diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
new file mode 100644
index 00000000..55870dbc
--- /dev/null
+++ b/.github/workflows/python-package.yml
@@ -0,0 +1,58 @@
+---
+name: CI
+
+on:
+ push:
+ branches: ["main","github-actions"]
+ pull_request:
+ branches: ["main"]
+ workflow_dispatch:
+
+jobs:
+ tests:
+ name: "Python ${{ matrix.python-version }}"
+ runs-on: "ubuntu-latest"
+
+ strategy:
+ matrix:
+ # python-version: ["3.7", "3.8", "3.9"]
+ python-version: ["3.8", "3.9", "3.10"]
+
+ steps:
+ - uses: "actions/checkout@v2"
+ - uses: "actions/setup-python@v2"
+ - uses: "s-weigand/setup-conda@v1"
+ with:
+ python-version: "${{ matrix.python-version }}"
+
+ - name: Install solvers
+ run: sudo apt-get install -y glpk-utils coinor-cbc
+
+ - name: "Install dependencies"
+ run: |
+ set -xe
+ python -VV
+ python -m site
+ python -m pip install --upgrade pip setuptools wheel
+ python -m pip install --upgrade coverage[toml] virtualenv tox tox-gh-actions
+ conda install -c conda-forge ipopt
+ conda install -c conda-forge pyscipopt
+
+ - name: "Run tox targets with lean testing environment for ${{ matrix.python-version }}"
+ run: "tox -re leanenv"
+
+ - name: "Run tox targets for ${{ matrix.python-version }}"
+ run: "tox"
+
+ # - name: "Run tox notebooks targets for ${{ matrix.python-version }}"
+ # run: |
+ # shopt -s globstar
+ # tox -e notebooks docs/**/*.ipynb
+
+ - name: "Convert coverage"
+ run: "python -m coverage xml"
+
+ - name: "Upload coverage to Codecov"
+ uses: "codecov/codecov-action@v2"
+ with:
+ fail_ci_if_error: true
diff --git a/.gitignore b/.gitignore
index b6e47617..1b77d315 100644
--- a/.gitignore
+++ b/.gitignore
@@ -127,3 +127,4 @@ dmypy.json
# Pyre type checker
.pyre/
+.vscode/settings.json
diff --git a/docs/conf.py b/docs/conf.py
index a5e75db8..a85d176d 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -7,10 +7,10 @@
# All configuration values have a default; values that are commented out
# serve to show the default.
-import os
-import sys
import inspect
+import os
import shutil
+import sys
# -- Path setup --------------------------------------------------------------
@@ -77,7 +77,7 @@
"sphinx.ext.doctest",
"sphinx.ext.ifconfig",
"sphinx.ext.mathjax",
- "sphinx.ext.napoleon"
+ "sphinx.ext.napoleon",
]
# Add any paths that contain templates here, relative to this directory.
@@ -147,7 +147,7 @@
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
-#html_theme = "furo"
+# html_theme = "furo"
html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
diff --git a/docs/notebooks/data/build_sin_quadratic_csv.py b/docs/notebooks/data/build_sin_quadratic_csv.py
index c03f34dd..6506022a 100644
--- a/docs/notebooks/data/build_sin_quadratic_csv.py
+++ b/docs/notebooks/data/build_sin_quadratic_csv.py
@@ -1,15 +1,20 @@
+from random import random
+
import numpy as np
import pandas as pd
-from random import random
n_samples = 10000
w = 5
-x = np.linspace(-2,2,n_samples)
-df = pd.DataFrame(x, columns=['x'])
-df['y'] = np.sin(w*x) + x**2 + np.array([np.random.uniform()*0.1 for _ in range(n_samples)])
+x = np.linspace(-2, 2, n_samples)
+df = pd.DataFrame(x, columns=["x"])
+df["y"] = (
+ np.sin(w * x)
+ + x**2
+ + np.array([np.random.uniform() * 0.1 for _ in range(n_samples)])
+)
-plt.plot(df['x'],df['y'])
+plt.plot(df["x"], df["y"])
plt.show()
-df.to_csv("sin_quadratic.csv")
\ No newline at end of file
+df.to_csv("sin_quadratic.csv")
diff --git a/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb b/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb
index f286f23b..78c4e1a9 100644
--- a/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb
+++ b/docs/notebooks/neuralnet/auto-thermal-reformer-relu.ipynb
@@ -50,7 +50,7 @@
"- `pandas`: used for data import and management
\n",
"- `tensorflow`: the machine learning language we use to train our neural network\n",
"- `pyomo`: the algebraic modeling language for Python, it is used to define the optimization model passed to the solver\n",
- "- `onnx`: used to express trained neural network models\n",
+ "- `onnx`: used to express trained neural network models\n",
"- `omlt`: The package this notebook demonstates. OMLT can formulate machine learning models (such as neural networks) within Pyomo\n",
"\n",
"**NOTE:** This notebook also assumes you have a working MIP solver executable (e.g., CBC, Gurobi) to solve optimization problems in Pyomo. The open-source solver CBC is called by default."
@@ -64,7 +64,18 @@
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING: DEPRECATED: Declaring class 'OmltBlockData' derived from\n",
+ "'_BlockData'. The class '_BlockData' has been renamed to 'BlockData'.\n",
+ "(deprecated in 6.7.2) (called from\n",
+ "/home/codespace/.python/current/lib/python3.10/site-packages/omlt/block.py:33)\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # suppress CUDA warnings from tensorflow\n",
@@ -167,7 +178,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -199,7 +210,16 @@
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/codespace/.python/current/lib/python3.10/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ }
+ ],
"source": [
"# create our Keras Sequential model\n",
"nn = Sequential(name='reformer_relu_4_20')\n",
@@ -225,205 +245,205 @@
"output_type": "stream",
"text": [
"Epoch 1/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.8370\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step - loss: 0.9315\n",
"Epoch 2/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.4563\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.6021 \n",
"Epoch 3/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.2696\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 0.2147\n",
"Epoch 4/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.1227\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 962us/step - loss: 0.0938\n",
"Epoch 5/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0698\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 972us/step - loss: 0.0583\n",
"Epoch 6/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0440\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0440\n",
"Epoch 7/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0258\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 934us/step - loss: 0.0354\n",
"Epoch 8/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0154\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0265 \n",
"Epoch 9/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0103\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - loss: 0.0208\n",
"Epoch 10/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0076\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0171\n",
"Epoch 11/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0061\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 948us/step - loss: 0.0149\n",
"Epoch 12/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0051\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 917us/step - loss: 0.0121\n",
"Epoch 13/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 0.0043\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0102\n",
"Epoch 14/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0038\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step - loss: 0.0084\n",
"Epoch 15/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0035\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 930us/step - loss: 0.0074\n",
"Epoch 16/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0031\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 911us/step - loss: 0.0061\n",
"Epoch 17/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0028\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - loss: 0.0055\n",
"Epoch 18/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0026\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 919us/step - loss: 0.0050\n",
"Epoch 19/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0023\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 0.0046\n",
"Epoch 20/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0022\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 917us/step - loss: 0.0044\n",
"Epoch 21/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0020\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 923us/step - loss: 0.0038\n",
"Epoch 22/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 0.0018\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 938us/step - loss: 0.0037\n",
"Epoch 23/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0017\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 0.0033\n",
"Epoch 24/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0016\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - loss: 0.0030\n",
"Epoch 25/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0015\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 894us/step - loss: 0.0028\n",
"Epoch 26/100\n",
- "88/88 [==============================] - ETA: 0s - loss: 0.001 - 0s 3ms/step - loss: 0.0014\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - loss: 0.0027\n",
"Epoch 27/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0026\n",
"Epoch 28/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step - loss: 0.0026\n",
"Epoch 29/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 946us/step - loss: 0.0024\n",
"Epoch 30/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0022\n",
"Epoch 31/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0010\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 944us/step - loss: 0.0022\n",
"Epoch 32/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 9.5515e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - loss: 0.0021\n",
"Epoch 33/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 9.2159e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - loss: 0.0019\n",
"Epoch 34/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 8.7369e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 986us/step - loss: 0.0018\n",
"Epoch 35/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 8.0810e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0017\n",
"Epoch 36/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 7.7885e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 969us/step - loss: 0.0017\n",
"Epoch 37/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 7.4054e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 956us/step - loss: 0.0016\n",
"Epoch 38/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 7.2014e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - loss: 0.0015\n",
"Epoch 39/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.8355e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - loss: 0.0014\n",
"Epoch 40/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 6.6854e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0014 \n",
"Epoch 41/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.2248e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0014\n",
"Epoch 42/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 6.2566e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 905us/step - loss: 0.0014\n",
"Epoch 43/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.8445e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0013 \n",
"Epoch 44/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.5951e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - loss: 0.0013\n",
"Epoch 45/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.3668e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 914us/step - loss: 0.0013\n",
"Epoch 46/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.3497e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - loss: 0.0012\n",
"Epoch 47/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.2125e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - loss: 0.0012\n",
"Epoch 48/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 4.9190e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 934us/step - loss: 0.0011 \n",
"Epoch 49/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.7993e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011 \n",
"Epoch 50/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.6690e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 961us/step - loss: 0.0011\n",
"Epoch 51/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 4.5492e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011 \n",
"Epoch 52/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 4.3848e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 998us/step - loss: 0.0010 \n",
"Epoch 53/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 4.4862e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0010\n",
"Epoch 54/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.3271e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0010 \n",
"Epoch 55/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.9621e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step - loss: 0.0010\n",
"Epoch 56/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.7816e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 943us/step - loss: 9.7793e-04\n",
"Epoch 57/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 3.6440e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 9.5539e-04 \n",
"Epoch 58/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 3.6122e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 951us/step - loss: 9.8643e-04\n",
"Epoch 59/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.4262e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 964us/step - loss: 9.5467e-04\n",
"Epoch 60/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.3973e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 9.5569e-04\n",
"Epoch 61/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.4042e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 937us/step - loss: 8.9545e-04\n",
"Epoch 62/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.4183e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 959us/step - loss: 8.9153e-04\n",
"Epoch 63/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.0932e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 991us/step - loss: 8.8198e-04\n",
"Epoch 64/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 3.1305e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 953us/step - loss: 8.7606e-04\n",
"Epoch 65/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 2.9894e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - loss: 8.2828e-04\n",
"Epoch 66/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.9626e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.4195e-04\n",
"Epoch 67/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.8854e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - loss: 8.8572e-04\n",
"Epoch 68/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.8529e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.8402e-04 \n",
"Epoch 69/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.6655e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 993us/step - loss: 7.8691e-04\n",
"Epoch 70/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.6622e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 919us/step - loss: 8.2283e-04\n",
"Epoch 71/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.7927e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - loss: 7.8774e-04\n",
"Epoch 72/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.5607e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 937us/step - loss: 7.3661e-04\n",
"Epoch 73/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.7671e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 940us/step - loss: 7.9336e-04\n",
"Epoch 74/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.5296e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 993us/step - loss: 7.3721e-04\n",
"Epoch 75/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.5474e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step - loss: 7.4315e-04\n",
"Epoch 76/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.3464e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.2666e-04\n",
"Epoch 77/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.4455e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1000us/step - loss: 7.2654e-04\n",
"Epoch 78/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.2040e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.9702e-04\n",
"Epoch 79/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.1218e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.8081e-04\n",
"Epoch 80/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.5060e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.0167e-04\n",
"Epoch 81/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.2401e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 923us/step - loss: 7.1075e-04\n",
"Epoch 82/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.1947e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 947us/step - loss: 6.6085e-04\n",
"Epoch 83/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.0758e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 6.5808e-04\n",
"Epoch 84/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.0181e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 896us/step - loss: 6.1667e-04\n",
"Epoch 85/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9040e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 918us/step - loss: 6.0925e-04\n",
"Epoch 86/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9628e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 921us/step - loss: 6.3800e-04\n",
"Epoch 87/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.1624e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.2445e-04 \n",
"Epoch 88/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.2154e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 993us/step - loss: 6.2050e-04\n",
"Epoch 89/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 1.9279e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.9191e-04\n",
"Epoch 90/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.0530e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 928us/step - loss: 6.0064e-04\n",
"Epoch 91/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 1.8791e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 950us/step - loss: 5.6989e-04\n",
"Epoch 92/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9119e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - loss: 6.0071e-04\n",
"Epoch 93/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.7840e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 938us/step - loss: 5.7475e-04\n",
"Epoch 94/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 1.8819e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 927us/step - loss: 5.8762e-04\n",
"Epoch 95/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9525e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.9248e-04\n",
"Epoch 96/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.0329e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 962us/step - loss: 5.6154e-04\n",
"Epoch 97/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.7023e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.5977e-04\n",
"Epoch 98/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9264e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 5.5252e-04\n",
"Epoch 99/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.7761e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 948us/step - loss: 5.4744e-04\n",
"Epoch 100/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.9651e-04\n"
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step - loss: 5.6862e-04\n"
]
}
],
@@ -443,20 +463,12 @@
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: reformer_nn_relu/assets\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# save the model to disk\n",
"# While not technically necessary, this shows how we can load a previously saved model into\n",
"# our optimization formulation)\n",
- "nn.save('reformer_nn_relu')"
+ "nn.save('reformer_nn_relu.keras')"
]
},
{
@@ -501,7 +513,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 13,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -510,7 +522,7 @@
"outputs": [],
"source": [
"# load the Keras model\n",
- "nn_reformer = keras.models.load_model('reformer_nn_relu', compile=False)\n",
+ "nn_reformer = keras.models.load_model('reformer_nn_relu.keras', compile=False)\n",
"\n",
"# Note: The neural network is in the scaled space. We want access to the\n",
"# variables in the unscaled space. Therefore, we need to tell OMLT about the\n",
@@ -533,7 +545,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 14,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -550,7 +562,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 15,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -565,7 +577,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 16,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -577,8 +589,8 @@
"output_type": "stream",
"text": [
"Bypass Fraction: 0.1\n",
- "NG Steam Ratio: 1.1186717\n",
- "H2 Concentration: 0.33157189\n",
+ "NG Steam Ratio: 1.1404918\n",
+ "H2 Concentration: 0.33255362\n",
"N2 Concentration: 0.34\n"
]
}
@@ -607,7 +619,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.12"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb b/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb
index 8e65296b..650f5700 100644
--- a/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb
+++ b/docs/notebooks/neuralnet/auto-thermal-reformer.ipynb
@@ -50,7 +50,7 @@
"- `pandas`: used for data import and management
\n",
"- `tensorflow`: the machine learning language we use to train our neural network\n",
"- `pyomo`: the algebraic modeling language for Python, it is used to define the optimization model passed to the solver\n",
- "- `onnx`: used to express trained neural network models\n",
+ "- `onnx`: used to express trained neural network models\n",
"- `omlt`: The package this notebook demonstates. OMLT can formulate machine learning models (such as neural networks) within Pyomo\n",
"\n",
"**NOTE:** This notebook also assumes you have a working MIP solver executable (e.g., CBC, Gurobi) to solve optimization problems in Pyomo. The open-source solver IPOPT is called by default."
@@ -58,7 +58,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 22,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -84,7 +84,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 23,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -149,7 +149,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 24,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -167,7 +167,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 25,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -193,13 +193,22 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 26,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/codespace/.python/current/lib/python3.10/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ }
+ ],
"source": [
"# create our Keras Sequential model\n",
"nn = Sequential(name='reformer_sigmoid_4_20')\n",
@@ -213,7 +222,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 27,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -225,205 +234,205 @@
"output_type": "stream",
"text": [
"Epoch 1/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.0341\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 988us/step - loss: 1.1144\n",
"Epoch 2/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.9957\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 914us/step - loss: 0.9900\n",
"Epoch 3/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.9706\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.9766\n",
"Epoch 4/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.7485\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.8390 \n",
"Epoch 5/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.2584\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 930us/step - loss: 0.2823\n",
"Epoch 6/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.1501\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1576 \n",
"Epoch 7/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.1265\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 891us/step - loss: 0.1403\n",
"Epoch 8/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.1111\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step - loss: 0.1267\n",
"Epoch 9/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0998\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 898us/step - loss: 0.1145\n",
"Epoch 10/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0907\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 886us/step - loss: 0.1074\n",
"Epoch 11/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0828\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1041\n",
"Epoch 12/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0741\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 934us/step - loss: 0.1006\n",
"Epoch 13/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0640\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 982us/step - loss: 0.0973\n",
"Epoch 14/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0511\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 909us/step - loss: 0.0939\n",
"Epoch 15/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0374\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 870us/step - loss: 0.0898\n",
"Epoch 16/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0266\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 949us/step - loss: 0.0862\n",
"Epoch 17/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0196\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0843\n",
"Epoch 18/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0153\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0815\n",
"Epoch 19/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0124\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0789\n",
"Epoch 20/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0102\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0765\n",
"Epoch 21/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0086\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0726\n",
"Epoch 22/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0072\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0720\n",
"Epoch 23/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0062\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0754\n",
"Epoch 24/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0705\n",
"Epoch 25/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0047\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0714\n",
"Epoch 26/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0041\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0713\n",
"Epoch 27/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0037\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0709\n",
"Epoch 28/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0033\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0679\n",
"Epoch 29/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0029\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0686\n",
"Epoch 30/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0027\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0672\n",
"Epoch 31/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0024\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0657\n",
"Epoch 32/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0022\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0654\n",
"Epoch 33/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0020\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0631\n",
"Epoch 34/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0019\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - loss: 0.0578\n",
"Epoch 35/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0017\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - loss: 0.0485\n",
"Epoch 36/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0016\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0336\n",
"Epoch 37/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0016\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0203\n",
"Epoch 38/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0014\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0147\n",
"Epoch 39/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0014\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - loss: 0.0113\n",
"Epoch 40/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0013\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0086 \n",
"Epoch 41/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0012\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step - loss: 0.0071\n",
"Epoch 42/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 908us/step - loss: 0.0059\n",
"Epoch 43/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 945us/step - loss: 0.0052\n",
"Epoch 44/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 0.0010\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 883us/step - loss: 0.0042\n",
"Epoch 45/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 9.7936e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 886us/step - loss: 0.0037\n",
"Epoch 46/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 9.2880e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 901us/step - loss: 0.0035\n",
"Epoch 47/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 9.0375e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 886us/step - loss: 0.0030\n",
"Epoch 48/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 8.6779e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 0.0027\n",
"Epoch 49/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 8.5856e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 891us/step - loss: 0.0027\n",
"Epoch 50/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 8.0145e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 913us/step - loss: 0.0023\n",
"Epoch 51/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 8.0115e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0021 \n",
"Epoch 52/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 7.9738e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step - loss: 0.0020\n",
"Epoch 53/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.9619e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 913us/step - loss: 0.0019\n",
"Epoch 54/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 6.7135e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 963us/step - loss: 0.0017\n",
"Epoch 55/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.5336e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 903us/step - loss: 0.0016\n",
"Epoch 56/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.6119e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 966us/step - loss: 0.0015\n",
"Epoch 57/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 6.0447e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 919us/step - loss: 0.0015\n",
"Epoch 58/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.9642e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 890us/step - loss: 0.0014\n",
"Epoch 59/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 5.8340e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 910us/step - loss: 0.0012\n",
"Epoch 60/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 5.9287e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 0.0012\n",
"Epoch 61/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.4710e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 871us/step - loss: 0.0012 \n",
"Epoch 62/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 5.1789e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 946us/step - loss: 0.0011 \n",
"Epoch 63/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.9301e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 919us/step - loss: 0.0011 \n",
"Epoch 64/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.8124e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 933us/step - loss: 9.5829e-04\n",
"Epoch 65/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.6044e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 910us/step - loss: 9.6994e-04\n",
"Epoch 66/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.3224e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 906us/step - loss: 9.0896e-04\n",
"Epoch 67/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.2608e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 906us/step - loss: 9.1381e-04\n",
"Epoch 68/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.0868e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 896us/step - loss: 8.5913e-04\n",
"Epoch 69/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.9811e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 994us/step - loss: 9.0463e-04\n",
"Epoch 70/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.9089e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.8907e-04\n",
"Epoch 71/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 4.0310e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 925us/step - loss: 7.9675e-04\n",
"Epoch 72/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.6990e-04A: 0s - loss: 3.5289e-0\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.2875e-04\n",
"Epoch 73/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.7645e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 900us/step - loss: 7.3307e-04\n",
"Epoch 74/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.2927e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.3824e-04\n",
"Epoch 75/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.3896e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.3988e-04 \n",
"Epoch 76/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 3.3238e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.4647e-04\n",
"Epoch 77/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 3.2586e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 898us/step - loss: 5.9410e-04\n",
"Epoch 78/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 3.0942e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 889us/step - loss: 5.9625e-04\n",
"Epoch 79/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.8561e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 5.2871e-04\n",
"Epoch 80/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 2.8161e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 5.6454e-04\n",
"Epoch 81/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.6297e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 941us/step - loss: 5.6161e-04\n",
"Epoch 82/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.6181e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 890us/step - loss: 5.1684e-04\n",
"Epoch 83/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.6130e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 935us/step - loss: 5.1329e-04\n",
"Epoch 84/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.4854e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 881us/step - loss: 4.4305e-04\n",
"Epoch 85/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 2.6028e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 902us/step - loss: 4.8435e-04\n",
"Epoch 86/100\n",
- "88/88 [==============================] - 0s 4ms/step - loss: 2.3970e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 922us/step - loss: 4.2593e-04\n",
"Epoch 87/100\n",
- "88/88 [==============================] - 1s 6ms/step - loss: 2.2274e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.2300e-04 \n",
"Epoch 88/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 2.2896e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 4.4135e-04\n",
"Epoch 89/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 2.3039e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 922us/step - loss: 4.1130e-04\n",
"Epoch 90/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 2.4000e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 3.9683e-04\n",
"Epoch 91/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 1.8690e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 904us/step - loss: 3.9107e-04\n",
"Epoch 92/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 1.9249e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 896us/step - loss: 3.5425e-04\n",
"Epoch 93/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 2.0807e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 897us/step - loss: 3.7474e-04\n",
"Epoch 94/100\n",
- "88/88 [==============================] - 1s 6ms/step - loss: 1.8234e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 905us/step - loss: 3.5553e-04\n",
"Epoch 95/100\n",
- "88/88 [==============================] - 1s 7ms/step - loss: 1.8770e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step - loss: 3.5410e-04\n",
"Epoch 96/100\n",
- "88/88 [==============================] - 1s 6ms/step - loss: 1.6957e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.3268e-04 \n",
"Epoch 97/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 1.6235e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 878us/step - loss: 3.1562e-04\n",
"Epoch 98/100\n",
- "88/88 [==============================] - 0s 5ms/step - loss: 1.7383e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 3.0199e-04\n",
"Epoch 99/100\n",
- "88/88 [==============================] - 0s 2ms/step - loss: 1.7169e-04\n",
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 907us/step - loss: 2.9183e-04\n",
"Epoch 100/100\n",
- "88/88 [==============================] - 0s 3ms/step - loss: 1.6411e-04\n"
+ "\u001b[1m88/88\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 907us/step - loss: 2.9824e-04\n"
]
}
],
@@ -437,26 +446,18 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 28,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: reformer_nn/assets\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# save the model to disk\n",
"# While not technically necessary, this shows how we can load a previously saved model into\n",
"# our optimization formulation)\n",
- "nn.save('reformer_nn')"
+ "nn.save('reformer_nn.keras')"
]
},
{
@@ -473,7 +474,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 29,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -487,7 +488,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 30,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -501,7 +502,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 31,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -510,7 +511,7 @@
"outputs": [],
"source": [
"# load the Keras model\n",
- "nn_reformer = keras.models.load_model('reformer_nn', compile=False)\n",
+ "nn_reformer = keras.models.load_model('reformer_nn.keras', compile=False)\n",
"\n",
"# Note: The neural network is in the scaled space. We want access to the\n",
"# variables in the unscaled space. Therefore, we need to tell OMLT about the\n",
@@ -533,7 +534,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 32,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -550,7 +551,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 33,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -561,7 +562,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt 3.13.3: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
@@ -569,7 +570,7 @@
" For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version 3.13.3, running with linear solver ma27.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 1812\n",
"Number of nonzeros in inequality constraint Jacobian.: 1\n",
@@ -586,80 +587,101 @@
" inequality constraints with only upper bounds: 1\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 0 -0.0000000e+00 2.32e+04 3.68e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
- " 1 -2.6030116e-03 2.29e+04 1.06e+00 -1.0 7.05e+03 - 5.34e-03 1.11e-02f 1\n",
- " 2 -6.0669949e-03 2.27e+04 1.96e+00 -1.0 1.59e+04 - 1.18e-02 9.02e-03f 1\n",
- " 3 -6.2596751e-03 2.27e+04 4.65e+01 -1.0 2.84e+04 - 6.23e-03 4.77e-04h 1\n",
- " 4 -6.2616127e-03 2.27e+04 6.65e+03 -1.0 1.47e+04 - 4.48e-03 2.90e-05h 1\n",
- " 5 -6.2581094e-03 2.27e+04 6.22e+05 -1.0 2.50e+04 - 3.05e-03 3.23e-05h 1\n",
- " 6r-6.2581094e-03 2.27e+04 9.99e+02 2.5 0.00e+00 - 0.00e+00 1.97e-07R 2\n",
- " 7r-5.9558091e-03 2.20e+04 2.14e+03 2.5 2.66e+04 - 1.15e-02 3.11e-04f 1\n",
- " 8r-7.6598374e-03 2.09e+02 2.07e+03 1.1 8.43e+04 - 4.26e-04 3.31e-03f 1\n",
- " 9 -7.7647208e-03 2.09e+02 1.36e+00 -1.0 9.30e+03 - 6.42e-04 3.06e-04h 1\n",
+ " 0 -0.0000000e+00 2.32e+04 3.10e-04 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ " 1 -3.1527294e-03 2.29e+04 1.04e+00 -1.0 1.12e+03 - 6.41e-03 1.32e-02f 1\n",
+ " 2 -6.2152944e-03 2.27e+04 5.37e+00 -1.0 2.29e+04 - 1.61e-02 8.91e-03f 1\n",
+ " 3 -6.2618863e-03 2.27e+04 2.13e+02 -1.0 7.53e+03 - 1.53e-02 2.94e-04h 1\n",
+ " 4 -6.2980596e-03 2.26e+04 9.10e+02 -1.0 1.95e+01 - 2.38e-03 4.06e-04h 1\n",
+ " 5 -6.3144679e-03 2.26e+04 6.08e+04 -1.0 2.09e+04 - 2.93e-03 4.46e-05h 1\n",
+ " 6 -6.3198672e-03 2.26e+04 1.26e+07 -1.0 2.32e+04 - 2.81e-03 1.37e-05h 1\n",
+ " 7r-6.3198672e-03 2.26e+04 9.99e+02 2.5 0.00e+00 - 0.00e+00 7.31e-08R 2\n",
+ " 8r-6.0332788e-03 2.19e+04 1.92e+03 2.5 3.55e+04 - 1.19e-02 2.52e-04f 1\n",
+ " 9r-8.0808049e-03 2.08e+02 1.76e+03 1.1 5.87e+04 - 7.19e-04 4.73e-03f 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 10 -8.1576012e-03 2.08e+02 4.95e+00 -1.0 1.79e+04 - 1.23e-03 2.37e-04h 1\n",
+ " 11 -8.1674126e-03 2.08e+02 2.15e+03 -1.0 4.50e+03 - 1.60e-02 6.30e-05h 1\n",
+ " 12 -8.1719233e-03 2.08e+02 1.11e+06 -1.0 1.83e+04 - 6.31e-03 1.19e-05h 1\n",
+ " 13r-8.1719233e-03 2.08e+02 9.99e+02 0.4 0.00e+00 - 0.00e+00 2.36e-07R 2\n",
+ " 14r-3.6071845e-02 1.98e+00 1.00e+03 0.4 5.28e+03 - 1.04e-04 1.00e-03f 1\n",
+ " 15r-3.6071845e-02 1.98e+00 9.99e+02 0.3 0.00e+00 - 0.00e+00 3.75e-07R 5\n",
+ " 16r-6.1605099e-02 1.96e+00 9.98e+02 0.3 6.30e+03 - 1.02e-03 8.08e-04f 1\n",
+ " 17r-1.2559031e-01 1.90e+00 9.97e+02 0.3 6.27e+03 - 7.32e-04 2.51e-03f 1\n",
+ " 18 -1.2871206e-01 1.85e+00 9.78e-01 -1.0 1.02e+04 - 2.09e-02 2.20e-02f 1\n",
+ " 19 -1.3332528e-01 1.81e+00 1.24e+01 -1.0 2.11e+04 - 6.42e-02 2.50e-02f 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 20 -1.3461035e-01 1.79e+00 5.73e+01 -1.0 1.41e+04 - 6.22e-02 1.16e-02h 1\n",
+ " 21 -1.3467457e-01 1.79e+00 3.24e+03 -1.0 1.34e+04 - 4.47e-02 4.13e-04h 1\n",
+ " 22 -1.3484205e-01 1.78e+00 8.30e+04 -1.0 4.52e+03 - 2.40e-02 9.45e-04h 1\n",
+ " 23 -1.3484927e-01 1.78e+00 2.78e+07 -1.0 5.88e+02 - 1.33e-02 4.05e-05h 1\n",
+ " 24r-1.3484927e-01 1.78e+00 9.99e+02 0.2 0.00e+00 - 0.00e+00 2.10e-07R 2\n",
+ " 25r-1.4779559e-01 1.76e+00 9.98e+02 0.2 1.43e+05 - 1.55e-03 9.11e-04f 1\n",
+ " 26r-1.9083218e-01 1.67e+00 9.94e+02 0.2 1.43e+05 - 3.50e-03 3.83e-03f 1\n",
+ " 27 -2.3722606e-01 7.32e-01 4.81e+03 -1.0 1.68e+04 - 1.29e-02 5.61e-01f 1\n",
+ " 28 -2.3903999e-01 4.88e-01 2.89e+03 -1.0 1.73e+00 2.0 1.36e-02 3.33e-01f 1\n",
+ " 29 -2.3894926e-01 4.79e-01 2.87e+03 -1.0 1.29e+00 1.5 3.00e-01 2.03e-02h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 10 -7.7729541e-03 2.09e+02 2.65e+03 -1.0 1.75e+04 - 2.10e-02 3.24e-05h 1\n",
- " 11 -7.7716836e-03 2.09e+02 3.58e+05 -1.0 2.09e+04 - 1.54e-03 1.11e-05h 1\n",
- " 12r-7.7716836e-03 2.09e+02 9.99e+02 0.4 0.00e+00 - 0.00e+00 2.22e-07R 2\n",
- " 13r-2.5415884e-02 8.08e+01 9.99e+02 0.4 2.65e+03 - 2.04e-04 6.20e-04f 1\n",
- " 14r-2.5415884e-02 8.08e+01 9.99e+02 0.4 0.00e+00 - 0.00e+00 2.54e-07R 5\n",
- " 15r-3.9234936e-02 6.38e+01 9.99e+02 0.4 5.96e+02 - 6.52e-04 4.72e-04f 1\n",
- " 16r-8.3935109e-02 2.62e+01 9.97e+02 0.4 4.70e+02 - 5.99e-04 1.71e-03f 1\n",
- " 17r-1.3934552e-01 5.58e+00 9.95e+02 0.4 2.45e+02 - 1.67e-03 2.76e-03f 1\n",
- " 18 -1.4363902e-01 5.40e+00 3.72e+00 -1.0 3.92e+03 - 6.89e-03 3.28e-02f 1\n",
- " 19 -1.6446259e-01 4.80e+00 8.56e+00 -1.0 1.60e+04 - 8.07e-02 1.11e-01f 1\n",
+ " 30 -2.3851472e-01 4.74e-01 5.90e+04 -1.0 3.67e+00 1.0 3.11e-01 1.02e-02h 1\n",
+ " 31 -2.3168898e-01 3.98e-01 4.74e+04 -1.0 3.70e+00 0.6 4.40e-01 1.59e-01h 1\n",
+ " 32 -2.2358201e-01 1.15e-01 2.59e+04 -1.0 3.45e+02 - 4.72e-01 7.11e-01h 1\n",
+ " 33 -2.1064898e-01 7.56e-02 1.41e+04 -1.0 2.37e+03 - 4.40e-01 3.44e-01h 1\n",
+ " 34 -2.1047547e-01 7.53e-02 1.38e+04 -1.0 2.38e+03 - 1.08e-02 4.64e-03h 1\n",
+ " 35 -2.0536031e-01 5.73e-02 1.98e+04 -1.0 2.26e+03 - 2.46e-02 2.39e-01f 1\n",
+ " 36 -2.0531335e-01 5.70e-02 1.83e+04 -1.0 2.21e+03 - 2.35e-02 3.90e-03h 1\n",
+ " 37 -2.0667730e-01 7.73e-02 2.31e+04 -1.0 2.13e+03 - 7.16e-02 4.18e-01f 1\n",
+ " 38 -2.0667907e-01 7.73e-02 5.57e+03 -1.0 1.36e+03 - 2.17e-01 2.13e-04h 6\n",
+ " 39 -2.0674307e-01 7.64e-02 6.46e+03 -1.0 8.11e+01 - 1.94e-03 1.25e-02f 2\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 20 -1.7215140e-01 4.48e+00 7.06e+01 -1.0 6.18e+03 - 2.94e-01 6.74e-02h 1\n",
- " 21 -1.7784726e-01 4.23e+00 6.31e+02 -1.0 9.08e+03 - 4.03e-01 5.47e-02h 1\n",
- " 22 -1.8642388e-01 3.85e+00 8.97e+02 -1.0 7.01e+03 - 3.33e-01 9.04e-02h 1\n",
- " 23 -2.3017104e-01 2.09e+00 8.13e+04 -1.0 1.22e+04 - 2.45e-03 4.58e-01f 1\n",
- " 24 -2.7788252e-01 3.25e-02 1.62e+04 -1.0 8.46e+03 - 2.03e-03 1.00e+00f 1\n",
- " 25 -2.7625877e-01 3.29e-04 1.28e+02 -1.0 9.08e-02 2.0 8.50e-01 1.00e+00f 1\n",
- " 26 -2.7633917e-01 7.21e-07 1.04e-01 -1.0 1.59e+01 - 1.00e+00 1.00e+00h 1\n",
- " 27 -2.7678608e-01 8.97e-06 2.15e-03 -2.5 7.21e+01 - 1.00e+00 1.00e+00h 1\n",
- " 28 -2.8773008e-01 4.94e-03 2.06e-02 -3.8 1.77e+03 - 7.86e-01 1.00e+00h 1\n",
- " 29 -3.0628413e-01 1.68e-02 6.62e-04 -3.8 3.63e+03 - 9.95e-01 1.00e+00h 1\n",
+ " 40 -2.1130252e-01 3.51e-03 1.28e+03 -1.0 7.01e+02 - 1.41e-01 1.00e+00f 1\n",
+ " 41 -2.1107713e-01 3.05e-03 1.20e+02 -1.0 1.69e+02 - 4.83e-01 1.00e+00f 1\n",
+ " 42 -2.1118142e-01 4.58e-03 3.62e+01 -1.0 2.69e+02 - 7.13e-01 1.00e+00f 1\n",
+ " 43 -2.2385213e-01 5.24e-03 2.45e+01 -1.0 9.72e+02 - 1.70e-01 1.00e+00f 1\n",
+ " 44 -2.4208782e-01 1.30e-02 1.17e+01 -1.0 1.46e+03 - 6.06e-01 1.00e+00f 1\n",
+ " 45 -2.7496917e-01 5.99e-02 3.53e+00 -1.0 3.32e+03 - 6.81e-01 1.00e+00f 1\n",
+ " 46 -2.8094542e-01 5.97e-03 4.74e-02 -1.0 1.89e+03 - 1.00e+00 1.00e+00f 1\n",
+ " 47 -2.8143139e-01 9.21e-05 2.35e-01 -2.5 2.25e+02 - 9.43e-01 1.00e+00h 1\n",
+ " 48 -2.8471889e-01 5.06e-04 1.52e-03 -2.5 5.87e+02 - 1.00e+00 1.00e+00h 1\n",
+ " 49 -2.9285237e-01 3.11e-03 1.78e-02 -3.8 1.51e+03 - 8.18e-01 1.00e+00h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 30 -3.1281609e-01 4.16e-02 1.09e-01 -3.8 2.12e+03 - 6.47e-01 4.46e-01h 1\n",
- " 31 -3.2328324e-01 2.01e-02 8.47e-02 -3.8 3.96e+03 - 1.00e+00 7.31e-01h 1\n",
- " 32 -3.2676931e-01 3.50e-02 2.48e+00 -3.8 4.58e+03 - 5.28e-02 1.00e+00H 1\n",
- " 33 -3.2673051e-01 7.54e-03 1.58e-03 -3.8 5.36e+02 - 9.98e-01 1.00e+00h 1\n",
- " 34 -3.2769712e-01 1.87e-02 1.82e-03 -3.8 1.82e+03 - 5.30e-01 5.19e-01h 1\n",
- " 35 -3.2579448e-01 5.47e-03 1.41e-04 -3.8 1.83e+03 - 1.00e+00 1.00e+00f 1\n",
- " 36 -3.2581048e-01 1.50e-03 5.66e-06 -3.8 3.23e+02 - 1.00e+00 1.00e+00h 1\n",
- " 37 -3.2778102e-01 1.39e-02 2.44e-02 -5.7 4.01e+03 - 4.61e-01 5.40e-01h 1\n",
- " 38 -3.3081682e-01 6.98e-02 3.75e-02 -5.7 5.65e+03 - 5.42e-01 8.01e-01h 1\n",
- " 39 -3.3146434e-01 5.19e-02 3.12e-02 -5.7 4.30e+03 - 2.86e-02 2.85e-01h 1\n",
+ " 50 -3.1047007e-01 1.68e-02 6.69e-04 -3.8 3.61e+03 - 1.00e+00 9.82e-01h 1\n",
+ " 51 -3.1805376e-01 7.91e-03 3.86e-03 -3.8 3.08e+03 - 1.00e+00 1.00e+00h 1\n",
+ " 52 -3.2496988e-01 1.07e-02 2.10e-01 -3.8 4.72e+03 - 1.00e+00 4.42e-01h 1\n",
+ " 53 -3.2596317e-01 1.38e-02 1.13e+00 -3.8 2.85e+03 - 4.77e-01 1.00e+00h 1\n",
+ " 54 -3.2609714e-01 2.18e-03 1.21e-04 -3.8 8.58e+02 - 1.00e+00 1.00e+00h 1\n",
+ " 55 -3.2634535e-01 2.20e-05 3.62e-07 -3.8 1.59e+02 - 1.00e+00 1.00e+00h 1\n",
+ " 56 -3.2840186e-01 5.71e-03 4.99e-02 -5.7 4.39e+03 - 6.38e-01 4.96e-01h 1\n",
+ " 57 -3.3229603e-01 2.85e-02 7.58e-03 -5.7 7.33e+03 - 6.50e-01 7.37e-01h 1\n",
+ " 58 -3.3222233e-01 2.04e-02 2.60e-02 -5.7 8.51e+01 - 5.17e-01 2.82e-01h 1\n",
+ " 59 -3.3182603e-01 2.03e-04 2.60e-01 -5.7 3.64e+02 - 6.85e-02 1.00e+00h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 40 -3.3127607e-01 6.17e-04 9.41e-02 -5.7 7.26e+00 - 3.83e-01 1.00e+00h 1\n",
- " 41 -3.3130748e-01 3.54e-04 3.08e-02 -5.7 1.29e+02 - 1.00e+00 4.35e-01h 1\n",
- " 42 -3.3129128e-01 1.51e-06 9.28e-03 -5.7 2.63e+01 - 7.55e-01 1.00e+00f 1\n",
- " 43 -3.3129839e-01 2.97e-07 9.66e-07 -5.7 1.24e+01 - 1.00e+00 1.00e+00h 1\n",
- " 44 -3.3130682e-01 1.84e-07 1.60e-03 -8.6 1.25e+01 - 1.00e+00 8.58e-01h 1\n",
- " 45 -3.3130710e-01 9.64e-10 1.06e-01 -8.6 2.73e-01 - 1.69e-01 1.00e+00h 1\n",
- " 46 -3.3130710e-01 3.66e-15 2.50e-14 -8.6 1.29e-03 - 1.00e+00 1.00e+00h 1\n",
+ " 60 -3.3184336e-01 2.96e-05 2.87e-02 -5.7 3.19e+01 - 1.00e+00 8.56e-01h 1\n",
+ " 61 -3.3183702e-01 5.23e-08 6.23e+01 -5.7 9.02e+00 - 3.66e-04 1.00e+00f 1\n",
+ " 62 -3.3183667e-01 1.66e-10 1.84e-11 -5.7 4.99e-01 - 1.00e+00 1.00e+00h 1\n",
+ " 63 -3.3184437e-01 3.06e-08 6.72e-04 -8.6 8.36e+00 - 1.00e+00 9.15e-01h 1\n",
+ " 64 -3.3184455e-01 9.27e-11 2.88e-02 -8.6 1.50e-01 - 4.59e-01 1.00e+00f 1\n",
+ " 65 -3.3184455e-01 1.89e-15 2.51e-14 -8.6 5.16e-04 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 46\n",
+ "Number of Iterations....: 65\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: -3.3130709928730723e-01 -3.3130709928730723e-01\n",
- "Dual infeasibility......: 2.5035529205297280e-14 2.5035529205297280e-14\n",
- "Constraint violation....: 3.6637359812630166e-15 3.6637359812630166e-15\n",
- "Complementarity.........: 2.7713235776435301e-09 2.7713235776435301e-09\n",
- "Overall NLP error.......: 2.7713235776435301e-09 2.7713235776435301e-09\n",
+ "Objective...............: -3.3184454733260904e-01 -3.3184454733260904e-01\n",
+ "Dual infeasibility......: 2.5091040356528538e-14 2.5091040356528538e-14\n",
+ "Constraint violation....: 1.8873791418627661e-15 1.8873791418627661e-15\n",
+ "Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00\n",
+ "Complementarity.........: 2.6249489091631699e-09 2.6249489091631699e-09\n",
+ "Overall NLP error.......: 2.6249489091631699e-09 2.6249489091631699e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 58\n",
- "Number of objective gradient evaluations = 44\n",
- "Number of equality constraint evaluations = 58\n",
- "Number of inequality constraint evaluations = 58\n",
- "Number of equality constraint Jacobian evaluations = 50\n",
- "Number of inequality constraint Jacobian evaluations = 50\n",
- "Number of Lagrangian Hessian evaluations = 46\n",
- "Total CPU secs in IPOPT (w/o function evaluations) = 0.015\n",
- "Total CPU secs in NLP function evaluations = 0.001\n",
+ "Number of objective function evaluations = 83\n",
+ "Number of objective gradient evaluations = 63\n",
+ "Number of equality constraint evaluations = 83\n",
+ "Number of inequality constraint evaluations = 83\n",
+ "Number of equality constraint Jacobian evaluations = 70\n",
+ "Number of inequality constraint Jacobian evaluations = 70\n",
+ "Number of Lagrangian Hessian evaluations = 65\n",
+ "Total seconds in IPOPT = 0.094\n",
"\n",
"EXIT: Optimal Solution Found.\n",
- "\b\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ "\b"
]
}
],
@@ -671,7 +693,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 34,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -682,10 +704,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Bypass Fraction: 0.10000025307452928\n",
- "NG Steam Ratio: 1.1197517732543654\n",
- "H2 Concentration: 0.3313070992873072\n",
- "N2 Concentration: 0.34000000393182694\n"
+ "Bypass Fraction: 0.1000002111229052\n",
+ "NG Steam Ratio: 1.1131313973800456\n",
+ "H2 Concentration: 0.33184454733260904\n",
+ "N2 Concentration: 0.3400000044198399\n"
]
}
],
@@ -713,7 +735,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.12"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb b/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb
index dd1e74dd..69cb9675 100644
--- a/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb
+++ b/docs/notebooks/neuralnet/graph_neural_network_formulation.ipynb
@@ -39,9 +39,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:32:39.757240: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-05-16 17:32:39.808990: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"import numpy as np\n",
"import torch\n",
@@ -161,10 +171,10 @@
"output_type": "stream",
"text": [
"Welcome to the CBC MILP Solver \n",
- "Version: 2.9.9 \n",
- "Build Date: Oct 13 2018 \n",
+ "Version: 2.10.10 \n",
+ "Build Date: Apr 19 2023 \n",
"\n",
- "command line - /rds/general/user/sz421/home/anaconda3/envs/OMLT_test/bin/cbc -printingOptions all -import /var/tmp/pbs.8259409.pbs/tmpp27h4a9g.pyomo.lp -stat=1 -solve -solu /var/tmp/pbs.8259409.pbs/tmpp27h4a9g.pyomo.soln (default strategy 1)\n",
+ "command line - /opt/conda/bin/cbc -printingOptions all -import /tmp/tmpwsv2x1xb.pyomo.lp -stat=1 -solve -solu /tmp/tmpwsv2x1xb.pyomo.soln (default strategy 1)\n",
"Option for printingOptions changed from normal to all\n",
"Presolve 172 (-222) rows, 111 (-75) columns and 608 (-267) elements\n",
"Statistics for presolved model\n",
@@ -197,80 +207,81 @@
"Continuous objective value is 0.315152 - 0.00 seconds\n",
"Cgl0003I 0 fixed, 0 tightened bounds, 2 strengthened rows, 0 substitutions\n",
"Cgl0004I processed model has 166 rows, 105 columns (25 integer (25 of which binary)) and 670 elements\n",
- "Cbc0038I Initial state - 5 integers unsatisfied sum - 0.191951\n",
- "Cbc0038I Pass 1: suminf. 0.00000 (0) obj. 0.317969 iterations 17\n",
+ "Cbc0038I Initial state - 5 integers unsatisfied sum - 0.124759\n",
+ "Cbc0038I Pass 1: suminf. 0.00000 (0) obj. 0.317969 iterations 41\n",
"Cbc0038I Solution found of 0.317969\n",
"Cbc0038I Relaxing continuous gives 0.317969\n",
- "Cbc0038I Before mini branch and bound, 19 integers at bound fixed and 48 continuous\n",
- "Cbc0038I Full problem 166 rows 105 columns, reduced to 49 rows 27 columns\n",
- "Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)\n",
+ "Cbc0038I Before mini branch and bound, 20 integers at bound fixed and 63 continuous\n",
+ "Cbc0038I Full problem 166 rows 105 columns, reduced to 17 rows 13 columns\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.02 seconds)\n",
"Cbc0038I Round again with cutoff of 0.317791\n",
- "Cbc0038I Pass 2: suminf. 0.00876 (1) obj. 0.317791 iterations 1\n",
- "Cbc0038I Pass 3: suminf. 0.18897 (1) obj. 0.317791 iterations 25\n",
- "Cbc0038I Pass 4: suminf. 0.00876 (1) obj. 0.317791 iterations 45\n",
- "Cbc0038I Pass 5: suminf. 0.18897 (1) obj. 0.317791 iterations 10\n",
- "Cbc0038I Pass 6: suminf. 0.00876 (1) obj. 0.317791 iterations 9\n",
- "Cbc0038I Pass 7: suminf. 0.00876 (1) obj. 0.317791 iterations 20\n",
- "Cbc0038I Pass 8: suminf. 0.18897 (1) obj. 0.317791 iterations 11\n",
- "Cbc0038I Pass 9: suminf. 0.00876 (1) obj. 0.317791 iterations 11\n",
- "Cbc0038I Pass 10: suminf. 0.00876 (1) obj. 0.317791 iterations 4\n",
- "Cbc0038I Pass 11: suminf. 0.18897 (1) obj. 0.317791 iterations 10\n",
- "Cbc0038I Pass 12: suminf. 0.00876 (1) obj. 0.317791 iterations 8\n",
- "Cbc0038I Pass 13: suminf. 0.00876 (1) obj. 0.317791 iterations 6\n",
- "Cbc0038I Pass 14: suminf. 0.18897 (1) obj. 0.317791 iterations 9\n",
- "Cbc0038I Pass 15: suminf. 0.00876 (1) obj. 0.317791 iterations 9\n",
- "Cbc0038I Pass 16: suminf. 0.00876 (1) obj. 0.317791 iterations 6\n",
- "Cbc0038I Pass 17: suminf. 0.18897 (1) obj. 0.317791 iterations 17\n",
- "Cbc0038I Pass 18: suminf. 0.00876 (1) obj. 0.317791 iterations 18\n",
- "Cbc0038I Pass 19: suminf. 0.00876 (1) obj. 0.317791 iterations 8\n",
- "Cbc0038I Pass 20: suminf. 0.18897 (1) obj. 0.317791 iterations 15\n",
- "Cbc0038I Pass 21: suminf. 0.00876 (1) obj. 0.317791 iterations 19\n",
- "Cbc0038I Pass 22: suminf. 0.00876 (1) obj. 0.317791 iterations 25\n",
+ "Cbc0038I Pass 2: suminf. 0.00876 (1) obj. 0.317791 iterations 11\n",
+ "Cbc0038I Pass 3: suminf. 0.18897 (1) obj. 0.317791 iterations 20\n",
+ "Cbc0038I Pass 4: suminf. 0.00876 (1) obj. 0.317791 iterations 58\n",
+ "Cbc0038I Pass 5: suminf. 0.18897 (1) obj. 0.317791 iterations 13\n",
+ "Cbc0038I Pass 6: suminf. 0.00876 (1) obj. 0.317791 iterations 21\n",
+ "Cbc0038I Pass 7: suminf. 0.00876 (1) obj. 0.317791 iterations 32\n",
+ "Cbc0038I Pass 8: suminf. 0.18897 (1) obj. 0.317791 iterations 16\n",
+ "Cbc0038I Pass 9: suminf. 0.00876 (1) obj. 0.317791 iterations 19\n",
+ "Cbc0038I Pass 10: suminf. 0.00876 (1) obj. 0.317791 iterations 57\n",
+ "Cbc0038I Pass 11: suminf. 0.18897 (1) obj. 0.317791 iterations 7\n",
+ "Cbc0038I Pass 12: suminf. 0.00876 (1) obj. 0.317791 iterations 7\n",
+ "Cbc0038I Pass 13: suminf. 0.00876 (1) obj. 0.317791 iterations 5\n",
+ "Cbc0038I Pass 14: suminf. 0.18897 (1) obj. 0.317791 iterations 7\n",
+ "Cbc0038I Pass 15: suminf. 0.00876 (1) obj. 0.317791 iterations 7\n",
+ "Cbc0038I Pass 16: suminf. 0.00876 (1) obj. 0.317791 iterations 10\n",
+ "Cbc0038I Pass 17: suminf. 0.18897 (1) obj. 0.317791 iterations 9\n",
+ "Cbc0038I Pass 18: suminf. 0.00876 (1) obj. 0.317791 iterations 8\n",
+ "Cbc0038I Pass 19: suminf. 0.00876 (1) obj. 0.317791 iterations 22\n",
+ "Cbc0038I Pass 20: suminf. 0.18897 (1) obj. 0.317791 iterations 6\n",
+ "Cbc0038I Pass 21: suminf. 0.00876 (1) obj. 0.317791 iterations 9\n",
+ "Cbc0038I Pass 22: suminf. 0.00876 (1) obj. 0.317791 iterations 17\n",
"Cbc0038I Pass 23: suminf. 0.18897 (1) obj. 0.317791 iterations 6\n",
"Cbc0038I Pass 24: suminf. 0.00876 (1) obj. 0.317791 iterations 5\n",
- "Cbc0038I Pass 25: suminf. 0.00876 (1) obj. 0.317791 iterations 12\n",
+ "Cbc0038I Pass 25: suminf. 0.00876 (1) obj. 0.317791 iterations 10\n",
"Cbc0038I Pass 26: suminf. 0.18897 (1) obj. 0.317791 iterations 6\n",
"Cbc0038I Pass 27: suminf. 0.00876 (1) obj. 0.317791 iterations 5\n",
- "Cbc0038I Pass 28: suminf. 0.00876 (1) obj. 0.317791 iterations 13\n",
- "Cbc0038I Pass 29: suminf. 0.18897 (1) obj. 0.317791 iterations 6\n",
- "Cbc0038I Pass 30: suminf. 0.00876 (1) obj. 0.317791 iterations 15\n",
- "Cbc0038I Pass 31: suminf. 0.00876 (1) obj. 0.317791 iterations 6\n",
+ "Cbc0038I Pass 28: suminf. 0.00876 (1) obj. 0.317791 iterations 30\n",
+ "Cbc0038I Pass 29: suminf. 0.18897 (1) obj. 0.317791 iterations 5\n",
+ "Cbc0038I Pass 30: suminf. 0.00876 (1) obj. 0.317791 iterations 6\n",
+ "Cbc0038I Pass 31: suminf. 0.00876 (1) obj. 0.317791 iterations 3\n",
"Cbc0038I No solution found this major pass\n",
- "Cbc0038I Before mini branch and bound, 1 integers at bound fixed and 46 continuous\n",
+ "Cbc0038I Before mini branch and bound, 1 integers at bound fixed and 47 continuous\n",
"Cbc0038I Full problem 166 rows 105 columns, reduced to 48 rows 27 columns\n",
- "Cbc0038I Mini branch and bound did not improve solution (0.02 seconds)\n",
- "Cbc0038I After 0.02 seconds - Feasibility pump exiting with objective of 0.317969 - took 0.02 seconds\n",
- "Cbc0012I Integer solution of 0.31796885 found by feasibility pump after 0 iterations and 0 nodes (0.02 seconds)\n",
- "Cbc0038I Full problem 166 rows 105 columns, reduced to 49 rows 27 columns\n",
- "Cbc0031I 6 added rows had average density of 5.5\n",
- "Cbc0013I At root node, 25 cuts changed objective from 0.31628066 to 0.31796885 in 1 passes\n",
- "Cbc0014I Cut generator 0 (Probing) - 11 row cuts average 3.0 elements, 1 column cuts (1 active) in 0.000 seconds - new frequency is 1\n",
- "Cbc0014I Cut generator 1 (Gomory) - 2 row cuts average 13.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.04 seconds)\n",
+ "Cbc0038I After 0.04 seconds - Feasibility pump exiting with objective of 0.317969 - took 0.03 seconds\n",
+ "Cbc0012I Integer solution of 0.31796885 found by feasibility pump after 0 iterations and 0 nodes (0.05 seconds)\n",
+ "Cbc0038I Full problem 166 rows 105 columns, reduced to 48 rows 27 columns\n",
+ "Cbc0031I 3 added rows had average density of 3.3333333\n",
+ "Cbc0013I At root node, 31 cuts changed objective from 0.31628066 to 0.31796885 in 1 passes\n",
+ "Cbc0014I Cut generator 0 (Probing) - 19 row cuts average 3.0 elements, 1 column cuts (1 active) in 0.000 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 1 (Gomory) - 3 row cuts average 8.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
"Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 4 row cuts average 3.2 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 3 row cuts average 3.3 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
"Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 6 (TwoMirCuts) - 8 row cuts average 6.8 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
- "Cbc0001I Search completed - best objective 0.3179688539269278, took 31 iterations and 0 nodes (0.02 seconds)\n",
+ "Cbc0014I Cut generator 6 (TwoMirCuts) - 6 row cuts average 6.2 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is 1\n",
+ "Cbc0001I Search completed - best objective 0.3179688539269278, took 17 iterations and 0 nodes (0.06 seconds)\n",
"Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
"Cuts at root node changed objective from 0.316281 to 0.317969\n",
- "Probing was tried 1 times and created 12 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
- "Gomory was tried 1 times and created 2 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "Probing was tried 1 times and created 20 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "Gomory was tried 1 times and created 3 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"Knapsack was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"Clique was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
- "MixedIntegerRounding2 was tried 1 times and created 4 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "MixedIntegerRounding2 was tried 1 times and created 3 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"FlowCover was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
- "TwoMirCuts was tried 1 times and created 8 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "TwoMirCuts was tried 1 times and created 6 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"\n",
"Result - Optimal solution found\n",
"\n",
"Objective value: 0.31796885\n",
"Enumerated nodes: 0\n",
- "Total iterations: 31\n",
- "Time (CPU seconds): 0.03\n",
+ "Total iterations: 17\n",
+ "Time (CPU seconds): 0.06\n",
"Time (Wallclock seconds): 0.03\n",
"\n",
- "Total time (CPU seconds): 0.03 (Wallclock seconds): 0.03\n",
+ "Total time (CPU seconds): 0.07 (Wallclock seconds): 0.04\n",
"\n"
]
}
@@ -343,7 +354,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt 3.14.12: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
@@ -351,7 +362,7 @@
" For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version 3.14.12, running with linear solver MUMPS 5.2.1.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 395\n",
"Number of nonzeros in inequality constraint Jacobian.: 276\n",
@@ -388,66 +399,43 @@
" 16 4.8086057e-01 1.98e-06 3.65e+05 -1.0 1.21e-05 - 1.00e+00 6.18e-01h 1\n",
" 17 4.8086191e-01 6.64e-07 6.79e+05 -1.0 4.84e-06 - 1.00e+00 6.65e-01h 1\n",
" 18 4.8086192e-01 6.47e-07 3.49e+06 -1.0 1.54e-06 - 1.00e+00 2.47e-02f 6\n",
- " 19 4.8086258e-01 1.04e-10 1.00e-06 -1.0 1.52e-06 - 1.00e+00 1.00e+00h 1\n",
- "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 20 4.8086253e-01 1.78e-10 4.52e+02 -8.6 7.22e-05 - 1.00e+00 1.00e+00h 1\n",
- " 21 4.8001913e-01 2.86e-02 2.80e+02 -8.6 1.17e+00 - 5.24e-01 1.00e+00f 1\n",
- " 22 4.8001744e-01 1.08e-02 2.14e+01 -8.6 2.16e-01 - 9.00e-01 6.57e-01h 1\n",
- " 23 4.8001271e-01 1.79e-03 2.28e+01 -8.6 3.03e-01 - 8.31e-01 1.00e+00h 1\n",
- " 24 4.8000768e-01 1.81e-04 4.39e+01 -8.6 9.74e-02 - 7.32e-01 1.00e+00h 1\n",
- " 25 4.8000768e-01 1.80e-04 5.02e+01 -8.6 1.67e-02 - 5.11e-01 4.80e-03h 1\n",
- " 26 4.8000768e-01 1.80e-04 7.68e+01 -8.6 1.72e-02 - 2.92e-01 2.94e-04f 2\n",
- " 27 4.8000768e-01 1.80e-04 1.18e+02 -8.6 1.73e-02 - 6.38e-01 2.96e-04h 1\n",
- " 28 4.8000768e-01 1.80e-04 1.25e+02 -8.6 1.76e-02 - 3.09e-01 6.58e-05h 2\n",
- " 29 4.8000768e-01 1.80e-04 1.41e+02 -8.6 1.76e-02 - 1.00e+00 2.94e-04h 1\n",
+ " 19 4.8086258e-01 3.98e-10 1.00e-06 -1.0 1.52e-06 - 1.00e+00 1.00e+00h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 30 4.8000669e-01 5.04e-06 2.97e+00 -8.6 1.77e-02 - 2.99e-01 1.00e+00f 1\n",
- " 31 4.8000669e-01 5.04e-06 8.32e+01 -8.6 4.66e-05 - 5.89e-01 2.35e-04h 1\n",
- " 32 4.8000669e-01 5.04e-06 1.14e+02 -8.6 5.90e-05 - 5.27e-01 3.92e-05h 1\n",
- " 33 4.8000669e-01 5.04e-06 1.25e+02 -8.6 6.02e-05 - 3.88e-01 6.73e-06f 2\n",
- " 34 4.8000669e-01 5.04e-06 1.25e+02 -8.6 6.06e-05 - 5.49e-02 2.21e-05h 1\n",
- " 35 4.8000669e-01 5.04e-06 1.27e+02 -8.6 4.61e-04 - 8.33e-02 7.28e-08f 2\n",
- " 36 4.8000669e-01 5.04e-06 1.34e+02 -8.6 6.09e-05 - 4.80e-01 7.71e-05f 2\n",
- " 37 4.8000669e-01 5.04e-06 1.35e+02 -8.6 6.11e-05 - 1.75e-01 1.36e-05h 1\n",
- " 38 4.8000669e-01 5.04e-06 1.36e+02 -8.6 1.27e-04 - 9.83e-02 1.38e-07f 2\n",
- " 39 4.8000669e-01 5.04e-06 1.37e+02 -8.6 6.12e-05 - 2.54e-01 9.45e-04h 1\n",
+ " 20 4.8086253e-01 2.78e-09 4.52e+02 -8.6 7.22e-05 - 1.00e+00 1.00e+00h 1\n",
+ " 21 4.8001912e-01 2.86e-02 2.80e+02 -8.6 1.17e+00 - 5.23e-01 1.00e+00f 1\n",
+ " 22 4.8001743e-01 1.08e-02 2.14e+01 -8.6 2.15e-01 - 9.00e-01 6.57e-01h 1\n",
+ " 23 4.8001271e-01 1.79e-03 2.27e+01 -8.6 3.03e-01 - 8.31e-01 1.00e+00h 1\n",
+ " 24 4.8000768e-01 1.81e-04 4.21e+01 -8.6 9.74e-02 - 7.43e-01 1.00e+00h 1\n",
+ " 25 4.8000767e-01 1.79e-04 5.50e+02 -8.6 1.68e-02 - 1.00e+00 9.31e-03h 1\n",
+ " 26 4.8000669e-01 5.00e-06 9.43e-09 -8.6 1.76e-02 - 1.00e+00 1.00e+00f 1\n",
+ " 27 4.8000670e-01 7.87e-11 8.32e+01 -8.6 7.60e-05 - 4.17e-01 1.00e+00h 1\n",
+ " 28 4.8000670e-01 7.86e-11 1.41e+02 -8.6 8.40e-06 - 1.00e+00 1.93e-04h 1\n",
+ " 29 4.8000670e-01 3.40e-10 1.73e+02 -8.6 7.68e-06 - 6.59e-02 1.00e+00f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 40 4.8000669e-01 4.94e-06 1.35e+02 -8.6 6.12e-05 - 1.83e-01 1.89e-02f 1\n",
- " 41 4.8000669e-01 4.94e-06 1.36e+02 -8.6 2.85e-04 - 9.97e-02 1.10e-07f 2\n",
- " 42 4.8000669e-01 4.94e-06 1.37e+02 -8.6 6.00e-05 - 1.72e-01 4.40e-05h 1\n",
- " 43 4.8000669e-01 4.94e-06 1.37e+02 -8.6 1.41e-04 - 9.04e-02 1.49e-06f 2\n",
- " 44 4.8000669e-01 4.94e-06 1.38e+02 -8.6 6.01e-05 - 1.71e-01 5.97e-06f 2\n",
- " 45 4.8000670e-01 2.65e-11 6.17e+01 -8.6 6.01e-05 - 1.56e-01 1.00e+00h 1\n",
- " 46 4.8000670e-01 2.64e-11 5.65e+01 -8.6 3.45e-06 - 5.27e-02 4.42e-04h 1\n",
- " 47 4.8000670e-01 2.26e-11 7.42e+01 -8.6 1.45e-07 - 6.61e-01 1.47e-01f 2\n",
- " 48 4.8000670e-01 2.25e-11 8.82e+01 -8.6 7.27e-06 - 2.11e-01 1.18e-03h 1\n",
- " 49 4.8000670e-01 2.25e-11 1.30e+02 -8.6 8.86e-06 - 7.86e-01 1.57e-04f 2\n",
- "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 50 4.8000670e-01 2.25e-11 1.33e+02 -8.6 4.24e-05 - 2.54e-01 1.00e-04h 1\n",
- " 51 4.8000670e-01 2.25e-11 1.41e+02 -8.6 6.81e-05 - 1.00e+00 2.59e-05f 2\n",
- " 52 4.8000670e-01 3.10e-11 2.08e+00 -8.6 1.27e-07 - 2.54e-01 1.00e+00h 1\n",
- " 53 4.8000670e-01 3.19e-09 1.41e+02 -8.6 3.37e-05 - 1.00e+00 4.73e-04h 1\n",
- " 54 4.8000670e-01 9.74e-11 7.50e-11 -8.6 1.65e-08 - 1.00e+00 1.00e+00f 1\n",
+ " 30 4.8000670e-01 3.40e-10 1.74e+02 -8.6 2.35e-05 - 1.00e+00 2.85e-04h 1\n",
+ " 31 4.8000670e-01 5.51e-09 1.32e+00 -8.6 3.08e-08 - 2.22e-01 1.00e+00f 1\n",
+ " 32 4.8000670e-01 9.70e-11 1.23e+02 -8.6 1.11e-04 - 8.72e-01 1.82e-04h 2\n",
+ " 33 4.8000670e-01 1.52e-10 1.76e-10 -8.6 1.50e-08 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 54\n",
+ "Number of Iterations....: 33\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: 4.8000669509937166e-01 4.8000669509937166e-01\n",
- "Dual infeasibility......: 7.5043113584813605e-11 7.5043113584813605e-11\n",
- "Constraint violation....: 9.7397756526618195e-11 9.7397756526618195e-11\n",
+ "Objective...............: 4.8000669509919508e-01 4.8000669509919508e-01\n",
+ "Dual infeasibility......: 1.7627861836399183e-10 1.7627861836399183e-10\n",
+ "Constraint violation....: 1.5176976342345938e-10 1.5176976342345938e-10\n",
"Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00\n",
- "Complementarity.........: 2.5636037643218892e-09 2.5636037643218892e-09\n",
- "Overall NLP error.......: 9.7397756526618195e-11 2.5636037643218892e-09\n",
+ "Complementarity.........: 3.0531777979505568e-09 3.0531777979505568e-09\n",
+ "Overall NLP error.......: 1.5176976342345938e-10 3.0531777979505568e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 72\n",
- "Number of objective gradient evaluations = 55\n",
- "Number of equality constraint evaluations = 72\n",
- "Number of inequality constraint evaluations = 72\n",
- "Number of equality constraint Jacobian evaluations = 55\n",
- "Number of inequality constraint Jacobian evaluations = 55\n",
- "Number of Lagrangian Hessian evaluations = 54\n",
- "Total seconds in IPOPT = 0.125\n",
+ "Number of objective function evaluations = 40\n",
+ "Number of objective gradient evaluations = 34\n",
+ "Number of equality constraint evaluations = 40\n",
+ "Number of inequality constraint evaluations = 40\n",
+ "Number of equality constraint Jacobian evaluations = 34\n",
+ "Number of inequality constraint Jacobian evaluations = 34\n",
+ "Number of Lagrangian Hessian evaluations = 33\n",
+ "Total seconds in IPOPT = 0.052\n",
"\n",
"EXIT: Optimal Solution Found.\n",
"\b"
@@ -535,10 +523,10 @@
"output_type": "stream",
"text": [
"Welcome to the CBC MILP Solver \n",
- "Version: 2.9.9 \n",
- "Build Date: Oct 13 2018 \n",
+ "Version: 2.10.10 \n",
+ "Build Date: Apr 19 2023 \n",
"\n",
- "command line - /rds/general/user/sz421/home/anaconda3/envs/OMLT_test/bin/cbc -printingOptions all -import /var/tmp/pbs.8259409.pbs/tmp1n22ks_r.pyomo.lp -stat=1 -solve -solu /var/tmp/pbs.8259409.pbs/tmp1n22ks_r.pyomo.soln (default strategy 1)\n",
+ "command line - /opt/conda/bin/cbc -printingOptions all -import /tmp/tmp0s5lbbp6.pyomo.lp -stat=1 -solve -solu /tmp/tmp0s5lbbp6.pyomo.soln (default strategy 1)\n",
"Option for printingOptions changed from normal to all\n",
"Presolve 260 (-137) rows, 141 (-51) columns and 852 (-173) elements\n",
"Statistics for presolved model\n",
@@ -571,17 +559,16 @@
"56 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
"0 of type Free \n",
"Continuous objective value is 0.107106 - 0.00 seconds\n",
- "Cgl0003I 0 fixed, 0 tightened bounds, 1 strengthened rows, 0 substitutions\n",
"Cgl0004I processed model has 237 rows, 118 columns (29 integer (29 of which binary)) and 969 elements\n",
- "Cbc0038I Initial state - 17 integers unsatisfied sum - 1.66726\n",
- "Cbc0038I Pass 1: suminf. 1.01765 (9) obj. 0.107106 iterations 47\n",
+ "Cbc0038I Initial state - 17 integers unsatisfied sum - 1.61761\n",
+ "Cbc0038I Pass 1: suminf. 1.01765 (9) obj. 0.107106 iterations 46\n",
"Cbc0038I Solution found of 0.107106\n",
"Cbc0038I Relaxing continuous gives 0.107106\n",
- "Cbc0038I Before mini branch and bound, 12 integers at bound fixed and 40 continuous\n",
+ "Cbc0038I Before mini branch and bound, 12 integers at bound fixed and 38 continuous\n",
"Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)\n",
- "Cbc0038I After 0.01 seconds - Feasibility pump exiting with objective of 0.107106 - took 0.00 seconds\n",
- "Cbc0012I Integer solution of 0.10710584 found by feasibility pump after 0 iterations and 0 nodes (0.01 seconds)\n",
- "Cbc0001I Search completed - best objective 0.1071058437228203, took 0 iterations and 0 nodes (0.01 seconds)\n",
+ "Cbc0038I After 0.02 seconds - Feasibility pump exiting with objective of 0.107106 - took 0.01 seconds\n",
+ "Cbc0012I Integer solution of 0.10710584 found by feasibility pump after 0 iterations and 0 nodes (0.02 seconds)\n",
+ "Cbc0001I Search completed - best objective 0.1071058437228203, took 0 iterations and 0 nodes (0.02 seconds)\n",
"Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
"Cuts at root node changed objective from 0.107106 to 0.107106\n",
"Probing was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
@@ -591,16 +578,17 @@
"MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "ZeroHalf was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"\n",
"Result - Optimal solution found\n",
"\n",
"Objective value: 0.10710584\n",
"Enumerated nodes: 0\n",
"Total iterations: 0\n",
- "Time (CPU seconds): 0.01\n",
+ "Time (CPU seconds): 0.03\n",
"Time (Wallclock seconds): 0.02\n",
"\n",
- "Total time (CPU seconds): 0.01 (Wallclock seconds): 0.02\n",
+ "Total time (CPU seconds): 0.03 (Wallclock seconds): 0.02\n",
"\n"
]
}
@@ -644,9 +632,9 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python [conda env:OMLT_test]",
+ "display_name": "Python 3",
"language": "python",
- "name": "conda-env-OMLT_test-py"
+ "name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -658,7 +646,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.17"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/import_network.ipynb b/docs/notebooks/neuralnet/import_network.ipynb
index 60b48adf..3f056572 100644
--- a/docs/notebooks/neuralnet/import_network.ipynb
+++ b/docs/notebooks/neuralnet/import_network.ipynb
@@ -189,14 +189,12 @@
"outputs": [
{
"data": {
- "image/png": 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A4cA3JG2fR/BmZmZmRTZhohURGyPizvT4aWA1sDcwG1iSFlsCHJ0ezwYuiYhnI2ItsAY4uM1xm5mZmRXeDo0sLGkG8GbgFmAoIjZCloxJ2iMttjdwc9lq69O0ytc6CTgJYGhoiNHR0arvOTQJRmaONRImQM3Xy8uWLVs6/p6N6oUYoXfiNDMzm0jdiZakXYAfAKdGxFOSai5aZVpsMyFiMbAYYNasWTE8PFz1xc658HIWrWwoHwRg3fHVXy8vo6Oj1PoMRdELMULvxGlmZjaRuq46lLQjWZJ1YURcmiZvkjQtzZ8GbE7T1wPTy1bfB9jQnnDNzMzMekc9Vx0KOA9YHRFnl81aBsxNj+cCl5dNnyNpJ0n7AfsDt7YvZDMzM7PeUM8ZrUOAjwLvkbQi3Y4AFgLvk/QA8L70nIhYBSwF7gV+CJwcEVtzid7MzHqCuwqyQTVh46eIuJHq7a4ADq2xzgJgQQtxmZlZfyl1FXSnpF2BOyQtB+aRdRW0UNJ8sq6CTqvoKmgv4FpJr/Mfd+s1jbcyNzPrczPmX9n0uusWHtnGSPpHukq9dKX605LKuwoaTostAUaB0yjrKghYK6nUVdBNnY3crDVOtMzMrKO60VXQoHQb08znbKYLpVa043vope/TiZaZmXVMt7oKGpRuY5r5nPNaOIPbjHZ0v9RL36cHlTYzs45wV0E2iJxomZlZ7txVkA0qVx2aWeG10jjdCqPUVdBKSSvStDPIugZaKulE4EHgGMi6CpJU6ipoDHcVZD3KiZZZG0g6HzgK2BwRb0zTzgT+DPhFWuyMiLgqzTsdOBHYCnwyIq7ueNBmHeSugmxQ9W2i5cuzrcMuAL4GfLti+pcj4kvlE9w/kJnZ4HAbLbM2iIgbgMfqXPz5/oEiYi1Q6h/IzMz6TN+e0TIriFMknQDcTtYr9uPU2T8Q9GYfQXnE0ko/P0OTOttP0Hifvd+/JzPblhMts/x8EziLrO+fs4BFwMeos38g6M0+gvKIpZV+fkZmjrFoZed+6sbrI6jfvycz25arDs1yEhGbImJrRDwHnMsL1YPuH8jMbEA40TLLSakTxuTDwD3psfsHMjMbEK46NGsDSReTDYw7VdJ64LPAsKSDyKoF1wEfB/cPZGY2SJxombVBRBxXZfJ54yzv/oHMzAaAqw7NzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMzCwnTrTMzMzMcuJEy8zMzCwnEyZaks6XtFnSPWXTzpT0sKQV6XZE2bzTJa2RdL+kw/IK3MzMzKzo6jmjdQFweJXpX46Ig9LtKgBJBwBzgAPTOt+QtH27gjUzMzPrJRMmWhFxA/BYna83G7gkIp6NiLXAGl4Y383MzMxsoLTSM/wpkk4AbgdGIuJxYG/g5rJl1qdp25B0EnASwNDQEKOjo1XfZGgSjMwcayHMxtWKZTxbtmxpar1O6oUYoXfiNDMzm0izidY3gbPIxnA7C1gEfAxQlWWj2gtExGJgMcCsWbNieHi46hudc+HlLFrZ2ZGC1h1fPZbxjI6OUuszFEUvxAi9E6eZmdlEmrrqMCI2RcTWiHgOOJcXqgfXA9PLFt0H2NBaiGZm1ut8YZUNqqYSLUnTyp5+GCgVnGXAHEk7SdoP2B+4tbUQzcysD1yAL6yyATRhnZyki4FhYKqk9cBngWFJB5FVC64DPg4QEaskLQXuBcaAkyNiay6Rm5lZz4iIGyTNqHPx5y+sAtZKKl1YdVNe8ZnlZcJEKyKOqzL5vHGWXwAsaCUoMzMbGB25sGpQLrJp5nP2wgVnlXrp++xsK3MzM7MXdOzCqkG5yKaZzzlv/pX5BFNDMxecVeql79ND8JiZWVf4wiobBE60zMysK3xhlQ0CVx2amVnufGGVDSonWmZmljtfWGWDylWHZmZmZjlxomVmZmaWEydaZmZmZjlxG60qZjTRp8jIzDGG2x+KmZmZ9TCf0TIzMzPLiRMtszaQdL6kzZLuKZs2RdJySQ+k+93L5p0uaY2k+yUd1p2ozcwsb060zNrjAuDwimnzgesiYn/guvQcSQcAc4AD0zrfkLR950I1M7NOcaJl1gYRcQPwWMXk2cCS9HgJcHTZ9Esi4tmIWAus4YWhR8zMrI+4MbxZfoYiYiNARGyUtEeavjdwc9ly69O0bUg6CTgJYGhoqOZo9UUayT6PWEZmjjW97tCk1tZv1Hifvd+/JzPblhMts85TlWlRbcGIWAwsBpg1a1bUGq2+SCPZ5xHLvCauBC4ZmTnGopWd+6lbd/xwzXn9/j2Z2bZcdWiWn02lQXPT/eY0fT0wvWy5fYANHY7NzMw6wGe0zPKzDJgLLEz3l5dNv0jS2cBewP7ArV2J0NpuvH74RmaO1Tw7t27hkXmFZGZd5ETLrA0kXQwMA1MlrQc+S5ZgLZV0IvAgcAxARKyStBS4FxgDTo6IrV0J3MzMcuVEy6wNIuK4GrMOrbH8AmBBfhGZmVkRuI2WmZmZWU4mTLTc47WZmZlZc+o5o3UB7vHazMzMrGETttGKiBskzaiYPJus4S9kPV6PAqdR1uM1sFZSqcfrm9oUr5mZmVUxY/6V417Zat3RbGP4jvV43elenZs1NGn8HqGLoFd6gu6VOM2sfpLOB44CNkfEG9O0KcD3gBnAOuDYiHg8zTsdOBHYCnwyIq7uQthmLWv3VYdt7/H6nAsv72ivzs0amTnGsQXvZblXeoLulTjNrCEXAF8Dvl02rdQMZaGk+en5aRXNUPYCrpX0OneDYr2o2asO3eO1mZnVzQOv26Bq9lSRe7w2M7NWDeTA63kZmTnWE81t2vE99NL3OWGi5R6vzcysw/p64PW8zEuN4Yve3Ga8gdfr1UvfZz1XHbrHazMzy8MmSdPS2Sw3Q7G+5J7hzcysW0rNUGDbZihzJO0kaT/cDMV6WLHPL5qZWV9wMxQbVE60zMwsd26GYoPKVYdmZmZmOfEZrQKY0eRwCesWHtnmSMzMzPI1aMc8n9EyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4kTLTMzM7OcONEyMzMzy4l7hjczK4BB6y3bbFA40TLLmaR1wNPAVmAsImZJmgJ8D5gBrAOOjYjHuxWjmZnlw1WHZp3x7og4KCJmpefzgesiYn/guvTczMz6jBMts+6YDSxJj5cAR3cvFDMzy4urDs3yF8A1kgL4x4hYDAxFxEaAiNgoaY9qK0o6CTgJYGhoiNHR0apvsGXLlprzOi2PWEZmjjW97tCk1tZvpzxiaXZbF2mfMetnTrTM8ndIRGxIydRySffVu2JKyhYDzJo1K4aHh6suNzo6Sq15nZZHLPOabCgOWWKzaGUxfuryiGXd8cNNrVekfcasn7VUdShpnaSVklZIuj1NmyJpuaQH0v3u7QnVrDdFxIZ0vxm4DDgY2CRpGkC639y9CM3MLC/t+Gv17oh4pOx5qZHvQknz0/PT2vA+Zj1H0mRgu4h4Oj1+P/B5YBkwF1iY7i/vXpTWy5rtFuKCwye3ORIzqyaP8+mzgeH0eAkwihOtXDT6Azsyc4x58690vzudNQRcJgmy8nZRRPxQ0m3AUkknAg8Cx3QxRrOuchco1s9aTbRyb+RbpIas4xmaBOdc2NxJiZGZbQ6mhtK2LHoD2H5qpBsRPwXeVGX6o8ChnY/IrLBcO2J9qdVEK/dGvudceHlhGrKOp0gNbmspxdhs49lOcSNdM8O1I9YnWsoMyhv5SnpRI990NsuNfM3MbCID1QVKXkZmjvVMLVAzyr+/Xvo+m0603MjXzMzaZKC6QMnLvPlX9kTtSrPKa2N66fts5dtwI18zM2uZa0esnzWdaLmRr5mZtcq1I9bv+vP8opmZ9QrXjlhfc6JlZh3TbOea1r9cO2L9rqUheMzMzMysNidaZmZmZjlx1aGZmfW9lQ8/ybwmqq49ZJm1yme0zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLifrSsbs0On+J+aMzMbFD5jJaZmZlZTpxomZmZmeXEVYdmZmZWeOXNV0ZmjtU9pFK3m6840TIzMyuQZtvDWjE50RpALsRmZmad4TZaZmZmZjlxomVmZmaWk9yqDiUdDnwV2B74VkQszOu9zHpRO8vIyoefrLthaLlmG4mOV/3cSCNVs4n4WGKt6nYfkLkkWpK2B74OvA9YD9wmaVlE3JvH+1mxNbqTlw7U3b5SJE8uI2YTczmxfpDXGa2DgTUR8VMASZcAswEXDrOMy4jZxHq6nPjCIwNQRLT/RaU/BA6PiD9Nzz8KvC0iTilb5iTgpPT09cD9NV5uKvBI24Nsv16IsxdihHzj3DciXpnTa9etnjKSpvdiOSlSLFCseHollp4pJz1aRvLkz9kZdZeRvM5oqcq0F2V0EbEYWDzhC0m3R8SsdgWWl16IsxdihN6Js0UTlhHozXJSpFigWPE4loYN3LGkVf6cxZPXVYfrgellz/cBNuT0Xma9yGXEbGIuJ9bz8kq0bgP2l7SfpJcAc4BlOb2XWS9yGTGbmMuJ9bxcqg4jYkzSKcDVZJfknh8Rq5p8uQlPCRdEL8TZCzFC78TZtDaXESjWNitSLFCseBxLAwb0WNIqf86CyaUxvJmZmZm5Z3gzMzOz3DjRMjMzM8tJoRMtSYdLul/SGknzux1PNZLWSVopaYWk27sdT4mk8yVtlnRP2bQpkpZLeiDd717AGM+U9HDaniskHdHNGIuuCGWkWhno1L7W6H4u6fS0re6XdFiH4qm5T+cZj6Tpkq6XtFrSKkmfStO7tn26pQjlJC/dLH95KVq5bllEFPJG1vDxJ8CrgZcAdwEHdDuuKnGuA6Z2O44qcb0LeAtwT9m0LwLz0+P5wBcKGOOZwF91e/v1wq0oZaRaGejUvtbIfg4ckLbRTsB+adtt34F4qu7TeccDTAPekh7vCvw4vWfXtk83bkUpJzl+vq6Vvxw/U6HKdau3Ip/Ren7ohYj4DVAaesHqEBE3AI9VTJ4NLEmPlwBHdzKmSjVitPoVuYx0ZF9rcD+fDVwSEc9GxFpgDdk2zDueWnKNJyI2RsSd6fHTwGpgb7q4fbqkyOUkL4X6rW9U0cp1q4qcaO0NPFT2fH2aVjQBXCPpjjQURJENRcRGyH6EgT26HE8tp0i6O50+7qlT3h1WlDJSrQx0c1+r9d7d3F7V9umOxSNpBvBm4BaKuX3y1K+fq6Ro5S8vPbvfFjnRqmuIkgI4JCLeAnwAOFnSu7odUI/7JvAa4CBgI7Coq9EUW1HKSK+UgW5tr1r7dEfikbQL8APg1Ih4arxFOxFPF/Tr5yrplfKXl8J/v0VOtHpi6IWI2JDuNwOXUbBTlhU2SZoGkO43dzmebUTEpojYGhHPAedS7O3ZbYUoIzXKQDf3tVrv3ZXtNc4+nXs8knYkS7IujIhL0+RCbZ8O6NfPBRSy/OWlZ/fbIidahR96QdJkSbuWHgPvB+4Zf62uWgbMTY/nApd3MZaqSgUp+TDF3p7d1vUyMk4Z6Oa+Vuu9lwFzJO0kaT9gf+DWvIMZZ5/ONR5JAs4DVkfE2WWzCrV9OqDr5SQvBS1/eend/bbbrfHHuwFHkF0p8xPgM92Op0p8rya72uEuYFWRYgQuJqum+C1Zxn8i8ArgOuCBdD+lgDF+B1gJ3E1WgKZ1e1sW+dbtMlKrDHRqX2t0Pwc+k7bV/cAHOhRPzX06z3iAd5JVodwNrEi3I7q5fbp163Y5yfFzdbX85fi5ClWuW715CB4zMzOznBS56tDMzMyspznRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRapCkeZJurDHveEnXtOl9QtJrW3kfSWdK+m474jHrJ5JGJf1pety2cmtmVsmJVg2S3inpR5KelPSYpP+U9HvjrRMRF0bE++t47TMkbUm3X0vaWvZ81UTr1/s+Zr1K0jpJ7+3Ee7k8Wb9K5ehX6djyuKQrJU1P8y5If+g/VLHOV9L0eel5zZMLVh8nWlVIehlwBXAOMAXYG/gc8Gw7Xj8i/i4idomIXYBPADeVnkfEge14DzMzM+CD6VgzDdhEdlwr+TEwt/RE0g7AMcBPOhphn3OiVd3rACLi4ojYGhG/iohrIuLuygUl/V9JN0p6eWXmn/4VfELSA+nfxNclqYE43ltt3Srvc6Ck5enM2yZJZ1SJc0dJF0v6gaSXpGrFpZK+LelpSaskzSpbfq+07C8krZX0ybJ5B0u6XdJT6f3OTtN3lvRdSY9KekLSbZKGGvi8Zi9S2tclfSmVg7WSPlAx/6dpH14r6fg0/UXV5pJmpPK4Q633KHveark1K5yI+DXwfeCAssn/Ahwiaff0/HDgbuDnHQ6vrznRqu7HwFZJSyR9oGwnfJ6k7SSdC/wu8P6IeLLGax0F/B7wJuBY4LAG4phwXUm7AtcCPwT2Al4LXFexzCTgn8nOyB0bEb9Jsz4EXALsBiwDvlb6bGQF8C6ys3mHAqdKKr3/V4GvRsTLgNcAS9P0ucDLgenAK8jO1v2qgc9rVs3bgPuBqcAXgfOUmQz8PfCBiNgVeAewok3v2Uq5NSscSS8F/gi4uWzyr8l+++ek5ycA3+5waH3PiVYVEfEU8E4ggHOBX0haVnZ2ZkfgYrJqxQ9GxC/HebmFEfFERDwIXA8c1EAo9ax7FPDziFgUEb+OiKcj4pay+S8jS8J+AvxJRGwtm3djRFyVpn2H7KAC2QHmlRHx+Yj4TUT8lGw7lArjb4HXSpoaEVsi4uay6a8AXpvOBN6RtqVZK34WEeem/XQJWRVIqSw+B7xR0qSI2BgRE7ZxrFMr5dasSP5Z0hPAU8D7gP9bMf/bwAmSXg78AdmfcmsjJ1o1RMTqiJgXEfsAbyQ7W/SVNPu1wGzgc2Vnh2opPwX7S2CXBsKoZ93pjF+f/nays24LIyImeP2dU9XKvsBeqfrviVRIz+CFg9uJZNWr96XqwaPS9O8AVwOXSNog6YuSdpzwU5qN7/n9tOxPzS4R8QzZP/RPABtTQ9/fafd70ni5NSuSoyNiN2An4BTg3yXtWZoZETcCrwT+GrgiIlwL0WZOtOoQEfcBF5AlXACrgT8B/lXS67sVV/IQWfVdLdcA/we4roH2Ug8BayNit7LbrhFxBEBEPBARxwF7AF8Avi9pckT8NiI+FxEHkFXjHEV2KtosFxFxdUS8j+ws131kZ14BngFeWrbonpXrmg2SVMtwKbCVrMam3HeBEVxtmAsnWlVI+h1JI5L2Sc+nA8dRVrcdEReTneW5VtJ4iU7ergD2lHSqpJ0k7SrpbeULRMQXgYvIkq2pdbzmrcBTkk6TNEnS9pLeqNS9haSPSHplRDwHPJHW2Srp3ZJmStqe7DT1b8kKtVnbSRqS9KHUVutZYAsv7G8rgHdJelWqEjm9S2GaFUJq1zgb2J3sZEG5vyerVryh44ENACda1T1N1gD3FknPkCVY95Bl/M+LiCXA54F/kzSj00GmGJ4mKyAfJKvueAB4d5XlziKre79W0pQJXnNrer2DgLXAI8C3yBq6Q3ZlyipJW8gaxs9JV7TsSXZVy1NkBfnfyf4pmeVhO7IyuQF4jKx9yZ8DRMRy4HtkV1DdQfaHxGwQ/Uv6rX4KWADMrWzLGBGPRcR1VZqXWBvI29XMzMwsHz6jZWZmZpYTJ1pmZmZmOXGiZWZmZpYTJ1pmZmZmOXGiZdYjJP2w2zHYYPC+ZtY+2wyw2g1Tp06NGTNmVJ33zDPPMHny5M4GNA7HU1uRYoHq8dxxxx2PRMQr83g/SevIugbZCoxFxKzUlcb3gBnAOrKxJh9Py59O1sv+VuCTEXH1eK//spe97LBZs2ZVvUy4CNveMRQrjhZj6Mmhs3rpWFKL42yvvOJs6FgSEV2/vfWtb41arr/++przusHx1FakWCKqxwPcHjntx2SJ1NSKaV8E5qfH84EvpMcHkA3avROwH9kwStuP9/pFLyeO4QVFiKOVGPIsJ3neil5G6uE42yuvOBspI646NMvXbLKBkEn3R5dNvyQino2ItcAa4ODOh2dmZnkqRNWhWZ8I4BpJAfxjRCwGhiJiI0BEbJS0R1p2b8qGdALWp2kvIukk4CSAoaEhRkdHq77xli1bas7rFMdQrDiKEIOZOdEya6dDImJDSqaWS7pvnGVVZdo27a9SsrYYYNasWTE8PFz1xUZHR6k1r1McQ7HiKEIMZuarDs3aJiI2pPvNwGVkVYGbJE0DSPeb0+Lrgellq+9DNmafmZn1ESdaZm0gabKkXUuPgfeTDUS+DJibFpsLXJ4eLwPmSNpJ0n7A/sCtnY3azMzyVviqw5UPP8m8+Vc2vN66hUfmEI1ZTUPAZZIgK1cXRcQPJd0GLJV0IvAgcAxARKyStBS4FxgDTo6Irc2+ucuJmc1o4jcA/DuQt8InWma9ICJ+CrypyvRHgUNrrLMAWJBzaGZm1kWuOjQzMzPLiRMtMzMzs5w40TIzMzPLiRMtMzMzs5w40TIzs9xJmi7pekmrJa2S9Kk0/UxJD0takW5HlK1zuqQ1ku6XdFj3ojdrnq86NDOzThgDRiLiztTn3B2Slqd5X46IL5UvLOkAYA5wILAXcK2k17XSDYpZN/iMlpmZ5S4iNkbEnenx08BqqozvWcYDr1tf8BktMzPrKEkzgDcDtwCHAKdIOgG4neys1+P04cDr9WglzpGZY02t18z7DcL2bBcnWmZm1jGSdgF+AJwaEU9J+iZwFtmg6mcBi4CP0YcDr9ejlTibGR0CYN3xjb/fIGzPdnHVoZmZdYSkHcmSrAsj4lKAiNgUEVsj4jngXF6oHvTA69YXnGiZmVnulA0Eeh6wOiLOLps+rWyxD5MNxg4eeN36hKsOzcysEw4BPgqslLQiTTsDOE7SQWTVguuAj0P7B1436xYnWmZmlruIuJHq7a6uGmcdD7xuPc9Vh2ZmZmY5mTDRGqc33ymSlkt6IN3vXraOe/M1MzOzgVfPGa1Sb75vAN4OnJx67J0PXBcR+wPXpeeVvfkeDnxD0vZ5BG9mZmZWZBMmWuP05jsbWJIWWwIcnR67N18zMzMzGmwMX9Gb71BEbIQsGZO0R1qsrb35Dk1qrrfbvHqCLUIvs+WKFE+RYoHixWNmZoOn7kSrSm++NRetMq3p3nzPufByFq1s/OLIZnq6rUcRepktV6R4ihQLFC8eMzMbPHVddVitN19gU6mjuXS/OU13b75mZmZm1HfVYdXefMl67Z2bHs8FLi+b7t58beBI2l7Sf0m6Ij33lblmZgOunjq5Wr35LgSWSjoReBA4Btybrw20T5FdLPKy9Lx0Ze5CSfPT89MqrszdC7hW0utcTsysG2Y0MRj1yMwxhtsfSl+aMNEapzdfgENrrOPefG2gSNoHOJJsv//LNHk2PP9btAQYBU6j7MpcYK2k0pW5N3UwZDMz6wAPwWPWHl8BPg3sWjatpStzobeuzi3CVZ5FiKEocRQhBjNzomXWMklHAZsj4g5Jw/WsUmXaNlfmQm9dnVuEqzyLEENR4ihCDGbmRMusHQ4BPiTpCGBn4GWSvku6MjedzfKVuWZmA8iDSpu1KCJOj4h9ImIGWSP3f4uIj+Arc83MBp7PaJnlx1fmmpkNOCdaZm0UEaNkVxcSEY/iK3PNzAaaqw7NzMzMcuJEy8zMzCwnTrTMzCx3kqZLul7SakmrJH0qTfdQVdbXnGiZmVknjAEjEfEG4O3AyWk4qtJQVfsD16XnVAxVdTjwDUnbdyVysxa4MXwbNTNeFMC6hUe2ORIzs2JJoySURkp4WtJqshERPFSV9TUnWmZm1lGSZgBvBm6hxaGq6h2mqleGJGolzmaG4WrW0KT2DuGVlyJ87060zMysYyTtAvwAODUinpKqjUiVLVpl2jZDVdU7TFWvDEnUSpzzmqxVacbIzDGO7fPt2S5uo2VmZh0haUeyJOvCiLg0Td6UhqjCQ1VZP3KiZWZmuVN26uo8YHVEnF02y0NVWV9z1aGZmXXCIcBHgZWSVqRpZ+ChqqzPOdGqYryrB0dmjnW0HtzMrB9ExI1Ub3cFHqrK+pirDs3MzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxyMmGiJel8SZsl3VM27UxJD0takW5HlM3zaOtmZmZm1HdG6wKykdMrfTkiDkq3q8CjrZuZmZmVmzDRiogbgMfqfL3nR1uPiLVAabR1MzMzs4HTSoelp0g6AbgdGImIx6lztHWof8T1oUnNjUjeymjd471fs/GMp5VYizAyeUmRYoHixWNmZoOn2UTrm8BZZCOpnwUsAj5GnaOtQ/0jrp9z4eUsWtl4mOuOr/569Riv5/eRmWNNxTOeVmItwsjkJUWKBYoXj5mZDZ6mrjqMiE0RsTUingPO5YXqQY+2bgNH0s6SbpV0l6RVkj6Xpk+RtFzSA+l+97J1fNGImdkAaCrRkjSt7OmHgdIViR5t3QbRs8B7IuJNwEHA4ZLeDswHrouI/YHr0nNfNGJmNkAmrAOTdDEwDEyVtB74LDAs6SCyasF1wMfBo63bYIqIALakpzumW5BdHDKcpi8BRoHTKLtoBFgrqXTRyE2di9rMzDphwkQrIo6rMvm8cZb3aOs2cNIZqTuA1wJfj4hbJA1FxEaAiNgoaY+0eF9cNFKpCBcfFCGGosRRhBjMrLWrDs0sSWduD5K0G3CZpDeOs3hfXDRSqQgXHxQhhqLEUYQYzMxD8Ji1VUQ8QVZFeDiwqdSeMd1vTov5ohEzswHhRMusRZJemc5kIWkS8F7gPrKLQ+amxeYCl6fHvmjEzGxAuOrQrHXTgCWpndZ2wNKIuELSTcBSSScCDwLHgC8aMTMbJE60zFoUEXcDb64y/VHg0Brr+KIRGyiSzgeOAjZHxBvTtDOBPwN+kRY7o2zs3NOBE4GtwCcj4uqOB90lM8bpNNt6j6sOzcysEy4ga7tY6csRcVC6lZIs9zVnfcOJlpmZ5S4ibgAeq3Px5/uai4i1QKmvObOe46pDMzPrplMknQDcDoxExOPk0Ndcr/QrtmXLFkZmFr/J5tCk9vbDl5cifO9OtMzMrFu+CZxF1o/cWcAi4GPk0Ndcr/QrNjo6yqIbn+l2GBMamTnGsT2yPbv9vbvq0MzMuiIiNkXE1oh4DjiXF6oH3dec9Q0nWmZm1hWlDn2TDwP3pMfua876hqsOzcwsd5IuJhtkfaqk9cBngWFJB5FVC64DPg7ua876ixMtMzPLXUQcV2XyeeMs777mrC+46tDMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLStx2Wzph/ZbdDMDMzswE34RktSedL2izpnrJpUyQtl/RAut+9bN7pktZIul/SYXkFbmZmZlZ09VQdXgAcXjFtPnBdROwPXJeeI+kAYA5wYFrnG5K2b1u0ZmZmZj1kwkQrIm4AHquYPBtYkh4vAY4um35JRDwbEWuBNcDB7QnVzMzMrLc020ZrKCI2AkTERkl7pOl7AzeXLbc+TduGpJOAkwCGhoYYHR2t/kaTYGTmWJNhtl8e8dT67PXYsmVLS+u3U5Figc7FI2k68G1gT+A5YHFEfFXSFOB7wAxgHXBsRDye1jkdOBHYCnwyIq7OPVAzM+u4djeGV5VpUW3BiFgMLAaYNWtWDA8PV33Bcy68nEUri9Nmf2TmWNvjWXf8cNPrjo6OUmvbdVqRYoGOxjMGjETEnZJ2Be6QtByYR1bFvlDSfLIq9tMqqtj3Aq6V9LqI2NqJYM3MrHOa7d5hk6RpAOl+c5q+Hphettw+wIbmwzMrvojYGBF3psdPA6vJzuS6it3MbMA1e2pmGTAXWJjuLy+bfpGks8n+qe8P3NpqkGa9QtIM4M3ALfRAFXs7q1aLUHVchBiKEkcRYjCzOhItSRcDw8BUSeuBz5IlWEslnQg8CBwDEBGrJC0F7iWrTjnZ1SE2KCTtAvwAODUinpKq1aRni1aZ1pUq9laqrSsVoeq4CDEUJY4ixGBmdSRaEXFcjVmH1lh+AbCglaDMeo2kHcmSrAsj4tI0eZOkaelslqvYzcwGkIfgMWuRslNX5wGrI+LsslmlKnbYtop9jqSdJO2Hq9htALjzaxtUTrTMWncI8FHgPZJWpNsRZFXs75P0APC+9JyIWAWUqth/iKvYbTBcgDu/tgFUnH4TzHpURNxI9XZX4Cp2MyDr/DpdLFJuNlkbYMiuzB0FTqPsylxgraTSlbk3dSRYszZyomVmZt3SsStze+UqzC1btjAys/gnuIcmtfeq5bwU4Xt3omVmZkXT9itze+UqzNHRURbd+Ey3w5jQyMwxju2R7dnt791ttMzMrFvc+bX1PSdaZmbWLb4y1/qeqw7NzCx37vzaBpUTrQKYMf/KptZbt/DINkdiZpYPd35tg8pVh2ZmZmY5caJlZmZmlhNXHZoNMFdbm5nly2e0zMzMzHLiRMvMzMwsJ060zMzMzHLiNlpmZmbWMLfxrI/PaJmZmZnlxImWmZmZWU6caJmZmZnlxImWmZmZWU6caJmZmZnlxImWmZmZWU5a6t5B0jrgaWArMBYRsyRNAb4HzADWAcdGxOOthWlmZmbWe9pxRuvdEXFQRMxKz+cD10XE/sB16bmZmZnZwMmj6nA2sCQ9XgIcncN7mBWKpPMlbZZ0T9m0KZKWS3og3e9eNu90SWsk3S/psO5EbWZmeWu1Z/gArpEUwD9GxGJgKCI2AkTERkl7VFtR0knASQBDQ0OMjo5WfYOhSTAyc6zFMNunSPGMjo6yZcuWmtuu04oUC3Q8nguArwHfLptWOru7UNL89Pw0SQcAc4ADgb2AayW9LiK2dipYMzPrjFYTrUMiYkNKppZLuq/eFVNSthhg1qxZMTw8XHW5cy68nEUrizNS0MjMscLEs+74YUZHR6m17TqtSLFAZ+OJiBskzaiYPBsoBbAEGAVOS9MviYhngbWS1gAHAzd1JFgzM+uYljKGiNiQ7jdLuozsYLFJ0rR0NmsasLkNcZr1olpnd/cGbi5bbn2ato2invmtFkcRzmgWIYaixFGEGOrlC6usnzWdaEmaDGwXEU+nx+8HPg8sA+YCC9P95e0I1KyPqMq0qLZgUc/8rjt+2ziKcEazCDEUJY4ixNCgd0fEI2XPq1a9dyc0s+a18ss8BFwmqfQ6F0XEDyXdBiyVdCLwIHBM62Ga9aRaZ3fXA9PLltsH2NDx6MyKrVbVu1lPaTrRioifAm+qMv1R4NBWgjLrE7XO7i4DLpJ0Nllj+P2BW7sSoVkx5H5hVa9UpW7ZsoWRmcW/LqaV5gqd/B6K8L0Xo1W3WY+TdDHZv++pktYDnyVLsLY5uxsRqyQtBe4FxoCTfcWhDbjcL6zqlarU0dFRFt34TLfDmFArF4ZVa3qQlyJ87060zNogIo6rMavq2d2IWAAsyC8is97hC6usn3msQzMz6xpJkyXtWnpMdmHVPbxQ9Q6+sMp6mM9omZlZN/nCKutrTrTMzKxrfGGV9TsnWmbWsBnzr9xm2sjMMeZVmV5u3cIj8wrJzKyQ3EbLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCdOtMzMzMxy4kTLzMzMLCce69DMOqbaGIn18BiJ1oua2d9HZo7hQ3N/8RktMzMzs5w4bTYzM7OOGbQz2060etiM+VcyMnOMeQ3utL26s5qZNWvlw082/FsJ/r201rnq0MzMzCwnuSVakg6XdL+kNZLm5/U+Zr3KZcRsYi4n1utyqTqUtD3wdeB9wHrgNknLIuLePN7PrNe4jDSm3jYdzVSlV+PqomJwObF+kFcbrYOBNRHxUwBJlwCzAReOAhi0hogF5TJSYM2WEXA5aTOXE3tes91lDLc/lIbklWjtDTxU9nw98Lac3sv6VJ8nhC4jfarZi1TabaIYXE5sUHT7WJJXoqUq0+JFC0gnASelp1sk3V/jtaYCj7QxtpZ8coDj0RcmXKQQ26Yszmrx7NvRYGqbsIxAb5WTIpSNIsRQlDgmimGC8twz5STvMlLH715bFWHfqccgxNmuMpJXorUemF72fB9gQ/kCEbEYWDzRC0m6PSJmtTe85jme2ooUCxQvngoTlhHorXLiGIoVRxFiaIO+PZbU4jjbqwhx5nXV4W3A/pL2k/QSYA6wLKf3MutFLiNmE3M5sZ6XyxmtiBiTdApwNbA9cH5ErMrjvcx6kcuI2cRcTqwf5NYzfERcBVzVhpea8JRwhzme2ooUCxQvnhdpYxmBYnxWx/CCIsRRhBha1sfHklocZ3t1PU5FbNP+1szMzMzawEPwmJmZmeWk0IlWN4dekDRd0vWSVktaJelTafqZkh6WtCLdjuhgTOskrUzve3uaNkXSckkPpPvdOxTL68u2wQpJT0k6tVPbR9L5kjZLuqdsWs1tIen0tB/dL+mwPGLqhk6VkXHKQ8e3uaTtJf2XpCu6GMNukr4v6b60TX6/03FI+ov0Xdwj6WJJOw9iGahHN48l42mmXHUx1rrLXTc1WjY7IiIKeSNr+PgT4NXAS4C7gAM6+P7TgLekx7sCPwYOAM4E/qpL22QdMLVi2heB+enxfOALXfqufk7Wr0hHtg/wLuAtwD0TbYv0vd0F7ATsl/ar7bvxHeaw3TtSRsYpDx3f5sBfAhcBV3TreweWAH+aHr8E2K2TcZB15LkWmJSeLwXmDVoZqHNbdfVYMkFsDZWrLsdaV7nr9q2RstmpW5HPaD0/9EJE/AYoDb3QERGxMSLuTI+fBlaT/bgVzWyyHYt0f3QXYjgU+ElE/KxTbxgRNwCPVUyutS1mA5dExLMRsRZYQ7Z/9bqOlZFxykNHt7mkfYAjgW+VTe50DC8jS/TPA4iI30TEE52Og+xipkmSdgBeSta/1KCVgXp09VgynibKVVc0WO66pomy2RFFTrSqDb3QlURH0gzgzcAtadIpku5O1VedPAUZwDWS7lDWGzLAUERshKzQAnt0MJ6SOcDFZc+7tX1qbYvC7Ett1pXPVVEeOr3NvwJ8GniubFqnY3g18Avgn1JVyrckTe5kHBHxMPAl4EFgI/BkRFzTyRh6SE989jrLVbd8hfrLXTc1WjY7osiJVl1DlOQehLQL8APg1Ih4Cvgm8BrgILIfuEUdDOeQiHgL8AHgZEnv6uB7V6WsE8EPAf8vTerm9qmlEPtSDjr+uaqUh5qLVpnWUmySjgI2R8Qd9a7S7hiSHciqrb8ZEW8GniGrjuhYHOkPzGyyasC9gMmSPtLJGHpI4T97A+Wq45ood93UaNnsiCInWnUNUZInSTuS7fwXRsSlABGxKSK2RsRzwLl08PR7RGxI95uBy9J7b5I0LcU7DdjcqXiSDwB3RsSmFFvXtg+1t0XX96WcdPRzVSsPdHabHwJ8SNI6suqf90j6bodjKL3u+ogoneH+PtmPeyfjeC+wNiJ+ERG/BS4F3tHhGHpFoT97g+WqGxotd93UaNnsiCInWl0dekGSyOp5V0fE2WXTp5Ut9mHgnsp1c4pnsqRdS4+B96f3XgbMTYvNBS7vRDxljqOs2rBb2yeptS2WAXMk7SRpP2B/4NYOxpWXjpWRWuWBDm7ziDg9IvaJiBlkn/XfIuIjnYwhxfFz4CFJr0+TDgXu7XAcDwJvl/TS9N0cSta+Z9DKQD0KO4xPE+Wq45ood13TRNnsWGCFvQFHkF2F8RPgMx1+73eSnV6+G1iRbkcA3wFWpunLgGkdiufVZFfL3AWsKm0P4BXAdcAD6X5KB7fRS4FHgZeXTevI9iFL7jYCvyX7F3PieNsC+Ezaj+4HPtDJfSnn76AjZWSc8tCVbQ4M88LVTx2Pgaxq/Pa0Pf4Z2L3TcQCfA+4j+zPzHbIrCgeuDNS5rbp2LJkgrobLVZfjravcdTnGhspmJ27uGd7MzMwsJ0WuOjQzMzPraU60zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLiRMvMzMwsJ060zMzMzHLSM4mWpH+Q9Dd1Ljsq6U/zjqlTJK2T9N70+AxJ3+p2TN0g6XhJ13Q7DjMzs3oVJtFKycSvJD0t6QlJP5L0CUnbAUTEJyLirA7E0ZYkTdKwpOckbUmf6X5Jf9Lq60bE30VEV5JISSHpmfSZtkh6Isf3mpHeb4fStIi4MCLen9d7Wu9J5fVxSTt1OxazPEiaJ2mlpF9K+rmkb0rarc51n/+Tbt1TmEQr+WBE7ArsCywETgPO625ILdkQEbsALyP7LOdKOqBbwZQnLS14U0Tskm67teH1zJoiaQbw/wEBfKi70Zi1n6QR4AvA/wJeDryd7Pi4XNJLuhmb1a9oiRYAEfFkRCwD/giYK+mNki6Q9LcAknaXdIWkX6R/s1dI2qfiZV4j6VZJT0q6XNKU0gxJb09nzJ6QdJek4TR9AdkP99fSGZuvpem/I2m5pMfSmaljy17rCEn3prNWD0v6qyqfJyLin4HHgQMkbSdpvqSfSHpU0tKK+D4q6Wdp3mfKX0vSmZK+W/b8hLJl/6aimvFMSd+X9F1JTwHzJL1c0nmSNqZ4/1bS9mWv9zFJq9N2vVrSvhN9X+nM02vLnpd/V8OS1ksakbQ5ve+flC07SdKi9BmelHSjpEnADWmRJ9J38fvpn92NZeu+Q9Jtab3bJL2jbN6opLMk/Wf6bq6RNHWiz2I95QTgZuACYG5poqRXSPoXSU+l/eJvK/abmuXZrCgkvQz4HPA/I+KHEfHbiFgHHEuWbH2k/Lc2rTMsaX16/B3gVcC/pN/QT6fp7yw7/j0kaV6a/nJJ307H1Z9J+mulGqX02/ufkr6c1vtp+v2dl15js6TyMriTpC9JelDSJmVNfyZ1ZMMVUCETrZKIuBVYT5b8lNsO+Ceyne1VwK+Ar1UscwLwMWAvYAz4ewBJewNXAn8LTAH+CviBpFdGxGeA/wBOSWdsTpE0GVgOXATsARwHfEPSgel9zgM+ns7EvRH4t8rPkRKrDwO7ASuBTwJHA3+Q4nsc+Hpa9gDgm8BH07xXAJVJJGXLfgM4HphG9o9n74rFZgPfT+99IbAkbY/XAm8G3g/8aXq9o4EzgP8GvDJti4urvXeD9iyL7UTg65J2T/O+BLwVeAfZ9/Fp4DngXWn+bum7uKn8BVNieiXZ9/oK4GzgSkmvKFvsj4E/IfveXkL2XVv/OIFsn74QOEzSUJr+deAZsv1uLi9OwiYqz2ZF8Q5gZ+DS8okRsQX4V+B9460cER8FHiSrKdolIr4o6VVp3XPIfuMPAlakVc4h+51+Ndmx6QSy38+StwF3k/3eXgRcAvwe2bHkI2QnKHZJy34BeF16/deS/fb/7wY+e18pdKKVbCA7AD8vIh6NiB9ExC8j4mlgAdmOUe47EXFPRDwD/A1wbDpz8xHgqoi4KiKei4jlwO3AETXe/yhgXUT8U0SMRcSdwA+AP0zzf0t2luplEfF4ml+yl7J2TI8AnwU+GhH3Ax8HPhMR6yPiWeBM4A+VVe39IXBFRNyQ5v0NWeJRzR8C/xIRN0bEb8h25KhY5qaI+OeIeI6sCvMDwKkR8UxEbAa+DMxJy34c+D8RsToixoC/Aw6qOKt1Z/pH84Skv68RV6XfAp9P/8iuArYAr0//lj4GfCoiHo6IrRHxo/S5J3Ik8EBEfCd9LxcD9wEfLFvmnyLixxHxK2ApWaG3PiDpnWR/tJZGxB3AT4A/TmX8vwOfTb8P95L9uSiZqDybFcVU4JH0W1xpY5rfqOOBayPi4vR7/GhErEjl5o+A0yPi6XTmbBHZH/6StancbAW+B0wn+11/NiKuAX4DvFaSgD8D/iIiHkvH6L/jhePMwGlHm5287Q08Vj5B0kvJEoTDgdKZkV0lbZ92AoCHylb5GbAj2Y65L3CMpPID8o7A9TXef1/gbXpxw+8dgO+kx/8d+GtgoaS7gfllZ182RES1s1H7ApdJKk+gtgJDZGexno89Ip6R9GiN2CqX/WWVZcu3w75kn3VjVhaALNl+qGz+VyUtKltHZN/Bz9Lzt0TEmhrx1PJoxY/FL4FdyL6PnckOko3aqyymkp/x4jN6P6/yntYf5gLXRMQj6flFadrFZOWzfL+vLAPjlWezongEmCpphyrJ1rQ0v1HTqf57O5XsrH/5b2rl7+mmsse/AoiIymm7kJ0peylwR9lxRsD2DKhCJ1qSfo/si76R7LRlyQjweuBtEfFzSQcB/0X2ZZZML3v8KrKzKo+Q/eh+JyL+rMbbVp4Regj494ioepo2Im4DZkvaETiF7MzJ9GrLVrzmxyLiPytnSNoIvKHs+UvJTtVWs5FsO5SWnVRl2fLP8xDwLDC1xr+kh4AFEXHhBPFX+iVZwSrZk6zKdyKPAL8GXgPcVTGv8nuotIHsoFnuVcAP63hf62FpPz8W2F5SKZneiax6fIisanwf4MdpXnl5HLc8mxXITWS/1/+N7LgCPF/9/QGyZh5vZtvf3nLVjmcHV3mvR8iOkfsC96ZprwIebiLuR8iSrgMjopn1+04hqw4lvUzSUWR1wN+NiJUVi+xK9kU+kdrqfLbKy3xE0gEpUfk88P10tuu7wAclHSZpe0k7pwaEpTNPm8jqqEuuAF6nrIH6jun2e5LeIOklyvp2enlE/BZ4iuzM1ET+AVhQqpKT9EpJs9O87wNHpQaLL0mx1/qevp8+yzvSsp/jxcnmi0TERuAaYFHaxttJeo2kUrXrPwCnl9qrpMaRx9TxeVaQqm0kHc621bi14nkOOB84W9Jeaf3fV3ap/i/IqkxfXWP1q8i+lz+WtIOkPwIOIPu+rL8dTVbODiCrDj6I7M/Jf5C1K7kUOFPSSyX9TppWUrM8dzB+swlFxJNkv+nnSDo87aszgP9H9kf2O2S/vUdImiJpT+DUipepPJ5dCLxX0rHpd/MVkg5Kx8alZMelXdOx6S/JjpeNxv0ccC7wZUl7QNY2WtJhjb5WvyhaovUvkp4my7o/Q9bAuVrfU18BJpFlzjdT/SzGd8iuRvo5WfXUJwEi4iGyBuJnkB3MHyK7dLa0Lb5K1l7qcUl/n+qX309Wv7whvd4XyP5BQ1aHvU7ZVX2fIGsDNpGvAsuAa9LnvZl0xi4iVgEnk1WFbCRrKF/17FBa9n+SJaQbgaeBzWT/gmo5gewU8b3ptb9PdhqaiLgsfbZL0ue5h+yf00Q+RdY26gmyNgD/XMc6JX9FdoHAbWRVxF8AtouIX5K1vfvP1B7s7eUrRcSjZO1tRoBHyRrRH1VWlWT9ay5Z+7sHI+LnpRvZBTHHk51ZfjlZWf0OWXXiswB1lGezwoiIL5Idq75E9kf+FrJj1qGpLet3yGoD1pH9if5exUv8H+Cv02/oX0XEg2TtkUfIfm9XAG9Ky/5PsotIfkpWi3QR2R/hZpwGrAFuTseSaymrfRk0ipiohsZ6Rbri4wlg/4hY2+VwzApB0heAPSNi7oQLm5m1WdHOaFmDJH0wVZFMJvvXs5Ls343ZQFLWT9bvKnMwWZcil3U7LjMbTE60et9ssiqQDcD+wJzwaUobbLuStdN6hqzdySLg8q5GZGYDy1WHZmZmZjnxGS0zMzOznBSiH62pU6fGjBkzAHjmmWeYPHlydwPqIH/ezrrjjjseiYhXdi2AFpSXk5Jub89m9WrcMBix92o5qVZGSor2vRUpHsdS3XixNFRGIqLrt7e+9a1Rcv3118cg8eftLOD2KMA+38ytvJyUdHt7NqtX444YjNh7tZxUKyONfvZOKVI8jqW68WJppIy46tDMzMwsJ060zMzMzHLiRMusRWkYp1sl3SVplaTPpelTJC2X9EC6371sndMlrZF0/yAPTWGDRdI6SSslrZB0e5rmcmJ9zYmWWeueBd4TEW8iG3fv8DRk0HzguojYH7guPUfSAWRDwBwIHA58Q9LAjmxvA+fdEXFQRMxKz11OrK850TJrUWobuSU93THdgqwz2SVp+hKywZBJ0y+JiGcjGyppDXBw5yI2KxSXE+trhejeYTwz5l/Z1HrrFh7Z5kjMakv/tO8AXgt8PSJukTQUERsBImJjaSR7YG+ygcRL1qdp1V73JOAkgKGhIUZHR180f8uWLYyOjrLy4Sebinvm3i9var1WleLuRY69JQFcIymAf4yIxUBL5WSiMlKy+bEnOefCxgcIyKuMFOC7eJ5jqa5dsRQ+0TLrBRGxFThI0m7AZZLeOM7iqvYSNV53MbAYYNasWTE8PPyi+aOjowwPDzOv2T8kxw9PuEweSnH3IsfekkMiYkNKppZLum+cZesqJxOVkZJzLrycRSsbP+TlVUYK8F08z7FU165YXHVo1kYR8QQwStamZJOkaQDpfnNabD0wvWy1fcjGqjTraxGxId1vJhvo+2BcTqzPOdEya5GkV6YzWUiaBLwXuA9YBsxNi83lhYGNlwFzJO0kaT+ywcBv7WjQZh0mabKkXUuPgfcD9+ByYn3OVYdmrZsGLEnttLYDlkbEFZJuApZKOhF4EDgGICJWSVoK3AuMASenqkezfjZEVq0O2bHnooj4oaTbcDmxPjZhoiVpZ+AGYKe0/Pcj4rOSpgDfA2YA64BjI+LxtM7pwInAVuCTEXF1LtGbFUBE3A28ucr0R4FDa6yzAFiQc2hmhRERPwXeVGW6y4n1tXqqDt1HkJmZmVkTJky03EeQmZmZWXPqaqOVRx9Btfo+qey3YmTmWGOfKClKPxwTKVKfIZ0waJ/XzMwGW12JVh59BNXq+6Sy34pe6x+oUUXqM6QTBu3zmpnZYGuoewf3EWRmZmZWvwkTLfcRZGZmZtaceqoO3UeQmZmZWRMmTLTcR5CZmZlZczwEj5mZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZmZmZ5cSJlpmZmVlOnGiZtUjSdEnXS1otaZWkT6XpZ0p6WNKKdDuibJ3TJa2RdL+kw7oXvZmZ5amuQaXNbFxjwEhE3ClpV+AOScvTvC9HxJfKF5Z0ADAHOBDYC7hW0us8goKZWf/xGS2zFkXExoi4Mz1+GlgN7D3OKrOBSyLi2YhYC6wBDs4/UjMz6zSf0TJrI0kzyIasugU4BDhF0gnA7WRnvR4nS8JuLlttPTUSM0knAScBDA0NMTo6+qL5W7ZsYXR0lJGZY03FW/l6nVKKuxc5djNrhBMtszaRtAvwA+DUiHhK0jeBs4BI94uAjwGqsnpUe82IWAwsBpg1a1YMDw+/aP7o6CjDw8PMm39lUzGvO354wmXyUIq7Fzl2M2uEqw7N2kDSjmRJ1oURcSlARGyKiK0R8RxwLi9UD64Hppetvg+woZPxmnWLpO0l/ZekK9LzKZKWS3og3e9etqwvGrGe50TLrEWSBJwHrI6Is8umTytb7MPAPenxMmCOpJ0k7QfsD9zaqXjNuuxTZO0YS+YD10XE/sB16XnlRSOHA9+QtH2HYzVr2YSJli9dN5vQIcBHgfdUlIcvSlop6W7g3cBfAETEKmApcC/wQ+BkX3Fog0DSPsCRwLfKJs8GlqTHS4Cjy6b7ohHrefW00fKl62bjiIgbqd7u6qpx1lkALMgtKLNi+grwaWDXsmlDEbERsit4Je2Rptd10chEF4w8/yaTaOqikbwuHijShQmOpbp2xTJhopUKQKkQPC2p7kvXgbWSSv9Cbmo5WjMz60mSjgI2R8QdkobrWaXKtG0uGpnogpGScy68nEUrG7/+K68LRop0YYJjqa5dsTS017Xz0vVa/0IqM8heu2y9UUXK3jth0D6vmT3vEOBDqVp9Z+Blkr4LbJI0LZ3NmgZsTsv7ohHrC3UnWu2+dL3Wv5DKDLLXLltvVJGy904YtM9rZpmIOB04HSCd0fqriPiIpP8LzAUWpvvL0yrLgIsknU3WDMUXjVhPqivRqnXpetn8c4Er0lP/CzEzs3otBJZKOhF4EDgGsotGJJUuGhnDF41Yj5ow0Rrv0vVSA0a2vXTd/0LMzKyqiBgFRtPjR4FDayzni0as59VzRqt06fpKSSvStDOA4yQdRFYtuA74OPhfiJmZmVlJPVcd+tJ1MzMzsya4Z3gzMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy2zFkmaLul6SaslrZL0qTR9iqTlkh5I97uXrXO6pDWS7pd0WPeiNzOzPDnRMmvdGDASEW8A3g6cLOkAYD5wXUTsD1yXnpPmzQEOBA4HviFp+65EbmZmuXKiZdaiiNgYEXemx08Dq4G9gdnAkrTYEuDo9Hg2cElEPBsRa4E1wMEdDdrMzDqinkGlzaxOkmYAbwZuAYYiYiNkyZikPdJiewM3l622Pk2r9nonAScBDA0NMTo6+qL5W7ZsYXR0lJGZY03FW/l6nVKKuxc5djNrhBMtszaRtAvwA+DUiHhKqjYWe7ZolWlRbcGIWAwsBpg1a1YMDw+/aP7o6CjDw8PMm39lUzGvO354wmXyUIq7Fzl2M2vEhFWHbuhrNjFJO5IlWRdGxKVp8iZJ09L8acDmNH09ML1s9X2ADZ2K1czMOqeeM1qlhr53StoVuEPScmAeWUPfhZLmkzX0Pa2ioe9ewLWSXhcRW/P5CNXNaPIfPsC6hUe2MRLrd8pOXZ0HrI6Is8tmLQPmAgvT/eVl0y+SdDZZGdkfuLVzEZuZWadMeEbLDX3NJnQI8FHgPZJWpNsRZAnW+yQ9ALwvPSciVgFLgXuBHwInd/qPiJmZdUZDbbTa2dC3ViPfysaazTbybUUnG4sOWuPUfvy8EXEj1dtdARxaY50FwILcgjIzs0KoO9Fqd0PfWo18KxtrNtvItxWdbCA8aI1TB+3zmpnZYKurHy039DUzs1ZI2lnSrZLuShdWfS5N94VV1tfquepwooa+sG1D3zmSdpK0H27oa2Zm8Czwnoh4E3AQcLikt+MRFKzP1XNGyw19zcysJZHZkp7umG6BL6yyPjdhGy039DUzs3ZIZ6TuAF4LfD0ibpGUy4VVlYYmNXdxVV4X7xTpwiDHUl27YnHP8GZm1hGpduMgSbsBl0l64ziLt3RhVaVzLrycRSsbP+TldXFUkS4McizVtSsWDyptZmYdFRFPAKNkba98YZX1NSdaZmaWO0mvTGeykDQJeC9wH76wyvqcqw7NzKwTpgFLUjut7YClEXGFpJuApZJOBB4EjoHswipJpQurxvCFVdajnGiZmVnuIuJuspFFKqc/ii+ssj7mqkMzMzOznDjRMjMzM8uJEy0zMzOznLiNlpmZmfWtGfOvbGq9Cw6f3Jb39xktMzMzs5z4jJZZG0g6HzgK2BwRb0zTzgT+DPhFWuyMiLgqzTsdOBHYCnwyIq7ueNA0/09v3cIj2xyJmVl/8hkts/a4gKyX60pfjoiD0q2UZB0AzAEOTOt8I/UtZGZmfcaJllkbRMQNwGN1Lj4buCQino2ItcAa4ODcgjMzs65x1aFZvk6RdAJwOzASEY8DewM3ly2zPk3bhqSTgJMAhoaGthlJvjS6/MjMsRxCr63VEe1Lcfcix25mjZgw0erVtidmBfBN4Cwg0v0i4GOAqiwb1V4gIhYDiwFmzZoVlSPJl0aXn9dkW6tmrTt+eMJlxlOKuxc5djNrRD1VhxfgtidmDYuITRGxNSKeA87lherB9cD0skX3ATZ0Oj4zM8vfhImW256YNUfStLKnHwbuSY+XAXMk7SRpP2B/4NZOx2dmZvlrpY1WLm1PKtsQdLrtCbTe/qQRg9Zmol8/r6SLgWFgqqT1wGeBYUkHkVULrgM+DhARqyQtBe4FxoCTI2JrF8I2M7OcNZto5db2pLINQafbnkDr7U8aMWhtJvr180bEcVUmnzfO8guABflFZGZmRdBU9w5ue2JmZmY2saYSLbc9MTMzM5tYPd07uO2JmZmZWRMmTLTc9sTMzMysOe4ZvgoPtGtmZmbt4LEOzcwsd5KmS7pe0mpJqyR9Kk2fImm5pAfS/e5l65wuaY2k+yUd1r3ozZrnRMvMzDphjKzPxTcAbwdOTqOJzAeui4j9gevSc480Yn3DiZaZmeUuIjZGxJ3p8dPAarIOrWcDS9JiS4Cj02OPNGJ9wW20zMysoyTNAN4M3AIMRcRGyJIxSXukxeoaaaTWKCOVhiY1N9JIXiNZFGmUjH6PpdkRZtoVixMtMzPrGEm7AD8ATo2Ip6RqA4pki1aZts1II7VGGal0zoWXs2hl44e8vEYKKdIoGf0eS7MjzFxw+OS2xOKqQzMz6whJO5IlWRdGxKVp8qZSJ9jpfnOa7pFGrC840TIzs9wpO3V1HrA6Is4um7UMmJsezwUuL5vukUas57nq0MzMOuEQ4KPASkkr0rQzgIXAUkknAg8Cx4BHGrH+4UTLzMxyFxE3Ur3dFcChNdbxSCPW81x1aGZmZpYTJ1pmbSDpfEmbJd1TNs09XpuZDTgnWmbtcQFZ79Xl3OO1mdmAc6Jl1gYRcQPwWMVk93htZjbgJmwML+l84Chgc0S8MU2bAnwPmAGsA46NiMfTvNOBE4GtwCcj4upcIjcrvpZ6vIaJe70u9VzcbM/HzWq1t+Qi9UTdKMduZo2o56rDC4CvAd8um1aqElkoaX56flpFlchewLWSXudLcs1epK4er2HiXq9LvSg32/Nxs1rtLbtIPVE3yrGbWSMmrDp0lYhZ09zjtZnZgGu2H63cqkQqT213ukqkFc2ckh+0U/kD9nlLPV4vZNsery+SdDbZmV/3eG1m1qfa3WFpy1Uilae2O10l0opmqlMG7VR+v35eSRcDw8BUSeuBz9LHPV7PaLJcrlt4ZJsjMTMrtmYTrU2SpqWzWa4SsYEXEcfVmOUer83MBliz3Tt4EFAzMzOzCdTTvcNAVYmYWX5KVY4jM8caahbgKkcz61UTJlquEjEzMzNrjnuGNzMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zM8udpPMlbZZ0T9m0KZKWS3og3e9eNu90SWsk3S/psO5EbdY6J1pmZtYJFwCHV0ybD1wXEfsD16XnSDoAmAMcmNb5hqTtOxeqWfs40TIzs9xFxA3AYxWTZwNL0uMlwNFl0y+JiGcjYi2wBji4E3GatduEQ/CYmZnlZCgiNgJExEZJe6TpewM3ly23Pk3bhqSTgJMAhoaGGB0drf5Gk7IxNhtV6/VatWXLltxeu1H9Hksz33s7Y3GiZWZmRaMq06LaghGxGFgMMGvWrBgeHq76gudceDmLVjZ+yFt3fPXXa9Xo6Ci1Yu20fo+lkQHsy11w+OS2xOKqQ7OcSVonaaWkFZJuT9NqNgI2GyCbJE0DSPeb0/T1wPSy5fYBNnQ4NrO2aOmMlqR1wNPAVmAsImZJmgJ8D5gBrAOOjYjHWwuzN8xoImsemTnGcPtDseJ5d0Q8Uva81Ah4oaT56flp3QnNrGuWAXOBhen+8rLpF0k6G9gL2B+4tSsRmrWoHVWHPoCYNW42PJ9jLwFGcTmpqZk/MQDrFh7Z5kisWZIuJtvnp0paD3yWLMFaKulE4EHgGICIWCVpKXAvMAacHBFbuxK4WYvyaKPlA4jZiwVwjaQA/jG1KanVCPhFJmroW2qs2Wxjz25ptmFyo/Jo4FukhsON6mbsEXFcjVmH1lh+AbAgv4jMOqPVRKvtB5DKH4JeO4A0amhSfle1FFEvH6RacEhEbEhlYbmk++pdcaKGvqWGo8029uyWkZljTTVMblQeDZmL1HC4Ub0cu1mvavWXru0HkMofgl47gDRqZOYYxw7QD98g/tBHxIZ0v1nSZWT9AW2SNC39GSlvBGxmZn2kpasOyw8gwIsOILDNVSRmA0fSZEm7lh4D7wfu4YVGwPDiRsBmZtZHmk60fAAxq8sQcKOku8iumroyIn5I1gj4fZIeAN6XnpuZWZ9ppepwCLhMUul1LoqIH0q6jSpXkZgNooj4KfCmKtMfpUYjYDMz6x9NJ1o+gJiZmZmNzz3Dm5mZmeXEYx2aWd9yR6dm1m0+o2VmZmaWEydaZmZmZjlx1aGZWYXxqhxHZo6N25Gyqx3NrJwTrQJwOxIzM7P+5KpDMzMzs5w40TIzMzPLiRMtMzMzs5y4jZaZWRu5zaWZlXOi1cP8g25mZlZsrjo0MzMzy4kTLTMzM7OcONEyMzMzy0luiZakwyXdL2mNpPl5vY9Zr3IZMZuYy4n1ulwaw0vaHvg68D5gPXCbpGURcW8e72eNabYRfbOabXzfz439XUasUj/v781yObF+kNdVhwcDayLipwCSLgFmAy4cA6j8ADLROHEDxGXE2qKRBK28/PVIguZyYj0vr0Rrb+ChsufrgbeVLyDpJOCk9HSLpPvT46nAIznFVTif9OdtO31h3Nn75vneDZiwjMC45aSkJ/efXt7v+yX2fikndZSRkqa+twm2UyuKtB85lire/YVxY6m7jOSVaKnKtHjRk4jFwOJtVpRuj4hZOcVVOP68A2vCMgK1y8nzL9Kj27NX4wbH3mFNH0u2eaGCffYixeNYqmtXLHk1hl8PTC97vg+wIaf3MutFLiNmE3M5sZ6XV6J1G7C/pP0kvQSYAyzL6b3MepHLiNnEXE6s5+VSdRgRY5JOAa4GtgfOj4hVda4+4SngPuPPO4BaLCPlenV79mrc4Ng7po3lBIr32YsUj2Opri2xKGKbZiFmZmZm1gbuGd7MzMwsJ060zMzMzHJSmERr0IZZkHS+pM2S7ul2LJ0gabqk6yWtlrRK0qe6HVMvqbX9JE2RtFzSA+l+927HWouk7SX9l6Qr0vOeiF3SbpK+L+m+tP1/v4di/4u0v9wj6WJJO/dK7I2Y6PihzN+n+XdLeku96+YQy/Ephrsl/UjSm8rmrZO0UtIKSbd3IJZhSU+m91sh6X/Xu24OsfyvsjjukbRV0pQ0r93bZdzjb9v3l4jo+o2skeNPgFcDLwHuAg7odlw5f+Z3AW8B7ul2LB36vNOAt6THuwI/7vfvuBPbD/giMD9Nnw98oduxjvMZ/hK4CLgiPe+J2IElwJ+mxy8BduuF2Mk6+1wLTErPlwLzeiH2Bj/nhMcP4AjgX8n65Xo7cEu96+YQyzuA3dPjD5RiSc/XAVM7uF2GS+Wx0XXbHUvF8h8E/i2P7ZJeb9zjb7v3l6Kc0Xp+mIWI+A1QGmahb0XEDcBj3Y6jUyJiY0TcmR4/DawmOxBYHcbZfrPJEgHS/dFdCXACkvYBjgS+VTa58LFLehnZj/J5ABHxm4h4gh6IPdkBmCRpB+ClZH1Q9Urs9arn+DEb+HZkbgZ2kzStznXbGktE/CgiHk9PbybrGywPrXy2jm+XCscBF7fwfuOq4/jb1v2lKIlWtWEWfBDuU5JmAG8GbulyKD2pYvsNRcRGyJIxYI8uhjaerwCfBp4rm9YLsb8a+AXwT6na81uSJtMDsUfEw8CXgAeBjcCTEXENPRB7g+o5ftRapt3HnkZf70SyMyclAVwj6Q5lQwu1ot5Yfl/SXZL+VdKBDa7b7liQ9FLgcOAHZZPbuV3q0db9pSiJVl3DkVjvk7QLWQE6NSKe6nY8vaYXt5+ko4DNEXFHt2Npwg5kVQzfjIg3A8+QVbcVXmp7NRvYD9gLmCzpI92NKhf1HD9qLdPuY0/dryfp3WSJ1mllkw+JiLeQVSmeLOldOcdyJ7BvRLwJOAf45wbWbXcsJR8E/jMiys84tXO71KOt+0tREi0PszAAJO1IliRcGBGXdjueXlNj+21Kp7RJ95u7Fd84DgE+JGkd2an290j6Lr0R+3pgfUSUzr5+nyzx6oXY3wusjYhfRMRvgUvJ2gf1QuyNqOf4UWuZdh976no9Sb9LVo0+OyIeLU2PiA3pfjNwGVlVVW6xRMRTEbElPb4K2FHS1Ho/RztjKTOHimrDNm+XerR1fylKouVhFvqcJJG1c1kdEWd3O55eM872WwbMTY/nApd3OraJRMTpEbFPRMwgK9v/FhEfoTdi/znwkKTXp0mHAvfSA7GTVRm+XdJL0/5zKFnbvl6IvRH1HD+WASekq8neTlaNurHOddsai6RXkSW9H42IH5dNnyxp19Jj4P1AK1el1xPLnmnfQNLBZDnBo/Ws2+5YUgwvB/6Asn0yh+1Sj/buL+1qxd/qjayV/4/JWvR/ptvxdODzXkzWbuK3ZFnyid2OKefP+06yU6x3AyvS7Yhux9Urt1rbD3gFcB3wQLqf0u1YJ/gcw7xw1WFPxA4cBNyetv0/A7v3UOyfA+4jOzB9B9ipV2Jv8HNuc/wAPgF8Ij0W8PU0fyUwa7x1c47lW8DjZeX49jT91WRXsd0FrOpQLKek97qLrGH+O7q1XdLzecAlFevlsV22Of7mub94CB4zMzOznBSl6tDMzMys7zjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznDjRMjMzM8uJEy0zMzOznPz/WKNoOjibKlwAAAAASUVORK5CYII=\n",
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",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -282,7 +280,17 @@
"cell_type": "code",
"execution_count": 5,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:23:43.060702: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-05-16 17:23:43.107094: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"from omlt.io import write_onnx_model_with_bounds, load_onnx_neural_network_with_bounds"
]
@@ -330,13 +338,22 @@
"cell_type": "code",
"execution_count": 7,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/home/codespace/.python/current/lib/python3.10/site-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
+ " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' # or any {'0', '1', '2'}\n",
- "import tensorflow as tf\n",
- "from tensorflow.keras.models import Sequential\n",
- "from tensorflow.keras.layers import Dense\n",
+ "import keras\n",
+ "from keras.models import Sequential\n",
+ "from keras.layers import Dense\n",
"\n",
"model = Sequential()\n",
"model.add(Dense(12, input_dim=8, activation='relu'))\n",
@@ -363,311 +380,311 @@
"output_type": "stream",
"text": [
"Epoch 1/150\n",
- "77/77 [==============================] - 0s 1ms/step - loss: 6.3354 - accuracy: 0.5677\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.6556 - loss: 5.5503\n",
"Epoch 2/150\n",
- "77/77 [==============================] - 0s 1ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6514 - loss: 5.6185\n",
"Epoch 3/150\n",
- "77/77 [==============================] - 0s 1ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6416 - loss: 5.7775\n",
"Epoch 4/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6579 - loss: 5.5142\n",
"Epoch 5/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6483 - loss: 5.6692\n",
"Epoch 6/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6317 - loss: 5.9365\n",
"Epoch 7/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6459 - loss: 5.7081\n",
"Epoch 8/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6452 - loss: 5.7192\n",
"Epoch 9/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6770 - loss: 5.2058\n",
"Epoch 10/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6290 - loss: 5.9799\n",
"Epoch 11/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6771 - loss: 5.2039\n",
"Epoch 12/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6570 - loss: 5.5278\n",
"Epoch 13/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6566 - loss: 5.5354\n",
"Epoch 14/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6599 - loss: 5.4820\n",
"Epoch 15/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6489 - loss: 5.6587\n",
"Epoch 16/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6662 - loss: 5.3798\n",
"Epoch 17/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6313 - loss: 5.9427\n",
"Epoch 18/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6623 - loss: 5.4427\n",
"Epoch 19/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6502 - loss: 5.6380\n",
"Epoch 20/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7022 - loss: 4.8001\n",
"Epoch 21/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6584 - loss: 5.5060\n",
"Epoch 22/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6772 - loss: 5.2026\n",
"Epoch 23/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6578 - loss: 5.5153\n",
"Epoch 24/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6851 - loss: 5.0759\n",
"Epoch 25/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6703 - loss: 5.3147\n",
"Epoch 26/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6440 - loss: 5.7380\n",
"Epoch 27/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6467 - loss: 5.6945\n",
"Epoch 28/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6375 - loss: 5.8429\n",
"Epoch 29/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6296 - loss: 5.9698\n",
"Epoch 30/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6477 - loss: 5.6782\n",
"Epoch 31/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6452 - loss: 5.7189\n",
"Epoch 32/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6753 - loss: 5.2343\n",
"Epoch 33/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6440 - loss: 5.7387\n",
"Epoch 34/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6535 - loss: 5.5844\n",
"Epoch 35/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6307 - loss: 5.9523\n",
"Epoch 36/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6518 - loss: 5.6125\n",
"Epoch 37/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6640 - loss: 5.4163\n",
"Epoch 38/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6501 - loss: 5.6394\n",
"Epoch 39/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6997 - loss: 4.8408\n",
"Epoch 40/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6395 - loss: 5.8108\n",
"Epoch 41/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6608 - loss: 5.4667\n",
"Epoch 42/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6467 - loss: 5.6938\n",
"Epoch 43/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6377 - loss: 5.8393\n",
"Epoch 44/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6574 - loss: 5.5224\n",
"Epoch 45/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6220 - loss: 6.0925\n",
"Epoch 46/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6355 - loss: 5.8751\n",
"Epoch 47/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6315 - loss: 5.9388\n",
"Epoch 48/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6745 - loss: 5.2463\n",
"Epoch 49/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6943 - loss: 4.9269\n",
"Epoch 50/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6564 - loss: 5.5379\n",
"Epoch 51/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6453 - loss: 5.7168\n",
"Epoch 52/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6524 - loss: 5.6025 \n",
"Epoch 53/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 935us/step - accuracy: 0.6364 - loss: 5.8609\n",
"Epoch 54/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 950us/step - accuracy: 0.6476 - loss: 5.6793\n",
"Epoch 55/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6735 - loss: 5.2618\n",
"Epoch 56/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6405 - loss: 5.7944\n",
"Epoch 57/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6695 - loss: 5.3265\n",
"Epoch 58/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6572 - loss: 5.5254\n",
"Epoch 59/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6720 - loss: 5.2875\n",
"Epoch 60/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6537 - loss: 5.5818\n",
"Epoch 61/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6457 - loss: 5.7102\n",
"Epoch 62/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6308 - loss: 5.9513\n",
"Epoch 63/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6598 - loss: 5.4827\n",
"Epoch 64/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6293 - loss: 5.9745\n",
"Epoch 65/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6528 - loss: 5.5963\n",
"Epoch 66/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6677 - loss: 5.3561\n",
"Epoch 67/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6537 - loss: 5.5819\n",
"Epoch 68/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6589 - loss: 5.4973\n",
"Epoch 69/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6673 - loss: 5.3623\n",
"Epoch 70/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6625 - loss: 5.4391\n",
"Epoch 71/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6450 - loss: 5.7224\n",
"Epoch 72/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6457 - loss: 5.7109\n",
"Epoch 73/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510: 0s - loss: 5.0131 - accuracy\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6634 - loss: 5.4250\n",
"Epoch 74/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6510 - loss: 5.6259\n",
"Epoch 75/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6493 - loss: 5.6531\n",
"Epoch 76/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6166 - loss: 6.1800\n",
"Epoch 77/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6771 - loss: 5.2045\n",
"Epoch 78/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6628 - loss: 5.4357\n",
"Epoch 79/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6479 - loss: 5.6760\n",
"Epoch 80/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6261 - loss: 6.0264\n",
"Epoch 81/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6175 - loss: 6.1647\n",
"Epoch 82/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6728 - loss: 5.2734\n",
"Epoch 83/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6395 - loss: 5.8100\n",
"Epoch 84/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6401 - loss: 5.8015\n",
"Epoch 85/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6313 - loss: 5.9432\n",
"Epoch 86/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6628 - loss: 5.4345\n",
"Epoch 87/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6603 - loss: 5.4747\n",
"Epoch 88/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6418 - loss: 5.7727\n",
"Epoch 89/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6285 - loss: 5.9874\n",
"Epoch 90/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6408 - loss: 5.7903\n",
"Epoch 91/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6540 - loss: 5.5770\n",
"Epoch 92/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6400 - loss: 5.8018\n",
"Epoch 93/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6405 - loss: 5.7938\n",
"Epoch 94/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6485 - loss: 5.6650\n",
"Epoch 95/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6539 - loss: 5.5784\n",
"Epoch 96/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6647 - loss: 5.4040\n",
"Epoch 97/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.7045 - loss: 4.7636 \n",
"Epoch 98/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6365 - loss: 5.8596\n",
"Epoch 99/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6261 - loss: 6.0266\n",
"Epoch 100/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6576 - loss: 5.5193\n",
"Epoch 101/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6628 - loss: 5.4345\n",
"Epoch 102/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6722 - loss: 5.2833\n",
"Epoch 103/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6260 - loss: 6.0280\n",
"Epoch 104/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6428 - loss: 5.7568\n",
"Epoch 105/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6574 - loss: 5.5220\n",
"Epoch 106/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6772 - loss: 5.2030\n",
"Epoch 107/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6540 - loss: 5.5763\n",
"Epoch 108/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6572 - loss: 5.5246 \n",
"Epoch 109/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.6324 - loss: 5.9245\n",
"Epoch 110/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6569 - loss: 5.5308\n",
"Epoch 111/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6541 - loss: 5.5753\n",
"Epoch 112/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6757 - loss: 5.2275\n",
"Epoch 113/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6306 - loss: 5.9543\n",
"Epoch 114/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6765 - loss: 5.2146\n",
"Epoch 115/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6328 - loss: 5.9185\n",
"Epoch 116/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6585 - loss: 5.5045\n",
"Epoch 117/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6320 - loss: 5.9312\n",
"Epoch 118/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6413 - loss: 5.7809\n",
"Epoch 119/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6661 - loss: 5.3812\n",
"Epoch 120/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6779 - loss: 5.1913\n",
"Epoch 121/150\n",
- "77/77 [==============================] - ETA: 0s - loss: 5.4343 - accuracy: 0.64 - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6361 - loss: 5.8660\n",
"Epoch 122/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6555 - loss: 5.5521\n",
"Epoch 123/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6376 - loss: 5.8411\n",
"Epoch 124/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6536 - loss: 5.5841\n",
"Epoch 125/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6427 - loss: 5.7591\n",
"Epoch 126/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6446 - loss: 5.7281\n",
"Epoch 127/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6545 - loss: 5.5692\n",
"Epoch 128/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6402 - loss: 5.7988\n",
"Epoch 129/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6731 - loss: 5.2691\n",
"Epoch 130/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6671 - loss: 5.3662\n",
"Epoch 131/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6392 - loss: 5.8152\n",
"Epoch 132/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6591 - loss: 5.4942\n",
"Epoch 133/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6609 - loss: 5.4661\n",
"Epoch 134/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6521 - loss: 5.6080\n",
"Epoch 135/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6640 - loss: 5.4156\n",
"Epoch 136/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6496 - loss: 5.6477\n",
"Epoch 137/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6349 - loss: 5.8848\n",
"Epoch 138/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6604 - loss: 5.4742\n",
"Epoch 139/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6440 - loss: 5.7379\n",
"Epoch 140/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6441 - loss: 5.7370\n",
"Epoch 141/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6613 - loss: 5.4598\n",
"Epoch 142/150\n",
- "77/77 [==============================] - 0s 2ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6705 - loss: 5.3115\n",
"Epoch 143/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - accuracy: 0.6506 - loss: 5.6317\n",
"Epoch 144/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6447 - loss: 5.7266\n",
"Epoch 145/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.6659 - loss: 5.3855\n",
"Epoch 146/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step - accuracy: 0.6351 - loss: 5.8810\n",
"Epoch 147/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.6500 - loss: 5.6406\n",
"Epoch 148/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6295 - loss: 5.9721\n",
"Epoch 149/150\n",
- "77/77 [==============================] - 0s 4ms/step - loss: 5.3827 - accuracy: 0.6510\n",
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6328 - loss: 5.9180\n",
"Epoch 150/150\n",
- "77/77 [==============================] - 0s 3ms/step - loss: 5.3827 - accuracy: 0.6510\n"
+ "\u001b[1m77/77\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step - accuracy: 0.6430 - loss: 5.7547\n"
]
},
{
"data": {
"text/plain": [
- ""
+ ""
]
},
"execution_count": 8,
@@ -688,21 +705,36 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 9,
"metadata": {},
"outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:24:23.814799: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
+ "2024-05-16 17:24:23.814932: I tensorflow/core/grappler/clusters/single_machine.cc:361] Starting new session\n",
+ "2024-05-16 17:24:23.911262: I tensorflow/core/grappler/devices.cc:75] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
+ "2024-05-16 17:24:23.911400: I tensorflow/core/grappler/clusters/single_machine.cc:361] Starting new session\n"
+ ]
+ },
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Wrote ONNX model with bounds at /tmp/tmp2u0qvwd0.onnx\n"
+ "Wrote ONNX model with bounds at /tmp/tmpz_5cafcg.onnx\n"
]
}
],
"source": [
+ "# Add output_names for compatibility:\n",
+ "model.output_names = [output.name for output in model.outputs]\n",
+ "\n",
+ "from tensorflow import TensorSpec\n",
"import tf2onnx\n",
"\n",
- "onnx_model, _ = tf2onnx.convert.from_keras(model)\n",
+ "spec = [TensorSpec(input.shape, input.dtype, input.name) for input in model.inputs]\n",
+ "onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature=spec)\n",
"\n",
"with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as f:\n",
" write_onnx_model_with_bounds(f.name, onnx_model, input_bounds)\n",
@@ -718,17 +750,17 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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",
"text/plain": [
""
]
},
- "execution_count": 11,
+ "execution_count": 10,
"metadata": {
"image/png": {
"height": 600
@@ -757,28 +789,28 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Epoch number: 0 loss : 0.25130271911621094\n",
- "Epoch number: 10 loss : 0.25201597809791565\n",
- "Epoch number: 20 loss : 0.2500983774662018\n",
- "Epoch number: 30 loss : 0.2500978708267212\n",
- "Epoch number: 40 loss : 0.25245341658592224\n",
- "Epoch number: 50 loss : 0.2509850561618805\n",
- "Epoch number: 60 loss : 0.25141310691833496\n",
- "Epoch number: 70 loss : 0.2518356740474701\n",
- "Epoch number: 80 loss : 0.250247597694397\n",
- "Epoch number: 90 loss : 0.25029245018959045\n",
- "Epoch number: 100 loss : 0.2565183639526367\n",
- "Epoch number: 110 loss : 0.25006231665611267\n",
- "Epoch number: 120 loss : 0.2502576410770416\n",
- "Epoch number: 130 loss : 0.2532578110694885\n",
- "Epoch number: 140 loss : 0.2514439821243286\n"
+ "Epoch number: 0 loss : 0.2537655234336853\n",
+ "Epoch number: 10 loss : 0.251478910446167\n",
+ "Epoch number: 20 loss : 0.2516653537750244\n",
+ "Epoch number: 30 loss : 0.2530170977115631\n",
+ "Epoch number: 40 loss : 0.25084957480430603\n",
+ "Epoch number: 50 loss : 0.2542480528354645\n",
+ "Epoch number: 60 loss : 0.25108495354652405\n",
+ "Epoch number: 70 loss : 0.25102800130844116\n",
+ "Epoch number: 80 loss : 0.2500641345977783\n",
+ "Epoch number: 90 loss : 0.2532801032066345\n",
+ "Epoch number: 100 loss : 0.2516343593597412\n",
+ "Epoch number: 110 loss : 0.2513783872127533\n",
+ "Epoch number: 120 loss : 0.25228577852249146\n",
+ "Epoch number: 130 loss : 0.2504936456680298\n",
+ "Epoch number: 140 loss : 0.2519592344760895\n"
]
}
],
@@ -829,14 +861,14 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Wrote PyTorch model to /tmp/tmpw13siqwz.onnx\n"
+ "Wrote PyTorch model to /tmp/tmpnl9ub4y3.onnx\n"
]
}
],
@@ -865,17 +897,17 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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IBwEEXCXAapyr5OkXAQQQQAABBBAokQBpXIn42BkBBBBAAAEEEHCVAGmcq+TpFwEEiikQHBxczD3ZDQEEEDCXANfGmWs+GQ0CFhDg2jgLTDJDRAABhwRYjXOIiUoIIIAAAggggIDRBEjjjDYjxIMAAoUIlC9fvpAabEYAAQSsIcBJVWvMM6NEwEQCnFQ10WQyFAQQKJEAq3El4mNnBBBAAAEEEEDAVQKkca6Sp18EECimQL169Yq5J7shgAAC5hIgjTPXfDIaBMwrEBcXt2nTJhlfZGSkfMtvKTHvcBkZAgggULiAR+FVqIEAAggYQKBy5cqLFy/evXu3xDJp0qQjR4706tXLAHERAgIIIOAyAW5xcBk9HSOAQFEF9u3b98ADD9hvcdi/f3+bNm2K2gL1EUAAATMJcFLVTLPJWBAwuUC7du26du2alZXVrVs3cjiTTzbDQwABBwQ4qeoAElUQQMBhgdjY2JiYGIerF7nik08+uXfv3hEjRpw8ebLIOzu8Q7Vq1apWrepwdSoigAACrhHgpKpr3OkVAfMJLFv+r5WrViUn33IvXVbT0aWkpJQpU0bTLjJSk729yw4ZPPjJ4UM17YjGEUAAgZIIkMaVRI99EUDgd4FXps7YdeB0armGpdy1TbD0E09P8Uw81eWBP70xfbJ+ndITAgggUBQBro0rihZ1EUAgL4E35ry588fI1Ar3mieHk2F6lEnzbfHND2fmhP4jr0FThgACCLhegDTO9XNABAgoLXDx4sXvfzya5tNE6VHkF3yqT5M9P4RFRUXlV4FyBBBAwIUCpHEuxKdrBMwgcOrUqdj4JDOMJJ8xxCYkyxjz2UgxAggg4EoB0jhX6tM3AiYQuHLlSrqHrwkGkt8Q0twrXr58Ob+tlCOAAAIuFCCNcyE+XSNgBoGkpKSsjDQzjCS/MWSmyxjz20g5Aggg4EIB0jgX4tM1AggggAACCCBQfAEe/1t8O/ZEAIECBMp7e00c0TXgnuq3UtN37D/1z3X7CqjMJgQQQACBYgiwGlcMNHZBAIHCBWaNfezQiagBLy4d+frKhnX8enVsVvg+1EAAAQQQKIoAq3FF0aIuAgg4JlCruq+vT9n1O36S6im30qa/uzkjI1N+e7i7TRj+UPNGNUvZSi1csePH4xe7tG3UKbBRhfJl6taosmDFjhYBNYNa1Y++mhAyb216Rua//zFi696TXdsFXI5NWL72+1F/Da5fu+rsJf/dfeisY4FQCwEEEDCzAKtxZp5dxoaAqwQkJzt/KS6799Q0yeKy5M/HH2qRVSpr6KR/vjDn89fH9PT0cJdC/6oVxoWueSH089AJfbbuOSkLeDabrUPL+vbdfzp1afBLy5NT0ob2bvv87NUvzV83euCDrhoX/SKAAAKGEmA1zlDTQTAImERA8rOMzN+W3+Qz/bmecoWcLK0NeWl5m3vrxly7MfDR1lIuJXVqVJYfJyJ+e5zHhei4+OvJ4eeuyO/DJ6P8qvjYdz92e2tY+M+yYic/jp2Jrnj7Bx8EEEAAAVbjOAYQQMD5Ahcvx9X0//1hctMWb37+jdX2Pmy2UpFRsSfPXpb/pi3edDkm4c6+09Iz7H+m/+9H9tbsTVKSeXthjw8CCCCAAGkcxwACCDhf4GxUrJvN9nD7AGlarofr1uH3V3UdOHahQe1qR89En4z8JbBZnZvJqc7vmxYRQAABywiQxllmqhkoAvoKTFqwTm5N+Gz+U0umD45LuGnvfO22MLnubdW8Ee9MHnD6wlV9I6I3BBBAwGwCtqwsTk+YbVIZDwJ6CixZsuSD1bttlf+kZ6d69pUVd3xk/w6jRo3Ss1P6QgABBBwRYDXOESXqIIAAAggggAAChhMgjTPclBAQAggggAACCCDgiABpnCNK1EEAAQQQQAABBAwnQBpnuCkhIAQQQAABBBBAwBEB0jhHlKiDAAIIIIAAAggYToC3OBhuSggIAeUEvJLPlvk1WrmwHQw4JSW5VKkODlamGgIIIKCnAGmcntr0hYA5Bfr36zds2DBzjq1UqU8++cSsQ2NcCCCgugBpnOozSPwIuF7A29u7atWqro9DmwhkdDxfUxtaWkUAgZIKcG1cSQXZHwEEEEAAAQQQcIkAaZxL2OkUAQQQQAABBBAoqQBpXEkF2R8BBBBAAAEEEHCJAGmcS9jpFAEEEEAAAQQQKKkAaVxJBdkfAQSKLZCQkBAVFVXs3bN3PHr0aMkboQUEEEBAOQHSOOWmjIARUEBg/fr1ttsfDw+Pnj17pqam5hn0lClTkpKSZNOKFSuqV6++efNme7X9+/f37du3bdu27733Xp47LliwYMCAAf379//000+lwsKFC8+fP59nTQoRQAABEwvwwBETTy5DQ8DFAgcPHoyJiXnkkUe2bdsmyVyOaHbt2lWjRo3GjRtv3759x44dY8eOza6wdu1aSezkSR+yddSoUW5uf/gfzszMzGXLlskK3K1bt9q3bz9kyJA5c+aMHz/entK5eMx0jwACCOgowGqcjth0hYDFBMrf/sigK1WqlGPoKSkpixcvDgkJkfL777//o48+cnd3z64zd+5c2VGyN1mik+/Dhw/PnDlTHt72/PPPX758WUruueees2fPSibXvHlz2cvPz69bt24rV660GDDDRQABqwuQxln9CGD8CGgnIGtpQUFBPXr0aNSoUY5eJFF78cUXPT09pdzX11fOveYO44svvnjggQekvGXLlvJ4YWmqe/fud911l5QMGjRo6NCho0ePHj58uH1H+bFu3bobN27kbocSBBBAwKwCpHFmnVnGhYDrBRITE5OTk0+cOCGXsuWIRq57kwvgCghRtv7rX/9644037HVOnz4tSWF4eLj8+euvv8rC2/fff3/gwIHXXnstIyNDCuPj42U9z8fHp4A22YQAAgiYTIA0zmQTynAQMJCAnPeMiIiQK9hyZ1dywZykdxcuXMgzXPtZVMnV7Mt1YWFhcg2cXA9XtmxZOakaGxublpZm3zEuLk7al99yt8T06dPzbI1CBBBAwKwCeZzIMOtQGRcCCOgs0KJFC+mxfv36gwcPzt31rFmzJkyY8PHHH0uqN3XqVMnqKlasuHHjxvfff/+pp56Sk6d///vfZa9Jkybdd/sjv8eMGSPfsqlVq1aSCEoyJzdAyJ0QO3furFmzZu5Tt7k7pQQBBBAwkwBpnJlmk7EgYBSBPn36FPo6+WrVqsl9CV9//XXXrl1XrVp1Z+iHDh0qeCSzZ8++s4Lc1vrBBx8UvAtbEUAAAfMJkMaZb04ZEQLKCAwbNswpscr5Vqe0QyMIIICAWgJcG6fWfBEtAggggAACCCDwuwBpHIcCAggggAACCCCgpABpnJLTRtAIIIAAAggggABpHMcAAggggAACCCCgpABpnJLTRtAIIIAAAggggABpHMcAAgi4TCAyMjIpKanQ7n/66adC61ABAQQQsKAAaZwFJ50hI6CHwOrVqxs2bCgvrX/66aft78vK0WtqaurEiRPlDVpSLk/xlbem3rx5015n+fLl8uS5wMDAvXv3Sol8f/7553oETR8IIICAUgKkcUpNF8EioIiALLMNGTKkc+fOixcvlhczyDN+cwf+5ptvjhs3zsvLS16iJW9lkKTNXkfezXDkyJH169fLCx5mzpwphZLkSRonb03N3QglCCCAgJUFSOOsPPuMHQGtBDZt2iTLbAsWLPjrX/8qKV337t1z9CQvuZc33AcHB0u5vGJLErXsCvIe1YULF8qf5cqVq1Spkvyw2WySz7322mtahUu7CCCAgJoCpHFqzhtRI2BsgejoaHnXluRhEqb9O0e806ZNy36TvZxOzXM08+bNkyU9+6bGjRtXrlx53759edakEAEEELCmAGmcNeedUSOgrYC/v39MTIz99oVr167l7kwumztz5kzu8uySd999t0KFCj179swuOXv2bIMGDQrYhU0IIICA1QRI46w244wXAT0E5CyqXOIWEhKyZs2agIAAOceao9fJkyeHhoamp6fnGY28I/X06dOzZs3K3vrJJ59069Ytv3W7PBuhEAEEEDC9AGmc6aeYASLgAoGmTZsuXbp0y5Ytct1b3759e/TokSOIsmXLjh49+q233pLyDRs2DBo0KCwsbPjw4YsWLZJbGcaOHfvLL79IoXwkHYyNjf3yyy+HDRvmgpHQJQIIIGBgAQ8Dx0ZoCCCgsMCTtz8FDKBLly5yE2tcXFzv2587ayYmJt75p+R2d67MFdAmmxBAAAFLCZDGWWq6GSwCzhfw9vZ2cyvmur7cyupIQDNmzHCkmkZ1fHx88jv5q1GPNIsAAgg4KFDMf3wdbJ1qCCBgeoHq1asfPXrUxMOUs701atQw8QAZGgIIqCtAGqfu3BE5AoYQaNSokbkXq2R0cpeGIawJAgEEEPijAGkcRwQCCJRIoHbt2nfffbe8raFErRh1Z3kQcb169WrWrGnUAIkLAQQsLWDLysqyNACDRwABZwi888478pS4ESNGyFN/ndGe69u4evWqPPdEnl337LPPuj4aIkAAAQTyEiCNy0uFMgQQKLrAZ7c/N27c0DqTk7OcHh7a3p4lzy729fXt37+/vEys6BLsgQACCOgkQBqnEzTdIGARAXlng6xjaTrYli1bHj58WNMu/Pz87K9z1bQXGkcAAQRKKEAaV0JAdkcAAb0FgoKCdu/erXev9IcAAggYT4BbHIw3J0SEAAIFCkRGRha4nY0IIICAVQRYjbPKTDNOBEwjYLPxD5dpJpOBIIBAiQRYjSsRHzsjgID+AnLhmv6d0iMCCCBgQAH+p9aAk0JICCBQkACrcQXpsA0BBKwkwGqclWabsSKAAAIIIICAiQRI40w0mQwFAWsINGvWzBoDZZQIIIBAIQKcVC0EiM0IIGA0AU6qGm1GiAcBBFwlwGqcq+TpFwEEiinQrl27Yu7JbggggIC5BFiNM9d8MhoELCDAapwFJpkhIoCAQwKsxjnERCUEEEAAAQQQQMBoAqRxRpsR4kEAgUIEGjZsWEgNNiOAAALWEOCkqjXmmVEiYCIBTqqaaDIZCgIIlEiA1bgS8bEzAgjoJnDjxo2kpCTprlOnTvItv6VEt97pCAEEEDCgAGmcASeFkBBAIA8BHx+fXr16RURE7Ny5U77lt5TkUY8iBBBAwDICpHGWmWoGioD6Ak888URgYGCDBg3kW36rPyBGgAACCJRIgGvjSsTHzgggoLNA7dq1o6Ki7r777osXL+rcNd0hgAACRhNgNc5oM0I8CCBQkMDMmTNls/27oHpsQwABBCwgwGqcBSaZISJgeIGNGzd+u337reTkS1euFBrsuXPn7rnnnkKr1bqruleZssFdujz22GOFVqYCAgggoKIAaZyKs0bMCJhHICsra3D/fu6/XMpMiC+fmZHl1JHddHN3q+ibddfdn6xeLY8pcWrbNIYAAgi4XoA0zvVzQAQIWFlgUN++7meOV8tI0w7hqrunLeDeTz5fo10XtIwAAgi4RIBr41zCTqcIIPCbwPvvvuvx8zlNczjpxU9yxPNnP/rgA9ARQAABkwmQxplsQhkOAioJbPjPf7xv6vEIX++kxA3r1qlEQ6wIIICAAwKkcQ4gUQUBBDQQSElJ8S7t6Z2VqUHbOZssl5XhXiorNTU15wb+RgABBFQWII1TefaIHQGVBSSNu3ItXrcRXI1PuHXrlm7d0RECCCCggwBpnA7IdIEAAs4XeGn9ltJlyzq/XVpEAAEE1BHwUCdUIkUAAUsIPDxqTIeBQxLj4tw9PZMS4v81/rnrsTGWGDmDRAABBIoowGpcEcGojgAC2gt8veS9f/yl19ze3SN+2Ndx+AjtO6QHBBBAQEkB0jglp42gEbCIQNTxoz5Vqspg67ZoOWH1ugmfrx84a667Z+k7hz9127f2P+/r0bPn+IkWkWGYCCCAgAiQxnEYIICAQQU8vbyaP/zIubBDcnZ1SOj8ZWNHv9W/z63ExKBBQw0aMWEhgAAC+gpwbZy+3vSGAAIOCP1vhZ0AACAASURBVHR5amSbP/+lWt179n62ct+az6rXbyg7NX+4u3y7eXrUax24a8UyB5qhCgIIIGByAdI4k08ww0NARYEdS5fsXvVx0OAn/O6pl5WZWcpmS7qeEHXsqIxFvpNv5HxisJu7e2ZGhoojJWYEEECgJAKcVC2JHvsigICGAt9/vqpJcMfKNWtdPXfWu6JvwtUr5w4f9Pb19SrnfWev8b9cbtDmASm5u2kzDaOhaQQQQMB4AqRxxpsTIkIAgdsCGWlp2z96v8fz4+XHyldCnnrngxdWrrmv+6PR4Sdl6/mww3LBnPzY8s6CgTPnPPPhvzxKeyGHAAIIWErAlpWVZakBM1gEEDCIQHx8/GOPPto6/hd94vmhov9XW7f6+Pjo0x29IIAAAjoIsBqnAzJdIIAAAggggAACzhcgjXO+KS0igIAjAr6+vqnp6ZmOVC1xHbn9ITMzk6W4EkPSAAIIGEuANM5Y80E0CFhKoNE999x0c9dhyDfdPBrXq6dDR3SBAAII6ClAGqenNn0hgMAfBJ5+9tnLPpV1QLnsU+npMWN06IguEEAAAT0FSOP01KYvBBD4g0DHjh37Dh0WUc5XU5cz5Xz/OvzJoKAgTXuhcQQQQEB/Ae5U1d+cHhFA4A8CH777f19v3HA9NsYnJTndzWn/b+mZlXndq2yFqn7d+vR56pnRoCOAAALmEyCNM9+cMiIE1BOIjY09ceJEVFRUYmJiodFPnz592rRphVaTGxpq1arVrFmzypX1OG9baDxUQAABBJwuQBrndFIaRAABbQU6dOiwZ88ebfugdQQQQEAFAdI4FWaJGBFA4A4Bm41/uDggEEAAgd8EnHYZCpwIIICAPgJNmzbVpyN6QQABBAwuwP/UGnyCCA8BBHIKsBqXU4S/EUDAqgKsxll15hk3AsoKsBqn7NQROAIIOFmA1Tgng9IcAghoLcBqnNbCtI8AAqoIsBqnykwRJwII/C7QqVMnLBBAAAEERIDVOA4DBBBQTIDVOMUmjHARQEAzAVbjNKOlYQQQQAABBBBAQEsB0jgtdWkbAQQQQAABBBDQTIA0TjNaGkYAAQQQQAABBLQUII3TUpe2EUBAAwFucdAAlSYRQEBJAW5xUHLaCBoBKwtwi4OVZ5+xI4DAnQKsxnE8IICAGgKpqan2QJs1a2b/kV2ixgCIEgEEEHC2AGmcs0VpDwEEtBHIzMwcOnRoSkrKsWPH5Ft+S4k2XdEqAgggoIYAaZwa80SUCCBQpkyZhg0btm/fvn79+vItv6UEFgQQQMDKAlwbZ+XZZ+wIKCaQkJBQq1atxMTE8uXL//zzzxUrVlRsAISLAAIIOFWA1TinctIYAghoKSB5W0hIiPQg3+RwWkrTNgIIqCHgoUaYRIkAAioInDx5Mjw8PCYmRrtgK1WqdNddd8n3kiVLtOulWrVqTZo0CQgI0K4LWkYAAQRKLsBJ1ZIb0gICCJSSew6eHDHyl9iExLTSWe7KX7Jmy0gp75F6l3+lfy79wMvLiwlGAAEEjClAGmfMeSEqBFQSkByuT78hv9rqlirrp1LchcaadOUuz0tffPZx6dKlC61LBQQQQEB/AdI4/c3pEQGzCQwc/ETEjWpmy+Hss5R0pUmVhI//tdRsc8Z4EEDAFALc4mCKaWQQCLhOQK6Hk3Op5szhRNXbPyo65tSpU64DpmcEEEAgXwHSuHxp2IAAAo4IyD0Ncj2cIzUVrZOY7iWpqqLBEzYCCJhbgDTO3PPL6BDQXEDuS81yL6t5N67rQEZ35coV1/VPzwgggEC+AqRx+dKwAQEEEEAAAQQQMLIAz40z8uwQGwIKC5T39po4omvAPdVvpabv2H/qn+v2KTwYQkcAAQQMKcBqnCGnhaAQUF9g1tjHDp2IGvDi0pGvr2xYx69Xx2bqj4kRIIAAAsYSYDXOWPNBNAiYQ6BWdV9fn7Lrd/wkw0m5lTb93c0ZGZny28PdbcLwh5o3qlnKVmrhih0/Hr/YpW2jToGNKpQvU7dGlQUrdrQIqBnUqn701YSQeWvTMzL//Y8RW/ee7Nou4HJswvK134/6a3D92lVnL/nv7kNnzQHFKBBAAIGSCLAaVxI99kUAgbwFJCc7fykue1tqmmRxWfLn4w+1yCqVNXTSP1+Y8/nrY3p6erhLoX/VCuNC17wQ+nnohD5b95yUBTybzdahZX377j+dujT4peXJKWlDe7d9fvbql+avGz3wwbx7pRQBBBCwmACrcRabcIaLgC4Ckp9lZP62/Caf6c/1lCvkZGltyEvL29xbN+bajYGPtpZyKalTo7L8OBFxWb4vRMfFX08OP/fbPaGHT0b5VfGx737s9taw8J9lxU5+HDsTXfH2Dz4IIIAAAqzGcQwggIDzBS5ejqvp72tvd9rizc+/sdr+22YrFRkVe/LsZflv2uJNl2MS7uw7LT3D/mf6/35kb83eJCWZtxf2+CCAAAIIkMZxDCCAgPMFzkbFutlsD7cPkKblerhuHZrY+zhw7EKD2tWOnok+GflLYLM6N5NTnd83LSKAAAKWESCNs8xUM1AE9BWYtGCd3Jrw2fynlkwfHJdw09752m1hct3bqnkj3pk84PSFq/pGRG8IIICA2QRsWVmcnjDbpDIeBPQUWLJkyQerd9sq/0nPTvXsKyvu+Mj+HUaNGqVnp/SFAAIIOCLAapwjStRBAAEEEEAAAQQMJ0AaZ7gpISAEEEAAAQQQQMARAdI4R5SogwACCCCAAAIIGE6ANM5wU0JACCCAAAIIIICAIwKkcY4oUQcBBBBAAAEEEDCcAG9xMNyUEBAC6glkJJe6dU29sB2MWEbHBwEEEDCkAGmcIaeFoBBQSqCK5/Wq5aKUCrkIwcYmXy9CbaoigAACOgqQxumITVcImFTgL3/pa+LHqsmD8Xi+pkmPXIaFgPICXBun/BQyAAQQQAABBBCwpgBpnDXnnVEjgAACCCCAgPICpHHKTyEDQAABBBBAAAFrCpDGWXPeGTUCCCCAAAIIKC9AGqf8FDIABBBAAAEEELCmAGmcNeedUSNgCIGEhISoKCc8qeTo0aOGGA9BIIAAAvoKkMbp601vCFhDYP369bbbHw8Pj549e6ampuY57ilTpiQlJcmmFStWVK9effPmzXlWk0J5oEnVqlVv3rxpr7B8+fI+ffoEBgbu3btXShYuXHj+/Pn89qUcAQQQMKsAaZxZZ5ZxIeB6gYMHD0pm9uWXX27bti13NLt27apRo0bjxo23b9++Y8eOsWPH5q5jL5k+fXqrVq0kabP/mZaWduTIEckUP/7445kzZ0rhnDlzJk+enN/ulCOAAAJmFSCNM+vMMi4EXC9Q/vZH4qhUqVKOaFJSUhYvXhwSEiLl999//0cffeTu7p5d5/3339+6dWt8fPzTTz8tj94dM2bMnY8X9vT0lOU3qVyuXDl7y35+ft26dVu5cqXrx0wECCCAgI4CpHE6YtMVAhYTkJW2oKCgHj16NGrUKMfQ586d++KLL0pCJuW+vr5y7vXOCiNHjly9evXAgQNfeeUVOTMrp1PzlJs3b96QIUPsm4YPH75u3bobN27kWZNCBBBAwJQCpHGmnFYGhYAhBBITE5OTk0+cOLFgwYIcAbVt23b//v35RSnXwMXFxclK26VLl/Kr8+6771aoUEEuvLNXkKU7Wc/z8fHJrz7lCCCAgPkESOPMN6eMCAGjCJw9ezYiIuLWrVu5s6tHHnlE0rsLFy7kGausq73xxhty6ds333yT5/tMly1bdvr06VmzZmXvLndLyCV0ebZGIQIIIGBWAdI4s84s40LA9QItWrS499575Qq2wYMH545GkjDJvaRcUr1BgwbJlW1ypvWZZ56RkmHDhjVp0kTOtE6bNk1Oqm7YsEEqhIWFyZnTRYsWycKb3A/xyy+/SKF85I6HnTt31qxZM/ep29ydUoIAAgiYSeAP16OYaWCMBQEEXCggTwPJcxXtzpCqVasm9yV8/fXXXbt2XbVqVQHR9r79ubOCnK698095XskHH3xQQAtsQgABBEwpQBpnymllUAioISCrbk4JVM6xOqUdGkEAAQTUEuCkqlrzRbQIIIAAAggggMDvAqRxHAoIIIAAAggggICSAqRxSk4bQSOAAAIIIIAAAqRxHAMIIIAAAggggICSAqRxSk4bQSOAAAIIIIAAAqRxHAMIIOAygcjIyKSkpBzdy8NEzp0757KY6BgBBBBQR4A0Tp25IlIElBKQl6I2bNhQXlovr7fPyMjIHXtqaurEiRPlDVqySd58Ly9OlXdwyW83Nzd53Wp6enruXShBAAEEELhTgDSO4wEBBJwvIMts8tL6zp07L168eOPGjfKM39x9vPnmm+PGjfPy8pKXaLVq1SowMNBex9vb+9lnn50/f37uXShBAAEEELhTgMf/cjwggIDzBTZt2iTLbAsWLJA3ccnb6+U7Rx/h4eG//vprcHCwlI8ZM0aW4uQ9qtl15L0Oa9eulVey1q9f3/nB0SICCCBgFgFW48wyk4wDASMJREdHy7u27Nlb7hxOIpWXpWa/yV5yuNyxyxtXp06dmrucEgQQQACBbAHSOA4GBBBwvoC/v39MTIz99oVr167l7kAumztz5kzu8uwSWa4LCAgooAKbEEAAAQRI4zgGEEDA+QLdu3dPS0sLCQlZs2aNZGNyjjVHH5MnTw4NDc3vPga5++Gtt96aNGmS8yOjRQQQQMBEAqRxJppMhoKAYQSaNm26dOnSLVu2yHVvffv27dGjR47QypYtO3r0aMnVpHzDhg2DBg0KCwsbPnz4okWLpEQyPLn7oXTp0oYZEIEggAACRhTgFgcjzgoxIWACgSdvfwoYSJcuXeQm1ri4uN63P9k1r169mpCQEBQUVMC+bEIAAQQQEAHSOA4DBBAokUCFChXk/GnxmpBbWXPvKI+aM9TTRuQBKB4e/FOZe6IoQQAB1wtwUtX1c0AECCgtULdu3f379ys9hIKD37t3b7169Qquw1YEEEDAJQKkcS5hp1MEzCPQunXrlJQU84znjyPJysqStUZ5OrFZB8i4EEBAaQHSOKWnj+ARcL2AnHCUWxOee+4514eiQQTyPgm5wo+TqhrQ0iQCCDhBgAs+nIBIEwhYXEBexiDP+O3Tp8/f/vY3eSCcPPhXdRB56J081u6jjz6SpxDfd999qg+H+BFAwKwCNjllYNaxMS4EENBTQN7c8Mknnxw/flxuNdW0XzmHW6ZMGU27kNssmjVrNmzYsOrVq2vaEY0jgAACJREgjSuJHvsigIALBGw2/uFyATtdIoCAAQW4Ns6Ak0JICCBQkICvr29Bm9mGAAIIWEaANM4yU81AETCLQPPmzc0yFMaBAAIIlEiAcxMl4mNnBBDQX4CTqvqb0yMCCBhTgNU4Y84LUSGAQL4CDz74YL7b2IAAAghYSYDVOCvNNmNFwBQCrMaZYhoZBAIIOEGA1TgnINIEAgjoKVClShU9u6MvBBBAwLACrMYZdmoIDAEE8hZgNS5vF0oRQMB6AqzGWW/OGTECCCCAAAIImEKANM4U08ggELCSgLz7y0rDZawIIIBAvgKcVM2Xhg0IIGBMAU6qGnNeiAoBBPQXYDVOf3N6RACBEgnUqVOnRPuzMwIIIGAWAVbjzDKTjAMBywiwGmeZqWagCCBQiACrcYUAsRkBBBBAAAEEEDCmAGmcMeeFqBBAIKdAbGzs4cOHpTQgIEC+5beU5KzE3wgggICVBEjjrDTbjBUBlQWqVq06fvz49evXh4eHy7f8lhKVB0TsCCCAQEkFuDaupILsjwACugns3Lmzc+fO5cuXT0xM/Oabbzp16qRb13SEAAIIGFCANM6Ak0JICCCQr0C7du3279/ftm3bffv25VuJDQgggIA1BEjjrDHPjBIBFQSuXr1aaJiSvfXr12/NmjWSzxVa2c/Pr9A6VEAAAQTUFSCNU3fuiBwBkwjIGdI5M6Z/t2dvHX+/S7G/FjqqpKQkb2/vQqvVrFr5wpWYBzt0ePX11x2pX2iDVEAAAQSMJkAaZ7QZIR4ErCXw8/nzAwYObJQYVy4zw7NUlnMHn1bKdtPNPbxcpS9Wr67BQ4Odi0trCCBgAAHSOANMAiEgYFUBWVd7qHPndjditAb43qfqrm+/8/Ly0roj2kcAAQT0FOCBI3pq0xcCCPxBYO6MGY1uXtMBpfHN+DnTp+vQEV0ggAACegqQxumpTV8IIPAHgW/37CmfmaEDSrmszO/27NGhI7pAAAEE9BQgjdNTm74QQOD/C8THx1evXKm0s6+Hy5PYKyuzWsUK169fz3MrhQgggICiAqRxik4cYSNgBoGfY/R7m1ZUTKzNZjODGmNAAAEE/idAGsexgAACSgq8tH5L6bJllQydoBFAAAEnCXg4qR2aQQABBJwj8PCoMR0GDkmMi3P39ExKiP/X+Oeux2p+K6tzQqcVBBBAQF8BVuP09aY3BBBwQODrJe/94y+95vbuHvHDvo7DRziwB1UQQAABKwqQxllx1hkzAqoIRB0/6lOlqkRbt0XLCavXTfh8/cBZc909S98Z/9Rt39r/vK9Hz57jJ6oyNOJEAAEESi5AGldyQ1pAAAFNBDy9vJo//Mi5sENydnVI6PxlY0e/1b/PrcTEoEFDNemPRhFAAAHVBLg2TrUZI14ELCDQ5amRbf78l2p179n72cp9az6rXr+hDLr5w93l283To17rwF0rllmAgSEigAAChQiQxhUCxGYEENBfYMfSJbtXfRw0+Am/e+plZWaWstmSridEHTsqkch38o0bOUJyc3fPzNDjMcL6U9AjAgggUIAAJ1ULwGETAgi4UuD7z1c1Ce5YuWatq+fOelf0Tbh65dzhg96+vl7lvO8MK/6Xyw3aPCAldzdt5spw6RsBBBDQXYA0TndyOkQAAccEMtLStn/0fo/nx8uPla+EPPXOBy+sXHNf90ejw09KA+fDDssFc/JjyzsLBs6c88yH//Io7eVYw9RCAAEETCJgy8rKMslQGAYCCCglIC/jeuzRR1vH/6JP1D9U9P9q61YfHx99uqMXBBBAQAcBVuN0QKYLBBDIQyAzM/OuKpXz2KBNUY0qlfi/Vm1oaRUBBFwmQBrnMno6RsDiApUrV75yLT61lB7vOZVeYhNuVKhQweLmDB8BBEwmQBpnsgllOAioJNChXbskm7sOEd+0ubdv106HjugCAQQQ0FOAa+P01KYvBBD4g8CVK1f++ufHW8Vf0drlx4r+/9m4sWrV314IwQcBBBAwjQCrcaaZSgaCgHoC/v7+S5Z8eLiiX6JNq3+LpOVDFf2WL1tGDqfe8UHECCBQmACrcYUJsR0BBDQWuHjx4luhc3b/cKBehfLxqWmF9paenu7hUfijy329Skcm3Ahu0+bFV1+tVatWoc1SAQEEEFBOgDROuSkjYARMK3DmzBm5fbXQ4bVs2fLw4cOFVnNzc2vY8Le3ePFBAAEEzCpAGmfWmWVcCJhWICgoaPfu3aYdHgNDAAEEHBbQ6noUhwOgIgIIIFA0gcjIyKLtQG0EEEDApAKsxpl0YhkWAuYVsNn4h8u8s8vIEECgKAKsxhVFi7oIIGAAAT8/PwNEQQgIIICA6wX4n1rXzwERIIBAkQRYjSsSF5URQMDEAqzGmXhyGRoCCCCAAAIImFmANM7Ms8vYEDClQLNmzUw5LgaFAAIIFFWAk6pFFaM+Agi4WICTqi6eALpHAAHDCLAaZ5ipIBAEEHBMoB0vuXcMiloIIGB6AVbjTD/FDBABswmwGme2GWU8CCBQXAFW44orx34IIIAAAggggIBLBUjjXMpP5wggUHQB3pRadDP2QAABcwpwUtWc88qoEDCxACdVTTy5DA0BBIokwGpckbiojAACLhO4ceNGUlKSdN+pUyf5lt9S4rJo6BgBBBAwgABpnAEmgRAQQMABAR8fn169ekVEROzcuVO+5beUOLAfVRBAAAHTCpDGmXZqGRgC5hN44oknAgMDGzRoIN/y23wDZEQIIIBAkQS4Nq5IXFRGAAEXC9SuXTsqKuruu+++ePGii0OhewQQQMDVAqzGuXoG6B8BBIoiMHPmTKlu/y7KftRFAAEETCjAapwJJ5UhIeASAbnnYOmyf/54KCwhPk7TAM6dO3fPPfdo2kVF38ptWrd8asTfypQpo2lHNI4AAgiURIA0riR67IsAAr8L/HjoyJgxozPKNy7lVTHLvazqLraM5FK3EtxvhL///oct72um+nCIHwEEzCpAGmfWmWVcCOgn8P2+Ay9PmZVUub1+XerVU9m4Pe8tnN2sGZmcXuL0gwACRREgjSuKFnURQCCXQGZm5mOP//VK6Zal3DxybVS/IDPNP+3IpnWr5ZnD6g+GESCAgNkEuMXBbDPKeBDQWeDQoUPxianmzOGE0s3z+s1bMkadVekOAQQQcESANM4RJeoggEC+AmfPnr3lXjnfzepvSLJVljGqPw5GgAACJhQgjTPhpDIkBPQUSEhIyMrK1LNHvfvKyrx27ZrendIfAggg4IAAaZwDSFRBAAEEEEAAAQSMJ2DGS5KNp0xECFhQoLy318QRXQPuqX4rNX3H/lP/XLfPgggMGQEEENBUgNU4TXlpHAHrCswa+9ihE1EDXlw68vWVDev49erIMzusezAwcgQQ0EiA1TiNYGkWAUsL1Kru6+tTdv2On0Qh5Vba9Hc3Z2T8dv2ch7vbhOEPNW9Us5St1MIVO348frFL20adAhtVKF+mbo0qC1bsaBFQM6hV/eirCSHz1qZnZP77HyO27j3ZtV3A5diE5Wu/H/XX4Pq1q85e8t/dh7jnwNIHGINHAAG7AKtxHAkIIOB8AcnJzl/6/6/kSk2TLC5Lunn8oRZZpbKGTvrnC3M+f31MT08Pdyn0r1phXOiaF0I/D53QZ+uek7KAJw9p69Cyvj2sn05dGvzS8uSUtKG92z4/e/VL89eNHvig8yOmRQQQQEBBAVbjFJw0QkbA8AKSn2Vk/n776vTnesoVcrK0NuSl5W3urRtz7cbAR1vLCKSkTo3fnlRyIuKyfF+Ijou/nhx+7or8Pnwyyq+Kj32Ux25vDQv/WVbs5MexM9EVb//ggwACCCDAahzHAAIIOF/g4uW4mv6+9nanLd78/Bur7b/lVQiRUbEnz16W/6Yt3nQ5JuHOvtPSM+x/pv/vR/bW7E1Sknl7YY8PAggggABpHMcAAgg4X+BsVKybzfZw+wBpWq6H69ahib2PA8cuNKhd7eiZ6JORvwQ2q3MzOdX5fdMiAgggYBkB0jjLTDUDRUBfgUkL1smtCZ/Nf2rJ9MFxCTftna/dFibXva2aN+KdyQNOX7iqb0T0hgACCJhNwJaVxekJs00q40FAT4ElS5Z8sHq3rfKf9OxUz76y4o6P7N9h1KhRenZKXwgggIAjAqzGOaJEHQQQQAABBBBAwHACpHGGmxICQgABBBBAAAEEHBEgjXNEiToIIIAAAggggIDhBEjjDDclBIQAAggggAACCDgiQBrniBJ1EEAAAQQQQAABwwnwFgfDTQkBIaCeQEpMqWsn1AvbwYhldHwQQAABQwqQxhlyWggKAaUE7m969/33369UyEUI9uDBg0WoTVUEEEBARwHSOB2x6QoBkwpIDmfix6rJg/F4vqZJj1yGhYDyAlwbp/wUMgAEEEAAAQQQsKYAaZw1551RI4AAAggggIDyAqRxyk8hA0AAAQQQQAABawqQxllz3hk1AggggAACCCgvQBqn/BQyAATUFUhISIiKiipq/ImJiefOnSvqXtRHAAEEzCdAGme+OWVECLheYP369bbbHw8Pj549e6ampuYZ05QpU5KSkmTTihUrqlevvnnz5jyrSaHcCVu1atWbN2/Kbzc3txdffDE9PT2/ypQjgAACFhEgjbPIRDNMBFwgIE9ck8zsyy+/3LZtW+7ud+3aVaNGjcaNG2/fvn3Hjh1jx47NXcdeMn369FatWgUGBtr/9Pb2fvbZZ+fPn59ffcoRQAABiwiQxllkohkmAi4QKH/7Ix1XqlQpR/cpKSmLFy8OCQmRcnns3EcffeTu7p5d5/3339+6dWt8fPzTTz8tz2wbM2ZMjufSde3a9cKFC2fPnnXBqOgSAQQQMIwAaZxhpoJAEDCdgKy0BQUF9ejRo1GjRjkGN3fuXDkx6unpKeW+vr5y7vXOCiNHjly9evXAgQNfeeUVOTMrp1Nz28yaNWvq1Km5yylBAAEErCNAGmeduWakCOgtIPciJCcnnzhxYsGCBTn6btu27f79+/MLSK6Bi4uLkzW8S5cu5VcnPDw8ICAgv62UI4AAAlYQII2zwiwzRgRcIyAnPSMiIm7duuXj45MjgkceeUTSOzkxmmdk69ate+ONNz7++ONvvvkmzxdhyT0Tb7311qRJk/LcnUIEEEDAIgKkcRaZaIaJgAsEWrRoce+995YrV27w4MG5u5ezonKnqpRLqjdo0KCVK1fKmdZnnnlGSoYNG9akSRM50zpt2jQ5qbphwwapEBYWNnz48EWLFkmF0NDQcePGlS5dOnezlCCAAALWEfjD9SjWGTYjRQABTQX69OmT5yranZ1Wq1atW7duX3/9tdyvsGrVqgLi6X37k13h6tWr8sA5uequgF3YhAACCFhBgDTOCrPMGBEwqICsuhUjMj8/P542Ugw3dkEAAfMJcFLVfHPKiBBAAAEEEEDAEgKkcZaYZgaJAAIIIIAAAuYTII0z35wyIgQQQAABBBCwhABpnCWmmUEigAACCCCAgPkESOPMN6eMCAEEEEAAAQQsIUAaZ4lpZpAIGFMgMjIyKSkpR2zy7odz584ZM2CiQgABBAwlQBpnqOkgGATMIyAvRW3YsKE8HEReb5+RkZF7YPImhokTJ7q7u8smefO9vDhV3sElv93c3OR1q+np6bl3oQQBBBBA4E4B0jiOBwQQcL6ALLMNGTKkc+fOixcv3rhxozzjN3cfb775e2OlWgAAEthJREFUpryJwcvLa/r06a1atQoMDLTX8fb2fvbZZ3kyXG4xShBAAIEcAjz+l0MCAQScL7Bp0yZZZluwYIG8iatnz57ynaMPebH9r7/+GhwcLOVjxoyRpTh5j2p2HXmvw9q1a+WVrPXr13d+cLSIAAIImEWA1TizzCTjQMBIAtHR0fKuLXv2ljuHk0jlZamyCGcPWXK43LHLG1enTp2au5wSBBBAAIFsAdI4DgYEEHC+gL+/f0xMjP32hWvXruXuQC6bO3PmTO7y7BJZrgsICCigApsQQAABBEjjOAYQQMD5At27d09LSwsJCVmzZo1kY3KONUcfkydPDg0Nze8+Brn74a233po0aZLzI6NFBBBAwEQCpHEmmkyGgoBhBJo2bbp06dItW7bIdW99+/bt0aNHjtDKli07evRoydWkfMOGDYMGDQoLCxs+fPiiRYukRDI8ufuhdOnShhkQgSCAAAJGFOAWByPOCjEhYAKBJ29/ChhIly5d5CbWuLi43rc/2TWvXr2akJAQFBRUwL5sQgABBBAQAdI4DgMEEHCZgNzKmrtvedQcTxvJzUIJAgggkFuAk6q5TShBAIEiCMgdqbnfxFCE/Q1fVR5KnOe9tIYPnAARQMD8AqRx5p9jRoiApgJyz+mRI0c07cK1jR86dEgu9XNtDPSOAAII5ClAGpcnC4UIIOCoQLNmzWw2mzwfxNEdlKp3/Phxec9EkyZNlIqaYBFAwCoCpHFWmWnGiYB2AnKJm9xzKi/g0q4Ll7QcERHx9ttv22+ndUkAdIoAAggULGDLysoquAZbEUAAgUIF4uPjR44cWadOHXl9llwtV2h9g1eQZxefO3fu/PnzS5YsqVixosGjJTwEELCsAGmcZaeegSPgfIHdu3fL2VV5Yojzm76jxY8//njYsGGadiF3y8qJ1A4dOmjaC40jgAACJRQgjSshILsjgIDeAnIpHqcR9EanPwQQMKQA18YZcloICgEEEEAAAQQQKEyANK4wIbYjgIDBBB544AGDRUQ4CCCAgGsEODfhGnd6RQCBYgtwUrXYdOyIAAImE2A1zmQTynAQML9AQECA+QfJCBFAAAEHBFiNcwCJKgggYCQBT0/PtLQ0I0VELAgggIBrBEjjXONOrwggUGwBTqoWm44dEUDAZAKcVDXZhDIcBBBAAAEEELCKAGmcVWaacSJgGoEyZcqYZiwMBAEEECiJACdVS6LHvggg4AIBTqq6AJ0uEUDAkAKsxhlyWggKAQTyF2jTpk3+G9mCAAIIWEiA1TgLTTZDRcAcAqzGmWMeGQUCCJRcgNW4khvSAgII6CrQunVrXfujMwQQQMCoAqzGGXVmiAsBBPIRYDUuHxiKEUDAcgKsxlluyhkwAggggAACCJhDgDTOHPPIKBCwkECrVq0sNFqGigACCOQvQBqXvw1bEEDAkAKHDh3KHdegQYPOnj2buzy7RN7f9dVXX+VZITAwMM/yPAuvXr36/PPP57mJQgQQQEBnAdI4ncHpDgEESirg5+dXjCbi4uKWLl1ajB1z7CK9v/POO8VoR/LIwYMH+/v7nzp1qhi7swsCCCCQW4A0LrcJJQggYGgBWQ/LM74PP/ywU6dODz30kGRsUuHll1/u1avX448/fuTIEfnzySef3L179/jx4+V3SEhIt27dunfvHhERIX9mZmZOnDixefPmM2bMyN2ydNe/f39paujQoSkpKZKEDRs2LD4+/rHbn5YtW8pCoOw1ZcoU6Ss4OPjEiRPy57///e9ly5bJj8WLF2/cuFF+PPHEE1Khffv2ubugBAEEECimQBYfBBBAQCkB+ccud7wDBw5cu3atlC9atOi1117LyMiQpE3+3LVrl+RP8uP8+fP9+vWTH9u2bZOUTn6Eh4dLgiU/6tSpI7marJbVrl1bErUcjS9fvlzalELJz+x7ST5nryP5n2SNx48f37lz59/+9jcpPHnyZOfOneWHbBo5cqR0LWdg7ZVjY2Plh2Ry0m+OLvgTAQQQKJ4Aq3HFTH/ZDQEEXCXw4IMP5tm1LKdJueRJBw8edHNzu3nzpqy6yRLd9evX76y/b98+WYeTksaNG8sam/yodvvj4eEhLUiylaPxLl26SCMLFy7MfTJXztJ26NChadOmkjJK/jd//vzNmzfbV+PkqSjt2rWTvrKX36pUqZJn2BQigAACxRYgjSs2HTsigIBrBL799ttCO5ZTn7Nnz5ZTpaGhoYVWzq4gmVzuyrJEt337dnd396CgoDszQlnAkzTu1VdflV3kf6MlKZS8TT5ffPGFlEg+t3//fknp5L6KHTt25G6WEgQQQKDkAqRxJTekBQQQMISA/Ro4OU8qTySRa9caNWok9xMcPXo0R3CSacl5VSmMjo5etWpVoaHLilpSUpKcG5WzpfaVNvsuEyZMmDNnjpeXl/wpa3Jy0la+mzVrdvr0aSnp2LHje++9J8mfpHrSY6G9UAEBBBAohgBpXDHQ2AUBBFwpkN9z4w4cOPDwww+vWbNm3Lhxbdq0kZOqcoJVluWSk5MPHz4sp0QvXLggNzF07drV19f30UcfHTFihNygUOhIKlSo8Mwzz8hdDrLklv0esMjIyA0bNrz99tt//vOf5V4KyfCqV68up2jlXtQ//elP0mb58uXlvKr8kEzO29tbfsybN0/C27Nnj1xFJ4EV2i8VEEAAgUIFeBlXoURUQAABYwnwMi5jzQfRIICA6wTyuBDEdcHQMwIIIFC4QO5bDQrfpyg1ZOFN7lfI3uPTTz+VBbmiNEBdBBBAQCcBVuN0gqYbBBBwlgCrcc6SpB0EEFBdgGvjVJ9B4kcAAQQQQAABiwqQxll04hk2AuoKyKsa1A2eyBFAAAEnCnBS1YmYNIUAAnoIcFJVD2X6QAABFQRYjVNhlogRAQTuELjrrrvwQAABBBAQAVbjOAwQQEAxAVbjFJswwkUAAc0EWI3TjJaGEUAAAQQQQAABLQVI47TUpW0EENBAoF69ehq0SpMIIICAegKcVFVvzogYAYsLcFLV4gcAw0cAgWwBVuM4GBBAQDGBpk2bKhYx4SKAAALaCLAap40rrSKAgGYCrMZpRkvDCCCgmACrcYpNGOEiYFmBa9euzZgxI3v48ltKLKvBwBFAAAERII3jMEAAATUEKlWqdO7cueDg4MDAQHmRg/yWEjVCJ0oEEEBAGwFOqmrjSqsIIKCBwNmzZxs0aGBvOCIion79+hp0QpMIIICAMgKsxikzVQSKAAKStz355JPiIN/kcBwPCCCAgAcECCCAgP4CsbGxZ86cuXLlSkZGRpF6b9u27aZNm+T7iy++KNKO7u7u1atXl8W8qlWrFmlHKiOAAAKGFeCkqmGnhsAQMK3AP/7xjyNHjpQvX7527dopKSn6jNPLyysqKurmzZstW7YcP368Pp3SCwIIIKCpAGmcprw0jgACOQXkDtNq1aoNHDgw5wa9/v7000/j4+OnTJmiV4f0gwACCGglQBqnlSztIoBAbgFZh5NFuAEDBuTepGeJZHLp6enjxo3Ts1P6QgABBJwuwC0OTielQQQQyFsgLi7uhx9+cHkOJ8ENGTJk9+7dCQkJeQdKKQIIIKCIAGmcIhNFmAioLyD3NBjnSW++vr4Sj/qojAABBCwtQBpn6eln8AjoKSD3pdaqVUvPHgvoSyKReAqowCYEEEDA+AKkccafIyJEwCQCmZmZt27dMshgUlNTJR6DBEMYCCCAQPEESOOK58ZeCCCgq4CsnP3nP/8pUpcrV668fv16kXahMgIIIKCWAGmcWvNFtAhYVODixYs7d+50fPBZWVlr165NTEx0fBdqIoAAAsoJkMYpN2UEjIDZBD755JMHHnigffv2CxcutI/tvvvus/+QFTh5zpy8SnXs2LFbtmx56qmnpPzll1+eOXPmo48+KtU2btwoJatWrZo7d659l3bt2smp2+nTpx84cGDw4MFFSv7MJst4EEDA7AK8jMvsM8z4EDC2QHh4+Icffrht2zZPT89BgwbJKxY6duyYI2R5fWpoaKisrr399tv2TVL5yy+/lLcydO/evVOnTrmH+Prrr8vDTT766KMaNWrk3koJAgggYA4BVuPMMY+MAgFVBb777rtHHnlEngksL8vq06fPrl27HBlJq1atpNrdd9/dokULSQQd2YU6CCCAgPkESOPMN6eMCAGVBOQiNpvNlh2x/Gn/LW9ZcHAY9htOHa/vYLNUQwABBIwvQBpn/DkiQgTMLBAcHPzVV18lJyenpaXJhW4PPvigjFbOhMoqnfw4cuRInoPfu3evlF+6dCksLKxJkyZSX0oyMjJiY2Ojo6Pz3IVCBBBAwHwCpHHmm1NGhIBKApKEjRgx4qGHHpJL3ORGh86dO0v0r7zyygsvvNCvX7/s58w1aNDgxx9/DAkJsY/N3d29d+/ePXv2lGvmKlSoILmgn59fhw4d5J2t1atXt9dp06bN8OHDDx8+rBIHsSKAAAJFEbBln8Ioyl7URQABBIossG7dOknFXnzxxSLv+ccd5E5VSfu6detWknbmzZsnWeNjjz1WkkbYFwEEEHCtAKtxrvWndwQQQAABBBBAoJgCpHHFhGM3BBAoqkC5cuUqV65c1L00ql+pUiWJR6PGaRYBBBDQR4A0Th9nekEAgVLy+Df7rQkltJAn/ZbwjKoEIJFIPCWMhN0RQAAB1wqQxrnWn94RsJBAvXr1/P39ExISXD7muLi4mjVr1qlTx+WREAACCCBQEgHSuJLosS8CCBRNQO4/ffXVV4u2jwa1p0yZMm7cOA0apkkEEEBAVwHuVNWVm84QQOD8+fPDhg0bPXq0vINBnvdmf3ivDixubm7ySDl5f9fixYs/++wz6V2HTukCAQQQ0FSANE5TXhpHAIE8BFJSUuQ9qvLk3sTERN3Osco9Dd7e3vfdd9+oUaNKly6dR1gUIYAAAqoJkMapNmPEiwACCCCAAAII3Bbg2jgOBAQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQAAB0jiOAQQQQAABBBBAQEkB0jglp42gEUAAAQQQQACB/wcOJ1VZwS9mowAAAABJRU5ErkJggg==",
"text/plain": [
""
]
},
- "execution_count": 14,
+ "execution_count": 13,
"metadata": {
"image/png": {
"height": 500
@@ -899,7 +931,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
@@ -915,7 +947,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"outputs": [
{
@@ -931,7 +963,7 @@
" 7: (21.0, 81.0)}"
]
},
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
@@ -949,7 +981,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {},
"outputs": [
{
@@ -997,7 +1029,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb b/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb
index d33266c6..1de8f770 100644
--- a/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb
+++ b/docs/notebooks/neuralnet/mnist_example_convolutional.ipynb
@@ -25,7 +25,7 @@
"- `torch`: the machine learning language we use to train our neural network\n",
"- `torchvision`: a package containing the MNIST dataset\n",
"- `pyomo`: the algebraic modeling language for Python, it is used to define the optimization model passed to the solver\n",
- "- `onnx`: used to express trained neural network models\n",
+ "- `onnx`: used to express trained neural network models\n",
"- `omlt`: the package this notebook demonstates. OMLT can formulate machine learning models (such as neural networks) within Pyomo\n",
"\n",
"**NOTE:** This notebook also assumes you have a working MIP solver executable (e.g., CBC, Gurobi) to solve optimization problems in Pyomo. The open-source solver CBC is called by default."
@@ -33,9 +33,19 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:34:36.631157: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-05-16 17:34:36.660941: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"#Import requisite packages\n",
"#data manipulation\n",
@@ -70,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -94,7 +104,7 @@
},
{
"cell_type": "code",
- "execution_count": 56,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -133,7 +143,7 @@
},
{
"cell_type": "code",
- "execution_count": 57,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -176,52 +186,52 @@
},
{
"cell_type": "code",
- "execution_count": 58,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.301070\n",
- "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 1.012006\n",
- "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.381090\n",
- "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.395724\n",
- "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.263946\n",
+ "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.322958\n",
+ "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.536660\n",
+ "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.259742\n",
+ "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.356392\n",
+ "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.196987\n",
"\n",
- "Test set: Average loss: 0.3262, Accuracy: 9075/10000 (91%)\n",
+ "Test set: Average loss: 0.3235, Accuracy: 9024/10000 (90%)\n",
"\n",
- "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.524031\n",
- "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.282691\n",
- "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.493126\n",
- "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.268222\n",
- "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.199386\n",
+ "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.392934\n",
+ "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.356313\n",
+ "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.350179\n",
+ "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.324098\n",
+ "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.309080\n",
"\n",
- "Test set: Average loss: 0.2783, Accuracy: 9183/10000 (92%)\n",
+ "Test set: Average loss: 0.2853, Accuracy: 9160/10000 (92%)\n",
"\n",
- "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.396457\n",
- "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.449215\n",
- "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.221934\n",
- "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.314683\n",
- "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.140539\n",
+ "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.594435\n",
+ "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.523681\n",
+ "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.236852\n",
+ "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.260963\n",
+ "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.184333\n",
"\n",
- "Test set: Average loss: 0.2462, Accuracy: 9295/10000 (93%)\n",
+ "Test set: Average loss: 0.2406, Accuracy: 9291/10000 (93%)\n",
"\n",
- "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.490455\n",
- "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.305711\n",
- "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.286548\n",
- "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.306441\n",
- "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.280397\n",
+ "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.270577\n",
+ "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.211996\n",
+ "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.167667\n",
+ "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.139197\n",
+ "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.104304\n",
"\n",
- "Test set: Average loss: 0.2280, Accuracy: 9360/10000 (94%)\n",
+ "Test set: Average loss: 0.2217, Accuracy: 9354/10000 (94%)\n",
"\n",
- "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.212264\n",
- "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.144381\n",
- "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.381677\n",
- "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.124658\n",
- "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.205714\n",
+ "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.190964\n",
+ "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.364933\n",
+ "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.268525\n",
+ "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.141043\n",
+ "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.414204\n",
"\n",
- "Test set: Average loss: 0.2085, Accuracy: 9401/10000 (94%)\n",
+ "Test set: Average loss: 0.2092, Accuracy: 9406/10000 (94%)\n",
"\n"
]
}
@@ -257,7 +267,7 @@
},
{
"cell_type": "code",
- "execution_count": 60,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -266,7 +276,7 @@
""
]
},
- "execution_count": 60,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -317,7 +327,7 @@
},
{
"cell_type": "code",
- "execution_count": 61,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -350,7 +360,7 @@
},
{
"cell_type": "code",
- "execution_count": 62,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -382,7 +392,7 @@
},
{
"cell_type": "code",
- "execution_count": 63,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -411,7 +421,7 @@
},
{
"cell_type": "code",
- "execution_count": 64,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -429,9 +439,364 @@
},
{
"cell_type": "code",
- "execution_count": 65,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,6]' to a numeric value `0`\n",
+ "outside the bounds (0.3284117877483368, 0.33041176199913025).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,7]' to a numeric value `0`\n",
+ "outside the bounds (0.724490225315094, 0.7264901995658875).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,8]' to a numeric value `0`\n",
+ "outside the bounds (0.6225294470787048, 0.6245294213294983).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,9]' to a numeric value `0`\n",
+ "outside the bounds (0.5911568999290466, 0.5931568741798401).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,10]' to a numeric value `0`\n",
+ "outside the bounds (0.2342941164970398, 0.23629412055015564).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,7,11]' to a numeric value `0`\n",
+ "outside the bounds (0.14017647504806519, 0.14217647910118103).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,6]' to a numeric value `0`\n",
+ "outside the bounds (0.8695882558822632, 0.8715882301330566).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,7]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,8]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,9]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,10]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,11]' to a numeric value `0`\n",
+ "outside the bounds (0.9440980553627014, 0.9460980296134949).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,12]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,13]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,14]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,15]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,16]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,17]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,18]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,19]' to a numeric value `0`\n",
+ "outside the bounds (0.7754706144332886, 0.777470588684082).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,20]' to a numeric value `0`\n",
+ "outside the bounds (0.6656666994094849, 0.6676666736602783).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,8,21]' to a numeric value `0`\n",
+ "outside the bounds (0.2029215693473816, 0.20492157340049744).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,6]' to a numeric value `0`\n",
+ "outside the bounds (0.2617451250553131, 0.26374509930610657).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,7]' to a numeric value `0`\n",
+ "outside the bounds (0.44605883955955505, 0.4480588138103485).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,8]' to a numeric value `0`\n",
+ "outside the bounds (0.2813529670238495, 0.28335294127464294).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,9]' to a numeric value `0`\n",
+ "outside the bounds (0.44605883955955505, 0.4480588138103485).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,10]' to a numeric value `0`\n",
+ "outside the bounds (0.6382157206535339, 0.6402156949043274).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,11]' to a numeric value `0`\n",
+ "outside the bounds (0.8891960978507996, 0.891196072101593).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,12]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,13]' to a numeric value `0`\n",
+ "outside the bounds (0.881352961063385, 0.8833529353141785).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,14]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,15]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,16]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,17]' to a numeric value `0`\n",
+ "outside the bounds (0.9793921709060669, 0.9813921451568604).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,18]' to a numeric value `0`\n",
+ "outside the bounds (0.8970392346382141, 0.8990392088890076).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,19]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,20]' to a numeric value `0`\n",
+ "outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,9,21]' to a numeric value `0`\n",
+ "outside the bounds (0.5480196475982666, 0.5500196218490601).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,11]' to a numeric value\n",
+ "`0` outside the bounds (0.06566666811704636, 0.0676666721701622).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,12]' to a numeric value\n",
+ "`0` outside the bounds (0.25782355666160583, 0.2598235309123993).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,13]' to a numeric value\n",
+ "`0` outside the bounds (0.05390196293592453, 0.05590195953845978).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,14]' to a numeric value\n",
+ "`0` outside the bounds (0.2617451250553131, 0.26374509930610657).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,15]' to a numeric value\n",
+ "`0` outside the bounds (0.2617451250553131, 0.26374509930610657).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,16]' to a numeric value\n",
+ "`0` outside the bounds (0.2617451250553131, 0.26374509930610657).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,17]' to a numeric value\n",
+ "`0` outside the bounds (0.23037254810333252, 0.23237255215644836).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,18]' to a numeric value\n",
+ "`0` outside the bounds (0.08135294169187546, 0.0833529457449913).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,19]' to a numeric value\n",
+ "`0` outside the bounds (0.924490213394165, 0.9264901876449585).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,20]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,10,21]' to a numeric value\n",
+ "`0` outside the bounds (0.41468629240989685, 0.4166862666606903).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,11,18]' to a numeric value\n",
+ "`0` outside the bounds (0.3244902193546295, 0.326490193605423).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,11,19]' to a numeric value\n",
+ "`0` outside the bounds (0.9911568760871887, 0.9931568503379822).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,11,20]' to a numeric value\n",
+ "`0` outside the bounds (0.8186078667640686, 0.8206078410148621).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,11,21]' to a numeric value\n",
+ "`0` outside the bounds (0.06958823651075363, 0.07158824056386948).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,12,17]' to a numeric value\n",
+ "`0` outside the bounds (0.08527451008558273, 0.08727451413869858).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,12,18]' to a numeric value\n",
+ "`0` outside the bounds (0.9127255082130432, 0.9147254824638367).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,12,19]' to a numeric value\n",
+ "`0` outside the bounds (0.9990000128746033, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,12,20]' to a numeric value\n",
+ "`0` outside the bounds (0.3244902193546295, 0.326490193605423).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,13,17]' to a numeric value\n",
+ "`0` outside the bounds (0.5048823952674866, 0.50688236951828).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,13,18]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,13,19]' to a numeric value\n",
+ "`0` outside the bounds (0.9323333501815796, 0.934333324432373).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,13,20]' to a numeric value\n",
+ "`0` outside the bounds (0.1715490221977234, 0.17354902625083923).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,14,16]' to a numeric value\n",
+ "`0` outside the bounds (0.23037254810333252, 0.23237255215644836).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,14,17]' to a numeric value\n",
+ "`0` outside the bounds (0.9754706025123596, 0.9774705767631531).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,14,18]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,14,19]' to a numeric value\n",
+ "`0` outside the bounds (0.24213725328445435, 0.2441372573375702).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,15,16]' to a numeric value\n",
+ "`0` outside the bounds (0.5205686688423157, 0.5225686430931091).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,15,17]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,15,18]' to a numeric value\n",
+ "`0` outside the bounds (0.7323333621025085, 0.734333336353302).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,15,19]' to a numeric value\n",
+ "`0` outside the bounds (0.018607843667268753, 0.0206078439950943).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,16,15]' to a numeric value\n",
+ "`0` outside the bounds (0.03429412096738815, 0.0362941175699234).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,16,16]' to a numeric value\n",
+ "`0` outside the bounds (0.8029215931892395, 0.804921567440033).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,16,17]' to a numeric value\n",
+ "`0` outside the bounds (0.9715490341186523, 0.9735490083694458).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,16,18]' to a numeric value\n",
+ "`0` outside the bounds (0.22645097970962524, 0.2284509837627411).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,17,15]' to a numeric value\n",
+ "`0` outside the bounds (0.49311766028404236, 0.4951176345348358).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,17,16]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,17,17]' to a numeric value\n",
+ "`0` outside the bounds (0.7127255201339722, 0.7147254943847656).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,18,14]' to a numeric value\n",
+ "`0` outside the bounds (0.2931176722049713, 0.29511764645576477).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,18,15]' to a numeric value\n",
+ "`0` outside the bounds (0.9833137392997742, 0.9853137135505676).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,18,16]' to a numeric value\n",
+ "`0` outside the bounds (0.9401764869689941, 0.9421764612197876).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,18,17]' to a numeric value\n",
+ "`0` outside the bounds (0.22252941131591797, 0.2245294153690338).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,19,13]' to a numeric value\n",
+ "`0` outside the bounds (0.07350980490446091, 0.07550980895757675).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,19,14]' to a numeric value\n",
+ "`0` outside the bounds (0.8656666874885559, 0.8676666617393494).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,19,15]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,19,16]' to a numeric value\n",
+ "`0` outside the bounds (0.6499804258346558, 0.6519804000854492).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,20,12]' to a numeric value\n",
+ "`0` outside the bounds (0.010764705948531628, 0.012764706276357174).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,20,13]' to a numeric value\n",
+ "`0` outside the bounds (0.795078456401825, 0.7970784306526184).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,20,14]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,20,15]' to a numeric value\n",
+ "`0` outside the bounds (0.8578235507011414, 0.8598235249519348).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,20,16]' to a numeric value\n",
+ "`0` outside the bounds (0.1362549066543579, 0.13825491070747375).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,21,12]' to a numeric value\n",
+ "`0` outside the bounds (0.14801961183547974, 0.15001961588859558).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,21,13]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,21,14]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,21,15]' to a numeric value\n",
+ "`0` outside the bounds (0.30096080899238586, 0.3029607832431793).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,22,11]' to a numeric value\n",
+ "`0` outside the bounds (0.12056862562894821, 0.12256862968206406).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,22,12]' to a numeric value\n",
+ "`0` outside the bounds (0.8774313926696777, 0.8794313669204712).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,22,13]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,22,14]' to a numeric value\n",
+ "`0` outside the bounds (0.44998040795326233, 0.4519803822040558).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,22,15]' to a numeric value\n",
+ "`0` outside the bounds (0.0029215686954557896, 0.004921569023281336).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,23,11]' to a numeric value\n",
+ "`0` outside the bounds (0.5205686688423157, 0.5225686430931091).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,23,12]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,23,13]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,23,14]' to a numeric value\n",
+ "`0` outside the bounds (0.2029215693473816, 0.20492157340049744).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,24,10]' to a numeric value\n",
+ "`0` outside the bounds (0.23821568489074707, 0.24021568894386292).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,24,11]' to a numeric value\n",
+ "`0` outside the bounds (0.9480196237564087, 0.9500195980072021).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,24,12]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,24,13]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,24,14]' to a numeric value\n",
+ "`0` outside the bounds (0.2029215693473816, 0.20492157340049744).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,25,10]' to a numeric value\n",
+ "`0` outside the bounds (0.473509818315506, 0.47550979256629944).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,25,11]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,25,12]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,25,13]' to a numeric value\n",
+ "`0` outside the bounds (0.8578235507011414, 0.8598235249519348).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,25,14]' to a numeric value\n",
+ "`0` outside the bounds (0.1558627486228943, 0.15786275267601013).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,26,10]' to a numeric value\n",
+ "`0` outside the bounds (0.473509818315506, 0.47550979256629944).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,26,11]' to a numeric value\n",
+ "`0` outside the bounds (0.995078444480896, 0.9970784187316895).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,26,12]' to a numeric value\n",
+ "`0` outside the bounds (0.810764729976654, 0.8127647042274475).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[0,26,13]' to a numeric value\n",
+ "`0` outside the bounds (0.06958823651075363, 0.07158824056386948).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n"
+ ]
+ }
+ ],
"source": [
"#create pyomo model\n",
"m = pyo.ConcreteModel()\n",
@@ -450,7 +815,7 @@
},
{
"cell_type": "code",
- "execution_count": 66,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -467,7 +832,7 @@
},
{
"cell_type": "code",
- "execution_count": 67,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
@@ -475,152 +840,164 @@
"output_type": "stream",
"text": [
"Welcome to the CBC MILP Solver \n",
- "Version: 2.10.5 \n",
- "Build Date: Oct 15 2020 \n",
+ "Version: 2.10.10 \n",
+ "Build Date: Apr 19 2023 \n",
"\n",
- "command line - /home/jhjalvi/anaconda3/envs/tensorflow/bin/cbc -printingOptions all -import /tmp/tmptpf8ezli.pyomo.lp -stat=1 -solve -solu /tmp/tmptpf8ezli.pyomo.soln (default strategy 1)\n",
+ "command line - /opt/conda/bin/cbc -printingOptions all -import /tmp/tmpm8l62uuz.pyomo.lp -stat=1 -solve -solu /tmp/tmpm8l62uuz.pyomo.soln (default strategy 1)\n",
"Option for printingOptions changed from normal to all\n",
- "Presolve 1243 (-2356) rows, 1675 (-1912) columns and 9017 (-5270) elements\n",
+ "Presolve 1216 (-2382) rows, 1655 (-1931) columns and 8081 (-6315) elements\n",
"Statistics for presolved model\n",
"Original problem has 398 integers (398 of which binary)\n",
- "Presolved problem has 171 integers (171 of which binary)\n",
- "==== 1665 zero objective 11 different\n",
- "1 variables have objective of -0.799653\n",
- "1 variables have objective of -0.692429\n",
- "1 variables have objective of -0.432872\n",
- "1 variables have objective of -0.381614\n",
- "1 variables have objective of -0.166969\n",
- "1 variables have objective of -0.0541137\n",
- "1665 variables have objective of 0\n",
- "1 variables have objective of 0.25157\n",
- "1 variables have objective of 0.258075\n",
- "1 variables have objective of 0.551109\n",
- "1 variables have objective of 0.969763\n",
- "==== absolute objective values 11 different\n",
- "1665 variables have objective of 0\n",
- "1 variables have objective of 0.0541137\n",
- "1 variables have objective of 0.166969\n",
- "1 variables have objective of 0.25157\n",
- "1 variables have objective of 0.258075\n",
- "1 variables have objective of 0.381614\n",
- "1 variables have objective of 0.432872\n",
- "1 variables have objective of 0.551109\n",
- "1 variables have objective of 0.692429\n",
- "1 variables have objective of 0.799653\n",
- "1 variables have objective of 0.969763\n",
- "==== for integers 171 zero objective 1 different\n",
- "171 variables have objective of 0\n",
+ "Presolved problem has 229 integers (229 of which binary)\n",
+ "==== 1649 zero objective 7 different\n",
+ "1 variables have objective of -0.576795\n",
+ "1 variables have objective of -0.489737\n",
+ "1649 variables have objective of 0\n",
+ "1 variables have objective of 0.206289\n",
+ "1 variables have objective of 0.207359\n",
+ "1 variables have objective of 0.905509\n",
+ "1 variables have objective of 1.00525\n",
+ "==== absolute objective values 7 different\n",
+ "1649 variables have objective of 0\n",
+ "1 variables have objective of 0.206289\n",
+ "1 variables have objective of 0.207359\n",
+ "1 variables have objective of 0.489737\n",
+ "1 variables have objective of 0.576795\n",
+ "1 variables have objective of 0.905509\n",
+ "1 variables have objective of 1.00525\n",
+ "==== for integers 229 zero objective 1 different\n",
+ "229 variables have objective of 0\n",
"==== for integers absolute objective values 1 different\n",
- "171 variables have objective of 0\n",
+ "229 variables have objective of 0\n",
"===== end objective counts\n",
"\n",
"\n",
- "Problem has 1243 rows, 1675 columns (10 with objective) and 9017 elements\n",
+ "Problem has 1216 rows, 1655 columns (6 with objective) and 8081 elements\n",
"Column breakdown:\n",
- "0 of type 0.0->inf, 1142 of type 0.0->up, 0 of type lo->inf, \n",
- "362 of type lo->up, 0 of type free, 0 of type fixed, \n",
- "0 of type -inf->0.0, 0 of type -inf->up, 171 of type 0.0->1.0 \n",
+ "0 of type 0.0->inf, 885 of type 0.0->up, 0 of type lo->inf, \n",
+ "541 of type lo->up, 0 of type free, 0 of type fixed, \n",
+ "0 of type -inf->0.0, 0 of type -inf->up, 229 of type 0.0->1.0 \n",
"Row breakdown:\n",
- "347 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, \n",
- "9 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
- "0 of type G other, 716 of type L 0.0, 0 of type L 1.0, \n",
- "171 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
+ "320 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, \n",
+ "6 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
+ "0 of type G other, 658 of type L 0.0, 0 of type L 1.0, \n",
+ "229 of type L other, 0 of type Range 0.0->1.0, 3 of type Range other, \n",
"0 of type Free \n",
- "Continuous objective value is -2.12429 - 0.02 seconds\n",
- "Cgl0003I 3 fixed, 0 tightened bounds, 0 strengthened rows, 0 substitutions\n",
- "Cgl0004I processed model has 937 rows, 1367 columns (115 integer (115 of which binary)) and 11817 elements\n",
- "Cbc0038I Initial state - 72 integers unsatisfied sum - 20.8814\n",
- "Cbc0038I Pass 1: suminf. 0.00000 (0) obj. 11.809 iterations 335\n",
- "Cbc0038I Solution found of 11.809\n",
- "Cbc0038I Relaxing continuous gives 11.7955\n",
- "Cbc0038I Before mini branch and bound, 43 integers at bound fixed and 641 continuous\n",
- "Cbc0038I Full problem 937 rows 1367 columns, reduced to 619 rows 530 columns - 16 fixed gives 607, 518 - still too large\n",
- "Cbc0038I Full problem 937 rows 1367 columns, reduced to 554 rows 470 columns\n",
- "Cbc0038I Mini branch and bound improved solution from 11.7955 to 11.7944 (0.48 seconds)\n",
- "Cbc0038I Freeing continuous variables gives a solution of 11.7937\n",
- "Cbc0038I Round again with cutoff of 11.7921\n",
- "Cbc0038I Pass 2: suminf. 0.20702 (1) obj. 11.7921 iterations 174\n",
- "Cbc0038I Pass 3: suminf. 0.39088 (1) obj. 11.7921 iterations 64\n",
- "Cbc0038I Pass 4: suminf. 0.31986 (1) obj. 11.7921 iterations 254\n",
- "Cbc0038I Pass 5: suminf. 0.24400 (1) obj. 11.7921 iterations 35\n",
- "Cbc0038I Pass 6: suminf. 0.31986 (1) obj. 11.7921 iterations 49\n",
- "Cbc0038I Pass 7: suminf. 0.56513 (2) obj. 11.7921 iterations 321\n",
- "Cbc0038I Pass 8: suminf. 0.81697 (2) obj. 11.7921 iterations 63\n",
- "Cbc0038I Pass 9: suminf. 0.56513 (2) obj. 11.7921 iterations 66\n",
- "Cbc0038I Pass 10: suminf. 1.29356 (7) obj. 11.7921 iterations 295\n",
- "Cbc0038I Pass 11: suminf. 1.35733 (4) obj. 11.7921 iterations 165\n",
- "Cbc0038I Pass 12: suminf. 1.27012 (4) obj. 11.7921 iterations 13\n",
- "Cbc0038I Pass 13: suminf. 0.77889 (3) obj. 11.7921 iterations 57\n",
- "Cbc0038I Pass 14: suminf. 0.75944 (3) obj. 11.7921 iterations 7\n",
- "Cbc0038I Pass 15: suminf. 1.47773 (4) obj. 11.7921 iterations 33\n",
- "Cbc0038I Pass 16: suminf. 1.33195 (4) obj. 11.7921 iterations 20\n",
- "Cbc0038I Pass 17: suminf. 1.38082 (4) obj. 11.7921 iterations 56\n",
- "Cbc0038I Pass 18: suminf. 1.25857 (4) obj. 11.7921 iterations 18\n",
- "Cbc0038I Pass 19: suminf. 1.01070 (4) obj. 11.7921 iterations 326\n",
- "Cbc0038I Pass 20: suminf. 0.94775 (5) obj. 11.7921 iterations 16\n",
- "Cbc0038I Pass 21: suminf. 1.08402 (4) obj. 11.7921 iterations 63\n",
- "Cbc0038I Pass 22: suminf. 1.05834 (4) obj. 11.7921 iterations 6\n",
- "Cbc0038I Pass 23: suminf. 1.03028 (3) obj. 11.7921 iterations 72\n",
- "Cbc0038I Pass 24: suminf. 0.97431 (3) obj. 11.7921 iterations 10\n",
- "Cbc0038I Pass 25: suminf. 0.72150 (2) obj. 11.7921 iterations 87\n",
- "Cbc0038I Pass 26: suminf. 0.64025 (2) obj. 11.7921 iterations 33\n",
- "Cbc0038I Pass 27: suminf. 0.63795 (2) obj. 11.7921 iterations 65\n",
- "Cbc0038I Pass 28: suminf. 0.59735 (2) obj. 11.7921 iterations 12\n",
- "Cbc0038I Pass 29: suminf. 0.56341 (2) obj. 11.7921 iterations 324\n",
- "Cbc0038I Pass 30: suminf. 0.74595 (2) obj. 11.7921 iterations 42\n",
- "Cbc0038I Pass 31: suminf. 0.61016 (2) obj. 11.7921 iterations 40\n",
+ "Continuous objective value is 11.399 - 0.03 seconds\n",
+ "Cgl0004I processed model has 1000 rows, 1439 columns (229 integer (229 of which binary)) and 11386 elements\n",
+ "Cbc0038I Initial state - 147 integers unsatisfied sum - 33.3817\n",
+ "Cbc0038I Pass 1: suminf. 0.16220 (3) obj. 11.4839 iterations 832\n",
+ "Cbc0038I Solution found of 11.4839\n",
+ "Cbc0038I Relaxing continuous gives 11.4374\n",
+ "Cbc0038I Before mini branch and bound, 82 integers at bound fixed and 547 continuous\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 685 rows 704 columns - 29 fixed gives 654, 673 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 586 rows 608 columns - too large\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.18 seconds)\n",
+ "Cbc0038I Round again with cutoff of 11.4336\n",
+ "Cbc0038I Pass 2: suminf. 0.35199 (3) obj. 11.4336 iterations 310\n",
+ "Cbc0038I Pass 3: suminf. 0.09461 (1) obj. 11.4336 iterations 626\n",
+ "Cbc0038I Pass 4: suminf. 0.28351 (1) obj. 11.4336 iterations 355\n",
+ "Cbc0038I Pass 5: suminf. 0.35646 (5) obj. 11.4336 iterations 775\n",
+ "Cbc0038I Pass 6: suminf. 0.12369 (2) obj. 11.4336 iterations 588\n",
+ "Cbc0038I Pass 7: suminf. 0.30731 (2) obj. 11.4336 iterations 400\n",
+ "Cbc0038I Pass 8: suminf. 0.61796 (3) obj. 11.4336 iterations 718\n",
+ "Cbc0038I Pass 9: suminf. 0.45612 (3) obj. 11.4336 iterations 249\n",
+ "Cbc0038I Pass 10: suminf. 0.37185 (1) obj. 11.4336 iterations 348\n",
+ "Cbc0038I Pass 11: suminf. 0.30010 (3) obj. 11.4336 iterations 337\n",
+ "Cbc0038I Pass 12: suminf. 1.07965 (14) obj. 11.4336 iterations 833\n",
+ "Cbc0038I Pass 13: suminf. 1.07524 (15) obj. 11.4336 iterations 25\n",
+ "Cbc0038I Pass 14: suminf. 0.42849 (2) obj. 11.4336 iterations 483\n",
+ "Cbc0038I Pass 15: suminf. 0.41940 (2) obj. 11.4336 iterations 184\n",
+ "Cbc0038I Pass 16: suminf. 0.48151 (5) obj. 11.4336 iterations 778\n",
+ "Cbc0038I Pass 17: suminf. 0.45259 (1) obj. 11.4336 iterations 224\n",
+ "Cbc0038I Pass 18: suminf. 0.43680 (1) obj. 11.4336 iterations 157\n",
+ "Cbc0038I Pass 19: suminf. 0.89838 (4) obj. 11.4336 iterations 649\n",
+ "Cbc0038I Pass 20: suminf. 0.48534 (1) obj. 11.4336 iterations 281\n",
+ "Cbc0038I Pass 21: suminf. 0.44387 (1) obj. 11.4336 iterations 115\n",
+ "Cbc0038I Pass 22: suminf. 1.13864 (15) obj. 11.4336 iterations 826\n",
+ "Cbc0038I Pass 23: suminf. 0.48847 (3) obj. 11.4336 iterations 478\n",
+ "Cbc0038I Pass 24: suminf. 0.48408 (3) obj. 11.4336 iterations 21\n",
+ "Cbc0038I Pass 25: suminf. 0.49814 (1) obj. 11.4336 iterations 145\n",
+ "Cbc0038I Pass 26: suminf. 0.45900 (1) obj. 11.4336 iterations 117\n",
+ "Cbc0038I Pass 27: suminf. 1.35634 (10) obj. 11.4336 iterations 647\n",
+ "Cbc0038I Pass 28: suminf. 1.20108 (11) obj. 11.4336 iterations 35\n",
+ "Cbc0038I Pass 29: suminf. 0.82478 (5) obj. 11.4336 iterations 323\n",
+ "Cbc0038I Pass 30: suminf. 0.81980 (5) obj. 11.4336 iterations 10\n",
+ "Cbc0038I Pass 31: suminf. 0.54350 (4) obj. 11.4336 iterations 206\n",
"Cbc0038I No solution found this major pass\n",
- "Cbc0038I Before mini branch and bound, 14 integers at bound fixed and 517 continuous\n",
- "Cbc0038I Full problem 937 rows 1367 columns, reduced to 666 rows 669 columns - 46 fixed gives 617, 620 - still too large\n",
- "Cbc0038I Full problem 937 rows 1367 columns, reduced to 593 rows 599 columns - too large\n",
- "Cbc0038I Mini branch and bound did not improve solution (0.70 seconds)\n",
- "Cbc0038I After 0.70 seconds - Feasibility pump exiting with objective of 11.7937 - took 0.55 seconds\n",
- "Cbc0012I Integer solution of 11.7937 found by feasibility pump after 0 iterations and 0 nodes (0.78 seconds)\n",
- "Cbc0038I Full problem 937 rows 1367 columns, reduced to 813 rows 1247 columns - 42 fixed gives 771, 1205 - still too large\n",
- "Cbc0012I Integer solution of 11.792721 found by DiveCoefficient after 944 iterations and 0 nodes (1.27 seconds)\n",
- "Cbc0031I 106 added rows had average density of 32.424528\n",
- "Cbc0013I At root node, 106 cuts changed objective from 11.777607 to 11.789992 in 10 passes\n",
- "Cbc0014I Cut generator 0 (Probing) - 1 row cuts average 16.0 elements, 0 column cuts (0 active) in 0.013 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 1 (Gomory) - 381 row cuts average 82.5 elements, 0 column cuts (0 active) in 0.022 seconds - new frequency is 1\n",
- "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.018 seconds - new frequency is -100\n",
+ "Cbc0038I Before mini branch and bound, 12 integers at bound fixed and 249 continuous\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 863 rows 1093 columns - 24 fixed gives 838, 1068 - still too large\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.59 seconds)\n",
+ "Cbc0038I After 0.59 seconds - Feasibility pump exiting with objective of 11.4374 - took 0.51 seconds\n",
+ "Cbc0012I Integer solution of 11.437437 found by feasibility pump after 0 iterations and 0 nodes (0.64 seconds)\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 791 rows 1230 columns - 88 fixed gives 703, 1142 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 674 rows 1113 columns - too large\n",
+ "Cbc0012I Integer solution of 11.430827 found by DiveCoefficient after 3020 iterations and 0 nodes (1.33 seconds)\n",
+ "Cbc0031I 247 added rows had average density of 37.910931\n",
+ "Cbc0013I At root node, 247 cuts changed objective from 11.399027 to 11.416462 in 10 passes\n",
+ "Cbc0014I Cut generator 0 (Probing) - 349 row cuts average 2.1 elements, 0 column cuts (104 active) in 0.014 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 1 (Gomory) - 663 row cuts average 78.3 elements, 0 column cuts (0 active) in 0.034 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.015 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 33 row cuts average 20.0 elements, 0 column cuts (0 active) in 0.009 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.019 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 6 (TwoMirCuts) - 295 row cuts average 54.4 elements, 0 column cuts (0 active) in 0.025 seconds - new frequency is -100\n",
- "Cbc0010I After 0 nodes, 1 on tree, 11.792721 best solution, best possible 11.789992 (1.38 seconds)\n",
- "Cbc0012I Integer solution of 11.79261 found by DiveCoefficient after 1301 iterations and 4 nodes (2.22 seconds)\n",
- "Cbc0001I Search completed - best objective 11.79260967177679, took 1540 iterations and 7 nodes (2.45 seconds)\n",
- "Cbc0032I Strong branching done 172 times (2219 iterations), fathomed 1 nodes and fixed 13 variables\n",
- "Cbc0035I Maximum depth 3, 0 variables fixed on reduced cost\n",
- "Cuts at root node changed objective from 11.7776 to 11.79\n",
- "Probing was tried 10 times and created 1 cuts of which 0 were active after adding rounds of cuts (0.013 seconds)\n",
- "Gomory was tried 22 times and created 447 cuts of which 0 were active after adding rounds of cuts (0.032 seconds)\n",
- "Knapsack was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.018 seconds)\n",
+ "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 441 row cuts average 11.9 elements, 0 column cuts (0 active) in 0.015 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 5 (FlowCover) - 3 row cuts average 3.3 elements, 0 column cuts (0 active) in 0.012 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 6 (TwoMirCuts) - 576 row cuts average 76.8 elements, 0 column cuts (0 active) in 0.030 seconds - new frequency is -100\n",
+ "Cbc0010I After 0 nodes, 1 on tree, 11.430827 best solution, best possible 11.416462 (1.36 seconds)\n",
+ "Cbc0012I Integer solution of 11.43068 found by DiveCoefficient after 3132 iterations and 1 nodes (1.60 seconds)\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 688 rows 1127 columns - 6 fixed gives 682, 1121 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 645 rows 1084 columns - too large\n",
+ "Cbc0012I Integer solution of 11.430373 found by rounding after 5500 iterations and 65 nodes (4.04 seconds)\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 694 rows 1133 columns - 10 fixed gives 684, 1123 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 649 rows 1088 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 722 rows 1161 columns - 17 fixed gives 705, 1144 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 668 rows 1107 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 617 rows 771 columns - 17 fixed gives 603, 756 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 581 rows 735 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 617 rows 771 columns - 16 fixed gives 604, 757 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 582 rows 736 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 621 rows 775 columns - 19 fixed gives 605, 758 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 583 rows 737 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 606 rows 794 columns - 10 fixed gives 598, 785 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 576 rows 764 columns - too large\n",
+ "Cbc0012I Integer solution of 11.430259 found by rounding after 46471 iterations and 982 nodes (19.93 seconds)\n",
+ "Cbc0010I After 1000 nodes, 15 on tree, 11.430259 best solution, best possible 11.416475 (20.00 seconds)\n",
+ "Cbc0012I Integer solution of 11.430193 found by rounding after 47023 iterations and 1040 nodes (20.27 seconds)\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 615 rows 769 columns - 15 fixed gives 602, 755 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 581 rows 735 columns - too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 638 rows 792 columns - 20 fixed gives 621, 774 - still too large\n",
+ "Cbc0038I Full problem 1000 rows 1439 columns, reduced to 598 rows 752 columns - too large\n",
+ "Cbc0001I Search completed - best objective 11.43019346267776, took 55037 iterations and 1209 nodes (23.48 seconds)\n",
+ "Cbc0032I Strong branching done 4126 times (63874 iterations), fathomed 86 nodes and fixed 436 variables\n",
+ "Cbc0035I Maximum depth 55, 199 variables fixed on reduced cost\n",
+ "Cuts at root node changed objective from 11.399 to 11.4165\n",
+ "Probing was tried 245 times and created 1770 cuts of which 104 were active after adding rounds of cuts (0.147 seconds)\n",
+ "Gomory was tried 236 times and created 2134 cuts of which 0 were active after adding rounds of cuts (0.271 seconds)\n",
+ "Knapsack was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.015 seconds)\n",
"Clique was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
- "MixedIntegerRounding2 was tried 10 times and created 33 cuts of which 0 were active after adding rounds of cuts (0.009 seconds)\n",
- "FlowCover was tried 10 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.019 seconds)\n",
- "TwoMirCuts was tried 10 times and created 295 cuts of which 0 were active after adding rounds of cuts (0.025 seconds)\n",
+ "MixedIntegerRounding2 was tried 236 times and created 1628 cuts of which 0 were active after adding rounds of cuts (0.235 seconds)\n",
+ "FlowCover was tried 10 times and created 3 cuts of which 0 were active after adding rounds of cuts (0.012 seconds)\n",
+ "TwoMirCuts was tried 10 times and created 576 cuts of which 0 were active after adding rounds of cuts (0.030 seconds)\n",
"ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"\n",
"Result - Optimal solution found\n",
"\n",
- "Objective value: 11.79260967\n",
- "Enumerated nodes: 7\n",
- "Total iterations: 1540\n",
- "Time (CPU seconds): 2.79\n",
- "Time (Wallclock seconds): 3.16\n",
+ "Objective value: 11.43019346\n",
+ "Enumerated nodes: 1209\n",
+ "Total iterations: 55037\n",
+ "Time (CPU seconds): 23.57\n",
+ "Time (Wallclock seconds): 25.18\n",
"\n",
- "Total time (CPU seconds): 2.82 (Wallclock seconds): 3.19\n",
+ "Total time (CPU seconds): 23.62 (Wallclock seconds): 25.21\n",
"\n"
]
},
{
"data": {
"text/plain": [
- "{'Problem': [{'Name': 'unknown', 'Lower bound': 11.79260967, 'Upper bound': 11.79260967, 'Number of objectives': 1, 'Number of constraints': 1243, 'Number of variables': 1675, 'Number of binary variables': 398, 'Number of integer variables': 398, 'Number of nonzeros': 10, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 2.82, 'Wallclock time': 3.19, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': 7, 'Number of created subproblems': 7}, 'Black box': {'Number of iterations': 1540}}, 'Error rc': 0, 'Time': 3.2129950523376465}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}"
+ "{'Problem': [{'Name': 'unknown', 'Lower bound': 11.43019346, 'Upper bound': 11.43019346, 'Number of objectives': 1, 'Number of constraints': 1216, 'Number of variables': 1655, 'Number of binary variables': 398, 'Number of integer variables': 398, 'Number of nonzeros': 6, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 23.62, 'Wallclock time': 25.21, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': 1209, 'Number of created subproblems': 1209}, 'Black box': {'Number of iterations': 55037}}, 'Error rc': 0, 'Time': 25.219436407089233}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}"
]
},
- "execution_count": 67,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -657,7 +1034,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/mnist_example_dense.ipynb b/docs/notebooks/neuralnet/mnist_example_dense.ipynb
index ccc84365..e7af1f06 100644
--- a/docs/notebooks/neuralnet/mnist_example_dense.ipynb
+++ b/docs/notebooks/neuralnet/mnist_example_dense.ipynb
@@ -24,7 +24,7 @@
"- `torch`: the machine learning language we use to train our neural network\n",
"- `torchvision`: a package containing the MNIST dataset\n",
"- `pyomo`: the algebraic modeling language for Python, it is used to define the optimization model passed to the solver\n",
- "- `onnx`: used to express trained neural network models\n",
+ "- `onnx`: used to express trained neural network models\n",
"- `omlt`: the package this notebook demonstates. OMLT can formulate machine learning models (such as neural networks) within Pyomo\n",
"\n",
"**NOTE:** This notebook also assumes you have a working MIP solver executable (e.g., CBC, Gurobi) to solve optimization problems in Pyomo. The open-source solver CBC is called by default."
@@ -34,7 +34,17 @@
"cell_type": "code",
"execution_count": 1,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:36:49.569530: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-05-16 17:36:49.599228: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"#Import requisite packages\n",
"#data manipulation\n",
@@ -69,91 +79,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100.0%\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "102.8%\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "100.0%\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
- "\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
- "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "112.7%"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
- "\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
+ "outputs": [],
"source": [
"#set training and test batch sizes\n",
"train_kwargs = {'batch_size': 64}\n",
@@ -175,7 +103,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -211,7 +139,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
@@ -254,52 +182,52 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.312474\n",
- "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.433773\n",
- "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.337540\n",
- "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.466846\n",
- "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.088567\n",
+ "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.309185\n",
+ "Train Epoch: 0 [12800/60000 (21%)]\tLoss: 0.233512\n",
+ "Train Epoch: 0 [25600/60000 (43%)]\tLoss: 0.294385\n",
+ "Train Epoch: 0 [38400/60000 (64%)]\tLoss: 0.198371\n",
+ "Train Epoch: 0 [51200/60000 (85%)]\tLoss: 0.192688\n",
"\n",
- "Test set: Average loss: 0.1634, Accuracy: 9508/10000 (95%)\n",
+ "Test set: Average loss: 0.1485, Accuracy: 9534/10000 (95%)\n",
"\n",
- "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.137867\n",
- "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.057379\n",
- "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.045729\n",
- "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.377446\n",
- "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.218694\n",
+ "Train Epoch: 1 [0/60000 (0%)]\tLoss: 0.091085\n",
+ "Train Epoch: 1 [12800/60000 (21%)]\tLoss: 0.186301\n",
+ "Train Epoch: 1 [25600/60000 (43%)]\tLoss: 0.122492\n",
+ "Train Epoch: 1 [38400/60000 (64%)]\tLoss: 0.110627\n",
+ "Train Epoch: 1 [51200/60000 (85%)]\tLoss: 0.084353\n",
"\n",
- "Test set: Average loss: 0.1208, Accuracy: 9630/10000 (96%)\n",
+ "Test set: Average loss: 0.1107, Accuracy: 9662/10000 (97%)\n",
"\n",
- "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.133075\n",
- "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.137646\n",
- "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.026231\n",
- "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.020423\n",
- "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.073325\n",
+ "Train Epoch: 2 [0/60000 (0%)]\tLoss: 0.132310\n",
+ "Train Epoch: 2 [12800/60000 (21%)]\tLoss: 0.084304\n",
+ "Train Epoch: 2 [25600/60000 (43%)]\tLoss: 0.181169\n",
+ "Train Epoch: 2 [38400/60000 (64%)]\tLoss: 0.031130\n",
+ "Train Epoch: 2 [51200/60000 (85%)]\tLoss: 0.014465\n",
"\n",
- "Test set: Average loss: 0.1031, Accuracy: 9677/10000 (97%)\n",
+ "Test set: Average loss: 0.1083, Accuracy: 9694/10000 (97%)\n",
"\n",
- "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.037360\n",
- "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.119995\n",
- "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.018661\n",
- "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.071436\n",
- "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.048075\n",
+ "Train Epoch: 3 [0/60000 (0%)]\tLoss: 0.073255\n",
+ "Train Epoch: 3 [12800/60000 (21%)]\tLoss: 0.186617\n",
+ "Train Epoch: 3 [25600/60000 (43%)]\tLoss: 0.009313\n",
+ "Train Epoch: 3 [38400/60000 (64%)]\tLoss: 0.120100\n",
+ "Train Epoch: 3 [51200/60000 (85%)]\tLoss: 0.045455\n",
"\n",
- "Test set: Average loss: 0.0930, Accuracy: 9713/10000 (97%)\n",
+ "Test set: Average loss: 0.0945, Accuracy: 9732/10000 (97%)\n",
"\n",
- "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.031118\n",
- "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.022899\n",
- "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.052135\n",
- "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.047121\n",
- "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.053384\n",
+ "Train Epoch: 4 [0/60000 (0%)]\tLoss: 0.184483\n",
+ "Train Epoch: 4 [12800/60000 (21%)]\tLoss: 0.061680\n",
+ "Train Epoch: 4 [25600/60000 (43%)]\tLoss: 0.044517\n",
+ "Train Epoch: 4 [38400/60000 (64%)]\tLoss: 0.044902\n",
+ "Train Epoch: 4 [51200/60000 (85%)]\tLoss: 0.024778\n",
"\n",
- "Test set: Average loss: 0.0881, Accuracy: 9728/10000 (97%)\n",
+ "Test set: Average loss: 0.0930, Accuracy: 9728/10000 (97%)\n",
"\n"
]
}
@@ -335,7 +263,7 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
@@ -344,7 +272,7 @@
""
]
},
- "execution_count": 12,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
@@ -392,7 +320,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
@@ -424,7 +352,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
@@ -456,7 +384,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
@@ -484,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
@@ -502,9 +430,352 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 11,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[202]' to a numeric value `0`\n",
+ "outside the bounds (0.27941176295280457, 0.3794117867946625).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[203]' to a numeric value `0`\n",
+ "outside the bounds (0.6754902005195618, 0.7754902243614197).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[204]' to a numeric value `0`\n",
+ "outside the bounds (0.5735294222831726, 0.6735294461250305).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[205]' to a numeric value `0`\n",
+ "outside the bounds (0.5421568751335144, 0.6421568989753723).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[206]' to a numeric value `0`\n",
+ "outside the bounds (0.18529412150382996, 0.2852941155433655).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[207]' to a numeric value `0`\n",
+ "outside the bounds (0.09117648005485535, 0.19117647409439087).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[230]' to a numeric value `0`\n",
+ "outside the bounds (0.820588231086731, 0.9205882549285889).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[231]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[232]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[233]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[234]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[235]' to a numeric value `0`\n",
+ "outside the bounds (0.8950980305671692, 0.9950980544090271).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[236]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[237]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[238]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[239]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[240]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[241]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[242]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[243]' to a numeric value `0`\n",
+ "outside the bounds (0.7264705896377563, 0.8264706134796143).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[244]' to a numeric value `0`\n",
+ "outside the bounds (0.6166666746139526, 0.7166666984558105).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[245]' to a numeric value `0`\n",
+ "outside the bounds (0.15392157435417175, 0.2539215683937073).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[258]' to a numeric value `0`\n",
+ "outside the bounds (0.21274511516094208, 0.3127451241016388).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[259]' to a numeric value `0`\n",
+ "outside the bounds (0.3970588147640228, 0.49705883860588074).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[260]' to a numeric value `0`\n",
+ "outside the bounds (0.23235295712947845, 0.33235296607017517).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[261]' to a numeric value `0`\n",
+ "outside the bounds (0.3970588147640228, 0.49705883860588074).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[262]' to a numeric value `0`\n",
+ "outside the bounds (0.5892156958580017, 0.6892157196998596).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[263]' to a numeric value `0`\n",
+ "outside the bounds (0.8401960730552673, 0.9401960968971252).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[264]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[265]' to a numeric value `0`\n",
+ "outside the bounds (0.8323529362678528, 0.9323529601097107).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[266]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[267]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[268]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[269]' to a numeric value `0`\n",
+ "outside the bounds (0.9303921461105347, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[270]' to a numeric value `0`\n",
+ "outside the bounds (0.8480392098426819, 0.9480392336845398).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[271]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[272]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[273]' to a numeric value `0`\n",
+ "outside the bounds (0.4990196228027344, 0.5990196466445923).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[291]' to a numeric value `0`\n",
+ "outside the bounds (0.01666666939854622, 0.11666667461395264).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[292]' to a numeric value `0`\n",
+ "outside the bounds (0.2088235467672348, 0.3088235557079315).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[293]' to a numeric value `0`\n",
+ "outside the bounds (0.004901960492134094, 0.10490196198225021).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[294]' to a numeric value `0`\n",
+ "outside the bounds (0.21274511516094208, 0.3127451241016388).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[295]' to a numeric value `0`\n",
+ "outside the bounds (0.21274511516094208, 0.3127451241016388).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[296]' to a numeric value `0`\n",
+ "outside the bounds (0.21274511516094208, 0.3127451241016388).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[297]' to a numeric value `0`\n",
+ "outside the bounds (0.18137255311012268, 0.2813725471496582).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[298]' to a numeric value `0`\n",
+ "outside the bounds (0.03235294297337532, 0.13235294818878174).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[299]' to a numeric value `0`\n",
+ "outside the bounds (0.8754901885986328, 0.9754902124404907).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[300]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[301]' to a numeric value `0`\n",
+ "outside the bounds (0.3656862676143646, 0.46568629145622253).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[326]' to a numeric value `0`\n",
+ "outside the bounds (0.2754901945590973, 0.3754902184009552).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[327]' to a numeric value `0`\n",
+ "outside the bounds (0.9421568512916565, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[328]' to a numeric value `0`\n",
+ "outside the bounds (0.7696078419685364, 0.8696078658103943).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[329]' to a numeric value `0`\n",
+ "outside the bounds (0.020588237792253494, 0.12058824300765991).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[353]' to a numeric value `0`\n",
+ "outside the bounds (0.036274511367082596, 0.136274516582489).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[354]' to a numeric value `0`\n",
+ "outside the bounds (0.863725483417511, 0.9637255072593689).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[355]' to a numeric value `0`\n",
+ "outside the bounds (0.949999988079071, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[356]' to a numeric value `0`\n",
+ "outside the bounds (0.2754901945590973, 0.3754902184009552).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[381]' to a numeric value `0`\n",
+ "outside the bounds (0.45588237047195435, 0.5558823943138123).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[382]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[383]' to a numeric value `0`\n",
+ "outside the bounds (0.8833333253860474, 0.9833333492279053).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[384]' to a numeric value `0`\n",
+ "outside the bounds (0.12254902720451355, 0.22254902124404907).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[408]' to a numeric value `0`\n",
+ "outside the bounds (0.18137255311012268, 0.2813725471496582).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[409]' to a numeric value `0`\n",
+ "outside the bounds (0.9264705777168274, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[410]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[411]' to a numeric value `0`\n",
+ "outside the bounds (0.1931372582912445, 0.29313725233078003).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[436]' to a numeric value `0`\n",
+ "outside the bounds (0.47156864404678345, 0.5715686678886414).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[437]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[438]' to a numeric value `0`\n",
+ "outside the bounds (0.6833333373069763, 0.7833333611488342).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[464]' to a numeric value `0`\n",
+ "outside the bounds (0.7539215683937073, 0.8539215922355652).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[465]' to a numeric value `0`\n",
+ "outside the bounds (0.9225490093231201, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[466]' to a numeric value `0`\n",
+ "outside the bounds (0.1774509847164154, 0.2774509787559509).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[491]' to a numeric value `0`\n",
+ "outside the bounds (0.44411763548851013, 0.5441176295280457).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[492]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[493]' to a numeric value `0`\n",
+ "outside the bounds (0.6637254953384399, 0.7637255191802979).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[518]' to a numeric value `0`\n",
+ "outside the bounds (0.24411766231060028, 0.344117671251297).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[519]' to a numeric value `0`\n",
+ "outside the bounds (0.9343137145042419, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[520]' to a numeric value `0`\n",
+ "outside the bounds (0.8911764621734619, 0.9911764860153198).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[521]' to a numeric value `0`\n",
+ "outside the bounds (0.17352941632270813, 0.27352941036224365).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[545]' to a numeric value `0`\n",
+ "outside the bounds (0.02450980618596077, 0.12450981140136719).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[546]' to a numeric value `0`\n",
+ "outside the bounds (0.8166666626930237, 0.9166666865348816).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[547]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[548]' to a numeric value `0`\n",
+ "outside the bounds (0.6009804010391235, 0.7009804248809814).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[573]' to a numeric value `0`\n",
+ "outside the bounds (0.7460784316062927, 0.8460784554481506).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[574]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[575]' to a numeric value `0`\n",
+ "outside the bounds (0.8088235259056091, 0.908823549747467).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[576]' to a numeric value `0`\n",
+ "outside the bounds (0.08725491166114807, 0.1872549057006836).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[600]' to a numeric value `0`\n",
+ "outside the bounds (0.0990196168422699, 0.19901961088180542).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[601]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[602]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[603]' to a numeric value `0`\n",
+ "outside the bounds (0.25196078419685364, 0.35196080803871155).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[627]' to a numeric value `0`\n",
+ "outside the bounds (0.07156862318515778, 0.1715686321258545).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[628]' to a numeric value `0`\n",
+ "outside the bounds (0.8284313678741455, 0.9284313917160034).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[629]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[630]' to a numeric value `0`\n",
+ "outside the bounds (0.4009803831577301, 0.5009803771972656).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[655]' to a numeric value `0`\n",
+ "outside the bounds (0.47156864404678345, 0.5715686678886414).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[656]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[657]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[658]' to a numeric value `0`\n",
+ "outside the bounds (0.15392157435417175, 0.2539215683937073).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[682]' to a numeric value `0`\n",
+ "outside the bounds (0.18921568989753723, 0.28921568393707275).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[683]' to a numeric value `0`\n",
+ "outside the bounds (0.8990195989608765, 0.9990196228027344).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[684]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[685]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[686]' to a numeric value `0`\n",
+ "outside the bounds (0.15392157435417175, 0.2539215683937073).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[710]' to a numeric value `0`\n",
+ "outside the bounds (0.42450979351997375, 0.5245097875595093).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[711]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[712]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[713]' to a numeric value `0`\n",
+ "outside the bounds (0.8088235259056091, 0.908823549747467).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[714]' to a numeric value `0`\n",
+ "outside the bounds (0.10686275362968445, 0.20686274766921997).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[738]' to a numeric value `0`\n",
+ "outside the bounds (0.42450979351997375, 0.5245097875595093).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[739]' to a numeric value `0`\n",
+ "outside the bounds (0.9460784196853638, 1.0).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[740]' to a numeric value `0`\n",
+ "outside the bounds (0.7617647051811218, 0.8617647290229797).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n",
+ "WARNING (W1002): Setting Var 'nn.scaled_inputs[741]' to a numeric value `0`\n",
+ "outside the bounds (0.020588237792253494, 0.12058824300765991).\n",
+ " See also https://pyomo.readthedocs.io/en/stable/errors.html#w1002\n"
+ ]
+ }
+ ],
"source": [
"#create pyomo model\n",
"m = pyo.ConcreteModel()\n",
@@ -523,7 +794,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
@@ -540,7 +811,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 13,
"metadata": {
"scrolled": true
},
@@ -550,168 +821,141 @@
"output_type": "stream",
"text": [
"Welcome to the CBC MILP Solver \n",
- "Version: 2.10.5 \n",
- "Build Date: Oct 15 2020 \n",
+ "Version: 2.10.10 \n",
+ "Build Date: Apr 19 2023 \n",
"\n",
- "command line - /home/jhjalvi/anaconda3/envs/tensorflow/bin/cbc -printingOptions all -import /tmp/tmpdwk9ljju.pyomo.lp -stat=1 -solve -solu /tmp/tmpdwk9ljju.pyomo.soln (default strategy 1)\n",
+ "command line - /opt/conda/bin/cbc -printingOptions all -import /tmp/tmpoznymvux.pyomo.lp -stat=1 -solve -solu /tmp/tmpoznymvux.pyomo.soln (default strategy 1)\n",
"Option for printingOptions changed from normal to all\n",
- "Presolve 332 (-1777) rows, 1029 (-1664) columns and 31506 (-14801) elements\n",
+ "Presolve 359 (-1749) rows, 1048 (-1644) columns and 38227 (-8079) elements\n",
"Statistics for presolved model\n",
"Original problem has 100 integers (100 of which binary)\n",
- "Presolved problem has 71 integers (71 of which binary)\n",
- "==== 979 zero objective 51 different\n",
+ "Presolved problem has 74 integers (74 of which binary)\n",
+ "==== 998 zero objective 51 different\n",
"==== absolute objective values 51 different\n",
- "==== for integers 71 zero objective 1 different\n",
- "71 variables have objective of 0\n",
+ "==== for integers 74 zero objective 1 different\n",
+ "74 variables have objective of 0\n",
"==== for integers absolute objective values 1 different\n",
- "71 variables have objective of 0\n",
+ "74 variables have objective of 0\n",
"===== end objective counts\n",
"\n",
"\n",
- "Problem has 332 rows, 1029 columns (50 with objective) and 31506 elements\n",
+ "Problem has 359 rows, 1048 columns (50 with objective) and 38227 elements\n",
"Column breakdown:\n",
- "0 of type 0.0->inf, 759 of type 0.0->up, 0 of type lo->inf, \n",
- "199 of type lo->up, 0 of type free, 0 of type fixed, \n",
- "0 of type -inf->0.0, 0 of type -inf->up, 71 of type 0.0->1.0 \n",
+ "0 of type 0.0->inf, 746 of type 0.0->up, 0 of type lo->inf, \n",
+ "228 of type lo->up, 0 of type free, 0 of type fixed, \n",
+ "0 of type -inf->0.0, 0 of type -inf->up, 74 of type 0.0->1.0 \n",
"Row breakdown:\n",
"0 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, \n",
- "87 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
- "0 of type G other, 174 of type L 0.0, 0 of type L 1.0, \n",
- "71 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
+ "95 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
+ "0 of type G other, 190 of type L 0.0, 0 of type L 1.0, \n",
+ "74 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
"0 of type Free \n",
- "Continuous objective value is -8.70513 - 0.03 seconds\n",
- "Cgl0003I 0 fixed, 0 tightened bounds, 54 strengthened rows, 0 substitutions\n",
- "Cgl0003I 0 fixed, 0 tightened bounds, 1 strengthened rows, 0 substitutions\n",
- "Cgl0004I processed model has 258 rows, 955 columns (63 integer (63 of which binary)) and 52403 elements\n",
- "Cbc0038I Initial state - 45 integers unsatisfied sum - 16.9175\n",
- "Cbc0038I Pass 1: suminf. 6.67376 (26) obj. 8.1543 iterations 369\n",
- "Cbc0038I Pass 2: suminf. 0.00000 (0) obj. 10.7826 iterations 1051\n",
- "Cbc0038I Solution found of 10.7826\n",
- "Cbc0038I Relaxing continuous gives 7.01412\n",
- "Cbc0038I Before mini branch and bound, 18 integers at bound fixed and 356 continuous\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 195 rows 558 columns - 6 fixed gives 189, 552 - still too large\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 167 rows 535 columns - too large\n",
- "Cbc0038I Mini branch and bound did not improve solution (0.35 seconds)\n",
- "Cbc0038I Round again with cutoff of 5.95662\n",
- "Cbc0038I Pass 3: suminf. 7.02236 (29) obj. 5.95662 iterations 60\n",
- "Cbc0038I Pass 4: suminf. 1.95668 (13) obj. 5.95662 iterations 412\n",
- "Cbc0038I Pass 5: suminf. 0.37202 (1) obj. 5.95662 iterations 778\n",
- "Cbc0038I Pass 6: suminf. 0.20711 (1) obj. 5.95662 iterations 35\n",
- "Cbc0038I Pass 7: suminf. 1.75379 (6) obj. 5.95662 iterations 390\n",
- "Cbc0038I Pass 8: suminf. 0.24439 (2) obj. 5.95662 iterations 172\n",
- "Cbc0038I Pass 9: suminf. 0.31716 (1) obj. 5.95662 iterations 129\n",
- "Cbc0038I Pass 10: suminf. 0.15007 (1) obj. 5.95662 iterations 46\n",
- "Cbc0038I Pass 11: suminf. 2.30947 (9) obj. 5.95662 iterations 233\n",
- "Cbc0038I Pass 12: suminf. 0.21814 (1) obj. 5.95662 iterations 221\n",
- "Cbc0038I Pass 13: suminf. 0.37586 (1) obj. 5.95662 iterations 44\n",
- "Cbc0038I Pass 14: suminf. 3.21707 (16) obj. 5.95662 iterations 266\n",
- "Cbc0038I Pass 15: suminf. 2.95868 (15) obj. 5.95662 iterations 20\n",
- "Cbc0038I Pass 16: suminf. 2.95331 (15) obj. 5.95662 iterations 22\n",
- "Cbc0038I Pass 17: suminf. 0.31716 (1) obj. 5.95662 iterations 812\n",
- "Cbc0038I Pass 18: suminf. 0.15007 (1) obj. 5.95662 iterations 39\n",
- "Cbc0038I Pass 19: suminf. 3.13470 (12) obj. 5.95662 iterations 403\n",
- "Cbc0038I Pass 20: suminf. 2.54532 (10) obj. 5.95662 iterations 94\n",
- "Cbc0038I Pass 21: suminf. 5.93299 (26) obj. 5.95662 iterations 340\n",
- "Cbc0038I Pass 22: suminf. 0.28804 (1) obj. 5.95662 iterations 562\n",
- "Cbc0038I Pass 23: suminf. 0.10862 (1) obj. 5.95662 iterations 48\n",
- "Cbc0038I Pass 24: suminf. 2.17321 (10) obj. 5.95662 iterations 298\n",
- "Cbc0038I Pass 25: suminf. 0.28804 (1) obj. 5.95662 iterations 289\n",
- "Cbc0038I Pass 26: suminf. 0.10862 (1) obj. 5.95662 iterations 48\n",
- "Cbc0038I Pass 27: suminf. 3.49937 (14) obj. 5.95662 iterations 289\n",
- "Cbc0038I Pass 28: suminf. 0.37176 (1) obj. 5.95662 iterations 208\n",
- "Cbc0038I Pass 29: suminf. 0.20074 (1) obj. 5.95662 iterations 26\n",
- "Cbc0038I Pass 30: suminf. 0.91276 (5) obj. 5.95662 iterations 120\n",
- "Cbc0038I Pass 31: suminf. 2.70655 (11) obj. 5.95662 iterations 250\n",
- "Cbc0038I Pass 32: suminf. 2.64846 (13) obj. 5.95662 iterations 32\n",
- "Cbc0038I Rounding solution of 6.24461 is better than previous of 7.01412\n",
- "\n",
- "Cbc0038I Before mini branch and bound, 3 integers at bound fixed and 353 continuous\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 200 rows 566 columns - 10 fixed gives 190, 556 - still too large\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 163 rows 534 columns - too large\n",
- "Cbc0038I Mini branch and bound did not improve solution (1.01 seconds)\n",
- "Cbc0038I Round again with cutoff of 4.05313\n",
- "Cbc0038I Pass 32: suminf. 7.70811 (30) obj. 4.05313 iterations 7\n",
- "Cbc0038I Pass 33: suminf. 4.89653 (23) obj. 4.05313 iterations 121\n",
- "Cbc0038I Pass 34: suminf. 4.88223 (23) obj. 4.05313 iterations 23\n",
- "Cbc0038I Pass 35: suminf. 1.20216 (3) obj. 4.05313 iterations 1192\n",
- "Cbc0038I Pass 36: suminf. 0.91596 (3) obj. 4.05313 iterations 61\n",
- "Cbc0038I Pass 37: suminf. 0.90941 (3) obj. 4.05313 iterations 27\n",
- "Cbc0038I Pass 38: suminf. 2.77624 (12) obj. 4.05313 iterations 160\n",
- "Cbc0038I Pass 39: suminf. 1.18048 (3) obj. 4.05313 iterations 356\n",
- "Cbc0038I Pass 40: suminf. 0.86798 (2) obj. 4.05313 iterations 73\n",
- "Cbc0038I Pass 41: suminf. 0.85671 (3) obj. 4.05313 iterations 39\n",
- "Cbc0038I Pass 42: suminf. 2.64239 (10) obj. 4.05313 iterations 103\n",
- "Cbc0038I Pass 43: suminf. 1.20892 (3) obj. 4.05313 iterations 119\n",
- "Cbc0038I Pass 44: suminf. 0.90215 (3) obj. 4.05313 iterations 59\n",
- "Cbc0038I Pass 45: suminf. 0.89043 (3) obj. 4.05313 iterations 43\n",
- "Cbc0038I Pass 46: suminf. 4.06118 (14) obj. 4.05313 iterations 330\n",
- "Cbc0038I Pass 47: suminf. 3.86419 (13) obj. 4.05313 iterations 33\n",
- "Cbc0038I Pass 48: suminf. 0.92252 (3) obj. 4.05313 iterations 242\n",
- "Cbc0038I Pass 49: suminf. 4.29719 (14) obj. 4.05313 iterations 350\n",
- "Cbc0038I Pass 50: suminf. 4.17818 (15) obj. 4.05313 iterations 53\n",
- "Cbc0038I Pass 51: suminf. 0.91809 (3) obj. 4.05313 iterations 188\n",
- "Cbc0038I Pass 52: suminf. 0.89043 (3) obj. 4.05313 iterations 61\n",
- "Cbc0038I Pass 53: suminf. 1.21047 (3) obj. 4.05313 iterations 106\n",
- "Cbc0038I Pass 54: suminf. 0.89965 (3) obj. 4.05313 iterations 72\n",
- "Cbc0038I Pass 55: suminf. 2.95986 (11) obj. 4.05313 iterations 232\n",
- "Cbc0038I Pass 56: suminf. 2.80081 (11) obj. 4.05313 iterations 64\n",
- "Cbc0038I Pass 57: suminf. 0.97287 (3) obj. 4.05313 iterations 198\n",
- "Cbc0038I Pass 58: suminf. 0.95556 (3) obj. 4.05313 iterations 53\n",
- "Cbc0038I Pass 59: suminf. 1.23943 (3) obj. 4.05313 iterations 110\n",
- "Cbc0038I Pass 60: suminf. 0.96379 (3) obj. 4.05313 iterations 73\n",
- "Cbc0038I Pass 61: suminf. 3.20098 (12) obj. 4.05313 iterations 340\n",
+ "Continuous objective value is -8.72226 - 0.06 seconds\n",
+ "Cgl0004I processed model has 298 rows, 987 columns (74 integer (74 of which binary)) and 60742 elements\n",
+ "Cbc0038I Initial state - 57 integers unsatisfied sum - 21.3774\n",
+ "Cbc0038I Pass 1: suminf. 7.64588 (38) obj. 6.842 iterations 304\n",
+ "Cbc0038I Pass 2: suminf. 0.79348 (10) obj. 7.42356 iterations 472\n",
+ "Cbc0038I Solution found of 7.42356\n",
+ "Cbc0038I Relaxing continuous gives 5.71541\n",
+ "Cbc0038I Before mini branch and bound, 16 integers at bound fixed and 528 continuous\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 222 rows 410 columns - 10 fixed gives 212, 400 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 172 rows 366 columns\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.29 seconds)\n",
+ "Cbc0038I Freeing continuous variables gives a solution of 5.71541\n",
+ "Cbc0038I Round again with cutoff of 4.27163\n",
+ "Cbc0038I Pass 3: suminf. 8.08526 (38) obj. 4.27163 iterations 20\n",
+ "Cbc0038I Pass 4: suminf. 4.83676 (28) obj. 4.27163 iterations 252\n",
+ "Cbc0038I Pass 5: suminf. 2.47587 (17) obj. 4.27163 iterations 245\n",
+ "Cbc0038I Pass 6: suminf. 2.46886 (18) obj. 4.27163 iterations 20\n",
+ "Cbc0038I Pass 7: suminf. 0.96572 (8) obj. 4.27163 iterations 144\n",
+ "Cbc0038I Pass 8: suminf. 0.94235 (9) obj. 4.27163 iterations 22\n",
+ "Cbc0038I Pass 9: suminf. 0.53923 (2) obj. 4.27163 iterations 738\n",
+ "Cbc0038I Pass 10: suminf. 0.53608 (2) obj. 4.27163 iterations 23\n",
+ "Cbc0038I Pass 11: suminf. 0.46778 (2) obj. 4.27163 iterations 55\n",
+ "Cbc0038I Pass 12: suminf. 0.42897 (1) obj. 4.27163 iterations 30\n",
+ "Cbc0038I Pass 13: suminf. 3.59727 (16) obj. 4.27163 iterations 200\n",
+ "Cbc0038I Pass 14: suminf. 3.35016 (14) obj. 4.27163 iterations 18\n",
+ "Cbc0038I Pass 15: suminf. 0.53923 (2) obj. 4.27163 iterations 257\n",
+ "Cbc0038I Pass 16: suminf. 0.53608 (2) obj. 4.27163 iterations 23\n",
+ "Cbc0038I Pass 17: suminf. 0.46778 (2) obj. 4.27163 iterations 55\n",
+ "Cbc0038I Pass 18: suminf. 0.42897 (1) obj. 4.27163 iterations 30\n",
+ "Cbc0038I Pass 19: suminf. 2.88596 (13) obj. 4.27163 iterations 313\n",
+ "Cbc0038I Pass 20: suminf. 2.65913 (14) obj. 4.27163 iterations 113\n",
+ "Cbc0038I Pass 21: suminf. 2.24821 (12) obj. 4.27163 iterations 22\n",
+ "Cbc0038I Pass 22: suminf. 0.65487 (3) obj. 4.27163 iterations 314\n",
+ "Cbc0038I Pass 23: suminf. 0.56714 (2) obj. 4.27163 iterations 80\n",
+ "Cbc0038I Pass 24: suminf. 0.53781 (2) obj. 4.27163 iterations 24\n",
+ "Cbc0038I Pass 25: suminf. 0.64231 (2) obj. 4.27163 iterations 46\n",
+ "Cbc0038I Pass 26: suminf. 0.63473 (2) obj. 4.27163 iterations 29\n",
+ "Cbc0038I Pass 27: suminf. 4.08453 (19) obj. 4.27163 iterations 210\n",
+ "Cbc0038I Pass 28: suminf. 3.85029 (20) obj. 4.27163 iterations 28\n",
+ "Cbc0038I Pass 29: suminf. 1.17777 (8) obj. 4.27163 iterations 217\n",
+ "Cbc0038I Pass 30: suminf. 1.17106 (8) obj. 4.27163 iterations 35\n",
+ "Cbc0038I Pass 31: suminf. 0.55352 (4) obj. 4.27163 iterations 168\n",
+ "Cbc0038I Pass 32: suminf. 0.52158 (4) obj. 4.27163 iterations 29\n",
"Cbc0038I No solution found this major pass\n",
- "Cbc0038I Before mini branch and bound, 6 integers at bound fixed and 376 continuous\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 196 rows 538 columns - 6 fixed gives 190, 532 - still too large\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 164 rows 512 columns - too large\n",
- "Cbc0038I Mini branch and bound did not improve solution (1.49 seconds)\n",
- "Cbc0038I After 1.49 seconds - Feasibility pump exiting with objective of 6.24461 - took 1.35 seconds\n",
- "Cbc0012I Integer solution of 6.2446143 found by feasibility pump after 0 iterations and 0 nodes (1.54 seconds)\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 215 rows 912 columns - 30 fixed gives 184, 881 - still too large\n",
- "Cbc0031I 34 added rows had average density of 383.97059\n",
- "Cbc0013I At root node, 34 cuts changed objective from -3.5608111 to 0.11409384 in 100 passes\n",
- "Cbc0014I Cut generator 0 (Probing) - 132 row cuts average 52.4 elements, 0 column cuts (0 active) in 0.083 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 1 (Gomory) - 1147 row cuts average 815.8 elements, 0 column cuts (0 active) in 1.221 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 2 (Knapsack) - 3 row cuts average 3.7 elements, 0 column cuts (0 active) in 0.073 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.003 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 3392 row cuts average 281.4 elements, 0 column cuts (0 active) in 0.717 seconds - new frequency is 1\n",
- "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 1.634 seconds - new frequency is -100\n",
- "Cbc0014I Cut generator 6 (TwoMirCuts) - 290 row cuts average 367.1 elements, 0 column cuts (0 active) in 0.132 seconds - new frequency is 1\n",
- "Cbc0010I After 0 nodes, 1 on tree, 6.2446143 best solution, best possible 0.11409384 (9.72 seconds)\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 215 rows 910 columns - 18 fixed gives 195, 890 - still too large\n",
- "Cbc0038I Full problem 258 rows 955 columns, reduced to 212 rows 907 columns - 18 fixed gives 192, 887 - still too large\n",
- "Cbc0001I Search completed - best objective 6.244614291599583, took 69539 iterations and 156 nodes (29.56 seconds)\n",
- "Cbc0032I Strong branching done 1052 times (53050 iterations), fathomed 1 nodes and fixed 0 variables\n",
- "Cbc0035I Maximum depth 31, 2 variables fixed on reduced cost\n",
- "Cuts at root node changed objective from -3.56081 to 0.114094\n",
- "Probing was tried 100 times and created 132 cuts of which 0 were active after adding rounds of cuts (0.083 seconds)\n",
- "Gomory was tried 100 times and created 1147 cuts of which 0 were active after adding rounds of cuts (1.221 seconds)\n",
- "Knapsack was tried 100 times and created 3 cuts of which 0 were active after adding rounds of cuts (0.073 seconds)\n",
- "Clique was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.003 seconds)\n",
- "MixedIntegerRounding2 was tried 446 times and created 11432 cuts of which 0 were active after adding rounds of cuts (3.153 seconds)\n",
- "FlowCover was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (1.634 seconds)\n",
- "TwoMirCuts was tried 446 times and created 290 cuts of which 0 were active after adding rounds of cuts (0.406 seconds)\n",
+ "Cbc0038I Before mini branch and bound, 9 integers at bound fixed and 388 continuous\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 230 rows 554 columns - 38 fixed gives 192, 516 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 165 rows 495 columns - too large\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.77 seconds)\n",
+ "Cbc0038I After 0.77 seconds - Feasibility pump exiting with objective of 5.71541 - took 0.65 seconds\n",
+ "Cbc0012I Integer solution of 5.7154098 found by feasibility pump after 0 iterations and 0 nodes (0.84 seconds)\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 245 rows 934 columns - 38 fixed gives 206, 895 - still too large\n",
+ "Cbc0031I 65 added rows had average density of 239.36923\n",
+ "Cbc0013I At root node, 65 cuts changed objective from -8.72226 to -4.1250558 in 36 passes\n",
+ "Cbc0014I Cut generator 0 (Probing) - 7 row cuts average 255.9 elements, 0 column cuts (18 active) in 0.021 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 1 (Gomory) - 115 row cuts average 671.1 elements, 0 column cuts (0 active) in 0.402 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 2 (Knapsack) - 2 row cuts average 7.0 elements, 0 column cuts (0 active) in 0.032 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.001 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 1363 row cuts average 255.4 elements, 0 column cuts (0 active) in 0.207 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.490 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 6 (TwoMirCuts) - 152 row cuts average 379.7 elements, 0 column cuts (0 active) in 0.063 seconds - new frequency is 1\n",
+ "Cbc0010I After 0 nodes, 1 on tree, 5.7154098 best solution, best possible -4.1250558 (3.78 seconds)\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 257 rows 946 columns - 31 fixed gives 225, 914 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 261 rows 950 columns - 28 fixed gives 229, 918 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 185 rows 660 columns - 21 fixed gives 171, 643 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 155 rows 631 columns - too large\n",
+ "Cbc0012I Integer solution of 5.1974607 found by rounding after 49300 iterations and 229 nodes (18.59 seconds)\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 201 rows 596 columns - 23 fixed gives 178, 573 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 150 rows 550 columns - too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 209 rows 525 columns - 24 fixed gives 185, 501 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 200 rows 516 columns - 25 fixed gives 175, 491 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 213 rows 529 columns - 28 fixed gives 185, 501 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 210 rows 525 columns - 27 fixed gives 183, 498 - still too large\n",
+ "Cbc0038I Full problem 298 rows 987 columns, reduced to 157 rows 479 columns - too large\n",
+ "Cbc0010I After 1000 nodes, 24 on tree, 5.1974607 best solution, best possible -4.1250558 (37.52 seconds)\n",
+ "Cbc0001I Search completed - best objective 5.197460746436284, took 152711 iterations and 1107 nodes (42.02 seconds)\n",
+ "Cbc0032I Strong branching done 2298 times (79340 iterations), fathomed 33 nodes and fixed 3 variables\n",
+ "Cbc0035I Maximum depth 54, 2 variables fixed on reduced cost\n",
+ "Cuts at root node changed objective from -8.72226 to -4.12506\n",
+ "Probing was tried 36 times and created 7 cuts of which 18 were active after adding rounds of cuts (0.021 seconds)\n",
+ "Gomory was tried 36 times and created 115 cuts of which 0 were active after adding rounds of cuts (0.402 seconds)\n",
+ "Knapsack was tried 36 times and created 2 cuts of which 0 were active after adding rounds of cuts (0.032 seconds)\n",
+ "Clique was tried 36 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\n",
+ "MixedIntegerRounding2 was tried 1273 times and created 32180 cuts of which 0 were active after adding rounds of cuts (6.526 seconds)\n",
+ "FlowCover was tried 36 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.490 seconds)\n",
+ "TwoMirCuts was tried 1273 times and created 154 cuts of which 0 were active after adding rounds of cuts (0.787 seconds)\n",
"ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"\n",
"Result - Optimal solution found\n",
"\n",
- "Objective value: 6.24461429\n",
- "Enumerated nodes: 156\n",
- "Total iterations: 69539\n",
- "Time (CPU seconds): 29.77\n",
- "Time (Wallclock seconds): 31.00\n",
+ "Objective value: 5.19746075\n",
+ "Enumerated nodes: 1107\n",
+ "Total iterations: 152711\n",
+ "Time (CPU seconds): 42.15\n",
+ "Time (Wallclock seconds): 43.81\n",
"\n",
- "Total time (CPU seconds): 29.80 (Wallclock seconds): 31.04\n",
+ "Total time (CPU seconds): 42.27 (Wallclock seconds): 43.87\n",
"\n"
]
},
{
"data": {
"text/plain": [
- "{'Problem': [{'Name': 'unknown', 'Lower bound': 6.24461429, 'Upper bound': 6.24461429, 'Number of objectives': 1, 'Number of constraints': 332, 'Number of variables': 1029, 'Number of binary variables': 100, 'Number of integer variables': 100, 'Number of nonzeros': 50, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 29.8, 'Wallclock time': 31.04, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': 156, 'Number of created subproblems': 156}, 'Black box': {'Number of iterations': 69539}}, 'Error rc': 0, 'Time': 31.065782070159912}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}"
+ "{'Problem': [{'Name': 'unknown', 'Lower bound': 5.19746075, 'Upper bound': 5.19746075, 'Number of objectives': 1, 'Number of constraints': 359, 'Number of variables': 1048, 'Number of binary variables': 100, 'Number of integer variables': 100, 'Number of nonzeros': 50, 'Sense': 'minimize'}], 'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 42.27, 'Wallclock time': 43.87, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': 1107, 'Number of created subproblems': 1107}, 'Black box': {'Number of iterations': 152711}}, 'Error rc': 0, 'Time': 43.88876152038574}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}"
]
},
- "execution_count": 19,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -740,7 +984,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/docs/notebooks/neuralnet/neural_network_formulations.ipynb b/docs/notebooks/neuralnet/neural_network_formulations.ipynb
index e0e87a7f..3317acd9 100644
--- a/docs/notebooks/neuralnet/neural_network_formulations.ipynb
+++ b/docs/notebooks/neuralnet/neural_network_formulations.ipynb
@@ -2,6 +2,7 @@
"cells": [
{
"cell_type": "markdown",
+ "id": "d37935f9",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -20,6 +21,7 @@
},
{
"cell_type": "markdown",
+ "id": "ed87cc4f",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -42,13 +44,23 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 1,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2024-05-16 17:40:18.850942: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-05-16 17:40:18.880227: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"#Start by importing the following libraries\n",
"#data manipulation and plotting\n",
@@ -79,6 +91,7 @@
},
{
"cell_type": "markdown",
+ "id": "69a90a72",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -90,6 +103,7 @@
},
{
"cell_type": "markdown",
+ "id": "0aea62be",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -101,7 +115,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 2,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -114,6 +128,7 @@
},
{
"cell_type": "markdown",
+ "id": "d02d89ff",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -125,7 +140,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 3,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -134,14 +149,12 @@
"outputs": [
{
"data": {
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"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -174,6 +187,7 @@
},
{
"cell_type": "markdown",
+ "id": "2945e68c",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -190,7 +204,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 4,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -200,7 +214,7 @@
"source": [
"#sigmoid neural network\n",
"nn1 = Sequential(name='sin_wave_sigmoid')\n",
- "nn1.add(Input(1))\n",
+ "nn1.add(Input(np.array((1,))))\n",
"nn1.add(Dense(50, activation='sigmoid'))\n",
"nn1.add(Dense(50, activation='sigmoid'))\n",
"nn1.add(Dense(1))\n",
@@ -208,7 +222,7 @@
"\n",
"#relu neural network\n",
"nn2 = Sequential(name='sin_wave_relu')\n",
- "nn2.add(Input(1))\n",
+ "nn2.add(Input(np.array((1,))))\n",
"nn2.add(Dense(30, activation='relu'))\n",
"nn2.add(Dense(30, activation='relu'))\n",
"nn2.add(Dense(1))\n",
@@ -216,7 +230,7 @@
"\n",
"#mixed neural network\n",
"nn3 = Sequential(name='sin_wave_mixed')\n",
- "nn3.add(Input(1))\n",
+ "nn3.add(Input(np.array((1,))))\n",
"nn3.add(Dense(50, activation='sigmoid'))\n",
"nn3.add(Dense(50, activation='relu'))\n",
"nn3.add(Dense(1))\n",
@@ -225,7 +239,7 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 5,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -237,611 +251,606 @@
"output_type": "stream",
"text": [
"Epoch 1/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 1.0157\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 1.0194 \n",
"Epoch 2/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.9936\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 931us/step - loss: 0.9805\n",
"Epoch 3/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.9983\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 952us/step - loss: 0.9615\n",
"Epoch 4/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.9876\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 930us/step - loss: 0.8894\n",
"Epoch 5/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.9527\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.5624\n",
"Epoch 6/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.7003\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 948us/step - loss: 0.2872\n",
"Epoch 7/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.3571\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.2425\n",
"Epoch 8/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.2592\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 913us/step - loss: 0.2330\n",
"Epoch 9/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2364\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 909us/step - loss: 0.2218\n",
"Epoch 10/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2198\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 954us/step - loss: 0.2045\n",
"Epoch 11/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.2056\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 893us/step - loss: 0.1906\n",
"Epoch 12/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1914\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1763\n",
"Epoch 13/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1749\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 913us/step - loss: 0.1604\n",
"Epoch 14/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1572\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 913us/step - loss: 0.1417\n",
"Epoch 15/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1388\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 928us/step - loss: 0.1264\n",
"Epoch 16/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1219\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 945us/step - loss: 0.1102\n",
"Epoch 17/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1076\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 916us/step - loss: 0.0953\n",
"Epoch 18/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0969\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0812\n",
"Epoch 19/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0897\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0681\n",
"Epoch 20/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0844\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 951us/step - loss: 0.0575\n",
"Epoch 21/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0800\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0495\n",
"Epoch 22/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0778\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0410\n",
"Epoch 23/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0751\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0359\n",
"Epoch 24/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0719\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0314 \n",
"Epoch 25/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0685\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0269\n",
"Epoch 26/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0654\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 964us/step - loss: 0.0218\n",
"Epoch 27/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0609\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 0.0186\n",
"Epoch 28/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0561\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0147\n",
"Epoch 29/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0503\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0110\n",
"Epoch 30/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0434\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0084\n",
"Epoch 31/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0358\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0062\n",
"Epoch 32/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0277\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - loss: 0.0047\n",
"Epoch 33/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0204\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0040 \n",
"Epoch 34/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0144\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 935us/step - loss: 0.0033\n",
"Epoch 35/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0098\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 935us/step - loss: 0.0029\n",
"Epoch 36/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0065\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 901us/step - loss: 0.0026\n",
"Epoch 37/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0045\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0024\n",
"Epoch 38/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0033\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 945us/step - loss: 0.0021\n",
"Epoch 39/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0026\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0020\n",
"Epoch 40/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0022\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 914us/step - loss: 0.0019\n",
"Epoch 41/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0020\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0017\n",
"Epoch 42/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0019\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.0016\n",
"Epoch 43/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0017\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.0015\n",
"Epoch 44/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0015\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 944us/step - loss: 0.0014\n",
"Epoch 45/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0014\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 922us/step - loss: 0.0012\n",
"Epoch 46/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.0012 \n",
"Epoch 47/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0011\n",
"Epoch 48/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 896us/step - loss: 0.0011\n",
"Epoch 49/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 898us/step - loss: 0.0010\n",
"Epoch 50/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0010\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0010 \n",
"Epoch 51/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 9.6712e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 9.4040e-04\n",
"Epoch 52/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 9.4382e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 906us/step - loss: 9.5708e-04\n",
"Epoch 53/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 9.0115e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 8.4537e-04\n",
"Epoch 54/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 9.0252e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 911us/step - loss: 8.2021e-04\n",
"Epoch 55/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 8.2970e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 8.6811e-04\n",
"Epoch 56/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 8.1398e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 982us/step - loss: 8.1609e-04\n",
"Epoch 57/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 8.7276e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 893us/step - loss: 8.3628e-04\n",
"Epoch 58/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.5446e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 867us/step - loss: 7.5957e-04\n",
"Epoch 59/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.5136e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 923us/step - loss: 7.4459e-04\n",
"Epoch 60/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.5220e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 889us/step - loss: 8.4700e-04\n",
"Epoch 61/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.3402e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.2618e-04\n",
"Epoch 62/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.0150e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.1441e-04\n",
"Epoch 63/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.0766e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 948us/step - loss: 6.9701e-04\n",
"Epoch 64/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.0312e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 905us/step - loss: 7.5714e-04\n",
"Epoch 65/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.3476e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 934us/step - loss: 7.1533e-04\n",
"Epoch 66/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.2482e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 914us/step - loss: 7.1795e-04\n",
"Epoch 67/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.8576e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 949us/step - loss: 7.3062e-04\n",
"Epoch 68/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.7042e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 7.1659e-04\n",
"Epoch 69/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.2495e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 897us/step - loss: 6.6398e-04\n",
"Epoch 70/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.5771e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 878us/step - loss: 7.1466e-04\n",
"Epoch 71/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 7.0572e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 6.4845e-04 \n",
"Epoch 72/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.6288e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.0734e-04\n",
"Epoch 73/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.4062e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 6.9322e-04\n",
"Epoch 74/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 6.8181e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 7.5916e-04\n",
"Epoch 75/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 6.2752e-04\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 956us/step - loss: 6.8370e-04\n",
"Epoch 1/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.4294\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 897us/step - loss: 0.5942\n",
"Epoch 2/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.1710\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1814\n",
"Epoch 3/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.1113\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 979us/step - loss: 0.1477\n",
"Epoch 4/75\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.0904\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 882us/step - loss: 0.1218\n",
"Epoch 5/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0826\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 858us/step - loss: 0.0996\n",
"Epoch 6/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0759\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 892us/step - loss: 0.0865\n",
"Epoch 7/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0738\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 946us/step - loss: 0.0764\n",
"Epoch 8/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0713\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 950us/step - loss: 0.0735\n",
"Epoch 9/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0696\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.0699\n",
"Epoch 10/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0703\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.0743\n",
"Epoch 11/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0681\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 888us/step - loss: 0.0679\n",
"Epoch 12/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0686\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 915us/step - loss: 0.0671\n",
"Epoch 13/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0668\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 0.0694\n",
"Epoch 14/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0668\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 911us/step - loss: 0.0689\n",
"Epoch 15/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0673\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 860us/step - loss: 0.0677\n",
"Epoch 16/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0666\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 878us/step - loss: 0.0661\n",
"Epoch 17/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0667\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 879us/step - loss: 0.0659\n",
"Epoch 18/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0668\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 937us/step - loss: 0.0672\n",
"Epoch 19/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0662\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0667\n",
"Epoch 20/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0666\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0660\n",
"Epoch 21/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0670\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0697\n",
"Epoch 22/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0670\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0677 \n",
"Epoch 23/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0671\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 969us/step - loss: 0.0676\n",
"Epoch 24/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0670\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 911us/step - loss: 0.0672\n",
"Epoch 25/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0671\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 908us/step - loss: 0.0654\n",
"Epoch 26/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0663\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0658\n",
"Epoch 27/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0668\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 985us/step - loss: 0.0645\n",
"Epoch 28/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0663\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 945us/step - loss: 0.0648\n",
"Epoch 29/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0661\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 913us/step - loss: 0.0642\n",
"Epoch 30/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0661\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 909us/step - loss: 0.0686\n",
"Epoch 31/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0645\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 944us/step - loss: 0.0687\n",
"Epoch 32/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0610\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0641\n",
"Epoch 33/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0533\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 925us/step - loss: 0.0635\n",
"Epoch 34/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0413\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0608\n",
"Epoch 35/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0264\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 990us/step - loss: 0.0518\n",
"Epoch 36/75\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0139\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0431\n",
"Epoch 37/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0067\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 982us/step - loss: 0.0313\n",
"Epoch 38/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0034\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 902us/step - loss: 0.0236\n",
"Epoch 39/75\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0022\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 871us/step - loss: 0.0158\n",
"Epoch 40/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0016\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 882us/step - loss: 0.0126\n",
"Epoch 41/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0014\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 932us/step - loss: 0.0079\n",
"Epoch 42/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - loss: 0.0055\n",
"Epoch 43/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0042\n",
"Epoch 44/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 943us/step - loss: 0.0033\n",
"Epoch 45/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0024\n",
"Epoch 46/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 929us/step - loss: 0.0021\n",
"Epoch 47/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 908us/step - loss: 0.0016\n",
"Epoch 48/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0017\n",
"Epoch 49/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 916us/step - loss: 0.0015\n",
"Epoch 50/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0011 \n",
"Epoch 51/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 910us/step - loss: 0.0011\n",
"Epoch 52/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 886us/step - loss: 0.0014\n",
"Epoch 53/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0012\n",
"Epoch 54/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 890us/step - loss: 9.7089e-04\n",
"Epoch 55/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 905us/step - loss: 0.0010 \n",
"Epoch 56/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 902us/step - loss: 9.9088e-04\n",
"Epoch 57/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011\n",
"Epoch 58/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 917us/step - loss: 0.0011 \n",
"Epoch 59/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 895us/step - loss: 9.8774e-04\n",
"Epoch 60/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 880us/step - loss: 0.0010 \n",
"Epoch 61/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011\n",
"Epoch 62/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 897us/step - loss: 9.7171e-04\n",
"Epoch 63/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0013\n",
"Epoch 64/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0013\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 890us/step - loss: 0.0012\n",
"Epoch 65/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 899us/step - loss: 0.0011 \n",
"Epoch 66/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 9.6319e-04\n",
"Epoch 67/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0012\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 906us/step - loss: 9.7450e-04\n",
"Epoch 68/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011\n",
"Epoch 69/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 940us/step - loss: 0.0011 \n",
"Epoch 70/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0011\n",
"Epoch 71/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 949us/step - loss: 9.4181e-04\n",
"Epoch 72/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 933us/step - loss: 0.0012\n",
"Epoch 73/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0010\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 9.3774e-04\n",
"Epoch 74/75\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 898us/step - loss: 0.0010 \n",
"Epoch 75/75\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0011\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 898us/step - loss: 0.0011\n",
"Epoch 1/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.9257\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 929us/step - loss: 0.9351\n",
"Epoch 2/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.4758\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.4725 \n",
"Epoch 3/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2841\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 952us/step - loss: 0.2493\n",
"Epoch 4/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2647\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 990us/step - loss: 0.2212\n",
"Epoch 5/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2351\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 932us/step - loss: 0.1980\n",
"Epoch 6/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.2086\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 911us/step - loss: 0.1884\n",
"Epoch 7/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1918\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 938us/step - loss: 0.1818\n",
"Epoch 8/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1843\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 971us/step - loss: 0.1816\n",
"Epoch 9/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1809\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 969us/step - loss: 0.1847\n",
"Epoch 10/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.1806\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 928us/step - loss: 0.1817\n",
"Epoch 11/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1798\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 922us/step - loss: 0.1811\n",
"Epoch 12/150\n",
- "313/313 [==============================] - 2s 6ms/step - loss: 0.1802\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1821\n",
"Epoch 13/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1789\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 954us/step - loss: 0.1790\n",
"Epoch 14/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1789\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 940us/step - loss: 0.1801\n",
"Epoch 15/150\n",
- "313/313 [==============================] - 2s 6ms/step - loss: 0.1784\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1783 \n",
"Epoch 16/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1780\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1804 \n",
"Epoch 17/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1772\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1786\n",
"Epoch 18/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1776\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 948us/step - loss: 0.1781\n",
"Epoch 19/150\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.1756\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1764\n",
"Epoch 20/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1742\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 944us/step - loss: 0.1828\n",
"Epoch 21/150\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.1736\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 931us/step - loss: 0.1805\n",
"Epoch 22/150\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.1717\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1772 \n",
"Epoch 23/150\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.1715\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 952us/step - loss: 0.1793\n",
"Epoch 24/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.1704\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - loss: 0.1790\n",
"Epoch 25/150\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.1695\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 963us/step - loss: 0.1811\n",
"Epoch 26/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1677\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 939us/step - loss: 0.1798\n",
"Epoch 27/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1666\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.1749\n",
"Epoch 28/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1660\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1773\n",
"Epoch 29/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1648\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 964us/step - loss: 0.1751\n",
"Epoch 30/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1633\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1705\n",
"Epoch 31/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1622\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1676\n",
"Epoch 32/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1605\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 989us/step - loss: 0.1626\n",
"Epoch 33/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1587\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 967us/step - loss: 0.1613\n",
"Epoch 34/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1577\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 920us/step - loss: 0.1602\n",
"Epoch 35/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1567\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 988us/step - loss: 0.1542\n",
"Epoch 36/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1559\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 0.1524\n",
"Epoch 37/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1529\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1448\n",
"Epoch 38/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1508\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 932us/step - loss: 0.1402\n",
"Epoch 39/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1462\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 982us/step - loss: 0.1398\n",
"Epoch 40/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1406\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 930us/step - loss: 0.1347\n",
"Epoch 41/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1325\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1303\n",
"Epoch 42/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1183\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1289 \n",
"Epoch 43/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.1054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 940us/step - loss: 0.1210\n",
"Epoch 44/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0913\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 918us/step - loss: 0.1165\n",
"Epoch 45/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0775\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 946us/step - loss: 0.1151\n",
"Epoch 46/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0660\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1131\n",
"Epoch 47/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0562\n",
- "Epoch 48/150\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0481\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.1112\n",
+ "Epoch 48/150\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.1136\n",
"Epoch 49/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0406\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 960us/step - loss: 0.1092\n",
"Epoch 50/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 947us/step - loss: 0.1071\n",
"Epoch 51/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 910us/step - loss: 0.1031\n",
"Epoch 52/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0262\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 918us/step - loss: 0.1011\n",
"Epoch 53/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step - loss: 0.1005\n",
"Epoch 54/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 919us/step - loss: 0.0978\n",
"Epoch 55/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 935us/step - loss: 0.0915\n",
"Epoch 56/150\n",
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+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0962\n",
"Epoch 57/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0206\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 913us/step - loss: 0.0900\n",
"Epoch 58/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0191\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0883\n",
"Epoch 59/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0187\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 944us/step - loss: 0.0872\n",
"Epoch 60/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0193\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0845\n",
"Epoch 61/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0185\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 937us/step - loss: 0.0812\n",
"Epoch 62/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0178\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 924us/step - loss: 0.0816\n",
"Epoch 63/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0180\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 908us/step - loss: 0.0792\n",
"Epoch 64/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0178\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 939us/step - loss: 0.0766\n",
"Epoch 65/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0170\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 932us/step - loss: 0.0779\n",
"Epoch 66/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0168\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0759\n",
"Epoch 67/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0169\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.0706\n",
"Epoch 68/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0160\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 981us/step - loss: 0.0689\n",
"Epoch 69/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0164\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 974us/step - loss: 0.0653\n",
"Epoch 70/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0154\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0644\n",
"Epoch 71/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0155\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0612 \n",
"Epoch 72/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0153\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 918us/step - loss: 0.0574\n",
"Epoch 73/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0146\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 916us/step - loss: 0.0580\n",
"Epoch 74/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0140\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 930us/step - loss: 0.0561\n",
"Epoch 75/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0141\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0534\n",
"Epoch 76/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0138\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0525\n",
"Epoch 77/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0137\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 933us/step - loss: 0.0495\n",
"Epoch 78/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0132\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 886us/step - loss: 0.0449\n",
"Epoch 79/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0134\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 901us/step - loss: 0.0457\n",
"Epoch 80/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0130\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 924us/step - loss: 0.0420\n",
"Epoch 81/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0123\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0387\n",
"Epoch 82/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0125\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0364\n",
"Epoch 83/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0119\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 930us/step - loss: 0.0326\n",
"Epoch 84/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0119\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 939us/step - loss: 0.0334\n",
"Epoch 85/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0113\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 948us/step - loss: 0.0293\n",
"Epoch 86/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0113\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0283\n",
"Epoch 87/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0109\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0276\n",
"Epoch 88/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0105\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 944us/step - loss: 0.0254\n",
"Epoch 89/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0102\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 960us/step - loss: 0.0243\n",
"Epoch 90/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0100\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 944us/step - loss: 0.0224\n",
"Epoch 91/150\n",
- "313/313 [==============================] - 2s 5ms/step - loss: 0.0103\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0212\n",
"Epoch 92/150\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.0096\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 989us/step - loss: 0.0200\n",
"Epoch 93/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0100\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 987us/step - loss: 0.0190\n",
"Epoch 94/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0090\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 949us/step - loss: 0.0185\n",
"Epoch 95/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0091\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 915us/step - loss: 0.0169\n",
"Epoch 96/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0091\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 987us/step - loss: 0.0157\n",
"Epoch 97/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0090\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 999us/step - loss: 0.0159\n",
"Epoch 98/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0086\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 958us/step - loss: 0.0155\n",
"Epoch 99/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0085\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 914us/step - loss: 0.0148\n",
"Epoch 100/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0084\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 929us/step - loss: 0.0132\n",
"Epoch 101/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0086\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 926us/step - loss: 0.0148\n",
"Epoch 102/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0082\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0138\n",
"Epoch 103/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0073\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 934us/step - loss: 0.0127\n",
"Epoch 104/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0084\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0128\n",
"Epoch 105/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0073\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.0120\n",
"Epoch 106/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0074\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 937us/step - loss: 0.0123\n",
"Epoch 107/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0069\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 914us/step - loss: 0.0114\n",
"Epoch 108/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0068\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 966us/step - loss: 0.0120\n",
"Epoch 109/150\n",
- "313/313 [==============================] - 1s 5ms/step - loss: 0.0071\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0110\n",
"Epoch 110/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0063\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 913us/step - loss: 0.0112\n",
"Epoch 111/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0064\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 994us/step - loss: 0.0110\n",
"Epoch 112/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0062\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0112\n",
"Epoch 113/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0062\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 921us/step - loss: 0.0109\n",
"Epoch 114/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0064\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0106\n",
"Epoch 115/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0060\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 942us/step - loss: 0.0111\n",
"Epoch 116/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0057\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0117\n",
"Epoch 117/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0059\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 926us/step - loss: 0.0118\n",
"Epoch 118/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 0.0107\n",
"Epoch 119/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0096\n",
"Epoch 120/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0099\n",
"Epoch 121/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 916us/step - loss: 0.0103\n",
"Epoch 122/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0050\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0097\n",
"Epoch 123/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0051\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 948us/step - loss: 0.0108\n",
"Epoch 124/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0054\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 920us/step - loss: 0.0099\n",
"Epoch 125/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0050\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0104\n",
"Epoch 126/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0053\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 902us/step - loss: 0.0111\n",
"Epoch 127/150\n",
- "313/313 [==============================] - 0s 2ms/step - loss: 0.0046\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0101\n",
"Epoch 128/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0047\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.0100\n",
"Epoch 129/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0048\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.0095\n",
"Epoch 130/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0046\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 923us/step - loss: 0.0099\n",
"Epoch 131/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0047\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 911us/step - loss: 0.0095\n",
"Epoch 132/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0044\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0107\n",
"Epoch 133/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0044\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 916us/step - loss: 0.0100\n",
"Epoch 134/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0041\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0108\n",
"Epoch 135/150\n",
- "313/313 [==============================] - 1s 4ms/step - loss: 0.0045\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 917us/step - loss: 0.0099\n",
"Epoch 136/150\n",
- "313/313 [==============================] - 1s 3ms/step - loss: 0.0044\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 942us/step - loss: 0.0094\n",
"Epoch 137/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0043\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0094\n",
"Epoch 138/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0045\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0097\n",
"Epoch 139/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0042\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 943us/step - loss: 0.0094\n",
"Epoch 140/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0039\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 912us/step - loss: 0.0095\n",
"Epoch 141/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0040\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 962us/step - loss: 0.0092\n",
"Epoch 142/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0042\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 936us/step - loss: 0.0088\n",
"Epoch 143/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0040\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 942us/step - loss: 0.0089\n",
"Epoch 144/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0039\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 931us/step - loss: 0.0102\n",
"Epoch 145/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0041\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.0089\n",
"Epoch 146/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0040\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 931us/step - loss: 0.0092\n",
"Epoch 147/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0039\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 927us/step - loss: 0.0091\n",
"Epoch 148/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0040\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1ms/step - loss: 0.0096\n",
"Epoch 149/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0045\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 923us/step - loss: 0.0090\n",
"Epoch 150/150\n",
- "313/313 [==============================] - 1s 2ms/step - loss: 0.0038\n"
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 929us/step - loss: 0.0087\n"
+
]
}
],
@@ -854,6 +863,7 @@
},
{
"cell_type": "markdown",
+ "id": "0a92e066",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -866,13 +876,23 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 6,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 828us/step\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 827us/step\n",
+ "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
+ ]
+ }
+ ],
"source": [
"#note: we calculate the unscaled output for each neural network to check the predictions\n",
"#nn1\n",
@@ -890,7 +910,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 7,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -899,14 +919,12 @@
"outputs": [
{
"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"
}
],
@@ -924,6 +942,7 @@
},
{
"cell_type": "markdown",
+ "id": "d41b8e3c",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -981,6 +1000,7 @@
},
{
"cell_type": "markdown",
+ "id": "6bddc73d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1015,6 +1035,7 @@
},
{
"cell_type": "markdown",
+ "id": "26e4475d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1029,7 +1050,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 8,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1040,7 +1061,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\n",
+ "\n",
"Scaled input bounds: {0: (-1.7317910151019957, 1.7317910151019957)}\n"
]
}
@@ -1061,6 +1082,7 @@
},
{
"cell_type": "markdown",
+ "id": "1ecd8d9d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1076,7 +1098,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 9,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1088,15 +1110,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt trunk: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
" Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
- " For more information visit http://projects.coin-or.org/Ipopt\n",
+ " For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version trunk, running with linear solver ma27.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 10\n",
"Number of nonzeros in inequality constraint Jacobian.: 0\n",
@@ -1113,41 +1135,48 @@
" inequality constraints with only upper bounds: 0\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 0 0.0000000e+00 1.38e+00 3.78e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
- " 1 -9.5106884e+00 9.82e+00 1.05e+01 -1.0 1.30e+01 - 4.30e-01 7.32e-01f 1\n",
- " 2 2.9457246e+00 5.80e-02 5.51e+00 -1.0 1.25e+01 - 1.74e-01 1.00e+00h 1\n",
- " 3 -2.7063957e+00 3.38e+00 1.27e+00 -1.0 5.65e+00 - 1.00e+00 1.00e+00f 1\n",
- " 4 -2.4280958e+00 2.84e+00 3.22e+02 -1.0 2.09e+00 2.0 1.00e+00 2.07e-01h 2\n",
- " 5 1.4877467e+00 2.89e-05 3.51e+00 -1.0 3.92e+00 - 1.00e+00 1.00e+00h 1\n",
- " 6 1.1574839e+00 1.25e-01 2.24e-01 -1.0 3.30e-01 - 1.00e+00 1.00e+00f 1\n",
- " 7 1.3301105e+00 3.30e-06 1.78e-06 -1.7 1.73e-01 - 1.00e+00 1.00e+00h 1\n",
- " 8 1.3299507e+00 5.88e-05 3.08e-05 -3.8 2.78e-03 - 1.00e+00 1.00e+00h 1\n",
- " 9 1.3300317e+00 1.01e-08 5.11e-09 -5.7 8.11e-05 - 1.00e+00 1.00e+00h 1\n",
+ " 0 0.0000000e+00 1.38e+00 3.79e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ " 1 -9.7109089e+00 9.94e+00 9.96e+00 -1.0 1.33e+01 - 4.30e-01 7.33e-01f 1\n",
+ " 2 3.0399169e+00 1.08e-01 5.32e+00 -1.0 1.28e+01 - 1.79e-01 1.00e+00h 1\n",
+ " 3 -4.8527966e+00 4.82e+00 3.49e+00 -1.0 7.89e+00 - 5.97e-01 1.00e+00f 1\n",
+ " 4 -1.1738476e+01 1.11e+01 2.82e+01 -1.0 7.46e+01 0.0 3.43e-02 9.23e-02f 1\n",
+ " 5 4.0647885e+00 6.99e-01 1.31e+01 -1.0 1.58e+01 - 1.00e+00 1.00e+00h 1\n",
+ " 6 2.5376230e+00 6.70e-02 2.88e+00 -1.0 1.53e+00 -0.5 8.80e-01 1.00e+00f 1\n",
+ " 7 -2.1307021e+01 1.85e+01 5.65e+00 -1.0 3.46e+01 - 2.92e-01 6.88e-01f 1\n",
+ " 8 -3.4923942e+01 2.79e+01 8.07e+00 -1.0 2.09e+01 -1.0 9.12e-03 6.50e-01f 1\n",
+ " 9 5.4733784e+00 3.32e+00 9.32e+00 -1.0 4.04e+01 - 1.00e+00 1.00e+00h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 10 1.3300318e+00 5.24e-13 2.62e-13 -8.6 2.62e-07 - 1.00e+00 1.00e+00h 1\n",
+ " 10 6.2585992e+00 1.93e+00 8.03e+00 -1.0 1.69e+00 0.4 1.00e+00 1.00e+00h 1\n",
+ " 11 3.6221165e+00 5.40e-04 2.88e-01 -1.0 2.64e+00 - 1.00e+00 1.00e+00f 1\n",
+ " 12 3.5580841e+00 4.26e-03 3.93e-01 -1.7 2.66e-01 - 1.00e+00 2.54e-01f 2\n",
+ " 13 3.5291931e+00 1.81e-04 4.83e-03 -1.7 2.89e-02 - 1.00e+00 1.00e+00h 1\n",
+ " 14 3.4762808e+00 5.13e-04 1.95e-02 -3.8 5.39e-02 - 9.95e-01 9.81e-01f 1\n",
+ " 15 3.4758624e+00 4.84e-09 4.22e-07 -3.8 4.18e-04 - 1.00e+00 1.00e+00h 1\n",
+ " 16 3.4755696e+00 5.01e-09 4.35e-07 -5.7 2.93e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 17 3.4755659e+00 7.89e-13 6.91e-11 -8.6 3.70e-06 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 10\n",
+ "Number of Iterations....: 17\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: 1.3300317561605992e+00 1.3300317561605992e+00\n",
- "Dual infeasibility......: 2.6201750238320983e-13 2.6201750238320983e-13\n",
- "Constraint violation....: 5.2395587868403481e-13 5.2395587868403481e-13\n",
- "Complementarity.........: 2.5067660651846794e-09 2.5067660651846794e-09\n",
- "Overall NLP error.......: 2.5067660651846794e-09 2.5067660651846794e-09\n",
+ "Objective...............: 3.4755659182795648e+00 3.4755659182795648e+00\n",
+ "Dual infeasibility......: 6.9056316220894587e-11 6.9056316220894587e-11\n",
+ "Constraint violation....: 7.8914652590356127e-13 7.8914652590356127e-13\n",
+ "Variable bound violation: 1.5551582244199835e-08 1.5551582244199835e-08\n",
+ "Complementarity.........: 3.0181336980863786e-09 3.0181336980863786e-09\n",
+ "Overall NLP error.......: 3.0181336980863786e-09 3.0181336980863786e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 13\n",
- "Number of objective gradient evaluations = 11\n",
- "Number of equality constraint evaluations = 13\n",
+ "Number of objective function evaluations = 21\n",
+ "Number of objective gradient evaluations = 18\n",
+ "Number of equality constraint evaluations = 21\n",
"Number of inequality constraint evaluations = 0\n",
- "Number of equality constraint Jacobian evaluations = 11\n",
+ "Number of equality constraint Jacobian evaluations = 18\n",
"Number of inequality constraint Jacobian evaluations = 0\n",
- "Number of Lagrangian Hessian evaluations = 10\n",
- "Total CPU secs in IPOPT (w/o function evaluations) = 0.024\n",
- "Total CPU secs in NLP function evaluations = 0.032\n",
+ "Number of Lagrangian Hessian evaluations = 17\n",
+ "Total seconds in IPOPT = 0.010\n",
"\n",
"EXIT: Optimal Solution Found.\n",
- "\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ "\b"
]
}
],
@@ -1184,7 +1213,7 @@
},
{
"cell_type": "code",
- "execution_count": 32,
+ "execution_count": 10,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1198,9 +1227,9 @@
"Reduced Space Solution:\n",
"# of variables: 6\n",
"# of constraints: 5\n",
- "x = -1.4353817202941686\n",
- "y = 1.3300317561605992\n",
- "Solve Time: 0.0739603042602539\n"
+ "x = 2.0000000155515822\n",
+ "y = 3.475565918279565\n",
+ "Solve Time: 0.024790048599243164\n"
]
}
],
@@ -1216,6 +1245,7 @@
},
{
"cell_type": "markdown",
+ "id": "9ddd63fa",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1230,7 +1260,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 11,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1241,15 +1271,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt trunk: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
" Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
- " For more information visit http://projects.coin-or.org/Ipopt\n",
+ " For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version trunk, running with linear solver ma27.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 2915\n",
"Number of nonzeros in inequality constraint Jacobian.: 0\n",
@@ -1266,113 +1296,153 @@
" inequality constraints with only upper bounds: 0\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 0 0.0000000e+00 6.09e+00 8.45e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
- " 1 -7.1339577e-02 6.07e+00 1.72e-01 -1.0 3.70e+01 - 1.63e-03 1.93e-03h 1\n",
- " 2 -7.5247495e-02 6.07e+00 6.54e+01 -1.0 5.54e+01 - 2.48e-03 7.06e-05h 1\n",
- " 3 -7.9254570e-02 6.07e+00 2.01e+03 -1.0 6.23e+01 - 2.02e-03 6.43e-05h 1\n",
- " 4r-7.9254570e-02 6.07e+00 9.99e+02 0.8 0.00e+00 - 0.00e+00 3.33e-07R 2\n",
- " 5r-6.2158937e-02 5.82e+00 9.99e+02 0.8 1.02e+03 - 2.64e-04 2.49e-04f 1\n",
- " 6r-3.0300263e-02 5.57e+00 9.98e+02 0.8 6.37e+02 - 4.33e-04 3.94e-04f 1\n",
- " 7r 2.4178689e-02 5.14e+00 9.98e+02 0.8 5.26e+02 - 8.85e-04 8.12e-04f 1\n",
- " 8r 2.4178689e-02 5.14e+00 9.99e+02 0.7 0.00e+00 - 0.00e+00 2.76e-07R 4\n",
- " 9r 6.2872635e-02 4.91e+00 9.98e+02 0.7 4.51e+02 - 1.33e-03 5.12e-04f 1\n",
+ " 0 0.0000000e+00 6.95e+00 2.28e-02 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ " 1 -4.0740903e-02 6.94e+00 5.95e-01 -1.0 4.63e+01 - 1.32e-03 8.80e-04h 1\n",
+ " 2 -4.1300350e-02 6.94e+00 1.31e+02 -1.0 3.98e+01 - 6.10e-04 1.41e-05h 1\n",
+ " 3 -5.1148271e-02 6.94e+00 3.02e+02 -1.0 7.21e+01 - 1.57e-04 3.44e-04f 1\n",
+ " 4r-5.1148271e-02 6.94e+00 9.99e+02 0.8 0.00e+00 - 0.00e+00 4.32e-07R 4\n",
+ " 5r-3.1179870e-02 6.67e+00 9.99e+02 0.8 8.44e+02 - 4.03e-04 3.16e-04f 1\n",
+ " 6r 5.4986374e-03 6.37e+00 9.98e+02 0.8 5.94e+02 - 3.80e-04 6.16e-04f 1\n",
+ " 7r 4.0604225e-02 6.14e+00 9.97e+02 0.8 4.90e+02 - 1.48e-03 6.33e-04f 1\n",
+ " 8 3.8375120e-02 6.14e+00 5.20e+00 -1.0 4.06e+01 - 2.85e-04 5.48e-05h 1\n",
+ " 9 3.7391021e-02 6.14e+00 3.46e+02 -1.0 7.11e+01 - 5.67e-04 3.92e-05h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 10 3.2490611e-02 6.14e+00 2.56e+02 -1.0 7.19e+01 - 1.29e-04 1.98e-04f 1\n",
+ " 11r 3.2490611e-02 6.14e+00 9.99e+02 0.8 0.00e+00 - 0.00e+00 2.51e-07R 4\n",
+ " 12r 3.4070427e-02 6.12e+00 9.99e+02 0.8 5.66e+02 - 7.45e-04 3.14e-05f 1\n",
+ " 13r 1.1894166e-01 5.52e+00 9.97e+02 0.8 4.43e+02 - 1.91e-03 1.78e-03f 1\n",
+ " 14 1.1709483e-01 5.52e+00 1.00e+00 -1.0 3.81e+01 - 5.89e-05 4.85e-05h 1\n",
+ " 15r 1.1709483e-01 5.52e+00 9.99e+02 0.7 0.00e+00 - 0.00e+00 2.68e-07R 6\n",
+ " 16r 1.1949108e-01 5.53e+00 9.99e+02 0.7 5.72e+02 - 4.95e-04 7.88e-05f 1\n",
+ " 17r 2.2036232e-01 4.63e+00 9.96e+02 0.7 4.12e+02 - 2.55e-03 3.22e-03f 1\n",
+ " 18 2.0437600e-01 4.63e+00 2.60e+00 -1.0 2.57e+01 - 2.35e-03 6.78e-04h 1\n",
+ " 19 1.9248698e-01 4.62e+00 2.91e+01 -1.0 3.46e+01 - 4.29e-03 5.63e-04h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 20r 1.9248698e-01 4.62e+00 9.99e+02 0.7 0.00e+00 - 0.00e+00 3.85e-07R 5\n",
+ " 21r 1.9291305e-01 4.59e+00 9.99e+02 0.7 6.87e+02 - 2.67e-03 6.04e-05f 1\n",
+ " 22r 2.1019282e-01 4.12e+00 9.96e+02 0.7 3.90e+02 - 5.73e-03 2.11e-03f 1\n",
+ " 23 2.0769986e-01 4.12e+00 1.71e+00 -1.0 2.03e+01 - 4.74e-05 1.29e-04h 1\n",
+ " 24 2.0615392e-01 4.12e+00 3.91e+00 -1.0 3.41e+01 - 4.15e-04 9.59e-05h 1\n",
+ " 25r 2.0615392e-01 4.12e+00 9.99e+02 0.6 0.00e+00 - 0.00e+00 3.34e-07R 3\n",
+ " 26r 2.0779159e-01 4.11e+00 9.99e+02 0.6 4.52e+02 - 2.14e-03 1.41e-04f 1\n",
+ " 27r 2.0653116e-01 3.45e+00 9.96e+02 0.6 2.29e+02 - 7.47e-03 3.09e-03f 1\n",
+ " 28 2.0024100e-01 3.45e+00 3.24e+00 -1.0 2.11e+01 - 1.95e-03 4.80e-04h 1\n",
+ " 29 1.9990707e-01 3.45e+00 3.21e+02 -1.0 3.06e+01 - 2.24e-03 3.50e-05h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 10r 2.6127893e-01 3.46e+00 9.95e+02 0.7 4.25e+02 - 1.73e-03 3.41e-03f 1\n",
- " 11 2.5138535e-01 3.46e+00 6.71e+00 -1.0 2.40e+01 - 6.85e-05 5.33e-04f 1\n",
- " 12 2.2883453e-01 3.46e+00 6.87e+00 -1.0 3.81e+01 - 1.05e-03 6.33e-04f 1\n",
- " 13r 2.2883453e-01 3.46e+00 9.99e+02 0.5 0.00e+00 - 0.00e+00 2.55e-07R 6\n",
- " 14r 2.3019790e-01 3.40e+00 9.98e+02 0.5 6.70e+02 - 3.20e-03 9.03e-05f 1\n",
- " 15r 2.2011443e-01 2.10e+00 9.94e+02 0.5 5.23e+02 - 6.04e-03 3.82e-03f 1\n",
- " 16 2.1807122e-01 2.10e+00 8.31e+00 -1.0 3.62e+01 - 7.32e-04 9.10e-05h 1\n",
- " 17 2.1209811e-01 2.10e+00 5.91e+01 -1.0 4.93e+01 - 1.09e-03 2.07e-04h 1\n",
- " 18r 2.1209811e-01 2.10e+00 9.99e+02 0.3 0.00e+00 - 0.00e+00 3.30e-07R 4\n",
- " 19r 2.1251496e-01 2.09e+00 9.99e+02 0.3 5.89e+02 - 2.34e-03 4.29e-05f 1\n",
+ " 30r 1.9990707e-01 3.45e+00 9.99e+02 0.5 0.00e+00 - 0.00e+00 3.50e-07R 2\n",
+ " 31r 1.9836742e-01 3.39e+00 9.98e+02 0.5 3.43e+02 - 5.27e-03 1.64e-04f 1\n",
+ " 32r 1.4575203e-01 2.27e+00 9.90e+02 0.5 1.51e+02 - 1.34e-02 7.48e-03f 1\n",
+ " 33 1.4477427e-01 2.27e+00 1.02e+01 -1.0 1.70e+01 - 1.69e-03 1.80e-04h 1\n",
+ " 34 1.4469234e-01 2.27e+00 7.63e+02 -1.0 2.99e+01 - 2.18e-03 3.04e-05h 1\n",
+ " 35r 1.4469234e-01 2.27e+00 9.99e+02 0.4 0.00e+00 - 0.00e+00 4.15e-07R 2\n",
+ " 36r 1.4286779e-01 2.21e+00 9.98e+02 0.4 3.79e+02 - 5.15e-03 1.48e-04f 1\n",
+ " 37r 5.1000476e-02 1.04e+00 9.90e+02 0.4 1.79e+02 - 1.92e-02 6.58e-03f 1\n",
+ " 38 5.0625345e-02 1.04e+00 4.13e+01 -1.0 1.64e+01 - 3.12e-03 1.19e-04h 1\n",
+ " 39 5.0530511e-02 1.04e+00 1.08e+04 -1.0 3.40e+01 - 3.48e-03 1.57e-05h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 20r 1.8668110e-01 1.77e+00 9.95e+02 0.3 3.66e+02 - 4.29e-03 3.80e-03f 1\n",
- " 21 1.8545839e-01 1.77e+00 1.68e+00 -1.0 1.67e+01 - 4.39e-04 1.80e-04h 1\n",
- " 22 1.8396458e-01 1.77e+00 5.74e+01 -1.0 4.63e+01 - 7.32e-04 8.30e-05h 1\n",
- " 23r 1.8396458e-01 1.77e+00 9.99e+02 0.2 0.00e+00 - 0.00e+00 4.63e-07R 3\n",
- " 24r 1.7641383e-01 1.71e+00 9.98e+02 0.2 3.36e+02 - 2.80e-03 3.68e-04f 1\n",
- " 25r 1.1457888e-01 1.00e+00 9.94e+02 0.2 2.27e+02 - 2.50e-03 4.60e-03f 1\n",
- " 26 1.1272351e-01 1.00e+00 5.98e+00 -1.0 1.70e+01 - 2.34e-03 4.45e-04h 1\n",
- " 27 1.1190732e-01 1.00e+00 4.49e+02 -1.0 4.25e+01 - 2.50e-03 6.84e-05h 1\n",
- " 28r 1.1190732e-01 1.00e+00 9.99e+02 -0.0 0.00e+00 - 0.00e+00 3.26e-07R 3\n",
- " 29r 9.3921694e-02 7.18e-01 9.98e+02 -0.0 3.23e+02 - 2.16e-03 9.32e-04f 1\n",
+ " 40r 5.0530511e-02 1.04e+00 9.99e+02 0.0 0.00e+00 - 0.00e+00 1.02e-07R 2\n",
+ " 41r 3.8919654e-02 7.46e-01 1.04e+03 0.0 4.98e+02 - 3.35e-03 6.19e-04f 1\n",
+ " 42r 3.8919654e-02 7.46e-01 9.99e+02 -0.1 0.00e+00 - 0.00e+00 4.58e-07R 4\n",
+ " 43r 4.3279805e-03 6.24e-01 9.97e+02 -0.1 4.24e+02 - 2.56e-03 1.77e-03f 1\n",
+ " 44 1.5721501e-04 6.22e-01 9.97e-01 -1.0 1.92e+01 - 3.52e-03 2.94e-03h 1\n",
+ " 45 3.9615166e-05 6.22e-01 5.60e+01 -1.0 2.50e+01 - 1.03e-02 6.92e-04h 1\n",
+ " 46 -5.4603067e-06 6.22e-01 3.19e+04 -1.0 1.98e+01 - 7.63e-03 1.53e-05h 1\n",
+ " 47 -1.3336510e-04 6.22e-01 1.48e+07 -1.0 3.46e+01 - 1.13e-02 2.42e-05h 1\n",
+ " 48r-1.3336510e-04 6.22e-01 9.99e+02 -0.2 0.00e+00 - 0.00e+00 1.21e-07R 2\n",
+ " 49r-6.8416215e-03 6.20e-01 1.00e+03 -0.2 2.28e+02 - 7.72e-04 4.29e-04f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 30 9.3828300e-02 7.18e-01 4.95e+02 -1.0 3.10e+01 - 4.16e-03 1.21e-05h 1\n",
- " 31r 9.3828300e-02 7.18e-01 9.99e+02 -0.1 0.00e+00 - 0.00e+00 3.77e-07R 4\n",
- " 32r 6.2918044e-02 5.98e-01 9.97e+02 -0.1 3.48e+02 - 2.03e-03 2.22e-03f 1\n",
- " 33 6.2833156e-02 5.98e-01 5.52e+02 -1.0 1.58e+01 - 9.73e-03 3.34e-05h 1\n",
- " 34r 6.2833156e-02 5.98e-01 9.99e+02 -0.2 0.00e+00 - 0.00e+00 2.38e-07R 2\n",
- " 35r 7.1530977e-02 5.72e-01 9.95e+02 -0.2 1.97e+02 - 1.82e-03 3.79e-03f 1\n",
- " 36r 1.1109302e-01 5.68e-01 9.95e+02 -0.2 2.53e+02 - 1.37e-03 2.68e-03f 1\n",
- " 37r 1.2938546e-01 5.74e-01 1.21e+03 -0.2 2.24e+02 - 3.15e-03 5.94e-04f 1\n",
- " 38r 1.8274522e-01 5.96e-01 1.31e+03 -0.2 1.47e+02 - 3.21e-03 2.85e-03f 1\n",
- " 39r 1.6012761e-01 6.04e-01 1.31e+03 -0.2 6.15e+02 - 7.98e-04 8.34e-04f 1\n",
+ " 50r-2.1457919e-02 6.14e-01 9.97e+02 -0.2 2.64e+02 - 1.14e-03 1.52e-03f 1\n",
+ " 51r-2.4674731e-02 5.98e-01 9.95e+02 -0.2 2.60e+02 - 2.38e-03 2.36e-03f 1\n",
+ " 52r-1.0027863e-02 5.88e-01 1.12e+03 -0.2 2.84e+02 - 5.06e-03 2.58e-03f 1\n",
+ " 53r 8.8990076e-03 5.95e-01 1.38e+03 -0.2 2.58e+02 - 3.39e-03 1.55e-03f 1\n",
+ " 54r 2.0354606e-02 6.06e-01 1.03e+03 -0.2 3.07e+02 - 1.37e-03 3.15e-03f 1\n",
+ " 55r 2.9933282e-02 6.10e-01 1.11e+03 -0.2 1.48e+02 - 1.53e-03 9.77e-04f 1\n",
+ " 56r 5.9781463e-02 6.19e-01 1.34e+03 -0.2 1.34e+02 - 3.38e-03 2.51e-03f 1\n",
+ " 57r 1.2716349e-01 6.25e-01 1.37e+03 -0.2 2.69e+02 - 2.34e-03 2.08e-03f 1\n",
+ " 58r 2.0055619e-01 6.31e-01 1.37e+03 -0.2 2.19e+02 - 3.26e-03 2.46e-03f 1\n",
+ " 59r 2.5292029e-01 6.36e-01 1.37e+03 -0.2 1.43e+02 - 3.66e-03 2.61e-03f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 40r 1.3917437e-01 6.20e-01 1.31e+03 -0.2 3.06e+02 - 3.14e-03 1.65e-03f 1\n",
- " 41r 1.4328022e-01 6.31e-01 1.30e+03 -0.2 4.90e+01 - 5.50e-03 2.05e-03f 1\n",
- " 42r 1.4534640e-01 6.37e-01 1.45e+03 -0.2 4.32e+01 - 3.75e-03 1.74e-03f 1\n",
- " 43r 1.7324460e-01 6.42e-01 1.44e+03 -0.2 1.59e+02 - 1.22e-03 2.13e-03f 1\n",
- " 44r 1.9727319e-01 6.47e-01 1.44e+03 -0.2 1.55e+02 - 1.71e-03 1.81e-03f 1\n",
- " 45r 2.6103933e-01 6.54e-01 2.79e+03 -0.2 3.99e+02 - 8.10e-04 3.34e-03f 1\n",
- " 46r 2.6081200e-01 6.54e-01 2.79e+03 -0.2 2.35e+02 0.0 1.70e-03 3.62e-04f 1\n",
- " 47r 2.9790995e-01 6.53e-01 2.79e+03 -0.2 1.79e+03 -0.5 4.43e-05 7.17e-04f 1\n",
- " 48r 3.0116462e-01 6.53e-01 2.79e+03 -0.2 2.57e+02 - 6.01e-04 4.98e-04f 1\n",
- " 49r 2.9308511e-01 6.51e-01 2.79e+03 -0.2 1.01e+03 - 5.82e-04 4.35e-04f 1\n",
+ " 60r 3.3947915e-01 6.37e-01 1.36e+03 -0.2 3.61e+02 - 8.07e-04 1.71e-03f 1\n",
+ " 61r 3.7477628e-01 6.38e-01 1.36e+03 -0.2 1.42e+02 - 3.44e-03 1.26e-03f 1\n",
+ " 62r 4.2790672e-01 6.42e-01 1.36e+03 -0.2 1.24e+02 - 1.98e-03 3.84e-03f 1\n",
+ " 63r 4.4861908e-01 6.44e-01 1.35e+03 -0.2 1.95e+02 - 1.80e-03 1.41e-03f 1\n",
+ " 64r 5.2915872e-01 6.43e-01 1.35e+03 -0.2 6.36e+03 - 5.42e-05 1.20e-04f 1\n",
+ " 65r 5.7491918e-01 6.43e-01 1.35e+03 -0.2 8.32e+02 - 2.67e-04 5.99e-04f 1\n",
+ " 66r 5.8136208e-01 6.44e-01 1.35e+03 -0.2 2.28e+02 - 3.28e-03 8.03e-04f 1\n",
+ " 67r 6.0893637e-01 6.44e-01 1.35e+03 -0.2 1.48e+02 0.0 1.62e-03 6.73e-03f 1\n",
+ " 68r 6.1721890e-01 6.44e-01 1.33e+03 -0.2 5.50e+01 0.4 9.76e-03 7.75e-03f 1\n",
+ " 69r 5.9280222e-01 6.42e-01 1.33e+03 -0.2 2.32e+02 -0.1 2.79e-04 2.09e-03f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 50r 3.0664746e-01 6.50e-01 2.79e+03 -0.2 4.23e+02 - 1.01e-03 9.03e-04f 1\n",
- " 51r 3.4410676e-01 6.50e-01 2.78e+03 -0.2 4.19e+02 - 2.94e-03 1.64e-03f 1\n",
- " 52r 3.7003823e-01 6.49e-01 2.77e+03 -0.2 2.61e+02 - 9.63e-04 3.40e-03f 1\n",
- " 53r 3.7318066e-01 6.41e-01 2.76e+03 -0.2 2.16e+02 - 2.42e-03 3.13e-03f 1\n",
- " 54r 3.8975736e-01 6.39e-01 2.76e+03 -0.2 8.01e+02 - 8.12e-04 2.36e-04f 1\n",
- " 55r 4.1628881e-01 6.34e-01 2.75e+03 -0.2 2.67e+02 - 1.11e-03 3.77e-03f 1\n",
- " 56r 4.2335383e-01 6.32e-01 2.75e+03 -0.2 2.39e+02 - 4.98e-03 1.50e-03f 1\n",
- " 57r 4.3744081e-01 6.43e-01 2.74e+03 -0.2 1.55e+02 - 1.32e-02 2.92e-03f 1\n",
- " 58r 4.5823302e-01 6.38e-01 2.72e+03 -0.2 2.24e+02 - 6.79e-04 7.94e-03f 1\n",
- " 59r 4.7928638e-01 6.31e-01 2.71e+03 -0.2 1.85e+02 - 2.35e-03 4.20e-03f 1\n",
+ " 70r 5.6890773e-01 6.40e-01 1.33e+03 -0.2 9.62e+02 - 6.75e-04 2.96e-04f 1\n",
+ " 71r 6.0587761e-01 6.40e-01 1.33e+03 -0.2 1.26e+02 - 3.26e-03 3.19e-03f 1\n",
+ " 72r 6.1282044e-01 6.37e-01 1.35e+03 -0.2 1.26e+02 - 3.33e-04 3.55e-03f 1\n",
+ " 73r 6.2591560e-01 6.36e-01 1.32e+03 -0.2 7.04e+01 - 7.50e-03 3.17e-03f 1\n",
+ " 74r 6.0988842e-01 6.25e-01 1.32e+03 -0.2 9.57e+02 - 2.01e-04 9.43e-04f 1\n",
+ " 75r 5.8604058e-01 6.21e-01 1.31e+03 -0.2 6.80e+02 - 1.86e-03 5.50e-04f 1\n",
+ " 76r 5.7477530e-01 6.19e-01 1.31e+03 -0.2 3.05e+02 - 1.49e-03 4.66e-04f 1\n",
+ " 77r 5.3145906e-01 6.12e-01 1.31e+03 -0.2 2.26e+02 - 1.99e-03 2.35e-03f 1\n",
+ " 78r 5.4201911e-01 6.10e-01 1.35e+03 -0.2 5.42e+01 - 1.76e-04 4.13e-03f 1\n",
+ " 79r 5.6713031e-01 6.08e-01 1.30e+03 -0.2 1.59e+02 - 7.69e-03 1.56e-03f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 60r 4.8794775e-01 6.19e-01 2.70e+03 -0.2 1.53e+02 - 1.59e-03 1.91e-03f 1\n",
- " 61r 5.0165368e-01 5.82e-01 2.69e+03 -0.2 1.53e+02 - 4.14e-03 5.31e-03f 1\n",
- " 62r 6.6674037e-01 5.33e-01 2.68e+03 -0.2 6.29e+02 - 4.39e-04 1.38e-03f 1\n",
- " 63 6.5615964e-01 5.20e-01 1.59e+03 -1.0 2.99e+00 - 1.51e-01 2.62e-02f 1\n",
- " 64 5.8865601e-01 3.39e-01 3.09e+04 -1.0 2.50e+00 - 1.59e-02 3.47e-01f 1\n",
- " 65 5.7989436e-01 3.19e-01 2.90e+04 -1.0 1.62e+00 - 4.65e-02 6.06e-02h 1\n",
- " 66 5.0842376e-01 4.14e-03 7.77e+04 -1.0 1.40e+00 - 1.89e-02 9.87e-01h 1\n",
- " 67 5.0888864e-01 4.04e-05 3.33e+04 -1.0 3.23e-02 - 9.46e-01 9.90e-01h 1\n",
- " 68 5.3601450e-01 2.27e-06 2.14e-02 -1.0 1.22e-01 - 1.00e+00 1.00e+00H 1\n",
- " 69 2.0484095e-01 1.53e-02 1.32e+06 -5.7 3.56e+00 - 2.19e-01 4.31e-01f 1\n",
+ " 80r 6.3557574e-01 6.02e-01 1.30e+03 -0.2 3.48e+02 - 7.04e-05 1.35e-03f 1\n",
+ " 81r 6.6149225e-01 5.99e-01 1.29e+03 -0.2 4.12e+02 - 1.35e-03 1.02e-03f 1\n",
+ " 82r 7.0330515e-01 5.94e-01 1.29e+03 -0.2 4.06e+02 - 5.21e-04 1.36e-03f 1\n",
+ " 83r 7.3806437e-01 5.90e-01 1.29e+03 -0.2 3.33e+02 - 8.82e-04 1.12e-03f 1\n",
+ " 84r 7.8312861e-01 5.85e-01 1.29e+03 -0.2 1.77e+02 - 1.05e-03 2.56e-03f 1\n",
+ " 85r 8.1278418e-01 5.82e-01 1.28e+03 -0.2 9.98e+01 - 6.67e-03 2.84e-03f 1\n",
+ " 86r 8.6191480e-01 5.70e-01 1.28e+03 -0.2 8.96e+01 - 2.90e-03 6.00e-03f 1\n",
+ " 87r 9.2113171e-01 5.43e-01 1.36e+03 -0.2 3.55e+02 - 5.07e-04 1.53e-03f 1\n",
+ " 88 9.2258112e-01 5.40e-01 5.10e+03 -1.0 3.51e+00 - 9.38e-02 5.06e-03f 1\n",
+ " 89 9.2441270e-01 5.34e-01 1.11e+04 -1.0 3.08e+00 - 5.31e-03 1.13e-02f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 70 -8.7188062e-02 3.42e-02 5.97e+06 -5.7 1.56e+00 - 1.84e-02 1.00e+00f 1\n",
- " 71 -3.4078960e-02 6.67e-06 3.51e+06 -5.7 5.31e-02 - 4.38e-01 1.00e+00h 1\n",
- " 72 -7.2685236e-02 1.32e-03 7.07e+05 -5.7 3.22e-01 - 7.93e-01 1.00e+00f 1\n",
- " 73 -1.4565891e-01 1.01e-02 2.58e+05 -5.7 7.91e-01 - 6.41e-01 1.00e+00h 1\n",
- " 74 -1.2430807e-01 1.01e-03 4.59e+04 -5.7 2.22e-01 - 8.29e-01 1.00e+00h 1\n",
- " 75 -1.2166233e-01 8.01e-07 7.24e-07 -5.7 6.11e-03 - 1.00e+00 1.00e+00h 1\n",
- " 76 -1.2166022e-01 3.48e-10 3.60e-10 -8.6 1.27e-04 - 1.00e+00 1.00e+00h 1\n",
+ " 90 9.2434422e-01 5.33e-01 1.10e+04 -1.0 2.28e+00 - 4.35e-04 1.02e-03h 1\n",
+ " 91 8.6368151e-01 2.51e-01 6.92e+04 -1.0 2.12e+00 - 1.71e-02 5.29e-01f 1\n",
+ " 92 9.2391326e-01 2.46e-03 1.91e+05 -1.0 1.37e+00 - 2.22e-01 9.90e-01h 1\n",
+ " 93 9.0831043e-01 6.29e-05 1.42e+05 -1.0 5.07e-02 0.0 8.33e-01 9.92e-01h 1\n",
+ " 94 9.0105069e-01 8.19e-05 7.63e+06 -1.0 5.30e-01 - 1.00e+00 6.25e-02h 5\n",
+ " 95 8.3789577e-01 1.61e-03 6.79e+06 -1.0 1.28e+00 - 1.00e+00 2.21e-01f 3\n",
+ " 96 7.7240813e-01 2.16e-03 5.18e+06 -1.0 1.03e+00 - 1.00e+00 2.50e-01f 3\n",
+ " 97 6.4093759e-01 3.95e-03 2.59e+06 -1.0 9.40e-01 - 1.00e+00 5.00e-01f 2\n",
+ " 98 5.5439882e-01 4.86e-03 6.51e+06 -1.0 2.02e+00 - 9.74e-01 1.37e-01f 3\n",
+ " 99 4.2151393e-01 5.94e-03 2.08e+06 -1.0 7.85e-01 - 1.00e+00 5.00e-01f 2\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 100 3.2204724e-01 6.90e-03 3.12e+06 -1.0 1.35e+00 - 7.43e-01 2.12e-01f 3\n",
+ " 101 2.2429491e-01 7.96e-03 5.15e+06 -1.0 1.56e+00 - 1.00e+00 1.77e-01f 3\n",
+ " 102 1.5370840e-01 7.16e-03 5.17e+05 -1.0 7.71e-01 - 1.00e+00 2.50e-01f 3\n",
+ " 103 -5.6325943e-02 1.49e-02 1.24e+06 -1.0 1.30e+00 - 1.00e+00 4.41e-01f 2\n",
+ " 104 -4.9483630e-01 3.55e-02 1.06e-01 -1.0 1.17e+00 - 1.00e+00 1.00e+00w 1\n",
+ " 105 -8.8191026e-01 2.25e-02 7.20e+06 -2.5 1.18e+00 - 3.36e-01 1.00e+00f 1\n",
+ " 106 -8.6457261e-01 6.67e-03 6.13e+06 -2.5 4.34e-01 - 1.30e-01 1.00e+00h 1\n",
+ " 107 -8.5476376e-01 1.91e-04 3.51e+05 -2.5 7.34e-02 - 9.40e-01 1.00e+00h 1\n",
+ " 108 -8.8359862e-01 4.69e-03 2.16e-03 -2.5 3.62e-01 - 1.00e+00 1.00e+00h 1\n",
+ " 109 -8.7384947e-01 3.44e-04 2.49e+04 -3.8 1.04e-01 - 9.07e-01 1.00e+00h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 110 -8.7297456e-01 4.91e-08 5.97e-07 -3.8 1.35e-03 - 1.00e+00 1.00e+00h 1\n",
+ " 111 -8.7297555e-01 2.16e-07 3.90e-07 -5.7 2.61e-03 - 1.00e+00 1.00e+00h 1\n",
+ " 112 -8.7297500e-01 3.68e-11 5.83e-11 -8.6 3.41e-05 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 76\n",
+ "Number of Iterations....: 112\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: -1.2166022451801017e-01 -1.2166022451801017e-01\n",
- "Dual infeasibility......: 3.6034897278835850e-10 3.6034897278835850e-10\n",
- "Constraint violation....: 3.4823799399674726e-10 3.4823799399674726e-10\n",
- "Complementarity.........: 2.6332158051441440e-09 2.6332158051441440e-09\n",
- "Overall NLP error.......: 2.6332158051441440e-09 2.6332158051441440e-09\n",
+ "Objective...............: -8.7297499690442715e-01 -8.7297499690442715e-01\n",
+ "Dual infeasibility......: 5.8297584736706334e-11 5.8297584736706334e-11\n",
+ "Constraint violation....: 3.6792457969170300e-11 3.6792457969170300e-11\n",
+ "Variable bound violation: 0.0000000000000000e+00 0.0000000000000000e+00\n",
+ "Complementarity.........: 2.5595344838331209e-09 2.5595344838331209e-09\n",
+ "Overall NLP error.......: 2.5595344838331209e-09 2.5595344838331209e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 106\n",
- "Number of objective gradient evaluations = 44\n",
- "Number of equality constraint evaluations = 106\n",
+ "Number of objective function evaluations = 196\n",
+ "Number of objective gradient evaluations = 67\n",
+ "Number of equality constraint evaluations = 196\n",
"Number of inequality constraint evaluations = 0\n",
- "Number of equality constraint Jacobian evaluations = 85\n",
+ "Number of equality constraint Jacobian evaluations = 123\n",
"Number of inequality constraint Jacobian evaluations = 0\n",
- "Number of Lagrangian Hessian evaluations = 76\n",
- "Total CPU secs in IPOPT (w/o function evaluations) = 0.194\n",
- "Total CPU secs in NLP function evaluations = 0.015\n",
+ "Number of Lagrangian Hessian evaluations = 112\n",
+ "Total seconds in IPOPT = 0.212\n",
"\n",
"EXIT: Optimal Solution Found.\n",
- "\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ "\b"
]
}
],
@@ -1402,7 +1472,7 @@
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 12,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1416,9 +1486,9 @@
"Full Space Solution:\n",
"# of variables: 209\n",
"# of constraints: 208\n",
- "x = 0.8800743078211596\n",
- "y = -0.12166022451801017\n",
- "Solve Time: 0.14703655242919922\n"
+ "x = -0.27612966130338\n",
+ "y = -0.8729749969044271\n",
+ "Solve Time: 0.23176932334899902\n"
]
}
],
@@ -1434,6 +1504,7 @@
},
{
"cell_type": "markdown",
+ "id": "96bec3ef",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1449,7 +1520,7 @@
},
{
"cell_type": "code",
- "execution_count": 35,
+ "execution_count": 13,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1460,15 +1531,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt trunk: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
" Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
- " For more information visit http://projects.coin-or.org/Ipopt\n",
+ " For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version trunk, running with linear solver ma27.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 1215\n",
"Number of nonzeros in inequality constraint Jacobian.: 180\n",
@@ -1485,71 +1556,114 @@
" inequality constraints with only upper bounds: 60\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 0 0.0000000e+00 1.38e+00 1.23e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
- " 1 3.2457639e-02 1.35e+00 1.19e+00 -1.0 1.29e+00 - 2.57e-02 2.51e-02f 1\n",
- " 2 2.1293657e-01 1.16e+00 8.12e+00 -1.0 1.28e+00 - 3.32e-02 1.41e-01f 1\n",
- " 3 4.0536698e-01 8.85e-01 6.54e+00 -1.0 8.37e-01 - 2.27e-01 2.36e-01f 1\n",
- " 4 1.7514949e-01 6.63e-01 5.29e+00 -1.0 1.31e+00 - 2.53e-01 2.51e-01h 1\n",
- " 5 -6.7821031e-02 5.83e-01 1.23e+02 -1.0 2.03e+00 - 9.89e-01 1.20e-01h 1\n",
- " 6 -3.9492120e-01 2.66e-01 1.66e+02 -1.0 8.59e-01 - 1.00e+00 5.45e-01h 1\n",
- " 7 -6.0986326e-01 1.60e-01 3.39e+02 -1.0 5.86e-01 - 1.00e+00 3.97e-01h 1\n",
- " 8 -7.4904928e-01 6.18e-02 4.12e+02 -1.0 2.81e-01 - 1.00e+00 6.14e-01h 1\n",
- " 9 -8.0825872e-01 2.83e-02 1.17e+03 -1.0 1.24e-01 - 1.00e+00 5.42e-01h 1\n",
+ " 0 0.0000000e+00 1.38e+00 1.11e+00 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ " 1 2.1523013e-02 1.34e+00 1.12e+00 -1.0 6.53e-01 - 2.86e-02 3.30e-02f 1\n",
+ " 2 1.2939043e-02 1.31e+00 1.27e+00 -1.0 1.25e+00 - 3.04e-02 2.20e-02f 1\n",
+ " 3 -1.6755924e-02 9.74e-01 1.37e+01 -1.0 1.21e+00 - 3.98e-02 2.56e-01f 1\n",
+ " 4 -1.6839056e-01 7.79e-01 1.12e+01 -1.0 1.26e+00 - 2.34e-01 2.00e-01f 1\n",
+ " 5 -2.9388224e-01 5.82e-01 8.36e+00 -1.0 1.12e+00 - 2.05e-01 2.53e-01h 1\n",
+ " 6 -3.1578455e-01 4.60e-01 5.54e+01 -1.0 1.33e+00 - 4.98e-01 2.10e-01h 1\n",
+ " 7 -2.6605668e-01 2.89e-01 2.44e+01 -1.0 5.27e-01 - 3.20e-01 3.72e-01h 1\n",
+ " 8 -4.5440899e-01 2.88e-01 2.44e+01 -1.0 7.34e+01 -2.0 2.63e-03 2.56e-03h 1\n",
+ " 9 -5.3336662e-01 2.56e-01 6.61e+01 -1.0 1.04e+00 - 1.77e-01 1.12e-01h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 10 -5.5095381e-01 1.71e-01 3.42e+02 -1.0 4.65e-01 - 7.66e-01 3.30e-01h 1\n",
+ " 11 -6.7686979e-01 7.60e-02 6.01e+02 -1.0 2.88e-01 - 1.00e+00 5.56e-01h 1\n",
+ " 12 -6.7984217e-01 7.46e-02 5.90e+02 -1.0 2.71e+00 -0.7 1.89e-02 1.89e-02h 1\n",
+ " 13 -6.9284406e-01 4.15e-02 3.91e+03 -1.0 1.89e-01 - 8.56e-01 4.43e-01h 1\n",
+ " 14 -6.4107811e-01 1.71e-02 1.21e+03 -1.0 9.64e-02 - 7.35e-01 5.88e-01h 1\n",
+ " 15 -6.2662979e-01 7.85e-03 4.11e+03 -1.0 5.40e-02 - 8.61e-01 5.41e-01h 1\n",
+ " 16 -6.2672162e-01 2.80e-03 1.02e+04 -1.0 6.76e-03 - 1.00e+00 6.43e-01h 1\n",
+ " 17 -6.2562987e-01 1.22e-03 3.43e+04 -1.0 6.08e-03 - 1.00e+00 5.66e-01h 1\n",
+ " 18 -6.3154247e-01 4.84e-04 7.62e+04 -1.0 9.82e-03 - 1.00e+00 6.02e-01h 1\n",
+ " 19 -6.3308981e-01 1.98e-04 1.88e+05 -1.0 2.62e-03 - 1.00e+00 5.90e-01h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 10 -8.4218416e-01 1.14e-02 2.43e+03 -1.0 6.18e-02 - 1.00e+00 5.96e-01h 1\n",
- " 11 -8.5864468e-01 4.82e-03 6.16e+03 -1.0 2.89e-02 - 1.00e+00 5.79e-01h 1\n",
- " 12 -8.6760342e-01 1.98e-03 1.45e+04 -1.0 1.52e-02 - 1.00e+00 5.88e-01h 1\n",
- " 13 -8.7245163e-01 8.21e-04 3.52e+04 -1.0 8.27e-03 - 1.00e+00 5.86e-01h 1\n",
- " 14 -8.7541491e-01 3.36e-04 8.37e+04 -1.0 5.02e-03 - 1.00e+00 5.90e-01h 1\n",
- " 15 -8.7737642e-01 1.36e-04 1.96e+05 -1.0 3.29e-03 - 1.00e+00 5.96e-01h 1\n",
- " 16 -8.7879948e-01 5.26e-05 4.37e+05 -1.0 2.32e-03 - 1.00e+00 6.12e-01h 1\n",
- " 17 -8.7987379e-01 1.84e-05 8.63e+05 -1.0 1.65e-03 - 1.00e+00 6.51e-01h 1\n",
- " 18 -8.8068977e-01 5.22e-06 1.34e+06 -1.0 1.14e-03 - 1.00e+00 7.16e-01h 1\n",
- " 19 -8.8124416e-01 1.24e-06 1.48e+06 -1.0 7.52e-04 - 1.00e+00 7.62e-01h 1\n",
+ " 20 -6.4297177e-01 7.91e-05 4.23e+05 -1.0 1.66e-02 - 1.00e+00 6.01e-01h 1\n",
+ " 21 -6.5224443e-01 3.10e-05 8.88e+05 -1.0 1.53e-02 - 1.00e+00 6.08e-01h 1\n",
+ " 22 -6.6363610e-01 1.53e-05 2.01e+06 -1.0 2.27e-02 - 1.00e+00 5.07e-01h 1\n",
+ " 23 -6.7033577e-01 4.83e-06 1.72e+06 -1.0 9.88e-03 - 1.00e+00 6.84e-01h 1\n",
+ " 24 -6.7656797e-01 1.18e-06 1.92e+06 -1.0 8.33e-03 - 1.00e+00 7.56e-01h 1\n",
+ " 25 -6.7898126e-01 6.66e-16 1.46e+04 -1.0 2.44e-03 - 1.00e+00 1.00e+00h 1\n",
+ " 26 -6.7952651e-01 6.66e-16 7.32e+03 -1.7 5.51e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 27 -6.8102195e-01 6.66e-16 6.40e+05 -1.7 5.78e-03 1.6 5.86e-01 2.60e-01f 2\n",
+ " 28 -6.8122013e-01 8.88e-16 9.76e+06 -1.7 3.77e-03 2.9 1.00e+00 5.25e-02f 4\n",
+ " 29 -6.8126778e-01 6.66e-16 1.73e+01 -1.7 4.77e-05 - 1.00e+00 1.00e+00f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 20 -8.8124638e-01 1.24e-06 7.95e+06 -1.0 4.39e-04 - 1.00e+00 5.37e-03f 8\n",
- " 21 -8.8181978e-01 4.58e-16 6.02e+03 -1.0 6.31e-04 - 1.00e+00 1.00e+00h 1\n",
- " 22 -8.8191168e-01 1.67e-16 1.45e+03 -2.5 1.03e-04 - 1.00e+00 1.00e+00h 1\n",
- " 23 -8.8191549e-01 2.08e-16 1.00e+01 -2.5 5.10e-06 4.0 1.00e+00 1.00e+00f 1\n",
- " 24 -8.8209647e-01 4.44e-16 7.42e+03 -3.8 1.50e-02 - 2.97e-02 1.56e-02f 2\n",
- " 25 -8.8216895e-01 2.22e-16 4.10e+06 -3.8 3.00e-03 3.5 1.00e+00 5.00e-02f 2\n",
- " 26 -8.8215085e-01 3.89e-16 1.86e+06 -3.8 1.15e-04 - 6.10e-01 1.00e+00f 1\n",
- " 27 -8.8213197e-01 4.44e-16 4.43e+00 -3.8 4.50e-05 - 1.00e+00 1.00e+00h 1\n",
- " 28 -8.8219173e-01 4.44e-16 7.02e-02 -3.8 1.20e-04 - 1.00e+00 1.00e+00f 1\n",
- " 29 -8.8221836e-01 4.02e-16 4.77e+03 -5.7 2.66e-05 - 8.37e-01 1.00e+00f 1\n",
+ " 30 -6.8184006e-01 4.44e-16 1.13e+06 -3.8 5.72e-04 - 7.05e-01 1.00e+00f 1\n",
+ " 31 -6.8197616e-01 4.44e-16 4.51e-01 -3.8 1.36e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 32 -6.8239575e-01 4.44e-16 1.54e+03 -3.8 1.47e-03 - 3.93e-01 2.86e-01f 2\n",
+ " 33 -6.8277018e-01 6.66e-16 4.68e-01 -3.8 3.74e-04 2.4 1.00e+00 1.00e+00f 1\n",
+ " 34 -6.8377585e-01 6.66e-16 5.32e+04 -3.8 1.03e-03 1.9 4.23e-01 1.00e+00F 1\n",
+ " 35 -6.8420962e-01 6.66e-16 6.88e+04 -3.8 3.99e-03 1.5 3.88e-01 1.09e-01f 2\n",
+ " 36 -6.8620010e-01 6.66e-16 5.35e+04 -3.8 7.24e-03 - 3.46e-01 2.75e-01f 1\n",
+ " 37 -6.8668666e-01 4.44e-16 1.14e+06 -3.8 8.92e-03 1.0 1.00e+00 5.45e-02f 1\n",
+ " 38 -6.9721019e-01 4.44e-16 2.73e+05 -3.8 1.41e-02 - 2.89e-01 7.45e-01f 1\n",
+ " 39 -7.1283625e-01 8.88e-16 3.78e+05 -3.8 1.50e+01 - 2.22e-03 1.04e-03f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 30 -8.8228690e-01 2.22e-16 2.45e+03 -5.7 1.49e-04 - 1.00e+00 4.60e-01f 1\n",
- " 31 -8.8229523e-01 2.08e-16 1.84e-02 -5.7 1.38e-05 - 1.00e+00 1.00e+00f 1\n",
- " 32 -8.8229861e-01 3.19e-16 1.25e-05 -5.7 5.18e-06 - 1.00e+00 1.00e+00h 1\n",
- " 33 -8.8230256e-01 2.22e-16 4.93e+01 -8.6 3.95e-06 - 8.64e-01 1.00e+00f 1\n",
- " 34 -8.8230390e-01 5.13e-16 1.47e+01 -8.6 1.34e-06 - 6.98e-01 1.00e+00h 1\n",
- " 35 -8.8230501e-01 5.13e-16 3.38e+00 -8.6 1.11e-06 - 7.56e-01 1.00e+00f 1\n",
- " 36 -8.8230568e-01 4.58e-16 4.25e-01 -8.6 6.69e-07 - 8.57e-01 1.00e+00h 1\n",
- " 37 -8.8230588e-01 5.27e-16 2.36e-08 -8.6 2.04e-07 - 1.00e+00 1.00e+00h 1\n",
- " 38 -8.8230596e-01 2.43e-16 5.62e-09 -9.0 7.64e-08 - 1.00e+00 1.00e+00h 1\n",
+ " 40 -7.4236216e-01 6.66e-16 4.49e+03 -3.8 2.95e-02 0.5 1.00e+00 1.00e+00f 1\n",
+ " 41 -7.6210060e-01 4.44e-16 9.73e+03 -3.8 8.90e-02 0.0 2.31e-01 2.22e-01f 1\n",
+ " 42 -7.6083045e-01 4.64e-08 6.47e+05 -3.8 1.02e-02 - 9.23e-01 1.25e-01f 4\n",
+ " 43 -7.6090712e-01 4.03e-08 3.36e+05 -3.8 4.94e-04 - 1.00e+00 1.55e-01f 2\n",
+ " 44 -7.6164755e-01 4.77e-09 1.01e+02 -3.8 7.40e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 45 -7.6209913e-01 3.33e-16 1.39e+03 -3.8 4.52e-04 - 8.79e-01 1.00e+00f 1\n",
+ " 46 -7.6213859e-01 6.66e-16 8.23e-01 -3.8 7.33e-05 3.2 1.00e+00 1.00e+00f 1\n",
+ " 47 -7.6229913e-01 1.00e-09 2.91e+03 -3.8 2.24e-04 - 6.87e-01 1.00e+00F 1\n",
+ " 48 -7.6235349e-01 5.00e-16 1.21e+00 -3.8 1.10e-04 2.7 1.00e+00 1.00e+00f 1\n",
+ " 49 -7.6254478e-01 6.66e-16 2.05e+00 -3.8 2.97e-04 2.2 1.00e+00 1.00e+00F 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 50 -7.6307778e-01 4.44e-16 7.51e+00 -3.8 8.65e-04 1.7 1.00e+00 1.00e+00f 1\n",
+ " 51 -7.6433608e-01 2.97e-10 9.89e+02 -3.8 1.08e-02 - 1.44e-01 1.92e-01F 1\n",
+ " 52 -7.6715888e-01 2.97e-10 1.02e+03 -3.8 1.11e+02 - 3.55e-05 4.17e-05f 1\n",
+ " 53 -8.0984247e-01 2.97e-10 1.05e+03 -3.8 3.77e+02 - 1.84e-04 1.86e-04f 1\n",
+ " 54 -8.3901822e-01 2.97e-10 8.65e+02 -3.8 1.40e+02 - 3.81e-04 3.42e-04f 1\n",
+ " 55 -8.3696468e-01 3.50e-07 7.66e+03 -3.8 1.29e-02 - 1.00e+00 2.50e-01f 3\n",
+ " 56 -8.3733455e-01 2.36e-07 2.57e+04 -3.8 1.62e-03 - 1.00e+00 3.60e-01f 2\n",
+ " 57 -8.3826460e-01 5.41e-08 1.13e+01 -3.8 1.48e-03 - 1.00e+00 1.00e+00f 1\n",
+ " 58 -8.3882684e-01 4.21e-09 1.26e+00 -3.8 9.13e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 59 -8.3892768e-01 6.66e-16 2.27e+03 -3.8 4.87e-04 - 6.41e-01 3.76e-01f 2\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 60 -8.3893258e-01 6.66e-16 6.43e-01 -3.8 2.59e-05 3.1 1.00e+00 1.00e+00h 1\n",
+ " 61 -8.3898079e-01 4.44e-16 2.47e+03 -3.8 9.92e-04 - 2.17e-01 1.07e-01f 2\n",
+ " 62 -8.3899317e-01 6.66e-16 1.19e+04 -3.8 1.95e-04 2.6 1.00e+00 2.50e-01h 3\n",
+ " 63 -8.3900197e-01 6.66e-16 6.18e-02 -3.8 4.46e-05 - 1.00e+00 1.00e+00h 1\n",
+ " 64 -8.3904044e-01 4.44e-16 5.51e+03 -5.7 4.05e-04 - 3.23e-01 5.11e-01f 1\n",
+ " 65 -8.3910952e-01 4.44e-16 5.53e+03 -5.7 3.41e+00 - 1.44e-04 3.45e-04f 1\n",
+ " 66 -8.4550348e-01 6.66e-16 5.23e+03 -5.7 3.85e+00 - 3.22e-02 2.83e-02f 1\n",
+ " 67 -8.4548968e-01 4.44e-16 5.79e+03 -5.7 9.31e-04 - 5.85e-01 2.50e-01f 3\n",
+ " 68 -8.4550291e-01 4.44e-16 2.49e+03 -5.7 3.48e-04 - 8.62e-01 6.77e-01h 1\n",
+ " 69 -8.4550207e-01 4.44e-16 2.01e+03 -5.7 2.50e-05 - 1.00e+00 3.69e-01f 2\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 70 -8.4550066e-01 4.44e-16 1.99e-02 -5.7 1.33e-05 - 1.00e+00 1.00e+00h 1\n",
+ " 71 -8.4550066e-01 4.44e-16 7.93e-08 -5.7 6.03e-08 - 1.00e+00 1.00e+00h 1\n",
+ " 72 -8.4550569e-01 8.88e-16 5.08e+01 -8.6 1.56e-05 - 8.56e-01 1.00e+00f 1\n",
+ " 73 -8.4550785e-01 4.44e-16 1.48e+01 -8.6 2.16e-06 - 6.94e-01 1.00e+00h 1\n",
+ " 74 -8.4550945e-01 6.66e-16 3.49e+00 -8.6 1.60e-06 - 7.59e-01 1.00e+00f 1\n",
+ " 75 -8.4551007e-01 6.66e-16 5.18e-01 -8.6 6.29e-07 - 8.48e-01 1.00e+00f 1\n",
+ " 76 -8.4551025e-01 6.66e-16 1.41e-08 -8.6 1.74e-07 - 1.00e+00 1.00e+00h 1\n",
+ " 77 -8.4551033e-01 1.11e-15 3.53e-09 -9.0 8.62e-08 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 38\n",
+ "Number of Iterations....: 77\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: -8.8230595646701349e-01 -8.8230595646701349e-01\n",
- "Dual infeasibility......: 5.6164118911183891e-09 5.6164118911183891e-09\n",
- "Constraint violation....: 2.4286128663675299e-16 2.4286128663675299e-16\n",
- "Complementarity.........: 1.2411801603550043e-09 1.2411801603550043e-09\n",
- "Overall NLP error.......: 5.6164118911183891e-09 5.6164118911183891e-09\n",
+ "Objective...............: -8.4551033463177583e-01 -8.4551033463177583e-01\n",
+ "Dual infeasibility......: 3.5349462246259122e-09 3.5349462246259122e-09\n",
+ "Constraint violation....: 1.1102230246251565e-15 1.1102230246251565e-15\n",
+ "Variable bound violation: 9.9381632800225759e-09 9.9381632800225759e-09\n",
+ "Complementarity.........: 1.2114999317118539e-09 1.2114999317118539e-09\n",
+ "Overall NLP error.......: 3.5349462246259122e-09 3.5349462246259122e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 51\n",
- "Number of objective gradient evaluations = 39\n",
- "Number of equality constraint evaluations = 51\n",
- "Number of inequality constraint evaluations = 51\n",
- "Number of equality constraint Jacobian evaluations = 39\n",
- "Number of inequality constraint Jacobian evaluations = 39\n",
- "Number of Lagrangian Hessian evaluations = 38\n",
- "Total CPU secs in IPOPT (w/o function evaluations) = 0.137\n",
- "Total CPU secs in NLP function evaluations = 0.008\n",
+ "Number of objective function evaluations = 120\n",
+ "Number of objective gradient evaluations = 78\n",
+ "Number of equality constraint evaluations = 120\n",
+ "Number of inequality constraint evaluations = 120\n",
+ "Number of equality constraint Jacobian evaluations = 78\n",
+ "Number of inequality constraint Jacobian evaluations = 78\n",
+ "Number of Lagrangian Hessian evaluations = 77\n",
+ "Total seconds in IPOPT = 0.127\n",
"\n",
"EXIT: Optimal Solution Found.\n",
- "\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ "\b"
]
}
],
@@ -1579,7 +1693,7 @@
},
{
"cell_type": "code",
- "execution_count": 36,
+ "execution_count": 14,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1593,9 +1707,9 @@
"ReLU Complementarity Solution:\n",
"# of variables: 189\n",
"# of constraints: 248\n",
- "x = -0.26491612663085007\n",
- "y = -0.8823059564670135\n",
- "Solve Time: 0.09547257423400879\n"
+ "x = -0.30985268358479867\n",
+ "y = -0.8455103346317758\n",
+ "Solve Time: 0.13968920707702637\n"
]
}
],
@@ -1611,6 +1725,7 @@
},
{
"cell_type": "markdown",
+ "id": "333ff2ba",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1625,7 +1740,7 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 15,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1658,7 +1773,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 16,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1672,9 +1787,9 @@
"ReLU BigM Solution:\n",
"# of variables: 189\n",
"# of constraints: 308\n",
- "x = -0.26491679\n",
- "y = -0.88230334\n",
- "Solve Time: 4.298674821853638\n"
+ "x = -0.30985269\n",
+ "y = -0.84550685\n",
+ "Solve Time: 1.8677217960357666\n"
]
}
],
@@ -1690,6 +1805,7 @@
},
{
"cell_type": "markdown",
+ "id": "60341a6d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1705,13 +1821,140 @@
},
{
"cell_type": "code",
- "execution_count": 39,
+ "execution_count": 17,
"metadata": {
"pycharm": {
"name": "#%%\n"
}
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Welcome to the CBC MILP Solver \n",
+ "Version: 2.10.7 \n",
+ "Build Date: Feb 14 2022 \n",
+ "\n",
+ "command line - /usr/bin/cbc -printingOptions normal -printingOptions all -import /tmp/tmp_uvuil91.pyomo.lp -stat=1 -solve -solu /tmp/tmp_uvuil91.pyomo.soln (default strategy 1)\n",
+ "Option for printingOptions changed from normal to all\n",
+ "Presolve 256 (-172) rows, 124 (-65) columns and 3072 (-660) elements\n",
+ "Statistics for presolved model\n",
+ "Original problem has 60 integers (60 of which binary)\n",
+ "Presolved problem has 58 integers (58 of which binary)\n",
+ "==== 94 zero objective 31 different\n",
+ "==== absolute objective values 31 different\n",
+ "==== for integers 58 zero objective 1 different\n",
+ "58 variables have objective of 0\n",
+ "==== for integers absolute objective values 1 different\n",
+ "58 variables have objective of 0\n",
+ "===== end objective counts\n",
+ "\n",
+ "\n",
+ "Problem has 256 rows, 124 columns (30 with objective) and 3072 elements\n",
+ "There are 7 singletons with objective \n",
+ "Column breakdown:\n",
+ "0 of type 0.0->inf, 58 of type 0.0->up, 0 of type lo->inf, \n",
+ "8 of type lo->up, 0 of type free, 0 of type fixed, \n",
+ "0 of type -inf->0.0, 0 of type -inf->up, 58 of type 0.0->1.0 \n",
+ "Row breakdown:\n",
+ "0 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, \n",
+ "7 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
+ "7 of type G other, 24 of type L 0.0, 0 of type L 1.0, \n",
+ "218 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
+ "0 of type Free \n",
+ "Continuous objective value is -17.2784 - 0.00 seconds\n",
+ "Cgl0003I 0 fixed, 0 tightened bounds, 35 strengthened rows, 0 substitutions\n",
+ "Cgl0004I processed model has 388 rows, 168 columns (55 integer (55 of which binary)) and 3424 elements\n",
+ "Cbc0038I Initial state - 50 integers unsatisfied sum - 16.5378\n",
+ "Cbc0038I Pass 1: suminf. 4.33557 (27) obj. -0.210609 iterations 49\n",
+ "Cbc0038I Pass 2: suminf. 0.05645 (2) obj. -0.210609 iterations 67\n",
+ "Cbc0038I Pass 3: suminf. 0.00000 (0) obj. -0.210609 iterations 4\n",
+ "Cbc0038I Solution found of -0.210609\n",
+ "Cbc0038I Relaxing continuous gives -0.210609\n",
+ "Cbc0038I Before mini branch and bound, 5 integers at bound fixed and 19 continuous\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 241 rows 102 columns - 9 fixed gives 229, 96 - still too large\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 229 rows 96 columns - too large\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.01 seconds)\n",
+ "Cbc0038I Round again with cutoff of -1.88827\n",
+ "Cbc0038I Pass 4: suminf. 4.53896 (29) obj. -1.88827 iterations 5\n",
+ "Cbc0038I Pass 5: suminf. 1.48787 (17) obj. -1.88827 iterations 54\n",
+ "Cbc0038I Pass 6: suminf. 0.21136 (2) obj. -1.88827 iterations 44\n",
+ "Cbc0038I Pass 7: suminf. 0.46323 (2) obj. -1.88827 iterations 10\n",
+ "Cbc0038I Pass 8: suminf. 2.44728 (15) obj. -1.88827 iterations 28\n",
+ "Cbc0038I Pass 9: suminf. 0.19780 (2) obj. -1.88827 iterations 30\n",
+ "Cbc0038I Pass 10: suminf. 0.44303 (2) obj. -1.88827 iterations 11\n",
+ "Cbc0038I Pass 11: suminf. 3.47403 (20) obj. -1.88827 iterations 45\n",
+ "Cbc0038I Pass 12: suminf. 0.14308 (1) obj. -1.88827 iterations 44\n",
+ "Cbc0038I Pass 13: suminf. 1.90399 (14) obj. -1.88827 iterations 26\n",
+ "Cbc0038I Pass 14: suminf. 0.43625 (2) obj. -1.88827 iterations 29\n",
+ "Cbc0038I Pass 15: suminf. 0.19483 (2) obj. -1.88827 iterations 7\n",
+ "Cbc0038I Pass 16: suminf. 1.71231 (11) obj. -1.88827 iterations 22\n",
+ "Cbc0038I Pass 17: suminf. 0.44626 (2) obj. -1.88827 iterations 28\n",
+ "Cbc0038I Pass 18: suminf. 0.19932 (2) obj. -1.88827 iterations 7\n",
+ "Cbc0038I Pass 19: suminf. 1.33985 (12) obj. -1.88827 iterations 25\n",
+ "Cbc0038I Pass 20: suminf. 0.43625 (2) obj. -1.88827 iterations 28\n",
+ "Cbc0038I Pass 21: suminf. 0.19483 (2) obj. -1.88827 iterations 7\n",
+ "Cbc0038I Pass 22: suminf. 0.97602 (7) obj. -1.88827 iterations 13\n",
+ "Cbc0038I Pass 23: suminf. 2.37465 (15) obj. -1.88827 iterations 23\n",
+ "Cbc0038I Pass 24: suminf. 3.18019 (19) obj. -1.88827 iterations 11\n",
+ "Cbc0038I Pass 25: suminf. 2.87988 (19) obj. -1.88827 iterations 6\n",
+ "Cbc0038I Pass 26: suminf. 0.44626 (2) obj. -1.88827 iterations 40\n",
+ "Cbc0038I Pass 27: suminf. 0.19932 (2) obj. -1.88827 iterations 7\n",
+ "Cbc0038I Pass 28: suminf. 1.21662 (9) obj. -1.88827 iterations 16\n",
+ "Cbc0038I Pass 29: suminf. 2.29875 (17) obj. -1.88827 iterations 25\n",
+ "Cbc0038I Pass 30: suminf. 0.43625 (2) obj. -1.88827 iterations 40\n",
+ "Cbc0038I Pass 31: suminf. 0.19483 (2) obj. -1.88827 iterations 7\n",
+ "Cbc0038I Pass 32: suminf. 1.36943 (10) obj. -1.88827 iterations 16\n",
+ "Cbc0038I Pass 33: suminf. 2.90392 (17) obj. -1.88827 iterations 12\n",
+ "Cbc0038I No solution found this major pass\n",
+ "Cbc0038I Before mini branch and bound, 0 integers at bound fixed and 17 continuous\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 279 rows 118 columns - 10 fixed gives 249, 103 - still too large\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 248 rows 103 columns - too large\n",
+ "Cbc0038I Mini branch and bound did not improve solution (0.04 seconds)\n",
+ "Cbc0038I After 0.04 seconds - Feasibility pump exiting with objective of -0.210609 - took 0.04 seconds\n",
+ "Cbc0012I Integer solution of -0.21060906 found by feasibility pump after 0 iterations and 0 nodes (0.05 seconds)\n",
+ "Cbc0031I 50 added rows had average density of 82.42\n",
+ "Cbc0013I At root node, 50 cuts changed objective from -16.987081 to -5.5113161 in 100 passes\n",
+ "Cbc0014I Cut generator 0 (Probing) - 237 row cuts average 2.3 elements, 0 column cuts (28 active) in 0.088 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 1 (Gomory) - 3895 row cuts average 115.5 elements, 0 column cuts (0 active) in 0.171 seconds - new frequency is 1\n",
+ "Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.020 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.002 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 45 row cuts average 19.8 elements, 0 column cuts (0 active) in 0.102 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.019 seconds - new frequency is -100\n",
+ "Cbc0014I Cut generator 6 (TwoMirCuts) - 343 row cuts average 60.2 elements, 0 column cuts (0 active) in 0.028 seconds - new frequency is 1\n",
+ "Cbc0010I After 0 nodes, 1 on tree, -0.21060906 best solution, best possible -5.5113161 (1.56 seconds)\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 119 rows 50 columns\n",
+ "Cbc0016I Integer solution of -0.86488116 found by strong branching after 8325 iterations and 44 nodes (2.70 seconds)\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 279 rows 121 columns - 20 fixed gives 154, 62 - ok now\n",
+ "Cbc0038I Full problem 388 rows 168 columns, reduced to 128 rows 50 columns\n",
+ "Cbc0001I Search completed - best objective -0.8648811606598734, took 16109 iterations and 180 nodes (5.99 seconds)\n",
+ "Cbc0032I Strong branching done 3008 times (51330 iterations), fathomed 57 nodes and fixed 153 variables\n",
+ "Cbc0035I Maximum depth 20, 0 variables fixed on reduced cost\n",
+ "Cuts at root node changed objective from -16.9871 to -5.51132\n",
+ "Probing was tried 100 times and created 237 cuts of which 28 were active after adding rounds of cuts (0.088 seconds)\n",
+ "Gomory was tried 412 times and created 4647 cuts of which 0 were active after adding rounds of cuts (0.336 seconds)\n",
+ "Knapsack was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.020 seconds)\n",
+ "Clique was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.002 seconds)\n",
+ "MixedIntegerRounding2 was tried 100 times and created 45 cuts of which 0 were active after adding rounds of cuts (0.102 seconds)\n",
+ "FlowCover was tried 100 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.019 seconds)\n",
+ "TwoMirCuts was tried 412 times and created 943 cuts of which 0 were active after adding rounds of cuts (0.107 seconds)\n",
+ "ZeroHalf was tried 1 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
+ "ImplicationCuts was tried 268 times and created 122 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\n",
+ "\n",
+ "Result - Optimal solution found\n",
+ "\n",
+ "Objective value: -0.86488116\n",
+ "Enumerated nodes: 180\n",
+ "Total iterations: 16109\n",
+ "Time (CPU seconds): 6.03\n",
+ "Time (Wallclock seconds): 6.31\n",
+ "\n",
+ "Total time (CPU seconds): 6.03 (Wallclock seconds): 6.31\n",
+ "\n"
+ ]
+ }
+ ],
"source": [
"net_relu_partition = keras_reader.load_keras_sequential(nn2,scaler,input_bounds)\n",
"\n",
@@ -1744,13 +1987,15 @@
"def connect_outputs(mdl):\n",
" return mdl.y == mdl.nn.outputs[0]\n",
"\n",
- "status_2_partition = pyo.SolverFactory('cbc').solve(model2_partition, tee=False)\n",
+ "solver = pyo.SolverFactory('cbc')\n",
+ "solver.options[\"printingOptions\"] = \"normal\"\n",
+ "status_2_partition=solver.solve(model2_partition, tee=True)\n",
"solution_2_partition = (pyo.value(model2_partition.x),pyo.value(model2_partition.y))"
]
},
{
"cell_type": "code",
- "execution_count": 40,
+ "execution_count": 18,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1764,9 +2009,9 @@
"ReLU Partition Solution:\n",
"# of variables: 249\n",
"# of constraints: 428\n",
- "x = -0.26491679\n",
- "y = -0.88230334\n",
- "Solve Time: 5.003722667694092\n"
+ "x = -0.30985269\n",
+ "y = -0.84550685\n",
+ "Solve Time: 5.177385568618774\n"
]
}
],
@@ -1782,6 +2027,7 @@
},
{
"cell_type": "markdown",
+ "id": "af1df2ec",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1794,7 +2040,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 19,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1805,15 +2051,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Ipopt trunk: \n",
+ "Ipopt 3.14.16: \n",
"\n",
"******************************************************************************\n",
"This program contains Ipopt, a library for large-scale nonlinear optimization.\n",
" Ipopt is released as open source code under the Eclipse Public License (EPL).\n",
- " For more information visit http://projects.coin-or.org/Ipopt\n",
+ " For more information visit https://github.com/coin-or/Ipopt\n",
"******************************************************************************\n",
"\n",
- "This is Ipopt version trunk, running with linear solver ma27.\n",
+ "This is Ipopt version 3.14.16, running with linear solver MUMPS 5.7.1.\n",
"\n",
"Number of nonzeros in equality constraint Jacobian...: 2965\n",
"Number of nonzeros in inequality constraint Jacobian.: 150\n",
@@ -1830,76 +2076,99 @@
" inequality constraints with only upper bounds: 50\n",
"\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 0 0.0000000e+00 2.52e+00 7.94e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
- " 1 -4.6409314e-02 2.51e+00 8.04e-01 -1.0 2.23e+01 - 2.05e-03 2.08e-03f 1\n",
- " 2 -4.1860656e-02 2.49e+00 1.86e+00 -1.0 1.03e+01 - 2.59e-03 1.08e-02f 1\n",
- " 3 2.4536586e-02 2.34e+00 2.88e+00 -1.0 9.82e+00 - 1.42e-02 5.84e-02f 1\n",
- " 4 8.1271545e-02 1.62e+00 7.05e+00 -1.0 8.82e+00 - 6.37e-02 3.07e-01f 1\n",
- " 5 4.8810763e-02 1.34e+00 3.57e+00 -1.0 5.77e+00 - 4.49e-01 1.72e-01h 1\n",
- " 6 1.2961364e-02 7.88e-01 8.94e+00 -1.0 5.02e+00 - 6.30e-01 4.13e-01h 1\n",
- " 7 -2.0106918e-01 4.55e-01 4.22e+01 -1.0 3.79e+00 - 9.42e-01 4.23e-01h 1\n",
- " 8 -6.0116605e-01 2.47e-01 1.60e+02 -1.0 3.43e+00 - 1.00e+00 4.57e-01h 1\n",
- " 9 -7.3191200e-01 9.88e-02 2.51e+02 -1.0 1.30e+00 - 1.00e+00 5.99e-01h 1\n",
+ " 0 0.0000000e+00 1.38e+00 6.80e-01 -1.0 0.00e+00 - 0.00e+00 0.00e+00 0\n",
+ " 1 -4.2213234e-03 1.38e+00 7.02e-01 -1.0 1.09e+01 - 1.81e-03 1.85e-03f 1\n",
+ " 2 -7.1514143e-03 1.36e+00 1.51e+00 -1.0 1.46e+01 - 4.11e-03 1.39e-02f 1\n",
+ " 3 1.1925103e-01 1.26e+00 2.92e+00 -1.0 1.28e+01 - 1.56e-02 7.18e-02f 1\n",
+ " 4 6.0819010e-01 7.46e-01 6.89e+00 -1.0 1.18e+01 - 1.02e-01 4.10e-01f 1\n",
+ " 5 6.9742648e-01 5.78e-01 8.74e+00 -1.0 6.14e+00 - 6.22e-01 2.26e-01h 1\n",
+ " 6 9.7915102e-01 2.98e-01 1.86e+01 -1.0 4.83e+00 - 8.22e-01 4.84e-01h 1\n",
+ " 7 1.0151316e+00 1.41e-01 5.42e+01 -1.0 2.35e+00 - 9.82e-01 5.27e-01h 1\n",
+ " 8 9.2586119e-01 1.19e-01 7.81e+01 -1.0 1.84e+00 - 2.32e-01 1.59e-01h 1\n",
+ " 9 6.6692297e-01 9.12e-02 9.92e+01 -1.0 2.95e+00 - 1.93e-01 2.31e-01h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 10 -7.6842130e-01 4.87e-02 1.23e+03 -1.0 4.84e-01 - 1.00e+00 5.07e-01h 1\n",
- " 11 -7.9099629e-01 1.96e-02 2.23e+03 -1.0 2.40e-01 - 1.00e+00 5.99e-01h 1\n",
- " 12 -8.0158674e-01 8.31e-03 5.95e+03 -1.0 1.02e-01 - 1.00e+00 5.75e-01h 1\n",
- " 13 -8.0783181e-01 3.42e-03 1.37e+04 -1.0 4.77e-02 - 1.00e+00 5.89e-01h 1\n",
- " 14 -8.1132311e-01 1.42e-03 3.37e+04 -1.0 2.22e-02 - 1.00e+00 5.85e-01h 1\n",
- " 15 -8.1347790e-01 5.81e-04 7.97e+04 -1.0 1.10e-02 - 1.00e+00 5.90e-01h 1\n",
- " 16 -8.1482416e-01 2.35e-04 1.88e+05 -1.0 5.69e-03 - 1.00e+00 5.95e-01h 1\n",
- " 17 -8.1570393e-01 9.19e-05 4.20e+05 -1.0 3.09e-03 - 1.00e+00 6.10e-01h 1\n",
- " 18 -8.1629939e-01 3.27e-05 8.40e+05 -1.0 1.75e-03 - 1.00e+00 6.44e-01h 1\n",
- " 19 -8.1669516e-01 1.05e-05 1.44e+06 -1.0 1.00e-03 - 1.00e+00 6.80e-01h 1\n",
+ " 10 2.4412105e-01 8.98e-02 3.62e+01 -1.0 5.59e+01 - 1.89e-02 1.58e-02f 1\n",
+ " 11 -2.2316706e-01 7.11e-02 1.24e+03 -1.0 4.65e+00 - 6.83e-01 2.08e-01h 1\n",
+ " 12 -4.7732641e-01 5.77e-02 5.09e+03 -1.0 2.73e+00 - 1.00e+00 1.89e-01h 1\n",
+ " 13 -4.9330881e-01 3.35e-02 6.06e+03 -1.0 3.35e-01 - 1.00e+00 4.19e-01h 1\n",
+ " 14 -4.9910360e-01 1.91e-02 7.04e+03 -1.0 2.40e-01 - 8.17e-01 4.29e-01h 1\n",
+ " 15 -5.2133966e-01 1.40e-02 1.77e+04 -1.0 2.32e-01 - 1.00e+00 2.66e-01h 1\n",
+ " 16 -5.2834468e-01 1.04e-02 8.58e+04 -1.0 1.07e-01 - 1.00e+00 2.58e-01h 1\n",
+ " 17 -5.3496075e-01 4.22e-03 1.06e+05 -1.0 6.47e-02 - 1.00e+00 5.95e-01h 1\n",
+ " 18 -5.3637181e-01 1.69e-03 1.06e+05 -1.0 2.90e-02 4.0 1.00e+00 5.98e-01h 1\n",
+ " 19 -5.3760651e-01 1.18e-03 1.13e+06 -1.0 1.51e-02 - 1.00e+00 3.06e-01h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 20 -8.1698248e-01 1.99e-06 1.14e+06 -1.0 5.70e-04 - 1.00e+00 8.10e-01h 1\n",
- " 21 -8.1717000e-01 2.87e-10 5.48e+02 -1.0 2.87e-04 - 1.00e+00 1.00e+00h 1\n",
- " 22 -8.1721030e-01 1.33e-11 2.01e+02 -2.5 6.09e-05 - 1.00e+00 1.00e+00h 1\n",
- " 23 -8.1721306e-01 4.44e-15 5.21e+00 -2.5 2.77e-06 4.0 1.00e+00 1.00e+00f 1\n",
- " 24 -8.1721717e-01 3.92e-13 2.15e+00 -3.8 1.05e-05 3.5 1.00e+00 1.00e+00f 1\n",
- " 25 -8.1728949e-01 1.68e-10 2.58e+03 -3.8 5.85e-03 - 6.49e-02 3.69e-02f 2\n",
- " 26 -8.1729296e-01 1.41e-12 2.20e-02 -3.8 1.98e-05 3.0 1.00e+00 1.00e+00h 1\n",
- " 27 -8.1736151e-01 3.16e-10 1.31e+04 -5.7 2.97e-04 - 5.49e-01 1.00e+00f 1\n",
- " 28 -8.1736080e-01 3.20e-14 5.67e+03 -5.7 2.98e-06 2.6 6.37e-01 1.00e+00h 1\n",
- " 29 -8.1736450e-01 2.88e-08 3.87e+03 -5.7 2.83e-03 - 1.28e-01 1.00e+00f 1\n",
+ " 20 -5.3955151e-01 1.45e-04 1.98e+05 -1.0 8.67e-03 - 1.00e+00 8.77e-01h 1\n",
+ " 21 -5.3949283e-01 9.23e-05 2.38e+06 -1.0 1.15e-03 - 1.00e+00 3.63e-01h 1\n",
+ " 22 -5.3956061e-01 3.50e-05 2.40e+06 -1.0 5.94e-04 - 1.00e+00 6.21e-01h 1\n",
+ " 23 -5.3956541e-01 1.43e-05 3.04e+06 -1.0 2.91e-04 - 1.00e+00 5.91e-01h 1\n",
+ " 24 -5.3957103e-01 5.60e-06 2.52e+06 -1.0 1.35e-04 - 1.00e+00 6.09e-01h 1\n",
+ " 25 -5.3957167e-01 2.01e-06 1.47e+06 -1.0 5.05e-05 - 1.00e+00 6.41e-01h 1\n",
+ " 26 -5.3957541e-01 5.50e-07 1.72e+06 -1.0 1.46e-05 - 1.00e+00 7.26e-01h 1\n",
+ " 27 -5.3957544e-01 5.49e-07 7.97e+06 -1.0 4.13e-05 - 1.00e+00 1.88e-03f 10\n",
+ " 28 -5.3958752e-01 2.67e-12 7.88e+01 -1.0 3.44e-05 - 1.00e+00 1.00e+00h 1\n",
+ " 29 -5.3959089e-01 2.74e-13 2.79e+05 -3.8 1.12e-05 3.5 9.86e-01 1.00e+00h 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 30 -8.1737530e-01 2.14e-08 4.69e+01 -5.7 4.20e-05 2.1 1.00e+00 2.57e-01h 1\n",
- " 31 -8.1748935e-01 2.33e-06 4.16e+01 -5.7 2.55e-02 - 1.25e-01 1.00e+00f 1\n",
- " 32 -8.1759102e-01 4.28e-06 1.43e+02 -5.7 4.17e-01 - 2.83e-01 5.77e-02h 1\n",
- " 33 -8.1851759e-01 2.18e-04 1.33e+02 -5.7 2.46e-01 - 2.67e-01 1.00e+00f 1\n",
- " 34 -8.1814563e-01 4.76e-05 1.17e+00 -5.7 9.57e-03 - 1.00e+00 7.82e-01h 1\n",
- " 35 -8.1813261e-01 4.16e-05 8.17e+01 -5.7 2.64e-03 - 1.00e+00 1.25e-01f 4\n",
- " 36 -8.1804149e-01 1.21e-08 9.51e-02 -5.7 2.23e-03 - 1.00e+00 1.00e+00h 1\n",
- " 37 -8.1804147e-01 2.60e-11 4.78e-04 -5.7 8.37e-05 - 1.00e+00 1.00e+00h 1\n",
- " 38 -8.1804147e-01 1.78e-15 8.20e-09 -5.7 1.59e-07 - 1.00e+00 1.00e+00h 1\n",
- " 39 -8.1804147e-01 4.97e-10 4.25e+00 -8.6 3.65e-04 - 9.87e-01 1.00e+00h 1\n",
+ " 30 -5.3962685e-01 3.55e-11 1.89e+00 -3.8 1.28e-04 - 1.00e+00 1.00e+00f 1\n",
+ " 31 -5.3963813e-01 2.38e-12 1.77e-01 -3.8 3.30e-05 3.0 1.00e+00 1.00e+00f 1\n",
+ " 32 -5.3976973e-01 4.48e-10 8.87e+02 -3.8 7.33e-03 - 1.17e-01 6.17e-02f 2\n",
+ " 33 -5.3979375e-01 2.00e-11 3.55e-02 -3.8 9.59e-05 2.6 1.00e+00 1.00e+00h 1\n",
+ " 34 -5.4016606e-01 4.02e-09 4.09e+02 -5.7 1.20e-02 - 9.91e-02 1.13e-01f 1\n",
+ " 35 -5.4023618e-01 1.53e-10 2.44e+04 -5.7 2.65e-04 2.1 5.19e-01 1.00e+00f 1\n",
+ " 36 -5.4045325e-01 1.48e-09 9.08e+03 -5.7 8.24e-04 1.6 2.69e-01 1.00e+00f 1\n",
+ " 37 -5.4108669e-01 1.24e-08 3.27e-02 -5.7 2.38e-03 1.1 1.00e+00 1.00e+00f 1\n",
+ " 38 -5.4661515e-01 9.58e-07 2.53e+01 -5.7 1.21e+01 - 4.96e-04 1.72e-03f 1\n",
+ " 39 -5.4851901e-01 1.12e-07 3.20e-01 -5.7 7.18e-03 0.7 1.00e+00 1.00e+00f 1\n",
"iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
- " 40 -8.1804157e-01 5.69e-13 7.14e-01 -8.6 1.24e-05 - 8.25e-01 1.00e+00h 1\n",
- " 41 -8.1804169e-01 9.47e-14 1.46e-06 -8.6 5.04e-06 - 1.00e+00 1.00e+00h 1\n",
- " 42 -8.1804171e-01 2.66e-15 5.64e-09 -8.6 2.25e-07 - 1.00e+00 1.00e+00h 1\n",
+ " 40 -5.5364118e-01 8.19e-07 7.94e+00 -5.7 2.15e-02 0.2 1.00e+00 8.96e-01f 1\n",
+ " 41 -5.7086614e-01 9.09e-06 1.36e+00 -5.7 6.49e-02 -0.3 1.00e+00 1.00e+00f 1\n",
+ " 42 -5.9830981e-01 2.71e-05 9.22e-01 -5.7 1.97e-01 -0.8 5.18e-01 5.23e-01f 1\n",
+ " 43 -7.6056123e-01 7.68e-04 8.89e+00 -5.7 6.10e-01 -1.2 1.00e+00 1.00e+00f 1\n",
+ " 44 -7.9944754e-01 7.51e-04 8.07e+00 -5.7 1.98e+00 -1.7 1.04e-01 7.37e-02f 1\n",
+ " 45 -7.8973071e-01 7.77e-04 1.18e+00 -5.7 5.92e-02 - 1.00e+00 1.00e+00h 1\n",
+ " 46 -8.0680051e-01 1.29e-04 3.58e-01 -5.7 4.41e-02 - 1.00e+00 1.00e+00h 1\n",
+ " 47 -8.1840701e-01 1.74e-05 4.40e-01 -5.7 2.49e-01 - 3.69e-01 3.69e-01f 1\n",
+ " 48 -8.7645813e-01 7.65e-04 1.38e+01 -5.7 2.41e+00 -2.2 2.67e-01 2.72e-01f 1\n",
+ " 49 -8.6821723e-01 5.52e-04 1.00e+03 -5.7 3.14e-01 - 1.00e+00 2.99e-01h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 50 -8.7755204e-01 4.70e-05 1.12e+02 -5.7 1.07e-01 - 8.70e-01 9.46e-01f 1\n",
+ " 51 -8.7775200e-01 4.56e-05 3.87e+04 -5.7 6.55e-03 - 1.00e+00 3.05e-02f 1\n",
+ " 52 -8.7832336e-01 6.60e-06 6.85e+03 -5.7 3.98e-03 - 4.52e-01 8.56e-01f 1\n",
+ " 53 -8.7872676e-01 5.14e-06 2.23e+06 -5.7 8.27e-03 - 2.07e-03 2.21e-01h 1\n",
+ " 54 -8.7874167e-01 1.59e-12 7.35e+04 -5.7 5.09e-05 5.6 1.01e-02 1.00e+00f 1\n",
+ " 55 -8.7874339e-01 1.78e-15 2.51e-01 -5.7 1.72e-06 5.2 1.00e+00 1.00e+00h 1\n",
+ " 56 -8.7875323e-01 9.79e-13 6.23e+00 -5.7 1.75e-04 - 2.28e-01 1.33e-01f 2\n",
+ " 57 -8.7880211e-01 5.03e-10 4.29e+03 -5.7 2.83e-04 - 1.00e+00 1.73e-01f 1\n",
+ " 58 -8.7879876e-01 4.89e-13 7.98e-03 -5.7 1.65e-05 - 1.00e+00 1.00e+00f 1\n",
+ " 59 -8.7879858e-01 1.78e-15 3.66e-07 -5.7 4.35e-07 - 1.00e+00 1.00e+00h 1\n",
+ "iter objective inf_pr inf_du lg(mu) ||d|| lg(rg) alpha_du alpha_pr ls\n",
+ " 60 -8.7880397e-01 1.16e-12 1.06e+01 -8.6 2.55e-05 - 9.69e-01 9.99e-01f 1\n",
+ " 61 -8.7880412e-01 3.55e-15 3.30e+00 -8.6 1.47e-07 - 7.00e-01 1.00e+00f 1\n",
+ " 62 -8.7880423e-01 1.78e-15 4.52e-01 -8.6 1.05e-07 - 8.63e-01 1.00e+00h 1\n",
+ " 63 -8.7880431e-01 3.55e-15 4.53e-09 -8.6 8.62e-08 - 1.00e+00 1.00e+00h 1\n",
"\n",
- "Number of Iterations....: 42\n",
+ "Number of Iterations....: 63\n",
"\n",
" (scaled) (unscaled)\n",
- "Objective...............: -8.1804171339081455e-01 -8.1804171339081455e-01\n",
- "Dual infeasibility......: 5.6423246075354427e-09 5.6423246075354427e-09\n",
- "Constraint violation....: 2.6645352591003757e-15 2.6645352591003757e-15\n",
- "Complementarity.........: 2.6308254411353257e-09 2.6308254411353257e-09\n",
- "Overall NLP error.......: 5.6423246075354427e-09 5.6423246075354427e-09\n",
+ "Objective...............: -8.7880431160674222e-01 -8.7880431160674222e-01\n",
+ "Dual infeasibility......: 4.5283724343658262e-09 4.5283724343658262e-09\n",
+ "Constraint violation....: 3.5527136788005009e-15 3.5527136788005009e-15\n",
+ "Variable bound violation: 7.3203456619894392e-09 7.3203456619894392e-09\n",
+ "Complementarity.........: 3.0363333719947935e-09 3.0363333719947935e-09\n",
+ "Overall NLP error.......: 4.5283724343658262e-09 4.5283724343658262e-09\n",
"\n",
"\n",
- "Number of objective function evaluations = 49\n",
- "Number of objective gradient evaluations = 43\n",
- "Number of equality constraint evaluations = 49\n",
- "Number of inequality constraint evaluations = 49\n",
- "Number of equality constraint Jacobian evaluations = 43\n",
- "Number of inequality constraint Jacobian evaluations = 43\n",
- "Number of Lagrangian Hessian evaluations = 42\n",
- "Total CPU secs in IPOPT (w/o function evaluations) = 0.170\n",
- "Total CPU secs in NLP function evaluations = 0.009\n",
+ "Number of objective function evaluations = 78\n",
+ "Number of objective gradient evaluations = 64\n",
+ "Number of equality constraint evaluations = 78\n",
+ "Number of inequality constraint evaluations = 78\n",
+ "Number of equality constraint Jacobian evaluations = 64\n",
+ "Number of inequality constraint Jacobian evaluations = 64\n",
+ "Number of Lagrangian Hessian evaluations = 63\n",
+ "Total seconds in IPOPT = 0.184\n",
"\n",
"EXIT: Optimal Solution Found.\n",
- "\b\b\b\b\b\b\b\b\b\b\b\b\b"
+ "\b"
]
}
],
@@ -1924,13 +2193,14 @@
"def connect_outputs(mdl):\n",
" return mdl.y == mdl.nn.outputs[0]\n",
"\n",
- "status_3_mixed = pyo.SolverFactory('ipopt').solve(model3_mixed, tee=True)\n",
+ "solver = pyo.SolverFactory('ipopt')\n",
+ "status_3_mixed = solver.solve(model3_mixed, tee='true')\n",
"solution_3_mixed = (pyo.value(model3_mixed.x),pyo.value(model3_mixed.y))"
]
},
{
"cell_type": "code",
- "execution_count": 42,
+ "execution_count": 20,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1944,9 +2214,9 @@
"Mixed NN Solution:\n",
"# of variables: 259\n",
"# of constraints: 308\n",
- "x = -0.23830882868021425\n",
- "y = -0.8180417133908146\n",
- "Solve Time: 0.129364013671875\n"
+ "x = -0.2978469190009904\n",
+ "y = -0.8788043116067422\n",
+ "Solve Time: 0.20247483253479004\n"
]
}
],
@@ -1962,6 +2232,7 @@
},
{
"cell_type": "markdown",
+ "id": "fa0db35d",
"metadata": {
"pycharm": {
"name": "#%% md\n"
@@ -1981,7 +2252,7 @@
},
{
"cell_type": "code",
- "execution_count": 43,
+ "execution_count": 21,
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -1990,14 +2261,12 @@
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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pRGpqqri/cuXKIreRnJxc5GOIiIiIyHQMXc8z9bW/4goNDcXs2bMxY8YM8TkHBwesW7dO72jfhIQEcTsuLg6LFy8uctv5+6zFvY7IRDKRZeHU1kRUary9vXHq1CnMmDEDlStXFp9Xq9W4ePEivv/+e/Tu3RtVqlTB559/jpycnCK3oVQqxW1HR0ejjnF2di5SG0Ud1WuK9aA1z8vJycmoY/KX07ygWZx2jX0/jY2PiIiIyBIZWjsuJSWlxG2oVKoS10FERERExVeU63mmuPZXEprXWYHcWRrr1Kmj95jS6LPyOiLR84GJZCIqVa6urvjkk09w9+5dHDt2DF9++SV69eoFLy8vsUxycjKmTp2KPn36iGt6GEszKZyenm7UMWlpaUVqQwqa52VsvPnLubi4lKjd8vR+EhEREZWW/BfDHj9+DEEQivTYt29fmcSqVqvLpB0iIiIiKh0xMTGYMGGC1nNnzpzRO601oN1nffXVV4vcXxUEAREREVp18joi0fOBiWQiKhPW1tYIDQ3FBx98gM2bN+PBgwc4ePAgXn31VbHMn3/+id9//71I9WompDXXmdPH2HJS0pxC8fbt20Ydc+vWLXHb1ta2WInk4rTLNf2IiIjoeebu7q61TMv9+/fLrG0bGxtx25hRzaYYiUJERERE0sjOzsbAgQPFZGzt2rXF12bPno2TJ08WeqzmKGZT9Vd5HZHo+cBEMhFJwsrKCq1atcKWLVvQsWNH8fmtW7cWqZ6GDRuK21euXDFqOucTJ04UqQ0pNGrUSNw+ceKEUdN+HzlyROv44kyzo9nusWPHDJYXBAHHjx8vcjtERERE5UnTpk3F7cOHD5dZu66uruJ2UlKSwfIXL14szXCIiIiIqBTNnDkTp06dApCbGD5w4ACGDBkCIPemwgEDBmhNN60pNDRU3D537pxJRgYX9TqiUqnEpUuXStwuEZUtJpKJSFIymQw9evQQ9x88eFCk40NCQlCpUiUAuXflbdy4UW95tVqNX3/9teiBlrEWLVqII1sSExOxbds2veXVajVWrVol7rdv375Y7bZr107c3r59Ox4/fqy3/N69ey1ihDcRERFRaXrllVfE7SVLlhR5uZbi8vPzE7fPnz9vsF1DfWUiIiIiMk/79+/HF198Ie6vWrUKlSpVwsKFC1GrVi0AwI0bNzB+/Hidx9eqVQvBwcEAgKysLPzwww8ljknzOuKGDRuQnZ2tt/yGDRuQmZlZ4naJqGwxkUxEpSI1NRVZWVlGldWc0iQvKWwsKysr8c47IHcaF33Jz0WLFiEmJqZIbUjB3d0d4eHh4v6kSZP0jrZetGiROMLEysoKb7/9drHa7dSpE3x8fADkrm3y4YcfFlo2IyMD77//frHaISIiIipPRo4cCXd3dwC5a9RFRkYafeyjR4+Mmn1Gl+DgYHE5k3v37mHXrl2Flt22bZvBmxOJiIiIyPwkJyfjzTffhFqtBgCMGzcOXbt2BQC4uLjgl19+gVwuB5CbYN60aZPOeiZPnixuz5gxo0iz1eiaDnvAgAFwdHQEkHt9d968eYUen5SUhJkzZxrdHhGZDyaSiahUnD59Gn5+fpg9ezaio6N1lsnJycGGDRuwcOFC8bm8TlBRvP/++6hQoQKA3PWPO3fujBs3bmiVEQQB33//PSZOnKi1hp05mzlzJpydnQEAMTEx6Ny5M27evKlVRq1W47vvvsPEiRPF58aOHas1OqUorK2t8cknn4j7P/zwAyZMmICMjAytcvfv30ePHj1w/vx52NraFqstIiIiovLCzc0N33zzjbgfGRmJIUOGFLpWnCAIOHz4MMaMGYMaNWrg6dOnxWpXLpejX79+4v6IESMK9L0FQcDatWvRr18/i+kHExEREdEzo0aNEgfi1K1bF19++aXW682aNcNHH30k7o8cOVLnDIKDBg0SZzFMTU1Fq1atsGzZskIHAykUCvzyyy8ICwvDO++8U+B1Nzc3rUEoM2fOxLx58wrcJHn9+nV07NgRCQkJvI5IZIHkUgdAROXXvXv3EBkZicjISHh7e6Nhw4bw9vaGXC7HgwcPcPr0aSQkJIjlW7dujf79+xe5ncqVK2PZsmUIDw+HWq3GqVOnUKdOHbRu3RoBAQFIS0vDoUOHxA7Xt99+K3Z+rKzM934af39/rFy5EgMHDkROTg6OHj2K2rVro3Xr1vD394dSqcTBgwdx9+5d8ZhmzZppTXNTHEOGDMH27dvFqQ+/++47/PTTT2jXrh08PT0RHx+PqKgoZGZmombNmujZsye+/fbbErVJREREZOkiIiJw8+ZN8aa8n376Cb/88gsaNmyIOnXqwNnZGUqlEnfu3MG5c+eQkpJiknZnzJiB9evXIy0tDfHx8WjYsCHatm2LWrVqQaFQ4MiRI7h9+zbkcjmWLl2K4cOHm6RdIiIiIip9q1evFq/R2dnZYd26dbC3ty9Qbvr06di1axcOHz6Mx48fY8iQIdizZw9kMplYxtraGhs3bkTHjh1x9uxZKBQKjBo1Ch9++CGaN2+OatWqwdraGsnJybh27RquXLkClUoFAOjTp4/O+KZOnYrdu3fj8OHDEAQBU6ZMwXfffYe2bdvC2dkZN27cwMGDB5GTk4PQ0FD4+/tj3bp1pfBOEVFpYSKZiEqFg4MD5HK52Nm4f/8+/vnnn0LL9+3bFz/++GOxE7t9+/bF2rVrMXLkSCiVSuTk5GDfvn3Yt2+fWMbOzg4LFy5EWFiY+Jyrq2ux2isr4eHhcHJywvDhw/HgwQOoVCpERUUhKiqqQNk33ngDK1eu1NmZLKqff/4ZDg4OWLNmDYDcKXT++OMPrTJ16tTB5s2bsX79+hK3R0RERFQefPzxx6hXrx7ee+89JCQkICcnB6dPn8bp06cLPaZp06awsbEpdpt+fn7YtGkT+vTpg/T0dGRnZ2PPnj1aZVxdXbFq1So0bty42O0QERERUdmKjY3VGgk8d+5cvPDCCzrLWltb4+eff0aDBg2gUCiwd+9efPnllwWWrfP09MThw4cxceJErFy5EiqVCgqFAjt37iw0DgcHB7z44os6X7O1tcX27dsRHh4uXvu9d+9egeuFLVq0wKZNmzB16lSjzp2IzAcTyURUKkJDQ/Hw4UPs2bMHhw4dwtmzZxEbG4ukpCTk5OTA1dUV/v7+aNasGQYNGoSmTZuWuM0BAwagdevWWLhwIbZt24bbt29DJpOhevXq6NSpE0aNGoU6derg+PHj4jF5a9mZs1deeQU3btzAjz/+iL///huXL1/Go0eP4ODggKpVq6Jdu3YYPHgwQkNDTdamjY0NVq9ejcGDB2P58uU4fPgwHj58CA8PDwQEBKBfv34YNmyYOPU2EREREeXq168fevbsifXr12Pnzp04efIkEhMToVQq4eTkhGrVqiE4OBitW7dGt27dEBQUVOI2u3TpgqtXr2L+/PnYuXMn4uPjYW1tjRo1aqBHjx4YPXo0atSogVu3bpX8BImIiIio1KlUKgwcOBBKpRIA0KlTJ0yYMEHvMX5+fvj+++8xaNAgAMBHH32EDh06FLiZ0MHBAUuWLMHkyZPx888/Y+/evYiJiUFSUhLUajXc3NxQq1YtNGjQAC+//DK6dOmidzCOq6srduzYgT/++AOrV6/GyZMn8fjxY3h5eSE4OBgDBw7EoEGDSnTzJBFJRyYIgiB1EEREZWnFihV4++23AeSuMbJkyRKJIyIiIiIiIiIiIiIiIjIv5rs4KBFRKdmwYYO43aRJEwkjISIiIiIiIiIiIiIiMk8ckUxEz5U//vgDffr0AQDY29sjISEBHh4eEkdFRERERERERERERERkXjgimYjKhSNHjmDEiBE4d+6cztczMzPx7bff4o033hCfe/vtt5lEJiIiIiIiIiIiIiIi0oEjkomoXNi3bx/atWsHAPDx8UHDhg1RuXJlCIKAu3fv4ujRo0hJSRHLh4SE4MSJE3BycpIqZCIiIiIiIiIiIiIiIrMllzoAIiJTi4+PR3x8fKGvd+7cGevWrWMSmYiIiIiIiIiIiIiIqBAckUxE5YJarcb+/fuxfft2nDx5Evfu3cOjR4+gUCjg6uqKqlWrolWrVujfvz/atm0rdbhERERERERERERERERmjYnkfNRqNRISEuDi4gKZTCZ1OERERPScEQQBqampqFq1KqysrKQOh4zA/iMRERFJjX1Iy8M+JBEREUnJ2P4jp7bOJyEhAT4+PlKHQURERM+5+Ph4VK9eXeowyAjsPxIREZG5YB/ScrAPSURERObAUP+RieR8XFxcAOS+ca6urhJHQ0RERM8bhUIBHx8fsU9C5o/9RyIiIpIa+5CWh31IIiIikpKx/UcmkvPJm0rG1dWVnTgiIiKSDKe3sxzsPxIREZG5YB/ScrAPSURERObAUP+Ri6YQEREREREREREREREREZEWJpKJiIiIiIiIiIiIiIiIiEgLE8lERERERERERERERERERKSFiWQiIiIiIiIiIiIiIiIiItLCRDIREREREREREREREREREWlhIpmIiIiIiIiIiIiIiIiIiLQwkUxERERERERERERERERERFqYSCYiIiIiIiIiIiIiIiIiIi1MJBMRERERERERERERERERkRYmkomIiIiIiIiIiIiIiIiISAsTyUREREREREREREREREREpIWJZCIiIiIiIiIiIiIiIiIi0sJEMhERERERERERERERERERaZFLHQBRfmq1GiqVCmq1WupQiIieS1ZWVpDL5bCy4v1mRFQ62N8jIiJD2CclIiIiIpIeE8lkFtRqNZRKJRQKBZRKJQRBkDokIqLnmkwmg7OzM1xdXeHs7MwLeERUYuzvERFRUbFPSkREREQkLSaSSXJqtRp37txBWloa7O3tUbFiRdjb28PKygoymUzq8IiIniuCIECtViMjIwMKhQJ3796Fk5MTqlevzgt3RFRs7O8REVFRsE9KRERERGQemEgmSeVdVExPT0eNGjXg5OQkdUhERATAyckJnp6eSEtLQ3x8PO7cucMLd0RULOzvERFRcbFPSkREREQkLfa8SVJKpRJpaWnw8fHhRUUiIjPk5OQEHx8fpKWlQalUSh0OEVkg9veIiKik2CclIiIiIpIGE8kkKYVCAXt7e15UJCIyY05OTrC3t4dCoZA6FCKyQOzvERGRKbBPSkRERERU9phIJsmo1WoolUq4urpKHQoRERng6uoKpVIJtVotdShEZEHY3yMiIlNin5SIiIiIqGwxkUySUalUEAQB9vb2UodCREQG2NvbQxAEqFQqqUMhIgvC/h4REZkS+6RERERERGWLiWSSTN4dxFZW/DUkIjJ3eZ/VHP1BREXB/h4REZkS+6RERERERGWLV3RIcjKZTOoQiIjIAH5WE1FJ8DOEiIhMgd8nRERERERli4lkIiIiIiIiIiIiIiIiIiLSwkSylNQ5UkdARERERBZGEASpQyAiIiIiS8L+IxERERWTXOoAnisZj4CYBUBKNKC4AqTFA30fA1b8MRARERGRbusvrce/N/9FzOMYxCTFYFKLSZjYfKLUYRERERGRuYqPBxYvBq5dA2JigJQU4M4dqaMiIiIiC8QMZlm79In2vjIOcA2UJhYiIiIiMnu7Yndh1blV4n5MUoyE0RARERGR2Xv6FJg3T/u5x4+BChWkiYeIiIgsFqe2Lkv2XoCdl/ZziivSxEL0nNm3bx9kMhlkMhnCwsKkDsdi5b2HMplM6lCIiJ4bgRW0bzpkIpmo/Ll165bYx/Lz85M6HCpn2Icneg7VrAnI840funZNmliIiIjIojGRXNZcg7X3mUgmIiIiIj2CPIO09plIJiIiIiK9bGwAf3/t52LYhyQiIqKi49TWZa1GP8CzSW5C2S0YcKsndUREREREZMYaeDfA8EbDEeQZhEDPwAKJZSIiKl23bt1CzZo1AQC+vr64deuWtAGVgrCwMOzfvx8AEBUVxVmciMqDIUNy10auXRsICgLq15c6IiIiIrJATCSXtdrjpI6AiIiIiCxIQIUArHh1hdRhEBEREZElmTpV6giIiIioHGAimYiIiIiIiIionBIEQeoQiIiIiIjIQjGRTFRESqUSN27cQGZmJuzs7BAQEABnZ2epwyIiIiIiE2F/j4iIiIiIiIiIiWQio0RHR2Pp0qXYvn07bt68qXVHt0wmQ61atdCtWzeMGjUKISEhEkZKRERERMXB/h4RERERERERkTYrqQMgMmdxcXHo1KkT6tatiyVLliA2NrbAtGCCICA2NhZLlixB3bp10alTJ8TFxUkUcdmQyWTiI8/58+fx7rvvol69eqhQoQJkMhl69eql8/ikpCR89dVX6NixI3x8fGBvbw93d3eEhIRg7NixOHXqVJHiuX79OiZMmIA6derAyckJFSpUQMOGDTFz5kzcuXPH6HpWr14tnldERITB8rdu3RLL+/n5GdXGgwcP8MUXX6Bjx46oUaMGHBwc4ODggBo1aqBr16744osvcOvWLYP1ZGdnY+3atejXrx9q1aoFFxcXODk5oWbNmnjjjTewefPmIk1hl5KSgrlz56JJkybw8PCAs7MzateujREjRuD06dNG11Mce/fuxVtvvYX69evD3d0dcrkcjo6OqF69Olq3bo0JEybg77//RlZWls7jdf0+njx5EsOHD0dQUJD4O9G0aVPMnTsXCoXCqLhSUlLw66+/YuTIkQgNDYWXlxdsbW3h6uoKf39/vPHGG9i4cSPUanWRz/nChQuYMmUKQkND4e3tDVtbW/E9Dw8Pxw8//ICUlBSD9Zj6b4mI6HnE/p5xFAoFFi5ciB49esDPzw/Ozs6ws7ND1apV8fLLLyMyMhKXL1/WW8ejR4/w+eefo23btqhSpQrs7Ozg5eWFRo0aYdKkSYiOjjYYR2H9r4MHD+LNN99EQEAAHB0d4ebmhrCwMKxbt05nnygqKgqvv/46AgMD4eDggEqVKqF79+7YsWOHwRhmz54txjB79mwAQFpaGhYvXozWrVvD29sb9vb28PX1xcCBA7F//36DdRaHKfoBus4lIyMDy5YtQ1hYGKpUqQJbW1tUr14dgwcP1vkzUiqVWLx4MVq1aoUqVarA3t4e/v7+GDt2bJH64kDu+7hkyRL06NEDvr6+cHR0hIuLCwIDAzFs2DDs3bvXYB2F9ek3b96MHj16oEaNGrCzs0OlSpXQqVMn/Pzzz4X2m/Pqqlmzpvjcf//9p9X/1NUX1XT69GnMnTsXr7zyCmrVqgVnZ2fY2tqicuXKaNGiBaZPn47bt28b9f74+fmJbeX9nyE2NhbTp09Ho0aNULFiRVhZWaFhw4Zax+mLMe95zd/Tdu3a6Ty/1atXAwB69uwpPjd37lyjYgeAWbNmice99tprRh9HREREREQSEkhLSkqKAEBISUkpmwbVOYKQ+bhs2jIzT58+FaKjo4WnT59KHYpOK1asEOzt7QW5XC4AMPohl8sFe3t7YcWKFVKfQqnRPF9BEIRZs2YJ1tbWBd6Lnj17Fjh20aJFgpubm973UCaTCcOGDRMyMzMNxrJ48WLB3t6+0Lrc3d2FrVu3ClFRUeJzbdu21VnXqlWrxDJDhgwx2HZcXJxY3tfXV2/ZnJwcITIyUnB0dDT4O2RlZSVcvny50LqioqIEf39/g/U0a9ZMuHPnjsHzOHjwoFC1alW98URGRgqCUPBnXxJKpVJ49dVXjf7bKuxvStfvo5WVVaH1VKtWTThy5Ije2H7//XfBzs7OqLgaNGgg3Lx506hzTk5OFsLDwwWZTGaw3sqVK+uty9R/S4aY+2d2eVLmfREqMSl+ZmlZaWXWVkmY+2cH+3vGWbJkieDh4WHUe7Njxw6ddfzwww8Gv7esra2FCRMmCCqVqtBY8ve/VCqVMGHCBL31Dh06VFCr1YIgCEJaWprQs2dPveUnTZqk9/2YNWuWWHbWrFnC1atXheDgYL11jhgxokjnZYip+gH5zyU2NlZo0KBBoXXa2dkJ//zzj3j8iRMnhGrVqhVa3tXVVTh69KjB8xEEQdi4caPg7e1t8HfslVdeEZ48eVJoPfn79E+ePDHY5+zSpYuQnp6uty5jHvk1adLEqONsbGyEefPmGXyPfH19xWPi4uKEZcuW6fy/UIMGDbSO0xdjUc5v1apVgiAIwl9//SU+FxgYaDBuQcj9/1CNGjXE47Zt22bUcfmZ+/eKlNiHtDxl/jPLyRGENMvoQxIREVHpM7YvwqmtpZB4GIhZDCiuAIprgGco0CFK6qhIw5w5czBjxoxiHatSqaBSqTBixAg8ePAA06dPN3F05uXLL79EZGQkAMDf3x9NmzaFo6Mjbt26BRsbG62yEyZMwHfffSfue3l5oXnz5vD29kZGRgbOnj2LS5cuQRAE/Pjjj0hISMC2bdtgZaV78oRly5Zh7Nix4r6NjQ3CwsLg6+uLx48fY9++fXj8+DH69u2Lzz77rBTO3jg5OTl4/fXXsXnzZvE5W1tbNG/eHH5+frCxscH9+/dx+vRp3Lt3D2q1utDRt7/99hsGDhyI7OxsAICDgwOaNWsGPz8/WFlZISYmBkePHoVKpcKxY8fQvHlznDx5EpUrV9ZZ3+nTp9G1a1colUrxuZdeegn169dHVlYWjh07htjYWMyaNQseHh4mfFeAQYMGYevWreJ+QEAAGjVqhAoVKiA7OxuJiYm4ePGiUSO08yxYsED8fQwICEBoaChsbW1x8eJFcVTO3bt30aVLF+zfv7/AaI08Dx8+RGZmJgCgevXqCAkJgbe3NxwdHaFUKnHlyhWcOXMGgiDg/PnzaNOmDc6dOwdPT89CY0tISED79u1x7do18Tl3d3e0bNkSVapUQXZ2Nm7fvo3Tp09DoVAgIyOj0LpM/bdEROZPkanAh7s/xPXH1xGTFIM7ijt4NOkRPB0L/9wh/djfM8748eOxcOFCcd/a2hpNmjRBYGAg7O3tkZiYiHPnzonf17q+v+bPn49JkyaJ+3Z2dmjbti1q1KiB5ORkREVF4fHjx8jJycG3336L27dvY9OmTYWO8NQ0Y8YMfPvtt7CyskKTJk0QEhIClUqFgwcPijGtWrUKgYGBmDx5Mvr27YsdO3ZALpejZcuWCAgIQHp6OqKionD//n0Auf3bxo0bo3///gbbT0lJQdeuXREXFwc7OzuEhYXBx8cHSUlJiIqKwpMnTwAAK1asQEZGBn766SeDdRpSWv0AhUKBrl27IiYmBq6urmjbti28vb1x//59/Pvvv0hPT0dmZiZ69+6NixcvIjs7Gx06dIBCoYCXlxfatGkDT09P3L59G3v37kV2djYUCgV69eqFa9euwc3NrdC2v/nmG7z//vviyGBXV1c0b94c1atXR05ODi5fvoxTp05BEAT8/fffCAsLw+HDh+Ho6Kj3nFQqFfr06YN///0Xtra2aNGiBfz9/ZGRkYGDBw+KI4H/+ecfTJw4EUuWLNE6Pjg4GGPHjkVqaqr4s3NxccHgwYP1tpsnr347OzvUrVsXAQEBcHNzgyAIuHfvHo4fP45Hjx4hOzsbkydPBgB8+OGHRtX922+/iWWrVq2Kli1bws3NDQkJCXj8+LFRdQAQ/z+1efNmJCQkAAB69eqFatWqFSgbHBwMAOjatSt8fHwQHx+P69ev48CBA2jTpo3ednbv3i2+H9WrV0eXLl2MjpGISuD334H164GYGOD6dWDYMGDRIqmjIiIiIktS2hltS1MmdwPe/kMQfsGzx+/6R52VV+Z6J/GKFSuKdFe2ocfKlSulPiWT0zw/uVwuuLm5CZs3by5QLiMjQ9z+4YcftEYmrFixQsjKyipwzN69e7VGNRR2Z35MTIzW3fdt27YV4uPjC7SfN0LF1tZWq6wupTUiefLkyVrv2bhx44RHjx7pLHv8+HFh8ODBwqVLlwq8dunSJcHBwUEcYfLBBx8IycnJBcrFxsYKrVq1Etvr2rWrzrYyMzO1Rs/4+PjoHKm7Zs0awc7OTus9LOnXx7lz58R6nJ2dhe3btxdaNjY2Vvj000+FrVu36nxdMyZbW1vB3t5e+PnnnwuUO3TokNbvVv369XX+DgqCIGzdulWYO3eucP369ULjunnzptC5c2exvrfeeqvQstnZ2ULLli3Fsg4ODsKiRYt0tp+ZmSls3bpV6NWrl866TP23ZCxz/cwujziaxPKUxc8sOydbkH8sFzAb4uNovHGj/KRkrp8d7O8ZZ8mSJVrn2a9fP+H27ds6y168eFEYP368sHPnTq3nDx8+rDVzTdeuXYX79+9rlcnIyBAmTZqk1dZXX32lsx3N/peNjY0gk8mEOnXqCGfPntUql52drTVS2cPDQ4iMjBQACK1atSowm0h6errQr18/sXytWrXEUcz5aY7izesfdezYUbh3716BOseMGaN1XuvWrTN4Xvr6labuB2ieS95sLCNGjBAUCoVWufj4eKFOnTpafeUXX3xRkMlkwuzZswuMer506ZLW6OK82W102bNnjzibjK2trfD5558LaTpGzJ09e1YICQkR6xw9erTO+jT79Hnn1LVr1wIz9WRnZwsffPCBWFYmkwlxcXE66yzqiPE8o0ePFrZt26ZztLMgCIJKpRJWrVolODk5ib/T+ma60RyRLJfLBVtbW2H58uUFflc1/x8mCMbNKtS2bVuxTFRUlMFz0/zdGTx4sMHyr7/+ulj+o48+Mli+MOb6vWIO2Ie0PGXyM/vkE0EAnj06dCi9toiIiMiiGNsXYSI5nzLpxD25op1I/gXP5fTW5vgfwJs3b+qdJrk4D3t7e6OnvbUUmudnZWUl7N+/X295hUIhuLu7ixeHjh07prd8dHS0+HPw9PTUeSFpwIABYgx169bVWSbP8OHDtWIuy0TytWvXtKZZnjt3rsF6C9O+fXuxnq+//lpvWaVSqXWhTdd7vnz5cq3f0ytXrhRa388//1zgd7skFi5cKNYzffr0EtWVP67169cXWvbSpUtaU1b/8MMPJWo7KytLeOGFF8T38PFj3Z/lmgkLGxsb4cCBA8VqrzT+loxljp/Z5RUvAlqesvqZBS0M0kokrzm3plTbMwVz/Oxgf884jx8/FlxcXMRzHDVqVLHqadOmjVhHixYt9E6zPH78eK0kaf5kpiBo978ACBUrViyQwM2jUqmE2rVra5UPDg4uNKmnUCiEChUqiGWPHz+us5xmAg2A0LBhQ72/44MGDRLL+vn5CTk5OXrPq7B+ZWn0A/Kfy6BBgwqt79ChQwV+92fNmlVoec3+Y3BwsM4yOTk5QmBgoFjujz/+0HtO9+7dEypXriz2qfLfSCoIBaejbt26tZCdna2zPrVarTX99Oeff66zXHETycZav369WP+HH35YaDnNRDIAnTdP6mJMH76oieTbt2+L/89xdHTU+x346NEj8aYLfQl7Y5jj94q5YB/S8pTJz2zDBu1Eso9P6bVFREREFsXYvgjnuJSCiz8gyzereMpVaWIhLSNHjoRKpTJpnSqVCiNHjjRpneakb9++Bqcx+/HHH8Vp/caMGYPQ0FC95YODgzFkyBAAQFJSEv755x+t1588eYLff/9d3P/iiy/0Tmv3xRdfwMnJSW+bpeWbb76BWq0GADRr1kycsq6ozp8/j7179wIAGjVqhAkTJugt7+TkhI8++kjc/+WXXwqUWblypbj9zjvvoE6dOoXWN3DgQLRo0aKIURdOoVCI2xUrVjRZva1bt0Z4eHihr9etW1drOvQVK1aUqD0bGxsMHDgQQO5UnocOHdJZ7quvvhK3J06ciNatWxerPVP/LRGRZQnyDNLaj0mKkSgSy8b+nnGWL1+O1NRUAICvry++/fbbItdx5coVHDhwQNxftGgRbG1tCy3/2WefwcvLC0BuX2HdunUG25g2bRq8vb11vmZtbY1+/fppPTd37lw4ODjoLO/i4oLu3buL+ydOnDDYPpD7PW9vb1/o619//TXs7OwAALdu3cLu3buNqje/0u4H2NraYv78+YW+3rJlS9SoUUPcr1y5MqZNm1Zo+ddee038eV+9elX8fdL0119/4fr16wByp1Pu3bu33hi9vb3FfnB2djY2btyotzwAfPvtt5DLda/qJZPJMHToUHHf2J+5qfXt2xfOzs4AgD179hh1TNOmTcV+qBR8fHzE6anT09Px66+/Flp27dq14tI9HTp0gJ+fX1mESEQAULu29n58PJCeLk0sREREZJGYSJaClQ1Qbybw0mLg5b1A73uAVzOpo3ruRUdHY/fu3aVyYXH37t24cuWKSes1F8asHbd9+3Zxe8CAAUbV2759e3E7f3LuyJEj4vq1lSpVMri+loeHB1599VWj2jU1zQt248aNM2qtP10038M33njDqHr0vYepqanimsEAjFrnLe9CpCn4+PiI2z/99BPSTfQf2aKex8mTJ5GWlqa3/JMnT/DPP//gq6++wrRp0zB+/HiMGzdOfOzatUsse+7cuQLH//fff7h69dnNQuPGjTPiTHQz9d8SEVmWgfUHYk77Ofjt9d9wftR5TGtdeAKHdGN/z3iafZgRI0aIidCiiIqKErcbNmyIRo0a6S3v5OSEN954Q+fxhenbt6/e1+vXry9uOzg4oFu3bnrL16tXT9yOi4sz2H716tXRrl07vWUqVqyo1a4x56VLafcDWrdujcqVK+sto/n+9OjRQ++NAQ4ODvD39wcACIIgrlmtqbTPqVatWmjcuLHeMpq/l7piNJULFy5gzZo1iIyMxPvvv6/Vn3z33XfF/v3FixfFG1H1Meb/YaXt7bffFrd/+OGHQstpvjZ8+PBSjYmI8gkMBN5/H1i+HNi3D7h3DyjkhioiIiIiXXTflkulr/5HhstQmVq6dCnkcrnJLywCgFwux5IlS7BgwQKT1y21F1980WCZo0ePitvLly/HmjVrDB5z584dcTs+Pl7rtbNnz4rbTZs2hZWV4Xtimjdvrvcu+dLw4MEDrYtRhi4y6qP5HkZFReG///4zeIwgCOJ2/vfwwoUL4gUqFxcX1K1b12B9zZs3NzZcg7p16wYnJyekpaXhzJkzqFOnDt566y10794djRo1grW1dbHqNSbG+vXrw9nZGUqlEjk5Obhw4YLO4+7cuYMpU6Zg06ZN4o0Lhjx69KjAc8eOHRO3AwMDUb16daPq0sXUf0tEZFn615M+aWDp2N8z3vHjx8Xt4vZhNPtsxs5s0rJlSyxcuBAAcObMGb1l3dzcDH6venh4iNtBQUGwsbHRW75ChQrituYMKoVp1qyZUTf4NW/eHJs3bwag/b4URWn3AzSTxIXRfD+N6T8aej81z+n333/H/v37DdaZkpIibhs6J80bCQrj6empN8aSWrNmDT777DPExBg3i0R2djZSUlK03mtdjPl/WGl75ZVXULVqVSQkJODkyZO4ePFigff8xIkTuHTpEgDAy8sLvXr1kiBSoueYoyOgZ7YJIiIiIkOYSCb6v+3bt5fKRUUgd5TKjh07SqVuqRmallipVGpNY6c5nbKxkpOTtfYTExPFbc3p9fQxtpwpPXjwQNy2s7ND1apVi11XQkKCuF2c3yV976GPj49RF0BN+R56enpi5cqVGDx4MLKzsxEfH4/Zs2dj9uzZcHZ2RmhoKNq2bYsePXqgYcOGRtdrTIwymQzVq1cXRwlrvhd5zp49i5dffrnA+2aIrikbNX8PatWqVaT6NJXG3xIR0fOG/T3jKBQKPH36VNwv7veX5nesr6+vUcdoTnmr6wYtTW5ubgbr05zSuKjls7OzDZYvTl9UV9/DkLLoB0jxfmr2cTds2GCwvvxMcU6aNxcY8zM3liAIeOutt7Bq1aoiH5uammowkWzK5WGKy9raGsOGDcOnn34KIHfkcf5p8DVHI7/55pt6R7ETEREREZH54dTWRMj9j/rNmzdLtY3Y2FgolcpSbUMKha0xl0dzxEBx5b/gq/k+6lsbWZMUayRrXuzLW/OsuEr6Pubk5Gjtm8N72L9/f5w4cQK9e/fWuoCnVCrx77//YubMmWjUqBFeeuklHDx40Kg6i3Mu+ZO/mZmZ6NOnj3hhsmLFipgxYwaioqIQHx+PtLQ0qNVqCIIAQRC0Lg7qmobQVL8HpfG3RET0PGF/z3j5vxuL+/2l+V4Y24/Q9x2dX1GXDCnuEiP6mKLvYYyy6AdI8X6W9LxMfU6mtGLFCq1+YpcuXbBmzRpcvHgRycnJyMzMFPuTgiBo3WxhzNTWhv4fVlaGDx8uzhD1888/i2shA7lrJ69fv16rLBERERERWRaOSCZC7kU/zWmAS4MgCLhx40aRRleWB/kvGj5+/Njg3fWGaF7MNHZtXUPr4BaHoQs8Li4u4nZJLyprvo9//PEHevfuXaL6zOU9bNiwIf744w88efIEBw4cwKFDh3Do0CGcOnVKHBFy+vRptGvXDr/++itef/11vfWlp6drve+F0TyX/OV///13cU3EatWq4eTJk6hSpUqhdRm6GGyq34PS+FsiInqesL9nvPzfjUqlsljJZM1jjO1H6PuONkfF6UcV57zKaz/AyclJTCafOXPG4DralmS+xlSykZGRmDlzpt7yxbnBwBz4+vqiY8eO2LlzJ5KSkrBlyxb069cPAPDbb7+J04U3b94cISEhUoZKRERERETFwBHJRIDR659aSjvmxN3dHXZ2duL+/fv3S1yn5jRut2/fNuoYY9aG1RwVa8zITUMjKCpXrixuZ2Zm4t69ewbrNKYuU7+Hd+7cMerCemmur+vu7o5XX30VX3zxBY4cOYJHjx5h1apV4jSQOTk5GDNmjNY0m7oY8/sgCALu3r0r7nt5eWm9/u+//4rbEyZM0JtEBmBwvWrNn11egro4SuNviYjoecL+nvFcXV21RjsW9/urOH22W7duidv5v6PNUXH6osU5r/LaDzB1H9dcxMfH4/r16wByf3ZTp07VW16hUFj0EiRvv/22uK05lbXmNkcjExERERFZJiaSzYEgAOl3gacPDJelUqF5UaY8tGNumjZtKm4fPny4xPVpjlQ4efKkUVO/HT161GAZV1dXcTspKclg+YsXL+p9vXLlylrr/O3du9dgnYUJDQ0Vt03xHr7wwgviFHQKhQLR0dEGjzHmPTQVV1dXREREYO/eveLfzaNHjwzGcOzYMYN1X7p0SRzxYW1tjQYNGmi9rrlWX/369Q3Wd+DAAb2vN2vWTNyOiYnBnTt3DNZZGFP/LRGRZctQZeBh2kOpw7AY7O8VjWbfo7h9GM0+25EjR4w6RrNc48aNi9VuWTp+/LhR5TT7MMU9r/LYDzB1H7e0FHWKbM3+ZJ06dbRuWNXl0KFDpT5jgiElmQb81Vdfhbe3NwBgz549uH37NmJiYsTlaZydncVRykQkMUEAEhKAcnDjGxEREZUNJpKldPUbYGco8JsbsKU6ELNI6oieWwEBAaW+fpZMJkNAQECptmGuXnnlFXF7yZIlJb5I0qJFC/Ei7YMHD7Br1y695VNSUrB161aD9Womfc+fP28wzo0bNxqss2vXruL24sWLi33umu/hH3/8gQcPSnbjiYuLC1566SVxf+3atQaP+emnn0rUZnH4+/ujbt264r6h8/75558N1ql5Hk2aNCkwVWRegh0wPF3l6dOncfLkSb1lfH19ERwcLO4vXrzYYIyFMfXfEhFZnr9j/kbnnzuj5nc14TjHEcO3coSXsdjfKxrNPsyKFSuKNdK6ffv24vbZs2dx4cIFveXzr6eqeby5io+Px759+/SWefToEbZv3y7ut2vXrlhtlcd+gOY5/fjjj8jIyJAwmsLZ29uL23nLr+hTlP4kkPvzlFpRz1GTXC7H0KFDAeQu/7Nq1Sr8+OOP4uv9+/cv9lrrRGQiw4YBL74IuLoC1aoBJ05IHRERERFZCCaSpZQWDySdAFT/XwtJcUXaeJ5jzs7OqFWrVqm24e/v/9z+53nkyJFwd3cHkLv2WWRkpNHHPnr0CDk5OVrPubu7o0+fPuL+hx9+qHfK48mTJxu1Nm1wcLC4Zt29e/f0Jqi3bduGbdu2GaxzwoQJ4oWko0ePYt68eQaP0aVp06YICwsDADx9+hRvvvkmsrKyjDo2KytL51R5mtPLLViwADExMYXWsX79ehw6dKhoQevx6NEjo8rl5ORoTQleqVIlveX37duHTZs2Ffr6lStXsGjRs5t2dE2xp/lZoO8GhPT0dK1p/PSZOHGiuP3VV1+JozOKytR/S0RkeR4/fYxdsbtw68ktCBAQk1T4ZzdpY3+vaEaMGCGey3///YcJEyYUuY46deqgTZs24v64ceP0JqhmzJiBhw9zR9m7urpiwIABRW5TCh988IHeRPsHH3wgJkjz1pMtjvLYD+jTp49488W9e/cwZswYoxPkSqXS6LW3S8rd3V3s0ycmJhpMtNasWVO8ceXSpUu4efNmoWU3bNiAv//+23TBFpOnp6e4rbkMjLGGDx8unvOqVauwZs0ardeISGInTwJnzgB510auXZM2HiIiIrIYTCRLyS1Ye1/BTpyUunXrBrlcXip1y+VyrVEdzxs3Nzd888034n5kZCSGDBlS6JpygiDg8OHDGDNmDGrUqKEzSTxz5kxxVPLFixfRvXv3Ahc8MjMz8cEHH2DZsmWwtbU1GKdcLteacm3EiBEFpnwWBAFr165Fv379jJq6MigoCO+//764P3XqVLzzzjt4/PixzvInTpxAREQELl++XOC1hQsXihd0d+/ejTZt2uidTjEmJgaffPIJ/Pz8dE4VOHjwYNSuXRtAbnK6Y8eOOuv75ZdfMHToUKPeQ2NNmjQJbdq0wU8//YQnT57oLJOUlIQRI0aIiWRXV1e0aNFCb722trYYPHgwfv311wKvHT16FJ07dxYv5NatWxdvvvlmgXI9evQQt9esWYOvvvqqwIXXGzduoFOnTjhz5kyBEc26REREiLFnZ2ejS5cu+P7773VehMzKysJff/2F3r17F3itNP6WiMiyBFYI1NqPTY6FSq2SKBrLw/6e8Tw8PLRugFu6dCnCw8MLXaLh8uXLePfddwvciDd37lxYW1sDAA4ePIg+ffqIyeI8WVlZmDp1qtZ33KxZsywiKW9ra4vTp0+jV69eBWZOycjIwPjx47USanPmzNEarVoU5bEfYG1tjSVLloi/I6tWrUL37t1x5UrhN1mfO3cOkydPho+PT7HX7y4qOzs7BAbmfv5mZ2djy5Ytest7eXmJy5uo1Wr07dsX1/IlbdRqNRYvXow333wT1tbWWiOCpVCvXj1xe9OmTUUe8V6rVi28/PLLAHJvPslb87p+/fpaU5gTkUSCgrT3mUgmIiIiI5XOVRQyjmtt7f2ndwFBDciY35fCqFGjsHDhwlKpW6VSYfTo0aVSt6WIiIjAzZs38cknnwDInV74l19+QcOGDVGnTh04OztDqVTizp07OHfuHFJSUvTWV7t2bXz99dcYO3YsACAqKgq1atVCWFgYfH19kZycjKioKCQlJcHW1hZz5szBpEmTDMY5Y8YMrF+/HmlpaYiPj0fDhg3Rtm1b1KpVCwqFAkeOHMHt27chl8uxdOlSo+6u/+yzz3D16lX89ddfAIBFixZh+fLlaN68OWrWrAm5XI779+/j9OnTYtJU16ifevXq4ddff0V4eDjS09Nx/PhxNGvWDP7+/mjcuDEqVKiAjIwMPHz4EBcuXDA4ksDOzg5r165Fu3btkJaWhtu3b6NZs2Zo2rQp6tWrh6ysLBw7dgw3btwAkDtqefz48QbP1xiCIODgwYM4ePAgrK2tUadOHQQHB8PDwwNPnz7F3bt3cfjwYa1R1/Pnz4eDg4Peer/44gtMmDABAwYMwKxZsxAaGgobGxtcunRJawpqZ2dnrFmzRmdyvFOnTmjTpg0OHDgAQRDwwQcfYPHixWjcuDHc3Nxw/fp1HDlyBDk5OahWrRreffddfPjhh3rjksvl2LBhA9q3b4/r168jPT0dY8eOxfTp09GyZUtUqVIFKpUK//33H06fPg2FQgE3NzeddZn6b4mILEuQp/ZFQJVahdspt1HLo3RH2pYX7O8VzZgxY3Dp0iVx2t2NGzfi999/R5MmTRAUFAR7e3skJibi7NmzuHXrFoCC0za3aNECn3/+udgP++uvv1CjRg20a9cOPj4+Wn22PL1798Z7771XNidZQqNHj8aff/6Jf/75B35+fggLC4OPjw+SkpIQFRWlNSvMgAEDMHDgwBK1Vx77AR06dMCSJUswevRo5OTkYMeOHfjnn38QEhKCF154Aa6urkhPT8e9e/dw/vx5JCYmShJnnz598NlnnwEABg4ciNWrVyMgIEBr/eP58+eL25988gk6deoEtVqNs2fPon79+mjZsiVq1aoFpVKJgwcPin3/OXPmYPny5fjvv//K9qQ0vPbaa5g2bRoEQcC2bdvwwgsvoEWLFuKMTUDuFNWay+Pk9/bbb2PPnj1az7311lulFjMRFUHtfNcg9cyUQERERKRFIC0pKSkCACElJaX0G8t8LAhXFwpCwi5BUP4nCOqc0m/TjDx9+lSIjo4Wnj59KnUooo4dOwpyuVwAYLKHXC4XOnbsKPWpmZTm+RXVhg0bhKpVqxr9/jVt2lTIyMgotL4FCxYIdnZ2hR7v5uYm/Pnnn0JUVJT4XNu2bfXGuGPHDsHR0bHQOl1dXYXff/9diIuLE5/z9fXVW2dOTo4wbdo0vbHmPaytrYUrV64UWte5c+eEF1980ej30M/PTzh79myh9e3fv1/w9vYu9HgrKyth1qxZgiCU7Gevady4cUbH7+LiIixfvrzQuvLH9NFHHwkymazQ+qpWrSocOnRIb3z3798XGjdurDeukJAQ4fLly8KqVavE54YMGaK33qSkJKF3795GnXe1atX01mXqvyVDzPEzu7wq074ImURZ/8zmHpwrrL+4XjiTcEZQZCjKpM3iMsfPDvb3iu7bb78VXF1dDb4PMplM2Llzp846Vq5cabAOa2tr4d133xVUKlWhsRSl/yUIQpH6gIIgGPW9PmvWLLHMrFmzhCtXrgi1a9fWe27Dhg0TsrOzTXZepuoH5D8XQ4YMGSKWX7VqlcHybdu2FctHRUXpLbt3714hMDDQ6HOqW7eucPfu3QL1FKVvJgjGv/dPnjwR6tSpozem/JYsWaL388bKykqYOXOmoFarBV9fX/H5uLg4nTEYU0YXfTFqmjp1qt7zM/Qzz8rKEipVqiSWt7OzE5KSkoyO0xjm+L1iLtiHtDxl+jM7flwQvv9eEPbsEYT4eEHIeb6uQRIREVFBxvZFOCJZSrYeQO1xUkdBGpYtW4aQkBCoVKabIlIul2PZsmUmq8/S9evXDz179sT69euxc+dOnDx5EomJiVAqlXByckK1atUQHByM1q1bo1u3bgjKP/1SPu+88w46d+6MRYsW4Z9//sGdO3dgZ2cHHx8fvPLKKxg1ahRq1KiBffv2GR1jly5dcPXqVcyfPx87d+5EfHw8rK2tUaNGDfTo0QOjR49GjRo1xJE3xrCyssKcOXMwatQorF69Grt378aNGzfw6NEjyOVyVKpUCXXr1sXLL7+M8PBwVKtWrdC6GjRogFOnTmHXrl3YsmULDh8+jISEBDx58gR2dnaoWLEiateujdDQUHTu3BnNmzcX1yvTpU2bNrhy5QoWL16MP/74A7GxscjOzkbVqlXRpk0bjBw5Ek2bNjX6XI2xcOFCjBkzBnv27MGxY8dw+fJl3L59G6mpqZDL5fD09ETdunXRqVMnvPnmmwbXRtb08ccfo1u3bli+fDkOHjyIhIQE2NjYICAgAK+99hrGjh1b6GjfPJUrV8aRI0ewcuVKrF+/HpcuXUJ6ejoqVaqE2rVrIzw8HAMHDoSjoyNOnDhhdGwVKlTAH3/8gZMnT2LdunXYt28f7ty5g+TkZDg4OKB69epo2LAhunTpgr59++qty9R/S0RkOaa0miJ1CBaN/b2ie/fddzFo0CCsXr0aO3fuRHR0NB49egQgdwrf4OBgtG3bFuHh4eL0v/m99dZb6NmzJ1asWIEdO3YgJiYGjx8/houLC3x8fNChQwcMGzYMISEhZXlqJlGnTh2cPHkSP/74IzZu3IgbN27gyZMnqFy5Mlq2bIm33367wEjtkiqP/YB27drhypUr2LJlC7Zt24Zjx47h/v37UCgUcHR0ROXKlVGnTh20aNECXbt2RcOGDcs0Pjc3N5w8eRLff/89tm3bhitXruDJkyd610seNWoUWrZsiW+++QZRUVFISEiAg4MDqlWrhvbt22PYsGFo1KhRGZ6Ffp999hlatWqFVatW4fTp03jw4AHS09ONPt7GxgavvPIKfvzxRwC5swtUqFChtMIloqJo2jT3QURERFREMkEo4sI35VzedKIpKSlwdXWVOpxyLSMjA3FxcahZs6bk60FpWrlyJUaMGGHS+jidF1Hp0UyS8yut9JjrZ3Z5xL6I5eHPrHDm+tnB/h6VxOzZsxEZGQkgdy3n2bNnSxsQkZkQBAH+/v7i2tV79uwR1002FXP9XjEH7I+UnYkTJ2qtWe/r61ukG83z8GdGREREUjK2L8LFeInyGT58OD799FOT1DVnzhxeVCQiIiIyM+zvERGZXlRUlJhErlWrFtq3by9xRESmd+LECXz33XdSh0FERERUZphIJtJh+vTpWLFiBezt7SGXF20GeLlcDnt7e6xcuRLTpk0rpQiJiIiIqCTY3yMiMq0FCxaI2yNHjtS7vA6RJcrOzsbw4cOhVqulDoWIiIiozDCRTFSI4cOHIzo6WlzPzNAFxrzX27Vrh+joaI5MISIiIjJz7O8REZnG1q1b8eeffwIAXFxcMHz4cIkjIjK9efPm4eLFiwCAAQMGSBwNERERUdlgItlcCAKQkQg8PATkZEgdDf1fzZo1sWvXLly+fBmjR49GQEBAgbuqZTIZAgICMHr0aERHR2PXrl2oWbOmRBETERHR8yQrJwtXH11FSkaK1KFYLPb3iIiK7saNG5gwYQLeeecddO7cGb169RJfmzRpEipUqCBdcESl4OrVq+KyGAMHDkTHjh0ljqgEBAF4+BC4cEHqSIiIiMgCFG0ONzI9dQ6wpzWguApkJec+1+UMUKGRtHGRlpCQEHGaLqVSiRs3biAzMxN2dnYICAiAs7OzxBESERHR8yRiSwQO3T6EuCdxUAtq/N7vd7wW/JrUYVk09veIiIx3584dnevEtmzZEpMnT5YgIqLSIwgChg8fjszMTHh4eODrr7/G9u3bpQ6r6E6fBsaMAa5dA1JSgCpVgIQEqaMiIiIiM8dEstSsrIH0u8+SyACQGsNEshlzdnZGw4YNpQ6DiIiInmO3U24jNjlW3I9JipEwmvKH/T0iIuPZ2tqiZs2aCA8Px5QpU2Brayt1SEQmtWTJEhw+fBgA8OWXX6JSpUoSR1RMDg7AiRPP9u/dA1JTARcX6WIiIiIis8dEsjlwrQ2k3362r7gmXSxERBZGEASpQyAiKnOBFQIRdStK3GcimajszJ49G7Nnz5Y6DCJJhYWFsR9Oz4X4+HhMmTIFANC6dWsMGzZM4ohKwN8fsLIC1Opnz8XEAC++KF1MREREZPa4RrI5cK39bNvKFlClShcLEREREZm9IM8grf2HaQ8lioSIiIio/BozZgxSU1Nha2uLZcuWQSaTSR1S8dnZAX5+2vv37kkWDhEREVkGjkg2B/5vAVW65iaUnXwBK/5YiIiIiKhw3YO6o4pLFQR5BiGwQiDc7N2kDomIiIioXFm/fj3+/vtvAMDkyZMRHBwscUQm8PXXgL09ULs24OMDWFtLHRERERGZOWYszYFHw9wHEREREZER6njVQR2vOlKHQURERFQuJSUlYfz48QCAoKAgTJ8+XeKITKRnT6kjICIiIgvDRDIRERERERERERHR/7333ntITEwEACxduhR2dnYlrjMzMxOZmZnivkKhKHGdRERERKWNayQTERERERERERERAdi1axfWrl0LABgyZAjatWtnknrnzp0LNzc38eHj42OSeomIiIhKU7lJJE+cOBEymUx8+Pn5SR0SERERERERERERWYi0tDSMHDkSAODp6Yn58+ebrO6pU6ciJSVFfMTHx5usbiIiIqLSUi6mtj5x4gS+++47qcMgIiIiIiIiIiIiCzV9+nTcunULAPDVV1/By8vLZHXb2dmZZIpsIiIiorJk8SOSs7OzMXz4cKjVaqlDMY2sZODRceD+v1JHQkREREQWQKVW4cbjGzh0+5DUoRARERFZrDNnzmDhwoUAgHbt2mHIkCESR1TKkpKAw4eB6GipIyEiIiIzZvEjkufNm4eLFy8CAAYMGIB169ZJHFExJewEjr4JZCbm7jv7A6/ekDYmIiIiIjJb5+6fQ/imcNxMvgmVWgUXWxekTEmBTCaTOjQiIiIii3PhwgVxoMrt27fRrFmzQssmJiaK2/fu3dMq+9FHH6F79+6lF2hJffwxsGgRkHcO48YB/0+gExEREeVn0Ynkq1ev4tNPPwUADBw4EB06dLDcRLJdhWdJZABIiwNyMgFrTnlDREREVBomTpyIb775Rtz39fUVpzK0BB72HohJihH3U7NS8SDtAbydvSWMioiIiMjyxcbGIjY21qiyWVlZOH78uLivmWQ2SzLZsyQyAFy9Kl0sREREZPYsdmprQRAwfPhwZGZmwsPDA19//bXUIZWMS5D2vqAGlDeliYWIiIionDtx4gS+++47qcMoER83H9jlu+lQM7FMRERERFRAnTra+0wkExERkR4Wm0hesmQJDh8+DAD48ssvUalSJYkjKiFbN8C+cu62zBpwCQSynkgaEhEREVF5lJ2djeHDh4tTF1oqK5kVAj0DxX0Pew88fvpYwoiIiIiILFdERAQEQTDqsWrVKvE4X19frdciIiKkOwljBAc/27axAdzcgKws6eIhIiIis2aRU1vHx8djypQpAIDWrVtj2LBhEkdkIq02AfYVAaeagLWt1NEQERERlUvz5s3DxYsXAQADBgyw3KVRACzosgAONg4IrBAIT0dPqcMhIiIiInMXGAhs3Zo7MrlmTUBukZeHiYiIqIxYZE9hzJgxSE1Nha2tLZYtWwaZTCZ1SKZRqZXUERARERGVa1evXsWnn34KABg4cCA6dOhg0YnkdjXbSR0CEREREVkSOzugRw+poyAiIiILYXFTW69fvx5///03AGDy5MkI1pyOhYiIiIioEIIgYPjw4cjMzISHhwe+/vprqUMiIiIiIiIiIiIyWxaVSE5KSsL48eMBAEFBQZg+fbrEERERERGRpViyZAkOHz4MAPjyyy9RqVIliSMiIiIiIiIiIiIyXxaVSH7vvfeQmJgIAFi6dCns7OxKXGdmZiYUCoXWg4iMk5SUhMjISISGhsLDwwPW1taQyWSQyWRYvXp1qbad146+qe3DwsLEMvv27SvVeIiIyLzFx8djypQpAIDWrVtj2LBhEkdEZN6M6WsVVURERJn1FYmIiIiIiIio5CxmjeRdu3Zh7dq1AIAhQ4agXTvTrAc3d+5cREZGmqQuoufJzZs30aZNG9y9e1fqUIiIiAwaM2YMUlNTYWtri2XLlpk0OUZEREREz5eIiAhERERIHQYRERFRqbOIRHJaWhpGjhwJAPD09MT8+fNNVvfUqVMxceJEcV+hUMDHx8dk9RdLtgJIvQ4orgPu9XIfRGZm5MiRYhLZwcEBHTp0QLVq1WBtbQ0AXL+ciIjMxvr16/H3338DACZPnlwuv6Ny1DmIV8QjJikG3s7eeKHyC1KHRERERETmTqEArl0DrlwBunQBuPQLERER5WMRieTp06fj1q1bAICvvvoKXl5eJqvbzs7OJFNkm8yhfsDt357t1/+YiWQyO/fu3cOePXsA5P4NnT9/HoGBgRJHRUREVFBSUhLGjx8PAAgKCsL06dMljsj0Ptz9IRYcX4DMnEwAwDtN38GCrgskjoqIiIiIzFqDBsCFC8/2t24FevSQLh4iIiIyS2afSD5z5gwWLlwIAGjXrh2GDBkicUSlzK6i9n7qdWnioEIplcCNG0BmJmBnBwQEAM7OUkdVts6ePStut27dmklkIiIyW++99x4SExMBAEuXLjXJDYSZmZnIzMwU9xUKRYnrLAknGycxiQwAMUkxEkZTPrC/VzpWr17NtZGJiIjMhaur9v7Vq0wkExERUQFmn0i+cOEC1Go1AOD27dto1qxZoWXzLhICuSMmNct+9NFH6N69e+kFaiou+RJyTCSbhehoYOlSYPt24OZNQBCevSaTAbVqAd26AaNGASEh0sVZVpKTk8XtKlWqSBgJERFR4Xbt2oW1a9cCAIYMGYJ27dqZpN65c+ciMjLSJHWZQpBnkNY+E8nFw/4eERERPVfq1AEOHXq2f/WqdLEQERGR2TL7RLKm2NhYxMbGGlU2KysLx48fF/c1k8xmLS+RLLMCHGsATjWkjec5FxcHjBwJ7N4NyOWASlWwjCAAsbHAkiXAwoVAx47AsmVAzZplH29Zyc7OFretrKwkjISIiEi3tLQ0jBw5EgDg6emJ+fPnm6zuqVOnYuLEieK+QqGAj4+PyeovqkDPZzciutq5opJTJagFNaxk/I42Bvt7RERE9FwKDs7918oK8PcHTLiUIBEREZUfvLpkbiq1AbpHA/3SgZ5xQKsNUkf03Fq5Mne0SVRU7r6ui4qa8l6Piso9buXK0o2vrO3btw8ymQwymQxDhw4Vn1+zZo34fN4jIiJCfH316tU6ny/MrVu3xPJ+fn6mP5ESio+PR2RkJNq0aYPKlSvDzs4Otra28PT0RIMGDTBgwAAsWbIE9+/f13l8RESEeH55UzsmJSVh3rx5aNq0KSpWrAgHBwf4+/vj7bff1ppGXB+1Wo2DBw9i5syZ6NSpE2rUqAFHR0fY2dmhSpUqaN++PebMmYNHjx4V+ZwVCgUWLlyIHj16wM/PD87OzrCzs0PVqlXx8ssvIzIyEpcvXzZYjyAI2Lx5M4YMGYKgoCC4ubnB3t4ePj4+6NWrF9asWQOVoT80IiIjTZ8+Hbdu3QIAfPXVV/Ay4YUxOzs7uLq6aj2kVLdiXRwcehAPPniAJ5Of4NjwY0wiG4n9vaI5efIkhg8fjqCgIDg5OaFChQpo2rQp5s6da9QU77r6QfpcvXoV48ePR+3atcX2GjZsiI8++gjx8fEAtPuoYWFhOusprMzff/+N1157DX5+frC3t4enpye6du2K7du3F6hDrVbjzz//xCuvvIKaNWvC3t4eVapUweuvv45jx44ZPBciIiKz88YbwKVLQHo6EBMDzJsndURERERkjoRyZNWqVQIAAYDg6+tbrDpSUlIEAEJKSoppg6MCnj59KkRHRwtPnz6VOpQCPv1UEHLHnpTs8emnUp+J6URFRYl/X4YeQ4YMEY/T/LvUfL4wcXFxRv0da7ZXmLZt24ploqKijD/ZQixbtkxwcHAw6j1o2bKlzjqGDBkillm1apVw5MgRoWrVqoXWY21tLcyaNUtvXFlZWUK1atWMisvJyUlYu3at0ee8ZMkSwcPDw6i6d+zYUWg958+fFxo2bGiwjtq1awuXL182Oj4qO+b8mV3esC9ScqdPnxasrKwEAEK7du30lmX/sXSZ82cH+3v65e9rzZo1S/y70vWoVq2acOTIEb115u8H6fPNN98Itra2hbbn5uYmbN26VauP2rZtW5115S+TlpYm9O/fX2+fRLP/9fDhQ6FFixaFlpXJZMLChQuL8vYSUTGZ8/eK1NgfsTz8mREREZGUjO2LWNTU1kRlYeVKYMYM09Q1Ywbg7Q289ZZp6pNStWrVMHbsWAC5o0P+/fdfAECdOnXw8ssva5XVt5a5pdqyZYs4RSoAuLq6onnz5qhevTrkcjlSUlIQExODS5cuISsry6g6//vvP0ycOBHJyclwdnZG+/btUblyZSQkJCAqKgrp6enIyclBZGQk1Go1Pv74Y5315OTk4O7duwAAZ2dn1K1bF7Vq1YKrqyuys7Nx584dHDt2DAqFAmlpaXjzzTdhY2OD8PBwvfGNHz8eCxcuFPetra3RpEkTBAYGwt7eHomJiTh37pw44i8jI0NnPQcOHECPHj3EkUo2NjZiPTY2Nrh16xYOHTqEjIwMXLt2DS1atMDRo0cRnDfNFhFREV24cAFqtRoAcPv2bb3fS5rLn9y7d0+r7EcffYTu3buXXqAkGfb3imbBggXiuuABAQEIDQ2Fra0tLl68iFOnTgEA7t69iy5dumD//v1o2LBhidt77733xH07Ozu0bdsWNWrUQHJyMvbv349Hjx6hb9++mDt3bpHrf+utt7B+/XrI5XK0bNkSAQEBSE9Px969e/HgwQMAQGRkJGrXro1evXqhU6dOOHfuHOzt7dGmTRvUqFEDT548wb///ovk5GQIgoDx48fjxRdfRPPmzUt07kRERERERETmhIlkIg1xccA775i2znHjgPbtLX8NvcDAQCxatAhA7nTVeYnk0NBQ8fnyLO/iKQCMGzcO8+bNg6OjY4FySqUSO3bswOnTpw3W+dlnnyErKwsDBw7E999/rzU1anJyMoYPH44//vgDADBnzhx06dIFLVq0KFCPlZUVhg4disGDB6Nly5awsbEpUCYzMxMLFizAtGnToFKpMGrUKHTv3h3Ozs46Y1u6dKlWErlfv36YP3++zjVAL126hBUrVuh8P+7fv4/XX39dTCIPHjwYn3/+OapUqaJV7sGDBxg9ejQ2b96MlJQUhIeH4+zZs7C2ttYZHxGRsWJjYxEbG2tU2aysLBw/flzc10wyU/nB/l7RTZo0Cfb29li5ciUGDhyo9drhw4cRHh6Ou3fvQqFQYPDgwTh9+rTO/ogxrly5gkmTJon7HTt2xJo1a7T6DtnZ2Zg1axbmzp2LadOmFan+Y8eOITMzEy1atMDatWtRq1Yt8bWnT59iyJAh+O233wAAs2bNwtGjR3Hu3Dn07t0bS5cuRaVKlcTyycnJ6NWrFw4cOABBEDB9+nTs3bu3WOdNREREREREZI64eBqRhpEjDa+NV1QqVW69ZLmUSiXOnTsHAPDx8cGCBQt0Jk2B3BHBr7/+Oj7//HOD9WZlZaFbt2746aefCqyv6eHhgQ0bNojr+KnVakyZMkVnPba2tvjxxx8RFhZW6EVbOzs7TJo0CZ9++ikA4MmTJ1i7dq3OssnJyfjwww/F/VGjRmHDhg06k8gAUK9ePXz33Xfo1KlTgdemT5+Ohw8fAsgd4Zz/QnCeypUr47fffkP79u0BABcvXsSmTZt0tkdERFQS7O8VXVZWFlavXl0giQwALVu2xM6dO2FnZwcg9zu8sD6GMSIjI8XZXRo0aICtW7cW6DvY2Njgs88+w/jx45GZmVmk+jMzM1G7dm3s2rVLK4kMAA4ODvjhhx9QoUIFAMD169excOFCtG/fHps2bdJKIgO5/bWffvpJvPFt3759uH//fpHiISIiIiIiIjJnTCQT/V90NLB7d+lcWNy9G7hyxbT1UtnJG00LAJ6enpDJZCapVyaTYcGCBbCy0v1RLJfLsWDBAnH/4MGDuHbtWonaHDp0qLi9Z88enWWWL1+O1NRUAICvry++/fbbYrWVmJiIn3/+GQDg7e2NefPm6S1vbW2NOXPmiPu//PJLsdolIoqIiIAgCEY9Vq1aJR7n6+ur9VpERIR0J0Glgv294mndurXeJTHq1q0rLoECACtWrChWO8nJydi8ebO4/+WXX8Le3r7Q8p9++mmBm/GM8fnnn8PJyUnnay4uLgWmtP/6668L7a/5+vqKM8YIgiBO9U1ERERERERUHnBqa3OVrQSUN4DU64DyFhAyyeAhVDJLlwJyuekvLAK59S5ZAmjkBMmCeHl5wd7eHhkZGbh06RIOHz6Mli1blrjeFi1awN/fX2+Z+vXro1GjRjh79iwAICoqCrVr1y60vFqtxunTp3Hu3DncuXMHCoUC2dnZOsvmjbLO759//hG3R4wYIY4wKqo9e/aII4pee+01vReC84SGhsLJyQlpaWk4dOhQsdolInpeqQU17iruIiYpBjFJMRjcYDCcbHUny55X7O8Vz+DBgw2WGTJkCL7++msAwMmTJ5GWllZosrYwR44cEfsO3t7eePnll/WWd3FxQc+ePYs0AtrBwcHg2uf169cXtwMCAtCgQQO95evVq4eDBw8CAOLi4oyOhYiIyCykpQExMcDVq0BCAvD++1JHRERERGakXCWSIyIiysfIEeUtYGu+Bdb8hwF2npKE87zYvr10LioCufXu2FE6dVPps7W1Ra9evbB+/XqoVCq0b98e4eHh6Nu3L9q0aQN3d/di1du8eXOjy+UlkvP+zU+lUmHBggX45ptvcOfOHaPqffTokc7nNdcHbdeunVF16XL06FFx+8KFCxg3blyRjk9OTi7WRWgioudRdk42KnxRAcospfhcaPVQNK7SWMKozA/7e8VjTJ+lfv36cHZ2hlKpRE5ODi5cuGB0XyeP5k1uTZo0KXQUsKbQ0NAiJZKDgoIMrt/s4eEhbtetW9dgnXlTYQPaM9kQERGZvYsXgRdeeLYvkwGjRwOFLOdFREREz59ylUguNxyrAzI5IGhc5Uq9zkRyKUpNBW7eLN02YmMBpRJwdi7ddqh0fPPNNzh9+jSuX7+OrKwsrF27FmvXroWVlRXq1q2L1q1bo2PHjujatavRI3hr1KhR5HKJiYkFXs/MzMSrr76KXbt2GXcy/5c3fbUmhUKBp0+fivv51w4sioSEBHH70KFDxRphnJyczEQyEZERbKxt4OXopZVIjkmKYSJZA/t7xWdMn0Umk6F69eq4evUqAN19FkM0j/Hx8THqmOrVqxepDTc3N4Nl5PJn/00uavnCZoIhIiIyS/n/zy8IwPXrgIHZOIiIiOj5wTWSzZGVHHDO15FLvS5NLM+J2NjcvnJpEgTgxo3SbYNKj7e3N06dOoUZM2agcuXK4vNqtRoXL17E999/j969e6NKlSr4/PPPkZOTY7BORyPv8NVMpOpK/kZGRopJZJlMhvDwcGzcuBFXrlxBSkoKsrKytNb8zCPo+KXPX79zCa6Ep6SkFPvYPKrSGjZGRFQOBXkGae3HJMVIFIl5Yn+v+EzVZzFEqXx2I4SxbRa1ryKTyUq1PBERkUVxcgLy3zD2/5vCiIiIiAAmks2XSyAAGeBYA6jcHrBxlzqici0zs3y1Y8nUarXUIRTK1dUVn3zyCe7evYtjx47hyy+/RK9eveDl5SWWSU5OxtSpU9GnTx+diVpN6enpRrWblpYmbru4uGi9lpmZiYULF4r7q1evxvr16/H666+jTp06cHV11Zq+0dBF3fz1a17QLSrNi8lff/21VjLb2Iefn1+x2yciet4EVggEADjbOqNxlcao4FDBwBHPF/b3is8UfRZjaCaFi9MmERERFUOdOrlTWvv5AV26AEbMxkFERETPD05tba6a/QjIXQC5g9SRPBeMnInYYtoxJ5pJTGNGl5piFGtps7a2RmhoKEJDQ/HBBx9ArVbjyJEj+PLLL7F161YAwJ9//onff/8dffv2LbSe27dvG9VefHy8uK2ZtAaAEydOiMneunXrYvDgwXrr+u+///S+7urqCgcHB3F667i4OHh7exsVZ36aI7fv379frDqIiEpbREQEIiIipA7DJKa1nobprafD29mboyh1YH+v+G7fvm1wrWBBEHD37l1xP3+fxRiax9y5c8eoY4wtR0RERIVYvTo3ecx1kYmIiEgHjkg2V/aVmEQuQwEBuTdfliaZLLed542rq6u4nZSUZLD8xYsXSzOcUmFlZYVWrVphy5Yt6Nixo/h8XlK5MMeOHTOq/qNHj4rbjRtrr3WpuQ5x/fr1DdZ14MABg2VCQ0PF7b179xoTosF6Dh8+XOx6iIjIOFVdqqKKSxUmkQvB/l7xGdNnuXTpkjjzibW1NRoUY23Fhg0bitsnT540OLsLkHtTHREREZVAlSpMIhMREVGhmEgmAuDsDNSqZbhcSfj757bzvNGcmvj8+fMGLwhu3LixlCMqPTKZDD169BD3Hzx4oLf84cOHERcXp7fM5cuXcebMGXE/LCxM63Urq2cf44amgFSr1Vi+fLneMgDQtWtXcXvFihXILOYcnZ07d4ZcnjvxxZEjR3D+/Pli1UNERGQK7O8V388//2ywzE8//SRuN2nSRGuJC2O1aNECtra2AIB79+4ZvKFNqVRiy5YtRW6HiIiIiIiIiIzDRDLR/3XrBshLabJ3uRzQyM09V4KDg8U18u7du4ddu3YVWnbbtm3Ytm1bWYVmtNTUVGRlZRlVVnMa6kqVKuktKwgC3n333UKT6zk5ORg/fry436pVK9SpU0erTC2NK+L79+/XOzX4l19+aVQyd8SIEeIahf/99x8mTJhg8BhdqlWrhkGDBgHIPdfBgwdDoVAYdaxarUZiYmKx2iUiIioM+3vFs2/fPmzatKnQ169cuYJFixaJ+8OHDy9WOxUqVEDPnj3F/Q8//FDvDW0zZ860iGVRiIiIiIiIyEJFRwPffw9s2AAcOAAkJ0sdUZljIpno/0aNAoxYwrdYVCpg9OjSqdvcyeVy9OvXT9wfMWIEoqOjtcoIgoC1a9eiX79+sDPDhQVPnz4NPz8/zJ49u0DseXJycrBhwwYsXLhQfK6rgavJtra2+OuvvxARESFOBZknOTkZb7zxhjgSRyaTYe7cuQXqaNSoEapVqwYgd33p119/XWu6awDIzMzEzJkzMWXKFKNGB3l4eGDevHni/tKlSxEeHl7oGoSXL1/Gu+++q/MmgTlz5qBKlSoAgAsXLqBp06Z6bya4c+cOvvnmG9SuXRsbNmwwGCsREVFRsL9XPLa2thg8eDB+/fXXAq8dPXoUnTt3RkZGBgCgbt26ePPNN4vd1qxZs8RRyWfOnEHPnj0LzPKSnZ2Njz76CN98841Z9h2JiIiIiIionDhxAhg7FujfH2jbFqhQAdAYTPY8KKX78YksT0gI0LEjEBVl2guMcjnQrh0QHGy6Oi3NjBkzsH79eqSlpSE+Ph4NGzZE27ZtUatWLSgUChw5cgS3b9+GXC7H0qVLiz2KpTTdu3cPkZGRiIyMhLe3Nxo2bAhvb2/I5XI8ePAAp0+f1krgtm7dGv3799db59SpU/Hdd9/hp59+wubNm9G+fXtUqlQJ9+/fx969e5GWlqZVtlWrVgXqsLKywieffIJhw4YBAHbv3o2goCC0aNECvr6+SEpKwr59+5D8/zulli9fjoEDBxo83zFjxuDSpUtYsmQJgNwpx3///Xc0adIEQUFBsLe3R2JiIs6ePYtbt24BANq1a1egnqpVq+LPP/9Et27d8OjRI1y7dg2dO3dGtWrV0LRpU1SsWBHZ2dl49OgRLl26ZHCqbyIiopJgf694vvjiC0yYMAEDBgzArFmzEBoaChsbG1y6dAknT54Uyzk7O2PNmjViIrg46tati88//xwTJ04EAOzcuRO+vr4ICwtDjRo1kJycjP379yMxMRG2trb47LPP8P777wPQXvKDiIiIiIiISC9BAHbuBOrVA6pX110m/83Lnp6Fly2nmEg2Z6p0QBkLpF7PfbgEAT69pY6qXFu2LPcCo6kvLC5bZrr6LJGfnx82bdqEPn36ID09HdnZ2dizZ49WGVdXV6xatQqNGzeWKMrCOTg4QC6XQ/X/X4z79+/jn3/+KbR837598eOPPxq8mOnn54dt27ahb9++uHfvHv78888CZaytrTFlyhR8+umnhdYzdOhQ3LhxA5999hkAIC0tDbt379YqY29vj2+//RYDBgwwKpEMAN9//z1q166NmTNnQqFQICcnB8eOHcOxY8cKlJXJZHB0dNRZT5MmTXDq1Cm89dZb+PfffwEAd+/exebNmwttu3LlyggMDDQqTiIiekYQBNxT3sP1pOuISYqBndwOgxsMljoss8L+XtG9++67SEpKwqefforr16/j+vXrBcpUrVoVGzduxIsvvlji9t577z3k5ORg2rRpyM7ORmZmJnbu3KlVxs3NDWvXroW9vb34nKura4nbJiIiei5lZgLXrwNXr+Y+qlYF/n/DOhERUbl07RowZgywd2/ud94PP+gup/F/TgBA06aATFZ4vYKg/3ULxESyObvwEXD162f7NV5nIrmU1awJLFwIjBhhujoXLcqt93nXpUsXXL16FfPnz8fOnTsRHx8Pa2tr1KhRAz169MDo0aNRo0YNcXSrOQkNDcXDhw+xZ88eHDp0CGfPnkVsbCySkpKQk5MDV1dX+Pv7o1mzZhg0aBCaNm1qdN0tWrTA+fPnsXz5cmzevBm3bt2CUqlE1apV0b59e4wZM8ao5PqcOXPQtWtXLFq0CIcOHUJiYiJcXFxQvXp1dOnSBW+99VaxErPvvvsuBg0ahNWrV2Pnzp2Ijo7Go0ePAABeXl4IDg5G27ZtER4errd+X19f7NmzB0ePHsVvv/2GAwcOID4+HsnJyZDL5fD09ERgYCBeeukldOrUCWFhYZCX1iKWRETl2I9nf8Twv57N7FG/Un0mkvNhf694Pv74Y3Tr1g3Lly/HwYMHkZCQABsbGwQEBOC1117D2LFj4ebmZrL2PvjgA3Tv3h2LFy/Grl27cOfOHdjZ2Yl9x5EjR8LHx0drKQx3d3eTtU9E5cuIrSOgyFJgROMR6FCrg9ThEJmfb78Fpkx5tt+mDRPJRERUfq1fDwwdCvx/iSasXg1MnAjUrVuwbJUqQMuWQGIicOsW0KRJ4fWeOweMH5+blC5Hg6RkgiAIUgdhThQKBdzc3JCSkiL9He3XlwInNRZa82gIdD0rWTimlpGRgbi4ONSsWVNrJIE5mDMHmDHDNPVMm1byeqj8iIiIwJo1awAAq1atQkREhLQBERnJnD+zyxuz6ouQUczpZ7b/1n6ErQkT9+3l9kiblgYrmTRT/przZwf7e+XD9OnTxVlZPv/8c0yePFniiIioNBX3e6XRskY4d/8cAOCjNh9hdthsyb4bS4s59UfIOGb1M/vzT6BXr2f7Xl65F8yJiIjKo/PncxPC2dnPnnv11dzvQ32ysnKTz7q+twUB6NAhd4SzgwPwxRe5ayub8ehkY/si5avXXN645LtjIfVG7i8jlbrp04EVK3JnLSjqoEi5PPe4lSt5UZGIiIjKVpBnkNZ+hioDdxR3JIrGvLG/Z/kEQcBvv/0m7jfRd2c4ET3XsnKyxO1PDnyCoX8OlTAaIjMUHKy9/+gRE8lERFR+NWgAzJr1bL96daB3b8P5N1tb3UlkANi2LTeJDABPnwLvvJN74aEcYCLZnLkE5P7rUBWo1Bbw7Q+oM6WN6TkyfDgQHQ20a5e7b+gCY97r7drlHvfWW6UbHxEREVF+3s7ecLZ1hoPcAfUr1Uef4D7Izsk2fOBziv09y/bNN9+I6zVXq1YNbdu2lTgiIpKKSq3C0+ynOl8TBEErkSyDDH2C+5RVaESWoVat3IvjVavmjqYaPx7IyZE6KiIiotIzeTLQrFnud961a0BERMlGDy9Zor3v4ZF70aEc4AKU5szRB+inBOROUkfy3KpZE9i1K/dC4dKlwI4dQGys9o0pMhng7w907QqMHl3wJk4iIjKRnKzcD10rG6kjITJbMpkM19+5jkpOlcrdlJ2lhf0987Np0yYcO3YMb7/9NoKCggq8rlAo8MUXX4hTWgPA+++/D2tr67IMk4jMgCAI2H59OybtnoQeQT0wr+M8neXa1GiD2MexECDgs5c/w6u1Xy3jSInMnFyeOwrZxUXqSIiIiMqGXA7s2wfY2Zmmvo0bc5PTixcDVlbAhg25N2qVA0wkmzOZFZPIZiIkBFiwIHdbqQRu3AAyM3M/YwICAGdnaeMjInou3NkCHBkIuNcDPEOBaj0A746Ata3UkRGZFW9nb6lDsEjs75kPpVKJr776Cl999RUCAgLwwgsvwMvLC9nZ2fjvv/9w7NgxpKeni+Xbt2+Pd999V8KIiUgKMUkxGLNtDP6N+xcAEJsci9FNRsPP3U+rnEwmww89f8Ab9d/AlqtbMLkl11In0olJZCIiKm/S0nL/U1/Y9GOmSiIDgJMTsGgR0L177t3pHTuarm6JMZFMVETOzkDDhlJHQURUzqjSgKvfAv5vAQ6FJMEyEwFBBSSfy33E/gC89gCwrlCGgRLR84D9PfNx48YN3LhxQ+drMpkMAwcOxIoVK2BlxRH4RM8bGWTY/99+cT8rJwvT/p2GdX3W6SzfoVYHdKjVoazCIyIiIiIpqVTA66/njg5ev77s7g7v2rVs2ilD/N82ERERSevBPmD7C8CFGcCV+YWXy3ykvV85DLBjEpmIqLwZMGAA/v77b4wbNw4tWrSAv78/3NzcYGNjAy8vLzRu3BgTJkzA6dOnsXbtWtjb20sdMhFJINAzEGNeGqP13LWka4WulWzItUfXMOD3AVBkKkwRHhERERFJadKk3LWrtm0DWrcG7t6VOqJcV68CR45IHUWRcEQyEVEZWr16NVavXi11GETm49oC4PQEAP9fjPT6EiBkMmBfsWDZ/Ink6r1LOzoiIpKAra0tunfvju7du0sdChGZuZltZ2LN+TVwtnXGZy9/hkEvDIKVrOhjJpKfJqPHrz1w/fF1nH9wHlv7b4V/Bf9SiJiIiIiISt3mzcC33z7bP3cOeOUV4MwZQCaTKirgwgWgQwcgIwP491+gSRPpYikCjkgmIiIi6VjZQkwiA0BOOnD1a91lX/gY6LAfaDgPqNgSqN6z8HrjtwCxqwBBKLwMEREREVk0T0dP/DPoH8S8E4PBDQYXK4msUqvQb1M/XH98HQAQnRiNpiub4vz986YOl4iIiIjKgkqlPZW1nR2weLG0SeTTp4F27YDERCA1NXct5Zs3pYunCDgi2dwJaiAlGki9AShjcx8NPgNs3aWOjIiIqOQCRwGKa8C1b3P3K7YCfPvrLmvrAVRqk/sI+bDwOrOeACdHARkPgFtrgWZrACcfU0dOZNYS0xIRkxQjPvqE9MFLVV+SOiwiIiKTa1a9WYmOv5l8E+fun9N6zs/dD4GegSWql8gi/fcfcP48cOUKEB0N9OkDvPqq1FEREREVzeuvAy+8APTrlzsKeMECoEULaWP69lvg8eNn+4mJwDvv5E69beaYSDZ3Qg6wo2Huv3lqDQU8LWPIOxERkUGN5ufeMOXZFKg3HSjGSBIt56bmJpEB4EFU7vrLnQ4DbiElj5XIQvTa0AtH4p+tuVPRqSITyUREZLHUgrpYo42NEeQZhJMjTqLHrz1w6eEleDt748/+f8LRxrFU2iMya++/D/z++7N9Ly8mkomIyDLVrg0cOwasWwcMGyZ1NMDKlcCDB8Du3bn7LVoAFrIEJqe2NndWNoCTr/ZzqTekiYWIiKg0WFkDbbYA9T8qeRJZEQPcWKb9nMcLgEvtktVLZGGCPIO09mOSYiSKhIiIqGQEQcC1R9dwM/kmslRZpdKGn7sfjgw7gn51+2FL+BZUd61eKu0Qmb2QfDffRkdLEwcREZEpODgAb70l7ZTWeezsgE2bgPr1c0dK//svULGi1FEZhSOSLYFzAKDUmCtdGStdLERERKXByto09bgGAWHbgBOjgPTbgI0b0Hyt6eonshBBFbQTyXnrPhIREVma5IxkpGWnIS07Dcmpych8monKWZVhb29v0nZc7Fywoe8Gk9ZJZHGYSCYiIio9rq7Avn2AuztgZTnjfJlItgQuAUDKRcDZP3fb/QWpIyIiIiq6J5cA93ql307VrkC388DJ0UD1XoBTjdJvk8jMBHoGwtbaFgEVAhDkGYTQaqFSh0RERFRkakGNu4q74r4AAemqdNhY20gSz6yoWWju0xxdArpI0j5RqQsJATw9c/8NCQHq1gUEwTxGchEREZUHFSpIHUGRMZFsCV5aCDRZLHUUpUYQBKlDICIiA0r8WX33b2B/D8B/eO6ayLZupgmsMLbuQMtf9ZfJOydeFKFyqGftnkiflg5rjsYnIiIL9jT7KbLV2VrPedh7SJJI/vHsj/j4wMewklnhq05f4d3QdyFjP5LKm/r1gcRE/h+JiIgsT2Zm7vTRZHKWM3b6eVbS9SLNlLV17oXNnJwciSMhIiJD8j6r8z67i3ZwBnBqXO527EpgWwiQsMOE0RXTxVnAiRFAvouTROWBjbUNk8hERGTxnGydUL9SfVRyqgQZZHCQO8DRxrHM4zh0+xBG/T0KQO4o6fd2voex28eWeRxEpU4mYxKZiIgsz+XLQOXKwMiRwJEjzwaPWJrffweGDTO7+MtnhpIsglwuh1wuh1KplDoUIiIyQKlUip/bRXbtOyDtv2f7TxOAp/dNF1xx3PwJuPQJEPsDENUVyHoibTxEREREpJONtQ1quNVA3Up1Uc2lmiQxrLu4rsDI6KbVmkoSCxERERHls2YNkJICLF8OtGwJtGsndURFk5YGDB8O9O0LrFoFrFghdURamEgmychkMri5uSElJYWjkomIzFhOTg5SUlLg5uZWvOn7crIAK9tn+5XaALUiTBZfkT3YD5wYrrH/L7CnjcGRyUqlEufOncPx48dx7tw53ghFREREVIbs5fawt7GXpO3F3Rbj47CPxf2JzSYiomGEJLEQERERkQaVCvj5Z+3nmlrQDX+CAHTpAvzww7PnJkwArl6VLKT8mEgmSbm7uwMA/vvvP2RlZUkbDBERFZCVlYX//ssdTZz3mV1k9T8CXrkK1AgHIAMafSXtdGlZyYAs35S/gaMBq4Jr7UVHR2P8+PEICAiAq6srGjVqhGbNmqFRo0ZwdXVFQEAAxo8fj+jo6DIKnoiIiIjKmkwmw0dtP8Km1zehb0hffNHxC6lDIiIiIiIAOHMGuJ9v5sMhQ6SJpThkMmDyZO3nnj4F1q2TJh4dZIJgZpNtS0yhUIijZF1dXaUO57mQmZmJ+Ph4qFQqODk5wcnJCXZ2drCysireyDciIio2QRCgVquRmZmJtLQ0pKWlQS6Xw8fHB3Z2diVvIDUWcPEveT0l9egEcOBVIOMBUPs94MWvtV6Oi4vDyJEjsXv3bsjlcqhUqkKrynu9Y8eOWLZsGWrWrFmi0NgXsTz8mRUuIyMDcXFxqFmzJuztpRlFRkRE5Qe/VwrH/ojl4c+MiIjIROLjgbVrc6e4dnEBTp2SOqKiGzcOWLwYcHYGFi0CBg8u9YE4xvZFirHQIUki/Q7w+AygjM29CO9aB6g9TuqoTMLOzg5+fn5ISUmBUqnEw4cPwfsbiIikJZPJ4ODggIoVK8LNza14ayPrYg5JZADwagp0Pg5cWwg0nKf10sqVK/HOO++IyWN9SWTN16OiohASEoKFCxdi+PDheo8hKgsZqgycu38OMUkxiEmKwd3Uu1jVc5XUYREREemVmpkKZ1tni7yx/I7iDr44/AXmdZgHBxsHqcMhKr7du4FLl4Do6NzH0qVA/fpSR0VERKSbjw8wbRowdSrw6JHU0RTPl18CCgUwcyYQECB1NFqYSLYUcT8B56c/2/fuUG4SyUDuaC5PT094enpCrVZDpVJBrVZLHRYR0XPJysoKcrkcVlblfAUMJ1+g8Xytp+bMmYMZM2YUqzqVSgWVSoURI0bgwYMHmD59uuGDiEpRfEo8mv/QXOu5+R3nw9PRU6KIiIiI9EvNTMW1pGtwsnFCddfqcLFzkToko6VlpaHn+p44c+8Mjt89ji3hW1DFpYrUYREVz9tvA7duPdu/cIGJZCIiMn8yGVCxotRRFI+DA/DTT1JHoRMTyZbCOd8IrtRYaeIoA1ZWVrC1tZU6DCIies6sXLlSK4ns4QQMbAks2lX0umbMmAFvb2+89dZbJoyQqGj83P0gt5JDpX42qv764+tMJBMRkdlKSE0AAKRlp+Fa0jV4OXrBz91P2qCMoBbUiPgzAmfunQEAnLh7Ak1WNMHOQTtRt1JdiaMjKoaQEO1EcnS0ZKEQERGRtMr5UKNyxCXfUPb0/4CcLGliISIi0kdQA0/vSx1FkcTFxeGdd94R9x1sgb8+ABYOAZYMA6yKMbPiuHHjEBcXZ8IoiYrGxtoGNd211+y+nnRdomiIiIj0S89OR2pWqtZzDnLLmB76etJ17LyxU+s5RxtHVHWpKlFERCUUEqK9z0QyERHRc4uJZEvh7A/YVwK8WgB+bwL1ZgFqJpKJiMgM3f8X2OIDHAoHHuwHLGDd+5EjR4prHcutgY3jgZZBua+Nehn4eUzu80WhUqkwcuRIE0dKVDR1K9VFbc/a6BHUA+83fx91vOpIHRKRRZo9ezZkMhlkMhlmz55tsnr37dsn1hsWFmayeokskTJLqbUvt5KjopNlTE1Y26s2jr51VLyBy83ODX+98Rc8HDwkjoyomBo3Bpo2BYYOzV2z8b33pI6IiIiIJMKprS2FrTvw2gOpoyAiIjLs+mJAUAG3N+Y+fPoCrX+TOqpCRUdHY/fu3eJ+q9pA1wbaZcKCgSruQHyS8fWqVCrs3r0bV65cQXBwsGmCJSqiP/r9AZmsGEPqiYiIylglp0qoYF8BKZkpeJLxBI42jrCSWc74h7qV6uLEiBPov6k/Pmz5IWp71ZY6JKLie+ON3AcREZG5+vprIDgY6NABsLGROppyzXJ65ERERGT+0u8Ad//Sfs67vTSxGGnp0qWQy5/dW7cvGnj9OyAzO3f/sRLoNK9oSeQ8crkcS5YsMVGkREXHJDJRQRxdTGS+5NZyeDp6wr+CP6q4VJE6nCLzcvTC7jd3o5N/J6lDISIiIiq/Hj8GJk8GunUDvL2Bt98G7t2TOqpyi4lkIiIiMp3EIwA0EldyZ8BvkGThGGP79u3itNZ5Np8Cun8J3H8CdP0CuBRfvLpVKhV27NhR8iCJiIiIyCLwJi4iIiKiUrZ5M5B3Le/xY2DtWsDZWdqYyjFObU1ERESm49sPqNQWuL0BiFsLeDQAbFykjqpQqampuHnzps7X/r0M1HoPeJpVsjZiY2OhVCrhzA4tkcXIUmbh8Y3HUGWqILeTo0JABdg620odFklo9uzZJh29nCcsLAyCIJi8XiIiIiIionLrt3xL6HXrBriY7/VHS8dEMhEREZmWQ2Wg9vjcR04Js7ClLDY2Vu8F/JImkQFAEATcuHEDDRs2LHllRFRqEqMTcWrpKVzffh3JN5MBzY8GGeBRywOB3QLx0qiXUDGkomRxEhERERERET3XXn4ZSEoCTp3K3e/XT9p4yjkmkomIiKj0WJv3CL7MzMxy1Q4RFV1yXDL+Hvk3bu6+CZlcBkGl4+YSAUiOTcbJJSdxYuEJ1OpYC68sewUeNT3KPmAiIiIiIiKi59mkSbmP+Hhg69bcEclUarhGsiVR5wB3twFXvwNOjQf2dQfSE6SOioiIyGLZ2dmVq3aIdLmedB2/XPgFs/fNxoDfB2DB8QVSh2Q2zqw8g+9DvsetqFsAoDuJrCHv9VtRt/B9yPc4s/JMaYcoGZlMJj7ynDx5EsOHD0dQUBCcnJxQoUIFNG3aFHPnzoVCoTCq3pSUFPz6668YOXIkQkND4eXlBVtbW7i6usLf3x9vvPEGNm7cCLVabbCu1atXizFGREQAAHJycrB+/Xr07NkTtWrVgoODA2QyGbZs2YKwsDDIZDJERkaKdURGRmqda/768syePVt8Lf8U13mvtWvXTnxu//79Ouv18/PTOnbfvn3ia2FhYUa9hzt37sSwYcMQFBQEV1dXODg4wNfXF71798bq1auRnZ1tsI6IiAix3dWrVwMA0tPT8f3336NVq1aoXLky7Ozs4OPjgzfeeAOHDx82Kjai4rivvI9H6Y+QnWP4d5eIylBKCrB4MTBuHNC+PeDjAxjxHUNERFRmfHyAsWM5rXUp44hkSyKzAg6HA6q0Z8+lxgCOVaWLiYiIyIIFBARAJpOV6vqUMpkMAQEBpVY/kSGrz63GZ4c+E/eVWUqMDx0vYUTm4cCcA4iaEVWsY9UqNdQqNf4a8ReUD5RoM72NiaMzP7Nnz8Ynn3yileBNT0/HyZMncfLkSSxevBi//fYbmjdvXmgdf/zxBwYMGKBzlobs7Gxx3fr169ejQYMG2Lx5M2rWrGl0jAkJCQgPD8ehQ4eKdnIW4uHDhxgwYAD+/fffAq/dvn0bt2/fxpYtW/DZZ59h3bp1eOmll4yuOzo6Gn379sWVK1e0nr9z5w7Wr1+P9evXY+bMmVpJeCJTUAtqJKQmQC3kfrY42zrD180XDjYOEkdGRFCrc5PImq5fB0JCpImHiIiIJMFEsiWRyQBnf+DJhWfPpd4AKodJFhIREZElc3Z2Rq1atRAbG1tqbfj7+8PZ2bnU6icyJMgzSGv/+uPrEkViPs6sPFPsJHJ+UTOi4OztjMZvNTZJfeZowYIFYgIxICAAoaGhsLW1xcWLF3Hq/2tS3b17F126dMH+/fsLXRP+4cOHYhK5evXqCAkJgbe3NxwdHaFUKnHlyhWcOXMGgiDg/PnzaNOmDc6dOwdPT0+DMWZmZuLVV1/F6dOnIZfL0aJFC/j7+yMzMxNnzuSOHO/duzfq1auHEydO4OTJkwCAJk2aoGnTpgXqa9asmdHvT9OmTTF27FjcvXsXW7ZsAQBUrVoVvXv3LlDWmHPR5cGDB2jZsqXW95W/vz9CQ0NhZ2eH6OhoHD9+HABw/fp1tGvXDv/88w9atmxpsO6EhAR06NAB9+7dg7u7O1q3bg1vb288evQIe/fuRUpKCgDg448/RkhICMLDw4t1DkS6pGamiklkIPdmJ7kVL1URmQUPD6BKFeDevWfPXb7MRDIREdFzhr1zS5M/kay8IV0sREREACAIwOnxQOX2QJXOgNxR6oiKpFu3bliyZAlUKpXJ65bL5ejatavJ6yUqikDPQK392MexUKlVz+2F+uS4ZOx4Z4dJ69wxbgdqtq9ZbtdMnjRpEuzt7bFy5UoMHDhQ67XDhw8jPDwcd+/ehUKhwODBg3H69GnY2NgUqKdatWqYO3cu+vbtW+hMDXFxcRg9ejR27tyJO3fuYPLkyVi5cqXBGDdt2gSVSoW2bdti9erVBaaQzszMFJcZmD17tphI7tatW4GpqouqW7du6NatG/bt2ycmkgMDA7Fo0aIS1atp6NChYhLZyckJK1euRP/+/bXKnDp1CuHh4bh58yaUSiXeeOMNXLhwAe7u7nrr/vjjj5GZmYnJkydj5syZcHR89j3++PFjvP7669i7dy8AYNq0aejXr5/WlOdEJfEk44nWvrOtM2ysC35+EJFE6tUrmEh+/XXp4iEiIqIyxzWSLU2FxoBnM8BvEFBvFlCVi4gTEZHEHp8GYhYBB18DfvcCDrwGZCuljspoo0aNKpUkMgCoVCqMHj26VOomMlaQZxD8PfzRNaArxjcdj286fwOVunR+5y3B3yP/hlpleP3dolCr1Ph75N8mrdOcZGVlYfXq1QWSyADQsmVL7Ny5U0zSXrx4EWvXrtVZT48ePTBlyhS90/3XrFkTf/31F1544QUAwC+//ILk5GSDMapUKtSvXx87duwokEQGLHut+qioKOzY8ezmhw0bNhRIIgPASy+9hH///Rdubm4AgPj4eCxYYHhN9MzMTEydOhWff/65VhIZACpUqIB169bByckJAHDz5k2cOHGiJKdDpMXFzgXu9u6wkuVennK3d5c2ICLS1rkz0L8/8OmnwJYtwIgRUkdERETPK7UaKKXrd6QfE8mWpt4MoPNRoMVa4IXZQKXyvx4bERGZufg/nm3nPAWeXATkTtLFU0QhISHo2LEj5HLTjs6Uy+Xo2LEjgoODTVovUVF5OXrhxvgb2D5wO77r+h3GNh0Le7m91GFJIjE6ETd33yyVRPLN3TeReCXRpPWai9atW+udzrhu3boYO3asuL9ixYoStWdjYyMmrTP+x959h0dVpm8c/86khxTSCC1AIISS0KWroDRFQV1YC/aya+9tLWv7rcuuZddedxVURLGvIioWQHoLhNBC7xAI6Y0kM78/jiYMNUAy75T7c13n4jwnZ865IUqGec77vuXldV7z+J///CdhYb63ruqbb75Zsz969GjOO++8o57bpk0bHn744Zr6jTfewOl0HvP6CQkJPPbYY0f9emJioss91UiW+hQbFktKbArdE7uTEptCbGis6UgicrB774XJk+GRR+CCC6BFC9OJRETEXy1cCImJcPXV8MUXUFJiOpHf8M/57ERERKT+bP/ctU76A3jZlJdvvvkmnTt3rteRyYGBgS4f/ouIeYvfWIwt0Iaz6tiNtZNhD7Sz+PXFnPuS701nf9VVVx33nKuvvpp//etfACxatIiSkpKaUaxHkp+fz/z581m5ciW5ubkUFxfjcNQ2+NesWVOzv2zZMkaNGnXM+8fExDB8+PDj5vRGv/xSu573ddddd9zzr732Wh566CEcDge7du1i7dq1dOzY8ajnjxo1itDQYz9c0qNHD6ZMmQLA5s2b6xZc5ATY7XaNRhYRERGRo/v6a9i/H957z9p69IClS02n8gtqJIuIiMjJc1RB8jWwaxrsnQvOKquR7GWSk5N5+eWX+VM9TtX2yiuvkJycXG/XE5FTt+7bdQ3SRAZrVPL6aesb5Nqm9e/f/7jndOnShYiICIqLi6muriYzM/OIr9u+fTt/+ctf+PTTT6moqKjT/fft23fcc7p3705AQECdrudNduzYQU5OTk09YMCA474mISGB1NTUmmb80qVLj9lI7tKly3GvGRcXV7NfWFh43PNFRERERETq1bffutY++iCxJ9LU1iIiInLy7IGQ9hcYOhPG7IMzv4S43qZTnZQbbriBv/3tb/Vyraeffprrr7++Xq4lIvWjoqiCvI3HX2v3VOzfsJ8DxQca9B4mtGrV6rjn2Gw2WrZsWVPv3Xv4NN8ZGRl07dqVSZMm1bmJDFBUVHTccxISEup8PW9y8J9jWFhYnX+fB68TfbxG/O9rKh9LUFBQzX5lZWWdMoiIiIiIiNSL3bth2TLXYyNHGonij9RIFhERkfoRHA0tLwCb9769eOSRR3j77bcJDQ094TWTAwMDCQ0N5T//+Y/L+pQi4hnyNuRBwwxGruWE/ev3N/BN3C88PLxO5x08lfWhzd+KigrGjBlDXp7VzE9ISODRRx/ll19+Ydu2bZSUlOBwOHA6nTidTt59992a1x485fXR+OLayADFxcU1+8eaKvxQx/peHMrmZctRiIiIiIiIn2naFLKz4cUXYcQIa63kOsycJfVDU1uLiIiIHOSGG25gyJAh3HjjjUyfPp3AwMBjrp38+9fPOuss3nzzTU1nLeKhqirqbw10T7iPO5WWlhIZGXnc80pKSmr2Dz3/s88+Y9OmTQC0aNGCRYsW0axZs6Neqy6jkP1BREREzf7Bf77Hc6zvhYgncDqdeohBxFuVlYGPPsAlIiIerH17a7vjDqiqghMcACInT3/S3ih/Jez5GYo3QNEGaJwO3cebTiUiIuIzkpOT+eGHH1i1ahVvvPEG06ZNY8OGDTidtcMZbTYb7dq149xzz+Xmm2+mU6dOBhOLHJvT6WTKyilk52azbv86snOzmXDhBDrGH33dVF8TGOKef/q46z7utHXrVtLS0o55jtPpZMeOHTV1fHy8y9d/+umnmv277rrrmE1kgC1btpxEUt9z8FTWZWVl7Nu377A/2yPZvHlzzX5dzhdxJ6fTyYqcFYQFhhEZEklUSBRhgWFqLIt4qo0b4fXXISvL2gIC4KCfMyIiIm6nJrJb6U/bG+3+EZbeVVtXHHvNKxERETk5nTt35qWXXgKs6UXXr19PRUUFISEhpKSkuIwUE/FkNpuNe364h51FO2uOrd672q8aybEpsWCjYae3tv12Hx8zf/784zaSs7KyakYRBwQE0K1bN5ev79xZ+99ely5djnvPWbNmnUTSummoZlVDXLdFixY0adKEnJwcAObOncvo0aOP+Zp9+/aRnZ1dU/fs2bPec4mcitLKUg5UH+BA9QEKKgoA6NKkCyGBIYaTicgRFRXBc8+5HisshKgoM3lERETErbx3EUN/FpniWhdvMJNDRET8m8P3pm89loiICLp3707fvn3p3r27msjidVLjUl3q7Nzso5zpm4IjgolpG9Og94htF0twRHCD3sOEDz744LjnvPfeezX7vXv3Pmw9X7u99p+epaWlx7zWkiVLWLRo0QmmrLvQ0NCa/crKSo+/7llnnVWzP2HChOOeP2HChJp1pZs3b06HDh3qLYtIfSisKHSpQwND1UQW8WQdOlijkA+2apWZLCIiIuJ2aiR7o4h2rnXFXqgsPPK5IiIiDaF4M3wWD7+OhQ3/hdIdx32JiJjVPra9S+1vjWSA9iPbYwtsmNGo9kA7KeemHP9ELzRjxgw+/fTTo3599erVvPLKKzX1DTfccNg5bdu2rdn/3//+d9RrlZaW8uc///kkk9ZNXFxczf7B03F76nVvvPHGmv0vvviC77///qjnbtmyhaefftrltZouWDxN0QHXNdAjg7WOt4hHCw211qQ8WFaWmSwiIiLidmoke6OIZIjrC63HQfpfod9EsAUc/3UiIiL1Zc/PUFkA2z6DBTfAtB7gdJhOJSLH0LdFX4a1HcatvW/lxXNe5E+9/mQ6ktuddtNpOKsaZm5rR5WD024+rUGubVpwcDBXXXUVkydPPuxr8+bNY8SIEZSXlwOQlpbGlVdeedh5o0aNqtmfOHEizz//PNXV1S7nrF+/nuHDh7N06dLDRjTXp/T09Jr9H374gYKCgnq5bnJyMuHh4YDV0K2vUdVnnXUW5557bk09duxYPvnkk8POW7JkCUOHDiU/Px+ApKQk7rjjjnrJIFKfkhsn0zamLfHh8QQHBBMVoulxRTzeddfBo4/CRx9ZTeSrrjKdSERE/MHevfD99/DbvzfFDK2R7I0CQmDEfNMpRETEn+35xbVOHAw2PZ8m4smu73k91/e83nQMoxI6J9B2WFs2/7IZR1X9PfxiD7TT5qw2JHRKqLdrepJnnnmGu+66i3HjxvH444/Tt29fgoKCyMrKcmmWRkREMHHiRIKDD5/ee/jw4Zx55pnMmjULp9PJfffdx6uvvkrPnj2Jjo5m3bp1zJ07l+rqalq0aMGdd97JAw880CC/nz59+pCUlMS2bdvYtWsXHTt2ZPjw4cTHx9eM3u3duzeXXHLJCV03ICCACy+8kA8//BCAwYMHc84559CqVSsCfpsSNDY2locffviEM7/77rsMHDiQDRs2UFxczMUXX0z79u3p27cvwcHBrFq1igULFuB0Wg9KNGrUiMmTJ9O4ceMTvpdIQwsKCCI2LJbYMGtN+d//uxURD3b//aYTiIiIP/rf/+CGGyAsDM46Cy6+GK6+2nQqv6NGsoiIiJwYp/MIjeSzjnyuiIiHOf/N83mt82v13kg+/83z6+16nubOO+8kNzeXv/3tb6xbt45169Yddk7z5s2ZMmUKvXr1Oup1pkyZwsiRI1m6dCkAmzZtYtOmTS7ndO7cmU8++YSFCxfW72/iIHa7nddee40xY8Zw4MABdu/e7bLGM8DVV199wo1kgL///e/8/PPP7N69m9LSUj7//HOXr7du3fqkGsmJiYnMmTOHcePG8fPPPwMc9XuRkpLChx9+SO/evU/4PiImaPp1ERERETmiH3+0fi0rg2+/hYgINZIN0NAhEREROXFDZ0Kft61lFsKaQRM1kkXEO8Qkx3Duy+ce/8QTcO4r5xKTHFOv1/Q0Tz31FHPnzuXaa68lJSWF8PBwoqOj6dWrF08//TSrVq1i4MCBx7xGYmIic+fO5ZVXXuH000+ncePGBAcH07JlS4YMGcJbb73FokWL6Ny5c4P/fs4//3wWL17MjTfeSFpaGpGRkfXSzGrdujXLly/nr3/9K3379iUmJobAwPp5fjsxMZGffvqJadOmcc0115CSkkJERAQhISEkJSUxevRo3nnnHVatWqUmsoiIiIiIeDeHA376yfXY0KFmsvg5m1NzCLkoLCwkOjqagoICoqK0To+IiMhx/f5WQqNJ6oXei3gffc+Orry8nE2bNpGcnExoaKjpOC5mPT2LXx795fgnHsfZT5/NGQ+fUQ+JPMvBTVX9k1FEPIUn/1wxTe9HvI++ZyIiIsewZw+MHAkZGbWfPW7cCMnJZnP5kLq+F9HU1iIiInJq1EAWES905iNnEpEYwbTbp+GocpzQVNf2QDv2QDvnvnIuPa/v2YApRURERERERPxQYiIsWQK5ufDLL9a+mshGaGprERERERHxSz1v6Mktq26hzVltAKtBfCy/f73NWW24ZdUtaiKLiJyAkgMlVFZXmo4hIvWhtBS2bTOdQkRE/EFcHIwdC+PHm07itzQi2Vs5nbDudShaD8UbrO2MzyCqg+lkIiIiIuKhNuzfwJSVU8jen012bjYRwRF8f8X3pmMZFZMcw5U/XMneVXtZ/MZi1k9bz/4N++Hg2ZxtENsulpRzUzjt5tNI6JRgLK+IiLfamLeRiuoKwgLDiAqJIqFRAqGBmp5axGvMmgX//jdkZcGGDTBwIPz6q+lUIiIi0sDUSPZWNhtkPQXle2qPFWarkSwiIiIiR7UxbyMP//xwTR0VEoXT6XRZD9dfJXRO4NyXzgXgQPEB9q/fT1VFFYEhgcSmxBIcEWw4oYiI96qoqqCiugKAsqoyyqrKiAuPM5xKRE5IYSF8+WVtnZVlDXTR+0gRERGfpkayN4to59pILl5vLouIiPiHovUQ3hICNHpExBulxqW61IUVheSU5JAYkWgokWcKjgimafempmOIiPiMwopClzrQHkhYYJihNCJyUtLTXev8fNi5E1q0MBJHRERE3EONZG8WmQL75tbWRRvMZREREf/wywgo3Q6xvSB+ALS/GSLbmU4lInWUFJ1ESEBIzagwgOzcbDWSRUSkQVU7q7Hb7DicDsCaEUOzYYin2rdvH3PmzGHhwoWsWLGCDRs2sHPnToqLiwkKCiImJob09HQGDx7MVVddRQt/aaS2agUREVBcbNU2G6xbp0ayiIiIj1Mj2Zs1OROqK6yGckQ7iDvNdCIREfFlZbuheKO1v2+etSVfaTaTiJwQu83O5V0uJzggmNS4VFLjUklvkn78F4rfcTqdxz9JRKSOmkY0pUmjJpQeKKXwQCHhQeGmI4kc1TXXXMPUqVOP+LWqqirKysrYuXMnP/zwA08++SQPPfQQf/3rX7Hb7W5O6mZ2O/ztb9C4sTU6uVMnCNf/yyIiUs8KC+HOO2HoUBgyBJpqtjDT1Ej2Zu2utzYRERF3OHgWDIDASIhWA0rE2/z3gv+ajiAiIn7IbrMTERJBREiE6SgidRYfH0+nTp1o3bo1ERERlJaWsn79ehYuXEhVVRUVFRU88cQTbNy4kYkTJ5qO2/DuvNN0AhER8XWzZsGECdYG0KsXLFxoPdAkRqiRLCIiInVTtAGwAb+NUovvB/YAk4lERERERETq1eDBgxk1ahRDhgwhJSXliOfs2bOHu+++m8mTJwPw3nvvMWrUKMaOHevOqCIiIr5n5kzXulEjNZENUyNZRERE6qbz/ZDyZ8hdAHvnWksriIiIiIiI+JD77rvvuOckJiYyadIk9uzZw88//wzAm2++qUayiIjIqZo1y7UeNMhMDqmhNr6IiIjUXXA0NBsOXZ+A5CtMpxERERERETHCZrNx7bXX1tQZGRkG04iIiPiISy+F886D6GirViPZOI1IFhERERERERERETlBCQkJNftFRUUGk4iIiPiIu++2tupqyMyEjh1NJ/J7aiSLiIiIiPg5p9OJzWZr0OuLiIj/KT5QTGllKRHBEYQFhp3yzxr9PBFPs2rVqpr9Nm3amAtiwoEDsHYtOJ3QtavpNCIi4msCAqBHD9MpBDWSvd++hbDtEyhab22Nu8DAD02nEhEREREP9kHmB8zYPIPs3Gyyc7N59MxHua3PbfV+H7vdWknH4XDU+7VFRMTz7S/bT05JDgB2m53ERom0iGpx0tf7/efJ7z9fREzauXMnzz33XE3tN+sjf/gh/POfsHo1VFbChRfCF1+YTiUiIiINRI1kb1eQBaufO+iAns4VERERkWObtn4aH66offhwXe66BrlPYGAgNpuN8vJyGjVq1CD3EBERz1V8oLhm3+F0YLedWgO4vLwcm81GYKA+zhIzSktL2bx5M9OmTeOZZ54hJ8d6UKJTp0785S9/MZzOTSorralGf3fwvoiIiPgcvfP2dpEprnXxRnA64BT/cSYiIlKjfB/s+Rni+kCj1tCA09+KiHu0j23vUmfvz26Q+9jtdiIiIigsLCQuLq5B7iEiIp6p2lFNWWWZy7GI4IhTumZhYSEREREakSxuM3v2bM4444xjnjNy5EgmTZpEZGSkm1IZdug01hs3QlER+MvvX0RExM94TSN53759zJkzh4ULF7JixQo2bNjAzp07KS4uJigoiJiYGNLT0xk8eDBXXXUVLVqc/FRJXiXikEZydRmU7YJwP/n9i4hIw8uZCXMusfZD4qHpcBg4yWwmETklqXGpLnV2bsM0kgGioqLYsWMHJSUlGpUsIuJHHE4HMWExlBwooaK6AoDwoPCTvl5JSQnl5eV6MEk8RkxMDK+99hqXXnppnc6vqKigoqKipi4sLGyoaA2rUydr3crqaqtu3Rp27ICOHc3mEhERkQbhNY3ka665hqlTpx7xa1VVVZSVlbFz505++OEHnnzySR566CH++te/+v5TqmHNoPVl1gixyBSrsRyif1SJiEg92r+odr9iH1TsNZdFROpFj6Y9uKHHDaTGpdI+rj0d4jo02L0iIiJo1KgR27ZtIykpSc1kERE/ERQQRNuYtgBUVldSVlVGgD3gpK5VUlLCtm3baNSoERERpzaqWeRENG/enFtvvRUAp9NJUVERa9euZenSpeTl5XHZZZfx1ltv8cYbb5CamnrMa40fP54nn3zSHbEbVmgoTJhgNZC7dIHGjU0nEhERX/CHP0B5OQwaBGeeCaedBkFBplMJYHM6nV6xqO75559f00iOj4+nU6dOtG7dmoiICEpLS1m/fj0LFy6kqqqq5jVXXXUVEydOPKH7FBYWEh0dTUFBAVFRUfX6exAREfFKPw2xprb+Xdoj0O1v5vL4OL0X8T76nh2fw+Fg+/btlJSUEBoaSlRUFKGhodjtdmyaLl9ERA7hdDpxOByUl5dTWFhIeXk5jRo1omXLlr4/YOAk6f2Ie+3cuZNHHnmECRMmANbo5BkzZtD10GmfD3KkEclJSUn6nomIiBw4YD2YVHbQsihffgkXXGAqkV+o6/tHrxmRPHjwYEaNGsWQIUNISUk54jl79uzh7rvvZvLkyQC89957jBo1irFjx7ozqoiIiG8JiYfgWDiw36rjepvNIyJex26307JlS4qLiyksLGTv3r14yfOsIiJikM1mIyIigri4OK2NLB6lefPmvPvuu0RFRfHSSy+Rl5fHpZdeyooVKwgIOPLI+5CQEEJCQtycVERExAssXuzaRAY44wwzWeQwXjMiua6cTidDhw7l55+tkVNDhw5l+vTpdX69nuAUERE5AqcTSjZB7iJoOlTLKDQgvRepP/v27WPOnDksXLiQFStWsGHDBnbu3ElxcTFBQUHExMSQnp7O4MGDueqqq2jRosVJ3UffsxPncDioqqrC4XCYjiIiIh7KbrcTGBio5nEd6f2IGaWlpTRr1qxmveOvv/6a888/v06v1fdMRETkN88+Cw88UFunp8OKFeby+AmfG5FcVzabjWuvvbamkZyRkWE4kYiIiA+w2SCirbWJeIlrrrmmZmmUQ1VVVVFWVsbOnTv54YcfePLJJ3nooYf461//qg+s3cButxMcHGw6hoiIiMgpCQ8PZ8CAAXz33XcAzJkzp86NZBEREfnN5ZdDixYwdy7MmwcDBphOJAfxuUYyQEJCQs1+UVGRwSQiIiIi4gni4+Pp1KkTrVu3JiIigtLSUtavX8/ChQupqqqioqKCJ554go0bNzJx4kTTcUVERETES8TExNTs5+bmGkwiIiLipZo3h3HjrA2smRHFY/hkI3nVqlU1+23atDEXxBSnE3CCTaNpRERExH8NHjyYUaNGMWTIEFJSUo54zp49e7j77ruZPHkyAO+99x6jRo1i7Nix7ozqEUorSwkPCjcdQ0REfMCfv/4zjUMb069lP/q37E+zyGamI4k0mF27dtXsx8bGGkxigMMBGzdCZibExcGgQaYTiYiIL7DZTCeQg/hcI3nnzp0899xzNbXffAi4/FEoXA1FG6B4PZz9E8T3NZ1KRERExJj77rvvuOckJiYyadIk9uzZU7M0yptvvukX7yHzyvJ46KeHyM7NZt3+dewo3EHhQ4VEBEeYjiYiIl6srLKMd5e9S5WjqubYr9f+yumtTjeYSqRh5ObmMm/evJq6U6dOBtO42fPPw+OPQ0mJVf/xj2oki4iI+CCfGLJaWlrKqlWreP755+nRowc7d+4ErDdvf/nLXwync5MtH8O2zyF/OVSVQPEG04lEREREvILNZuPaa6+tqTMyMgymcZ+woDDeWvIWv2z+he2F23HiZF3uOtOxRETEyy3eudiliWy32enetLu5QCInYP/+/XU+1+FwcNttt1FRUQFASEiIf62PHB1d20QGWL7cXBYRERFpMF7ZSJ49ezY2m61ma9SoEWlpadx3333k5OQAMHLkSObOnUtkZKThtG4Sech0jUXrzeQQERHfsWs6rBwPu3+EA/mm04g0qISEhJr9oqIig0ncJzQwlFbRrVyOZedmG0ojIiK+Yt72eS5118Sumu1CvMZ7771H7969ee+99ygsLDzqeZmZmYwcOZKPPvqo5tj9999PXFycO2J6hm7dXOt166C01EwWERERaTA+N7V1TEwMr732Gpdeemmdzq+oqKh5chA45ptEjxbRzrVWI1lERE7V1k9gw9u1dYc7odcLxuKINKRVq1bV7Ldp08ZcEDdLjUtlS8GWmlqNZBEROVVDkofw1zP/yrzt81iwfQH9W/Y3HUnkhCxevJirr76awMBAOnbsSIcOHYiJicFms5Gbm0tmZibr17t+7jZmzBgef/xxQ4kNSUsDu91aJzk+3mos798P4eGmk4mIiLc4cMB6CKlxY9NJ5Bi8spHcvHlzbr31VgCcTidFRUWsXbuWpUuXkpeXx2WXXcZbb73FG2+8QWpq6jGvNX78eJ588kl3xG5Yzc+FoAiroRzRDqI6mk4kIiLeLu+Q6X3DW5rJIdLAdu7cyXPPPVdT+8P6yL+7qttVnNXmLFLjUkmNSyUlNuX4LxIRETmGXs170at5LwCqHdWUVJYc5xUiniMkJKRmv6qqiqysLLKyso56fmRkJE888QR33nknAQEB7ojoOcLD4ZdfIDUVEhPBZjOdSEREvM2vv8KwYdC5MwwYAGeeCVdcYTqVHMLmdDqdpkPUl507d/LII48wYcIEwBqdPGPGDLp27XrU1xxpRHJSUhIFBQVERUU1dGQRERHP5KiCKRHgqP0ZydnToelQc5n8RGFhIdHR0Xov0sBKS0vZvHkz06ZN45lnnqlZHqVTp04sWLDghJZH0fdMRERETNP7kfqTnZ3Njz/+yIIFC1i5ciVbt24lPz8fgKioKJo1a0b37t0ZOnQoY8aMISLi5KZu1/dMRET83v/9Hzz2WG3dpw8sWGAuj5+p63sRrxyRfDTNmzfn3XffJSoqipdeeom8vDwuvfRSVqxYcdSnAkNCQlyeNhQRERGgqhiSr7JGJeevsBrKMT1MpxI5abNnz+aMM8445jkjR45k0qRJJ9REFhERERHfkpqaSmpqKrfccovpKCIiIr5t3jzXesAAMznkmOymAzSE8ePH13TPV69ezbRp0wwnEhER8TLBjaHvW3DOIri4GM5bBSFxplOJNIiYmBgmT57M1KlTaVyHdXkqKiooLCx02UREREREREREpI6cTsg4ZFk9NZI9kk82ksPDwxlw0H9wc+bMMZhGRETEy9kDIbqT6RQip6R58+bceuut3Hrrrdxyyy1ceeWV9OnTh8DAQPLy8rjssss4++yzyc7OPu61xo8fT3R0dM2WlJTkht+BiIiIiIiIiIiPsNlg82ZrVPLzz8PYsWokeyifmtr6YDExMTX7ubm5BpOIiIiIiGlt27bllVdeOez4zp07eeSRR5gwYQK//PIL/fr1Y8aMGXTt2vWo13rooYe45557aurCwkI1k0VExO85nU4AbDab4SQiIiIi4hVCQqBfP2sTj+WzjeRdu3bV7MfGxhpMYojTCdVlEBhuOomIiIiIx2revDnvvvsuUVFRvPTSS+Tl5XHppZeyYsUKAgICjviakJAQQkJC3JzUPcoqyyg+UExCowTTUURExMvM3jqbSz+7lP4t+9O/ZX8GJA2gf1J/07FEpKE5nbBrF2RmWlunTjBqlOlUIiIiUk98cmrr3Nxc5h20SHenTn4yHeeemTD7YpjWEz5tDDPPN51IRERExCuMHz+eqKgoAFavXs20adMMJ3KfL1Z/wfD3h9PmhTY0+nsjbvn2FtORRETEC83bPo+dRTv5bPVn3Df9Pm6eerPpSCLiDvfdBy1awLnnwoMPwqRJphOJiIhIPfKKRvL+/fvrfK7D4eC2226joqICsEaMnH++nzRUK/bC1k8gLwMqC6Fog+lEIiIiIl4hPDycAQetxTNnzhyDadxrX+k+pm+czpaCLThxkp17/HWiRUREDjV/+3yXul9LTVEo4hc6dHCtMzLM5BAREZEG4RWN5Pfee4/evXvz3nvvUVhYeNTzMjMzGTlyJB999FHNsfvvv5+4uDh3xDQvMsW1Lt0G1RVmsoiIiPfa8F9YeDOsexP2LYSqMtOJRNwiJiamZj83N9dgEvdqH9fepV6Xuw6H02EojYiIeCOn08m87fNcjvVvqWmtRfxCjx6u9bp1UFxsJouIiIjUO69ZI3nx4sVcffXVBAYG0rFjRzp06EBMTAw2m43c3FwyMzNZv369y2vGjBnD448/biixARHtDjnghOJNEN3RSBwREfFS276End/U1h3vhZ7PGYsj4i67du2q2Y+NjTWYxL1S41Jd6rKqMnYV7aJFVAtDiURExBvNuHoG87bPY962eczbPk/rI4v4i/R0CAiAkBDo1s1qLJeWQkSE6WQiIuKpVq8GhwM6drR+hohH84pGckhISM1+VVUVWVlZZGVlHfX8yMhInnjiCe68804C/Ok/wqBISP8rhLWAyHZWYzm8lelUIiLibfIOmYospseRzxPxIbm5ucybVzuSqlOnTgbTuFeziGY8ffbTpMSmkBqXSkpsChHB+uBPRETqzmaz0SG+Ax3iO3BN92tMxxERdwoLg+xsaN1azQAREambp5+GSZOsh4569YJbb4U//tF0KjkKr2gk33zzzQwZMoQff/yRBQsWsHLlSrZu3Up+fj4AUVFRNGvWjO7duzN06FDGjBlDhL8+9db1KdMJRETEm5XvhbIdrsdi1UgW77N///46jyp2OBzcdtttVFRYS4KEhIRw/vnnN2Q8j2Kz2Xj4jIdNxxARERERb9W2rekEIiLiTRYtsn4tLoaZM+GSS8zmkWPyikYyQGpqKqmpqdxyyy2mo4iIiPgumx26/9MalZyXAaU7ILKD6VQiJ+y9995j0qRJ3H777Vx44YVERUUd8bzMzEweeOABvv/++5pj999/P3Fxce6KKiIiIiIiIiLiH/LzrZksDtanj5EoUjde00gWERERNwiJg84P1NbV5WDX9GTinRYvXszVV19NYGAgHTt2pEOHDsTExGCz2cjNzSUzM5P169e7vGbMmDE8/vjjhhKLiIiIiIiIiPiwrVshKQm2bbPq4GDo0sVsJjkmNZJFRETk6AJCTScQOSkhISE1+1VVVWRlZZGVlXXU8yMjI3niiSe48847CdDabiIiIiIiIiIi9a9rV6uZvHu3NcX1jh1WM1k8lhrJIiIiIuJzbr75ZoYMGcKPP/7IggULWLlyJVu3biU/Px+AqKgomjVrRvfu3Rk6dChjxowhIiLCbGgREREvVFRRREhgCMEB+gBQREREROqoaVMYNcp0CqkDNZJ9mdMJB/ZDYCONKBMRERG/k5qaSmpqKrfccovpKF6loqqCjXkbaRnVksiQSNNxRETEwz0/73nGzx5PWkIaPZr2YHSH0VzQ8QLTsUTE3YqLYflyyMiwti5d4K67TKcSERGRU6RGsi9a8GfIy4Ci9VCZD2dPh6ZDTacSEREREQ92+eeXM3/7fDbnb8bhdPDNZd9wXup5pmOJiIiHy9idwYHqA2TsziBjdwaNQxurkSzij557Dp58srYePFiNZBERER9gNx1AGsD+JbB/sdVEBquhLCIiIiJyDJvyNrExbyMOpwOA7Nxsw4lERMQbLN211KXu0ayHoSQiYlSPQ/7fX7bMmi1RREREvJoayb4osp1rrUayiIjUxcp/wC/nQMb9sOl9KNpgOpGIuFFqXKpLrUayiIgcz/6y/Wwv3O5yrEdTNZJF/NKhjeT8fNiyxUgUERERqT+a2toXRaS41sUbzeQQERHvkjMDdn1vbQBpD0O3p41GEhH3ObSRvKdkj6EkIiLiLWLDYsl7MI9lu5eRsSuDzJxMOsR3MB1LRExISoLEREhIsJrKPXpAo0amU4mIiCeZPBmioqBnT2jWzHQaqSM1kn1RywugURJEtIPIFAhvZTqRiIh4g/wVrnXjrmZyiIgRF3S4gDaN25Aal0r72PZEh0abjiQiIl6gcWhjBrcZzOA2g01HERGTbDbYvh0C9XGziIgcxb33wq5d1n5iInz0EQwebDSSHJ9+svui+L7WJiIiUlcVuVC20/WYGskifiWtSRppTdJMxxARERERb6UmsoiIHM2uXbVNZIA9ezQq2Uvop7uIiIhAQCgM/BjyM62RyUVrIbK96VQiIiIiIiIiIiLi7TIyXOuICGivzx69gRrJIiIiAoGNoPXF1iYiIiIiIiIiIiJSXwID4YwzrIZycTF07w52u+lUUgdqJIuIiIiIiIiIiIiIiIhIwxg+3NocDtiwAYqKTCeSOlIjWURERERERERETsjGvI04nU7axrTFZrOZjiMiIiIi3sBu15TWXkbjxv1BRS4UZptOISIiIiJeospRxfr965m7ba7pKCIi4qH+/uvfSXk5hcb/bMygCYOYlDnJdCQR8QRbt8LEiXDXXTBoENx0k+lEIiIicgo0ItlX7ZkJy/4CRdlwYD9Ep8N5K0ynEhEREREPtmjHIq744go25m2kylFFfHg8e+/fazqWiIh4oIzdGQAUVhQya8ssLuhwgeFEIuIRvv0Wbr65tt6zx1wWEREROWUakeyrbDbInW81kQGK1oHTYTaTiIh4JqfTdAIR8RDRodFk52ZT5agCYF/pPvaX7TecSkREPE1ldSVZOVkux3o07WEojYh4lJ49XevsbK2DKSIi4sXUSPZVkYfMMe+ogNJtZrKIiIhnW/l3+F8KzLoIMh+DnFmmE4mIIcmNkwmwBbgcW5e7zlAaERHxVNsKtxEVEuVyrHvT7mbCiIhn6doVAg+aBNPphIwMc3lERETklGhqa18V2hQCI6Cq2KptgVCyDRq1NptLREQ8T94yKN5gbdu/hAMF0ORM06lExICggCDaxrRl3X6reRwXFkduWa7hVCIi4mnaxrQl574cdhTtIGNXBuv3rycmLMZ0LBHxBKGhMGwYBAXBaadZW/fuplOJiIhJDz4IzZtbs1Z06wZRUcd/jXgMNZJ9lc0Gvd+AkFhrdHKjNmDXt1tERI6gYIVr3biLmRwi4hFeP+91IoIjaB/XntiwWNNxRETEQ9lsNlpGtaRlVEvTUUTE03z7rekEIiLiKUpK4NlnXZfWW7oUemhZFG+hzqIvS77cdAIREfF0VaVQdMi0tY27mskiIh5hSNshpiOIiIiIiIiIiC9Yvty1iRwQAB07mssjJ0yNZBEREX9mD4ERiyB/BeRnWr82TjOdSkRERERERERERLxdRoZr3akThIWZySInRY1kERERf2YPgNie1iYiIiIiIiIiIiJSX9q3h6uugsxMWLVKU1p7ITWSRURERERERERERERERKR+DR9ubQCVlVBcbDaPnDA1kkVEREREREREpE5mbp5JeVU5PZr1oEmjJqbjiIg3cDqhsBCio00nERERk4KCICbGdAo5QWok+4sDBVC0DsKaQnhL02lERERExMNVO6rZWrCV7NxsWkS1IL1JuulIIiLiAcbPHs/3G74HoHlkc54++2mu6X6N2VAi4nm2bIF334XFi60tKgqys02nEhERkROkRrKvW3QbbPsEynOsusdz0Oles5lERERExKPd9d1dvL74dQ5UHwDgvv738ezwZw2nEhER05xOJxm7M2rqnUU7CQ8KN5hIRDxWbi48+WRtvWcPFBRoVLKIiIiXsZsOIA2surS2iQxQpCf/RETkN9Xl4KgynUJEPFB4UHhNExkge7/eQ4qICOwq3kVOSY7LsR5NexhKIyIeLT0dgoNdjy1daiaLiIiInDQ1kn1dZKprXbTOTA4REfE8m96HKY3g264w5zJY/7bpRCLiIVLjXN9DZueqkSwiIpBXlke/lv0ICwwDIDI4knax7QynEhGPFBwMXbq4HlMjuX59+inMmQP791u10wkVFWYziYiIz9HU1r4u6qAPAW12cBw4+rkiIuJfClZaPxfyV1ibsxpS/mQ6lYh4gPax7Wv2o0OiiQ+Px+l0YrPZDKYSERHT0pqkMe/6eVQ7qsnOzWZb4TbsNo1REJGjuPxyGDQITjsNevWClBTTiXxHdTVcccXhjeN774XnnjOTSUTkUOPGQZMm0LWrtXXpAiEhplPJCVIj2dclnA5nfmWNTI5oCwHBx3+NiIj4h4JVrnVUZzM5RMTjdG/andnXziY1LpX48Hg1kEVExEWAPYBOCZ3olNDJdBQR8WR33206ge/avPnIo48zM90eRUTkiAoLYfJk12PLlkG3bkbiyMlTI9nXhTaBlqNNpxAREU9UsNK1bpxmJoeIeJxGwY0Y2Gqg6RgiIiIiInIkq1cf+fjy5e7NISJyNCtWuNaBgdCxo5ksckrUSBYREfFXIzOtUckFK60t9jTTiURERERERETkeKqroXNnWHXITGM5ObBnDyQmmsklIvK7Q2dI6NhR01p7KTWSRURE/FVIHDQ5w9pERERERERExDtccIG1HTgA/ftDmzZw1VXWGqRNmphOJyJi/d301FNWQzkzE7p3N51ITpIaySIiIiIiIiIiIiIi3iY4GJYsMZ1CRORw3bu7No8dDlNJ5BSpkSwiIiIiIiIiIsf0xeov2Fu6l/Qm6aQ3SScqJMp0JBHxNgUF1hq+Z55pOomIiLib3W46gZwkNZL9SWUxFK2D4g3QaqzpNCIiIiLi4RxOB9sLt5Odm012bjbX9biO0MBQ07FERMSA1xe/zvSN02vqZ4c9y30D7jOYSES8Ql4e3H03LFgAa9ZYx3JyICHBbC4RERGpEzWS/UHpTvi+N5TtrD02Zp+1NqaIiIiIyBGUVZYR90wcZVVlNccGtR5EWpM0g6lERMSUrJwsl7p1dGtDSUTEq0RGwiefQGlp7bEFC+D8881lEhERkTrTWHJ/ENoEKva6HitcayaLiIiYV10BxZvBqbVJROTowoLCaBza2OVYdm62mTAiImLU/rL97Cre5XIsvUm6oTQi4lUCA6FXL9djCxaYySIiIiInTI1kf2APhMj2rscKV5vJIiIi5u1fAv9Lhk+i4LvesOBP4HSaTiUiHig1LtWlViNZRMQ/lRwo4ZK0S0hLSCPQHkhwQDApsSmmY4mIt+jbt3Y/ONhaK1lO3qefwoQJsHAhFBcf/vX9+2HPHrfHEhER36Sprf1FVCcoWAXYIKKt6TQiImJSwUrr16oS2L8YqkvBZjObSUQ8UvvY9szcMpOI4AhS41IPG6EsIiL+ISk6iY/GfgRARVUFWwq2EBQQZDiViHiNP/4RWrWyGsrdukFIiOlE3u2FF2DOnNr61VchKgomT4bMTNi+He65B55/3lhEEfFzQ4ZAWBh07Wptw4ZBnJZa9VZqJPuLLk9A+mMQlQoBoabTiIiISb83kn8X1dlMDhHxeI8PfpynznqKphFNsemBExERAUICQw6bsUJE5Jj69LE2OXVOJ6w85N/0rVtb04V/+23tseXL3ZtLROR3FRUwYwY4HDB1qnVs/nw1kr2YGsn+orHWLhIRkd+UbHato9OMxBARz9cyqqXpCCIiIiIi8rvduyE/3/VYWhqUl7seW77cajrrYVARcbe1a60m8sE6axCLN1MjWURExN+c8QWU7YLCVZC/EhIGmk4kIiIiIiIiIsdTVgYXXmiNSt6wAUJDrWnDKytdzysshNxciI83ElNE/Nihsya0agWRkWaySL1QI1lERMTf2GwQ3tzamg41nUZERERERERE6qJtW/jiC2u/vBy2bgW7Hdq1g4cegvR0ax3q1FQI0lr2ImLAGWfAe+9ZDeWVKyEx0XQiOUVqJIuIiIiIiIiIiIiIeJPQUKthDFYz+e9/N5tHRASgZUu48krTKaQeqZEsIiIiIiIiIiJH9OWaL5m7bS5dmnQhvUk6HeM7EhYUZjqWiHirqirIyoIFC+Dss6F9e9OJRERE5BjUSPY31eVQmA2Fq8Fmh1Z/NJ1IRERERDyY0+lkV/EusnOzyc7NJiI4gnFdxpmOJSIibvL12q95Z9k7NfW13a/lnQveOcYrRESO4tpr4eOPrXV+AV54Ae6802gkEREROTY1kv3Jpkkw/ypwOqy6cVc1kkVERETkmF5f/Dq3fntrTX1a89PUSBYR8SNZe7Nc6i5NuhhKIiJez26vbSKDNSpZREREPJrddABxo0ZJtU1kgMK14Kg2l0dERNxvz0wo2wVOp+kkIuIlUmJTXOrs3Gyc+jtERMQvOJwOVuasdDmW3iTdUBoR8Xp9+rjWaiSLiIh4PI1I9idRHV1rRwWUboGItmbyiIiIex3Ih58GW/vBMRDdGU7/FMKamkwlIh4uNS7VpS6sKCSnJIfEiERDiURExF0qqiq4s++dZO3NIisni415G9VIFpGT17dv7X67dlZdWQlBQeYyeZNp02DOHEhLg86doUMHCA098rmlpdZa1I0bQ2rqkc8REalvTifYbKZTSD1TI9mfhCRAcCwc2A/hSVZjubrcdCoREXGXglW1+wfyYN8C6+eCiMgxJEUlERwQTIAtgPZx7UmNS6W8Su8hRUT8QVhQGE8PebqmLj5QTKOgRgYTiYhXS0+HqVOtkcnx8abTeJ+vvoI336ytr7gC3n/f9ZznnoO334Z166yGzj33wPPPuzeniPiviy+GlSutB17S0uCii6BbN9Op5BSpkexPbDYYNgfCW0JQhOk0IiLibgc3kgGiUiEg2EwWEfEaAfYAtty1hSaNmmC3aWUcERF/FhGszxJE5BQEBsLIkaZTeK8VK1zrLkdYs764GLKza+vlyxs2k4jIwZYtg/XrYfVq+PRTSE5WI9kHqJHsb6I7Hv8cERHxTZUFEBAO1aVWHdXZbB4R8RpNIzQFvoiIiIiIMQ7H4Y3krl0PP+/QY8uXa6pZEXGPsjLYsMH1WGd99ugL1EgWERHxF53uhY53Q8lWKFgJwY1NJxIRERERERGR46mogNtug8xMa9u27ciN5INH/tlsEBsLRUUQFeW+rCLin9autR5cOVinTmaySL1SI1lERMSf2OwQ0cbaRERERERERMTzhYXB3/9eW+flQePGh5+XnGytkdy1q7UmdXi42yKKiJ/r0AHmzIFVq6x1kvftgwgti+IL1EgWEREREREREREREfEWMTFHPm63ww03uDeLiAhYD7wMGGBt4lPUSBYRERERERERERffr/+et5a+RVpCGulN0unetDupcammY4mIr9i1C+bOtbbGjeGvfzWdSERERI5AjWR/5KiG4o1QkAX5WRDfF5oNN51KRERERDxYTkkO2bnZNdul6ZfSvWl307FERKSBzN02l89Xf87nqz8HYHi74Xx/xfeGU4mIT3jvPbj66to6OVmNZBEREQ+lRrI/mn8tbH6/tm5/sxrJIiIiInJMIyeNZMmuJTV1UlSSGskiIj4sa2+WS52ekG4oiYj4nO7dXetNm6wRys2aGYkjIiIiR2c3HUAMiO7kWudnHfk8ERHxHTu+gZxfoSLXdBIR8VKHTmeanZttKImIiLhDVs4hjeQmaiSLSD1JS4OoKNdjc+eaySIiIiLHpBHJ/ij6kH/8FWSB0wk2m5k8IiLS8BbeCGU7rf3QRBj4ESQONhpJRLzLoY3kdfvXGUoiIiLu8MgZj7B893Ky9maRlZNFWpM005FExFcEBEC/fjBvHvTvDwMGWM1lObLPP4cPP4SuXa2tZ09o1er4r6uqgjVrYNkya/rwgQMbPKqI+KnycggOBrvGrvoiNZL9UePfGsmhTaymcnQ6OA5AQIjZXCIi0jAO5Nc2kQHK91g/A0RETkD72PaEBISQEptCalwqA5P0QZSIiC+7qttV0K22djqd5sKIiO/54AOIjbWaynJsM2fCZ59ZG8DYsfDJJ8d+zWOPwTPPQEWFVV9/vRrJItJwnn0W/vEP6NABOnaE886Dyy83nUrqiRrJ/qhRa/iDmggiIn6jYJVrbQuEiBQzWUTEa12cdjGXpl9KgF0f9omI+CObZjETkfqUkGA6gffIzHStu3Y9/msaN65tIoM1KllEpKGsWQOlpZCRYW3x8Wok+xCNM/dHNruayCIi/qS6HBp3AXuwVUelQkCw2Uwi4nWCAoLURBYRERERcSen8+QayT16uNZZWVBZWX+5REQOtmaNa92hg5kc0iA0IllERMTXNT0bRmaCowqKN1hTXYuIiIiIiIiIZ3M4rCljMzOtbfnyujWSux20NkFSktVYLiiwRgmKiNQnh+PwRnLHjmaySINQI1lERMRf2AMhSk8EioiIiIiIiHiFgAC47rrauq7r1cfGWmsrp6VBXFzDZBMRAbDZYN06q5m8Zg2sXQvp6aZTST1SI1lEREREREREREREzPq9Sao12Y/uRP5szjyz4XKIiPzOZoPmza3t7LNNp5EGoEayiIiIiIiIiIgAsGjHIm74+gbSm6STlpBGt8RunJd6nulYIuKLnE6YNw/mzoU5c6xfZ83S2poiIiIeRI1kf1a6A/IzIT8LCrKg3fXQRE+qiYiIiMiRlVWWsWz3MrJzs8nOzSanJIe3R79tOpaIiNSj5XuWk7knk8w9mQB0iu+kRrKINJw//hF27qyt1UgWERHxKGok+7O5l0POzNo6qoMaySIiIiJyVBvyNjDgnQEux/59zr+JCI4wlEhEROpbVk6WS53eRGvciX/ZvHkz06dPZ+bMmaxYsYKtW7dSXFxMZGQkLVu2pH///owbN45BgwaZjur9bDZr+uWPPqo9NmsW/OlP5jKJiIiICzWS/Vl0umsjOX+FuSwiItIwtv8PKnIhOg2iO0OQmj0icvLaxbTDhg0nzppj63LX0aNZD4OpRESkPq3Icf1sQI1k8RcZGRncdNNNLFy48Ihfz8vLIy8vjxUrVvDWW28xePBgJk6cSKtWrdyc1Mcc2kieOdOa8lrrJIuIiHgENZL9WeND/jGYn3Xk80RExHtlvwK7p9fWPZ6FTveZyyMiXi0sKIyk6CS2FmytObZuvxrJIiK+5O9n/52lu5aSlZNF1t4sTmt+mulIIm6xdu3aw5rIqamppKenEx8fT35+PnPnzmX79u0AzJgxg/79+/Prr7/Stm1bE5F9w6BBkJpq/XrmmdamJrLl3/+G//wHeva0tjPPhF69TuwaTids3QoZGdbWsSNcdlnD5BUR/3PgAOTmQtOm+rvbh6mR7M+i0yEoyvq1cTrEdDedSERE6lvBStc6XE/Li8ipSUtIIywwjNS4VFLjUkmJTTEdSURE6lHfln3p27Kv6RgixqSkpHDDDTdwxRVX0KJFC5evORwOJkyYwO23305paSk7d+7k8ssvZ+7cudj0AfrJ6dwZ1q41ncIzLVoEq1ZZ2wcfwJ//DG++eWLXeOQRGD++th41So1kEak/mZnQuzdERVkPqqSlwX//q6ayj1Ej2Z8lDICx+fqfWkTEVx3Ih7Kdrsei04xEERHfMXXcVH1QKiIiIj6nWbNmvPvuu1x55ZUEBAQc8Ry73c51111HTEwMf/jDHwCYP38+P/zwAyNGjHBnXPEHS5e61j17nvg10g75DCAj4+TziIgcas0a69fCQli40BqdrM8LfI7ddAAxyGbX/9QiIr6ssghaXgSRqb/9nR8Ike1NpxIRL6cmsoiIiPiiQYMGcc011xy1iXywiy66iD59+tTUU6dObcho4o+KiiA72/XYyTSSu3d3rbdvh337TjqWiIiL3xvJv+vY0UwOaVAakSwiIuKrGiXBmZ9b+9XlULwZAoKNRhIREREREfEFAwcOrFlTefPmzWbDiO8JDYW5c61RyUuXwrJl0KXLiV+nQwfrWmFhVlO5Rw+oqqrvtCLirzZscK3VSPZJaiSLiIj4g4BQiNabORERERERkfpw8Cwt1dXVBpOITwoKgn79rO1UBAbCpk2QmKiZKUWk/k2aBP/4hzUyee3ak5s5QTye1zSSN2/ezPTp05k5cyYrVqxg69atFBcXExkZScuWLenfvz/jxo1j0KBBpqOKiIiIiIiIiHgVp9OJEyd2m1ZBE6mLFStW1OwnJSUZTOKD8vKsKZhPZgSuHK5pU9MJRMRX2e3QurW1jRhhOo00EI9vJGdkZHDTTTfVTBVzqLy8PPLy8lixYgVvvfUWgwcPZuLEibRq1crNSUVEREREREREvNOOoh10fKUjaU3SSE9IJ71JOrf2uZVgLY0icpitW7fy888/19RDhw41mMZHrF0Lr74KM2fCihXW9KirVplOJSIi4vc8vpG8du3aw5rIqamppKenEx8fT35+PnPnzmX79u0AzJgxg/79+/Prr7/Stm1bE5G9T+4iyF0IecusretT0Pxc06lERERExENl52azaMcisnOzyd6fzZmtzuTm3jebjiUiIqdgxZ4VlFSWsHDHQhbuWEhkcCR39bvLdCwRj3TPPffUTGfdqlUrRo0aZTiRDygogJdfrq1Xr4acHGjSxFwmERER8fxG8u9SUlK44YYbuOKKK2jRooXL1xwOBxMmTOD222+ntLSUnTt3cvnllzN37lyX9UrkKDLuh5yZtXXuYjWSRUREROSo3lryFs/Pe76mrnJUqZEsIuLlsnKyXOr0Jun6TEXkCCZOnMhnn31WU48fP56QkJDjvq6iooKKioqaurCwsEHyea2ePaFRIygpqT02axaMHWsuk4iIiODxC980a9aMd999lzVr1vDggw8e1kQGsNvtXHfddXzwwQc1x+bPn88PP/zgzqjeK6a7a52/zEQKERGpT7umw/JHYPOHkLccqstNJxIxYvPmzbz99ttcccUVdOvWjZiYGIKCgoiNjaVr167ceOONzJw58/gXEhepcaku9brcdYaSiIhIfcnae3gjWURcLV68mJtuuqmmvuyyyxg3blydXjt+/Hiio6NrNq2rfIjAQBg4sLYOD4fdu83lMWnvXjhwwHQKERERAGxOp9NpOkR96tu3b81U2LfffjsvvfTSCb2+sLCQ6OhoCgoKiIqKaoiInmfjBJh/bW0d0RZGbzAWR0RE6sGSu2HtC7V1qz/C6VOMxZG688v3Ig0gIyODm2666bAlUo5m8ODBTJw4kVatWp3wvfzxe/bLpl84+72za+rwoHCKHirCbvP451RFROQo9hTvIXNPJlk5WWTlZDEiZQQXp11sOpbUkT++H3G3TZs2MWDAAHb/1tzs2rUrv/76a53/vI80IjkpKUnfs4NNnmytlTxkCPTtC8F+ukb7H/8IX38NPXpAnz4wbpz153Eq8vJg6VLIyLC2Tp3g0UfrJ6+I+Kc1a6zlB2JjTSeRk1TX949eM7V1XQ0cOLDmA8PNmzebDeMtYnpCdBrE9LBGJ8f0MJ1IREROVcFK1zqqs5kcIoasXbv2sCZyamoq6enpxMfHk5+fz9y5c9m+fTsAM2bMoH///vz666+0bdvWRGSvkhqXSkpsCqlxqbSPbU9qXCpVjiqCA/z0wz4RER+QGJHIsIhhDGs3zHQUEY+za9cuhg0bVtNEbtu2Ld99990JNYBDQkLqNAW2X7vsMtMJPMPChVBRAfPnW1u3bqfeSH77bXjwwdq6b181kkXk1IwYAVu3Wo3k9u3hxRdP/e8q8Ug+10g+eP2e6upqg0m8SExXOC/r+OeJiIj3KFjlWjdOM5NDxLCUlBRuuOEGrrjiisOWSHE4HEyYMIHbb7+d0tJSdu7cyeWXX87cuXO1JuRxtIhqwbrbNZ21iIiI+L7c3FyGDRvGhg3W7H3NmjXjxx9/pFmzZoaTiU/avdtqzBysT59Tv26PQwYOLV8OVVXWlOIiIieqvBy2bbP29++HBQv8dxYJP+Bzc8+tWLGiZl9rjYiIiF9yOqDNOGh2LjRqbR3TiGTxM82aNePdd99lzZo1PPjgg4c1kQHsdjvXXXcdH3zwQc2x+fPn88MPP7gzqoiIiIh4qMLCQkaMGMHKldaMT/Hx8fz4448kJycbTiY+66DPtgFo1MiahvpUHdpILi+3pqUVETkZGzbAoavmpqSYySINzqceOdq6dSs///xzTT106FCDaURERAyx2aHHM7V1ZREEhJvLI2LAoEGDGDRoUJ3Oveiii+jTp0/NVNhTp05lxIgRDRlP5IhyS3OJC48zHUNERESAkpISRo4cyZIlSwCIjo7mu+++o3NnPaQrDWjYMGt03+LF1gi/0lIICDj168bHW1PPRkVBz57QqxckJp76dUXEP+3YYc1oUFVl1U2bQmSk2UzSYHyqkXzPPffUTGfdqlUrRo0aZTiRiIiIBwjSGzmR4xk4cGBNI3nz5s1mw4hfenH+i/zt17/x/RXf07NZT9NxRERE/Fp5eTmjR49mzpw5AISHhzN16lR69eplOJn4hZgYq6E8rJ7XrF+zBuw+N0GpiJgwfLj1oMuWLbBuHRQXm04kDchnGskTJ07ks88+q6nHjx9PSEjIcV9XUVFBRUVFTV1YWNgg+cTHVZVCwUoo3W7VjVpDrD4AFBER8RYHr4n8+4OJIu7yTsY73PX9XQCcNfEsvh33LQNbDTQbSkT8yt6SvcSHx7v8PBTxV5WVlYwZM6Zm1sOQkBC++uorBg7Uz2a3cjisaZ5/+gk6dIDzzjOdyPupiSwi9SkoyJrOWlNa+zyfaCQvXryYm266qaa+7LLLGDduXJ1eO378eJ588smGiua9HNXgrISAUNNJPN+BPPi8GThqH0ggujOct9JcJhERETkhKw5aiywpKclgEvE3P2z4gT99/aeaurCikOEfDCf7tmxaRB2+treISH2rrK6k+b+aExUSRXqTdNIT0nls0GMkRmjKU/E/1dXVjBs3jm+//RaAwMBApkyZouXz3O211+CJJ2DvXqu+8EI1kkVERAzx+seQNm3axKhRoygvLwega9euvPHGG3V+/UMPPURBQUHNtm3btoaK6vl2TYeM++HHwfBpY1hX9z9HvxYcA/H9XI9Fab0cERERb7F169aaESeAPiisI6fTyUdZH/HUzKe48osr6fufvmzM22g6ltcZkDSAwW0Guxx77MzHaprIe4r3cO/39/Li/BcNpBMRf7AxbyNVjir2l+1n1pZZvLb4NYIDgk3HEnE7p9PJ9ddfz6effgqA3W7n/fffZ/To0YaT+aGIiNomMsCMGaBZg0RERIzw6hHJu3btYtiwYezevRuAtm3b8t133xEVFVXna4SEhNRpCmy/sOUj2PhObZ27yFwWb9PmcsiZWVtHp5nLIiIiIifknnvuqZnOulWrVowaNcpwIu9gs9m47dvbyC3LrTm2Zt8a2sa0NZjK+0QERzB13FQu/uRivs7+modOf4gHT3+QfaX7eGbOM7yy8BXKqsqIC4vjuh7XERmide9FpH6t2bfGpW7SqAkxYTGG0oiY8/rrrzNx4sSaul27dsyePZvZs2fX6fWvvPJKQ0XzP0OGuNb5+bB0KfTubSSOiIiIP/PaRnJubi7Dhg1jw4YNADRr1owff/yRZs2aGU7mxeL6uDaS96uRXGetxsKSOyD0t6m/Gqcf/dzyfRAcDfYg92QTEf+StwxW/sN6oCW6MzTuAlGpplOJeKyJEyfy2Wef1dTjx48/7kOGFRUVVFTULmlRWFjYYPk8XWpcKvO2z6ups3OzGdl+pMFE3ik0MJTPLv6MyVmTubLrlQBsyd/Cs3OfrTkntyyXVxa+wkNnPGQqpoj4qLW5a13qjvEdDSURMSsnJ8elXrduHevWravz69VIrkctWkDHjrBmDbRtazWWGzUynarhffYZdOsG7dqBu9asdzrddy8REfFKXtlILiwsZMSIEaxcaa1BGx8fz48//khycrLhZF4u7pCn+orWwYF8CG5sIo3nKd8HofFH/lpwDIzNO/6a0lUlMOMcaJQMAyeD3Sv/FxQRT5a7CLZ+XFtrzXaRo1q8eDE33XRTTX3ZZZcxbty4475u/PjxPPnkkw0ZzWscqZEsJycoIIirul1VU/dq3ovRHUbzv7X/qzn2woIXuHfAvZpyVkTq1d397ubCjheydt9a1uxbo7WRRcQzvPMONGsGbdqYTuIeubkwdqy136QJDBwIb7xh7denHTvg229hyRJrlHfjxvDDD/V7DxHxbVu3Qk4OtG8P0dGm04gb2JxOp9N0iBNRUlLCiBEjmDNnDgDR0dH89NNP9OrVq16uX1hYSHR0NAUFBSc0RbZPcFTCrIsgtifE9rYay2FNTafyDNu+gHlXwYBJ0PIk18ZxVMPsMbD9K6tOvgr6TdBTfyJSv5bcDWtfqK1b/RFOn2Isjpw4v34v4kabNm1iwIABNUukdO3alV9//bVOf+ZHGpGclJTkl9+zNxa/wRdrviA1NpXUuFT6texH7xaacvBo8svzaRzauM7nZ+zKoOdbPQmwBXBVt6t49MxHNXW4iIgckd5Deh99z8TF11/Dwetxh4VBQQEE1fOMht98Awcv5RMRYd3Hbq/f+4iI7/r73+GRR6z9hAS47DJ48UWzmeSk1PW9iFcNhywvL2f06NE1TeTw8HCmTp1ab01kv2cPgsHfmE7hefIyYe7lUF0Gsy6EXi9AhztO/DqZj9Q2kQE2vQeR7SH90fpKKiICBYeMPo7qbCaHiAfbtWsXw4YNq2kit23blu+++67OH+CFhIQcd/prf3HTaTdx02k3Hf9EYVvBNk5/93Su7X4tjw96HFsdHibs0awHL5/7MueknENKbIobUoqIiIiIEb993l2jT5/6byIDHPo5enExrFsHHTrU/71ExDcdvOzD3r1w4IC5LOIWXvOoUWVlJWPGjOHnn38GrA/wvvrqKwYOHGg4mfi0qlKYc6nVRAbACUvuhF3TT/xaTYe7Tn1tD4aw5vUSU0SkRttrIPV2SDwbQptC4zTTiUQ8Sm5uLsOGDWPDhg0ANGvWjB9//JFmzZoZTia+bE/xHoa+P5StBVt5cuaT3PvDvdR1Yqjb+tymJrKIiIiIrwsOhsSDlhZoqM+8mzWztoMtWdIw9xIR33RwIxmsKa7Fp3nFiOTq6mrGjRvHt99+C0BgYCBTpkxh6NChhpOJz6sqhfCWULi69li766HpSfy31/RsOP1TmDUaQprAGZ9DQv/6yyoiAtBmnLX9zrtWsBBpUIWFhYwYMYKVK62R+/Hx8fz4448kJycbTia+7ED1AUZ+ONJl/eh/z/83CeEJPHTGQwaTiYiIiIjHeOopePJJ2LDBGp3co0fD3evCC2H/fmt0cs+e0FtL04jICaiutqbDdzisWo1kn+fxjWSn08n111/Pp59+CoDdbuf9999n9OiTXKdW5ESExsNZ38Hq52H5w9C4C5z26smva9ziPOj/PjQZBOEt6jeriMiRaB12EQBKSkoYOXIkS3572j46OprvvvuOzp01/bs0rKKKIppHNmfprqU1xzondOZPvf5kMJWIiIiIl6quhoAA0ykahs0GKSnW1pBee61hry8ivm3ePGs6602bIDsb+muwnK/z+Eby66+/zsSJE2vqdu3aMXv2bGbPnl2n17/yyisNFU38hc0One+HxMEQFA0Bp7gm4sEjBUVERKTBlZeXM3r0aOb8tu5YeHg4U6dOpdeh64OJNIC48Dj+d+n/+GLNF9w+7XZCAkKYfuV04sPjT/naDqcDu81rVisSEQ+0vXA7USFRRIVEmY4iInJkZWUwaxZMmwbffQc33AD33Wc6lYiIfwsOttZW1/rqfsHjG8k5OTku9bp161h36Bzsx6BG8ikq2QoVuRDbgNOpeIs4TfMiIiLibSorKxkzZgw///wzACEhIXz11VcMbKg1x/xctaOa+dvnMyBpADbNiFDDZrPxh05/YGjboewt2UvzyOandL3FOxfzxIwn6NmsJ0+d9VQ9pRQRf3Tz1Jv5JvsbmkU0o0N8B+7qexcXdLzAdCwRkVr33guvv15bT5umRrKIiIgb6fF1Ody+BTD7EvgyCb5qDYtuNp3If2gtUxERkXpTXV3NuHHj+PbbbwEIDAxkypQpDB061HAy3+J0Opm9dTZ3TLuDpH8ncfq7p7Nk1xLTsTxSVEgU7WLbnfTrs3OzGT15NL3f7s3UdVN5Yf4L5Jbm1mNCEfE3a/atAWBX8S5mbJ5BYUWh4UQiIocYPty1/vVXKCoyk0VERMQPeXwj+YknnsDpdJ70JiehshC2ToHS7VadtxSqysxm8gd5y2D66bD7Z9NJREREvJ7T6eT666/n008/BcBut/P+++8zevRow8l80zVfXsPLC19mV/EuAKasnGI4kW+qdlTzTfY3NXXRgSKen/e8wUQi4s0qqirYlLfJ5VjH+I6G0oiIHMWQIRAUVFtXVsKMGcbiiIiI+BuPn9paDIjvZ60L7HRYtaMS9i+GJmeYzeUO+StgxzfQ4U4IDHfPPQ/kQ+ZjsO5V68884z44Z7H1PRARqasDBfDzEIhOg+jO1q9Nh0NAsOlkIka8/vrrTJw4saZu164ds2fPZvbs2XV6vZZHqTubzcbFaRczfvb4mmNTVk7hn0P/qemt61mnhE6M6zKOSSsm1Rz7eOXHPHXWUwTa9U87ETkxWwu24sT1AfwO8VrnTkQ8TGQkDBoEJSVw7rlwzjnQq5fpVPXno49g+XI46ywYOBAaNXJ/BqcTcnIgMdH99xYREY+nTxvkcEGR0LirNUIWIDgGynYZjeQ2y/4CO7+F7Fegy5PQ9hpo6A/ltn4K2S/X1nkZsPUTaH1Jw95XRHxLwUrYv8TaAGyBcHGJ2UwiBuXk5LjU69atY926dXV+vRrJJ+aStEtcGsnRodHsKdlD04imBlOZMXfbXP4x+x+8fO7LtG7cut6v/9igx5icNZmY0BgeGPgAt/a+VU1kETkp7ePaU/pwKev3r2fNvjVsKdhCVEiU6VgiIof77jsICDCdomG8/z58+y384x8QGAhPPw0PPNDw992xA156CZYutTaHA/bvBz0IKiLH8vXXkJQE7dpZD/qIX9AnDnJkHe8DRznED4CoDv4xOnbvXKuJDFC2Exb+CapKoOOdDXvfttfCmn9B4eraY2teUCNZRE5MwUrXOrK9RiOLiNt0TezK6A6j6dWsFxenXey3U6NWVldy4zc3kpWTxU+bfuKpwU9xZ78767XRmxqXyheXfMFZbc4iMkT/cBeRUxMSGEJakzTSmqSZjiIicnS+2kSurIRZs2rrqipoXf8PIh5RdTU884zrsU2boG1b99xfRLxPcTEcvFxYQgIsWADJyeYyiVv4QXdQTkry5dDueoju5B9NZICVT7vWoU2sP4OGZg+Abr/dO7ARpP8Vzvqu4e8rIr6lYJVrHa0PA8W/PfHEEzidzpPe5MTYbDa+uvQrHhv0mN82kQGen/c8WTlZAJRWlnLf9Pt4f/n79X6f0R1Gq4ksIiIi4u2WLLEaMwcbPNg9905Kgri4w/OIiBzNxo2u9d690NT/ZiHzR37SIRSpg453Q5PBtXXnv0BQhHvu3fJC6P4MjNoAXZ+C4Gj33FdEfEfyldDrRUj5MyScDvF9TScSEfErB6oP8NaSt1yOdW/anSu7XWkokYiIiIh4tCZN4OGHoX9/a1rrzp3dt06xzXb4WtMrVrjn3iLinQ5tJDdvDmFhZrKIW2lqa5HfNR1qbXvnWGskp/zZffe22aDz/e67n4j4ntie1iYiIkYEBwSz9MalPPTjQ7y55E0A3jr/La1fLCIiIiJH1rattSYyWCOTt21z7/0vvxx694aePa2mcqtW7r2/iHiXigpo2dJaY93ptNZJFr+gTzVEDpUw0NpERERERE5A49DGvH7+61zd/WrmbJ1D7xa93Xr/akc1ZVVlRAS7aVYdEREREROqqmD2bKuRcdZZptPUj4gI6NTJvfe86ir33k9EvNsll1hbeTls3gwHDphOJG6iqa1FRERERETqUb+W/bh3wL1uu1+1o5oPV3xI59c688hPj7jtviLivXJKcli2exmllaWmo4iI1N3SpXD11db0z2edBY89ZjqRiIj/CQ2Fjh2ha1fTScRNNCJZjq+qDPbNhT2/QLsbIKKN6UQiIiIi4sGqHFXM2DyDj7M+pmlEU/7v7P8zHclnLdyxkGu+vIbV+1YDsCV/Cw8MfIAWUS0MJxMRT/bF6i+4aepNALSKbsV57c/jtfNeM5xKROQ4du2C996rrefOhb17ISHBXCYREREfpxHJcmyzL4VPG8PPQ2Hl07D7B9OJ/IfTCbt/hFkXQuFa02lERERE6mTaumk0e74Zw94fxn8y/sPbS9+m2lFtOpbPSmyUyPr962vqiuoK/jH7HwYTiYg3+P3hE4CtBVvZV7rPYBoRkToaMgQaNaqtHQ74+mtzeURERPyAGslybDY7OA6a637PL+ay1LfqClj1DJTvNZ3kcBvehamd4OdhsP0ryNaT4SIiIuId2sW2c2lI7CnZw6wtswwmahiV1ZVMWDbBeJO8dePWXN/jepdjS3YtMZ5LRDzbwY1kgE7xbl6XU0TkZISGwogRtXWfPhATYy7PyTpwAKr1Xk1ERLyDGslybIlnudZ7frFGyvqCLR/BsgfhyySYfz3kZZpOVKtwteso5E0ToLLYWBwR8XDfdoPpp8OCP8OaF6Bst+lEIuLHUuNS6d60u8uxj1d+bCZMA/r3/H9z7VfX0u+//Vi6a6nRLA+f8TDBAcF0b9qdLy/5kjnXzSHAHmA0k4h4ttLKUmzYaupOCWoki4iXuPFG+Oc/YdMmWLAALrrIdKITN2mStc7zZZfBxInWlN2eYN8++P573/nsV0RE6oXWSJZj+72RbLND7GlWXV0OgWFmc50qpxPWvmDtOypg4ztQshmG/GQyVa32N8Pq54Df3rhVFsLmD6D9TUZjiYgHOlAA+b89CLN3jvVr06EQ1tRcJhHxe5ekXcKy3cs4rflpXNz5Yi5Ou9h0pHq1KW8TT8x4AoDFOxfT++3e/Gv4v7iz351G8iRFJ7HwhoV0SeyC3aZnhUXk+H699ldKK0vJzs1m9d7VnNn6TNORRETqZvhwa/Nm330Hubnw0UfWNnYsfPKJmSylpXDNNbBoEWzebB1btw5SUszkERHPlJkJGzdC27bWFhFhOpG4kRrJcmwR7WDwNIjvD8HRptPUn72/Qt4y12MdzHzwd0QRydD8PNj5DUSnQdtroeWFplOJiCcqdJ2WEFsARKaaySIi8pvre1zPHzv/kXax7UxHaRC3TbuNsqqymtrpdNI/qb/BRNCtaTej9xcR7xMeFE73pt0Pm0VCREQaUHU1TJ/ueuycc8xkAQgLg5kzISen9tiiRWoki4irSZPgmWdq62uvhXfeMZdH3EqPq8ux2WzQ/BzfaiIDhCZC8tVgD7bqiHZW49aTdHsaRiyCkSug070aXSgiR1aw0rWObA8BwWayiIj8JqFRgs82kQEeHPggHeM71tS39L6FPi36GEwkIiIiIl4hMxPy812PHbzus7vZbNC7t+uxxYvNZBERz7Vhg2udkGAmhxihEcnin6I6QP8J0P2fsP4NaNQaPG0duZiuphOIiDdoMRoGTbUayoWrIFQPnYiINLQzW5/JshuX8ezcZ3l32bs8ffbTpiOJiIiIiDfo0QN27oRvvoEvv4S9e6FlS7OZTjsNpk619sPCoLzcbB4R8TwbN7rWbduaySFG2JxOp9N0CE9SWFhIdHQ0BQUFREVFmY4jIiIifkbvRbyPvmf+7UD1AYI9eCaI/WX7qayuJDEi0XQUERFpQHo/4n30PatnlZUQFGQ6xYlzOMBueNLQzExYuNAamZyWBoEaeyYihzj/fMjIsB6EAWuK/qFDzWaSU1bX9yKa2lpEREREROQkeWoTOa8sj8d+eYw2L7Th4Z8eNh1HREREpP5VVMDXX8OVV0KTJrB9u+lEJ850Exmga1e44Qbo1k1NZBE5sm++gR07oLQUVq6Efv1MJxI38oCfVOKVnA7TCURERETEi1RUVfD12q85UH3AdBSf98OGH0h+MZn/m/V/FB0oYuLyiazfv950LBHxAJXVlUxZOYUVe1ZQUVVhOo6IyMlzOKBDBxg9Gj74wFp3+IMPTKcSEfFtYWHQuTNERJhOIm6kRrLUXXkObHof5oyDz5tAyTbTifyPoxJ2fgeVRaaTiIiIiNTJtHXTuObLa0h8LpHRH41m+obppiOdsPzyfNMRTki3xG4uDftqZzV/m/U3g4lExFOs37+eSz69hK5vdCX87+G0f7k9ZZVlpmOJiJw4ux2GDXM9NnEiaBVHERGReqVGstSN0wFTO8O8q2DLZKjIhV3fmU51YorWQ8Eq0ylOzp5fYMGf4POmMONc2PG16UQiIiIidfK3X//GxOUTKagoAODjlR8bTnRiNudvps0LbXhw+oOUHCgxHadOEiMSua3PbTW1DRsOpwOHZhUS8Xur962u2Xc4HRQfKCYsKMxgIhGRU3D11a71hg3WJiIiIvVGjWSpG5sdEoe4Hts5zUyWk7XiKZiaBj+eBVs/A0eV6UR1t+pZ2PAfOLDfqrd41wewItJAqjUdoYh4vkvSLnGpv1r7FeVV5YbSnBin08mt395KQUUBz8x9hvTX0/l23bemY9XJ/QPuJzI4kkvSLiHrlizeu+g97Db980/E363eu9ql7hTfyVASEZF6MHAgtGsHvXvDK6/Arl2QkmI61ZH973/w7rvWFNwiIiJeRJ8kSN01H+la7/kJvGWNu/K9sPW35mvODJg9FrJfNhrphLR2/QCWXd/BgQIzWUTEc8y6CD5PhJ/OhsW3w76FphOJiBxmbOex2LDV1F0Tu7K7eLfBRHX32erPXBrHm/M3MzV7qsFEdZfQKIFNd27io7Ef0Tmhs+k4IuIhAuwBtIhsUVOrkSwiXs1mg8WLYeFCuPVWiIsznejo/vEPuO46SEyECy6AefNMJzqy8nJYsABefVWju0VEBIBA0wHEizQfCQFhkHgWJP0BWoyGgGDTqepmw3/BcVDT2x4Mba4wl+dEtbzAyvz77yGmO5TthOBoo7FExLDCVdb69eU51hT48QMgvo/pVCIiLppHNufW3rfSLrYdYzuPpWVUS9OR6mx74XaC7EFUOioBaBrRlL8P+bvhVHUXF+7BH6aKiBF/Of0v/OX0v1BYUcjafWuJDIk0HUlE5NQ0bmw6wfFt3lzbOD5wwBqdfNNNRiMd0dix8NVXUPXbLI6vvQY332w2k4iY98Yb0KyZNQNE27YQHm46kbiZGslSd6EJMGYfBHrhXxQBYRCSABV7rbr1pdbvx1sEN4YOd1i/h1YXQ0Qb04lExLTKYijZ4nosWiPORMQzvTzSi2aCOchd/e5iRLsR3DT1JmZtmcWL57xIdKge5BMR7xcVEkXvFr1NxxAR8Q9TprjWsbEwdKiZLMcSGlrbRAZYtEiNZBF/V15++N8DK1ZAerqZPGKEpraWE+ONTWSAjnfChdug/wcQ3x/a32o60Ynr8Sx0fkBNZBGxFKxyrW12iOpgJouIiA/rlNCJGVfP4Ntx3/LHzn80HUdEREREvE2PHnDRRRASYtVjxkBQkNlMR3Laaa71okVmcoiI59iy5fBjrVu7P4cYpRHJ4j8CQiD5cmsTEfF2sT3h/DVWQ7lgJVTsg4BQ06lERHySzWbj3Pbnmo4hIiIiIt5o2DBrKyiwpo721JF8vX+bqcJmg44drdrptGoR8U+bN7vWcXEQqaVR/I0aySIiIt7IHmiNQI7qAEkXmU4jIiIerrCikEU7FjF/+3zSmqRxYccLTUcSERERqX8lJTB5srV98w2EhZlOVCs6Gq66ynSKo+vVC375BXr2hKgo02lExBMEBEC/flZDefduaNPGdCIxQI1kERERERERH/b4L4/zf7P+DydOAMZ2HqtGsoiIiPgWhwPuuQcmTLBG/gJ8/DFcc43JVN4lNBQGDzadQkQ8ydChtWu6l5VBXp7ZPGKE1kiWU1ORC+vegL1zTScRERERES9QVlnGZ6s+45JPL+HqL682HaeG0+nk9m9vZ/qG6aaj1Ls2jdvUNJEB5m+fbzCNiJjy/vL3+XLNl6zdt5YqR5XpOCIi9ctuh9Wra5vIAC+/bE3NLCIipy4sDJo3N51CDNCIZDk5e2bA2hdh51RwVEKrSyBhgOlUrorWQ6M21vSvvqaqDHb/CDv+B41aQ/qjphOJiIiIHNcvm35h1ORRlFSWABAaGMqrI18lIjjCcDL4Ys0XvLLoFV5Z9AqXd7mcf434F00aNTEdq170a9nPpd5euJ0dhTtoEdXCUCIRMeHu7+8mtywXgCB7ENMun8aQtkMMpxIRqUc33ww//FBbL10K8+dD//7mMomIiHg5jUiWk1OwErZ/aTWRAXZ8BZWFRiO5cFTBT0Pg6xRY/S84UHD813iLzR/BZ3EwazRs+A9seEdPV4qIiIhX6JrYlYrqipq6vKqcr9d+bTCRpbCikNun3V5TT1oxicETBuNwOgymqj8d4jsQHRJNkD2IPi36cEefO3zm9yYidbO3ZG9NExmg0lFJ68atDSYSEWkAo0ZB27bW/nnnwYwZ1tqeJuzeDYsXm7m3iIhIPVIjWU5Oq0vAdtBI3+py2Pa5uTyH2v4FlG6Fki2QcS98mWTt+4LojlBdVluXbIKCVebyiIj7VeRCZZHpFCIiJywuPI6hbYe6HJuyaoqhNLXeXvI2O4t2uhx7bNBj2G2+8c8lu83O3OvnUvhQIQtuWMCL575IUnSS6Vgi4kar9612qUMCQkhunGwojYhIAwkIgLffhqws+OYbGDQIbDYzWV56CXr3hoEDYcoUqNKSAiIi4p1845MRcb/QeGh+rrVvD4FWf4TIVLOZDrbm3651VEcIb2UmS31r3A3CD/ngb9c0M1lExIzVz8InUfBlK/jlXNjwrulEIiJ1dknaJdhtds5qcxZvnPcGb53/lulI3N3/bt48/00ahzYGYES7EVySdonZUPWsc0JnQgNDTccQEUNs2BjUelDNlP2pcakE2AMMpxIRaQBnnw1paWYzlJXBW7+9x507Fy65BB55xGymE+F0wrp18OGHcPfdMGmS6UQiImKQDy4eK27T8R5oeSEkjYHgaNNpah0oAGe167GO95h7ArG+2WxWE3/juxA/EJoNhxajTKcSEXfKX2n9WrrN2mK6G40jInIixnYeyzkp59A0oqnpKDXsNjt/7vVnRncYzYM/Psjjgx7H5ivvHUVEgDNan8GMa2YAsL9sP3tL9poNJCLiyyZNgtxc12PXXmsmy8m47z74179q64sugssvN5dHRMxYtQp+/hnatIHkZGjdGiIiTKcSA9RIlpOXONjaPE1wNAyfD/vmWSOT9y+GVmNMp6pfXZ6EHs9BUKTpJCJiQsFK1zra8NPWIiInICI4gohgz/zHZ9OIpky8cKLpGCIiDSo2LJbYsFjTMUREfFdgIDRvDjt/WzplxAjo2NFsphPRtatrvWiRmRwiYtbPP8Ptt9fWffrAggXm8ogxmtpafJPNBgkD4IxP4LxVYA8ynah+hTVVE1nEX1WXW6OQDxbd2UwWEREREREREW9RXX38c+rDNdfA5s0weTL06wd33OGe+9aX3r1d6+3bYdcuM1lExJzNm13r5GQjMcQ8jUgW3xcYZjqBiEj9CQiFi4uhcC0UrLJGJ0d50ZPNIiIiIiIiIu60cSM88QSUl8OUKe65Z1AQXHqptTmd7rlnfenQASIjISwM+va1tkC1EUT8zqZNrrUayX5LPwFERES8TUAIxHS1NhEROWGLdiwiOSaZ+PB401HcqtpRzaq9q5i/fT7zt89nT8kevhn3jelYIiIiIg0jNxceeQT++1+oqrKO/fornHGGe3PYbO6936kKCIB166BJE+/LLiL1p2NHa1aFTZtgzx5rrWTxS2okS/1yOiFnFgSEQXwf02lERERExAsUVhTy9dqvObf9uQ2+bmdRRREXfXwR5VXlPD/8ea7qdhU2P/mAbNnuZZz29mkux3JLc4kLjzOUSERERKQBBQXBJ5/UNpEBbrsNlizRCNvjSUw0nUBETHv66dr90lJzOcQ4rZEs9cPpgO1fwQ8D4KfBsOx+996/eJM1xas/c1RB+V7TKURERETq7PPVn3PRxxfR5NkmXPHFFXyx+osGv+dff/krO4p2kFuWyzVfXcPZ751NYUVhg9/XE3RN7EpoYKjLsYU7FhpKIyLu8sXqL3h+7vN8u+5bNuVtwuF0mI4kIuIeUVHWlNYHy8mB9euNxBER8Vrh4dYmfkmNZKkfW6bArAshd75V58yCvXPdd/+MB+DbLrDgBijd7r77mla2C9a/Bb+Ogc/iYfFtphOJiIiI1NmkFZP4cs2XVFRXADBlVcOuWbd452JeXviyy7EgexCRwZENel9PERQQxGnNXUckL9ixwFAaEXGX9zLf477p93Heh+fR9qW2PPTjQ6YjiYi4z803Q5cu1gjke++FtWut6VrrU0EBPPww5OXV73VFREQ8gBrJUj+SLoKwFq7HVjzhnnvvWwDbPrVGRW/4L3zdHnZ+5557m7bzW1h4I2z7HCoLIGeGNb24iIiIiBe4JO0Sl/qnjT+xt6ThZlhpFtGMizpeVFOHBoby+nmv+83U1gD9WvQjuXEyl6ZfygsjXuDitItNRxKRBrZ672qXumN8PTdQREQ8WWAgTJgAy5fDc89Zo5Tr2wMPwPjx0L49vPIKVFbW/z1EREQM0WIQUj8CQqDjPZBxr1VHpkL7m6ymZkN+MOd0wrIHD8kSBvH9Gu6eniTxLNe6PMea4rtxmpk8ItKwKvZD6TaI6gABocc/X0TEw53X/jzCg8IprSwl0B7I8HbDySvPI6FRQoPcr0VUCz69+FO+Xvs1t027jRt73Ui72HYNci9PNX7oeJ4d/qzpGCLiJgeqD7B+v+sUrp0SOhlKIyJiSM+eDXftH3+Et96y9nNz4fbbrVHPL7987NeJiIh4CTWSpf60vwm2fAjt/gTtrgN7kBtu6oSksZCXAZW/rW2X9ggEN3bDvT1Ao2QIbwWlW2uP5c5XI1nEV+2cBvOuAJsdItpB4hDo87rpVCIiJ61RcCP+euZfadKoCRd2vJDYsFi33HdUh1GclXwWwQHBbrmfJwm065+AIv6k5EAJV3e7mtX7VrN632ryy/PpFK9GsohIvXn1Vde6USO45x4zWepbcTHMnQsLFlhbRQVMn246lYiIuJk+RZD6ExgOIxY17AjkQ9ns0OE2aDUWMu6DnF8h9Vb33d80mw3aXGatlZx4FjQZDBFtTKcSkYZSuMr61emAonXW7A8iIl7uL6f/xch9I4IjjNxXRMSdYsJi+O8F/wXA6XSSU5JDdGi04VQiIh7E6bTWOG7c+OReP3ky3HILvPuuVT/7LCQn11s8ozIyYMSI2tpuh5ISq1kuIr7t3Xet/9+Tk6FNG2jbFsLCTKcSQ9RIlvplan25sKYw4AM4kOd/0712/4fpBCLiLgWrXOvozmZyiIiIiIjXsdlsJEYkmo4hIuI5SkrgmmsgKwtmzIDEk/g7MjQU/vtf6NsXvvwSbryxnkMa1KsXBARAdbVVOxywZAmceabZXCLS8F580Vpb/nf/+Q9cf725PGKU3XQAkXoVHGM6gYhIw6kscK3VSBYROabK6kqcTqfpGCIiIiLiaTZvhgED4NNPYc0aOPts2LTp5K5ls1kN5KlTrVG7viI8HLp0cT22YIGZLCLiPk7n4X8ftmljJIp4Bo1IFvdwOqFki6ZdFhE5FUN+hspiKFwDBSshcbDpRCIiHu2B6Q+wat8qXhv5Gu1i25mO47GqHFXYsBFgDzAdRURERKThOZ1w+eWQmVl7bNUquPde+Pzzo7+uvNwagXw0vtRE/t2gQdbvuW9fa9NoZBHfl58PhYWux3xlyn45KWokS8OrLISFN8LO7+DcjFNrJjsdsH8pxJ1Wb/FERLxKUIT1d6D+HhQRH1ftqD6lxubSXUt5aeFLOJwO0l9P57EzH+PeAfcSHBBcjym919drv2bOtjnM3z6fRTsX8e24bxnUZpDpWCIiIiINz2aDjz+2mqQbN1rHkpLgjTcOP7ey0moyf/IJvP02/PwzpKW5N69JL7xgOoGIuFt5OVx6qTUqefNm2LfP+jtS/JYPPiYlHmX/EpjWE7Z8BJX5MOtCq7F8sjIfhx/6wurnracHRURERMRn5JXl8W7Gu5w76VyGvj/0pK9T7ajmz1//GYfTAUB5VTlPznySbQXb6iuq13ti5hP8c84/mbllJqWVpczfPt90JBERERH3adkSfvkFOnWCsDBrfeMmTQ4/b9Uq6N4dnn4acnKsNUJ/XzNYRMQXNWsGkyfD/PmwezcUFUFQkOlUYpBGJEvDWvsSFG+orfOXw7xr4MxjTBNz1Gu9Aiv/Zu1n3Ad5y6HvWxBwjCll/FFlEeybD9FpEN7cdBoRERGROlm4YyGnv3M6lY7KmmPbC7fTMqrlCV9rU/4mdhfvdjn26JmPanrrg/Rr0Y+lu5bW1PN3qJEs4msW7ljIhGUT6BTfiY7xHUlrkkbzSP0bUUSkRqtWVqNk6VLo2fPI57Q85L3oggXw4otwzz0Nn09ExBOEhZlOIIZpRLI0rNNehsj2tXVoU0h/9MSvU7Eflj/semzLh7A/49Ty+ZKsp63R3582hl+Gw46vTCcSERERqbPuTbsTHhTucuyTlZ+c1LVSYlNYdesq7uhzBzZsdIrvxP0D7q+PmD6jX8t+LvWC7QtwasYfEZ8ye+tsXl/8Ond8dwfDPxjOHz7+g+lIIiKeJyoKBg8++tdjYw9fF3nt2gaNJCIi4knUSJaGFRQFg7+FkASI6gDD50HsUZ7wO5aQWBgwCbDVHuv1EiT0r7eoXq9wDeRlWOtIA+ydYzaPiIiIyAkIDgjmok4XuRz7cu2XJ329qJAoXjz3RRbcsIAJF04gJDDkFBP6lv5J/RmSPIRHzniEry/7muU3Lcdmsx3/hSLiNVbvXe1Sd07obCiJiIgXs9msUcmtW8Ott8LKlfDmm6ZTiYiIuI2mtpaGF5kCZ0+H8JYQEnfkc5xO643ZsbQcBT2egYz7odvTkHpL/Wf1ZgkDYfMHtfXe2eayiEj9qq6AHV9bU9ZHpoBd65KIiG+6uPPFTF4xmXPbn8vFnS/m/NTzT/mavVv0rodkviclNoUfr/rRdAwRaUCr97k2kjvFdzKURETEy2VmamrXg+XnQ2QkBASYTiIiIm6gRrK4R0y3Y399zb9h3WsQngT2YDj7+yOf1/Feq5HS/Nz6z+jtEgbW7tuDrGnEq0ogsJG5TCJSP4qyYfYfrX17EER2gHOWQECw2VwiIvVsaNuh5NyfQ1RIlOkoIiJe75K0S0iOSWb13tWs2beGTglqJIuInBR/byI7nfD669b60AsWWFN7L18OXbuaTiYiIm6gRrJ4hs3vQ/EGawOoLIagiMPPs9nURD6a6DTo/g+I7w+xvSHQz9/kiviSglW1+45KqCxQE1lEfFJQQBBBAZp1QUSkPtze9/aafYfToXXQRUTk5Nhs8OyzsHlz7bEFC9RIFvFFpaXw1FPWdP6tWllb586agcDPqZEs5uVnQd4y12PF6yGmu4k03stmh84Pmk4hIg2hYKVrHZ1mJoeIiIfK2JXBz5t+5s5+dxJo1z9xREQOZbfZQcugi4jIyerb9/BG8p/+ZCyOiDSQLVvgn/90PVZSAuHhZvKIR7CbDiDClo8PP1a03v05REQ8lS3Amq7+d9GdzWUREfEw1Y5q/vzNn7lv+n30frs3C3csNB1JRERERMS39O3rWi9daiaHiDSsrVtd6/h4NZFFI5LFA3R5DJIugvwVUFlkrf8Z29N0KhERz9HlcWur2A+FqyE4znQiERGP8fri11m8czEAy3Yvo99/+jF13FTOba/lUE7UjsIdlFeV0y62nekoIiIiIuJJBg+GK66wGsp9+0K3bqYTiUhDOLSR3KqVmRziUdRIFvN+bxyreSwicmwhsZAw0HQKERG32lO8h89Xf845KeeQHJPs8rWiiiIe/flRl2OpcamcnXy2OyN6te/Xf89/Mv7D/O3z2V64nXFdxjHpD5NMxxIRERERT9KjB7z/vukUItLQkpLgj3+0prjeutVaK1n8nhrJIiIiIiLicSavmMx/Mv7DjM0zcDgd/O2sv/HImY+4nBMZEsm0y6dx4zc3siJnBQBvnv8mIYEhJiJ7pc35m/l01ac19fzt8w2mEREREREREWPOOcfafudwmMsiHkONZBFf5KiGwjWQuwCqiqHDHaYTiYiIiJyQ2Vtn8/Omn2vqKaumHNZIBuif1J8lf17Cv+f/my35WxjUZpA7Y3q9fi37udQb8zayt2QvCY0SDCUSkVNVfKCYP3z8BzrFd6JTQic6xXdiQNIAggKCTEcTEREREW9it5tOIB5AjWQRX7NnBswcDVVFVh0cA6m3g81mNJaIiIjIibg47WJeW/xaTZ25J5M1+9bQMb7jYecGBQTxwMAH3BnPZ6Q1SSM8KJzSylIAokOiWb9/vRrJIl5szb41TN84nekbpwNgt9kpebiEINRIFhERERGRE6NGsoiviWhb20QGOJAHResgKtVcJhEREZETdHqr02kW0YxdxbtoFNSIUR1G4XQ6TcfyOYH2QJ4Y9ATx4fH0a9mPDvEdsNv01LmIN1u9d7VL3TamLaGBoYbSiIiIiIiIN1MjWcTXhCdBaFMo3117LHeBGski3mr92xDWDKI7Q6M2oA/3RcRPBNgD+PuQvxMRHMHI9iMJDwo3Hcln3T/wftMRRKQerdq7yqXuFN/JUBIREfFZlZWwfDlERkKHDqbTiIhIA1IjWcTX2GwQ3xe2f2U1leP6Qlhz06lE5GRUH4BFt4CzyqoDwmD4PIjpZjaXiIibXNP9Gpe62lHN6n2rSW+SbiaQiIgXOD/1fEIDQ1m9bzWr962me9PupiOJiIiveP99+M9/YNEiKCuDW2+FV14xnUpERBqQGskivqj7s9D7dWsUo4h4r+L1tU1kgOoyaNTaXB4REcPeXPImt0+7nTv73slTZz1FRHCE6UgiIh5nYKuBDGw10HQMERHxRTt2wKxZtfW8eeayiGf6+GN48knrQYOyMmvQ086d1q/i2ebNg6++gtatoVUrSEnRjAMCqJEs4pui2ptOICL1ocB1WkLCmkNwYyNRRERM21m0k4d+egiH08G/5/+bT1Z9wjuj32FYu2Gmo4mIiIiI+If+/V3r5cuhpAQaNTKTRzzT6tW1+xERR28iOxzWf1N9+8KoUTBoEAQHuyejHO7XX+Gf/6ythwyBH380l0c8hhZaFBER8VQBoZBwOgTHWHV0Z7N5REQMuuu7uyisKKyptxduJ9Cu52JFRERERNzmtNMgIKC2btcOtm83l0c8T6tWrnXEMWaRWrcOFi6El1+G4cOhZUuYOrVh88nRbd3qWh/6vRS/pU9eREREPFWL863N6YTyPVBZZDqRiIgRTqeTrold+d/a/1FRXQHA1d2u5qzkswwn8z2llaUs2bmEtblruaHnDabjiIiIiIgnadQI/v1vSE6Gfv0gPt50IvE0SUmudWTk0c9dsMC13rsXLr0UNmyAJk3qP5sc25YtrnVrLa8nFjWSRUREPJ3NBmFNrU1ExA/ZbDYePfNRLkm7hJun3syy3ct4bvhzpmP5lM35mxkzZQzLdy+n2lmNDRsXp11MVEiU6WgiIiIi4kluv910AjHJ4YCrr4aRI+HCCyEszPXrzZrBhx9aI5HDwo7dSJ4/v3bfbocLLoBbb4WEhAaJLsdx9tnW92zLFmt0shrJ8hub0+l0mg7hSQoLC4mOjqagoICoKH1oIiIiIu6l9yLeR98z93I6nWzO30xyTLLpKD6lvKqcqPFRVDoqa479dNVPnJ18tsFUIiJSV3o/4n30PRMRrzR/fu1a2Y0bw2WXwb/+BaGhJ36tlSvhiy9g0SJ46SU1LkXcrK7vRTQiWcSXle2B3AW/bQthwGQI1ZQzIiIi4r1sNpuayA0gNDCUHs16sHDHwppj87fPVyNZxMsMeW8IQfYguiZ2pWtiV0a0G0FCI43qETlR1dXVrFy5kkWLFrF48WIWLVpEZmYmlZXWA1eDBg1ixowZZkOKiJjw6ae1+/n58MsvEBJyctdKS7M2sJZ1ExGPpEayiK9yVMH/2kJ1ae2x3IXQYqS5TCIiIiLisfq26MvCHQux2+x0TexKTGiM6UjiIYoqirjjuzt46PSHSI1LNR1HjqK8qpyZm2dSB+r2nQAAhJxJREFU7azm+w3fAzD3urlqJIucoC+//JLLL7+c0tLS458sIuJPnE747DPXY2PHWkuynar6uIaINAi76QAi0kDsgRDby/XY/kVmsojnW3ovLPsLFK03nUREREQMuem0m5h5zUwK/1JIxo0Z3Nz7ZtORxEP8c84/mbBsAmmvpXHHtDvYV7rPdCQ5gtV7V1PtrHY5lt4k3VAaEe+Vn5+vJrKIyJFUVcF998HgwdaaxmA1kkXEp2lEsogviz0N9v5aW+9fYi6LmOV0Hv3JPqcTtkyGsl2w6hlodwN0expCNXLBqPVvQ1UJRKdBdGcIa66nM0VEpEF1TuhsOoJ4oO2F23l+3vMAVDmqeHnhy+SU5PDR2I8MJ5NDZe7JdKnbxrQlMiTSUBoR75eYmEjv3r1rtu+//54XX3zRdCwRz7J9O8ybB5s2wQMPmE4jDS0oCG691dpycuD776FrV9OpRKSBqZEs4stie0FQtPVrbC9ocqbpROJuTies+gcUroH+E498Tul2q4lsvQA2vA0VOXDml+5KKUey9iUoyKqt+7wFKX8yl0dERET80t9//TvlVeU1dYAtgMcHPW4wkRzN8HbD+Xjsx2TuySRzTyYtIluYjiTilc455xy2bNlCq1atXI4vWLDAUCIRD7R2LQwdajWSwRqdevPNEKkHmPxGkyZw5ZUNd/0NG+Dtt+HRRyEiouHuIyLH5VWN5OrqalauXMmiRYtYvHgxixYtIjMzk8rKSgAGDRrEjBkzzIYU8SStL4E24zSK0V85qmHxLbD+LatuOhySLz/8vNz5rnVwDPTSU9ZGOSqhaK3rsahOZrKIiIiIXxs/ZDxRIVG8MP8FKqor+HOvP9MpQe9LPFGzyGZcnHYxF6ddbDqKiFdr2rSp6Qginq9VK9izp7Z2OGDhQhgyxFwm8Q3TpsHLL8N331kDZJKT4cYbTafyfa++Ctu2QevW1v/f3btDCz2UKBavaSR/+eWXXH755VqjRORE2L3mf3Gpb04nLLoJNvyn9tjiWyBhIES0cT03thd0uBvWvwnVpdD/PWjU2q1x5RBF66xm8sEap5nJIiIiIn4tOjSafwz9BzefdjNPznzymKORN+ZtpKC8gB7NergxoYiIiLhdWBj06GE1j383b54ayXLqXnvNaib/7tVX4c9/1kCphjZ5MsyZU1u/+CLccYe5POJR7KYD1FV+fr6ayCIideWohAN5rseqiiFn1uHnRrSFXv+CUdnQ711ocb57MsrR2QKh7bUQ1wcCG0FYC2ukuIiIiEgDumPaHXyc9TFOp/Owr7Vu3Jp3LniHxIjEo77+/un30/Otntz27W1UOaoaMqqIiIiY1r+/9WvjxnDOOdCundE44iNuu821XrECfv3VTBZ/smWLa33I8g7i37xuuGJiYiK9e/eu2b7//ntefFFTsIqIuAgIhtOnQMb9sOZfYA+GgZMh6Q9Hf014C2h7jdsiyjFEpUK/d6x9pwPK95rNIyIifsXhdJCdm82C7QuYv30+N512E92adjMdSxrY1OypvLzwZV5e+DIfrPiA10a+RlJ0Up1fP3/7fD5f/TkAry56lfjweJ4Y/EQDpRURERHj7rjDmnK4QwdrjWTxXQUFEBgIjRo1/L2GDYOUFFi/HqKj4dprremWpeFs31673vnvkpPNZBGP5DWN5HPOOYctW7bQ6pAnIRYsWGAokYiIh7PZoefz0KgNhLeEpItO7XpVZYDDGiEr7mOzQ9jRR/6IyLFVV1ezcuVKFi1axOLFi1m0aBGZmZlUVlrTxw8aNIgZM2aYDSniYc549wzmbptbU6fGpaqR7OOKKoq4eerNNfU32d+QuSeT9bevJygg6LivdzqdPDD9AZdj/zfr/xjebjgDkgbUe14RERHxAG3bmk4g7vLqq/C3v8G558KYMXD++RAV1TD3stvh//4Piopg3Dj3NK/9XePG8OGH8OOPMH06FBdDerrpVOJBvKaR3LRpU9MRRES8U4fbT/0aBatg9iUQ2xP6Tzz164mIuMGXX37J5ZdfruVRRE5Q5/jOLo3k+TvmG0wj7jA5azLbCre5HHt80ON1aiIDVDurOTv5bH7dWjvt4GXpl5HeRB9AuUtldWWdv18iIiIiJ+TTT6GsDD7/3NpuvRVeeaXh7nfppQ13bTlcRARcdpm1OZ2wezcEBJhOJR7EaxrJInIKcmZD7nzYv8Taer0Ezc8xnUq8gdMJG9+BxbdDdRkUZEHSGGg52nQyEZHjys/PVxNZ5CT0bdmX/2T8p6aev12NZF/3p55/IiY0htun3c6ekj2cnXw213a/ts6vD7QH8sTgJwgOCOYfs//Ba+e9xhVdr2jAxHKovv/pS2FFIV0Tu9I1sStXdL2ClNgU07FE5CAVFRVUVFTU1IWFhQbTiIjU0caNkJHhemzMGDNZpOHZbNCsmekU4mHUSBbxB8v/Anvn1Nb7F6uRLHVTngNL77GayL9bfCskDoagBprCRkSkniUmJtK7d++a7fvvv+fFF180HUvk/9u777iorvz/4++hq0gRVFCxYMeOJXZFjcaSxBRT3PRkE9N3U9dsfim7yWazKbubaDZ+Ezd104uaaDT23nuv2AUEBASUOr8/bgTHBuoMZ8rr+XjcB/dc5s68dQbu5X7uOcdtdW/QXZJUr2Y9dW/QXd3rd1dJaYn8/bgr3VvZbDaNajNKg+IHaezssXqq51Oy2WwX/TzP9npWt7W/TQ3DG1b8YDhNUUmRNh/drMKSQu0+tls/bvtRSY2TKCQDbua1117Tyy+/bDoGAFycZcus4qLdbrWjo6U+fcxmAlClKCQDviCy8xmF5NXmssA1Nv5VUqmU8KzkH+K8561WV+r8rrTszvJtNn8pb58U0c55rwMALnDVVVdp3759atjQsaCxfPlyQ4kAz9A6urUO/PGAGoQ1MB0FVSyyWqTeH/H+Je/v7+dPEdmAHRk7VFhS6LCtXV3O1QF3M3bsWD3xxBNl7ZycHMXFxRlMBACVMHq0NGiQ9PPP0pQpUsOGUgBlJcCX8BMP+IJanR3bFJK9y4kUacvfpZJ8Kflzqet4KXaw856/ye3S3s+k1DlSyz9K7V+WAmo47/nhaNeH0rG1UkRbKbyNVbAPijSdCvBIMTExpiMAHsnfz58iMuBBNqZtdGjXr1lftarVMpQGwPkEBwcrODjYdAzA+fLzpVWrpCVLpKVLpSeflPr2NZ0KzlSnjnTPPdZiit1u9Y5u0UKKijKXA/BBFJIBXxDVVYpMtArKtX77ardbw5LA823+m1VElqTcXdK8YdK1+6Tq9Z3z/Dab1G2CVHjs7JsS4HyHpkiHfipvJzwrdfy7uTwAAABOUmovlZ/Nz3QMrzOy1Uitvn+1NqRu0IbUDQoJcOIIRQAAVGTwYGnxaSMhdupEIRnOc+yY9Pnn0v/9n7Rpk/TGG9JTT5lO5R0KCqSsLKluXdNJ4OZ8vpBcUFCggoKCsnZOTo7BNICLhLeWhtIL2Svl7Zd2nTH8YPxdzisinxIa79znw/llbXJsh7c1kwMAAHit9Px0BfkHKSw4rEpe70D2AT018yk1iWiivw/iBjlnCwkIUWJsohJjE01HAQD4oiuucCwkL1liLgu8zxNPSB9/XN7+v/+zer3TQeryzZ0rDR0qdewoDRlirffrZzoV3JDP3wr82muvKTw8vGxhbhIAHqVafannl1LUFVbbL0hq+4LZTLh0xXlSXrLjtvA2ZrIAAACv9ezMZ9XsnWZ6d/m7Z82t60wFxQV6beFrajW+lb7Z/I3eXvq2tqdvd9nrAQAAA3r1cmwvXSoVF5vJAu9z772O7Z07pfnzzWTxNr/+an1dt056/XXpz382Ggfuy+cLyWPHjlV2dnbZcuDAAdORAKDy/PylhjdIg5dKgxZKXcZLNRqaToVLVVosdXhVanSrNTeyfzUprJXpVAAAwItsTtusj9d/rKP5R/XY9MfUenxrrTi0wiWvtS97n16c96Lyi6xpWIpKi/Tkr0+65LUAAIAhvXpJfn5Wr8aHH5Y++EAqLTWdCt6iVy+pdevydny8dPy4uTze5FQh+ZTBg83kgNvz+aGtg4ODFRwcbDoGAFwem02q09taqpLdLqUvlaJ7MKSMMwSFS22eK2+Xllg3CwAAYMCxE8e09OBSLTu4TMsOLlOr6FZ6Z+g7pmPhMj035zmV2ssv7qbkpqhhuGtuRGwR1UJP9HhCry9+vWzb1J1TlXwsWU0im7jkNQEAQBWrW9eaZ7VmTdNJ4EwffCDVqCENGyZFRJjLYbNJDz0kLVggPfCAlJRk3biAy3PsmLR/v+M2Csk4D58vJAMALoHdLh36Sdr0ipS5Uho4V6rb33Qq70MRGXBLBQUFKigoKGvn5OQYTAO4zucbPtdj0x8rax/IYfQmT1dQXHDWtid7PKmY0BiXvebzfZ/XxLUTlV+Ur9va3aaHuz1MERkAAG9DEdm7lJZKL7wgpaRIAQFS377SG29IiYlm8jzyiLXAeSIjpYwMadkyacYMa27zrl1Np4KbopAM+KrSEsleJPmHmE4CT7RgpHRoSnl7898oJAPwGa+99ppefvll0zEAl+veoLtDe1v6Nh07cUyR1SINJcLlCg4I1uRbJmvR/kV6dtaz2pGxQ0/1fMqlrxkaFKofb/5RbWq34bPjAoePH1Z09WgF+QeZjgIAALzFihVWEVmy5rueM4ebBbxRYKDUp4+1ABdAIRnwJfu/lVJmScfWSVkbpU5vSi0eMp0Knih2iGMhOWWmlLVZimhjLhMAVJGxY8fqiSeeKGvn5OQoLi7OYCLANTrEdFCwf7AKSsp7sa44tEJDmg0xmArO0Lthby26e5H2Ze9TWHBYlbweXOP6r6/X6iOr1Tq6tdrXba/Hr3hcXevTmwS4XMOGDdPhw4cdtqWcKqpIWrVqlTp27HjWftOmTVO9evVcHQ8AXGvyZMd269ZS8+ZmsgAwjkIy4EsOTpH2fl7ePrbOWBRchmMbpLT5UpPbpaAIMxma3iNtfEkqOGq1q9WT8vZRSAbgE4KDgxUcHGw6BuByQf5B6tOojwqKC9S9QXddUf8KClRexGazqXFEY9MxcBlK7aXamLZRxaXF2pi2URvTNurODneajgV4hS1btmjfvn3n/X5eXp7Wr19/1vbCwkJXxgKAqtG1qzR8uDRrllRQIF17relEAAyikAz4ksiOFJK9wY53pd0fSuuelRrdLLV4RKrVuWoz+IdIzR6QUn6VWv5BirtBYjg9AAC8zq+3/SqbzWY6BoBz2HNsj/KL8h22ta/b3lAaAADOUFAgcQOuZ7r+emvJzZV+/VVq46YdR0pKrPmcAwNNJwG8GoVkwJdEdnRsZ2+USoslP34VeIyiHGnfl9Z6yQlpz8dSjfiqLyRLUrsXpA5/rfrX9VZ7v5KSP5bC20jhbaWoLlJEO9OpAAA+jiIy4L62p293aNeuXlt1Q+saSgN4l71795qOAHie9HRp2jRp8WJp0SKruLdunelUuByhoVZB2d3s2CF99JH06afS3/8u3X676USAV6N6BPiSyI5S499ZXyM7ShEdKCJ7mr1fSMV55W2bv9T0XjNZ/Ljbz6nSl0hHZliLJDW8Ser9tdlMAADA49ntdtlll5/Nz3QUBysPrdT4leP116S/Ki6ceeYvxfAWw5X1bJY2pm3UhtQNKiguqHgnAABcZccO6c7Tpliw2aSsLCkiwlQieKOnnpLeequ8/dFHFJIvRnGxNGGC1K+f1dOcG4dRCR5VQRo2bJgOHz7ssC0lJaVsfdWqVerYseNZ+02bNk316tVzdTzA/QVHST0/r/hxcF81m0v1RkhHpkv2Yqn+1VJ1fr95hexNju3wtmZyAAAAr7Ls4DKN+naUbml7i37X7nfqGNPRaE/zbzZ/o7eWvqUVh1ZIkhqENdArA14xlsfThYeEq3fD3urdsLfpKAAAX9e5szWUdcFvNzbZ7dLSpdLQoWZzwbt07erYnjtXSk6WmjQxk8fTrFkjPfKItV67tpSUJH3+OcOD44I8qpC8ZcsW7du377zfz8vL0/r168/aXlhY6MpYAFB1YgZay8mj0v5vzh6uHJ4re7NjO8JN558BAAAe5YuNX+jQ8UN6a+lbemvpW7q6xdWacusUY3nm7Z1XVkSWpA/WfKD/1/f/KTiAORQBAPBowcFWkW/RovJtixdTSIZzXXut1cs9K6t82/Tp0oMPmkrkWebOLV8/elTauJEiMirkUYVkAMBvQmpLLR42neLc7HaptEjyDzKdxHPYS6XO71q9krM3S1mb6JEMAHBbJaUl8vfzNx0DlVBUUqSvNztOldGlXhdDaSwPd31Y/1n1n7J2Wl6avtvynX7X/ncGUwEAAKcYMcLq5dirl9S7t9Spk+lEuBglJZK/m5/nh4RIo0dL334r3XabdPfdUrt2plN5jjlzHNtJSWZywKN4VCF57969piMAAM7nZJqU/Km0+0OpyZ1Sm7GmE3kOm5/U6CZJN5lOAgDAWQ4fP6yP1n6kjWkbtTFto44XHNf+P+43HQuVsPjAYh3NP+qwbXS70YbSWNrUaaP+jftr3t55iguL05guY3Rl0yuNZgIAAE7y7LOmE+BSlZRYw0O3bSsNH24tjRubTnVur7wi/fOfUhCdWC5as2bStm3S/t/+nqOQjEqw2e12u+kQ7iQnJ0fh4eHKzs5WWFiY6TgA4Bl2/kda/bjVE1mSQuOlq3daBVIAF4VzEecaNmyYDh8+7LAtJSVFqampkqQaNWqoWbNmZ+03bdo01atXuTnoec/g7balb1Pr8a0dth19+qiiq0cbSoSLsSltk77Y+IW+2PiF6obW1fL7lpuOpAX7FigjP0NXt7xaAX4edX874LY4H/E8vGcA3MrSpVLPno7bDhyQGjQwkweuY7db80rPnStdd51Uq5bpRDCksuci/MUG+DK7Xco/KPkHSyF1TKeBJ4vsVF5ElqTcPVLaAqluf2ORAECStmzZon379p33+3l5eVq/fv1Z2wsLC10ZC/AozWo1U7B/sApKCsq2bUzdqKQm3L3uCdrWaau/DfybXhnwio7mHa14hyrQt1Ff0xE83vqU9aoRVEPxkfHy4+ZNAABwuaZNc2wnJFBE9lY2mxQfby1AJfDXBuCLdoyXZg+Uvo+WJjeUdk80nQgXUloiFeWYTnFhUVecPafvwUlGogAAAOcK8AtQQu0Eh23rU8++AQPuzc/mp7qhdU3HgJM8+sujav5uc4W9FqbuH3bXtJ3TKt4JAADgfKZPd2wPH24mBwC3Q49kwBdlb5FS55S3j60zFgWVcHSRNHewFDNYaniDVP8aKdjNhhyx2aQmt0ubXrEyNr5NqtPfdCoA0N69e01HALzC9a2vV9d6XdW5XmclxiaqXZ12piMBPstut2tD6gZJUl5RnpYfWq5Se6nhVAAAwKP9+qs0Y4bVM/mXXygkAyhDIRnwRZEdHdsUkt3bgR+k0kLp8M/WEpkoDV1tOtXZmj8otXhECqhuOgkAAHCy5/s+bzoCgN8cyDmg7IJsh23t67Y3lAYAgAsoLpY2bpQaNpSiokynwYVERkq33GItJSVWpxFPYbdLS5ZIn30mPfmk1Ly56USAV2Foa8AXnVlILjgqnTbnHdyIvVQ6+IPjtgbXmslSkcCaFJEvxcHJ0uQm0vxrpPXPSwd/Mp0IAACgyhWVFOm7Ld9p8f7FpqO4vUM5hxRdPbqsHR4crriwOIOJAAA4wzvvSIMHW8XJxETpJ651eBR/f8nPQ0pH//631KyZ1Lu3NGGCVUwG4FT0SAZ8UXhbqe2LVkE5sqNUo5Fn3WXmS47vlE6mOm6Lu8FMFrjGsfVS3l5rOfSTVHeA1OBq06kAAIAH+9+G/6lZrWbqWr+r/GzufREwJTdFH6z+QO+vfl+Hjx/WkKZDNP226RXv6MN6xPVQ2lNpSs1L1YbUDUrPT5eNv+cAAO5kwQJp5szy9qJF0l13GYsDL5acLO3ZU97+/HPp5Ze51n2mZ56xbhDo00fq2VOKiDCdCB6EQjLgiwKqSe1fMp0ClRHWUro+TTr0s3TgeylvnxSeYDoVnClrg2M7gjknAQDApTtZfFJjpo5RbmGu6tWsp2tbXqvn+jynBmENTEc7p2k7p+mFeS+UtWfsnqEdGTvUIqqFwVTuz2azKSY0RjGhMaajAABwtl69pO+/L28vZsQRuMjtt1u9kk9JTrY+b717m8vkbkpKrN7aOTnS3/9uFdmnTpWGDjWdDB7CvW9NBgBIQRFSk9ukvj9KV63ijjpvc1YhmfntAADApZu1Z5ZyC3MlSYePH9aE1RMU7B9sONX53dr2VtWqVsth239W/sdQGgAA4BRnFvFKSqQTJ8xkgXdLTJRatbLWW7SQ/vpXqWlTs5nczcaNVhH5FLtd6tDBXB54HHokA4AncfOhCc9yfJe0/1sp/m6pGr0lzqn3d1Yx+dRSq7PpRAAAVKiopEh7ju1Ry+iWpqPgDJO2TXJo94rrpdo1apsJUwnVAqvpno736M2lb0qSesb1VJ9GfQynAgAAl6VjR+nJJ60hdHv1kurWNZ0I5/PNN1K/fp77HtlsVo/kyEipSxc64JzLokWO7fh4qV49M1ngkSgkAwCcb9u/pT0fSVnrrXZgmNTiYbOZ3FVke2sBAMDNpeam6sV5L2r1kdXakLpBpfZSHR97XCEBIaaj4TQto1qqVXQrbUvfJkka2Wqk2UCV8GDXB5V5IlMPd3tYibGJpuMAAIDLFRgovfmm6RSoyK5d0s03W+tdukjDh0vPPitVq2Y218UaPNh0AvfWs6c0dqy0cKG0YoU1TzJwESgkAwCcL2NFeRFZsnolU0gGAMCjVQ+srgmrJzhs25S2SV3qdTGUCOfydK+n9XSvp7UtfZsmbZuk61tfbzpSheIj4zXx2ommYwAAAPiWqVPL11etkg4ckF54wVweuEZiorVI0smTjsNcA5XgYWOkAnCJgkwpdb6Uu8d0EniLhqMc22kLpBOpZrIAAACnqBlcUy2iWjhsW3NkjaE0qEir6Fb6U+8/qXFEY9NR4ERzkudo+q7pOnz8sOx2u+k4AADAk/3yi2N76FDJj5KRVwsJkerUMZ0CHobfCoAvW/cnaVKc9H2UNLu/tO8r04lwSsocKflzqSDDdJJLEztECgi11kPqSM0ekEoLzWYCAACXrXNsZ4f2nmPciAhUpb8t/JuG/m+o6r9dX7XfqK2P1n5kOhIAAPBEdrsUESHVqFG+bdgwY3EAuC+GtgZ8WVGulH+wvH1sg7kscLT939KhKZLNT4rqLiU8IzW41nSqyguoJnV+RwptItXuI/n5m04EAACcYHS70eoU00mJsYlKjE1UZLVI05EAn2G327U+tXz6mIwTGQoPCTeYCAAAeCybTfrqK6mwUFq8WJoxQxo40HQq5ykqkpYtYz5gwAkoJAO+LLK9YzuLQrJbKDkppcyy1u2lUvoSqei42UyXoundphO4t5JCSaWSf4jpJAAAVNqIFiM0osUI0zHgI0rtpUrLS1NMaIzpKG4hNS9V6fnpDtva121/nkcDAOBGUlOlhQullBTpkUdMp8HpgoKkpCRr8XR2uzXX82efWUXyo0elnTulZs1MJwM8GkNbA74s4rSLDn5BUkB1qbTEXB5YUudJJfnlbZufFHuVsThwkdTZ0jeh0s8J0qJbpG3/Mp0IAAB4MG+ZL/d4wXF9t+U73TXpLsW+FauRX400HcltHDtxTD0a9FBokDWFTI3AGoqPjDecCgCAC1i7VmrZUoqJkUaNkp591uopCrhCcbE1PPe771pFZEn6/HOzmUwqLbWK68BlopAM+LKI9lLPL6Vhm6SbcqWrVjEEsTsICJXqXy35V7Pa0T2kkGizmeB8WRsle4mUs1Xa/7W0/1vTiQAAgIfam7VXcf+M08NTH9bM3TNVWFJoOtIlW3ZwmUZ9O0qfrP9EaXlpWnFohY7mHTUdyy20rt1aS+5douw/ZWv3Y7v18+if5Wfjsg4AwI01aCDt2FHezs+X1qwxlwfeLTBQuuUWx22ff+67xdQpU6SGDaVbb5Xee0/avNl0Ingo/uIAfFlAdanxLVJEG8kv0HQanFKnt9RvinRDhtR/mtT2BdOJ4ApnDiUf0c5MDgAA4PEmb5usQ8cP6b1V72nw54OVMD7BY3so923UVzUCa5S17bLrl12/GEzkfvxsfoqPjFf/xv1NRwEA4MJq15Zat3bctnChmSzwDbff7tiOiCjvnexrliyRDh60hvl++GHpscdMJ4KHopAMAO4qoJpUb6gUO9h0Eufy4B4yTpW10bEdwfx2AADg0kzaPsmh3adRH9lsNjNhLlNwQLCubHqlw7bVh1cbSgMAAC5b377W19BQacgQqVEjs3ng3bp2lYYPl15+Wdq+3ZozuU4d06nMWLbMsd2zp5kc8HgBpgMAALyc3S4dWysdnGwtNZtLfRjGWVcukrI3Wz2Tj22QavcynQgAgItit9u1N2uv1hxZo2tbXasAP/68NCHzRKYW7FvgsG1ky5FmwjjJ9a2uV35RvoY3H67hzYeraa2mpiMBAIBL9fjj0u9/L3XoIAVwvmhcSYl0441S797SVVdJCQmSh96AeE42m/Tzz6ZTmFdcbBXRT9e9u5ks8Hg2u6eOd+UiOTk5Cg8PV3Z2tsLCwkzHAQDPl/yZtPSO8nZAqHRDuuQfbC4T4MY4F/E8vGfwNQXFBRr+xXCtObJGx04ekyRtfHCj2tZpaziZ79qWvk0/bv1Rk7ZP0ua0zTr69FFVC6xmOhaAKsT5iOfhPQNgxLJlUo8e5e0GDay5c/k95F1KS6WNG633+9SycKEUHW06GdxIZc9FuAUIAOBaMYMl2ST9dt9Sca6UOscathsAAHic4IBg7czcWVZElqQ1R9ZQSDaoVXQrje0zVmP7jFXWySyKyAAAADi3GTMc22FhFJG9kZ+fNQpAhw7SAw+YTgMPxxzJACzF+VLGSmn3RCl7q+k08CbV6kq1z5iD4/B0M1kAAIBTdI7t7NBmDlv3ERESYToCXOCHrT9o3IpxWrBvgY6dOFbxDgAAAOdyZiH5qqvM5ADgMeiRDEBadJO0/zuV9Rjt9KYU3tpoJJ+07jkpa73Vgzd2sBTWynvmKKl/rXTiiNRgpLVE96xoDwAA4MYSYxP147YfJUlR1aKYHxlwsQ/XfKhfdv1S1n51wKt6rs9zBhMBAACP9Mwz0rRpVkF5/35pyBDTiQC4Of7aByAFRqisiCxJWRtMJfFtB3+UcrZJh6dZ7U5vSa2fMJvJWVr9QWr9lPcUxgEA8HGjEkapTe026lyvs+LC4mTjGA+41IZUx7/R4iPjDSUBAAAebeRIa7Hbpe3bpcaNDQdyseJiac4c6csvpWrVpPfeM50I8DgUkgFIEe0d2xSSq17efquIfLo6fcxkcQW/QNMJ3EfhMSn/kNXjnN5bAAAP1TK6pVpGtzQdAz7iRNEJzUmeo93HduuxKx4zHafKZeRn6NDxQw7b2tdtf55HAwDgpoqKpDVrpIULpQULpNtuk266yXQq32WzSa1amU7hWgsWSKNGSWlpVrtGDenNN6Xq1c3mAjwMV7ABSJGnLkLYpJrNpfA21l1p9CypOkd+dWwH1ZIiE81kgWsdni4tGS35h0jh7aSYQVLHv5lOBQAAPMzWo1t16Pgh9WvUT4H+3nnTXvKxZD36y6OakzxHJ4pPKMg/SPd0ukehQaGmo1WpE8UndHv727UhdYO2HN0im82mFlEtTMcCAODi3HGH9NVX5e3oaArJcK2WLaX09PJ2Xp7000/SzTeby1QVDh2yfr6Cg00ngZfwMx0AgBuo1UUaslK6KVe6ervU83OKyFUt7nqp19dS/D1S9QZWcdHP33QquMKxtdbXkpNS5kprXmwAAICL9J9V/9GVn12pOm/W0e0/3q65yXNNR3K6WtVqacbuGTpRfEKSVFhSqFl7ZhlOVfUahDXQp9d9qnVj1invuTxtfmgz85IDADxPjx6O7YULzeSA76hbVxo40HHbl1+ayVKV7rpLCguzfub++Edp3TrTieDhKCQDkAKqS1FdrK8wI7iW1OgmqftE6dr90hUTTSeCq5wqJJ8S2clMDgAA4LHsdrsmbZskSco6maXPN3yuJQeWmA3lAuEh4erbqK/Dtp93/GwojXsI9A9Us1rNTMcAAODi9XU8pmvXLunwYTNZ4DtuvdX62rWr9Pbb0vjxZvO4WkmJtHy5VFgoLVsm/etf0t69plPBw3ELKwC4G5tNCvTy4frsdmsu7oyVUrP7TKepWgWZjm0KyQAA4CKtObJGB3IOOGwb2WqkmTAuNrz5cM1JniM/m596xvVU13pdTUcCAACXol07KSJCio21isp9+kg1a5pO5TuOH/fN/+8bb7Q+a8185Ea8bdus9/p0V1xhJgu8BoVkAEDVKciUNr4sHZoi5e2VZJPqXy1Vq2s6WdUZuloqyJCOrZMy10i1e5pOBADAZcnIz9CaI2u05sga3db+NtUPq286ktfLLcxV13pdtfLwSklSs1rNlFA7wXAq17gx4UbFhMboqmZXqVa1WqbjAACAS+XvLx04IIV6eecJd1RQYBXwmzWTBg2ylqQk35hDt2ZN3yqgb95sdVKy2612o0bWew9cBgrJAICqE1BD2vNfqTj3tw126dBPvtcrOThKihloLQAAeLArPrxCKw6tKGs3iWyim9rcZDCRb+jXuJ9W/H6FDuYc1JTtUxTsHyybzWY6lks0DG+o0e1Gm44BAACcgSKyGcuWSXl50vr11vL221Jamm8Ukn3NTTdJQ4ZIK1da73tQkOlE8AIUkgEAVcc/WIq9SjrwXfm2g5N9r5AMAICXiKoW5dBefXg1heQq1CCsgR7q+pDpGAAAAHBns2Y5tjt1kqKjzWSB64WHl/c8B5zAz3QAAG6ktEjK2izt/VJaN1Y6tsF0Iu934sjZc+Z6uwbXlq/XaCRFtDGXBQAAXJbE2ESH9pqUNYaSAN7pk3Wf6A/T/6D/rv2vVh1epRNFJ0xHAgAAnmbRIsc2BUYAF4EeyQDK/dpLylxZ3q5WT4psby6PL9jyD2n7v6XIjlLdAVLDG6Xo7qZTuVb94VK7l62CckR7a94OAADgkTrHdpYkBfsHq0NMB3WJ7WI4EeBdJm2fpEnbJpW1/9j9j3p7yNvmAgEAAM8zY4a0YoXVM3nWLGnwYNOJzDp5Uvr1V6l7d6lOHdNpALdHIRlAufDWjoXkrI3msviK1DmS7NKxtdYSGO79heSgSKndC6ZTAAAAJxjQZIDWj1mv1tGtFegfaDoOfIjdbvfaeaFPtyHVcZSoDnU7GEoCAICL5OYyd7KrBQVJvXtby0svmU5jzowZ0uefS5MnS8ePS+PGSQ8/bDoV4PYY2hpAuYgzeh9nMbS1S508evb/ccwAM1ngetlbpP3fSrl7JbvddBoAAJwiPCRc7eu2p4iMKpGWl6ZP1n2im769SY3+1UiFJYWmI7lUTkGO9hzb47CtfV1GjAIAeLgTJ6RvvpEeeUTq0EGKipLy8kyngi/49FOrkHz8uNX+9luzeQAPQY9kAOVOFZKrN5AiOkhRV5jN4+2yt0j+1aSS3+Y5C6gh1epqNhNcZ9/X0qa/WOvB0VL8PVKn181mAgAAHuWrTV9p5aGV6tWwl3rF9VLd0LqmI1WZjPwMxb4Vq1J7adm2RfsXaUAT770Rs6S0RC/3f1kbUjdoQ+oG7cvep4TaCaZjAQBweUpKpNGjra+nLF3KvL1wvVGjpC++KG8vWCClpEgxMeYyOdMvv0itW0uNGjGVIJyKQjKAcnX6SDdkSMG1TCfxDXX7STcekzKWS6lzpeJ8yT/IdCq4Suaq8vWCdMlebC4LAADwSF9v/lqTtk3S28usOXKf6/2cXh34quFUVSOqepQ6xXTS6iOry7ZN3THVqwvJkdUi9UK/8ilhThSdUHBAsMFEAAA4QWio1LmzNWfvKQsWUEiG6w0ZYn3+cnOtQmvv3lJamncUknNypOHDrVEQo6OlLl2kiROlevVMJ4MXYGhrAOX8QygiVzX/YKlOX6ndi/ROtdu9d8hnu13KWOm4jd7nAADgItjtdi3ev9hhW6voVobSmDG8+XCH9tSdUw0lMaNaYDXTEQAAcI5+/RzbS5eayQHfUq2a9Kc/Se++Kx08aN3A0N5Lpg1Zs6b8ump6ujR7tjVsPOAE9EgGAJhTWiwdXSwdnCwdmiz1nSJFtDGdyvmK86RaiVYxuTDT2hZFIRkAAFTersxdOpp/1GFbz7iehtKYMaLFCL219C0NbjpYw5sP19DmQ01HAgAAl2LwYGnDBqug3L+/1UMZzjdtmlS/vtSuneRHn0JJ0p//bDqBa6xa5dhu104KZiQbOAeFZACAOTO6SsfWlbcPeWkhOTBUSppu3RmYlyxlrpZC402nAgDAaU4UndCG1A1ac2SNVh9Zrdvb365+jftVvCMqrXpgdb3Y70UtPrBYyw4uU43AGoqP9K3zic71OivjmQyGdwYAwNMNGsRQ1q5mt0v33mvNAVy7tjRwoPTCC9YcuvA+hYVSrVpS5m8dWLp0MZsHXoVCMgDAnOiejoXkg5OlNmONxXE5m80qIFNEBgB4mRFfjtCc5Dll7fo161NIdrL6YfX1Uv+XJEklpSU6kHNANpvNbKgq5mfzo4gMAABQGZs3W0VkSTp6VPrqK+mvfzWbCa7z3HPS2LHS3r1W7+SGDU0nghdhPAMAgDkNrnVsZyyXThwxkwUAAFyyTjGdHNprUtYYSuIb/P381TiisekYAAAAcFezZjm2GzWSmjY1kwVVw2aTmjSRRo2SrrjCdBp4EXokAzhb3j6rl+ix9VLWeqnNn635XeEceQekAz9IMQOk8DaSzYfv6anTXwoMk2o0sYrKDa6VQmJMpwIAABepc6zjvHarD682lATwDu+tfE/Tdk5T+7rt1b5ue3Wr383nhjIHAACXITBQio+X9uyx2oMGWYVGnJvdzv8PcB4UkgGcbd5wKXtzeTtmEIVkZzoyXVrzB2s9uLbU8Cap6zijkYzxD5KuSZaCa5lOAgAALkNibKJssqlldEt1ju2sxNhElZSWyN/P33Q0wCPN3TtXU3dO1dSdUyVJj3R9RO8Oe9dwKgAA4DEeftha9uyRZs+WEhJMJ3I/eXnStGnSt99KJ05IP/1kOhHgligkAzhbRHvHQvKx9eayeKPU8vkDVXBUKsw0l8UdUEQGAMDjNY9qrpyxOQoNCjUdBT4m+2S2ZifP1shWI+XnJSP9lJSWOMw5LkkdYzqaCQMAQFUoKZE2bJAKCxmS19ni460FjpYvl5KSrAKyJPn5SWlpUp06ZnMBbsg7/soC4FyRHRzbFJKdx253LCRLUt0BZrKgauwYL238i5QySyo6bjoNAAAu4Wfzo4iMKlNSWqK3l76tAZ8MUPQb0brhmxu05oj3zMu9/NByZZ5wvNn0yqZXGkoDAIALzZsnXXONFB0tJSZKzz1nOhF8Rfv2VvH4lNJS6YcfzOUB3Bg9kgGcLaKDFBRpfY1oL0VzJ6DTFOdJ9UZYxeS8vda2GArJXm3XB9Zc45I1H3a3D6Sm95jNBAAAPEZhSaFajmupTjGd1Cuul3o17KUu9boowM93/5z39/PX+6ve187MnWXbpu6Yqi71uhhM5TzdG3TXivtWlA1tXVBcoIbhDU3HAgDA+bKzHYcTXrpUKiiQgoPNZYJvqFZNGjFC+vrr8m3ffiuNGWMu06XIzJQ++kjq1Enq2FGqxciPcD7f/csTwPnFDpZuyJBsNtNJvE9gqNR9orWemywdXSLVaGI2E1ynKEfK3ljetpdKYS3N5QEAAB5nzZE12pu1V3uz9urHbT9KkjKfyVRktUjDycwa3ny4/rX8X2XtqTun6sX+L5oL5ER+Nj91rd9VXet31Uv9X9LJ4pOmIwEA4Bp9+ljXH+12q33ihLRypdS7t9lc8A2jRknffy8NGCBdf7107bWmE128Vaukp54qbyckSJs2cV0fTkUhGcDZvGRuMbcX2sRa4Ki0RMpYJgXXkcKam05zeTJWWMXjU/wCpVqdzeUBAAAeZ/H+xQ7tNrXb+HwRWZJGtBhRVkgO8g9SVPUoFZUUKdA/0GwwFwgJCDEdAQAA16hVyxpieP1vI7k1bSplZRmNBB8yYoQ1L3KkB59br13r2A4Lo4gMp6OQDABwD6nzpeRPpUM/SQVHpZZ/kDr/03SqyxMSa/070pdIx9ZKkYmSPxcCAQBA5S0+4FhI7hXXy1AS99KnUR892OVBDWk6RAPjBzJHNwAAnmrsWKmkROrXT6pf33Qaz/fuu9K+fVJSktXjOyzMdCL3FRzs+cOon1lI7tTJTA54NQrJAAD3kDpX2vPf8vbByVLi2559F11Em/JiePEJ6WSq2TwAALhY8rFkLT24VGuOrNHqI6t1Xavr9NgVj5mO5dHeGvyWRrYaqUX7F2nxgcXq06iP6UhuIcg/SO8Nf890DAAAcLluvtl0Au/y8cfSmjXSW29J/v7SuHGeN+8vKq9VK6lzZ2njRqmw0JonGXAyCskAAPfQ4Fpp08vl7bxkKXuTFNHOXCZnCqgmhTY2nQIAAJd6a+lbGr9yfFk7uno0heTL1CSyiZpENtEdHe6QJNlPzSEIAAAAnC4z07GHakmJ1Lq1uTxwvZdespaiImnrVik21nQieCEmQgUAuIfIjlL1huXt8LZSQYaxOAAA4OJ1ju3s0F59eLWhJN7L5smjteCCtqdv17KDy1RSWmI6CgAA8EQLFkin33QYEiJ1724uD6pOYKA133jt2qaTwAvRIxnA+aXOkzJXS8fWS1nrpa7vSbWZk+2SlJZIc4dItRKlOv2s/8egCNOp3IvNJiU8I5UWWr2TQ+NNJwIAABcpMTbRoZ2claxjJ44pslqkoUSA53h3xbsav3K8oqtHa1jzYbqrw11KapJkOhYAAPAUCQnSiy9Kc+ZIy5ZJPXt6/hzAVe34cemXX6RrrrEK8QAoJAO4gNV/sArIp2SuoZB8qbI3SqmzrWXrG5LNT7ruiBRSx3Qy99LiYdMJAADAZUionaAmEU3Upk4bdY7trMTYRIUEcAEGqIjdbtfUnVMlSen56fp0/adqGdWSQjIAAKi8Fi3KhzrOz5fS0kwn8hyffCJ99500c6ZUUCD9/LM0fLjpVIBboJAM4PwiOzgWko+tMxbF46UtcGzXaEIRGQAAeJ1A/0DteXyP6RjwQfuz92vqjqmaunOq/jnkn2oe1dx0pIuyNX2r9mbtddg2osUIM2EAADAlM9Mannn+fOnuu62henFpqleXGjc2ncJzfPihtGhRefuHHygkA7+hkAzg/CI6OLZPLyrj4qTNd2zX6WcmB6pG3gFpZi+pTn+pbj/ra2i8NXw3AABAJaTnpyvYP1g1g2uajuL2hnw+RL/u/rWsPSh+kP4Q9QdzgS5BWl6aWke31tb0rZKkBmEN1K5OO8OpAACoQqNGSd9/Xz7Hb0wMhWRUneuvdywkT54sTZggBVBCA/xMBwDgxqK6SLW6Sk3vkzq/K3V+x3Qiz9Xyj1Lb/yfV6Sv5BVNI9nZp86X8A9Lez6Tl90kzukr2UtOpAACAB3lryVuKeD1CnSZ00iPTHtGc5DmmI7mtBjUbOLQnbZtkJshl6N+4v7Y8vEV7Htujd4e+qz/1+pNs3IQIAPAldeqUF5Elq1cyUFWuu658PTBQ6tJFSk83l6ciO3ZIDz4o/d//SStXSidPmk4EL8btFADOr05f6aoVplN4hzq9rUWSSk46nhjD+6TNc2zX6Sv5+RuJAgAAPNPiA4tVai/VupR1WpeyTmHBYRrQZIDpWG5pRIsR+u+6/5a15++br+RjyWoS2cRgqkvTJLKJHun2iOkYAABUvX79pPfeK28vXCgVF9MjFFWjcWPpySelTp2sIa0jIkwnurClS6X33y9vN2sm7dxpLg+8Gr+FAaCq+YeYTuAZSgqllFnS/q+lgnSp/1TTiSov9cyhzPsbiQEAADxTYUmhVh5e6bCtV1wvQ2nc39DmQxVdPVq5hbka3Xa0Hu72sEcWkQEA8Gn9fhu9r1o1qWdPq33ypBQaajaXJygulvz9mVLtcr35pukElbd2rWO7HVOiwHUoJAMA3E/WRmlWP6nwWPm2vANSjThzmS5Gv8nW8Nap86zeyXX7Gw4EAIBZRSVFCvQPNB3DY+zI2KGS0hKHbT3iehhK4/5CAkL0/U3fq03tNoqqHmU6DgAAuBR160orVljzIgcHm07jWb79Vnr8cSkpyVoGDpSaNzedCq50ZiG5Y0cjMeAbKCQDANxPzZZnD/+9/1up9RNm8lys8ARraf4gw5gDAHzS+pT1mrx9slYfWa01R9aof+P++uy6z0zH8hht67RV9p+yterwKi0+sFj7s/erVrVapmO5tb6N+pqOAAAALlfXrqYTeKY5c6SjR6VvvrGWq6+WpkwxnQquNGqUFBtrFZR37rSG5AZchEIyAMD9+AdJcddJez4q37b/G88pJJ+OYYUAAD5oyYElenHei2Xt1YdXG0zjmaoFVlOfRn3Up1Ef01EAAADgzubOdWwnJZnJgarzyCPWIknHj0uBjP4E1/EzHQCAh7GXSmcMs4cLKMqlR+qlaniz9TW6p5T4L6nP90bjAACAykuMTXRob0vfptzCXENpAPf172X/1pifx+jnHT8rvyjfdBwAAOBpDh6Udu923EYh2bfUrCmFhJhOAS9Gj2QAFdv9kZSxQspab81d23eSFDPQdCrPsHKMlDJbqtPXWuoNl0Ibm07lGWIGSNfu95x5kQEAQJn2ddvL3+avErt1A6Jddq1PWa9eDXsZTga4l4/Xf6x1Kes0YfUEhQSEaNzQcbo38V7TsQAAgKeoX98qJM+day3r11vzTOPyHD1qDQ/+449S377SM8+YTgQYQyEZQMV2vidlripvH1tPIbky7HYpbb50MsUalnn/N1JXf6n5GNPJPINfIEVkAAA8VLXAarq9w+2KCI5QYmyiEmMT1Sq6lelY8DFrj6zV+JXj9f/6/j81imhkOs5ZDuUc0rqUdWXtk8Un1axWM3OBAACA57HZpPh4a7mXm9Gc4t//lp54QiottdqHDlFIhk+jkAygYhHtHQvJWevNZfEkeclS/kHHbXX6mcmCqpGxUgprJQXWNJ0EAADjPrr2I9MR4KN+3Pqj3lz6ppYcWCJJql29tl4b9JrhVGebtnOaQzs8OFw943oaSgMAgJsoLZU2bpTmz7eWjRulrVslf3/TyeAr2rcvLyJL0rp10p49VrEe8EHMkQygYpEdHNvHKCRXytGlju3g2laREd6ptEiaPVD6Pkqa1V/a/Jp0Mt10KgAA4GG+3PilNqVtUqm9tOIH45zmJM8pKyJL0odrP9TJ4pMGE53bwPiBen3Q6+rbqK/8bf66qtlVCvQPNB0LAACzjhyROnaUHn9c+uEHaedOacMG06ngS/r0kaKjHbd9/72ZLIAboEcygIpF95Qa3WoVlCM6nF1Yxrk1Hi1FX2ENb522QAoMt4abgXdKXyYVH7fW0+ZbS/zdZjMBAACPknkiU6N/GC1JigiJUI8GPTTxmomKrRlrOJlnebjbwxq3clxZOz0/Xd9s/kZ3dLjDYKqzxUfG65lez+iZXs/o2IljyinIMR0JAADz6teXmjWTdu0q3zZvntSpk7FI8DEBAdLIkdIXX0hDh0o33CANH246lWXNGunPf7Z6TbdvL3XoILVtazoVvByFZAAVi+oi9frCdArPY7NJNZtZS1PmKHGKolzp4I9SaLxUu5fpNI6OzHBsR3SQqsWYyQIAADzS6b1os05mae7euYqqHmUwkWdqFd1Kg+IHadaeWapXs54e6PyABjcdbDrWBUVWi1RktUjTMQAAcA/9+59dSP7jH02lgS965RVrruTq1U0ncbRqlTR9urVIUuvW0pYtZjPB61FIBgC4v4xV0o5x0oHvpOI8Ke569ysklxZK/iFSyW/DJsYOMZsHAAB4nMX7Fzu0u9XvpiD/IENpPNsLfV/Q/Yn3a2SrkQwXDQCAp0lKkmbNsr727299hSO7XZo5U+rRQ6pZ03Qa71O3rukE53bmMO/t25vJAZ9CIRkA4P6OrZGSPylvH/pJKsiQgt2oh06nf0jtXpSOzJQOTZYaXGM6EQAAbiW/KF+pualqEtnEdBS3VSOohhqGN9T+7P2SpF5xbnbjnAfp06iP6QgAAOBS3XKLNHq06RTubft2acgQyd9f6trVKra/+KIUHGw6GVxp40bHNoVkVAEKyQAA99fwJmnVY1JpgdUuLZL2fCK1fsJsrjMF1JDiRloLAADQ2iNr9c9l/9SaI2u0NX2r2tdtr7UPrDUdy2093/d5Pd/3eR3MOajF+xerTZ02piMBAABUPT8/0wnc39y51teSEmnZMmn/funVV81mguv98Y9Sr15Wz+SNGykko0pQSAYAuL+gCCnuBmnfF5LNX6p/jRTVzXQqAABQgZyCHH224bOy9qa0TSooLlBwAD0lLqRBWAPd3PZm0zHgIodyDql2jdoMWw64ucLCQn399df68ssvtXnzZqWmpioyMlJNmjTR9ddfr7vuukvR0dGmYwLwVfPnO7aTkiSbzUwWVJ2RI63lFLvdVBL4EArJAC6e3S7lH5QCw6SgcNNp3E9BppS5SoruKQWGmk7jPVo+JoW1kJreJ1WvbzoNAACohI4xHR3axaXF2pS2SZ3rdTYTCHADt/94u1YdXqXBTQdrePPhurrl1YquTjEKcCfbtm3TrbfeqnXr1jlsT0lJUUpKipYuXao33nhDH330kYYNG2YmJADfVlAgBQRIxcVWu18/s3l8QVqatHKlNHy46STluHkAVcDjxogoLCzUZ599pmHDhqlRo0YKCQlRbGysevbsqTfffFPp6emmIwLea+ub0pwh0g91pMkNpUNTTCdyTykzpblDpO8ipRndpU2vmE7kHaKvsOYgpogMAIDHCA8JV/NazcvafjY/7crcZTARfFlxabEmbZukhfsWGsuQfTJbC/cv1PHC4/p+6/e6Z8o9WrR/kbE8AM528OBBDRw4sKyIbLPZ1K9fP91zzz26+uqrVa1aNUlSWlqaRo4cqTlz5hhMC8Bn/fijlJUl/fqr9Oc/S1deaTqRdzp+XHrnHatQHxsrXXeddOyY6VRAlfKoHsncDQgYdnSRlPJreTtzjdTkdnN53FXab0PL2IuljOVScJTZPAAAAAY90eMJlZSWKDE2UR1iOqh6YHXTkeBjjuYd1cS1E/WfVf/R/uz9SmqcpDl3min8zNwzU8WlxWXtIP8gDYofZCQLgHMbPXq0Dh8+LElq1KiRJk+erA4dOpR9Pz09Xbfccotmz56toqIijRo1Srt371ZERIShxICPyMqSMjKkpk1NJ3EfNWpYBWSKyK71zDNWD3BJKi2Vvv1Wuv9+s5mAKuQxPZK5GxBwA5GJju3M1WZyuLu0M+YoqcPQMl6rOE+a2Ufa+pY13DsAADjLmC5j9HC3h9UjrgdFZBgxY/cMjZ09Vvuz90uS5u6dq81pm41k2ZS2yaHdv3F/hQYxHQ7gLqZNm6aFC61RC4KCgvTTTz85FJElKTo6WpMnT1Z8fLwkKTMzU//4xz+qPCvgEzZulJ5+WurSRYqKkh5+2HQi+JqaNaURIxy3ffaZmSyAIR5TSD7zbsC1a9dq3rx5mjhxoqZMmaL9+/dr4MCBklR2N2BWVpbBxIAXqnVGITlvrzVfMsqVFEiB4ZLttAEfKCR7r4M/WT311z4lTWoozR4kndbDBAAAoDJm7JqhP836k37a/pMy8jNMx/E6oxJGqXb12g7b3lv5npEsL/V/SYefOKyJ10zU9a2v16iEUUZyADi38ePHl63feeedateu3TkfV6NGDf3lL38pa0+YMEHFxfwtCDjd9u3Sm29Kq1dbPUEXLZKKikyngq+57bby9fBwKSFBKikxlweoYh5RSOZuQMBNRHWTEsZKvb+VrtktXbtPstlMp3Iv/sHS4CXSqCxpwEyp7QtnF+DhXFmbpDRD89zt+/K0hl2y+Ul+HjVrBOAzCgsL9dlnn2nYsGFq1KiRQkJCFBsbq549e+rNN99Uenq66YgAfNjXm7/W64tf1zVfXaPoN6L16LRHTUfyKsEBwbq/c/nwg93qd1O/xuZu9oytGat7Ot2j72/6Xvcl3mcsBwBHubm5mj17dln77rvvvuDjb7jhBoWGWiMKZGZmasGCBS7NB/ikfmccr/PypJUrzWSB7xo2TLr1VmtI65QUacIEyd+/ajNs2iQ1by7dcIP08svWHNl08EIV8YhCMncDAm4ipI7U8W9Swxul0HiKyBcSUEOKGSS1f1nyCzSdxvvY7VLKbGnuUGlaO2nlmKo/eSrIlI784rit8eiqzQCgUrZt26YrrrhCd9xxh3755Rft379fBQUFSklJ0dKlS/X000+rTZs2mjZtmumoAHyQ3W7XrD2zHLY1q9XMUBrv9UDnB3RHhzu0/L7lWn7fct3U5ibTkQC4mSVLlqjgtzkwa9Sooa5du17w8SEhIerRo0dZm2n2ABeoXVtq27a8HRws7d5tLg98U1CQ9MUX0o03SiEhZjKsXy/t2iX98IP00kvSH/7AtXlUGbcvJHM3IADgLGnzpDmDpCPTrXb2lvL1qhIYLvWfJsXfba37BUsNrqvaDAAqdPDgQQ0cOFDr1q2TJNlsNvXr10/33HOPrr76alWrVk2SlJaWppEjR3IBEECV25W5SwdyDjhsGxQ/yFAa7xUXHqdPRn6ibvW7mY4CwE1t3bq1bL1du3YKCKh4tKnExPIRyE7fH4ATPfCA9MIL0ty5UlaWdPvtphOZ9cEH1rJjBz1SfcmGDY7t9u3N5IBPcvtCMncDAgDOUqefFJ7guG1LFU9n4Odv9Trv/l/p+hRp0DwpKLxqMwCo0OjRo3X48GFJUqNGjbR27VrNmzdPEydO1JQpU7R//34NHDhQklRUVKRRo0YpKyvLYGLAu5WUlmhz2mZN28kIAKfUDK6pVwe8qqTGSQryD1JMaIwSaidUvCMAwKm2b99ett6oUaNK7dOwYcOy9W3btjk9EwBJjzxiDeXbv7+53qDu5LXXpPvvl1q2lOrXl375peJ94PkoJMMgty8kczcgAOAsNj+p1ZPl7ZAYKe56yV5qJo9/iBTd3cxrAzivadOmaeFCaw71oKAg/fTTT+rQoYPDY6KjozV58mTFx8dLska0+cc/qvjGFMAHbEvfpp4Teyrs72Fq+5+2uvGbG1VcyjREkhQTGqPn+jynOXfOUdazWZp1+yzZGKYOAKpcRkZG2XrdunUrtU9MTEzZemZmptMzAYCDAwek5OTy9pEjUr165vKg6rz+uvTf/1pDWg8cKF1xhelE8CFuX0jmbkAAwDk1/p0U0UHq9KZ0zW6p5aNWgRkAfjN+/Piy9TvvvFPt2rU75+Nq1Kihv/zlL2XtCRMmqLiYAhfgTFHVorT04FLlF+VLkk4Un9D29O0V7OV7qgVWU5s6bUzHgJMVFBeo70d99c7yd5R1Mst0HADnkZubW7Z+avqTipz+uNP3P5eCggLl5OQ4LABwUebPd2xHRkrn+TsXVeDECau4WxVDjLdvL919t/TPf0qzZknXXOP61wR+4/ZX3LkbEHBjhVlS6lwpZ4fpJO5h/f+Tdn1g/X8wR4nr+QdLQ9dKrZ+UAqqbTgPAzeTm5mr27Nll7bvvvvuCj7/hhhsUGhoqyTp/XLBggUvzAb6mdo3aiguLc9i2+shqQ2mAclkns7Ro/yKXvsZ3W77Twv0L9fj0x1X/7fq6/6f76ZEPuKGTJ0+WrQcFBVVqn+Dg4LL1EydOXPCxr732msLDw8uWuLi4Cz4eAM7SoIF0883SqfpHnz6Sn9uXeLxPQYE0frzUtKl0773Sjz+aTgS4lNv/luFuQMANbXpFmtJU+i5Smj1ASv7EdCLzinKkLX+TVtwv/dxS+rGelLXZdCrvx7CPAM5jyZIlKigokGT1OO7atesFHx8SEqIePXqUtefMmePSfIAvSowtn4KoQVgDFRQXGEwDX7cxdaPG/DxG9d+ur5FfjdSJogsXgC7He6veK1vPL8rX7mO7FeBX8bRdAKpWyGlzrxYWFlZqn1Pnm1LF1y3Hjh2r7OzssuXAgQOXFhSA7+rfX/rqK+nwYWn7dumVV0wn8k3XXmvN3X3kiNV+4QWppMRsJsCF3L6QzN2AgBsqzpVy95S3M1aay+Iuji52nJ+3MEMKjTeXB65RWiwt/731fgNwa1u3bi1bb9eunQICKr5gn5hYXuQ6fX8AzvFUz6f0y+9+UepTqTrwxwP6feffm44EH7U/e7/av99eE1ZPUH5RvjJOZOjrzV+75LU2pW3SkgNLHLY91OUhl7wWgMtzanQaqeLried63On7n0twcLDCwsIcFgAXyW6XNm+Wxo2TNmwwncYcm01q0YJhrU255x7H9ubNVoEf8FJuX0jmbkDADUV1c2xnrHQsovqitDOGQI3qJgVUbhQFeJAD30u7P5Rm9pZ+7Skd+JFhzAE3tX17+dyrjRo1qtQ+DRs2LFvftm2b0zMBvq53w966qtlVqlOjjukobqGktEQlpfRcMKFheEMNbjrYYdu7K96V3QXndW1qt9GcO+boxoQb5W/zV72a9XRNS+a0A9xRVFRU2Xpqamql9klJSSlbr1WrltMzATjN889bQzq3bSs9+qj0/femE8FX3XijNWfxKYMHS61bm8sDuJjbF5K5GxBwQ6cXkv2CpbBWUuExc3ncQdQVUqNbpGqxVrtOP7N5fFlJgbTldWnLP5z7vPZSafOr5e30pdLm15z7GgCcJiMjo2y9bt26ldon5tQ8U7LmSQYAV1qwb4Gi34jW9V9fr/Erxmt7+vaKd4LTPNL1kbJ1f5u/mkY2VX5RvtNfx2azKalJkr4d9a32/3G//nf9/xToH+j01wFw+Vq2bFm2vm/fvkrts3///rL1Vq1aOT0TgNMUFEhpaeXtuXPNZYFv8/OT/vpXqW9facECacYM6bQRzpyOYbNhmNtPysPdgIAbqlZfumKiFNlRCm8r+Vdu2HmvFjfSWux26fgueiObYLdLh36S1jwh5e62bnKof7UU7qQ7Ag9OkrI2Om5LeJp5mgE3lZubW7Ze0Qg153rc6fufqaCgwGEEnJycnEtICMDXzU6erayTWfpx24/6cduP6hzbWavuX2U6ls8Y1nyYujforsHxg3V/5/tVP6y+y1+zXs16qleznstfB8ClaX1ab7KNGzequLi4wulR1qxZc879AbhAUpL05pvl7WXLpPx8qXp1c5ngu66+2lrOd13QbnfONcPsbKsnfqtWUps2UkKC9NhjUgUdKAFncvseydwNCLghm01qeo9UK5Ei8plsNimsuVS9gekkvicvWVp4vVVElqTSAmnZ3ZKzhoysf63U/WOp+m9D34a3lRpc75znBuB0J0+eLFsPCqrcsSo4OLhs/UIj4bz22msKDw8vW+Li4i49KACfNWvPLIf2oPhBhpL4Jn8/fy29d6leTnq5SorIANxfz549y84H8/LytGrVhW/uKSgo0LJly8raAwYMcGk+wOf16SP5+1vrDRtKo0dL3NQLU2y2CxeKH33UKjT/8MPl9SjeskU6eVJat0763/+kF1+UKnmNA3AWty8kn+tuwIpwNyAA+KDQeKnl447bstZLx9ac+/EXy89fir9Tunq7lPhPqdOb1jYAbikkJKRsvbCwsFL7nN7L+EK9mMeOHavs7Oyy5cCBA5ceFIBPOl5wXKsOOxYoBjYZaCgNAECypscbOLD8d/HHH398wcf/8MMPOn78uCRrRMS+ffu6Mh6AmjWl776Tdu+W9u6VPv7Y6qnpC6ZMsQrpzz8vzZwp5eWZToQLOXnSKvr+/LN0ww1S8+bSr79e2nNt2eLYbt6cQjKqnNsXkrkbEABQae3/KtVsbq1HdZeGrpOiujr3NfxDpFZ/kOoNce7zAnCq0NOGebpQ7+LTnf640AsMExUcHKywsDCHBUDl2e12Hcw5qMnbJusv8/+iUnup6UhVrmZwTe39w159MvIT3d7+djWJaKJeDXuZjgUnKSop8snPNeANHnroobL1jz/+WJs3bz7n4/Lz8/XCCy+Ute+///4Kh8EG4AQjR0rx8b43zdisWdKiRdKrr0qDB1vFSbivn3+WsrLK28nJ0mlTuF6UM49DCQmXHAu4VG5fSOZuQABApQVUt4af7vSGdOUiKaxlhbsA8E5Rp/2RlpqaWql9UlJSytZr1arl9EwApIz8DMW8FaO4f8Zp5Ncj9eK8F5V8LNl0LCMahDXQHR3u0KfXfao9j+9R9UDm93MnB7IvfbSJ1xe/rl7/7aXVh1c7MRGAqjB8+HD16dNHktVZZcSIEdqwYYPDYzIyMjRy5Ejt2rVLknXe+Oyzz1Z5VgA+ZP58x/Zvv6fgpr76yrGdlCR17nxpz/XnP0tz5kjjxkkPPiiNGHH5+YCL5PaFZIm7AQEAF6F2T6n1Uww7Dfi4li3LbyTZt29fpfbZv39/2XqrVq2cngmAVKtaLdntdodtq49QbIP7KLWX6o3Fb6jpO001ZfuUi95/V+Yu/W3h37Ts4DJ1/aCrxvw8Rhn5GS5ICsBVvvjiC8XGxkqS9u7dq44dOyopKUn33Xefrr32WjVs2FAzZ86UJAUEBOibb75RRESEwcQAvFpmprRxo+O2fv3MZEHlfPSRtZwqHj/11KU/V1SUVYh++GHpvfeku+5ySkTgYnhEIZm7AQE3VlokZa6Rdr4v7Z5oOk3V2/x3aWYfaf3z0pGZUlGu6URwlp3/kXaMN50CwCVq3bp12frGjRtVXFxc4T5r1pTPqX76/gCcx2azKTE20WHbmiNrzvNooGql5aVp2P+G6ZlZz6iotEh3T75bB3MOVnr/jPwMXfX5VTpRbE2VYJddH6z5QAdyLr13M4Cq16BBA82ZM0cdO3aUZE3JMG/ePE2cOFFTpkxRfn6+JKl27dqaNGmSw0iKAOB0NWpYQyU/84x0xRXWXNFdnTyNG5yrZk2r4LtypbR4sXTVVed/7EcfSRMmSGfcbAu4E48oJEvcDQi4pX3fSN/UlKZ3llY+KG1723SiqpcyUzq6SNr8qjR3sLThhYr3gTl2u7TuT1LWpgs/Zuvb0sqHpVWPSBtf5mQO8EA9e/ZUcHCwJCkvL0+rVq264OMLCgq0bNmysvaAAQNcmg/wZZ1jrTvzw4LD1L9xfzWNbGo4EWD5aftPmrF7Rlk780SmRn8/WsWlFd+MJEkRIRG6Mv5Kh20PdH5AHWM6OjMmgCrQqlUrLV++XJ988omuuuoqxcXFKSgoSHXq1FH37t31j3/8Q1u2bNHw4cNNRwXg7YKDpWHDpNdfl5Ytk44etbbB/dlsUs+ekt95ynCHD0t/+IM0Zox03XVSenqVxgMqy2PGfT51N+Ctt96qdevWld0NOG/ePIfH1a5dWx999BF3AwJVoUYjqbSgvJ29VSrKkQLDzGWqSiWFUvpSx211GVrGrW18WdryurTtn1LCs1KrP0pBkY6P2fRXaeOLp+3zklSQLnV+xzoBBOARQkNDNXDgQE2bNk2SNT1K9+7dz/v4H374QcePH5dkjWzTt2/fKskJ+KIHuz6oezrdoyaRTeRn85h7m+ED7ul0j37Z9Yu+3/q9JMkmm/o1qvz5vb+fv94b/p7q1Kijvyz4izrU7aC/D/q7q+ICcLGgoCDdcccduuOOO0xHAXCm48elRYukuXOlBQukX3+VwnzkeiRFZO/x+ONSTo61PnmytGKFtTRoYDYXcAaP+quduwEBNxPZQfILPG2DXcr0oTnuMldKJSdO22CTavcxFgcVSP5M2vSytV5aaBWMc3ac/bjGoyX/EMdtwdEUkQEP9NBDD5Wtf/zxx9q8efM5H5efn68XXigfUeL+++9XQIDH3G8JeJwGYQ3UtFZTnywi2+12/bzjZ2WdzDIdBedgs9n04TUfqlF4I8WGxmrWHbP01wF/VYBf5Y8JNptNLye9rP9e819Nv226woJ95KI2AABVpbBQiomxeum+8Ya0fLm0cKHpVMDFWb9e+u47x21du0r165vJA1yAzW5nvM7T5eTkKDw8XNnZ2QrzlbuYgMsxvauUuUqq2VyK6ia1/IMU1cV0qqpRnCelLZDS5kup863i5FAfKqR7EnupNLO3Yw/y4GjpuhTJz//sx299U1r7tLXe4W9Swp8oJKPKcC7iXH379tXC3y4qNG7cWJMnT1b79u3Lvp+RkaFbb721bIqUWrVqaffu3Rc1RQrvGYDK2p6+Xa3Gt5KfzU9d6nXRwCYD9UK/FxQSEFLxzqgyG1M3qm5oXdWpUees7xWVFOmLjV/oq81f6adbf7qoIjPgSpyPeB7eM+Ay9O3rWDx+8knpzTfN5QEuxdSp0t13W8OVx8ZKGzZI0dHl38/IkGrV4pokXKay5yL8xQPg8vT4TKpW9+zhgX1BQA2p3lBrkaTSErN5cH42P2ngHGnDi9K2N63CcuxV5y4iS9YNEanzpKb3SnHXVWVSAE72xRdfqFu3bjpy5Ij27t2rjh07ql+/fmratKmOHj2qWbNmKT8/X5IUEBCgb7755qKKyABwMWYnz5YkldpLteLQCh3IPqBXB7xqOBXO1K5uu7O2lZSWaMLqCfrH4n9oX/Y+SdJXm77Sbe1vq+p4AAAgKcmxkHzG9JeARxg+3Coe33OPdTPE6UVkSerQwRr6OiFBatNGeuYZqWVLM1nh03xvLDEAzhXeyjeLyOdyvqIk3IN/iNTpdWnoeqnx76QG15z/sX4BUv+fKSIDXqBBgwaaM2eOOnbsKMkaVnbevHmaOHGipkyZUlZErl27tiZNmqSBAwcaTAvA283aM8uhPSh+kGz0MPAIfjY/fbr+07IisiS9tug1ldpLDaYCAMBHJSVJgYFS797S8897b2/ko0dNJ4CrxcRYPZPPvBaRnS0dOmTNB758ufTf/0pFRWYywufRIxkA4Fsi2ko9PzedAkAVatWqlZYvX66vvvpKX375pTZv3qzU1FRFREQoPj5e119/ve6++25Fn3n3LwA4mb+fv4L9g1VQUiBJGtiEm1c8hc1m03N9ntO1X11btm3L0S2auXumhjQbYjAZAAA+qHdv6dgxqUYN00lc58QJa77cevWkPn2s5dZbpZo1TSeDs53rxtItW6yv7dtbvZb9/aXmzas2F/Ab5kg+A/OTAAAAkzgX8Ty8Z8ClyT6ZrbUpa7XmyBqtPrJab1z5hurVrGc6lsudLD6pJQeWaNaeWXqk2yM+8W/2FqX2UnV8v6M2pm1Uz7ie+nOfP2tos6H0Kodb4HzE8/CeAbigefOsnten+PlZxXN+X/iGlBSptFT6/nvpscekVq2krVtNp4KXYY5kAAAAAIBbstvtavSvRsouyC7bdnObm3VNywtMPeElQgJCNKDJAA1oMsB0FFwkP5uf/n3Vv2Wz2dSvUT8KyAAAwHVOnwNakjp2pIjsS2JirCLy449b7X79zOaBT2OOZAC4WKUlUm6yxIAOAAAAl8Rms6ljTEeHbWuOrDETBrgISU2S1L9xf4rIAADAtdaccW7cp4+ZHDDDbrd6pYeGSgMGSH//u+lE8GH0SAbgHPmHpfQl0tHF1tek6VJQpOlUrpG1TpreRaoeJ9XpZy1N7z33fBYAAAA4p8TYRM3fN7+svfrIaoNpAAAAADfy/ffWPLkLF1rL0KGmE6Eq2WzSu+9K77zDNWcYRyEZwOUrKZSmxEulBeXb0pdJ9bz0BCd1nvU1/4C093MpY4XU7D6jkQAAADxN59jOCvQLVLu67ZQYk6ikJkkV7wQAAACci90upadLtWubTuIcfn5S27bW8uCDptPAFIrIcAMUkgFcPv8gKaqrdHRR+bajS7y3kJw237FdhzkqAAAALtYNCTfoxoQbFRwQbDoKAAAAPFFamvTjj9LcudYwwHXqSBs2mE4FAF6FQjIA54ju6VhITl9iLosr2e1SznbHbRSSAQAALlpIQIjpCFXm6V+fVqOIRhrYZKBaRbdifl0AAABn2LVLGjOmvJ2aKh096j29kgHADVBIBuActXtJu8Kl6B5WUbmulxZXbTZpxDYpZ6vVMzl1vvf+WwEAAHDZsk5m6e1lb6vUXipJqleznmbePlMJtRMMJwMAAPBwXbtK1atL+fnl2+bPl2680VwmAPAyFJIBOEe9YdKNmZLNz3QS17PZpPAEa2nOHCUAAAA4v3l755UVkSUp80Sm4iPjDSYCAADwEoGBUu/e0q+/lm9btYpCMgA4EYVkAM7hx68TAAAA4Eyz9sxyaPeK6+VTw3oDAAC41PXXS9HRUv/+UlKS1LSp6USXZ+1a6fhxqVs3KYRzRgDmUfkBAAAAALiNk8UnvarQelWzq5RbmKvZybN1MOegBsUPMh0JAADAezzwgLV4i3/+U/rsMykoyBq6+5FHpFtuMZ0KgA+jkAwAAAAAMCbzRKZm7p6pOclzNGfvHLWIaqGpo6eajuU0I1qM0IgWI2S327UjY4fCQ8JNRwIAAIC7WrjQ+lpYKC1eLN16q9k8AHwehWQAAAAAgDGz9szSLd+X97JIyU1RUUmRAv0DDaZyPpvNppbRLU3HAAAAgLs6eFDau9dxW58+RqIAwCl+pgMA8GL2Uqkg03QK59n/rZQ6Tyo+YToJAACA1+jfuL9DO7cwV6sOrzITBgAAADAlLU3q2FGy2ax2RITUtq3JRABAj2QATpabLB2cZBVcjy6U6iZJfb43nery2e3Sqkekk2mSX5AUdYXU+V9SrUTTyQAAADxanRp11K5OO21M21i2bf6++eoR18NgKgAAAKCKJSZKa9dK2dnSkiVWYdmPvoAAzKKQDMC50hZKa544rT3f6pls8/CTnpztVhFZkkoLrSJ5IPPbAQAAOMM1La9RXHicBjQeoAFNBqhDTAfTkQAAAOCJcnOlRYukunWlTp1Mp7k04eHS0KGmUwCAJArJAJytbj/HdkGGlL1ZimhnJo+zpM13bFerL4XGm8kCAADgZV4Z8IrpCE5XUloifz9/0zEAAAB8wxdfSO+9Jy1fLhUXS/feK334oelUAODxPLyLIAC3U6ORVKOx47aMlUaiOFVgmBTVrbxndZ1+5fOVAAAAAGe46bub1OX/uuhPs/6kmbtn6kTRCdORAAAAvFdqqrR4sVVElqS5c83mAQAvQY9kAM7X+HfSyVSpTn+rh3L1BqYTXb7Gt1pLYbaUtkAKqW06EQAAANxUcWmxZu+ZreyCbK0+slqvL35dn478VLd3uN10NAAAAO80YIBje88ead8+qVEjM3kAwEtQSAbgfB28b2jCMkHhUoOrTacAAACAG1t9eLWyC7Idtg2MH2goDQAAgA9o106KipIyMqSAAKlbNykzk0IyAFwmCskAAAAAADjR7OTZDu3W0a1Vr2Y9Q2kAAAB8gJ+f9M9/SrVrS717S6GhphNVXn6+9Ic/WLn79JEaN2ZKPQBug0IyAAAAAMCtFJUUaeXhlYquHq0WUS1Mx7loT/R4Qj0a9NDs5NmatWeWejToYToSAACA97vdQ6cRWb5c+uADa5GsXtQ7d0qBgWZzAYAoJAMAAAAA3MS3m7/VR+s+0oJ9C5RXlKcnuj+ht4a8ZTrWRQsJCFFSkyQlNUnSKwNekd1uNx0JAAAA7mrhQsd2dDRFZABug0IyAAAAAMAtbDm6Rb/s+qWsPWfvHINpnMfG0IQAAAA4nzMLyX36mMkBAOfgZzoAAB9wMl3a97V0bIPpJBdv5/vSkjukPR9LeQdMpwEAAPBqA+MHOrTXpaxTRn6GoTQAAABAFbjhBmnkSKsnskQhGYBboZAMwHV2fShN7yr9UEdafItVjPU0+7+T9n4mLbtbmtxQ2vCi6UQAAABeq1v9bqoeWL2s3a5OOx3MOWgwEQAAAOBiY8ZIP/4opaVJW7ZIV15pOhEAlGFoawCuc+KwlLmqvJ3yq7ksl6LkpJS+2HFbZCczWQAAAHxAkH+Q/jbgb4qtGav+jfurTo06piMBAADA09jt0u7d0ty51vLii1LLlqZTVcxmk1q3Np0CABxQSAbgOrGDpY2n9eDN3izlH5Kq1zeX6WKkL7WKyafY/KS6/Y3FAQAA8AWPd3/cdIRLtitzlwpLCtU6ujXzIgMAAJjSp4+0+LTOIT17ekYhGQDcEENbA3CdWl2kwAhr3b+6VG+YVJRtNNJFCWsldRkvxd0gBdWSIhOloAjTqQAAAOCm3l76ttq810b1366v23+8XTN3zzQdCQAAwPc0a+bYnjvXTA4A8AL0SAbgOn4BUpd3rR7I0T0l/2DTiS5OtVipxUPWYi+VTqaZTgQAAAA3Njt5tiTpSO4Rfb7hc7Wr005XNmWOOwAAgCqVlCR98kl5e948qbRU8qNfHQBcLArJAFyryW2mEziHzU+qFmM6BQAAANzUgewD2pGxw2HbwCYDDaUBAADwYUlJ1tc6daQBA6yluFgKCjKbCwA8EIVkAAAAAAAu077sfYoLi9OBnAOSpFrVaqljTEezoQAAAHxRw4bStm1SixaSzWY6zfk99JC0c6c1p3OfPtIVV0jVq5tOBQAOKCQDAAAAANxSWl6a5ibP1ZzkORrbZ6waRzQ2Hem8ejfsrX1/2Kddmbs0O3m28grz5O/nbzoWAACAb2rZ0nSCik2bJu3bJ82aZbXHj7eKywDgRigkAwAAAADczpWfXalZe2aVtbvW76r7Eu8zmKhiNptNzaOaq3lUc9NRAAAA4M4OHLCKyKfr08dMFgC4AGaXB4AzFWZLRcdNpwAAAPBp0dWjHdpzkucYSgIAAAA42aJFju3ISKlNGzNZAOACKCQDqDo5O6Wtb0uz+ktb3zSd5vx2T5S+q2Xl3PyalLXJdCIAAACfM6DxAIf2nOQ5stvthtIAAAAATnTVVdIPP0h//KPUpYvUv7/kR7kGgPthaGsAVWPDi9Kmv5S3S4uk1k+Zy3MhR2ZI9mIpbb615O6RrvjAdCoAAACfMqCJVUgODQpV30Z9NaDxABWVFinIP8hwMgAAAHikwkIpyE3OJSMjpeuusxZJKi01mwcAzoNCMoCqEd3dsZ2+VDpxRKoWaybP+RSfkI4ucNwWO8RMFgAAAB8WHxmvZfcuU2JsogL9A03HAQAAgKcpKpIWL5bmzJHmzpU2bZJSU92nmHw6eiMDcFMUkgFUjbpJUkBNqfjU3MN26eAkqfmDJlOdLWerHEb9t/lJMQONxQEAAPBVNptNVzS4wnSMCq1PWa//bfyfBsUPUu+GvVU9sLrpSAAAAJCk7GwpKclx28qVUq9eZvIAgAfiNhcAVcM/RKp/tRRUS2p6r5Q0Q2p6n+lUZ6uVKN2QLvX7WWp2v9RgpBQUaToVAAAA3NSU7VP0xpI3NOTzIYp8PVL3TXHDc1wAAABfFB0tdejguG3OHDNZAMBD0SMZQNXp/C8pKELyc/OhCQOqSfWHWwsAAABwAbOTZ5etF5YUKjQo1GAaAAAAOEhKktavL2/Pny/9v/9nLg8AeBgKyQCqTkht0wkAAAAAp8krzNOSA0sctg1swrQoAAAAbmPQIGnRIqugPGCA1Lu32Txjx0r9+kmDBzMvMgCPwG8qAAAAAIDHSM9PNx2hTGFJof7U+0/qGddT/jZ/+dv81a9xP9OxAAAAcMrw4da8yP/4h3TVVVKowdFjNmyQ/v53aehQqUUL6c03pRMnzOUBgEqgkAwAAAAAcGuHjx/Wu8vfVb+P+6num3WVfCzZdCRJUmS1SP0l6S9afM9iZT6bqbl3zlVYcJjpWAAAAHBH771Xvr57t/TWW1IAg8YCcG/8lgIAAAAAuK1Se6k6TeiktLy0sm3fbflOT/d62mCqs4UFh6lPoz6mYwAAAMAdZWdLn3/uuO3++6XAQDN5AKCS6JEMwKysjdLWN02nkA7/Im37t5S7x3QSAAAAnMbP5qdrW17rsO3bLd8aSgMAAABcgqAg6e23pfbtrba/v/T735vNBACVQCEZQNUrKbCKtr8kStPaS2uflo5tMJtpxzhpzR+kKU2lqW2lfV+bzQMAAIAyoxJGObS3pW9T5olMQ2kAAACAi1StmtUDed06adEia37kBg1MpwKACjG0NYCqZwuQtr4hnThUvi35EynyLTN5Co9JR34tb2dvluwlZrIAAADgLP0b91d8ZLx6xvXUqIRRGtx0sEICQkzHAgAAgKex26U9e6TMTKlr16p/fZtN6tXLWgDAA9AjGUDV8/OXmtzuuG3v/6TSYjN5DkyS7Ke9tl+wVP9qM1kAAABwlkD/QO18dKc+u+4zXdPyGuNF5APZBzRz90zZ7XajOQAAAFBJa9dKd90lNW4sNWsmPfyw6UQA4BEoJAMwo8md5evV6knxd0klJ8xkiewoNRsjBde22vWGSYE1zWQBAADAOfnZ3OfP178u+KsGfz5Y/T/pr0X7F5mOAwAAgIocOyZ98om0f7/VXr1ays42mwkAPID7/CUOwLeEt5LaPC8lzZCu3S91/Lu54m2tTlK3/0jXHZYGzJIS/mQmBwAAANzersxd+u/a/0qSFuxboD4f9dH4FeMNpwIAAMAF9eghBQWVt0tLpQULzOUBAA9BIRmAOR3+KsUOtoa6dgd+AVLMQCm6m+kkAAAAcFMvz39ZJfaSsna1gGq6IeEGg4kAAABQoWrVpJ49y9u1a1u9lF2pqEiaOFHKzXXt6wCAC1FIBgAAAACgkpIaJykuLK6s/Wi3RxUTGmMwEQAAACrloYekd96RNm2SUlOlO+5w7ev9+KN0331S/frSY49JW7e69vUAwAUCTAcAAAAAAOBiJR9L1vur3teJ4hN6Z+g7Vfa693S6R79r9zv93+r/07sr3tUzvZ6pstcGAADAZRg1qmpf7733rK85OdK770pr10oLF1ZtBgC4TBSSAQAAAAAeY3fmbj02/TH9svMX2WVXoF+gnu/7vOrUqFNlGYIDgvXoFY/q4W4Py8/GQF8AAAA4w+bN0vz5jtseeshMFgC4DPzFC8C9FOdLe7+Q7HbXvo7dLqUvc/3rAAAAwKkiQiI0J3mO7LLO44pKizRxzUQjWSgiAwAA4JzsdunqqyWbzWrXqSNdf73ZTABwCfirF4B7KMySNr0qTW4sLfmdlDrXta93dJH0aw/ppxbW6+YdcO3rAQAAwCmiqkfplra3OGybsHqCSu2lhhIBAAAAZ2jbVpoyRdqzRxo7Vnr6aSk42HQqALhoFJIBuIfZSdKG56WCo1Z7y99d+3q7Jlhfc3dZrztvqGtfDwAAAE7zUBdrWMDw4HA9fsXjmnHbDHoHAwAAwP00biz97W/SU0+ZTgIAl4S/tAG4h2YPOLZTZkqZq13zWidSpP3fOG5rcodrXgsAAABO17V+V3076lsdeuKQ/nXVv9QyuqXLXutE0QlN2zmNHs8AAADe5ORJad486cUXpdmzTacBALdFIRmAe4i/SwqpW96OHSLZAlzzWidTpIj25W3/ECn+Hte8FgAAAFzixoQbVSOohstf5+vNX2v4F8PV7J1memPxG8rIz3D5awIAAMCFnn9eioyUkpKkv/xF+vpr04kAwG25qEoDABfJP0Rq9aTVCznhWalWJ9e9VmRHachKKX2ZtONdKTBcCol23esBAADAY41fOV6SlJyVrGdmPaN5++Zp6uiphlMBAADgktWqZfVIPmXuXHNZAMDNUUgG4D4Snr6k3XJzpV27pIICKThYatZMCg2tYCebTardw1rs9kt6XQAAAHiGA7szNee7HcrLKVSNsCANuLGF4prWqnC/lYdWatXhVQ7b7k+831UxAQAAUBWSkhzbu3ZJBw5IcXFnPTQ3N1e7du1SQUGBgoOD1axZM4We78JjRoZ1obJRIxeEBgAzKCQD8Ehbtkjvvy9Nmybt2eNYC7bZpPh4adgwacwYKSGhgiez2VyaFQAAAFXv63Gr9fNLa1UrI0eROq5TZ3xHJU380wwdU01lRoVpxEuddPMjnc/5HHlFeepQt4PWp66XJDUMb6gRLUZUzT8AAAAArtGhgzW0dVaWlJhoFZZPuz64ZcsWvf/++5o2bZr27Nkj+2kXHm02m+Lj4zVs2DCNGTNGCadfeBw/Xnr5ZWnECOmhh6Qrr5T8mF0UgGez2e10xTtdTk6OwsPDlZ2drbCwMNNxAJwhOVl64AFp5kwpIEAqLj7/Y099/8orpQkTpCZNqi4nAFwqzkU8D+8Z4B7sdrsW7l+o8VM+VfUnOqpxcYZKZJO/zv8n76nv7wuI1s2fJ2nIzWffgWi327X04FKNXzlenWI66ameT7nynwEAl4TzEc/DewYYtny51KKFVVD+TXJysh544AHNnDlTAQEBKr7AhcdT37/yyis1YcIENWnQQGrcWDp8uPxBzz4r/f3vLvxHAMClq+y5CLfDAPAMdrs+/NDqXXxq2pILFZFP//7cudZ+H37o2ogAAACoeoUlhfrPyv+o/fvt1e/jfvomc6L86m6QpAsWkU//foPiDC285Uc90vPrsx5js9nUM66n/nf9/ygiAwAAeIsrrnAoIn/44YdKSEjQ3N8uPF6oiHz69+fOnauEhARNHTvWsYgsSTfd5NzMAGAAhWQA7i9jpQ5+1EtvvbhVJ09WXEA+U3GxdG3Hr/TuX9fr1VddExEAAABm+Nv89fri17UpbVPZttVdV1zcc8iuABWr9tJturvZJ86OCAAAADf26quv6ve//71OnjxZYQH5TMXFxTp58qRGvPWWlvbuXf6N7t2tYbMBwMNRSAbgvgqzpVWPyj79CjUIWaovHh6toICCi36aBrUO6MPf36c1ryYqas8Y/W/iUReEBQAAgAn+fv6KXdnTYdvexntV4ldyUc9zala8xrv36pFeZ/dMBgAAgPf58MMP9fzzzzvluXovWqR9HTtajT/+0SnPCQCmUUgG4L62/VPaMU42mzXkYKfG6/TG6Kcv6ilstlJ99MDdCg3Jk79fqcYMnKCrSlpp385jrkgMAACAKjbj6y3qPqmx/Iv9JUmxh2N178R75V/qf9HPdTT6qI5GH1XEkl2a8fUWZ0cFAACAG0lOTtajjz7qtOcrldR561ZlPPecNGqU054XAEyikAzAfSU8q73H2jhssskuVTDX3em6NV2hQW1nO2ybtOp6/f7hyPPsAQAAAE/y9W1zVTM/RIlrEtVmUxvd/dHdCjsedtHPk1c9T1+M/kIT752ofU126uvb5rogLQAAANzFAw884DCUdR1Jl1tWzi4p0a0rV0o2W8UPBgAPQCEZgNvasqOaRvz9K50oDJEkPfm/N/XYp++ofODBii3f1V3X/fMH5Z2sLkk6kNFAT/7vDc2cKW3d6orUAAAAqCpfj1utRsXp8pddQ2YM0Q3f36CgoqCLfp5i/2J9dctXOlbrmE5WO6kvbvtM6e1n6Nv/rHFBagAAAJi2ZcsWzZw5U8XFxeok6WNJ+yW9I2n4Bfa7WtI8SdXP8/3i4mLNnDlTW7nwCMBLUEgG4Lbef1/antJWY/7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1K2tPnTrVldHgIjExMWrYsKHpGLgE48ePL1u/88471a5du3M+rkaNGvrLX/5S1p4wYYKKi4tdng/Ox8+r7+B4DE/C59U3cUzyXJxD+hZ+Vn0Hx2N4Gj6zvodjkufi/NH38PPqOzzleEwh2YP16tWrbH3v3r3mggA+Jjc3V7Nnzy5r33333Rd8/A033KDQ0FBJUmZmphYsWODSfACqFsdjeBI+r4A5nEMCOIXjMTwNn1nADM4fAZzO1PGYQrIHO33885KSEoNJAN+yZMkSFRQUSLLu9uvatesFHx8SEqIePXqUtefMmePSfACqFsdjeBI+r4A5nEMCOIXjMTwNn1nADM4fAZzO1PGYQrIH27hxY9l6XFycwSSAb9m6dWvZert27RQQEFDhPomJiefcH4Dn43gMT8LnFTCHc0gAp3A8hqfhMwuYwfkjgNOZOh5TSPZQ+/fvd7ijaNCgQQbTAL5l+/btZeuNGjWq1D6nz2uxbds2p2cCYAbHY3gSPq+AWZxDApA4HsPz8JkFzOH8EcApJo/HFJI91BNPPFHWdb1hw4a6+uqrDScCfEdGRkbZet26dSu1T0xMTNl6Zmam0zMBMIPjMTwJn1fALM4hAUgcj+F5+MwC5nD+COAUk8djCske6JNPPtH3339f1n7ttdcUHBxsMBHgW3Jzc8vWq1WrVql9Tn/c6fsD8Fwcj+FJ+LwC5nEOCYDjMTwNn1nALM4fAUjmj8cUkj3MqlWrNGbMmLL2rbfeqtGjRxtMBPiekydPlq0HBQVVap/Tf7GfOHHC6ZkAVC2Ox/AkfF4B98A5JODbOB7D0/CZBczj/BGAOxyPK56dHRo3bpzGjRvn1Od85ZVXdOONN17UPsnJybr66qvLDiDt27fX+++/79RccJ/3G+4rJCSkbL2wsLBS+xQUFJStV/YOQgDuieMxKsNdzif4vFYdd3nP4b44hwR8F8djVJa7nE/wma0a7vJ+w31x/gj4Nnc5HlNIroT09HSHie2dISsr66Ief+TIEV155ZVKSUmRJMXHx2v69OkKCwtzai64x/sN9xYaGlq2Xtk7+05/3On7A/AsHI9RWe5wPsHntWq5w3sO98Y5JOCbOB7jYrjD+QSf2arjDu833Bvnj4DvcqfjMUNbe4CMjAxdeeWV2r17tyQpNjZWs2bNUmxsrOFkgG+KiooqW09NTa3UPqd+4UtSrVq1nJ4JgOtxPIYn4fMKuB/OIQHfw/EYnobPLOBeOH8EfJO7HY8pJFfCSy+9JLvd7tTlvvvuq9Rr5+TkaMiQIdq8ebMkKTo6WrNmzVKTJk1c+U/2aSbfb3iGli1blq3v27evUvvs37+/bL1Vq1ZOzwTAtTge42Jx/uh7OIdERTiHBHwLx2NcCs4hfQvnj6gI54+A73HH4zGFZDeWl5enYcOGafXq1ZKk8PBwTZ8+XQkJCYaTAb6tdevWZesbN25UcXFxhfusWbPmnPsDcH8cj+FJ+LwC7otzSMB3cDyGp+EzC7gnzh8B3+Kux2MKyW7q5MmTuuaaa7R48WJJUvXq1TV16lR17tzZcDIAPXv2VHBwsCTrl/uqVasu+PiCggItW7asrD1gwACX5gPgPByP4Un4vALujXNIwDdwPIan4TMLuC/OHwHf4c7HYwrJbqioqEg33HCD5syZI0kKDg7W5MmT1atXL8PJAEhSaGioBg4cWNb++OOPL/j4H374QcePH5dkzU3St29fV8YD4CQcj+FJ+LwC7o9zSMD7cTyGp+EzC7g3zh8B3+Dux2MKyW6mpKREo0eP1rRp0yRJAQEB+uabbzRo0CDDyQCc7qGHHipb//jjj8vmLDhTfn6+XnjhhbL2/fffr4CAAJfnA3B5OB7Dk/B5BTwH55CA9+J4DE/DZxbwDJw/At7NE47HFJLdiN1u17333qvvvvtOkuTn56fPPvtM11xzjeFkAM40fPhw9enTR5I1bMyIESO0YcMGh8dkZGRo5MiR2rVrlyTrTsBnn322yrMCuDgcj+FJ+LwCnoVzSMA7cTyGp+EzC3gOzh8B7+Upx2Ob3W63mw4By3vvvaeHH364rN28eXMNHjy40vuPGzfOFbHgYsOGDdPhw4cdtqWkpCg1NVWSVKNGDTVr1uys/aZNm6Z69epVSUac28GDB9WtWzcdOXJEkmSz2dSvXz81bdpUR48e1axZs5Sfny/JupNo+vTpDsPRwPPw8+obOB7Dk/B59V0ckzwX55C+hZ9V38DxGJ6Gz6xv4pjkuTh/9D38vPoGTzkeU0h2Iy+99JJefvnlS96ft9IzNW7cWPv27bvo/ZKTk9W4cWPnB8JF2bZtm2699VatW7fuvI+pXbu2PvroIw0fPrzqgsEl+Hn1DRyP4Un4vPoujkmejXNI38HPqm/geAxPw2fWN3FM8mycP/oWfl59g6ccjxkkHwAuQ6tWrbR8+XJ99dVX+vLLL7V582alpqYqIiJC8fHxuv7663X33XcrOjradFQAAAC4Cc4hAQAAcDE4fwRgCj2SAQAAAAAAAAAAAAAO/EwHAAAAAAAAAAAAAAC4FwrJAAAAAAAAAAAAAAAHFJIBAAAAAAAAAAAAAA4oJAMAAAAAAAAAAAAAHFBIBgAAAAAAAAAAAAA4oJAMAAAAAAAAAAAAAHBAIRkAAAAAAAAAAAAA4IBCMgAAAAAAAAAAAADAAYVkAAAAAAAAAAAAAIADCskAAAAAAAAAAAAAAAcUkgEAAAAAAAAAAAAADigkAwAAAAAAAAAAAAAcUEgGAAAAAAAAAAAAADigkAwAAAAAAAAAAAAAcEAhGQAAAAAAAAAAAADggEIyAAAAAAAAAAAAAMDB/webYyNRvk3pLAAAAABJRU5ErkJggg==",
"text/plain": [
- ""
+ ""
]
},
- "metadata": {
- "needs_background": "light"
- },
+ "metadata": {},
"output_type": "display_data"
}
],
@@ -2029,6 +2298,7 @@
{
"cell_type": "code",
"execution_count": null,
+ "id": "8a5dc39c",
"metadata": {
"pycharm": {
"name": "#%%\n"
@@ -2040,7 +2310,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -2054,7 +2324,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.3"
+ "version": "3.10.13"
}
},
"nbformat": 4,
diff --git a/src/omlt/__init__.py b/src/omlt/__init__.py
index 2d7e0d09..12aafdd5 100644
--- a/src/omlt/__init__.py
+++ b/src/omlt/__init__.py
@@ -8,6 +8,7 @@
sequential Keras and general ONNX models.
"""
+
import sys
if sys.version_info[:2] >= (3, 8):
diff --git a/src/omlt/gbt/__init__.py b/src/omlt/gbt/__init__.py
index 605c1824..f62ed421 100644
--- a/src/omlt/gbt/__init__.py
+++ b/src/omlt/gbt/__init__.py
@@ -22,5 +22,6 @@
F_{t,l} &:= \text{Weight of leaf $l$ in tree $t$}\\
\end{align*}
"""
+
from omlt.gbt.gbt_formulation import GBTBigMFormulation
from omlt.gbt.model import GradientBoostedTreeModel
diff --git a/src/omlt/io/__init__.py b/src/omlt/io/__init__.py
index 13b40c8c..6933e312 100644
--- a/src/omlt/io/__init__.py
+++ b/src/omlt/io/__init__.py
@@ -1,6 +1,6 @@
from omlt.dependencies import (
- onnx_available,
keras_available,
+ onnx_available,
torch_available,
torch_geometric_available,
)
diff --git a/src/omlt/io/torch_geometric/__init__.py b/src/omlt/io/torch_geometric/__init__.py
index 50dc3555..ae94d147 100644
--- a/src/omlt/io/torch_geometric/__init__.py
+++ b/src/omlt/io/torch_geometric/__init__.py
@@ -1,8 +1,7 @@
-from omlt.io.torch_geometric.torch_geometric_reader import (
- load_torch_geometric_sequential,
-)
-
from omlt.io.torch_geometric.build_gnn_formulation import (
gnn_with_fixed_graph,
gnn_with_non_fixed_graph,
)
+from omlt.io.torch_geometric.torch_geometric_reader import (
+ load_torch_geometric_sequential,
+)
diff --git a/src/omlt/io/torch_geometric/build_gnn_formulation.py b/src/omlt/io/torch_geometric/build_gnn_formulation.py
index f63af267..6e2e04ee 100644
--- a/src/omlt/io/torch_geometric/build_gnn_formulation.py
+++ b/src/omlt/io/torch_geometric/build_gnn_formulation.py
@@ -1,7 +1,10 @@
import numpy as np
import pyomo.environ as pyo
+
+from omlt.io.torch_geometric.torch_geometric_reader import (
+ load_torch_geometric_sequential,
+)
from omlt.neuralnet import FullSpaceNNFormulation
-from omlt.io.torch_geometric import load_torch_geometric_sequential
def gnn_with_non_fixed_graph(
diff --git a/src/omlt/io/torch_geometric/torch_geometric_reader.py b/src/omlt/io/torch_geometric/torch_geometric_reader.py
index beee1516..72d594cc 100644
--- a/src/omlt/io/torch_geometric/torch_geometric_reader.py
+++ b/src/omlt/io/torch_geometric/torch_geometric_reader.py
@@ -1,8 +1,9 @@
+import warnings
+
import numpy as np
-from omlt.neuralnet.layer import DenseLayer, InputLayer, GNNLayer
+from omlt.neuralnet.layer import DenseLayer, GNNLayer, InputLayer
from omlt.neuralnet.network_definition import NetworkDefinition
-import warnings
def _compute_gcn_norm(A):
diff --git a/src/omlt/linear_tree/__init__.py b/src/omlt/linear_tree/__init__.py
index b08e2684..2f89a669 100644
--- a/src/omlt/linear_tree/__init__.py
+++ b/src/omlt/linear_tree/__init__.py
@@ -17,8 +17,9 @@
b_{\ell} &:= \text{Bias term learned by the tree for leaf } \ell \in L\\
\end{align*}
"""
+
+from omlt.linear_tree.lt_definition import LinearTreeDefinition
from omlt.linear_tree.lt_formulation import (
LinearTreeGDPFormulation,
LinearTreeHybridBigMFormulation,
)
-from omlt.linear_tree.lt_definition import LinearTreeDefinition
diff --git a/src/omlt/linear_tree/lt_definition.py b/src/omlt/linear_tree/lt_definition.py
index e45274fd..6bd26c8f 100644
--- a/src/omlt/linear_tree/lt_definition.py
+++ b/src/omlt/linear_tree/lt_definition.py
@@ -1,5 +1,5 @@
-import numpy as np
import lineartree
+import numpy as np
class LinearTreeDefinition:
diff --git a/src/omlt/neuralnet/__init__.py b/src/omlt/neuralnet/__init__.py
index 9d8e8cf2..2b66fc97 100644
--- a/src/omlt/neuralnet/__init__.py
+++ b/src/omlt/neuralnet/__init__.py
@@ -15,6 +15,7 @@
where :math:`\mathbf z^{(0)}` is the output of `InputLayer`, :math:`\hat{\mathbf z}^{(l)}` is the pre-activation output of :math:`l`-th layer, :math:`\mathbf z^{(l)}` is the post-activation output of :math:`l`-th layer.
"""
+
from omlt.neuralnet.network_definition import NetworkDefinition
from omlt.neuralnet.nn_formulation import (
FullSpaceNNFormulation,
diff --git a/src/omlt/neuralnet/activations/__init__.py b/src/omlt/neuralnet/activations/__init__.py
index fcae4cc8..7918d9f1 100644
--- a/src/omlt/neuralnet/activations/__init__.py
+++ b/src/omlt/neuralnet/activations/__init__.py
@@ -2,6 +2,7 @@
Since all activation functions are element-wised, we only consider how to formulate activation functions for a single neuron, where :math:`x` denotes pre-activation variable, and :math:`y` denotes post-activation variable.
"""
+
from .linear import linear_activation_constraint, linear_activation_function
from .relu import ComplementarityReLUActivation, bigm_relu_activation_constraint
from .smooth import (
diff --git a/src/omlt/neuralnet/layers/__init__.py b/src/omlt/neuralnet/layers/__init__.py
index aa4944fd..f3699c3d 100644
--- a/src/omlt/neuralnet/layers/__init__.py
+++ b/src/omlt/neuralnet/layers/__init__.py
@@ -13,5 +13,6 @@
\end{align*}
"""
+
from .full_space import full_space_conv2d_layer, full_space_dense_layer
from .reduced_space import reduced_space_dense_layer
diff --git a/src/omlt/neuralnet/nn_formulation.py b/src/omlt/neuralnet/nn_formulation.py
index 1ff4d4fb..72d5986b 100644
--- a/src/omlt/neuralnet/nn_formulation.py
+++ b/src/omlt/neuralnet/nn_formulation.py
@@ -21,15 +21,15 @@
from omlt.neuralnet.layer import (
ConvLayer2D,
DenseLayer,
+ GNNLayer,
InputLayer,
PoolingLayer2D,
- GNNLayer,
)
from omlt.neuralnet.layers.full_space import (
full_space_conv2d_layer,
full_space_dense_layer,
- full_space_maxpool2d_layer,
full_space_gnn_layer,
+ full_space_maxpool2d_layer,
)
from omlt.neuralnet.layers.partition_based import (
default_partition_split_func,
diff --git a/tests/io/test_keras_reader.py b/tests/io/test_keras_reader.py
index d47b0920..21629c66 100644
--- a/tests/io/test_keras_reader.py
+++ b/tests/io/test_keras_reader.py
@@ -10,7 +10,7 @@
not keras_available, reason="Test only valid when keras is available"
)
def test_keras_reader(datadir):
- nn = keras.models.load_model(datadir.file("keras_linear_131"), compile=False)
+ nn = keras.models.load_model(datadir.file("keras_linear_131.keras"), compile=False)
net = load_keras_sequential(nn)
layers = list(net.layers)
@@ -21,7 +21,7 @@ def test_keras_reader(datadir):
assert layers[2].weights.shape == (3, 1)
nn = keras.models.load_model(
- datadir.file("keras_linear_131_sigmoid"), compile=False
+ datadir.file("keras_linear_131_sigmoid.keras"), compile=False
)
net = load_keras_sequential(nn)
layers = list(net.layers)
@@ -32,7 +32,7 @@ def test_keras_reader(datadir):
assert layers[2].weights.shape == (3, 1)
nn = keras.models.load_model(
- datadir.file("keras_linear_131_sigmoid_output_activation"), compile=False
+ datadir.file("keras_linear_131_sigmoid_output_activation.keras"), compile=False
)
net = load_keras_sequential(nn)
layers = list(net.layers)
@@ -42,7 +42,7 @@ def test_keras_reader(datadir):
assert layers[1].weights.shape == (1, 3)
assert layers[2].weights.shape == (3, 1)
- nn = keras.models.load_model(datadir.file("big"), compile=False)
+ nn = keras.models.load_model(datadir.file("big.keras"), compile=False)
net = load_keras_sequential(nn)
layers = list(net.layers)
assert len(layers) == 5
diff --git a/tests/io/test_torch_geometric.py b/tests/io/test_torch_geometric.py
index d80d176b..9cf6905f 100644
--- a/tests/io/test_torch_geometric.py
+++ b/tests/io/test_torch_geometric.py
@@ -1,8 +1,8 @@
-import pytest
import numpy as np
import pyomo.environ as pyo
-from omlt import OmltBlock
+import pytest
+from omlt import OmltBlock
from omlt.dependencies import (
torch,
torch_available,
@@ -12,12 +12,19 @@
if torch_available and torch_geometric_available:
from torch.nn import Linear, ReLU, Sigmoid, Softplus, Tanh
- from torch_geometric.nn import Sequential, GCNConv, SAGEConv
- from torch_geometric.nn import global_mean_pool, global_add_pool, global_max_pool
+ from torch_geometric.nn import (
+ GCNConv,
+ SAGEConv,
+ Sequential,
+ global_add_pool,
+ global_max_pool,
+ global_mean_pool,
+ )
+
from omlt.io.torch_geometric import (
- load_torch_geometric_sequential,
gnn_with_fixed_graph,
gnn_with_non_fixed_graph,
+ load_torch_geometric_sequential,
)
diff --git a/tests/linear_tree/test_lt_formulation.py b/tests/linear_tree/test_lt_formulation.py
index 0b1fc59d..28f6f873 100644
--- a/tests/linear_tree/test_lt_formulation.py
+++ b/tests/linear_tree/test_lt_formulation.py
@@ -1,9 +1,9 @@
import numpy as np
import pyomo.environ as pe
import pytest
+from pytest import approx
from omlt.dependencies import lineartree_available
-from pytest import approx
if lineartree_available:
from lineartree import LinearTreeRegressor
@@ -14,8 +14,8 @@
LinearTreeDefinition,
)
-from omlt import OmltBlock
import omlt
+from omlt import OmltBlock
scip_available = pe.SolverFactory("scip").available()
cbc_available = pe.SolverFactory("cbc").available()
diff --git a/tests/models/big/variables/variables.data-00000-of-00001 b/tests/models/big.keras
similarity index 88%
rename from tests/models/big/variables/variables.data-00000-of-00001
rename to tests/models/big.keras
index 2e6b0b39..f2cfdec1 100644
Binary files a/tests/models/big/variables/variables.data-00000-of-00001 and b/tests/models/big.keras differ
diff --git a/tests/models/big/saved_model.pb b/tests/models/big/saved_model.pb
deleted file mode 100644
index ccc1d6bd..00000000
Binary files a/tests/models/big/saved_model.pb and /dev/null differ
diff --git a/tests/models/big/variables/variables.index b/tests/models/big/variables/variables.index
deleted file mode 100644
index bc9e0cb5..00000000
Binary files a/tests/models/big/variables/variables.index and /dev/null differ
diff --git a/tests/models/keras_linear_131.keras b/tests/models/keras_linear_131.keras
new file mode 100644
index 00000000..e4f42efe
Binary files /dev/null and b/tests/models/keras_linear_131.keras differ
diff --git a/tests/models/keras_linear_131/saved_model.pb b/tests/models/keras_linear_131/saved_model.pb
deleted file mode 100644
index 4723d34f..00000000
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diff --git a/tests/models/keras_linear_131/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_131/variables/variables.data-00000-of-00001
deleted file mode 100644
index 416691ef..00000000
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diff --git a/tests/models/keras_linear_131/variables/variables.index b/tests/models/keras_linear_131/variables/variables.index
deleted file mode 100644
index c780e89f..00000000
Binary files a/tests/models/keras_linear_131/variables/variables.index and /dev/null differ
diff --git a/tests/models/keras_linear_131_relu.keras b/tests/models/keras_linear_131_relu.keras
new file mode 100644
index 00000000..08053262
Binary files /dev/null and b/tests/models/keras_linear_131_relu.keras differ
diff --git a/tests/models/keras_linear_131_relu/saved_model.pb b/tests/models/keras_linear_131_relu/saved_model.pb
deleted file mode 100644
index c2a499b4..00000000
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diff --git a/tests/models/keras_linear_131_relu/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_131_relu/variables/variables.data-00000-of-00001
deleted file mode 100644
index b642e071..00000000
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diff --git a/tests/models/keras_linear_131_relu/variables/variables.index b/tests/models/keras_linear_131_relu/variables/variables.index
deleted file mode 100644
index b543c030..00000000
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diff --git a/tests/models/keras_linear_131_relu_output_activation.keras b/tests/models/keras_linear_131_relu_output_activation.keras
new file mode 100644
index 00000000..004cef20
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diff --git a/tests/models/keras_linear_131_relu_output_activation/saved_model.pb b/tests/models/keras_linear_131_relu_output_activation/saved_model.pb
deleted file mode 100644
index 68cd37a6..00000000
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diff --git a/tests/models/keras_linear_131_relu_output_activation/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_131_relu_output_activation/variables/variables.data-00000-of-00001
deleted file mode 100644
index 487fb326..00000000
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diff --git a/tests/models/keras_linear_131_relu_output_activation/variables/variables.index b/tests/models/keras_linear_131_relu_output_activation/variables/variables.index
deleted file mode 100644
index 624177b6..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid.keras b/tests/models/keras_linear_131_sigmoid.keras
new file mode 100644
index 00000000..7fcd47a9
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diff --git a/tests/models/keras_linear_131_sigmoid/saved_model.pb b/tests/models/keras_linear_131_sigmoid/saved_model.pb
deleted file mode 100644
index 7d9fbb80..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_131_sigmoid/variables/variables.data-00000-of-00001
deleted file mode 100644
index f75e1d1c..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid/variables/variables.index b/tests/models/keras_linear_131_sigmoid/variables/variables.index
deleted file mode 100644
index 6ccd6488..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid_output_activation.keras b/tests/models/keras_linear_131_sigmoid_output_activation.keras
new file mode 100644
index 00000000..4f0ccd55
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diff --git a/tests/models/keras_linear_131_sigmoid_output_activation/saved_model.pb b/tests/models/keras_linear_131_sigmoid_output_activation/saved_model.pb
deleted file mode 100644
index 10363f25..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid_output_activation/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_131_sigmoid_output_activation/variables/variables.data-00000-of-00001
deleted file mode 100644
index e4924b6b..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid_output_activation/variables/variables.index b/tests/models/keras_linear_131_sigmoid_output_activation/variables/variables.index
deleted file mode 100644
index e5be42a8..00000000
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diff --git a/tests/models/keras_linear_131_sigmoid_softplus_output_activation.keras b/tests/models/keras_linear_131_sigmoid_softplus_output_activation.keras
new file mode 100644
index 00000000..ac11e8d2
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diff --git a/tests/models/keras_linear_131_sigmoid_softplus_output_activation/saved_model.pb b/tests/models/keras_linear_131_sigmoid_softplus_output_activation/saved_model.pb
deleted file mode 100644
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deleted file mode 100644
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diff --git a/tests/models/keras_linear_131_sigmoid_softplus_output_activation/variables/variables.index b/tests/models/keras_linear_131_sigmoid_softplus_output_activation/variables/variables.index
deleted file mode 100644
index d5e373d1..00000000
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diff --git a/tests/models/keras_linear_2353.keras b/tests/models/keras_linear_2353.keras
new file mode 100644
index 00000000..1e776330
Binary files /dev/null and b/tests/models/keras_linear_2353.keras differ
diff --git a/tests/models/keras_linear_2353/saved_model.pb b/tests/models/keras_linear_2353/saved_model.pb
deleted file mode 100644
index d37afada..00000000
Binary files a/tests/models/keras_linear_2353/saved_model.pb and /dev/null differ
diff --git a/tests/models/keras_linear_2353/variables/variables.data-00000-of-00001 b/tests/models/keras_linear_2353/variables/variables.data-00000-of-00001
deleted file mode 100644
index bce02613..00000000
Binary files a/tests/models/keras_linear_2353/variables/variables.data-00000-of-00001 and /dev/null differ
diff --git a/tests/models/keras_linear_2353/variables/variables.index b/tests/models/keras_linear_2353/variables/variables.index
deleted file mode 100644
index 14c98f13..00000000
Binary files a/tests/models/keras_linear_2353/variables/variables.index and /dev/null differ
diff --git a/tests/neuralnet/test_keras.py b/tests/neuralnet/test_keras.py
index 66b8a91b..02da81aa 100644
--- a/tests/neuralnet/test_keras.py
+++ b/tests/neuralnet/test_keras.py
@@ -108,27 +108,29 @@ def _test_keras_linear_big(keras_fname, reduced_space=False):
@pytest.mark.skipif(not keras_available, reason="Need keras for this test")
def test_keras_linear_131_full(datadir):
- _test_keras_linear_131(datadir.file("keras_linear_131"))
- _test_keras_linear_131(datadir.file("keras_linear_131_sigmoid"))
- _test_keras_linear_131(datadir.file("keras_linear_131_sigmoid_output_activation"))
+ _test_keras_linear_131(datadir.file("keras_linear_131.keras"))
+ _test_keras_linear_131(datadir.file("keras_linear_131_sigmoid.keras"))
_test_keras_linear_131(
- datadir.file("keras_linear_131_sigmoid_softplus_output_activation")
+ datadir.file("keras_linear_131_sigmoid_output_activation.keras")
+ )
+ _test_keras_linear_131(
+ datadir.file("keras_linear_131_sigmoid_softplus_output_activation.keras")
)
@pytest.mark.skipif(not keras_available, reason="Need keras for this test")
def test_keras_linear_131_reduced(datadir):
- _test_keras_linear_131(datadir.file("keras_linear_131"), reduced_space=True)
+ _test_keras_linear_131(datadir.file("keras_linear_131.keras"), reduced_space=True)
_test_keras_linear_131(
- datadir.file("keras_linear_131_sigmoid"),
+ datadir.file("keras_linear_131_sigmoid.keras"),
reduced_space=True,
)
_test_keras_linear_131(
- datadir.file("keras_linear_131_sigmoid_output_activation"),
+ datadir.file("keras_linear_131_sigmoid_output_activation.keras"),
reduced_space=True,
)
_test_keras_linear_131(
- datadir.file("keras_linear_131_sigmoid_softplus_output_activation"),
+ datadir.file("keras_linear_131_sigmoid_softplus_output_activation.keras"),
reduced_space=True,
)
@@ -136,26 +138,26 @@ def test_keras_linear_131_reduced(datadir):
@pytest.mark.skipif(not keras_available, reason="Need keras for this test")
def test_keras_linear_131_relu(datadir):
_test_keras_mip_relu_131(
- datadir.file("keras_linear_131_relu"),
+ datadir.file("keras_linear_131_relu.keras"),
)
_test_keras_complementarity_relu_131(
- datadir.file("keras_linear_131_relu"),
+ datadir.file("keras_linear_131_relu.keras"),
)
@pytest.mark.skipif(not keras_available, reason="Need keras for this test")
def test_keras_linear_big(datadir):
- _test_keras_linear_big(datadir.file("big"), reduced_space=False)
+ _test_keras_linear_big(datadir.file("big.keras"), reduced_space=False)
@pytest.mark.skip("Skip - this test is too big for now")
def test_keras_linear_big_reduced_space(datadir):
- _test_keras_linear_big("./models/big", reduced_space=True)
+ _test_keras_linear_big("./models/big.keras", reduced_space=True)
@pytest.mark.skipif(not keras_available, reason="Need keras for this test")
def test_scaling_NN_block(datadir):
- NN = keras.models.load_model(datadir.file("keras_linear_131_relu"))
+ NN = keras.models.load_model(datadir.file("keras_linear_131_relu.keras"))
model = pyo.ConcreteModel()
@@ -186,7 +188,7 @@ def obj(mdl):
result = pyo.SolverFactory("cbc").solve(model, tee=False)
x_s = (x - scale_x[0]) / scale_x[1]
- y_s = NN.predict(x=[x_s])
+ y_s = NN.predict([np.array((x_s,))])
y = y_s * scale_y[1] + scale_y[0]
assert y - pyo.value(model.nn.outputs[0]) <= 1e-3
diff --git a/tests/neuralnet/test_layer.py b/tests/neuralnet/test_layer.py
index 7b865faf..4a8944ac 100644
--- a/tests/neuralnet/test_layer.py
+++ b/tests/neuralnet/test_layer.py
@@ -4,10 +4,10 @@
from omlt.neuralnet.layer import (
ConvLayer2D,
DenseLayer,
+ GNNLayer,
IndexMapper,
InputLayer,
PoolingLayer2D,
- GNNLayer,
)
diff --git a/tests/neuralnet/test_nn_formulation.py b/tests/neuralnet/test_nn_formulation.py
index e8d54068..c37b641a 100644
--- a/tests/neuralnet/test_nn_formulation.py
+++ b/tests/neuralnet/test_nn_formulation.py
@@ -15,18 +15,18 @@
from omlt.neuralnet.layer import (
ConvLayer2D,
DenseLayer,
+ GNNLayer,
IndexMapper,
InputLayer,
PoolingLayer2D,
- GNNLayer,
)
from omlt.neuralnet.layers.full_space import (
- full_space_maxpool2d_layer,
_input_layer_and_block,
+ full_space_maxpool2d_layer,
)
from omlt.neuralnet.layers.partition_based import (
- partition_based_dense_relu_layer,
default_partition_split_func,
+ partition_based_dense_relu_layer,
)
from omlt.neuralnet.layers.reduced_space import reduced_space_dense_layer
diff --git a/tests/neuralnet/train_keras_models.py b/tests/neuralnet/train_keras_models.py
index 9bbd224c..c2de9dbc 100644
--- a/tests/neuralnet/train_keras_models.py
+++ b/tests/neuralnet/train_keras_models.py
@@ -1,11 +1,11 @@
import pytest
-import tensorflow.keras as keras
+import keras
# from conftest import get_neural_network_data
from keras.layers import Conv2D, Dense
from keras.models import Model, Sequential
from pyomo.common.fileutils import this_file_dir
-from tensorflow.keras.optimizers import Adamax
+from keras.optimizers import Adamax
from omlt.io import write_onnx_model_with_bounds
@@ -40,7 +40,7 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_131")
+ nn.save(this_file_dir() + "/models/keras_linear_131.keras")
x, y, x_test = get_neural_network_data("131")
nn = Sequential(name="keras_linear_131_sigmoid")
@@ -72,7 +72,7 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_131_sigmoid")
+ nn.save(this_file_dir() + "/models/keras_linear_131_sigmoid.keras")
x, y, x_test = get_neural_network_data("131")
nn = Sequential(name="keras_linear_131_sigmoid_output_activation")
@@ -105,7 +105,9 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_131_sigmoid_output_activation")
+ nn.save(
+ this_file_dir() + "/models/keras_linear_131_sigmoid_output_activation.keras"
+ )
x, y, x_test = get_neural_network_data("131")
nn = Sequential(name="keras_linear_131_relu")
@@ -137,7 +139,7 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_131_relu")
+ nn.save(this_file_dir() + "/models/keras_linear_131_relu.keras")
x, y, x_test = get_neural_network_data("131")
nn = Sequential(name="keras_linear_131_relu_output_activation")
@@ -170,7 +172,7 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_131_relu_output_activation")
+ nn.save(this_file_dir() + "/models/keras_linear_131_relu_output_activation.keras")
x, y, x_test = get_neural_network_data("131")
nn = Sequential(name="keras_linear_131_sigmoid_softplus_output_activation")
@@ -204,7 +206,8 @@ def train_models():
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
nn.save(
- this_file_dir() + "/models/keras_linear_131_sigmoid_softplus_output_activation"
+ this_file_dir()
+ + "/models/keras_linear_131_sigmoid_softplus_output_activation.keras"
)
x, y, x_test = get_neural_network_data("131")
@@ -263,7 +266,7 @@ def train_models():
history = nn.fit(
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/big")
+ nn.save(this_file_dir() + "/models/big.keras")
x, y, x_test = get_neural_network_data("2353")
nn = Sequential(name="keras_linear_2353")
@@ -306,7 +309,7 @@ def train_models():
x=x, y=y, validation_split=0.2, batch_size=16, verbose=1, epochs=15
)
- nn.save(this_file_dir() + "/models/keras_linear_2353")
+ nn.save(this_file_dir() + "/models/keras_linear_2353.keras")
def train_conv():
diff --git a/tox.ini b/tox.ini
index e1a56c01..e64ab1d8 100644
--- a/tox.ini
+++ b/tox.ini
@@ -4,15 +4,17 @@
[tox]
minversion = 3.15
-envlist = py36, py37, py38, py39, py310, lint
+envlist = py36, py37, py38, py39, py310, py311, py312, lint
[gh-actions]
python =
3.6: py36
3.7: py37
- 3.8: py38, lint
- 3.9: py39
+ 3.8: py38
+ 3.9: lint, py39
3.10: py310
+ 3.11: py311
+ 3.12: py312
[testenv]
deps = pytest
@@ -100,7 +102,7 @@ deps =
pep8-naming
commands =
flake8 --config=tox.ini src/omlt tests/
- black --check --diff src/omlt tests
+ black --check --diff src/omlt tests/
[testenv:format]
description = Format Python files using isort and black