyukKAkYWwWrbFErD6WM2VVAq5L+pq68uFlbieR7tKU2CmurUCLOH/RWI9TxmgXE/K/SI3vLZZLzeZua09o6Ojo6Ojo6Pje/AqYT3gHaQGCpHMRZWsamglMbMTqPMIKZFalkSbqw9SopHEi6dMSlxVGdfqvthuY1bspLolkk7qmmKvSpTKcj71HYLvl5K1mkvl1bKJFVW1jF3jrGoXq/OK/wyrTQA2hLpmqNZtRbwRQSWgrkJmT04ohFWybchqPb9UfaVFQR6aoIH6+qLqv1FeW2J7TnIXwbj1ojbvawct2Kqmm8KttBaApcYa0NHR0dHR0dHxPXiz05Xhs+qCF1+ForYa67S0Fq/jUuhU7ANV9bRKdMt3i1dSnNhYmb6v0/apTnnz0pPZFjBtK+nX6eh6LK0K+ELpE5AmNzWzznW74lm9tsV3inm+apOL6sfgrymncrxOSBdFthDtamtoD8CVYZ/6X4myX7xabOWvXly12BwaC61WP+756VUC25x/SzQvkddymC9atlay2iYKXIoKWxZbu451R0BHR0dHR0fH9+LNTlfFArr+nErIvq2B8VIU0oVkVoJmK1nLrKor4lX9paYJWKe2WyLoU9VtWD9U2TQVg6WZLV7LajutJDvXqe7sx1aLksxqu1ElJxbPgpmSkmElZNSAPAiRjImy05FgVW2tx+SdqVrFt1VYYxaCKJLljCQWtVWUHUaMBimTrBQ3bSwBhfTHRv3U9aHhvMvUuo9CJqsXtVwDhSXOa1MYVX3KhXjWLmPniiq8nkpg+MPO+e9OR0dHR0dHR8efA7nkzezo6Ojo6Ojo6Oj4reDt/nkdHR0dHR0dHR0df0F0wtrR0dHR0dHR0fGbRiesHR0dHR0dHR0dv2l0wtrR0dHR0dHR0fGbRiesHR0dHR0dHR0dv2l0wtrR0dHR0dHR0fGbxn8DW6E5uoNy7w0AAAAASUVORK5CYII=\n", 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { @@ -1750,40 +1642,36 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.38039213..1.0].\n" ] }, { @@ -1795,33 +1683,29 @@ }, { "data": { - "image/png": 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\n", 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ - "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" + "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). Got range [-0.92941177..0.9740809].\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1860,10 +1744,9 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [], "source": [ @@ -2017,7 +1900,7 @@ "name": "python3" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2031,9 +1914,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.5" + "version": "3.9.16" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } From b7a54986a606a863bb61e2642c4b9e612a6c9dc2 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Tue, 16 Jul 2024 20:29:19 +0000 Subject: [PATCH 25/25] Process tutorial notebooks --- projects/ComputerVision/em_synapses.ipynb | 186 +++++--------- .../ComputerVision/spectrogram_analysis.ipynb | 88 ++++--- .../sentiment_analysis.ipynb | 239 ++++++++++++------ projects/Neuroscience/blurry_vision.ipynb | 132 ++++++---- .../Neuroscience/cellular_segmentation.ipynb | 109 +++++--- projects/ReinforcementLearning/human_rl.ipynb | 64 +++-- .../ReinforcementLearning/lunar_lander.ipynb | 213 ++++++---------- 7 files changed, 551 insertions(+), 480 deletions(-) diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb index 3914df0e3..0ce02ae58 100644 --- a/projects/ComputerVision/em_synapses.ipynb +++ b/projects/ComputerVision/em_synapses.ipynb @@ -4,8 +4,7 @@ "cell_type": "markdown", "id": "2d9f0b20", "metadata": { - "execution": {}, - "id": "2d9f0b20" + "execution": {} }, "source": [ "\"Open   \"Open" @@ -15,8 +14,7 @@ "cell_type": "markdown", "id": "renayVUI7b9x", "metadata": { - "execution": {}, - "id": "renayVUI7b9x" + "execution": {} }, "source": [ "# Knowledge Extraction from a Convolutional Neural Network\n", @@ -32,8 +30,7 @@ "cell_type": "markdown", "id": "U6wofKujWp6X", "metadata": { - "execution": {}, - "id": "U6wofKujWp6X" + "execution": {} }, "source": [ "---\n", @@ -50,8 +47,7 @@ "cell_type": "markdown", "id": "zO4YN6W8W0Cp", "metadata": { - "execution": {}, - "id": "zO4YN6W8W0Cp" + "execution": {} }, "source": [ "---\n", @@ -60,11 +56,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "fO1IZwvkW9Me", "metadata": { "cellView": "form", - "id": "fO1IZwvkW9Me" + "execution": {} }, "outputs": [ { @@ -92,10 +88,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "gKkHjjTGWzUk", "metadata": { - "id": "gKkHjjTGWzUk" + "execution": {} }, "outputs": [ { @@ -128,8 +124,7 @@ "cell_type": "markdown", "id": "bd7d427d", "metadata": { - "execution": {}, - "id": "bd7d427d" + "execution": {} }, "source": [ "---\n", @@ -160,8 +155,7 @@ "cell_type": "markdown", "id": "5642d709", "metadata": { - "execution": {}, - "id": "5642d709" + "execution": {} }, "source": [ "---\n", @@ -174,8 +168,7 @@ "cell_type": "markdown", "id": "c61a11c6", "metadata": { - "execution": {}, - "id": "c61a11c6" + "execution": {} }, "source": [ "## Data Preparation" @@ -183,14 +176,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "821dc497", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "821dc497", - "outputId": "9871a113-d169-4d6a-b927-b00706fabd22" + "cellView": "form", + "execution": {} }, "outputs": [ { @@ -315,8 +305,7 @@ "cell_type": "markdown", "id": "0b84ec7b", "metadata": { - "execution": {}, - "id": "0b84ec7b" + "execution": {} }, "source": [ "## Classifier Training" @@ -326,8 +315,7 @@ "cell_type": "markdown", "id": "a79ab567", "metadata": { - "execution": {}, - "id": "a79ab567" + "execution": {} }, "source": [ "### Create and Inspect Datasets\n", @@ -337,14 +325,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "ae50b16a", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ae50b16a", - "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6" + "execution": {} }, "outputs": [ { @@ -413,8 +397,7 @@ "cell_type": "markdown", "id": "e9010bdc", "metadata": { - "execution": {}, - "id": "e9010bdc" + "execution": {} }, "source": [ "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" @@ -422,15 +405,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "3d8c6f3a", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 117 - }, - "id": "3d8c6f3a", - "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d" + "execution": {} }, "outputs": [ { @@ -463,8 +441,7 @@ "cell_type": "markdown", "id": "f882416f", "metadata": { - "execution": {}, - "id": "f882416f" + "execution": {} }, "source": [ "### Create a Model, Loss, and Optimizer" @@ -472,10 +449,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "54f177cc", "metadata": { - "id": "54f177cc" + "execution": {} }, "outputs": [], "source": [ @@ -570,10 +547,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "5da43245", "metadata": { - "id": "5da43245" + "execution": {} }, "outputs": [], "source": [ @@ -596,8 +573,7 @@ "cell_type": "markdown", "id": "01688095", "metadata": { - "execution": {}, - "id": "01688095" + "execution": {} }, "source": [ "### Train the Model" @@ -605,14 +581,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "fa65090d", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "fa65090d", - "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1" + "execution": {} }, "outputs": [ { @@ -634,8 +606,7 @@ "cell_type": "markdown", "id": "ecbab4f7", "metadata": { - "execution": {}, - "id": "ecbab4f7" + "execution": {} }, "source": [ "The next cell merely defines some convenience functions for training, validation, and testing:" @@ -643,10 +614,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "1a8c7fe9", "metadata": { - "id": "1a8c7fe9" + "execution": {} }, "outputs": [], "source": [ @@ -716,8 +687,7 @@ "cell_type": "markdown", "id": "68bcfbbf", "metadata": { - "execution": {}, - "id": "68bcfbbf" + "execution": {} }, "source": [ "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." @@ -725,10 +695,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "d0af7638", "metadata": { - "id": "d0af7638" + "execution": {} }, "outputs": [], "source": [ @@ -748,8 +718,7 @@ "cell_type": "markdown", "id": "45e31b87", "metadata": { - "execution": {}, - "id": "45e31b87" + "execution": {} }, "source": [ "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", @@ -759,11 +728,11 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "W5KA7zDIa3Lw", "metadata": { "cellView": "form", - "id": "W5KA7zDIa3Lw" + "execution": {} }, "outputs": [], "source": [ @@ -773,10 +742,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "53fb8dda", "metadata": { - "id": "53fb8dda" + "execution": {} }, "outputs": [], "source": [ @@ -796,14 +765,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "4f6e3663", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4f6e3663", - "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b" + "execution": {} }, "outputs": [ { @@ -837,8 +802,7 @@ "cell_type": "markdown", "id": "3f43bba5", "metadata": { - "execution": {}, - "id": "3f43bba5" + "execution": {} }, "source": [ "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", @@ -852,8 +816,7 @@ "cell_type": "markdown", "id": "72b5240c", "metadata": { - "execution": {}, - "id": "72b5240c" + "execution": {} }, "source": [ "---\n", @@ -864,11 +827,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "41c9e63b", "metadata": { "cellView": "form", - "id": "41c9e63b" + "execution": {} }, "outputs": [ { @@ -900,8 +863,7 @@ "cell_type": "markdown", "id": "e5da5c01", "metadata": { - "execution": {}, - "id": "e5da5c01" + "execution": {} }, "source": [ "In this example, we will translate between GABAergic and glutamatergic synapses.\n", @@ -911,14 +873,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "2b2519c4", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "2b2519c4", - "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c" + "execution": {} }, "outputs": [ { @@ -947,8 +905,7 @@ "cell_type": "markdown", "id": "0d328904", "metadata": { - "execution": {}, - "id": "0d328904" + "execution": {} }, "source": [ "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", @@ -958,14 +915,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "id": "a182c3bc", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "a182c3bc", - "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129" + "execution": {} }, "outputs": [], "source": [ @@ -986,8 +939,7 @@ "cell_type": "markdown", "id": "17fc1703", "metadata": { - "execution": {}, - "id": "17fc1703" + "execution": {} }, "source": [ "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" @@ -995,10 +947,10 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "id": "2a582ba6", "metadata": { - "id": "2a582ba6" + "execution": {} }, "outputs": [], "source": [ @@ -1020,8 +972,7 @@ "cell_type": "markdown", "id": "2cc0d486", "metadata": { - "execution": {}, - "id": "2cc0d486" + "execution": {} }, "source": [ "Show the top real and fake images that make the classifier change its mind:" @@ -1029,15 +980,10 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "id": "1567b00e", "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "1567b00e", - "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c" + "execution": {} }, "outputs": [], "source": [ @@ -1061,23 +1007,17 @@ " fake_B = imread(basename + '_fake.png')\n", " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "m7XsnW_R7wBN", - "metadata": { - "id": "m7XsnW_R7wBN" - }, - "outputs": [], - "source": [] } ], "metadata": { "accelerator": "GPU", "colab": { + "collapsed_sections": [], "gpuType": "T4", - "provenance": [] + "include_colab_link": true, + "name": "em_synapses", + "provenance": [], + "toc_visible": true }, "kernel": { "display_name": "Python 3", diff --git a/projects/ComputerVision/spectrogram_analysis.ipynb b/projects/ComputerVision/spectrogram_analysis.ipynb index 67f485971..8f2f42e5d 100644 --- a/projects/ComputerVision/spectrogram_analysis.ipynb +++ b/projects/ComputerVision/spectrogram_analysis.ipynb @@ -73,9 +73,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -88,8 +89,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Import necessary libraries.\n", @@ -112,8 +115,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "import requests\n", @@ -151,8 +156,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "from zipfile import ZipFile\n", @@ -175,8 +182,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -208,8 +217,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -254,8 +265,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -308,8 +321,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -339,8 +354,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -390,9 +407,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -432,8 +450,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -449,8 +469,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Create folder with training, testing and validation data.\n", @@ -497,8 +519,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Data loading.\n", @@ -524,8 +548,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Make a CNN & train it to predict genres.\n", @@ -655,8 +681,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { diff --git a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb index 8b10868a2..6fbe7549a 100644 --- a/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb +++ b/projects/NaturalLanguageProcessing/sentiment_analysis.ipynb @@ -78,9 +78,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [ { @@ -104,8 +105,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -155,8 +158,10 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -237,8 +242,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -360,8 +367,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "X = df.text.values\n", @@ -385,8 +394,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -416,8 +427,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -437,8 +450,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -485,8 +500,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -520,8 +537,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -547,8 +566,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -627,8 +648,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "vectorizer = CountVectorizer(binary=True)\n", @@ -638,8 +661,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -655,8 +680,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -689,8 +716,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1117,8 +1146,10 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1154,8 +1185,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1176,8 +1209,10 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1213,8 +1248,10 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1287,8 +1324,10 @@ }, { "cell_type": "code", - "execution_count": 20, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "def set_device():\n", @@ -1305,8 +1344,10 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1332,8 +1373,10 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1362,8 +1405,10 @@ }, { "cell_type": "code", - "execution_count": 23, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "num_words_dict = 30000\n", @@ -1382,8 +1427,10 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# dictionary to go from words to idx\n", @@ -1419,8 +1466,10 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# A function to convert list of tokens to list of indexes\n", @@ -1439,8 +1488,10 @@ }, { "cell_type": "code", - "execution_count": 26, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "x_train_idx = tokens_to_idx(x_train_token,word_to_idx)\n", @@ -1449,8 +1500,10 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1478,8 +1531,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1509,8 +1564,10 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ " # We choose the max length\n", @@ -1534,8 +1591,10 @@ }, { "cell_type": "code", - "execution_count": 30, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# We convert our list of tokens into a numpy matrix\n", @@ -1550,8 +1609,10 @@ }, { "cell_type": "code", - "execution_count": 31, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1581,8 +1642,10 @@ }, { "cell_type": "code", - "execution_count": 32, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# create Tensor datasets\n", @@ -1609,8 +1672,10 @@ }, { "cell_type": "code", - "execution_count": 33, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1667,8 +1732,10 @@ }, { "cell_type": "code", - "execution_count": 34, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "class SentimentRNN(nn.Module):\n", @@ -1745,8 +1812,10 @@ }, { "cell_type": "code", - "execution_count": 35, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Parameters of our network\n", @@ -1772,8 +1841,10 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1800,8 +1871,10 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1829,8 +1902,10 @@ }, { "cell_type": "code", - "execution_count": 38, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -1868,8 +1943,10 @@ }, { "cell_type": "code", - "execution_count": 39, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1997,8 +2074,10 @@ }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { diff --git a/projects/Neuroscience/blurry_vision.ipynb b/projects/Neuroscience/blurry_vision.ipynb index b3e073352..4ad804736 100644 --- a/projects/Neuroscience/blurry_vision.ipynb +++ b/projects/Neuroscience/blurry_vision.ipynb @@ -50,9 +50,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -63,8 +64,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Imports\n", @@ -96,9 +99,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -137,8 +141,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -173,9 +179,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -232,8 +239,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Define Preprocessing Filters\n", @@ -305,8 +314,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Get an example of a clear and noisy versions of cat and dog image\n", @@ -327,8 +338,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -405,8 +418,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Define AlexNet with different modules representing different brain areas\n", @@ -488,8 +503,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Create an neural network object and visualise it with an example image\n", @@ -501,8 +518,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -546,8 +565,10 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# define function for running some epochs of training\n", @@ -592,8 +613,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# define function to calculate current accuracy with a given dataloader\n", @@ -654,8 +677,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -895,8 +920,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1241,9 +1268,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -1280,8 +1308,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1327,8 +1357,10 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1398,8 +1430,10 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# choose intermidiate layers from which to get the output\n", @@ -1413,9 +1447,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -1455,8 +1490,10 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -1580,8 +1617,10 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -1744,9 +1783,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ diff --git a/projects/Neuroscience/cellular_segmentation.ipynb b/projects/Neuroscience/cellular_segmentation.ipynb index d6f1e6490..ef17b79d5 100644 --- a/projects/Neuroscience/cellular_segmentation.ipynb +++ b/projects/Neuroscience/cellular_segmentation.ipynb @@ -52,9 +52,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -66,8 +67,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# Imports\n", @@ -171,9 +174,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -234,8 +238,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -286,8 +292,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "labels_train = np.zeros((len(masks_train), 2,\n", @@ -318,8 +326,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -478,8 +488,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "def convbatchrelu(in_channels, out_channels, sz):\n", @@ -615,8 +627,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "kernel_size = 3\n", @@ -644,8 +658,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -861,9 +877,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -913,8 +930,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -972,9 +991,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -1142,8 +1162,10 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1219,8 +1241,10 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -1282,8 +1306,10 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1322,9 +1348,10 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [ { @@ -1372,8 +1399,10 @@ }, { "cell_type": "code", - "execution_count": 17, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1396,8 +1425,10 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -1460,8 +1491,10 @@ }, { "cell_type": "code", - "execution_count": 19, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { diff --git a/projects/ReinforcementLearning/human_rl.ipynb b/projects/ReinforcementLearning/human_rl.ipynb index 954a4bbba..19d71c316 100644 --- a/projects/ReinforcementLearning/human_rl.ipynb +++ b/projects/ReinforcementLearning/human_rl.ipynb @@ -54,9 +54,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [ { @@ -85,8 +86,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -118,9 +121,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "cellView": "form" + "cellView": "form", + "execution": {} }, "outputs": [], "source": [ @@ -220,8 +224,10 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -441,8 +447,10 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "class NBack(dm_env.Environment):\n", @@ -602,8 +610,10 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "class RandomAgent(acme.Actor):\n", @@ -641,8 +651,10 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stdout", @@ -677,8 +689,10 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "data": { @@ -777,8 +791,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [], "source": [ "# init a new N-back environment\n", @@ -796,8 +812,10 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", @@ -853,8 +871,10 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": {}, + "execution_count": null, + "metadata": { + "execution": {} + }, "outputs": [ { "name": "stderr", diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index adf6b71b6..405d1a454 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -3,8 +3,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "sgIXqXYCDBuR" + "execution": {} }, "source": [ "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", @@ -19,8 +18,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "AKlrTCmFDBuS" + "execution": {} }, "source": [ "---\n", @@ -32,8 +30,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "x5XKBDyYDBuS" + "execution": {} }, "source": [ "---\n", @@ -42,10 +39,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "cellView": "form", - "id": "WsHayfTHDBuS" + "execution": {} }, "outputs": [], "source": [ @@ -59,13 +56,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "6fooEJQSDBuT", - "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589" + "cellView": "form", + "execution": {} }, "outputs": [ { @@ -128,9 +122,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { - "id": "nA2Y9HGUDBuT" + "execution": {} }, "outputs": [], "source": [ @@ -162,14 +156,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "_M-76WwDDBuT", - "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2" + "execution": {} }, "outputs": [], "source": [ @@ -203,8 +193,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "UJnBSc5KDBuU" + "execution": {} }, "source": [ "---\n", @@ -248,8 +237,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "XuSpVWuGDBuU" + "execution": {} }, "source": [ "---\n", @@ -261,8 +249,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "dYwJRvx-DBuV" + "execution": {} }, "source": [ "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." @@ -270,13 +257,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "MvWRAJiSDBuV", - "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e" + "execution": {} }, "outputs": [], "source": [ @@ -290,8 +273,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "EHLx2d5xDBuV" + "execution": {} }, "source": [ "Now, let us setup our model and the DQN algorithm." @@ -299,9 +281,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { - "id": "4PzeDS2dDBuV" + "execution": {} }, "outputs": [], "source": [ @@ -346,8 +328,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "1FIshtazDBuW" + "execution": {} }, "source": [ "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", @@ -357,13 +338,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SyD6VwDhDBuW", - "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1" + "execution": {} }, "outputs": [ { @@ -384,14 +361,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - }, - "id": "68D-3iePDBuW", - "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622" + "execution": {} }, "outputs": [ { @@ -466,8 +438,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "fhtq8GDLDBuW" + "execution": {} }, "source": [ "From the video above, we see that the lander has crashed!\n", @@ -476,13 +447,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Xhl3ojMwDBuW", - "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e" + "execution": {} }, "outputs": [ { @@ -534,8 +501,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "IYynM83tDBuX" + "execution": {} }, "source": [ "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", @@ -551,8 +517,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "UFNQVKokDBuX" + "execution": {} }, "source": [ "Now, let us look at the visual performance of the lander.\n", @@ -562,14 +527,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - }, - "id": "hc0xXn5aDBuX", - "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537" + "execution": {} }, "outputs": [ { @@ -644,8 +604,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "cVCcx8GUDBuX" + "execution": {} }, "source": [ "The lander has landed safely!!\n", @@ -655,14 +614,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 510 - }, - "id": "_8ibUiTmDBuX", - "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6" + "execution": {} }, "outputs": [ { @@ -688,8 +642,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "2Zo8kpDUDBuX" + "execution": {} }, "source": [ "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", @@ -700,8 +653,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "D7JAEDEzDBuX" + "execution": {} }, "source": [ "---\n", @@ -711,8 +663,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "1m6YBf5nDBuX" + "execution": {} }, "source": [ "## 1 Play with exploration-exploitation trade-off\n", @@ -722,13 +673,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tnbb16KUDBuY", - "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f" + "execution": {} }, "outputs": [], "source": [ @@ -740,8 +687,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "794AyGDPDBuY" + "execution": {} }, "source": [ "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." @@ -750,8 +696,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "Ljf9XG5BDBuY" + "execution": {} }, "source": [ "## 2 Reward Shaping\n", @@ -761,9 +706,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": { - "id": "zAAhdiflDBuY" + "execution": {} }, "outputs": [], "source": [ @@ -797,8 +742,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "n7u1oEO2DBuY" + "execution": {} }, "source": [ "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." @@ -806,9 +750,9 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": { - "id": "463GUtbuDBuY" + "execution": {} }, "outputs": [], "source": [ @@ -987,8 +931,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "V-qZ4WYxDBuZ" + "execution": {} }, "source": [ "Once you have cutomized your own environment, you can execute that environment by just calling:" @@ -996,9 +939,9 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": { - "id": "yq4902DQDBuZ" + "execution": {} }, "outputs": [], "source": [ @@ -1009,8 +952,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "EAP-DUd6DBuZ" + "execution": {} }, "source": [ "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." @@ -1019,8 +961,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "QTq0hmHCDBuZ" + "execution": {} }, "source": [ "## 3 Identify the state information crucial to its performance.\n", @@ -1041,9 +982,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": { - "id": "sz45kgEaDBuZ" + "execution": {} }, "outputs": [], "source": [ @@ -1066,8 +1007,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "DXy9s2ymDBuZ" + "execution": {} }, "source": [ "## 4 Extension to Atari Games\n", @@ -1077,9 +1017,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": { - "id": "4RjAt0W-DBuZ" + "execution": {} }, "outputs": [ { @@ -1104,8 +1044,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "E6_fFAYhDBue" + "execution": {} }, "source": [ "## 5 Obstacle Avoidance and Transfer Learning\n", @@ -1119,9 +1058,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { - "id": "h6knZ3U8DBue" + "execution": {} }, "outputs": [], "source": [ @@ -1135,8 +1074,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "GXBIbO25DBue" + "execution": {} }, "source": [ "Following are some of the resources on transfer learning that you would want to start with.\n", @@ -1160,8 +1098,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "MlOksW3ODBue" + "execution": {} }, "source": [ "## 5(b) Transfer Learning in minigrid environment\n", @@ -1171,9 +1108,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": { - "id": "v7rB2JQlDBue" + "execution": {} }, "outputs": [], "source": [ @@ -1183,8 +1120,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "OPcds7ZtDBue" + "execution": {} }, "source": [ "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" @@ -1192,13 +1128,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "d0NiSkyeDBue", - "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22" + "execution": {} }, "outputs": [ { @@ -1220,8 +1152,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "7J92iMqqDBue" + "execution": {} }, "source": [ "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", @@ -1232,8 +1163,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "HL0W5M4uDBue" + "execution": {} }, "source": [ "## 6 Preference-Based RL (PBRL)\n", @@ -1256,8 +1186,7 @@ { "cell_type": "markdown", "metadata": { - "execution": {}, - "id": "QdW-XKCMDBue" + "execution": {} }, "source": [ "---\n", @@ -1273,6 +1202,8 @@ "metadata": { "accelerator": "GPU", "colab": { + "collapsed_sections": [], + "include_colab_link": true, "name": "lunar_lander", "provenance": [], "toc_visible": true