notebooks/classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from torch.optim import Adam, Optimizer\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from mil_autoencoder.model.vae import VAE\n",
    "from mil_autoencoder.data.loader import load_data, create_loaders\n",
    "from mil_autoencoder.data.transformers import simple_transform\n",
    "from mil_autoencoder.paths import DATA, TRAINED_MODELS\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import typing as tp\n",
    "from pathlib import Path\n",
    "\n",
    "class ClassificationHead(nn.Module):\n",
    "    def __init__(self, encoder: nn.Module, latent_size: int, start_dim: int = 32) -> None:\n",
    "        super().__init__()\n",
    "        \n",
    "        self.encoder = encoder\n",
    "        self.encoder.requires_grad_(False)\n",
    "        self.num_of_classes = 10\n",
    "        self.head = self.build_head(latent_size, start_dim)\n",
    "    \n",
    "    def build_head(self, latent_size: int, start_size: int = 32) -> nn.Module:\n",
    "        modules = []\n",
    "        dims = [start_size]\n",
    "        start_pow = len(f\"{start_size - 1:b}\") - 1\n",
    "        dims.extend([2 ** i for i in range(start_pow, 3, -1)])\n",
    "        prev_dim = latent_size\n",
    "\n",
    "        for dim in dims:\n",
    "            modules.append(\n",
    "                nn.Sequential(\n",
    "                    nn.Linear(prev_dim, dim),\n",
    "                    nn.BatchNorm1d(dim),\n",
    "                    nn.ReLU(),\n",
    "                ),\n",
    "            )\n",
    "            prev_dim = dim\n",
    "        \n",
    "        modules.append(nn.Linear(prev_dim, self.num_of_classes))\n",
    "        return nn.Sequential(*modules)\n",
    "\n",
    "    def forward(self, img: torch.Tensor):\n",
    "        latent_repr, _ = self.encoder(img)\n",
    "        return self.head(latent_repr)\n",
    "    \n",
    "    def unfreeze_encoder(self):\n",
    "        self.encoder.requires_grad_(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae_path = TRAINED_MODELS / \"smaller_vae_state_dict50\"\n",
    "vae_params = {\n",
    "    \"img_size\": 32,\n",
    "    \"downsamplings\": 3,\n",
    "    \"latent_size\": 64,\n",
    "    \"linear_hidden_size\": 128,\n",
    "    \"down_channels\": 4,\n",
    "    \"up_channels\": 4,\n",
    "}\n",
    "\n",
    "vae = VAE(**vae_params)\n",
    "vae.load_state_dict(torch.load(vae_path, map_location=torch.device('cpu')))\n",
    "\n",
    "model = ClassificationHead(encoder=vae.encoder, latent_size=vae_params[\"latent_size\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "trainset, testset, classes = load_data(DATA, simple_transform(), simple_transform())\n",
    "trainloader, testloader = create_loaders(\n",
    "    trainset,\n",
    "    testset,\n",
    "    train_batch=32,\n",
    "    test_batch=32,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(\n",
    "    model: nn.Module,\n",
    "    optim: Optimizer,\n",
    "    loss_func: tp.Callable[[torch.Tensor, torch.Tensor], torch.Tensor],\n",
    "    epochs: int,\n",
    "    trainloader: DataLoader,\n",
    "    testloader: DataLoader,\n",
    "    labels: tp.Set[str], \n",
    "    device: torch.device):\n",
    "    \n",
    "    train_loss_history = []\n",
    "    test_loss_hitory = []\n",
    "\n",
    "    model.to(device)\n",
    "\n",
    "    for ep in range(1, epochs + 1):\n",
    "        avg_loss = 0\n",
    "        total_batches = 0\n",
    "\n",
    "        model.train()\n",
    "        for imgs, target in tqdm(trainloader, desc=f\"Train #{ep}\"):\n",
    "            total_batches += 1\n",
    "            imgs.to(device)\n",
    "            preds = model(imgs)\n",
    "            loss = loss_func(preds, target)\n",
    "            optim.zero_grad()\n",
    "            loss.backward()\n",
    "            optim.step()\n",
    "            avg_loss += loss.item()\n",
    "        \n",
    "        avg_loss /= total_batches\n",
    "        train_loss_history.append(avg_loss)\n",
    "        print(f\"Epoch #{ep} | Train Avg Loss: {avg_loss:.4f} \")\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            avg_loss = 0\n",
    "            total_batches = 0\n",
    "            preds_labels = np.array([])\n",
    "            target_labels = np.array([])\n",
    "            for imgs, target in tqdm(testloader, desc=f\"Test #{ep}\"):\n",
    "                total_batches += 1\n",
    "                imgs.to(device)\n",
    "                preds = model(imgs)\n",
    "                avg_loss += loss_func(preds, target).item()\n",
    "                preds = preds.detach().cpu().numpy()\n",
    "                batch_pred_labels = preds.argmax(axis=1)\n",
    "                preds_labels = np.concatenate((preds_labels, batch_pred_labels))\n",
    "                target_labels = np.concatenate((target_labels, target.detach().numpy()))\n",
    "            \n",
    "            avg_loss /= total_batches\n",
    "            test_loss_hitory.append(avg_loss)\n",
    "            print(f\"Epoch #{ep} | Test Avg Loss: {avg_loss:.4f} \")\n",
    "            print(classification_report(target_labels.astype(int), preds_labels.astype(int), target_names=labels))\n",
    "    \n",
    "    return train_loss_history, test_loss_hitory"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Small Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae_path = TRAINED_MODELS / \"smaller_vae_state_dict50\"\n",
    "vae_params = {\n",
    "    \"img_size\": 32,\n",
    "    \"downsamplings\": 3,\n",
    "    \"latent_size\": 64,\n",
    "    \"linear_hidden_size\": 128,\n",
    "    \"down_channels\": 4,\n",
    "    \"up_channels\": 4,\n",
    "}\n",
    "\n",
    "vae = VAE(**vae_params)\n",
    "vae.load_state_dict(torch.load(vae_path, map_location=torch.device('cpu')))\n",
    "\n",
    "model = ClassificationHead(encoder=vae.encoder, latent_size=vae_params[\"latent_size\"])\n",
    "optim = Adam(model.parameters())\n",
    "loss_function = nn.CrossEntropyLoss()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #1: 100%|██████████| 1563/1563 [00:54<00:00, 28.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Train Avg Loss: 1.8915 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #1: 100%|██████████| 313/313 [00:07<00:00, 43.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Test Avg Loss: 1.6740 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.46      0.52      0.49      1000\n",
      "         car       0.48      0.54      0.51      1000\n",
      "        bird       0.30      0.16      0.21      1000\n",
      "         cat       0.29      0.15      0.20      1000\n",
      "        deer       0.34      0.31      0.33      1000\n",
      "         dog       0.37      0.35      0.36      1000\n",
      "        frog       0.35      0.54      0.42      1000\n",
      "       horse       0.41      0.42      0.42      1000\n",
      "        ship       0.47      0.59      0.52      1000\n",
      "       truck       0.47      0.47      0.47      1000\n",
      "\n",
      "    accuracy                           0.41     10000\n",
      "   macro avg       0.39      0.41      0.39     10000\n",
      "weighted avg       0.39      0.41      0.39     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #2: 100%|██████████| 1563/1563 [00:46<00:00, 33.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Train Avg Loss: 1.6743 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #2: 100%|██████████| 313/313 [00:06<00:00, 46.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Test Avg Loss: 1.5945 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.48      0.52      0.50      1000\n",
      "         car       0.53      0.56      0.54      1000\n",
      "        bird       0.28      0.17      0.22      1000\n",
      "         cat       0.30      0.19      0.23      1000\n",
      "        deer       0.35      0.37      0.36      1000\n",
      "         dog       0.40      0.34      0.36      1000\n",
      "        frog       0.37      0.56      0.44      1000\n",
      "       horse       0.46      0.47      0.47      1000\n",
      "        ship       0.52      0.59      0.56      1000\n",
      "       truck       0.46      0.47      0.47      1000\n",
      "\n",
      "    accuracy                           0.42     10000\n",
      "   macro avg       0.41      0.42      0.41     10000\n",
      "weighted avg       0.41      0.42      0.41     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #3: 100%|██████████| 1563/1563 [00:41<00:00, 37.82it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Train Avg Loss: 1.6161 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #3: 100%|██████████| 313/313 [00:08<00:00, 37.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Test Avg Loss: 1.5558 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.50      0.56      0.53      1000\n",
      "         car       0.54      0.60      0.57      1000\n",
      "        bird       0.31      0.25      0.28      1000\n",
      "         cat       0.30      0.25      0.27      1000\n",
      "        deer       0.36      0.36      0.36      1000\n",
      "         dog       0.42      0.30      0.35      1000\n",
      "        frog       0.39      0.62      0.48      1000\n",
      "       horse       0.52      0.45      0.48      1000\n",
      "        ship       0.56      0.60      0.58      1000\n",
      "       truck       0.51      0.47      0.49      1000\n",
      "\n",
      "    accuracy                           0.45     10000\n",
      "   macro avg       0.44      0.45      0.44     10000\n",
      "weighted avg       0.44      0.45      0.44     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #4: 100%|██████████| 1563/1563 [00:45<00:00, 34.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Train Avg Loss: 1.5863 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #4: 100%|██████████| 313/313 [00:06<00:00, 50.85it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Test Avg Loss: 1.5310 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.52      0.53      1000\n",
      "         car       0.54      0.60      0.57      1000\n",
      "        bird       0.31      0.27      0.29      1000\n",
      "         cat       0.30      0.21      0.24      1000\n",
      "        deer       0.37      0.36      0.37      1000\n",
      "         dog       0.40      0.39      0.39      1000\n",
      "        frog       0.41      0.56      0.47      1000\n",
      "       horse       0.53      0.45      0.49      1000\n",
      "        ship       0.54      0.63      0.58      1000\n",
      "       truck       0.50      0.52      0.51      1000\n",
      "\n",
      "    accuracy                           0.45     10000\n",
      "   macro avg       0.44      0.45      0.44     10000\n",
      "weighted avg       0.44      0.45      0.44     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #5: 100%|██████████| 1563/1563 [00:52<00:00, 29.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Train Avg Loss: 1.5662 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #5: 100%|██████████| 313/313 [00:14<00:00, 22.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Test Avg Loss: 1.5201 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.53      0.55      1000\n",
      "         car       0.59      0.57      0.58      1000\n",
      "        bird       0.34      0.22      0.27      1000\n",
      "         cat       0.29      0.18      0.22      1000\n",
      "        deer       0.36      0.41      0.39      1000\n",
      "         dog       0.40      0.37      0.39      1000\n",
      "        frog       0.38      0.62      0.48      1000\n",
      "       horse       0.50      0.51      0.51      1000\n",
      "        ship       0.58      0.61      0.59      1000\n",
      "       truck       0.51      0.53      0.52      1000\n",
      "\n",
      "    accuracy                           0.46     10000\n",
      "   macro avg       0.45      0.46      0.45     10000\n",
      "weighted avg       0.45      0.46      0.45     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #6: 100%|██████████| 1563/1563 [01:04<00:00, 24.27it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Train Avg Loss: 1.5572 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #6: 100%|██████████| 313/313 [00:12<00:00, 25.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Test Avg Loss: 1.5119 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.55      0.55      1000\n",
      "         car       0.57      0.56      0.57      1000\n",
      "        bird       0.35      0.26      0.30      1000\n",
      "         cat       0.34      0.12      0.18      1000\n",
      "        deer       0.39      0.41      0.40      1000\n",
      "         dog       0.37      0.41      0.39      1000\n",
      "        frog       0.41      0.62      0.49      1000\n",
      "       horse       0.49      0.52      0.50      1000\n",
      "        ship       0.59      0.62      0.60      1000\n",
      "       truck       0.50      0.54      0.52      1000\n",
      "\n",
      "    accuracy                           0.46     10000\n",
      "   macro avg       0.45      0.46      0.45     10000\n",
      "weighted avg       0.45      0.46      0.45     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #7: 100%|██████████| 1563/1563 [01:47<00:00, 14.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Train Avg Loss: 1.5434 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #7: 100%|██████████| 313/313 [00:08<00:00, 37.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Test Avg Loss: 1.5037 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.55      0.55      1000\n",
      "         car       0.56      0.56      0.56      1000\n",
      "        bird       0.35      0.24      0.29      1000\n",
      "         cat       0.33      0.21      0.26      1000\n",
      "        deer       0.36      0.45      0.40      1000\n",
      "         dog       0.38      0.36      0.37      1000\n",
      "        frog       0.43      0.57      0.49      1000\n",
      "       horse       0.47      0.52      0.50      1000\n",
      "        ship       0.56      0.64      0.59      1000\n",
      "       truck       0.54      0.48      0.51      1000\n",
      "\n",
      "    accuracy                           0.46     10000\n",
      "   macro avg       0.45      0.46      0.45     10000\n",
      "weighted avg       0.45      0.46      0.45     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #8: 100%|██████████| 1563/1563 [01:13<00:00, 21.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Train Avg Loss: 1.5374 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #8: 100%|██████████| 313/313 [00:13<00:00, 23.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Test Avg Loss: 1.4914 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.53      0.54      1000\n",
      "         car       0.58      0.57      0.58      1000\n",
      "        bird       0.34      0.27      0.30      1000\n",
      "         cat       0.33      0.23      0.27      1000\n",
      "        deer       0.42      0.35      0.38      1000\n",
      "         dog       0.38      0.37      0.38      1000\n",
      "        frog       0.42      0.65      0.51      1000\n",
      "       horse       0.48      0.54      0.51      1000\n",
      "        ship       0.56      0.64      0.60      1000\n",
      "       truck       0.53      0.51      0.52      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #9: 100%|██████████| 1563/1563 [00:48<00:00, 32.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Train Avg Loss: 1.5306 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #9: 100%|██████████| 313/313 [00:08<00:00, 38.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Test Avg Loss: 1.4923 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.54      0.54      1000\n",
      "         car       0.58      0.58      0.58      1000\n",
      "        bird       0.35      0.27      0.30      1000\n",
      "         cat       0.33      0.19      0.24      1000\n",
      "        deer       0.40      0.40      0.40      1000\n",
      "         dog       0.39      0.41      0.40      1000\n",
      "        frog       0.42      0.64      0.51      1000\n",
      "       horse       0.49      0.53      0.51      1000\n",
      "        ship       0.57      0.63      0.60      1000\n",
      "       truck       0.55      0.48      0.51      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #10: 100%|██████████| 1563/1563 [00:48<00:00, 32.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Train Avg Loss: 1.5267 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #10: 100%|██████████| 313/313 [00:06<00:00, 46.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Test Avg Loss: 1.4859 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.49      0.53      1000\n",
      "         car       0.57      0.58      0.58      1000\n",
      "        bird       0.32      0.33      0.33      1000\n",
      "         cat       0.32      0.22      0.26      1000\n",
      "        deer       0.42      0.32      0.36      1000\n",
      "         dog       0.38      0.41      0.39      1000\n",
      "        frog       0.45      0.60      0.51      1000\n",
      "       horse       0.48      0.54      0.51      1000\n",
      "        ship       0.60      0.60      0.60      1000\n",
      "       truck       0.51      0.56      0.53      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #11: 100%|██████████| 1563/1563 [00:43<00:00, 35.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #11 | Train Avg Loss: 1.5239 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #11: 100%|██████████| 313/313 [00:08<00:00, 36.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #11 | Test Avg Loss: 1.4792 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.55      0.54      1000\n",
      "         car       0.56      0.61      0.59      1000\n",
      "        bird       0.34      0.28      0.31      1000\n",
      "         cat       0.34      0.22      0.27      1000\n",
      "        deer       0.41      0.40      0.40      1000\n",
      "         dog       0.40      0.38      0.39      1000\n",
      "        frog       0.45      0.60      0.51      1000\n",
      "       horse       0.49      0.53      0.51      1000\n",
      "        ship       0.57      0.64      0.60      1000\n",
      "       truck       0.52      0.51      0.52      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #12: 100%|██████████| 1563/1563 [00:46<00:00, 33.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #12 | Train Avg Loss: 1.5191 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #12: 100%|██████████| 313/313 [00:08<00:00, 37.09it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #12 | Test Avg Loss: 1.4818 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.53      0.54      1000\n",
      "         car       0.60      0.55      0.57      1000\n",
      "        bird       0.35      0.29      0.32      1000\n",
      "         cat       0.34      0.21      0.26      1000\n",
      "        deer       0.39      0.41      0.40      1000\n",
      "         dog       0.40      0.40      0.40      1000\n",
      "        frog       0.44      0.58      0.50      1000\n",
      "       horse       0.49      0.55      0.51      1000\n",
      "        ship       0.55      0.66      0.60      1000\n",
      "       truck       0.53      0.53      0.53      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #13: 100%|██████████| 1563/1563 [00:44<00:00, 35.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #13 | Train Avg Loss: 1.5129 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #13: 100%|██████████| 313/313 [00:07<00:00, 43.84it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #13 | Test Avg Loss: 1.4807 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.60      0.46      0.52      1000\n",
      "         car       0.56      0.62      0.59      1000\n",
      "        bird       0.36      0.27      0.31      1000\n",
      "         cat       0.32      0.28      0.30      1000\n",
      "        deer       0.41      0.35      0.38      1000\n",
      "         dog       0.40      0.35      0.37      1000\n",
      "        frog       0.43      0.63      0.51      1000\n",
      "       horse       0.48      0.57      0.52      1000\n",
      "        ship       0.55      0.65      0.60      1000\n",
      "       truck       0.54      0.50      0.52      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.46     10000\n",
      "weighted avg       0.47      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #14: 100%|██████████| 1563/1563 [00:49<00:00, 31.63it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #14 | Train Avg Loss: 1.5147 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #14: 100%|██████████| 313/313 [00:07<00:00, 44.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #14 | Test Avg Loss: 1.4754 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.57      0.55      1000\n",
      "         car       0.57      0.61      0.59      1000\n",
      "        bird       0.36      0.28      0.32      1000\n",
      "         cat       0.33      0.24      0.28      1000\n",
      "        deer       0.41      0.35      0.38      1000\n",
      "         dog       0.46      0.30      0.37      1000\n",
      "        frog       0.41      0.66      0.51      1000\n",
      "       horse       0.48      0.54      0.51      1000\n",
      "        ship       0.56      0.66      0.61      1000\n",
      "       truck       0.54      0.49      0.52      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.46     10000\n",
      "weighted avg       0.47      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #15: 100%|██████████| 1563/1563 [00:45<00:00, 34.21it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #15 | Train Avg Loss: 1.5151 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #15: 100%|██████████| 313/313 [00:07<00:00, 42.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #15 | Test Avg Loss: 1.4696 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.48      0.53      1000\n",
      "         car       0.59      0.60      0.60      1000\n",
      "        bird       0.36      0.28      0.32      1000\n",
      "         cat       0.34      0.20      0.25      1000\n",
      "        deer       0.41      0.38      0.39      1000\n",
      "         dog       0.38      0.44      0.41      1000\n",
      "        frog       0.44      0.62      0.51      1000\n",
      "       horse       0.51      0.53      0.52      1000\n",
      "        ship       0.55      0.67      0.61      1000\n",
      "       truck       0.54      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #16: 100%|██████████| 1563/1563 [00:45<00:00, 34.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #16 | Train Avg Loss: 1.5051 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #16: 100%|██████████| 313/313 [00:07<00:00, 44.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #16 | Test Avg Loss: 1.4685 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.53      0.56      1000\n",
      "         car       0.58      0.60      0.59      1000\n",
      "        bird       0.34      0.31      0.32      1000\n",
      "         cat       0.35      0.20      0.26      1000\n",
      "        deer       0.38      0.41      0.39      1000\n",
      "         dog       0.43      0.38      0.41      1000\n",
      "        frog       0.44      0.62      0.51      1000\n",
      "       horse       0.49      0.55      0.52      1000\n",
      "        ship       0.58      0.63      0.61      1000\n",
      "       truck       0.54      0.54      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #17: 100%|██████████| 1563/1563 [00:46<00:00, 33.59it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #17 | Train Avg Loss: 1.5086 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #17: 100%|██████████| 313/313 [00:08<00:00, 36.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #17 | Test Avg Loss: 1.4662 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.55      0.55      1000\n",
      "         car       0.55      0.65      0.59      1000\n",
      "        bird       0.37      0.20      0.26      1000\n",
      "         cat       0.34      0.26      0.29      1000\n",
      "        deer       0.36      0.46      0.40      1000\n",
      "         dog       0.43      0.34      0.38      1000\n",
      "        frog       0.43      0.63      0.51      1000\n",
      "       horse       0.53      0.49      0.51      1000\n",
      "        ship       0.56      0.65      0.60      1000\n",
      "       truck       0.55      0.50      0.52      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.46     10000\n",
      "weighted avg       0.47      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #18: 100%|██████████| 1563/1563 [00:44<00:00, 34.81it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #18 | Train Avg Loss: 1.5016 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #18: 100%|██████████| 313/313 [00:08<00:00, 37.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #18 | Test Avg Loss: 1.4621 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.51      0.55      1000\n",
      "         car       0.54      0.65      0.59      1000\n",
      "        bird       0.34      0.33      0.33      1000\n",
      "         cat       0.34      0.18      0.23      1000\n",
      "        deer       0.40      0.38      0.39      1000\n",
      "         dog       0.40      0.42      0.41      1000\n",
      "        frog       0.44      0.64      0.52      1000\n",
      "       horse       0.54      0.49      0.52      1000\n",
      "        ship       0.59      0.65      0.62      1000\n",
      "       truck       0.54      0.52      0.53      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #19: 100%|██████████| 1563/1563 [00:43<00:00, 35.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #19 | Train Avg Loss: 1.5052 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #19: 100%|██████████| 313/313 [00:08<00:00, 38.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #19 | Test Avg Loss: 1.4685 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.50      0.54      1000\n",
      "         car       0.58      0.60      0.59      1000\n",
      "        bird       0.34      0.29      0.31      1000\n",
      "         cat       0.32      0.30      0.31      1000\n",
      "        deer       0.38      0.42      0.40      1000\n",
      "         dog       0.45      0.31      0.37      1000\n",
      "        frog       0.44      0.58      0.50      1000\n",
      "       horse       0.50      0.54      0.52      1000\n",
      "        ship       0.58      0.64      0.61      1000\n",
      "       truck       0.56      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.47     10000\n",
      "weighted avg       0.47      0.47      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #20: 100%|██████████| 1563/1563 [00:43<00:00, 35.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #20 | Train Avg Loss: 1.5019 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #20: 100%|██████████| 313/313 [00:08<00:00, 38.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #20 | Test Avg Loss: 1.4621 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.53      0.56      1000\n",
      "         car       0.57      0.61      0.59      1000\n",
      "        bird       0.36      0.28      0.32      1000\n",
      "         cat       0.33      0.23      0.27      1000\n",
      "        deer       0.38      0.45      0.42      1000\n",
      "         dog       0.42      0.36      0.38      1000\n",
      "        frog       0.44      0.63      0.52      1000\n",
      "       horse       0.54      0.51      0.52      1000\n",
      "        ship       0.57      0.67      0.61      1000\n",
      "       truck       0.54      0.50      0.52      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #21: 100%|██████████| 1563/1563 [00:44<00:00, 35.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #21 | Train Avg Loss: 1.4985 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #21: 100%|██████████| 313/313 [00:08<00:00, 36.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #21 | Test Avg Loss: 1.4654 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.60      0.57      1000\n",
      "         car       0.58      0.60      0.59      1000\n",
      "        bird       0.33      0.32      0.32      1000\n",
      "         cat       0.34      0.25      0.29      1000\n",
      "        deer       0.38      0.43      0.41      1000\n",
      "         dog       0.44      0.34      0.39      1000\n",
      "        frog       0.45      0.59      0.51      1000\n",
      "       horse       0.49      0.53      0.51      1000\n",
      "        ship       0.61      0.60      0.61      1000\n",
      "       truck       0.56      0.50      0.53      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #22: 100%|██████████| 1563/1563 [00:45<00:00, 34.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #22 | Train Avg Loss: 1.4997 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #22: 100%|██████████| 313/313 [00:09<00:00, 32.10it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #22 | Test Avg Loss: 1.4595 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.61      0.57      1000\n",
      "         car       0.54      0.65      0.59      1000\n",
      "        bird       0.39      0.22      0.28      1000\n",
      "         cat       0.32      0.30      0.31      1000\n",
      "        deer       0.37      0.47      0.41      1000\n",
      "         dog       0.45      0.31      0.37      1000\n",
      "        frog       0.45      0.60      0.52      1000\n",
      "       horse       0.52      0.52      0.52      1000\n",
      "        ship       0.61      0.60      0.61      1000\n",
      "       truck       0.55      0.49      0.52      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.47     10000\n",
      "weighted avg       0.48      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #23: 100%|██████████| 1563/1563 [00:44<00:00, 35.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #23 | Train Avg Loss: 1.4965 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #23: 100%|██████████| 313/313 [00:07<00:00, 43.98it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #23 | Test Avg Loss: 1.4576 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.57      0.57      1000\n",
      "         car       0.61      0.54      0.58      1000\n",
      "        bird       0.36      0.27      0.31      1000\n",
      "         cat       0.32      0.24      0.28      1000\n",
      "        deer       0.39      0.41      0.40      1000\n",
      "         dog       0.39      0.44      0.42      1000\n",
      "        frog       0.45      0.60      0.52      1000\n",
      "       horse       0.52      0.52      0.52      1000\n",
      "        ship       0.60      0.61      0.61      1000\n",
      "       truck       0.52      0.56      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #24: 100%|██████████| 1563/1563 [00:49<00:00, 31.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #24 | Train Avg Loss: 1.4994 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #24: 100%|██████████| 313/313 [00:07<00:00, 43.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #24 | Test Avg Loss: 1.4617 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.53      0.58      0.56      1000\n",
      "         car       0.56      0.60      0.58      1000\n",
      "        bird       0.35      0.30      0.33      1000\n",
      "         cat       0.34      0.25      0.29      1000\n",
      "        deer       0.39      0.42      0.40      1000\n",
      "         dog       0.45      0.33      0.38      1000\n",
      "        frog       0.44      0.65      0.53      1000\n",
      "       horse       0.56      0.47      0.51      1000\n",
      "        ship       0.59      0.63      0.61      1000\n",
      "       truck       0.54      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #25: 100%|██████████| 1563/1563 [00:45<00:00, 34.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #25 | Train Avg Loss: 1.4926 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #25: 100%|██████████| 313/313 [00:08<00:00, 37.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #25 | Test Avg Loss: 1.4593 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.56      0.56      1000\n",
      "         car       0.57      0.61      0.59      1000\n",
      "        bird       0.35      0.28      0.31      1000\n",
      "         cat       0.35      0.22      0.27      1000\n",
      "        deer       0.39      0.40      0.40      1000\n",
      "         dog       0.45      0.36      0.40      1000\n",
      "        frog       0.41      0.69      0.51      1000\n",
      "       horse       0.52      0.54      0.53      1000\n",
      "        ship       0.55      0.63      0.59      1000\n",
      "       truck       0.56      0.48      0.51      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_loss, test_loss = train(\n",
    "    model,\n",
    "    optim,\n",
    "    loss_function,\n",
    "    epochs=25,\n",
    "    device=device,\n",
    "    trainloader=trainloader,\n",
    "    testloader=testloader,\n",
    "    labels=classes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss, label=\"Avg train loss\")\n",
    "plt.plot(test_loss, label=\"Avg test loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #1: 100%|██████████| 3125/3125 [01:04<00:00, 48.47it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Train Avg Loss: 1.5538 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #1: 100%|██████████| 313/313 [00:07<00:00, 42.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Test Avg Loss: 1.4517 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.54      0.56      1000\n",
      "         car       0.56      0.61      0.59      1000\n",
      "        bird       0.36      0.28      0.31      1000\n",
      "         cat       0.34      0.25      0.29      1000\n",
      "        deer       0.40      0.39      0.39      1000\n",
      "         dog       0.43      0.37      0.40      1000\n",
      "        frog       0.42      0.66      0.52      1000\n",
      "       horse       0.53      0.53      0.53      1000\n",
      "        ship       0.60      0.61      0.60      1000\n",
      "       truck       0.53      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #2: 100%|██████████| 3125/3125 [00:58<00:00, 53.73it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Train Avg Loss: 1.5501 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #2: 100%|██████████| 313/313 [00:06<00:00, 45.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Test Avg Loss: 1.4553 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.52      0.55      1000\n",
      "         car       0.62      0.56      0.59      1000\n",
      "        bird       0.36      0.25      0.29      1000\n",
      "         cat       0.32      0.30      0.31      1000\n",
      "        deer       0.38      0.47      0.42      1000\n",
      "         dog       0.44      0.34      0.39      1000\n",
      "        frog       0.46      0.61      0.52      1000\n",
      "       horse       0.50      0.55      0.52      1000\n",
      "        ship       0.59      0.64      0.61      1000\n",
      "       truck       0.53      0.58      0.56      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #3: 100%|██████████| 3125/3125 [00:57<00:00, 53.94it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Train Avg Loss: 1.5467 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #3: 100%|██████████| 313/313 [00:07<00:00, 42.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Test Avg Loss: 1.4562 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.53      0.56      1000\n",
      "         car       0.61      0.58      0.59      1000\n",
      "        bird       0.36      0.26      0.30      1000\n",
      "         cat       0.32      0.29      0.30      1000\n",
      "        deer       0.39      0.44      0.42      1000\n",
      "         dog       0.43      0.39      0.41      1000\n",
      "        frog       0.45      0.62      0.52      1000\n",
      "       horse       0.52      0.55      0.53      1000\n",
      "        ship       0.59      0.64      0.61      1000\n",
      "       truck       0.56      0.53      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #4: 100%|██████████| 3125/3125 [01:03<00:00, 49.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Train Avg Loss: 1.5475 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #4: 100%|██████████| 313/313 [00:09<00:00, 34.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Test Avg Loss: 1.4532 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.54      0.55      1000\n",
      "         car       0.56      0.62      0.59      1000\n",
      "        bird       0.37      0.24      0.29      1000\n",
      "         cat       0.33      0.24      0.28      1000\n",
      "        deer       0.37      0.43      0.40      1000\n",
      "         dog       0.45      0.36      0.40      1000\n",
      "        frog       0.44      0.63      0.52      1000\n",
      "       horse       0.52      0.53      0.52      1000\n",
      "        ship       0.57      0.65      0.61      1000\n",
      "       truck       0.52      0.54      0.53      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #5: 100%|██████████| 3125/3125 [01:26<00:00, 36.01it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Train Avg Loss: 1.5476 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #5: 100%|██████████| 313/313 [00:08<00:00, 34.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Test Avg Loss: 1.4511 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.57      0.57      1000\n",
      "         car       0.59      0.58      0.58      1000\n",
      "        bird       0.35      0.30      0.32      1000\n",
      "         cat       0.36      0.24      0.28      1000\n",
      "        deer       0.39      0.43      0.41      1000\n",
      "         dog       0.44      0.37      0.40      1000\n",
      "        frog       0.45      0.64      0.53      1000\n",
      "       horse       0.52      0.52      0.52      1000\n",
      "        ship       0.58      0.67      0.62      1000\n",
      "       truck       0.53      0.54      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #6: 100%|██████████| 3125/3125 [01:32<00:00, 33.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Train Avg Loss: 1.5451 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #6: 100%|██████████| 313/313 [00:10<00:00, 29.80it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Test Avg Loss: 1.4555 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.55      0.56      1000\n",
      "         car       0.61      0.58      0.59      1000\n",
      "        bird       0.36      0.27      0.31      1000\n",
      "         cat       0.33      0.24      0.28      1000\n",
      "        deer       0.39      0.43      0.41      1000\n",
      "         dog       0.42      0.41      0.42      1000\n",
      "        frog       0.44      0.67      0.53      1000\n",
      "       horse       0.52      0.53      0.52      1000\n",
      "        ship       0.60      0.65      0.62      1000\n",
      "       truck       0.56      0.51      0.53      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #7: 100%|██████████| 3125/3125 [01:25<00:00, 36.64it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Train Avg Loss: 1.5472 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #7: 100%|██████████| 313/313 [00:08<00:00, 36.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Test Avg Loss: 1.4558 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.55      0.56      1000\n",
      "         car       0.61      0.57      0.59      1000\n",
      "        bird       0.35      0.30      0.32      1000\n",
      "         cat       0.34      0.24      0.28      1000\n",
      "        deer       0.38      0.45      0.41      1000\n",
      "         dog       0.44      0.37      0.40      1000\n",
      "        frog       0.43      0.62      0.51      1000\n",
      "       horse       0.55      0.50      0.52      1000\n",
      "        ship       0.55      0.65      0.60      1000\n",
      "       truck       0.55      0.52      0.53      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.47     10000\n",
      "weighted avg       0.48      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #8: 100%|██████████| 3125/3125 [01:12<00:00, 43.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Train Avg Loss: 1.5423 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #8: 100%|██████████| 313/313 [00:06<00:00, 48.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Test Avg Loss: 1.4565 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.54      0.56      1000\n",
      "         car       0.57      0.62      0.59      1000\n",
      "        bird       0.34      0.28      0.30      1000\n",
      "         cat       0.35      0.25      0.29      1000\n",
      "        deer       0.35      0.48      0.40      1000\n",
      "         dog       0.49      0.34      0.40      1000\n",
      "        frog       0.44      0.63      0.52      1000\n",
      "       horse       0.54      0.51      0.53      1000\n",
      "        ship       0.56      0.66      0.61      1000\n",
      "       truck       0.57      0.49      0.52      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.47     10000\n",
      "weighted avg       0.48      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #9: 100%|██████████| 3125/3125 [01:02<00:00, 49.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Train Avg Loss: 1.5418 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #9: 100%|██████████| 313/313 [00:07<00:00, 40.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Test Avg Loss: 1.4549 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.53      0.56      1000\n",
      "         car       0.58      0.58      0.58      1000\n",
      "        bird       0.37      0.24      0.30      1000\n",
      "         cat       0.32      0.23      0.27      1000\n",
      "        deer       0.38      0.43      0.41      1000\n",
      "         dog       0.43      0.38      0.40      1000\n",
      "        frog       0.43      0.66      0.52      1000\n",
      "       horse       0.52      0.54      0.53      1000\n",
      "        ship       0.61      0.64      0.62      1000\n",
      "       truck       0.52      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #10: 100%|██████████| 3125/3125 [00:59<00:00, 52.55it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Train Avg Loss: 1.5445 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #10: 100%|██████████| 313/313 [00:08<00:00, 36.79it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Test Avg Loss: 1.4663 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.53      0.56      1000\n",
      "         car       0.60      0.59      0.59      1000\n",
      "        bird       0.34      0.31      0.32      1000\n",
      "         cat       0.32      0.27      0.29      1000\n",
      "        deer       0.39      0.40      0.39      1000\n",
      "         dog       0.42      0.39      0.41      1000\n",
      "        frog       0.41      0.67      0.51      1000\n",
      "       horse       0.57      0.47      0.52      1000\n",
      "        ship       0.58      0.65      0.61      1000\n",
      "       truck       0.58      0.47      0.52      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.47     10000\n",
      "weighted avg       0.48      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #11:  10%|▉         | 306/3125 [00:08<01:19, 35.31it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[61], line 10\u001b[0m\n\u001b[1;32m      2\u001b[0m trainloader, testloader \u001b[39m=\u001b[39m create_loaders(\n\u001b[1;32m      3\u001b[0m     trainset,\n\u001b[1;32m      4\u001b[0m     testset,\n\u001b[1;32m      5\u001b[0m     train_batch\u001b[39m=\u001b[39m\u001b[39m16\u001b[39m,\n\u001b[1;32m      6\u001b[0m     test_batch\u001b[39m=\u001b[39m\u001b[39m32\u001b[39m,\n\u001b[1;32m      7\u001b[0m )\n\u001b[1;32m      9\u001b[0m optim \u001b[39m=\u001b[39m Adam(model\u001b[39m.\u001b[39mparameters(), lr\u001b[39m=\u001b[39m\u001b[39m1e-4\u001b[39m)\n\u001b[0;32m---> 10\u001b[0m train_loss, test_loss \u001b[39m=\u001b[39m train(\n\u001b[1;32m     11\u001b[0m     model,\n\u001b[1;32m     12\u001b[0m     optim,\n\u001b[1;32m     13\u001b[0m     loss_function,\n\u001b[1;32m     14\u001b[0m     epochs\u001b[39m=\u001b[39;49m\u001b[39m25\u001b[39;49m,\n\u001b[1;32m     15\u001b[0m     device\u001b[39m=\u001b[39;49mdevice,\n\u001b[1;32m     16\u001b[0m     trainloader\u001b[39m=\u001b[39;49mtrainloader,\n\u001b[1;32m     17\u001b[0m     testloader\u001b[39m=\u001b[39;49mtestloader,\n\u001b[1;32m     18\u001b[0m     labels\u001b[39m=\u001b[39;49mclasses\n\u001b[1;32m     19\u001b[0m )\n",
      "Cell \u001b[0;32mIn[57], line 21\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, optim, loss_func, epochs, trainloader, testloader, labels, device)\u001b[0m\n\u001b[1;32m     18\u001b[0m total_batches \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m     20\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m---> 21\u001b[0m \u001b[39mfor\u001b[39;00m imgs, target \u001b[39min\u001b[39;00m tqdm(trainloader, desc\u001b[39m=\u001b[39m\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mTrain #\u001b[39m\u001b[39m{\u001b[39;00mep\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m):\n\u001b[1;32m     22\u001b[0m     total_batches \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m     23\u001b[0m     imgs\u001b[39m.\u001b[39mto(device)\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/tqdm/std.py:1195\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1192\u001b[0m time \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_time\n\u001b[1;32m   1194\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1195\u001b[0m     \u001b[39mfor\u001b[39;00m obj \u001b[39min\u001b[39;00m iterable:\n\u001b[1;32m   1196\u001b[0m         \u001b[39myield\u001b[39;00m obj\n\u001b[1;32m   1197\u001b[0m         \u001b[39m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1198\u001b[0m         \u001b[39m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torch/utils/data/dataloader.py:628\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    625\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    626\u001b[0m     \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    627\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()  \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 628\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[1;32m    629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    630\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    631\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    632\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torch/utils/data/dataloader.py:671\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    669\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    670\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 671\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    672\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[1;32m    673\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:58\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     57\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     59\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     60\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torch/utils/data/_utils/fetch.py:58\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     56\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     57\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 58\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     59\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     60\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torchvision/datasets/cifar.py:118\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    115\u001b[0m img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(img)\n\u001b[1;32m    117\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 118\u001b[0m     img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform(img)\n\u001b[1;32m    120\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    121\u001b[0m     target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform(target)\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torchvision/transforms/transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[1;32m     94\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[0;32m---> 95\u001b[0m         img \u001b[39m=\u001b[39m t(img)\n\u001b[1;32m     96\u001b[0m     \u001b[39mreturn\u001b[39;00m img\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torchvision/transforms/transforms.py:135\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, pic):\n\u001b[1;32m    128\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    129\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[1;32m    130\u001b[0m \u001b[39m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    133\u001b[0m \u001b[39m        Tensor: Converted image.\u001b[39;00m\n\u001b[1;32m    134\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 135\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mto_tensor(pic)\n",
      "File \u001b[0;32m~/interview_assignments/MIL_autoencoder/.env/lib/python3.9/site-packages/torchvision/transforms/functional.py:167\u001b[0m, in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m    165\u001b[0m \u001b[39mif\u001b[39;00m pic\u001b[39m.\u001b[39mmode \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m1\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m    166\u001b[0m     img \u001b[39m=\u001b[39m \u001b[39m255\u001b[39m \u001b[39m*\u001b[39m img\n\u001b[0;32m--> 167\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39;49mview(pic\u001b[39m.\u001b[39;49msize[\u001b[39m1\u001b[39;49m], pic\u001b[39m.\u001b[39;49msize[\u001b[39m0\u001b[39;49m], \u001b[39mlen\u001b[39;49m(pic\u001b[39m.\u001b[39;49mgetbands()))\n\u001b[1;32m    168\u001b[0m \u001b[39m# put it from HWC to CHW format\u001b[39;00m\n\u001b[1;32m    169\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mpermute((\u001b[39m2\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mcontiguous()\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "trainset, testset, classes = load_data(DATA, simple_transform(), simple_transform())\n",
    "trainloader, testloader = create_loaders(\n",
    "    trainset,\n",
    "    testset,\n",
    "    train_batch=16,\n",
    "    test_batch=32,\n",
    ")\n",
    "\n",
    "optim = Adam(model.parameters(), lr=1e-4)\n",
    "train_loss, test_loss = train(\n",
    "    model,\n",
    "    optim,\n",
    "    loss_function,\n",
    "    epochs=25,\n",
    "    device=device,\n",
    "    trainloader=trainloader,\n",
    "    testloader=testloader,\n",
    "    labels=classes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(train_loss, label=\"Avg train loss\")\n",
    "plt.plot(test_loss, label=\"Avg test loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big model 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "trainset, testset, classes = load_data(DATA, simple_transform(), simple_transform())\n",
    "trainloader, testloader = create_loaders(\n",
    "    trainset,\n",
    "    testset,\n",
    "    train_batch=32,\n",
    "    test_batch=32,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae_path = TRAINED_MODELS / \"vae_state_dict_aug100\"\n",
    "vae_params = {\n",
    "    \"img_size\": 32,\n",
    "    \"downsamplings\": 3,\n",
    "    \"latent_size\": 128,\n",
    "    \"linear_hidden_size\": 256,\n",
    "    \"down_channels\": 4,\n",
    "    \"up_channels\": 8,\n",
    "}\n",
    "\n",
    "vae = VAE(**vae_params)\n",
    "vae.load_state_dict(torch.load(vae_path, map_location=torch.device('cpu')))\n",
    "\n",
    "model = ClassificationHead(encoder=vae.encoder, latent_size=vae_params[\"latent_size\"], start_dim=64)\n",
    "optim = Adam(model.parameters())\n",
    "loss_function = nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #1: 100%|██████████| 1563/1563 [00:57<00:00, 27.08it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Train Avg Loss: 1.8758 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #1: 100%|██████████| 313/313 [00:09<00:00, 31.51it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Test Avg Loss: 1.6572 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.48      0.49      0.49      1000\n",
      "         car       0.51      0.57      0.54      1000\n",
      "        bird       0.31      0.10      0.15      1000\n",
      "         cat       0.28      0.21      0.24      1000\n",
      "        deer       0.31      0.38      0.34      1000\n",
      "         dog       0.40      0.37      0.39      1000\n",
      "        frog       0.34      0.51      0.41      1000\n",
      "       horse       0.40      0.44      0.42      1000\n",
      "        ship       0.53      0.54      0.53      1000\n",
      "       truck       0.48      0.47      0.48      1000\n",
      "\n",
      "    accuracy                           0.41     10000\n",
      "   macro avg       0.40      0.41      0.40     10000\n",
      "weighted avg       0.40      0.41      0.40     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #2: 100%|██████████| 1563/1563 [01:07<00:00, 22.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Train Avg Loss: 1.6619 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #2: 100%|██████████| 313/313 [00:11<00:00, 27.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Test Avg Loss: 1.5665 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.49      0.53      0.51      1000\n",
      "         car       0.54      0.53      0.54      1000\n",
      "        bird       0.34      0.31      0.32      1000\n",
      "         cat       0.29      0.15      0.20      1000\n",
      "        deer       0.37      0.37      0.37      1000\n",
      "         dog       0.39      0.36      0.38      1000\n",
      "        frog       0.39      0.58      0.47      1000\n",
      "       horse       0.46      0.49      0.47      1000\n",
      "        ship       0.53      0.57      0.55      1000\n",
      "       truck       0.51      0.49      0.50      1000\n",
      "\n",
      "    accuracy                           0.44     10000\n",
      "   macro avg       0.43      0.44      0.43     10000\n",
      "weighted avg       0.43      0.44      0.43     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #3: 100%|██████████| 1563/1563 [01:07<00:00, 23.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Train Avg Loss: 1.5894 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #3: 100%|██████████| 313/313 [00:11<00:00, 26.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Test Avg Loss: 1.5163 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.55      0.54      1000\n",
      "         car       0.60      0.51      0.55      1000\n",
      "        bird       0.34      0.25      0.29      1000\n",
      "         cat       0.31      0.27      0.29      1000\n",
      "        deer       0.39      0.35      0.37      1000\n",
      "         dog       0.44      0.32      0.37      1000\n",
      "        frog       0.39      0.62      0.48      1000\n",
      "       horse       0.46      0.55      0.50      1000\n",
      "        ship       0.57      0.61      0.59      1000\n",
      "       truck       0.52      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.46     10000\n",
      "   macro avg       0.46      0.46      0.45     10000\n",
      "weighted avg       0.46      0.46      0.45     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #4: 100%|██████████| 1563/1563 [01:06<00:00, 23.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Train Avg Loss: 1.5499 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #4: 100%|██████████| 313/313 [00:12<00:00, 25.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Test Avg Loss: 1.4834 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.52      0.53      0.53      1000\n",
      "         car       0.58      0.56      0.57      1000\n",
      "        bird       0.36      0.26      0.30      1000\n",
      "         cat       0.31      0.30      0.30      1000\n",
      "        deer       0.38      0.39      0.39      1000\n",
      "         dog       0.41      0.35      0.38      1000\n",
      "        frog       0.46      0.56      0.50      1000\n",
      "       horse       0.51      0.50      0.51      1000\n",
      "        ship       0.55      0.66      0.60      1000\n",
      "       truck       0.51      0.55      0.53      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.46      0.47      0.46     10000\n",
      "weighted avg       0.46      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #5: 100%|██████████| 1563/1563 [01:07<00:00, 23.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Train Avg Loss: 1.5189 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #5: 100%|██████████| 313/313 [00:12<00:00, 24.35it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Test Avg Loss: 1.4678 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.49      0.53      1000\n",
      "         car       0.60      0.56      0.58      1000\n",
      "        bird       0.37      0.21      0.27      1000\n",
      "         cat       0.31      0.25      0.28      1000\n",
      "        deer       0.38      0.47      0.42      1000\n",
      "         dog       0.43      0.34      0.38      1000\n",
      "        frog       0.40      0.63      0.49      1000\n",
      "       horse       0.53      0.51      0.52      1000\n",
      "        ship       0.59      0.64      0.62      1000\n",
      "       truck       0.50      0.59      0.54      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.46     10000\n",
      "weighted avg       0.47      0.47      0.46     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #6: 100%|██████████| 1563/1563 [01:07<00:00, 23.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Train Avg Loss: 1.4973 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #6: 100%|██████████| 313/313 [00:11<00:00, 27.15it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Test Avg Loss: 1.4551 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.53      0.59      0.56      1000\n",
      "         car       0.58      0.60      0.59      1000\n",
      "        bird       0.36      0.26      0.30      1000\n",
      "         cat       0.33      0.33      0.33      1000\n",
      "        deer       0.38      0.44      0.41      1000\n",
      "         dog       0.46      0.31      0.37      1000\n",
      "        frog       0.46      0.53      0.49      1000\n",
      "       horse       0.54      0.52      0.53      1000\n",
      "        ship       0.53      0.64      0.58      1000\n",
      "       truck       0.53      0.50      0.51      1000\n",
      "\n",
      "    accuracy                           0.47     10000\n",
      "   macro avg       0.47      0.47      0.47     10000\n",
      "weighted avg       0.47      0.47      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #7: 100%|██████████| 1563/1563 [01:05<00:00, 23.93it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Train Avg Loss: 1.4857 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #7: 100%|██████████| 313/313 [00:11<00:00, 26.23it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Test Avg Loss: 1.4383 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.54      0.54      1000\n",
      "         car       0.59      0.56      0.57      1000\n",
      "        bird       0.36      0.34      0.35      1000\n",
      "         cat       0.35      0.20      0.25      1000\n",
      "        deer       0.41      0.39      0.40      1000\n",
      "         dog       0.40      0.41      0.41      1000\n",
      "        frog       0.48      0.54      0.51      1000\n",
      "       horse       0.52      0.60      0.56      1000\n",
      "        ship       0.55      0.67      0.60      1000\n",
      "       truck       0.52      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.47      0.48      0.47     10000\n",
      "weighted avg       0.47      0.48      0.47     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #8: 100%|██████████| 1563/1563 [01:10<00:00, 22.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Train Avg Loss: 1.4719 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #8: 100%|██████████| 313/313 [00:11<00:00, 26.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Test Avg Loss: 1.4373 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.55      0.56      1000\n",
      "         car       0.61      0.59      0.60      1000\n",
      "        bird       0.37      0.30      0.33      1000\n",
      "         cat       0.33      0.32      0.32      1000\n",
      "        deer       0.38      0.45      0.41      1000\n",
      "         dog       0.45      0.34      0.39      1000\n",
      "        frog       0.45      0.54      0.49      1000\n",
      "       horse       0.55      0.56      0.56      1000\n",
      "        ship       0.57      0.69      0.62      1000\n",
      "       truck       0.54      0.50      0.52      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #9: 100%|██████████| 1563/1563 [01:06<00:00, 23.65it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Train Avg Loss: 1.4624 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #9: 100%|██████████| 313/313 [00:11<00:00, 26.91it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Test Avg Loss: 1.4276 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.54      0.54      1000\n",
      "         car       0.58      0.64      0.61      1000\n",
      "        bird       0.38      0.28      0.33      1000\n",
      "         cat       0.33      0.27      0.30      1000\n",
      "        deer       0.39      0.43      0.41      1000\n",
      "         dog       0.43      0.36      0.39      1000\n",
      "        frog       0.45      0.59      0.51      1000\n",
      "       horse       0.49      0.61      0.54      1000\n",
      "        ship       0.60      0.62      0.61      1000\n",
      "       truck       0.59      0.49      0.54      1000\n",
      "\n",
      "    accuracy                           0.48     10000\n",
      "   macro avg       0.48      0.48      0.48     10000\n",
      "weighted avg       0.48      0.48      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #10: 100%|██████████| 1563/1563 [01:04<00:00, 24.06it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Train Avg Loss: 1.4509 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #10: 100%|██████████| 313/313 [00:11<00:00, 27.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Test Avg Loss: 1.4249 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.56      0.56      1000\n",
      "         car       0.57      0.66      0.61      1000\n",
      "        bird       0.38      0.29      0.33      1000\n",
      "         cat       0.32      0.34      0.33      1000\n",
      "        deer       0.40      0.39      0.39      1000\n",
      "         dog       0.43      0.34      0.38      1000\n",
      "        frog       0.49      0.58      0.53      1000\n",
      "       horse       0.53      0.58      0.55      1000\n",
      "        ship       0.63      0.61      0.62      1000\n",
      "       truck       0.53      0.53      0.53      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.48      0.49      0.48     10000\n",
      "weighted avg       0.48      0.49      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #11: 100%|██████████| 1563/1563 [01:04<00:00, 24.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #11 | Train Avg Loss: 1.4435 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #11: 100%|██████████| 313/313 [00:11<00:00, 26.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #11 | Test Avg Loss: 1.4206 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.56      0.55      1000\n",
      "         car       0.60      0.63      0.62      1000\n",
      "        bird       0.40      0.33      0.36      1000\n",
      "         cat       0.33      0.32      0.32      1000\n",
      "        deer       0.42      0.38      0.40      1000\n",
      "         dog       0.42      0.37      0.39      1000\n",
      "        frog       0.45      0.60      0.52      1000\n",
      "       horse       0.56      0.56      0.56      1000\n",
      "        ship       0.58      0.66      0.62      1000\n",
      "       truck       0.56      0.51      0.54      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #12: 100%|██████████| 1563/1563 [01:04<00:00, 24.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #12 | Train Avg Loss: 1.4405 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #12: 100%|██████████| 313/313 [00:11<00:00, 27.32it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #12 | Test Avg Loss: 1.4104 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.55      0.57      0.56      1000\n",
      "         car       0.61      0.62      0.61      1000\n",
      "        bird       0.37      0.32      0.34      1000\n",
      "         cat       0.32      0.27      0.29      1000\n",
      "        deer       0.42      0.39      0.40      1000\n",
      "         dog       0.41      0.40      0.40      1000\n",
      "        frog       0.48      0.61      0.54      1000\n",
      "       horse       0.54      0.56      0.55      1000\n",
      "        ship       0.60      0.65      0.62      1000\n",
      "       truck       0.56      0.54      0.55      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #13: 100%|██████████| 1563/1563 [01:03<00:00, 24.52it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #13 | Train Avg Loss: 1.4320 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #13: 100%|██████████| 313/313 [00:10<00:00, 28.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #13 | Test Avg Loss: 1.4115 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.59      0.56      1000\n",
      "         car       0.57      0.65      0.61      1000\n",
      "        bird       0.36      0.37      0.36      1000\n",
      "         cat       0.34      0.24      0.28      1000\n",
      "        deer       0.40      0.36      0.38      1000\n",
      "         dog       0.44      0.33      0.38      1000\n",
      "        frog       0.50      0.56      0.53      1000\n",
      "       horse       0.50      0.60      0.54      1000\n",
      "        ship       0.59      0.64      0.62      1000\n",
      "       truck       0.55      0.55      0.55      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.48      0.49      0.48     10000\n",
      "weighted avg       0.48      0.49      0.48     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #14: 100%|██████████| 1563/1563 [01:09<00:00, 22.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #14 | Train Avg Loss: 1.4258 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #14: 100%|██████████| 313/313 [00:10<00:00, 28.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #14 | Test Avg Loss: 1.4012 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.54      0.55      1000\n",
      "         car       0.60      0.61      0.61      1000\n",
      "        bird       0.39      0.36      0.37      1000\n",
      "         cat       0.36      0.21      0.27      1000\n",
      "        deer       0.41      0.40      0.40      1000\n",
      "         dog       0.40      0.42      0.41      1000\n",
      "        frog       0.47      0.61      0.53      1000\n",
      "       horse       0.56      0.57      0.56      1000\n",
      "        ship       0.59      0.64      0.62      1000\n",
      "       truck       0.54      0.58      0.56      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #15: 100%|██████████| 1563/1563 [01:02<00:00, 25.04it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #15 | Train Avg Loss: 1.4193 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #15: 100%|██████████| 313/313 [00:10<00:00, 29.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #15 | Test Avg Loss: 1.3989 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.60      0.56      0.58      1000\n",
      "         car       0.62      0.59      0.61      1000\n",
      "        bird       0.38      0.37      0.37      1000\n",
      "         cat       0.33      0.28      0.30      1000\n",
      "        deer       0.42      0.41      0.41      1000\n",
      "         dog       0.40      0.43      0.42      1000\n",
      "        frog       0.51      0.54      0.52      1000\n",
      "       horse       0.57      0.55      0.55      1000\n",
      "        ship       0.55      0.69      0.61      1000\n",
      "       truck       0.57      0.53      0.55      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #16: 100%|██████████| 1563/1563 [01:08<00:00, 22.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #16 | Train Avg Loss: 1.4181 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #16: 100%|██████████| 313/313 [00:16<00:00, 19.53it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #16 | Test Avg Loss: 1.4035 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.55      0.57      1000\n",
      "         car       0.60      0.61      0.61      1000\n",
      "        bird       0.39      0.29      0.33      1000\n",
      "         cat       0.32      0.34      0.33      1000\n",
      "        deer       0.42      0.43      0.42      1000\n",
      "         dog       0.45      0.32      0.37      1000\n",
      "        frog       0.46      0.63      0.53      1000\n",
      "       horse       0.58      0.54      0.56      1000\n",
      "        ship       0.61      0.64      0.63      1000\n",
      "       truck       0.51      0.59      0.55      1000\n",
      "\n",
      "    accuracy                           0.49     10000\n",
      "   macro avg       0.49      0.49      0.49     10000\n",
      "weighted avg       0.49      0.49      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #17: 100%|██████████| 1563/1563 [01:23<00:00, 18.67it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #17 | Train Avg Loss: 1.4071 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #17: 100%|██████████| 313/313 [00:30<00:00, 10.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #17 | Test Avg Loss: 1.4048 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.55      0.54      1000\n",
      "         car       0.61      0.60      0.61      1000\n",
      "        bird       0.40      0.35      0.37      1000\n",
      "         cat       0.34      0.28      0.31      1000\n",
      "        deer       0.41      0.44      0.42      1000\n",
      "         dog       0.47      0.33      0.39      1000\n",
      "        frog       0.45      0.65      0.53      1000\n",
      "       horse       0.54      0.59      0.56      1000\n",
      "        ship       0.60      0.65      0.62      1000\n",
      "       truck       0.57      0.53      0.55      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #18: 100%|██████████| 1563/1563 [01:12<00:00, 21.62it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #18 | Train Avg Loss: 1.4068 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #18: 100%|██████████| 313/313 [00:11<00:00, 28.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #18 | Test Avg Loss: 1.3976 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.60      0.51      0.55      1000\n",
      "         car       0.58      0.61      0.60      1000\n",
      "        bird       0.42      0.31      0.36      1000\n",
      "         cat       0.34      0.35      0.34      1000\n",
      "        deer       0.42      0.45      0.43      1000\n",
      "         dog       0.49      0.27      0.35      1000\n",
      "        frog       0.48      0.61      0.54      1000\n",
      "       horse       0.54      0.57      0.56      1000\n",
      "        ship       0.57      0.69      0.63      1000\n",
      "       truck       0.51      0.60      0.55      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #19: 100%|██████████| 1563/1563 [01:09<00:00, 22.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #19 | Train Avg Loss: 1.4005 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #19: 100%|██████████| 313/313 [00:17<00:00, 18.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #19 | Test Avg Loss: 1.3922 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.58      0.58      1000\n",
      "         car       0.58      0.67      0.62      1000\n",
      "        bird       0.40      0.29      0.34      1000\n",
      "         cat       0.34      0.29      0.32      1000\n",
      "        deer       0.42      0.39      0.40      1000\n",
      "         dog       0.45      0.34      0.39      1000\n",
      "        frog       0.46      0.65      0.54      1000\n",
      "       horse       0.52      0.60      0.56      1000\n",
      "        ship       0.62      0.65      0.63      1000\n",
      "       truck       0.56      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #20: 100%|██████████| 1563/1563 [01:35<00:00, 16.31it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #20 | Train Avg Loss: 1.3974 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #20: 100%|██████████| 313/313 [00:14<00:00, 21.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #20 | Test Avg Loss: 1.3994 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.54      0.61      0.57      1000\n",
      "         car       0.57      0.65      0.60      1000\n",
      "        bird       0.39      0.36      0.38      1000\n",
      "         cat       0.36      0.30      0.33      1000\n",
      "        deer       0.42      0.38      0.40      1000\n",
      "         dog       0.47      0.29      0.36      1000\n",
      "        frog       0.48      0.63      0.55      1000\n",
      "       horse       0.55      0.56      0.55      1000\n",
      "        ship       0.58      0.66      0.62      1000\n",
      "       truck       0.54      0.54      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #21: 100%|██████████| 1563/1563 [00:59<00:00, 26.34it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #21 | Train Avg Loss: 1.3962 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #21: 100%|██████████| 313/313 [00:06<00:00, 49.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #21 | Test Avg Loss: 1.3959 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.53      0.61      0.57      1000\n",
      "         car       0.62      0.63      0.62      1000\n",
      "        bird       0.39      0.32      0.35      1000\n",
      "         cat       0.35      0.29      0.31      1000\n",
      "        deer       0.42      0.42      0.42      1000\n",
      "         dog       0.41      0.39      0.40      1000\n",
      "        frog       0.50      0.57      0.53      1000\n",
      "       horse       0.54      0.57      0.56      1000\n",
      "        ship       0.60      0.65      0.62      1000\n",
      "       truck       0.57      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #22: 100%|██████████| 1563/1563 [00:39<00:00, 39.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #22 | Train Avg Loss: 1.3969 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #22: 100%|██████████| 313/313 [00:06<00:00, 49.50it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #22 | Test Avg Loss: 1.3861 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.58      0.57      1000\n",
      "         car       0.61      0.62      0.61      1000\n",
      "        bird       0.43      0.32      0.36      1000\n",
      "         cat       0.34      0.34      0.34      1000\n",
      "        deer       0.43      0.40      0.41      1000\n",
      "         dog       0.42      0.36      0.39      1000\n",
      "        frog       0.49      0.62      0.55      1000\n",
      "       horse       0.56      0.57      0.56      1000\n",
      "        ship       0.58      0.65      0.61      1000\n",
      "       truck       0.52      0.57      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #23: 100%|██████████| 1563/1563 [00:41<00:00, 37.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #23 | Train Avg Loss: 1.3907 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #23: 100%|██████████| 313/313 [00:07<00:00, 44.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #23 | Test Avg Loss: 1.3942 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.58      0.57      1000\n",
      "         car       0.57      0.64      0.60      1000\n",
      "        bird       0.38      0.35      0.36      1000\n",
      "         cat       0.34      0.27      0.30      1000\n",
      "        deer       0.42      0.39      0.40      1000\n",
      "         dog       0.44      0.38      0.41      1000\n",
      "        frog       0.48      0.64      0.55      1000\n",
      "       horse       0.54      0.59      0.56      1000\n",
      "        ship       0.65      0.63      0.64      1000\n",
      "       truck       0.56      0.54      0.55      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #24: 100%|██████████| 1563/1563 [00:41<00:00, 37.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #24 | Train Avg Loss: 1.3948 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #24: 100%|██████████| 313/313 [00:06<00:00, 45.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #24 | Test Avg Loss: 1.3795 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.57      0.58      1000\n",
      "         car       0.59      0.64      0.61      1000\n",
      "        bird       0.41      0.32      0.36      1000\n",
      "         cat       0.35      0.28      0.31      1000\n",
      "        deer       0.44      0.39      0.41      1000\n",
      "         dog       0.42      0.43      0.42      1000\n",
      "        frog       0.48      0.61      0.54      1000\n",
      "       horse       0.54      0.58      0.56      1000\n",
      "        ship       0.63      0.66      0.64      1000\n",
      "       truck       0.55      0.58      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #25: 100%|██████████| 1563/1563 [00:39<00:00, 39.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #25 | Train Avg Loss: 1.3879 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #25: 100%|██████████| 313/313 [00:06<00:00, 51.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #25 | Test Avg Loss: 1.3839 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.58      0.58      1000\n",
      "         car       0.60      0.61      0.61      1000\n",
      "        bird       0.40      0.33      0.36      1000\n",
      "         cat       0.31      0.34      0.33      1000\n",
      "        deer       0.43      0.36      0.39      1000\n",
      "         dog       0.44      0.36      0.39      1000\n",
      "        frog       0.48      0.64      0.55      1000\n",
      "       horse       0.58      0.55      0.56      1000\n",
      "        ship       0.59      0.68      0.63      1000\n",
      "       truck       0.53      0.53      0.53      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #26: 100%|██████████| 1563/1563 [00:38<00:00, 40.49it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #26 | Train Avg Loss: 1.3861 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #26: 100%|██████████| 313/313 [00:06<00:00, 48.02it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #26 | Test Avg Loss: 1.3863 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.52      0.54      1000\n",
      "         car       0.61      0.63      0.62      1000\n",
      "        bird       0.36      0.39      0.37      1000\n",
      "         cat       0.34      0.31      0.33      1000\n",
      "        deer       0.42      0.40      0.41      1000\n",
      "         dog       0.44      0.37      0.40      1000\n",
      "        frog       0.52      0.57      0.54      1000\n",
      "       horse       0.57      0.57      0.57      1000\n",
      "        ship       0.55      0.71      0.62      1000\n",
      "       truck       0.58      0.50      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.49      0.50      0.49     10000\n",
      "weighted avg       0.49      0.50      0.49     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #27: 100%|██████████| 1563/1563 [00:41<00:00, 38.12it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #27 | Train Avg Loss: 1.3799 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #27: 100%|██████████| 313/313 [00:05<00:00, 54.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #27 | Test Avg Loss: 1.3830 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.57      0.57      1000\n",
      "         car       0.61      0.63      0.62      1000\n",
      "        bird       0.38      0.32      0.35      1000\n",
      "         cat       0.35      0.35      0.35      1000\n",
      "        deer       0.44      0.40      0.41      1000\n",
      "         dog       0.44      0.33      0.38      1000\n",
      "        frog       0.45      0.65      0.53      1000\n",
      "       horse       0.57      0.57      0.57      1000\n",
      "        ship       0.62      0.65      0.63      1000\n",
      "       truck       0.57      0.55      0.56      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #28: 100%|██████████| 1563/1563 [00:41<00:00, 37.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #28 | Train Avg Loss: 1.3801 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #28: 100%|██████████| 313/313 [00:06<00:00, 45.01it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #28 | Test Avg Loss: 1.3828 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.56      0.56      1000\n",
      "         car       0.59      0.64      0.61      1000\n",
      "        bird       0.40      0.35      0.37      1000\n",
      "         cat       0.34      0.30      0.32      1000\n",
      "        deer       0.45      0.36      0.40      1000\n",
      "         dog       0.43      0.39      0.41      1000\n",
      "        frog       0.50      0.61      0.55      1000\n",
      "       horse       0.57      0.57      0.57      1000\n",
      "        ship       0.57      0.71      0.63      1000\n",
      "       truck       0.56      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #29: 100%|██████████| 1563/1563 [00:40<00:00, 38.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #29 | Train Avg Loss: 1.3763 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #29: 100%|██████████| 313/313 [00:05<00:00, 56.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #29 | Test Avg Loss: 1.3731 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.58      0.58      1000\n",
      "         car       0.58      0.65      0.61      1000\n",
      "        bird       0.39      0.36      0.38      1000\n",
      "         cat       0.36      0.35      0.35      1000\n",
      "        deer       0.42      0.38      0.40      1000\n",
      "         dog       0.48      0.32      0.38      1000\n",
      "        frog       0.50      0.61      0.55      1000\n",
      "       horse       0.56      0.60      0.58      1000\n",
      "        ship       0.60      0.68      0.63      1000\n",
      "       truck       0.55      0.55      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #30: 100%|██████████| 1563/1563 [00:40<00:00, 38.95it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #30 | Train Avg Loss: 1.3765 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #30: 100%|██████████| 313/313 [00:07<00:00, 40.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #30 | Test Avg Loss: 1.3795 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.59      0.58      1000\n",
      "         car       0.60      0.66      0.63      1000\n",
      "        bird       0.40      0.30      0.34      1000\n",
      "         cat       0.34      0.31      0.33      1000\n",
      "        deer       0.41      0.44      0.42      1000\n",
      "         dog       0.42      0.37      0.39      1000\n",
      "        frog       0.47      0.68      0.56      1000\n",
      "       horse       0.59      0.54      0.57      1000\n",
      "        ship       0.64      0.62      0.63      1000\n",
      "       truck       0.58      0.51      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #31: 100%|██████████| 1563/1563 [00:39<00:00, 39.70it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #31 | Train Avg Loss: 1.3722 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #31: 100%|██████████| 313/313 [00:06<00:00, 48.96it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #31 | Test Avg Loss: 1.3681 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.58      0.58      1000\n",
      "         car       0.64      0.60      0.62      1000\n",
      "        bird       0.42      0.36      0.38      1000\n",
      "         cat       0.34      0.28      0.31      1000\n",
      "        deer       0.41      0.45      0.43      1000\n",
      "         dog       0.44      0.38      0.41      1000\n",
      "        frog       0.50      0.59      0.54      1000\n",
      "       horse       0.55      0.58      0.57      1000\n",
      "        ship       0.60      0.67      0.63      1000\n",
      "       truck       0.54      0.55      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #32: 100%|██████████| 1563/1563 [00:40<00:00, 38.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #32 | Train Avg Loss: 1.3745 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #32: 100%|██████████| 313/313 [00:06<00:00, 47.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #32 | Test Avg Loss: 1.3783 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.60      0.59      1000\n",
      "         car       0.60      0.62      0.61      1000\n",
      "        bird       0.42      0.34      0.38      1000\n",
      "         cat       0.36      0.32      0.34      1000\n",
      "        deer       0.45      0.36      0.40      1000\n",
      "         dog       0.46      0.34      0.39      1000\n",
      "        frog       0.47      0.69      0.56      1000\n",
      "       horse       0.58      0.56      0.57      1000\n",
      "        ship       0.60      0.70      0.64      1000\n",
      "       truck       0.53      0.58      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #33: 100%|██████████| 1563/1563 [00:39<00:00, 39.22it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #33 | Train Avg Loss: 1.3699 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #33: 100%|██████████| 313/313 [00:06<00:00, 47.40it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #33 | Test Avg Loss: 1.3770 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.57      0.58      1000\n",
      "         car       0.59      0.61      0.60      1000\n",
      "        bird       0.38      0.36      0.37      1000\n",
      "         cat       0.35      0.28      0.31      1000\n",
      "        deer       0.42      0.39      0.40      1000\n",
      "         dog       0.45      0.37      0.41      1000\n",
      "        frog       0.47      0.66      0.55      1000\n",
      "       horse       0.57      0.57      0.57      1000\n",
      "        ship       0.58      0.69      0.63      1000\n",
      "       truck       0.54      0.52      0.53      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #34: 100%|██████████| 1563/1563 [00:36<00:00, 43.11it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #34 | Train Avg Loss: 1.3686 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #34: 100%|██████████| 313/313 [00:08<00:00, 35.41it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #34 | Test Avg Loss: 1.3769 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.60      0.59      1000\n",
      "         car       0.59      0.68      0.63      1000\n",
      "        bird       0.42      0.30      0.35      1000\n",
      "         cat       0.36      0.26      0.30      1000\n",
      "        deer       0.43      0.37      0.40      1000\n",
      "         dog       0.41      0.39      0.40      1000\n",
      "        frog       0.44      0.71      0.54      1000\n",
      "       horse       0.57      0.56      0.56      1000\n",
      "        ship       0.63      0.65      0.64      1000\n",
      "       truck       0.56      0.53      0.54      1000\n",
      "\n",
      "    accuracy                           0.50     10000\n",
      "   macro avg       0.50      0.50      0.50     10000\n",
      "weighted avg       0.50      0.50      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #35: 100%|██████████| 1563/1563 [00:41<00:00, 37.55it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #35 | Train Avg Loss: 1.3691 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #35: 100%|██████████| 313/313 [00:06<00:00, 47.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #35 | Test Avg Loss: 1.3686 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.61      0.59      1000\n",
      "         car       0.62      0.58      0.60      1000\n",
      "        bird       0.41      0.33      0.37      1000\n",
      "         cat       0.34      0.34      0.34      1000\n",
      "        deer       0.43      0.39      0.41      1000\n",
      "         dog       0.47      0.32      0.38      1000\n",
      "        frog       0.50      0.64      0.56      1000\n",
      "       horse       0.54      0.59      0.56      1000\n",
      "        ship       0.62      0.66      0.64      1000\n",
      "       truck       0.53      0.59      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #36: 100%|██████████| 1563/1563 [00:40<00:00, 38.44it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #36 | Train Avg Loss: 1.3677 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #36: 100%|██████████| 313/313 [00:07<00:00, 43.07it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #36 | Test Avg Loss: 1.3713 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.58      0.58      1000\n",
      "         car       0.59      0.66      0.62      1000\n",
      "        bird       0.42      0.31      0.35      1000\n",
      "         cat       0.35      0.24      0.28      1000\n",
      "        deer       0.41      0.48      0.44      1000\n",
      "         dog       0.43      0.42      0.43      1000\n",
      "        frog       0.49      0.65      0.56      1000\n",
      "       horse       0.57      0.57      0.57      1000\n",
      "        ship       0.60      0.66      0.63      1000\n",
      "       truck       0.58      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #37: 100%|██████████| 1563/1563 [00:39<00:00, 39.24it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #37 | Train Avg Loss: 1.3671 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #37: 100%|██████████| 313/313 [00:06<00:00, 47.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #37 | Test Avg Loss: 1.3629 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.63      0.57      0.60      1000\n",
      "         car       0.60      0.64      0.62      1000\n",
      "        bird       0.41      0.34      0.37      1000\n",
      "         cat       0.34      0.32      0.33      1000\n",
      "        deer       0.44      0.39      0.41      1000\n",
      "         dog       0.47      0.34      0.39      1000\n",
      "        frog       0.48      0.66      0.56      1000\n",
      "       horse       0.56      0.58      0.57      1000\n",
      "        ship       0.60      0.68      0.64      1000\n",
      "       truck       0.53      0.58      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #38: 100%|██████████| 1563/1563 [00:39<00:00, 39.74it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #38 | Train Avg Loss: 1.3603 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #38: 100%|██████████| 313/313 [00:06<00:00, 48.25it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #38 | Test Avg Loss: 1.3739 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.60      0.58      1000\n",
      "         car       0.61      0.66      0.63      1000\n",
      "        bird       0.43      0.29      0.35      1000\n",
      "         cat       0.34      0.35      0.35      1000\n",
      "        deer       0.40      0.50      0.44      1000\n",
      "         dog       0.46      0.33      0.39      1000\n",
      "        frog       0.49      0.63      0.55      1000\n",
      "       horse       0.57      0.56      0.57      1000\n",
      "        ship       0.66      0.62      0.64      1000\n",
      "       truck       0.57      0.54      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.50     10000\n",
      "weighted avg       0.51      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #39: 100%|██████████| 1563/1563 [00:51<00:00, 30.20it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #39 | Train Avg Loss: 1.3619 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #39: 100%|██████████| 313/313 [00:09<00:00, 32.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #39 | Test Avg Loss: 1.3616 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.59      0.59      1000\n",
      "         car       0.61      0.68      0.64      1000\n",
      "        bird       0.40      0.33      0.36      1000\n",
      "         cat       0.34      0.34      0.34      1000\n",
      "        deer       0.44      0.40      0.42      1000\n",
      "         dog       0.44      0.39      0.41      1000\n",
      "        frog       0.50      0.64      0.56      1000\n",
      "       horse       0.56      0.60      0.58      1000\n",
      "        ship       0.63      0.66      0.65      1000\n",
      "       truck       0.58      0.52      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #40: 100%|██████████| 1563/1563 [00:50<00:00, 31.05it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #40 | Train Avg Loss: 1.3593 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #40: 100%|██████████| 313/313 [00:10<00:00, 29.53it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #40 | Test Avg Loss: 1.3738 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.60      0.57      0.59      1000\n",
      "         car       0.60      0.60      0.60      1000\n",
      "        bird       0.44      0.32      0.37      1000\n",
      "         cat       0.35      0.28      0.31      1000\n",
      "        deer       0.42      0.40      0.41      1000\n",
      "         dog       0.42      0.41      0.42      1000\n",
      "        frog       0.48      0.66      0.55      1000\n",
      "       horse       0.54      0.61      0.57      1000\n",
      "        ship       0.61      0.68      0.64      1000\n",
      "       truck       0.54      0.54      0.54      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.50     10000\n",
      "weighted avg       0.50      0.51      0.50     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "train_loss, test_loss = train(\n",
    "    model,\n",
    "    optim,\n",
    "    loss_function,\n",
    "    epochs=40,\n",
    "    device=device,\n",
    "    trainloader=trainloader,\n",
    "    testloader=testloader,\n",
    "    labels=classes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAG1CAYAAAAr/fRyAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABbxUlEQVR4nO3dd3hUdd7+8ffMpHeSAEkgQGgB6V3EAsIKqCiIgooKK6uL3UeX3WV3BfSnD65dH11cFUFXFAUFWRUUVEAR6aEIBIFAAklogfQ6c35/HDIQSSBlJpNyv65rrkw753wOB8zt+TaLYRgGIiIiIh5i9XQBIiIi0rgpjIiIiIhHKYyIiIiIRymMiIiIiEcpjIiIiIhHKYyIiIiIRymMiIiIiEcpjIiIiIhHKYyIiIiIRymMiIiIiEdVOYysWbOGUaNGERMTg8ViYcmSJRfd5o033qBz5874+/sTHx/P+++/X51aRUREpAHyquoGubm59OjRg7vvvpubbrrpot+fPXs206ZN4+2336Zfv35s2LCBe+65hyZNmjBq1KhqFS0iIiINh6UmC+VZLBYWL17M6NGjK/zOZZddxqBBg3j++eed7z3++OOsX7+eH3/8sVLHcTgcpKamEhwcjMViqW65IiIiUosMwyA7O5uYmBis1oobY6p8Z6SqCgsL8fPzK/Oev78/GzZsoLi4GG9v73K3KSwsdL4+cuQIl1xyibtLFRERETdISUmhZcuWFX7u9jAyfPhw3nnnHUaPHk3v3r3ZvHkz77zzDsXFxZw4cYLo6Ojztpk1axZPPvnkee+npKQQEhLi7pJFRETEBbKysoiNjSU4OPiC33N7GHniiSdIT0/n0ksvxTAMmjdvzsSJE3nuuecqvGUzbdo0HnvsMefr0pMJCQlRGBEREalnLtbFwu1De/39/Xn33XfJy8vj4MGDJCcn06ZNG4KDg2natGm52/j6+jqDhwKIiIhIw+b2OyOlvL29ne1FCxYs4Prrr79gZxYRERFpHKocRnJycti3b5/zdVJSEgkJCYSHh9OqVSumTZvGkSNHnHOJ7N27lw0bNjBgwABOnTrFSy+9xM6dO3nvvfdcdxYiIiJSb1U5jGzatIkhQ4Y4X5f27Zg4cSLz5s0jLS2N5ORk5+d2u50XX3yRxMREvL29GTJkCD/99BNt2rSpefUiIuJSdrud4uJiT5ch9YS3tzc2m63G+6nRPCO1JSsri9DQUDIzM9V/RETEDQzDID09ndOnT3u6FKlnwsLCiIqKKreTamV/f9danxEREam7SoNIs2bNCAgI0ASTclGGYZCXl8exY8cAyp2qo7IURkREGjm73e4MIhEREZ4uR+oRf39/AI4dO0azZs2q3WSj4SwiIo1caR+RgIAAD1ci9VHp35ua9DVSGBEREeDiE1OJlMcVf28URkRERMSjFEZEREQ8bNKkSYwePdrj+/AUhREREan31q1bh81m47rrrquV482cOZOePXu6bH+vvvoq8+bNc9n+6ptGHUZyC0s4dDKX03lFni5FRERqYM6cOTz00EOsWbOG1NRUT5fjVNlOnaGhoYSFhbm3mDqsUYeRRxYkcNXzq/hyR5qnSxERkWrKycnh448/5r777uO6664rc4fh9ttvZ/z48WW+X1xcTGRkpHPZkuzsbCZMmEBgYCDR0dG8/PLLDB48mEcffbTc482bN48nn3ySbdu2YbFYsFgszmNaLBZmz57NDTfcQGBgIM888wx2u53JkycTFxeHv78/8fHxvPrqq2X2+dsmlsGDB/Pwww/z5z//mfDwcKKiopg5c2aV/lwKCwt5+OGHadasGX5+flx++eVs3LjR+fmpU6eYMGECTZs2xd/fnw4dOjB37lwAioqKePDBB4mOjsbPz4/WrVsza9asKh2/Khr1PCORQT4AZOTozoiIyLkMwyC/2F7rx/X3tlV5dMYnn3xCp06diI+P54477uDRRx9l2rRpWCwWJkyYwC233EJOTg5BQUEAfP311+Tl5TFmzBjAXNZk7dq1LF26lObNmzN9+nS2bNlSYTPM+PHj2blzJ8uXL2flypWAeWej1MyZM3n22Wd55ZVX8PLywuFw0LJlSxYuXEhERAQ//fQT9957L9HR0YwbN67C83rvvfd47LHHWL9+PevWrWPSpEkMGjSI3/3ud5X6c/nzn//Mp59+ynvvvUfr1q157rnnGD58OPv27SM8PJwnnniCXbt2sWzZMiIjI9m3bx/5+fkAvPbaayxdupRPPvmEVq1akZKSQkpKSqWOWx2NOoyEB5ph5GSuwoiIyLnyi+1cMv3rWj/urqeGE+BTtV9Nc+bM4Y477gBgxIgRZGZmsnr1agYPHszw4cMJDAxk8eLF3HnnnQB8+OGH3HDDDQQHB5Odnc17773Hhx9+yNChQwGYO3cuMTExFR7P39+foKAgvLy8iIqKOu/z22+/nd///vdl3nvyySedz+Pi4li3bh2ffPLJBcNI9+7dmTFjBgAdOnTg9ddf59tvv61UGMnNzWX27NnMmzePkSNHAvD222+zYsUK5syZw9SpU0lOTqZXr1707dsXoMyaccnJyXTo0IHLL78ci8VC69atL3rMmmjUzTQKIyIi9VtiYiIbNmzgtttuA8DLy4vx48czZ84c5+tx48Yxf/58wPwl/fnnnzNhwgQADhw4QHFxMf3793fuMzQ0lPj4+GrXVPrL/VxvvPEGffr0oWnTpgQFBfHWW2+VWVS2PN27dy/zOjo62jn1+sXs37+f4uJiBg0a5HzP29ub/v37s3v3bgDuu+8+FixYQM+ePfnzn//MTz/95PzupEmTSEhIID4+nocffphvvvmmUsetrkZ9ZyQyyBeAjNxCD1ciIlK3+Hvb2PXUcI8ctyrmzJlDSUlJmTsZhmHg6+vL66+/TmhoKBMmTOCqq67i2LFjrFixAn9/f0aMGOHq0p0CAwPLvF6wYAF/+tOfePHFFxk4cCDBwcE8//zzrF+//oL78fb2LvPaYrHgcDhcVufIkSM5dOgQX331FStWrGDo0KE88MADvPDCC/Tu3ZukpCSWLVvGypUrGTduHMOGDWPRokUuO/65dGcEOKk+IyIiZVgsFgJ8vGr9UZX+IiUlJbz//vu8+OKLJCQkOB/btm0jJiaGjz76CIDLLruM2NhYPv74Y+bPn88tt9zi/EXftm1bvL29y3TszMzMZO/evRc8to+PD3Z75frUrF27lssuu4z777+fXr160b59e/bv31/p86yOdu3a4ePjw9q1a53vFRcXs3HjRi655BLne02bNmXixIl88MEHvPLKK7z11lvOz0JCQhg/fjxvv/02H3/8MZ9++ikZGRluqbdR3xkpDSMZaqYREal3vvjiC06dOsXkyZPLdCAFGDt2LHPmzGHKlCmA2Y/jzTffZO/evXz//ffO7wUHBzNx4kSmTp1KeHg4zZo1Y8aMGVit1gsGozZt2pCUlERCQgItW7YkODgYX1/fcr/boUMH3n//fb7++mvi4uL4z3/+w8aNG4mLi3PBn0L5AgMDue+++5zn1apVK5577jny8vKYPHkyANOnT6dPnz506dKFwsJCvvjiCzp37gzASy+9RHR0NL169cJqtbJw4UKioqLcNvy4Ud8ZiQg6G0YMw/BwNSIiUhVz5sxh2LBh5wURMMPIpk2b2L59OwATJkxg165dtGjRokw/CjB/8Q4cOJDrr7+eYcOGMWjQIDp37oyfn1+Fxx47diwjRoxgyJAhNG3a1HkXpjx//OMfuemmmxg/fjwDBgzg5MmT3H///dU868p79tlnGTt2LHfeeSe9e/dm3759fP311zRp0gQw7+5MmzaN7t27c+WVV2Kz2ViwYAFghrTnnnuOvn370q9fPw4ePMhXX32F1eqe2GAx6sFv4aysLEJDQ8nMzCQkJMRl+y0ssRP/j+UAbJt+DaEB3hfZQkSk4SkoKCApKYm4uLgL/gJuLHJzc2nRogUvvvii8y6CVOxCf38q+/u7UTfT+HrZCPb1IruwhJO5hQojIiKN0NatW9mzZw/9+/cnMzOTp556CoAbb7zRw5U1Ho06jACEB/mcCSNFtG3q6WpERMQTXnjhBRITE/Hx8aFPnz788MMPREZGerqsRkNhJNCHQyfzNKJGRKSR6tWrF5s3b/Z0GY1ao+7AChARWDrXiMKIiIiIJyiMOIf3auIzERERT2j0YST8zPDeE2qmERER8YhGH0YiNPGZiIiIRymMBCmMiIiIeFKjDyPhZzqwauVeERERz2j0YSTCuVieOrCKiEjdN3PmTHr27OnpMlxKYeRMM82pPK1PIyJSX61btw6bzcZ1111XK8dzRyCYN2+e2xaiq+safRgpXbm32G6QVVDi4WpERKQ65syZw0MPPcSaNWtITU31dDlSRY0+jPh62QjyNSeiVVONiEj9k5OTw8cff8x9993Hddddx7x585yf3X777YwfP77M94uLi4mMjOT9998HIDs7mwkTJhAYGEh0dDQvv/wygwcP5tFHHy33ePPmzePJJ59k27ZtWCwWLBaL85inT5/mD3/4A02bNiUkJISrr76abdu2Obfdtm0bQ4YMITg4mJCQEPr06cOmTZtYtWoVv//978nMzHTuc+bMmZU6f4fDwVNPPUXLli3x9fWlZ8+eLF++3Pl5UVERDz74INHR0fj5+dG6dWtmzZoFgGEYzJw5k1atWuHr60tMTAwPP/xwpY7rSo1+Ongw747kFJaQofVpRERMhgHFebV/XO8AsFiqtMknn3xCp06diI+P54477uDRRx9l2rRpWCwWJkyYwC233EJOTg5BQUEAfP311+Tl5TFmzBgAHnvsMdauXcvSpUtp3rw506dPZ8uWLRU2w4wfP56dO3eyfPlyVq5cCUBoaCgAt9xyC/7+/ixbtozQ0FD+/e9/M3ToUPbu3Ut4eDgTJkygV69ezJ49G5vNRkJCAt7e3lx22WW88sorTJ8+ncTERABnvRfz6quv8uKLL/Lvf/+bXr168e6773LDDTfwyy+/0KFDB1577TWWLl3KJ598QqtWrUhJSSElJQWATz/9lJdffpkFCxbQpUsX0tPTy4Sn2qIwgtlvJDkjTyNqRERKFefB/8bU/nH/lgo+gVXaZM6cOdxxxx0AjBgxgszMTFavXs3gwYMZPnw4gYGBLF68mDvvvBOADz/8kBtuuIHg4GCys7N57733+PDDDxk6dCgAc+fOJSam4nP39/cnKCgILy8voqKinO//+OOPbNiwgWPHjuHra47UfOGFF1iyZAmLFi3i3nvvJTk5malTp9KpUycAOnTo4Nw+NDQUi8VSZp+V8cILL/CXv/yFW2+9FYB//vOffP/997zyyiu88cYbJCcn06FDBy6//HIsFgutW7d2bpucnExUVBTDhg3D29ubVq1a0b9//yod3xUafTMNaOIzEZH6KjExkQ0bNnDbbbcB4OXlxfjx45kzZ47z9bhx45g/fz4Aubm5fP7550yYMAGAAwcOUFxcXOYXcGhoKPHx8VWuZdu2beTk5BAREUFQUJDzkZSUxP79+wHzLswf/vAHhg0bxrPPPut8v7qysrJITU1l0KBBZd4fNGgQu3fvBmDSpEkkJCQQHx/Pww8/zDfffOP83i233EJ+fj5t27blnnvuYfHixZSU1H7/Sd0Z4WwnVvUZERE5wzvAvEvhieNWwZw5cygpKSlzJ8MwDHx9fXn99dcJDQ1lwoQJXHXVVRw7dowVK1bg7+/PiBEjXF05OTk5REdHs2rVqvM+Kx0lM3PmTG6//Xa+/PJLli1bxowZM1iwYIGzycgdevfuTVJSEsuWLWPlypWMGzeOYcOGsWjRImJjY0lMTGTlypWsWLGC+++/n+eff57Vq1fj7e3ttpp+S2EEiAjSxGciImVYLFVuLqltJSUlvP/++7z44otcc801ZT4bPXo0H330EVOmTOGyyy4jNjaWjz/+mGXLlnHLLbc4f9G2bdsWb29vNm7cSKtWrQDIzMxk7969XHnllRUe28fHB7vdXua93r17k56ejpeXF23atKlw244dO9KxY0f+53/+h9tuu425c+cyZsyYcvd5MSEhIcTExLB27Vquuuoq5/tr164tc7cnJCSE8ePHM378eG6++WZGjBhBRkYG4eHh+Pv7M2rUKEaNGsUDDzxAp06d2LFjB717965SLTWhMIKaaURE6qMvvviCU6dOMXnyZGcH0lJjx45lzpw5TJkyBTBH1bz55pvs3buX77//3vm94OBgJk6cyNSpUwkPD6dZs2bMmDEDq9WK5QIdadu0aUNSUhIJCQm0bNmS4OBghg0bxsCBAxk9ejTPPfccHTt2JDU1lS+//JIxY8bQpUsXpk6dys0330xcXByHDx9m48aNjB071rnPnJwcvv32W3r06EFAQAABARe/UzR16lRmzJhBu3bt6NmzJ3PnziUhIcHZNPXSSy8RHR1Nr169sFqtLFy4kKioKMLCwpg3bx52u50BAwYQEBDABx98gL+/f5l+JbXCqAcyMzMNwMjMzHTL/j/dnGK0/ssXxh3v/OyW/YuI1GX5+fnGrl27jPz8fE+XUiXXX3+9ce2115b72fr16w3A2LZtm2EYhrFr1y4DMFq3bm04HI4y383KyjJuv/12IyAgwIiKijJeeuklo3///sZf//rXCo9dUFBgjB071ggLCzMAY+7cuc59PfTQQ0ZMTIzh7e1txMbGGhMmTDCSk5ONwsJC49ZbbzViY2MNHx8fIyYmxnjwwQfL/LlPmTLFiIiIMABjxowZ5R57xowZRo8ePZyv7Xa7MXPmTKNFixaGt7e30aNHD2PZsmXOz9966y2jZ8+eRmBgoBESEmIMHTrU2LJli2EYhrF48WJjwIABRkhIiBEYGGhceumlxsqVKys87/Jc6O9PZX9/Wwyj7k87mpWVRWhoKJmZmYSEhLh8/6sSjzFp7kY6R4ew7JErXL5/EZG6rKCggKSkJOLi4vDz8/N0OR6Xm5tLixYtePHFF5k8ebKny6nzLvT3p7K/v9VMA0Se6TOSkasOrCIijc3WrVvZs2cP/fv3JzMzk6eeegqAG2+80cOVNR4KI5wdTZORa65Pc6F2QhERaXheeOEFEhMT8fHxoU+fPvzwww9ERkZ6uqxGQ2GE89enCfWvveFMIiLiWb169WLz5s2eLqNR06RngJ+3jUAfG6ARNSIiIrVNYeSMCPUbEZFGrh6MZ5A6yBV/bxRGzjg7C6vujIhI41I6AVhengcWxpN6r/TvTU1mbFWfkTNKJz7TLKwi0tjYbDbCwsI4duwYAAEBAerILxdlGAZ5eXkcO3aMsLAwbDZbtfelMHJGRJBmYRWRxqt0pdjSQCJSWWFhYVVeafi3FEbOCA88sz6NmmlEpBGyWCxER0fTrFkziouLPV2O1BPe3t41uiNSSmHkjLPr06gDq4g0XjabzSW/XESqQh1YzwhXnxERERGPUBg5o7TPiJppREREapfCyBkRgaXzjCiMiIiI1CaFkTPCg8quTyMiIiK1Q2HkjNIOrEV2B9mFJR6uRkREpPFQGDmjzPo06jciIiJSa6ocRtasWcOoUaOIiYnBYrGwZMmSi24zf/58evToQUBAANHR0dx9992cPHmyOvW6VWlTjUbUiIiI1J4qh5Hc3Fx69OjBG2+8Uanvr127lrvuuovJkyfzyy+/sHDhQjZs2MA999xT5WLd7ezEZ5prREREpLZUedKzkSNHMnLkyEp/f926dbRp04aHH34YgLi4OP74xz/yz3/+s6qHdrvIQE0JLyIiUtvc3mdk4MCBpKSk8NVXX2EYBkePHmXRokVce+21FW5TWFhIVlZWmUdt0MRnIiIitc/tYWTQoEHMnz+f8ePH4+PjQ1RUFKGhoRds5pk1axahoaHOR2xsrLvLBMoO7xUREZHa4fYwsmvXLh555BGmT5/O5s2bWb58OQcPHmTKlCkVbjNt2jQyMzOdj5SUFHeXCZwd3qs+IyIiIrXH7QvlzZo1i0GDBjF16lQAunfvTmBgIFdccQVPP/000dHR523j6+uLr6+vu0s7T+ksrGqmERERqT1uvzOSl5eH1Vr2MKUrQta1mU7VTCMiIlL7qhxGcnJySEhIICEhAYCkpCQSEhJITk4GzCaWu+66y/n9UaNG8dlnnzF79mwOHDjA2rVrefjhh+nfvz8xMTGuOQsXidBoGhERkVpX5WaaTZs2MWTIEOfrxx57DICJEycyb9480tLSnMEEYNKkSWRnZ/P666/z+OOPExYWxtVXX10nh/Y6R9PkmOvTWCwWD1ckIiLS8FmMutZWUo6srCxCQ0PJzMwkJCTEbcfJL7LTefpyAHbMvIZgP2+3HUtERKShq+zvb61Ncw5/HxsBpevTqKlGRESkViiM/EZpU80JLZYnIiJSKxRGfiMiyBzeqzsjIiIitUNh5DfOjqjRxGciIiK1QWHkN7Q+jYiISO1SGPmNiHOG94qIiIj7KYz8RoRmYRUREalVCiO/Ea71aURERGqVwshvqAOriIhI7VIY+Y1w9RkRERGpVQojv1HaZ+RkblGdW1VYRESkIVIY+Y2IM31Gikoc5BbZPVyNiIhIw6cw8hv+Pjb8vc31aU7mqN+IiIiIuymMlOPcphoRERFxL4WRcjhH1KgTq4iIiNspjJQjPFATn4mIiNQWhZFylE58dkJzjYiIiLidwkg5IoPUTCMiIlJbFEbKoWYaERGR2qMwUg7nLKwKIyIiIm6nMFKOs0N71WdERETE3RRGylE6C6v6jIiIiLifwkg5zm2m0fo0IiIi7qUwUo7SZppCrU8jIiLidgoj5Qjw8XKuT6OmGhEREfdSGKnA2aYadWIVERFxJ4WRCpQ21WiuEREREfdSGKmA886ImmlERETcSmGkAqXDezXxmYiIiHspjFTgbDON+oyIiIi4k8JIBTQlvIiISO1QGKmA+oyIiIjUDoWRCkRqNI2IiEitUBipQHjp+jQKIyIiIm6lMFKBiDPNNCdyCrU+jYiIiBspjFTg3PVp8rQ+jYiIiNsojFQgwMcLP2/zj0dNNSIiIu6jMHIBmvhMRETE/RRGLuDs8F5NfCYiIuIuCiMXUNpvRHdGRERE3Edh5AJK74yoz4iIiIj7KIxcQITCiIiIiNspjFxA6cRnJ9RnRERExG0URi4gQlPCi4iIuJ3CyAWomUZERMT9FEYuQCv3ioiIuJ/CyAVEBpVOeqY+IyIiIu6iMHIBpXdGCood5BWVeLgaERGRhklh5AICfGz4epl/RGqqERERcQ+FkQuwWCzOTqyahVVERMQ9FEYuIuJMv5EM9RsRERFxC4WRi9CIGhEREfdSGLkIzTUiIiLiXgojFxGuPiMiIiJuVeUwsmbNGkaNGkVMTAwWi4UlS5Zc8PuTJk3CYrGc9+jSpUt1a65VpX1G1EwjIiLiHlUOI7m5ufTo0YM33nijUt9/9dVXSUtLcz5SUlIIDw/nlltuqXKxnnC2mUYdWEVERNzBq6objBw5kpEjR1b6+6GhoYSGhjpfL1myhFOnTvH73/++qof2iHD1GREREXGrKoeRmpozZw7Dhg2jdevWFX6nsLCQwsKzdyKysrJqo7Ryla7ce0LNNCIiIm5Rqx1YU1NTWbZsGX/4wx8u+L1Zs2Y576iEhoYSGxtbSxWeLyKwdJ4RhRERERF3qNUw8t577xEWFsbo0aMv+L1p06aRmZnpfKSkpNROgeUIP3NnJL/YrvVpRERE3KDWmmkMw+Ddd9/lzjvvxMfH54Lf9fX1xdfXt5Yqu7BAHxs+XlaKShyczCkiILzWW7ZEREQatFq7M7J69Wr27dvH5MmTa+uQLmGxWIhUJ1YRERG3qXIYycnJISEhgYSEBACSkpJISEggOTkZMJtY7rrrrvO2mzNnDgMGDKBr1641q9gDSptqFEZERERcr8ptDps2bWLIkCHO14899hgAEydOZN68eaSlpTmDSanMzEw+/fRTXn311RqW6xnhZzqxahZWERER16tyGBk8eDCGYVT4+bx58857LzQ0lLy8vKoeqs6IdC6Wp4nPREREXE1r01SCJj4TERFxH4WRSijtM6JmGhEREddTGKmECN0ZERERcRuFkUoonYVVfUZERERcT2GkEtRMIyIi4j4KI5WgZhoRERH3URiphNLRNHlFdvKL7B6uRkREpGFRGKmEIF8vfLzMP6qTueo3IiIi4koKI5VgsVjUVCMiIuImCiOVVNpUo06sIiIirqUwUkkRQaXDexVGREREXElhpJLONtOoz4iIiIgrKYxUkpppRERE3ENhpJKci+WpmUZERMSlFEYqKVKzsIqIiLiFwkglhZeuT6MwIiIi4lIKI5UUrg6sIiIibqEwUkmlo2lO5hRhGIaHqxEREWk4FEYqKSrUD2+bhbwiOykZ+Z4uR0REpMFQGDEMcDgu+jU/bxvdW4YB8HPSSTcXJSIi0ng07jDy1Z/h+faw54tKfX1AXDgA6w9kuLMqERGRRqVxh5GSfMg7AWnbKvX1AW0jANhwUHdGREREXKVxh5HoHubPtIRKfb1P6ybYrBZSMvJJPa1+IyIiIq7QyMNIL/NnaoLZd+Qigny96BoTAsB69RsRERFxicYdRppfAhab2VSTnVapTZxNNUnqNyIiIuIKjTuMePtD007m80r2G+nfRp1YRUREXKlxhxE4p99I5cJIv7hwLBY4cCKXY1kFbixMRESkcVAYqWIYCfX3pnNUab8R3R0RERGpKYWR0jCSmlDpTQa0NZtq1G9ERESk5hRGoroBFshOhZxjldrEOfmZRtSIiIjUmMKIbxBEdjCfp22v1Cb948wRNXuP5pCRW+SuykRERBoFhRGo8uRn4YE+dGweBMAG3R0RERGpEYURqHInVoABZ+6OqBOriIhIzSiMQJXvjAD016J5IiIiLqEwAhDV3fx5OhnyKhcuSkfU7E7PIjOv2F2ViYiINHgKIwD+YdAkznyeXrlOrM2C/WgbGYhhwKZDujsiIiJSXQojparTb6Rt6RBfhREREZHqUhgpVY0wcrbfiEbUiIiIVJfCSKkajKjZmZpFTmGJO6oSERFp8BRGSkX3NH+e3AcFWZXaJCbMn9hwf+wOg82HTrmvNhERkQZMYaRUYASExprP03dUerP+bc7MN6KmGhERkWpRGDmXOrGKiIjUOoWRc1UjjFx6pt/I9sOnyS+yu6MqERGRBk1h5FzVCCOx4f5Eh/pRbDfYmqx+IyIiIlWlMHKu0jByIhGKciu1icVicQ7x/VlNNSIiIlWmMHKu4CgIigLDAUd/qfRmzkXz1IlVRESkyhRGfqsGnVi3ppymoFj9RkRERKpCYeS3qrGCb9vIQCKDfCkqcbD9cKZ76hIREWmgFEZ+qxp3RiwWCwM0NbyIiEi1KIz8VmkYObYbSgorvZnmGxEREakehZHfCm0J/uHgKKlWJ9bNh05RbHe4qzoREZEGR2HktywWiOlpPq9CU02HZkGEBXiTX2xnxxH1GxEREakshZHyVKPfiNVqoX+b0n4jaqoRERGpLIWR8lQjjAAMaHtmvpEkdWIVERGprCqHkTVr1jBq1ChiYmKwWCwsWbLkotsUFhby97//ndatW+Pr60ubNm149913q1Nv7SgNI0d/AXtxpTcrHVGz6eAp7A7DHZWJiIg0OF5V3SA3N5cePXpw9913c9NNN1Vqm3HjxnH06FHmzJlD+/btSUtLw+Gow508m8SBbygUZsLxRIjqWqnNOkeHEOznRXZBCbtSs+jWMtTNhYqIiNR/VQ4jI0eOZOTIkZX+/vLly1m9ejUHDhwgPNy8c9CmTZuqHrZ2WSwQ3R0O/mBOflbJMGKzWujXJpzv9hxjfdJJhREREZFKcHufkaVLl9K3b1+ee+45WrRoQceOHfnTn/5Efn5+hdsUFhaSlZVV5lHrqttvpHTRPHViFRERqZQq3xmpqgMHDvDjjz/i5+fH4sWLOXHiBPfffz8nT55k7ty55W4za9YsnnzySXeXdmHRPc2f1ezEuvFgBg6HgdVqcXFhIiIiDYvb74w4HA4sFgvz58+nf//+XHvttbz00ku89957Fd4dmTZtGpmZmc5HSkqKu8s8X+mdkfQd4Kj84nddYkII8LGRmV9M4tFsNxUnIiLScLg9jERHR9OiRQtCQ8/2n+jcuTOGYXD48OFyt/H19SUkJKTMo9ZFtAPvQCjOg5P7Kr2Zt81Kn9ZNAK1TIyIiUhluDyODBg0iNTWVnJwc53t79+7FarXSsmVLdx+++qw2iOpmPk9NqNKml55pqtlwUP1GRERELqbKYSQnJ4eEhAQSEhIASEpKIiEhgeTkZMBsYrnrrruc37/99tuJiIjg97//Pbt27WLNmjVMnTqVu+++G39/f9echbtUY1p4gP5nOrFuSMrAMDTfiIiIyIVUOYxs2rSJXr160atXLwAee+wxevXqxfTp0wFIS0tzBhOAoKAgVqxYwenTp+nbty8TJkxg1KhRvPbaay46BTeq5oia7i1D8fWyciKniP3Hcy6+gYiISCNW5dE0gwcPvuD/7c+bN++89zp16sSKFSuqeijPc3Zi3Q4OB1grl918vWz0btWEdQdO8vOBDNo3C3ZjkSIiIvWb1qa5kMh48PKDwiw4lVSlTS/vEAnAZ1vK76QrIiIiJoWRC7F5QfMu5vMqNtXc0rclPjYrW5JPs/nQKTcUJyIi0jAojFyMc/KzhCpt1izYj9G9YgB454cDrq1JRESkAVEYuZhqdmIF+MMVbQFY/ks6h07murIqERGRBkNh5GLODSNVHKbbsXkwV3VsimHAuz9Wrc+JiIhIY6EwcjHNOoPVG/JPQWbVp6W/90rz7sgnmw5zOq/I1dWJiIjUewojF+PlawYSqFZTzWXtIugcHUJ+sZ3565MvvoGIiEgjozBSGaVNNVWcFh7AYrFwzxVxAMz76SCFJZVfdE9ERKQxUBipjGpOC1/q+u4xNA/x5Xh2IUsTUl1Xl4iISAOgMFIZ5w7vrcZaMz5eVn4/yLw7MufHJK1XIyIicg6Fkcpo3gUsNsg9Dtnp1drFbf1bEehjY096Nj/8esLFBYqIiNRfCiOV4e0PTePN59Vsqgn192Zcv1gA3tYkaCIiIk4KI5VVg8nPSt09KA6rBX749QS707JcVJiIiEj9pjBSWc4wklDtXcSGBzCyWzQA7/ygSdBERERAYaTySjuxpiZUqxNrqXvOTBG/dNsRjmYV1LwuERGRek5hpLKiuoHNF7JT4egv1d5Nz9gw+rVpQrHdYN5PB11Xn4iISD2lMFJZvkHQ8Rrz+Y5ParSr0gX05v98iNzCkppWJiIiUq8pjFRFt3Hmzx2fgsNR7d0M69ycuMhAsgpKWLip6uvdiIiINCQKI1XR4RrwDYWsw5C8rtq7sVkt3H35mUnQ1iZhd2gSNBERabwURqrC2w8uGWU+r2FTzc29W9IkwJuUjHy+/qV6E6mJiIg0BAojVdXtFvPnL0ugpKjau/H3sXHHpa0BTYImIiKNm8JIVbW5AoKioOA07FtZo13dNbANPjYrW5NPs/lQhmvqExERqWcURqrKaoNuN5vPa9hU0zTYlzG9WgDw9hpNgiYiIo2Twkh1lIaRxGVQmF2jXf3hCrMj69e70jl4IremlYmIiNQ7CiPVEd0TIjpASQHs/qJGu+rQPJjB8U0xDHh3re6OiIhI46MwUh0Wy9mOrDsW1nh3956ZBG3hpsMczy6s8f5ERETqE4WR6iptqjnwPeQcq9GuBraLoEdsGPnFdl74OtEFxYmIiNQfCiPVFdEOWvQBwwG/LK7RriwWC9Ov7wzAJ5tT2Hkk0xUVioiI1AsKIzVROj389pqNqgHo0zqcG3rEYBjw1Be7MGqwMrCIiEh9ojBSE11vAosVjmyCk/trvLu/jOyEn7eVDUkZLNupWVlFRKRxUBipiaBm0Haw+XznpzXeXYswf+69sh0A//vVbgqK7TXep4iISF2nMFJT5zbVuKBpZcpVbYkK8ePwqXzm/KihviIi0vApjNRUp+vAyw9O/gpp22q8uwAfL/46shMAb3y/j2NZBTXep4iISF2mMFJTfiEQP9J87oI5RwBu7BlDr1Zh5BXZeU5DfUVEpIFTGHGF0qaaHYvAUfN+HuZQ30sAWLT5MNsPn67xPkVEROoqhRFXaD8M/MIgJx0O/uiSXfZq1cS5iN5T/9VQXxERabgURlzBywe6jDaf13Al33P9ZUQn/L1tbDp0iv9uT3PZfkVEROoShRFXKV2rZtd/odg1nU6jQv24b7A51PfZr3aTX6ShviIi0vAojLhKq8sgpAUUZsKv37hst/dc0ZaYUD9SMwt4+4cDLtuviIhIXaEw4ipWK3Qdaz53YVONv4+Nv15rrlsze9V+0jM11FdERBoWhRFX6n5mVM3ebyD/tMt2O6p7NH1bNyG/2M4/l+9x2X5FRETqAoURV2reFZp2Bnsh7P6vy3ZrsViYPsoc6rt46xG2JJ9y2b5FREQ8TWHElSwW6Haz+dxFE6CV6t4yjJv7tATMob4Oh4b6iohIw6Aw4mqlYSRpDWS5djju1OHxBPjYSEg5zdJtqS7dt4iIiKcojLhakzYQOwAwXLKS77mah/jxwJD2ADy7bA95RSUu3b+IiIgnKIy4Q+mcIy5uqgGYfHkcLZv4k55VwKvf/ury/YuIiNQ2hRF36DIGLDZIS4ATrg0Mft42/nGd2Zn136sP8KVmZhURkXpOYcQdAiOh/VDz+Zb3Xb77EV2juOeKOAAeX5jAziOZLj+GiIhIbVEYcZe+d5s/N7wNWa7vbPrXkZ25qmNTCood3Pv+Jo5nF7r8GCIiIrVBYcRdOo6A2EuhJB9WzXL57m1WC6/d1ou2TQNJzSxgygebKSzR2jUiIlL/KIy4i8UCv3vKfL71Azjm+plTQ/29eeeuvoT4ebH50Cn+sXgnhqH5R0REpH5RGHGnVgOg0/VgOODbp9xyiLZNg3j99t5YLbBw82HeXXvQLccRERFxF4URdxs6AyxWSPwSkn92yyGu7NiUv58ZYfPMl7tYvfe4W44jIiLiDgoj7ta0I/S603y+Yjq4qRnl7kFtGNe3JQ4DHvxwCweO57jlOCIiIq5W5TCyZs0aRo0aRUxMDBaLhSVLllzw+6tWrcJisZz3SE9Pr27N9c/gaeDlDynrYc+XbjmExWLh/43uSp/WTcguKOEP728iM7/YLccSERFxpSqHkdzcXHr06MEbb7xRpe0SExNJS0tzPpo1a1bVQ9dfIdEw8H7z+bdPgt0907j7etl4844+xIT6ceB4Lg9/tBW7FtQTEZE6rsphZOTIkTz99NOMGTOmSts1a9aMqKgo58NqbWQtRIMeAf9wOLEXEj5w22GaBvvy1l198fO2snrvcZ5dttttxxIREXGFWksEPXv2JDo6mt/97nesXbv2gt8tLCwkKyurzKPe8wuFK6eaz7+fBUW5bjtU1xahvHhLTwDe/iGJRZsPu+1YIiIiNeX2MBIdHc2bb77Jp59+yqeffkpsbCyDBw9my5YtFW4za9YsQkNDnY/Y2Fh3l1k7+k2GsFaQkw4/z3broa7rHs3DQzsA8LfPdrD50Cm3Hk9ERKS6LEYNZsmyWCwsXryY0aNHV2m7q666ilatWvGf//yn3M8LCwspLDw7vXlWVhaxsbFkZmYSEhJS3XLrhu2fwGf3gG8IPJwAgRFuO5TDYXDf/M18/ctRIoN8eO22XlzWLtJtxxMRETlXVlYWoaGhF/397ZGOG/3792ffvn0Vfu7r60tISEiZR4PR9WaI6g6FWfDDC249lNVq4aVxPekcHcKJnCJuf3s9f1+8g+wCjbIREZG6wyNhJCEhgejoaE8c2vOsVvjdk+bzDW/DqYNuPVygrxef/PFSbh/QCoD565MZ/vIaViUec+txRUREKqvKYSQnJ4eEhAQSEhIASEpKIiEhgeTkZACmTZvGXXfd5fz+K6+8wueff86+ffvYuXMnjz76KN999x0PPPCAa86gPmp3NbQdDI5i+O5ptx8u2M+b/x3TjQ/vGUCr8ABSMwuYNHcjf1q4jcw83SURERHPqnIY2bRpE7169aJXr14APPbYY/Tq1Yvp06cDkJaW5gwmAEVFRTz++ON069aNq666im3btrFy5UqGDh3qolOop4aduTuyYyGkbauVQ17WLpLlj17B3YPisFhg0ebDDHt5Nd/80ogmoBMRkTqnRh1Ya0tlO8DUO4smw85F0HYI3LWkVg+9+VAGUxdt58Bxc4jxqB4xzBx1CRFBvrVah4iINFx1ugOrnHH1P8DqDQe+h/3f1eqh+7QO56uHr+C+we2wWS38d1sqv3t5DUu3pVIP8qmIiDQgCiOeFB5nzj0CsGIGOBy1eng/bxt/GdGJJfcPolNUMBm5RTz80Vbu/c9mjmUV1GotIiLSeCmMeNqVU8EnGNK3wy+feaSEbi1DWfrg5fzPsI542yys2HWUa1/7gZ/2nfBIPSIi0rgojHhaYKS5bg2Yi+jleGbIrY+XlUeGdeCLh65wzktyx5z1zF61X802IiLiVgojdcHA+yGkBZxOhneGwYmKJ4Rzt/ioYBbffxk392mJw4B/Lt/Dvf/ZTJYmShMRETdRGKkLfALhrqXQpA2cPgRzfgfJ6z1Wjp+3jedv7s6sm7rhY7OyYtdRbvi/H9md1gAWLBQRkTpHYaSuiGwPk1dCTG/Iz4D3b4Dd//VYORaLhdv6t2LRfQNpEebPwZN5jPnXWj7VCsAiIuJiCiN1SVBTmPQFdBwJJQXw8Z3w85seLal7yzC+eOhyruzYlIJiB48v3MbfF++gsMTu0bpERKThUBipa3wCYfwH0HcyYMDyv8DXf6/1Yb/nahLow9xJ/Xh0WAcsFnN9m3FvruPI6XyP1SQiIg2HwkhdZPOC616EoTPM1+teh0/vhmLPzf1hs1p4dFhH5k7qR1iAN9sOZ3L9az+weu9xj9UkIiINg8JIXWWxwBWPwU1vm7O0/rIY/jMG8jI8Wtbg+Gb898HL6dYilFN5xUyau4FXV/5Kid1zd25ERKR+Uxip67qPgzs+Bd8QSP4J3h0Opw55tKTY8AAWThnIbf1bYRjw8sq9XPPyGr7akaY5SUREpMq0UF59cXQXzL8Zso5AUHO4/ROI6enpqvh082Ge+Wo3GblFAHRvGcpfRnRiUPtID1cmIiKeVtnf3woj9UlWKsy/BY7uBO9AGPs2dLrO01WRXVDMOz8k8c4PB8gtMkfZXN4+kr+M6ES3lqEerk5ERDxFYaShKsiCT+6EA6vM133vhmueAZ8Aj5YFcCKnkNe/28f89Ycotpt/ra7rFs3j13SkbdMgD1cnIiK1TWGkISspMtexWfe6+ToyHsa+A9HdPVvXGSkZeby8ci+Ltx7BMMyROOP6xvLI0A5Ehfp5ujwREaklCiONwf7vYfEUyEk3R9wMmwGXPgDWutEveU96Fi98ncjK3ebif75eVn4/KI77rmpHaIC3h6sTERF3UxhpLHJPwtKHIPFL83XbwTD6TQiJ9mhZ59p4MIN/LtvDpkOnAIgI9GHmDV24vns0FovFw9WJiIi7KIw0JoYBW96D5dOgOA/8m8ANr0Pn6z1dmZNhGHy35xjPLtvDr8dyABjWuTlPj+6qphsRkQZKYaQxOvErfDoZ0raZr/tMguH/a04xX0cUlTiYvWo/r3//K8V2g2BfL6Zd25lb+8ViteouiYhIQ6Iw0liVFMH3T8Pa1wADIjqYnVvrwJwk59p7NJu/fLqdrcmnAbi0bTjP3tSdNpF1JziJiEjNKIw0dgdWm51bs1PNzq1X/wMGPWJOM19H2B0G7/10kOe/TiS/2I6vl5XHfteRyZfH4WWrG51wRUSk+hRGxFzH5r+PwO6l5us+k+C6l8Bq82hZv5WSkce0z3bw474TAHRrEco/x3bnkhhdaxGR+qyyv7/1v58NWUA4jHvfXAHYYoXN82DxH8Fe7OnKyogND+A/k/vz3M3dCfHzYseRTG54/Ude+DqRgmK7p8sTERE3052RxmLnZ/DZPeAogfjr4Ja54OXr6arOcyy7gBmf/8KynekAtGsayMNDOzCiaxS+XnXrjo6IiFyYmmnkfHu/ho/vBHshtB0Ct86vUyNtzrV8ZxpPfP4Lx7MLAXNukvH9Yrl9QCtaNvH81PciInJxCiNSvqQ18OGtUJwLsZfChE/Ar24uZpeZV8x76w7y0YZk0jILALBa4OpOzblzYGuuaB+p4cAiInWYwohULGUjzB8LBZkQ3QPuWAyBEZ6uqkIldgcrdx/jg58POTu5ArSOCOCOAa25pW9LwgJ8PFihiIiUR2FELixtO/xnDOSdgKad4M4ldWoK+YrsP57DBz8fYtHmw2QXlADmmjejesRw18DWdG8Z5tkCRUTESWFELu74Xnj/RnMukiZt4K6l0KS1p6uqlLyiEpYmpPL+ukPsSstyvh8XGUjn6GA6Nj/7aBMRoHlLREQ8QGFEKufUQTOQnDoIwTFw1+fQtKOnq6o0wzDYknyaD34+xJfb0yiyO877jo/NSrtmQcQ3D6JD82DimwcTHxVMizB/9TkREXEjhRGpvKw0+M9oOL4HAiLhriUQ1c3TVVXZqdwith/J5Nej2SSmZ7P3aDZ7j+aQX8FcJYE+Nm7oGcMfr2ynaehFRNxAYUSqJvckfDDGXGTPLxSueBz8wsA32Hz4BIFv0JnnwebzOjhPyW85HAaHT+Wz92g2iUfNgJKYns2B47nOuyhWC1zfPYb7h7SjU5T+fomIuIrCiFRdQSbMHwcpP1fu+1ZvM5QER8Ol90PP2+vcVPMVKbE72HToFG+u3s+qxOPO94d1bsb9Q9rTu1UTD1YnItIwKIxI9RTlwtpX4eR+KMyGopyyPwtzoCS//G2bXQK/ewraD6tTC/JdzM4jmcxevZ+vdqRR+q9hYNsIHhjSnkHtI7DUo3MREalLFEbEfewlZUPKryvghxfMOysAcVeaoSSml2frrKL9x3P49+r9fLblCCUO859Fj5ah3D+kPb/r3FydXUVEqkhhRGpXXgb8+BKs/zfYi8z3ut0CV//DHDZcjxw5nc/baw6wYGMyBcVmv5IOzYK498q2XNstmkBfLw9XKCJSPyiMiGecOgTfPwPbPzZf23yg/71mh9iAcM/WVkUncgqZuzaJ9386RHahOcGan7eVYZ2bc0OPGK6Kb6rF+0RELkBhRDwrbRt88wQkrTZfl47Q6f9H8PbzbG1VlFVQzAc/H+KTjSkcPJnnfD/Ez4uRXaO5sWcMA9pGYFMzjohIGQoj4nmGAfu/hRUz4OhO873QWOg9EZp1gogOEN4WvOrHujKGYbDjSCafJ6TyxfZUjmYVOj9rFuzL9d1juKFnDD1ahqrTq4gICiNSlzjsZrPNd09D1pGyn1ls5hT0kR0hsoMZUEqfB0TU2VE5dofBhqQMlm47wlc70snML3Z+1joigBt6xDC6VwvaNQ3yYJUiIp6lMCJ1T3E+bP0ADm+CE3vhxK9QlF3x9/2bQGQ8xPaD1oOg1aXme3VMUYmDNXuP8/m2VFbuOlpmxteesWGM7d2CUT1itLKwiDQ6CiNS9xkG5Bw9E0z2wol9Z0NKZnI5G1igeRdofZn5aHUZBDev9bIvJLewhJW7j7Jk6xHW/HoC+5khwt42C0M7NWdsn5YMjm+KtxbuE5FGQGFE6reiPMjYD+k7IXkdHPoJTv56/vci2p8JJ4PMn2Gtar/WChzPLuTzhCN8tuVImZWFwwN9uKFHDGN7t6RrixD1LxGRBkthRBqenGNmKCl9HN0J/Oavb/OuZ2aBHeqREiuyOy2Lz7YcZklCKsezz3Z87dg8iJt6t+TartE0C/HFz1tDhUWk4VAYkYYv/zSkrIdDa81wkroVHOZ8IHQYDsOfMTvC1iEldgc/7DvBZ1uO8M0v6RSWOMp87uNlJdTf2/kI8fMq+/rMz0vbRhAbHuChsxARqRyFEWl88k/Bmhdg/ZtmKLF6mROuXfXnOtnxNaugmK+2p/HplsNsST7t7F9SGT5eVu4f3I77BrfTxGsiUmcpjEjjdWIffPMP2LvMfO3fBIb8Hfr8Hmx1cyp3wzDIKSwhM7/Y+cgq8/zsZ4dO5rLtsLkOUNvIQJ4e3ZXL2kd6+AxERM6nMCKy/ztY/jc4vtt83bST2XTTfphn66ohwzD4YnsaT32xy9n/ZEyvFvz9us5EBvl6uDoRkbMURkTAXGF4yzz47hnIzzDf63ANXPMMNO1Y+f047IAFrHVnSG5WQTEvfJ3If34+hGGY09P/dWRnbu0XqxWGRaROUBgROVf+aVjzfNn+JH3vNmd8LcyEgiwozKr4Z1EOBETC4L/WueaehJTT/O2zHc7hw71bhfG/N3WjU5T+rYiIZymMiJTnxD5Y8QQkflX9fTTtDCNmQbshrqurhkrsDt5bd4iXvkkkt8iOzWrhD5fH8ciwDgT41J3gJCKNi8KIyIXs/w42zzOf+4aYqwr7hoBfSPk/fUNg1xL4/hlz1A5A/LVwzdMQ0c5TZ3GetMx8nly6i+W/pAPQIsyfv13bmfbNgrBZLdisFrzO+2nFZiv7viZiExFXUBgRcYe8DFj9T9jwNhh2sHrDpffBlVPN4FJHfLv7KNM//4Ujp/OrvG2LMH9u6BnD6J4tiI8KdkN1ItJYuC2MrFmzhueff57NmzeTlpbG4sWLGT16dKW2Xbt2LVdddRVdu3YlISGh0sdUGJE659ge+PpvsP9b83VgUxg6HXpOAGstzfthGFCUC3knzzwyznl+kuKcE+w/eIijmXl8S3+WG5eS7/CmxGFgNwzsDuOic5t0igrmxp4tuKFnDC3C/GvnvESkwXBbGFm2bBlr166lT58+3HTTTZUOI6dPn6ZPnz60b9+eo0ePKoxI/WcY8Os3sHyauY4OQFR3GPlPc50cVyrKg5SfIWkNHFwLmYfN0GEvvPi2pfybmGGp793OpiXjTCgpORNMikocrDtwkiVbj/B94jGK7Wf/89A/LpzRPVtwbbcorUAsIpVSK800Foul0mHk1ltvpUOHDthsNpYsWaIwIg1HSRFseMtsvik8syBelzHQ43Zo0sZcvM/br+r7PLLZDB9Ja+DwBrAXlf9dmy8ERkJAOAREnP/Iy4Ct/4HMlLPbtB0MfSdD/EiweZe728y8Yr7amcaSrUdYn5ThfN/bZuGqjs0Y3SuGYZ2baz0dEalQnQojc+fOZfbs2fz00088/fTTFw0jhYWFFBae/T++rKwsYmNjFUakbss5Dt8/DZvfo+wCfhYIiTGDSZM20CTunOdtzCBhOCB9ByStNsPHoXVQnFt2/yEtIO4qiLsCmnU+Gza8A+BiHU4ddvh1BWx617ybU1pfcDT0vgt6T4TQFhVunno6n/9uS2VJQiq7z1mBONDHRpeYUOKjgukUHUynqGA6Ng8m2K+cgJOXAQWZENa6Ts3XIiLuU2fCyK+//srll1/ODz/8QMeOHZk5c+ZFw8jMmTN58sknz3tfYUTqhbTtsPZVOL4HTh005yi5EJ8gsNjM+U7OFRABcVeeeVwF4W0vHjoq49Qh2PIebHkfco+b71ms0HEk9Lsb2l59wbCw92g2S7Ye4fOE1Ao7yLYI86dTVDDxzYMY5J1I97RFBCUtx+IoNkcmRXWH6B7mI6YnRLSvvb42IlJr6kQYsdvtXHrppUyePJkpU6YAVCqM6M6INBiGYfbtOHXQfGQknX1+6iBkHcF5l8I3BFoPOhtAml3i3jsIJUWw57+waS4c/OHs+8ExZvNN/LXmXRiv8qeYNwyD3WnZ7EnPIjE9mz3p5vOjWYWEkMtY2xom2L6lvTXVuU0RXvhQct6+iq1+ZIZ2Ije8K8XNumFt0QufqE7YLV7kF9vJK7JTUGQnv/jMo8hOwZn3S98L9PFieJcojQASqUPqRBg5ffo0TZo0wWY7+388DocDwzCw2Wx88803XH311Rc9jvqMSINVUginU6A4zwwfnprZ9XiiGUq2fWg2pZTyCYJ2V5vBpMM1EBhx4f0c2Uzhz2/jtWsxNnsBAPkWP5Y6Luf9oqvZY8TS3nKErpaDdLUm0dWaxCWWQwRazu+IW2B485n9cl4uuZnjVH7V5fjmwdzQM4ZR3WNoFRFQ6e0qklVQTGZeMS2b+Gv+FZEqqhNhxOFwsGvXrjLv/etf/+K7775j0aJFxMXFERgYeNHjKIyI1JLiArPPSuJXkLgMctLPfmaxQuylZ++aRLY33y/KhR2LzP4oaQlnv9/sEnPkTvfxOHyCOXI6nyOn889blTgrrwDfzCQisnYTlZdI66JfaW/fTxBmE1A+vnzkNZolATdh8QnCz9tGgI8Nfx8bft42/M88Dp7MZfXe42VGAPWIDeOGHjFc3z2a5iF+YC+GXxbD1g/Mu0+XP1bm7pNhGKRk5LM5OYNNB0+x+dApEo9mYxjmMOdb+8UyulcLjSYSqSS3hZGcnBz27dsHQK9evXjppZcYMmQI4eHhtGrVimnTpnHkyBHef//9crevTDNNdU9GRFzI4TDDReIy83F0R9nPIzqYfT5+/ebsKCKbjzmSqO/dEDug+n1cHA5zKPOK6XB4o/leUHMY8jfoeUeFd5Ay84r5+pd0lm5L5af9JyidRiXYksfUpusZW/RfAgvOBixHp1Hs6PdPNqYWmuEj+ZRzJeRz2awW55wsPl5WRnaNYny/WC6Ni9CihCIX4LYwsmrVKoYMOX9NjokTJzJv3jwmTZrEwYMHWbVqVbnbK4yI1FOnkyFxuXnX5OCP4Cg++1mTOOj7ezMoXKwppyoMw5yGf+VMs48NQNNO8LunzGajC4SdY9kFrN64Da9NbzE09ytCLHkAnDBC2BZ8JVfmfI03xexytOYPRY+TSiRgDl3u2iKUPq2a0LdNE3q3aoKvl43Ptx3how0pZUYTtQoPYHy/WG7u09K88yIiZWg6eBFxn4JM2PctpG0zmzvaDnF/Z9tNc8y5XErXBoq7En73/8zROL91dBf89H+wY6EzNJ0KaMP7jOJfGX0oxIfelr382+clmlqyyLKG8W3Pl2nZfTDdWoRWOHeKYRjsPJLFgo3JLE1IJbvQ7IxrtcDVnZoxvl8rhsQ3xcumocsioDAiIg1R/mn44UVY/+bZSeC6j4ern4DQluaooLWvwb4VZ7dpdRkMehg6DAerlX3Hcvh+zzFCA7wZEJ5Hq28mY0nfYTYxXf8K9JpQqVLyikr4akc6H29MZuPBU873mwb7MqhdBM1D/YgK8aN5iB/NQ3xpFuxHsxBffL00hFkaD4UREWm4Th2C7/6feecDzFloI9rBsTMd5i1W6DwKLnsYWva98L6KcmHxFNi91Hw98EGzGagK857sO5bDwk0pLNp8mJO5FcyUC3hTwiX+p+kakEFHn5O0sR6jqTWb8FadaN6hL5aobhAa65r5ZETqAIUREWn4jmwxO7mWzpPi5Q+97oCB95uTxFWWwwGrnzWbgcDsjzL2HfALrVI5RSUO1u48QEbKLoyMJHyyDhGQe5gmRUeIsqcTxUlslgv/J9fuE4ItqitEdYXmZ342uwS8tVCh1D8KIyLSOBiGuXry6RTofEPNOtDu/AyW3A8l+RAZD7d95FxUsEJ5GXDoJzi01gxF6TspuxxAWQ4vf/KCWpHp24LjXtEkF/hhHN9LRw7RznIEH4v9/I0sVghvZwaTkBbmXRuLzXy/zHOr+dx65rWXnzkMOyS6+n8mIjWgMCIiUh2pW+Gj2yE71Vzp+Jb3oO1VZz/PPXEmeKw1fx7def4+gqIgPK7sOkThZ54HNj2vGSa3sISVu4/y5dZDpO7bRgfjEJ2tyXS2HKK7dwqhjszzj1FZ3gFm09Ogh8FXs9NK7VIYERGprux0WHC7uXKyxQZXPA75GWYAOb77/O9HxkOby6HNIHNK/+Coah/6VG4Ry3am83nCETYczMAwDJpymq62ZEY0PUn7oEJz4UPDcfanYcdi2MFhnH1uOIgsOkxs/h4AjICmWIb81VwUsYKVmivN4YDMZHOhxQqWCxABhRERkZopLoClD8GOT87/rNklZuhoc7n5M6ipW0pIzyzgi+2pfJ6Qyo4j1bk7YjDcupG/eC2grdWc7O24byv2dvsTzfqOoV2z4KpN2nZ8L2z/2PwzOZ0MYa3MEUjth1ajtgqUFJoz+hZmmXem/MPNnwFnfvqFalHFekRhRESkpgzDHEa850to3sUMHq0HuXZit0o6cDyHL7ankXo6H5vVcvZhsZR5bbVY8LJasFotFNsd7ErNYkfyCX6X/xWPeH1GhCUbgPWOTrxquRNLbF96xobRM7YJcZEBOAwosRvYHQYlDgeW3GOEHfgvkfsXE5RRTpMUQI/bYPj/moGhugwD9nwB3/zj7AR35bKYgeS8gOJVcd8Z53OL+bx5V+gyuuZ3iOSiFEZERAQwJ2tLyyxg54EUAje+Tr+0j/DBHIL8hf1SnisZT7LR3Pl9fwq4xrqJMba1XG7dgZfFAUCxYWO1oztL7JezznEJD3otYaLXN1gxyPduwqkrnyTqsjuxVnXSt/SdsPyvZ0dFBUVB64HmvDL5GeZEd3mnoCjbFX8cppCWcNmD0Psu8Ln4GmlSPQojIiJSvswjOL57Gsu2j7BgUGLxYqn3SL4t6sK1lp8YYmwggALn13dZ4/nOZwg/+l5BnncYNqsFh8NgT3o2l9gTedb7beKthwH4kV58ETuV9h07c2nbCDpHh2Arpykot7CE1NQU/H+YRcyBhVhxUGzxZpHPGF4uvJ6QkDAuaxfBZe0iGdg2gtAAb3Ohw/xTZ8JJxtnnBZlg2M/0oSntT+M457n97OclhbDrc8g9Zhbi3wT6/xH63+uRO16VYhjmPDgZB8w6XR2ejmyBH1+Gm94Gb9cua6AwIiIiF5a+05ynZf+353/WJM6c3bb7uAqHNxeW2NmWksmm/ek02/4mN2TOx8dSQq7hy/Ml43nffg2Bfj70axNO28hA0rIKOJyRR1pGFjcUfsHDXp8RYjFXZ/7CPoBnS27nsHF+/xuLBbrGhHJZezOc9GvThACf8hdLrJTiAtj2Eax9FU4lme95B5h3SQY+YPaFqStO7ocvH4MDq8zX4W1hzFsQ26/m+7YXw5oXYM3zZli76i/mYpQupDAiIiKVs/87+O4ZyEqFTteZIaRl3yrPBFt8dA+Fnz1I0FFzpeVtRgf+XDSZRKP0l7vBUOsW/u4139mhdr+tHZ9HP0hhzEBahgcQ28SfmDB/DhzP5af9J/hp/0n2Hcspcxxvm4VesU2c4aRnbBg+XtVYD8hhN++SrH3FXGcJzL4nXW+GQY9A80sq3jYvA078Cid/hRN74cQ+86d/E7h0ClwyumYdbUsK4cdXzOUP7IXmLMP+YZBz1OwHc/ljZnjw8qne/o/tgcV/NFfmBnO17eteqlm/n3IojIiISO1zOGDzXHOl5cIsDIsXCa0nsdH/cq49+iYtM342vxbQFOuw6dBzwkV/aR/NKmDd/pOs3WeGkyOn88t87m2z0CTAh7AAb8ICfAjz93a+Dg0489z/zGcB3nhZLRSWOCiyOygqcVBYbCfoyI+03v0WkcfXOfebHHkl21vcSpCXQVTxIZrkHSI49yB+p/dhzT954T+H8LYw6FHocWvVhz8fWG3eDTm5z3zd7mq49gUIiIBlfzZHNAFE9zDvkjTrVPl9O+zw87/g2/9nhhy/MLjuReh2c9VqrCSFERER8ZysVPjyT5D4Zdn3bT5w6X1wxZ/Ar+r/PTcMg+SMPH46E07W7T95wfWAqqqb5QBTvJYy0roR60Wm7k83IkixteSYTywZ/q3JDYqjY/FuBp5YiH9JFgC5vs34te0kDrcbh49/MP4+Nvy8bfh72wjwsdEmIvDs8Oqc4/DN38+GjaDmMGIWdLmp7F2qX5bAF4+a/WVsvjBsBgy47+IrZ586aM4wfGit+br9MLjhdbfO0KswIiIinlXa8fKrqWbzQqfr4Zr/V7V1gy56CIPUzAJO5RaRmV/MqbwiTucVc7r0Z/7Z56Wf2Q0DH5sVHy8rvl5WfLxs5vNz3mvpOMLwzIV0zfmJ07YmpFhasM8RzZ6SKHYUNuOAI5o8yu/sGUABt9m+4x6vL4mymCs6ZxhBzCsZwXv2a8gkyPndFmH+jO0dwyS/NYT/9AwUnAYs0O8PcPU/zKaZ8mSnw+cPnl2hus0VMHo2hMWW94cEW96Dr/8ORTngHQjDn4E+k9y+KKPCiIiI1A2FOZCdBpEdPF2JS9gdBln5xWTkFXEqt4hTecWcyi0iI6+InIISCort5BfbKSrMp2fG1wzL+JDmJakA5OHPUu/hvM/17M8Poo39IP/rPYc+1l8BOB3aCf8x/4dvm/4XL8QwzCaxr/8OxXngGwIjnzObhkpDRna6OXnfr9+Yr1tdBqP/ZS5PUAsURkREROoCewnsWmIOny1dy8jmgz1uMJb932I17OQYfrxUcgvv2a8hwM+XUT1iGNc3lh4tQ7Fc7O7Fyf2weAoc3mC+7jwKrn8VklbBl4+fac7xgaufMEcL1eIMtgojIiIidYlhmHcofngRUtaffb/zKNIGzuSTvQYLN6dw+NTZDrodmwcxrm8so3u1IDLobEfYvKISjmcXciKn0PyZmUubxDkMTP43NuzkW/zwN8y5Yk6GdObQFS/RrH1PYkL9q7YEQA0pjIiIiNRFhgGHfoIdCyF+JHQc7vzI4TD4+cBJPtmUwrKd6RSWmLPfelktdIkJ4XR+MSeyC8ktspe76y6Wg7zk/S/irYcpMay8YR/N/5WMpgRzXhYfLyutwwOIiwwkLjKQNpGBtIkIpG3TQJoF+178LkwVKYyIiIjUY5n5xfx3WyoLNx9mW8rp8z7397bRNNiXyCAfmgb7nnnuS/MA6HpiOcl+8WwtiuXgyVwOnMglJSOPYnvFv/L/3+iu3Hlpa5eeQ2V/f9dgCjsRERFxl1B/b+64tDV3XNqavUezOXA8h4ggX5oG+RIZ7Eugj+0CdzI60g247px3SuwOUk8XkHQyl4Mnckk6kcvBk+bPw6fyaRMRUAtnVT7dGREREWnkiu1mc5B3VRc5vAjdGREREZFKcXUIqSrPHl1EREQaPYURERER8SiFEREREfEohRERERHxKIURERER8SiFEREREfEohRERERHxKIURERER8SiFEREREfEohRERERHxKIURERER8SiFEREREfEohRERERHxqHqxaq9hGIC5FLGIiIjUD6W/t0t/j1ekXoSR7OxsAGJjYz1ciYiIiFRVdnY2oaGhFX5uMS4WV+oAh8NBamoqwcHBWCwWl+03KyuL2NhYUlJSCAkJcdl+6xqdZ8Oi82w4GsM5gs6zoanKeRqGQXZ2NjExMVitFfcMqRd3RqxWKy1btnTb/kNCQhr0X5xSOs+GRefZcDSGcwSdZ0NT2fO80B2RUurAKiIiIh6lMCIiIiIe1ajDiK+vLzNmzMDX19fTpbiVzrNh0Xk2HI3hHEHn2dC44zzrRQdWERERabga9Z0RERER8TyFEREREfEohRERERHxKIURERER8ahGHUbeeOMN2rRpg5+fHwMGDGDDhg2eLsmlZs6cicViKfPo1KmTp8uqsTVr1jBq1ChiYmKwWCwsWbKkzOeGYTB9+nSio6Px9/dn2LBh/Prrr54ptgYudp6TJk067/qOGDHCM8VW06xZs+jXrx/BwcE0a9aM0aNHk5iYWOY7BQUFPPDAA0RERBAUFMTYsWM5evSohyqunsqc5+DBg8+7nlOmTPFQxdUze/Zsunfv7pwMa+DAgSxbtsz5eUO4lhc7x4ZwHcvz7LPPYrFYePTRR53vufJ6Ntow8vHHH/PYY48xY8YMtmzZQo8ePRg+fDjHjh3zdGku1aVLF9LS0pyPH3/80dMl1Vhubi49evTgjTfeKPfz5557jtdee40333yT9evXExgYyPDhwykoKKjlSmvmYucJMGLEiDLX96OPPqrFCmtu9erVPPDAA/z888+sWLGC4uJirrnmGnJzc53f+Z//+R/++9//snDhQlavXk1qaio33XSTB6uuusqcJ8A999xT5no+99xzHqq4elq2bMmzzz7L5s2b2bRpE1dffTU33ngjv/zyC9AwruXFzhHq/3X8rY0bN/Lvf/+b7t27l3nfpdfTaKT69+9vPPDAA87XdrvdiImJMWbNmuXBqlxrxowZRo8ePTxdhlsBxuLFi52vHQ6HERUVZTz//PPO906fPm34+voaH330kQcqdI3fnqdhGMbEiRONG2+80SP1uMuxY8cMwFi9erVhGOa18/b2NhYuXOj8zu7duw3AWLdunafKrLHfnqdhGMZVV11lPPLII54ryk2aNGlivPPOOw32WhrG2XM0jIZ3HbOzs40OHToYK1asKHNurr6ejfLOSFFREZs3b2bYsGHO96xWK8OGDWPdunUerMz1fv31V2JiYmjbti0TJkwgOTnZ0yW5VVJSEunp6WWubWhoKAMGDGhw1xZg1apVNGvWjPj4eO677z5Onjzp6ZJqJDMzE4Dw8HAANm/eTHFxcZnr2alTJ1q1alWvr+dvz7PU/PnziYyMpGvXrkybNo28vDxPlOcSdrudBQsWkJuby8CBAxvktfztOZZqSNfxgQce4Lrrritz3cD1/zbrxUJ5rnbixAnsdjvNmzcv837z5s3Zs2ePh6pyvQEDBjBv3jzi4+NJS0vjySef5IorrmDnzp0EBwd7ujy3SE9PByj32pZ+1lCMGDGCm266ibi4OPbv38/f/vY3Ro4cybp167DZbJ4ur8ocDgePPvoogwYNomvXroB5PX18fAgLCyvz3fp8Pcs7T4Dbb7+d1q1bExMTw/bt2/nLX/5CYmIin332mQerrbodO3YwcOBACgoKCAoKYvHixVxyySUkJCQ0mGtZ0TlCw7mOAAsWLGDLli1s3LjxvM9c/W+zUYaRxmLkyJHO5927d2fAgAG0bt2aTz75hMmTJ3uwMnGFW2+91fm8W7dudO/enXbt2rFq1SqGDh3qwcqq54EHHmDnzp0Nol/ThVR0nvfee6/zebdu3YiOjmbo0KHs37+fdu3a1XaZ1RYfH09CQgKZmZksWrSIiRMnsnr1ak+X5VIVneMll1zSYK5jSkoKjzzyCCtWrMDPz8/tx2uUzTSRkZHYbLbzev0ePXqUqKgoD1XlfmFhYXTs2JF9+/Z5uhS3Kb1+je3aArRt25bIyMh6eX0ffPBBvvjiC77//ntatmzpfD8qKoqioiJOnz5d5vv19XpWdJ7lGTBgAEC9u54+Pj60b9+ePn36MGvWLHr06MGrr77aoK5lRedYnvp6HTdv3syxY8fo3bs3Xl5eeHl5sXr1al577TW8vLxo3ry5S69nowwjPj4+9OnTh2+//db5nsPh4Ntvvy3T7tfQ5OTksH//fqKjoz1ditvExcURFRVV5tpmZWWxfv36Bn1tAQ4fPszJkyfr1fU1DIMHH3yQxYsX89133xEXF1fm8z59+uDt7V3meiYmJpKcnFyvrufFzrM8CQkJAPXqepbH4XBQWFjYYK5leUrPsTz19ToOHTqUHTt2kJCQ4Hz07duXCRMmOJ+79Hq6pr9t/bNgwQLD19fXmDdvnrFr1y7j3nvvNcLCwoz09HRPl+Yyjz/+uLFq1SojKSnJWLt2rTFs2DAjMjLSOHbsmKdLq5Hs7Gxj69atxtatWw3AeOmll4ytW7cahw4dMgzDMJ599lkjLCzM+Pzzz43t27cbN954oxEXF2fk5+d7uPKqudB5ZmdnG3/605+MdevWGUlJScbKlSuN3r17Gx06dDAKCgo8XXql3XfffUZoaKixatUqIy0tzfnIy8tzfmfKlClGq1atjO+++87YtGmTMXDgQGPgwIEerLrqLnae+/btM5566ilj06ZNRlJSkvH5558bbdu2Na688koPV141f/3rX43Vq1cbSUlJxvbt242//vWvhsViMb755hvDMBrGtbzQOTaU61iR344UcuX1bLRhxDAM4//+7/+MVq1aGT4+Pkb//v2Nn3/+2dMludT48eON6Ohow8fHx2jRooUxfvx4Y9++fZ4uq8a+//57AzjvMXHiRMMwzOG9TzzxhNG8eXPD19fXGDp0qJGYmOjZoqvhQueZl5dnXHPNNUbTpk0Nb29vo3Xr1sY999xT78J0eecHGHPnznV+Jz8/37j//vuNJk2aGAEBAcaYMWOMtLQ0zxVdDRc7z+TkZOPKK680wsPDDV9fX6N9+/bG1KlTjczMTM8WXkV333230bp1a8PHx8do2rSpMXToUGcQMYyGcS0vdI4N5TpW5LdhxJXX02IYhlGNOzgiIiIiLtEo+4yIiIhI3aEwIiIiIh6lMCIiIiIepTAiIiIiHqUwIiIiIh6lMCIiIiIepTAiIiIiHqUwIiL1ksViYcmSJZ4uQ0RcQGFERKps0qRJWCyW8x4jRozwdGkiUg95eboAEamfRowYwdy5c8u85+vr66FqRKQ+050REakWX19foqKiyjyaNGkCmE0os2fPZuTIkfj7+9O2bVsWLVpUZvsdO3Zw9dVX4+/vT0REBPfeey85OTllvvPuu+/SpUsXfH19iY6O5sEHHyzz+YkTJxgzZgwBAQF06NCBpUuXuvekRcQtFEZExC2eeOIJxo4dy7Zt25gwYQK33noru3fvBiA3N5fhw4fTpEkTNm7cyMKFC1m5cmWZsDF79mweeOAB7r33Xnbs2MHSpUtp3759mWM8+eSTjBs3ju3bt3PttdcyYcIEMjIyavU8RcQFXLKUn4g0KhMnTjRsNpsRGBhY5vHMM88YhmGuUjtlypQy2wwYMMC47777DMMwjLfeesto0qSJkZOT4/z8yy+/NKxWq3Pl4ZiYGOPvf/97hTUAxj/+8Q/n65ycHAMwli1b5rLzFJHaoT4jIlItQ4YMYfbs2WXeCw8Pdz4fOHBgmc8GDhxIQkICALt376ZHjx4EBgY6Px80aBAOh4PExEQsFgupqakMHTr0gjV0797d+TwwMJCQkBCOHTtW3VMSEQ9RGBGRagkMDDyv2cRV/P39K/U9b2/vMq8tFgsOh8MdJYmIG6nPiIi4xc8//3ze686dOwPQuXNntm3bRm5urvPztWvXYrVaiY+PJzg4mDZt2vDtt9/Was0i4hm6MyIi1VJYWEh6enqZ97y8vIiMjARg4cKF9O3bl8svv5z58+ezYcMG5syZA8CECROYMWMGEydOZObMmRw/fpyHHnqIO++8k+bNmwMwc+ZMpkyZQrNmzRg5ciTZ2dmsXbuWhx56qHZPVETcTmFERKpl+fLlREdHl3kvPj6ePXv2AOZIlwULFnD//fcTHR3NRx99xCWXXAJAQEAAX3/9NY888gj9+vUjICCAsWPH8tJLLzn3NXHiRAoKCnj55Zf505/+RGRkJDfffHPtnaCI1BqLYRiGp4sQkYbFYrGwePFiRo8e7elSRKQeUJ8RERER8SiFEREREfEo9RkREZdT66+IVIXujIiIiIhHKYyIiIiIRymMiIiIiEcpjIiIiIhHKYyIiIiIRymMiIiIiEcpjIiIiIhHKYyIiIiIRymMiIiIiEf9fzt3h6fosk9tAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss, label=\"Avg train loss\")\n",
    "plt.plot(test_loss, label=\"Avg test loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), TRAINED_MODELS / \"vae_state_dict_aug100_cls\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #1: 100%|██████████| 1563/1563 [02:32<00:00, 10.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Train Avg Loss: 1.3394 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #1: 100%|██████████| 313/313 [00:19<00:00, 15.99it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #1 | Test Avg Loss: 1.3588 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.59      0.59      1000\n",
      "         car       0.60      0.66      0.63      1000\n",
      "        bird       0.40      0.35      0.37      1000\n",
      "         cat       0.33      0.28      0.30      1000\n",
      "        deer       0.43      0.39      0.41      1000\n",
      "         dog       0.42      0.42      0.42      1000\n",
      "        frog       0.50      0.62      0.56      1000\n",
      "       horse       0.57      0.59      0.58      1000\n",
      "        ship       0.61      0.67      0.64      1000\n",
      "       truck       0.58      0.53      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.51     10000\n",
      "weighted avg       0.50      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #2: 100%|██████████| 1563/1563 [02:06<00:00, 12.39it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Train Avg Loss: 1.3338 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #2: 100%|██████████| 313/313 [00:21<00:00, 14.61it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #2 | Test Avg Loss: 1.3540 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.63      0.60      1000\n",
      "         car       0.59      0.68      0.63      1000\n",
      "        bird       0.43      0.34      0.38      1000\n",
      "         cat       0.35      0.30      0.32      1000\n",
      "        deer       0.45      0.39      0.42      1000\n",
      "         dog       0.44      0.38      0.40      1000\n",
      "        frog       0.49      0.66      0.56      1000\n",
      "       horse       0.58      0.56      0.57      1000\n",
      "        ship       0.62      0.66      0.64      1000\n",
      "       truck       0.55      0.54      0.54      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #3: 100%|██████████| 1563/1563 [01:49<00:00, 14.29it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Train Avg Loss: 1.3268 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #3: 100%|██████████| 313/313 [00:14<00:00, 21.33it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #3 | Test Avg Loss: 1.3463 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.61      0.60      1000\n",
      "         car       0.59      0.68      0.63      1000\n",
      "        bird       0.42      0.33      0.37      1000\n",
      "         cat       0.35      0.33      0.34      1000\n",
      "        deer       0.42      0.44      0.43      1000\n",
      "         dog       0.48      0.34      0.40      1000\n",
      "        frog       0.52      0.64      0.57      1000\n",
      "       horse       0.56      0.59      0.57      1000\n",
      "        ship       0.63      0.65      0.64      1000\n",
      "       truck       0.56      0.55      0.55      1000\n",
      "\n",
      "    accuracy                           0.52     10000\n",
      "   macro avg       0.51      0.52      0.51     10000\n",
      "weighted avg       0.51      0.52      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #4: 100%|██████████| 1563/1563 [03:32<00:00,  7.36it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Train Avg Loss: 1.3269 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #4: 100%|██████████| 313/313 [00:43<00:00,  7.13it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #4 | Test Avg Loss: 1.3570 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.62      0.60      1000\n",
      "         car       0.61      0.65      0.63      1000\n",
      "        bird       0.39      0.37      0.38      1000\n",
      "         cat       0.35      0.28      0.31      1000\n",
      "        deer       0.42      0.41      0.42      1000\n",
      "         dog       0.45      0.37      0.41      1000\n",
      "        frog       0.50      0.62      0.55      1000\n",
      "       horse       0.56      0.57      0.57      1000\n",
      "        ship       0.60      0.67      0.64      1000\n",
      "       truck       0.58      0.54      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.50      0.51      0.51     10000\n",
      "weighted avg       0.50      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #5: 100%|██████████| 1563/1563 [01:52<00:00, 13.87it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Train Avg Loss: 1.3276 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #5: 100%|██████████| 313/313 [00:24<00:00, 12.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #5 | Test Avg Loss: 1.3511 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.59      0.59      1000\n",
      "         car       0.62      0.66      0.64      1000\n",
      "        bird       0.39      0.35      0.37      1000\n",
      "         cat       0.35      0.32      0.33      1000\n",
      "        deer       0.43      0.42      0.42      1000\n",
      "         dog       0.46      0.35      0.40      1000\n",
      "        frog       0.49      0.67      0.56      1000\n",
      "       horse       0.62      0.54      0.58      1000\n",
      "        ship       0.61      0.69      0.65      1000\n",
      "       truck       0.56      0.55      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #6: 100%|██████████| 1563/1563 [01:27<00:00, 17.89it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Train Avg Loss: 1.3239 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #6: 100%|██████████| 313/313 [00:16<00:00, 19.28it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #6 | Test Avg Loss: 1.3510 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.56      0.62      0.59      1000\n",
      "         car       0.62      0.62      0.62      1000\n",
      "        bird       0.40      0.34      0.37      1000\n",
      "         cat       0.35      0.31      0.33      1000\n",
      "        deer       0.41      0.43      0.42      1000\n",
      "         dog       0.44      0.38      0.41      1000\n",
      "        frog       0.52      0.62      0.57      1000\n",
      "       horse       0.58      0.59      0.58      1000\n",
      "        ship       0.61      0.67      0.64      1000\n",
      "       truck       0.57      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #7: 100%|██████████| 1563/1563 [01:39<00:00, 15.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Train Avg Loss: 1.3260 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #7: 100%|██████████| 313/313 [00:16<00:00, 19.54it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #7 | Test Avg Loss: 1.3475 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.59      0.61      0.60      1000\n",
      "         car       0.61      0.63      0.62      1000\n",
      "        bird       0.41      0.37      0.39      1000\n",
      "         cat       0.35      0.31      0.33      1000\n",
      "        deer       0.43      0.43      0.43      1000\n",
      "         dog       0.45      0.36      0.40      1000\n",
      "        frog       0.52      0.62      0.57      1000\n",
      "       horse       0.58      0.57      0.58      1000\n",
      "        ship       0.62      0.67      0.64      1000\n",
      "       truck       0.53      0.58      0.55      1000\n",
      "\n",
      "    accuracy                           0.52     10000\n",
      "   macro avg       0.51      0.52      0.51     10000\n",
      "weighted avg       0.51      0.52      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #8: 100%|██████████| 1563/1563 [01:27<00:00, 17.90it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Train Avg Loss: 1.3213 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #8: 100%|██████████| 313/313 [00:15<00:00, 20.30it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #8 | Test Avg Loss: 1.3482 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.60      0.59      1000\n",
      "         car       0.63      0.62      0.63      1000\n",
      "        bird       0.40      0.38      0.39      1000\n",
      "         cat       0.34      0.30      0.32      1000\n",
      "        deer       0.42      0.42      0.42      1000\n",
      "         dog       0.45      0.36      0.40      1000\n",
      "        frog       0.52      0.64      0.57      1000\n",
      "       horse       0.57      0.58      0.57      1000\n",
      "        ship       0.61      0.67      0.64      1000\n",
      "       truck       0.56      0.55      0.55      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #9: 100%|██████████| 1563/1563 [01:51<00:00, 13.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Train Avg Loss: 1.3228 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #9: 100%|██████████| 313/313 [00:21<00:00, 14.86it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #9 | Test Avg Loss: 1.3570 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.58      0.60      0.59      1000\n",
      "         car       0.60      0.66      0.63      1000\n",
      "        bird       0.42      0.36      0.38      1000\n",
      "         cat       0.35      0.30      0.32      1000\n",
      "        deer       0.41      0.41      0.41      1000\n",
      "         dog       0.46      0.36      0.40      1000\n",
      "        frog       0.48      0.67      0.56      1000\n",
      "       horse       0.61      0.56      0.58      1000\n",
      "        ship       0.59      0.70      0.64      1000\n",
      "       truck       0.57      0.52      0.54      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train #10: 100%|██████████| 1563/1563 [01:21<00:00, 19.26it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Train Avg Loss: 1.3229 \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Test #10: 100%|██████████| 313/313 [00:10<00:00, 30.14it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch #10 | Test Avg Loss: 1.3512 \n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.57      0.60      0.59      1000\n",
      "         car       0.63      0.63      0.63      1000\n",
      "        bird       0.39      0.36      0.38      1000\n",
      "         cat       0.34      0.28      0.31      1000\n",
      "        deer       0.42      0.40      0.41      1000\n",
      "         dog       0.44      0.38      0.41      1000\n",
      "        frog       0.49      0.66      0.56      1000\n",
      "       horse       0.59      0.57      0.58      1000\n",
      "        ship       0.61      0.66      0.64      1000\n",
      "       truck       0.57      0.54      0.56      1000\n",
      "\n",
      "    accuracy                           0.51     10000\n",
      "   macro avg       0.51      0.51      0.51     10000\n",
      "weighted avg       0.51      0.51      0.51     10000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "optim = Adam(model.parameters(), lr=1e-4)\n",
    "train_loss, test_loss = train(\n",
    "    model,\n",
    "    optim,\n",
    "    loss_function,\n",
    "    epochs=10,\n",
    "    device=device,\n",
    "    trainloader=trainloader,\n",
    "    testloader=testloader,\n",
    "    labels=classes\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAGwCAYAAABRgJRuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABxG0lEQVR4nO3deVxU9f7H8dewCggouAAC7or7ninlkuZSWeaaWVmpaaGm3rrJvZbWrcyytH6aZdcyTbPUNFvMm1bimisuuRuFG64JgorAnN8fR8coVFDgDMP7+XjMw5k5Z+Z8RmDmPd/zXWyGYRiIiIiIFHFuVhcgIiIikh8UakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYiIiLgED6sLKCx2u50jR47g7++PzWazuhwRERHJBcMwOHv2LGFhYbi5XbstptiEmiNHjhAREWF1GSIiInIDDh48SHh4+DX3KTahxt/fHzD/UwICAiyuRkRERHIjJSWFiIgIx+f4tRSbUHP5lFNAQIBCjYiISBGTm64jee4oHBcXR5cuXQgLC8Nms7Fo0aJr7r9q1Sqio6MJDg7Gx8eHqKgoJk6c+Lf9Dh8+zEMPPeTYr169emzcuNGx3TAMXnjhBUJDQ/Hx8aF9+/bs27cvr+WLiIiIi8pzqElLS6NBgwZMmTIlV/v7+fkxZMgQ4uLi2LVrF6NHj2b06NFMmzbNsc8ff/xBdHQ0np6eLFmyhJ07d/Lmm29SunRpxz6vv/4677zzDu+99x4///wzfn5+dOzYkQsXLuT1JYiIiIgLst3MKt02m42FCxfStWvXPD2uW7du+Pn5MWvWLABGjRrF6tWrWblyZY77G4ZBWFgY//jHP3jmmWcASE5Opnz58syYMYMHHnjgb49JT08nPT3dcfvyObnk5GSdfhIRESkiUlJSCAwMzNXnd6HPU7NlyxbWrFlD69atHfctXryYpk2b0rNnT8qVK0ejRo344IMPHNsTEhJISkqiffv2jvsCAwNp3rw5a9euzfE448aNIzAw0HHRyCcRERHXVmihJjw8HG9vb5o2bUpMTAwDBgxwbPv111+ZOnUq1atXZ+nSpTz55JMMGzaMjz/+GICkpCQAypcvn+05y5cv79j2V7GxsSQnJzsuBw8eLKBXJiIiIs6g0EY/rVy5ktTUVNatW8eoUaOoVq0affr0AcyJ8Zo2bcqrr74KQKNGjdixYwfvvfce/fr1u6HjeXt74+3tnW/1i4iIiHMrtJaaypUrU69ePQYOHMiIESMYO3asY1toaCi1a9fOtn+tWrVITEwEICQkBIBjx45l2+fYsWOObSIiIlK8WbL2k91uz9aJNzo6mj179mTbZ+/evVSsWBEwA1FISAjLly93bE9JSeHnn3+mRYsWhVO0iIiIOLU8n35KTU1l//79jtsJCQnEx8cTFBREZGQksbGxHD58mJkzZwIwZcoUIiMjiYqKAsx5biZMmMCwYcMczzFixAhatmzJq6++Sq9evVi/fj3Tpk1zDPu22WwMHz6cl19+merVq1O5cmWef/55wsLC8jzySkRERFxTnkPNxo0badu2reP2yJEjAejXrx8zZszg6NGjjtNGYLbKxMbGkpCQgIeHB1WrVmX8+PEMGjTIsU+zZs1YuHAhsbGxvPTSS1SuXJlJkybRt29fxz7//Oc/SUtL44knnuDMmTPcdtttfPfdd5QoUeKGXriIiIi4lpuap6Yoycs4dxEREXEOTj1PjYiIiEhBUKjJD4nrIO2U1VWIiIgUawo1NytpB3zSA6a3h1MHrK5GRESk2FKouVluHuBTGk7/Cv9tB7+vsboiERGRYkmh5maVi4KBy6FCEzj/B8y8D7Z9bnVVIiIixY5CTX4oWQ76fQ21ukDWRfhiIPw0HorHwDIRERGnoFCTX7x8oedMaHlpUsGfXoWFgyEz/dqPExERkXyhUJOf3Nygw3/gnolgc4dtc2FWNzh32urKREREXJ5CTUFo+jj0/Ry8/OH3VTD9TrMjsYiIiBQYhZqCUq099F8KAeFwaj/8t705n42IiIgUCIWaglS+jjkyKrQhnDsFH98L2+dbXZWIiIhLUqgpaP4h8Ni3UPNuyEqHBf0h7g2NjBIREclnCjWFwcsPes+CFkPM2z+8DF/GQOZFa+sSERFxIQo1hcXNHTq+AndNAJsbxM+GT7qZE/aJiIjITVOoKWy3DIQ+n4FXSfhtJUzvAKcTrK5KRESkyFOosUKNDvD4d+AfBif3miOjDm6wuioREZEiTaHGKiH1zJFRIfXh3En4+B74ZaHVVYmIiBRZCjVWCgiDx5ZAjU6QeQHmPQqrJmpklFxht1tdgYgUtKQdcPGc1VW4BIUaq3mXhAfmQPPB5u1lY+GrYZCVYWlZ4gTm94cJ1XVqUsSVbZ4F70XD+7dD6nGrqynyFGqcgZs7dB4PncabI6M2z4TZPeFCstWViVV+WwU75punJmd3h6TtVlckIvnt7DH437/N66f2w8yuWivwJinUOJNbB8MDn4KnH/z6I0zvCGcSra5KCpthwPL/mNc9fMxwO7MrnNhraVkiks++G2X+fZerAyVD4Pgv5lQf+kJ7wxRqnE3NTvD4EvAPhRO74IN2cHiT1VVJYdq/DA6uA48SMGjFlc7kM++DP36zujoRyQ97/we/fGG2znd9Fx75EnyD4cgWmN0LLqZZXWGRpFDjjEIbwIDlUL4epB2Hj+6GXV9ZXZUUBsOAHy610jQbAGVrwsOLoGwUnD1irh+WcsTSEkXkJl1Mg2/+YV6/9SkIawjlouDhhVAi0PxSM/dByLhgaZlFkUKNswqsYLbYVO8Amefhs4dhzf9pZJSr27UYjm41J2e8baR5n1+wGWxKV4Yzv5stNmknLS1TRG7Cj69CciIERkLbf125P7QB9F1g/v3/+hPM66fldPJIocaZefubfWyaDQAM+N9o+GYkZGVaXZkUBHsW/PCKeb1FjBlmLgsINZunAyqYEzbO6grnz1hRpYjcjCPxsO5d8/rdb5prA/5ZRDN48DPz9PPe7+CLgXrPzwOFGmfn7mGuF9XxVcAGGz+ET3vDhRSrK5P8tn0enNwDJUqZoeavSlc0g41fWXM01OwekJ5a6GWKyA3KyoSvngbDDnW6mbPL56TSbdB7Nrh5ws5FsHiI5qzKJYWaosBmMz/ken9ijobZvww+7ATJh6yuTPJL5kWzSRrgtuHmefWclKlunooqUQoObYBPH4CM84VUpIjclPXvw9F48++702vX3rd6e+g5A2zusPVT+PYf6n6QCwo1RUmte+Cxb6FkeXPo3wftzJ7yUvRtmWX2l/ErB7c8ce19Q+rCQ19cWRT1c513F3F6ZxKvnF6+8yXwL3/9x9S6B7pNw9FK/7/RCjbXoVBT1FRoDAOWQbnakJoEH90Fu7+1uiq5GRnnIe4N83qrZ/5+jj0n4U2unHfft9Q8727PKtg6ReTGGAZ88wxkpEFkS2j0SO4fW68H3Pt/5vW1k6+06EqOFGqKolKR5irfVe+AjHPm0L91U5Xgi6oN0+HsUQiMgCaP5v5xfzvvPlTn3UWc0c5F5pcPN0/oMgnc8vjR2/hh6Hzpi0/c6+YagZIjhZqiqkQgPPj5pQ9Bw5yZcsk/1Uu+qEk/C6veMq+3fg48vPP2+OrtoceH5nn3+Nnw3XMKtyLO5PwZWPKcef32f5hzT92I5k9A+7Hm9WVj4ef386E416NQU5S5e8I9k+DOS5O1rZ8Gc/uYH5RSNKx7D86dguBq0KDPjT1H7Xuh61TAZv4OLH8xX0sUkZuwbCykHoPg6nD7yJt7rttGQKt/mteX/NNcDFOyUagp6mw2iB4GvWZe6l/xP/iwMyQftroyuZ5zp80JFQHaxJrD929Ug95wz6UWn1UTIW7CzdcnIjfn97Ww6SPzepdJeW+JzUnbf0GLIeb1xUNh+/ybf04XkudQExcXR5cuXQgLC8Nms7Fo0aJr7r9q1Sqio6MJDg7Gx8eHqKgoJk7Mfj5w7Nix2Gy2bJeoqKhs+7Rp0+Zv+wwePDiv5buu2vfBo9+Yc5gc2w7/bWfOTCvOa807kJ4M5euac1bcrKaPQ4eXzes//MfsZyUi1si8CF8PN683etjsA5cfbDbz77zp44ABXzwBu77On+d2AXkONWlpaTRo0IApU6bkan8/Pz+GDBlCXFwcu3btYvTo0YwePZpp06Zl269OnTocPXrUcVm1atXfnmvgwIHZ9nn99dfzWr5rC29qrhlVNsrsePphZ9jzndVVSU7OHrtyTrztv/PecfBqWg6F1qPM69+Ngs0z8+d5RSRvVr8NJ3abXzTvfCl/n9tmg7veNE9ZG1kw/zFz/jIhz+3dnTt3pnPnzrnev1GjRjRq1Mhxu1KlSnzxxResXLmSJ564Mh+Hh4cHISEh13wuX1/f6+5T7JWuCI8vhc8fgYQVZh+bTuPNTmbiPFa9ZY5cq9AEaub+7ylX2oyCi6nm8M/Fw8DT1xwWKiKF4+T+K9M0dHoNfIPy/xhubnDvZHNKiJ2LYG5feGhB/rUIFVGF3qdmy5YtrFmzhtatW2e7f9++fYSFhVGlShX69u1LYmLi3x47e/ZsypQpQ926dYmNjeXcuXNXPU56ejopKSnZLsWGTynzl7vRw+Z03Euehe9iNY+Jszhz0JxIC6DdC+a3rvx0uXm6yWOAAQsHaS4jkcJiGOZpp6x0qNoO6nYvuGO5e0C3D6BGJ8i8AHN6w8ENBXe8IqDQQk14eDje3t40bdqUmJgYBgwY4NjWvHlzZsyYwXfffcfUqVNJSEjg9ttv5+zZK6N4HnzwQT755BN+/PFHYmNjmTVrFg899NBVjzdu3DgCAwMdl4iIiAJ9fU7H3dOcsKndGPP2unfhs4fMJe/FWnGvQ9ZFqHQ7VGlTMMew2eDut6B+b7Bnmqv9HvixYI4lIlfEzzZn+vbwMTvv5/eXlr/y8IKeH5vvJRdT4ZPuxbo/pc0wbnxSC5vNxsKFC+natet1901ISCA1NZV169YxatQoJk+eTJ8+OQ9hPXPmDBUrVuStt96if//+Oe7zww8/0K5dO/bv30/VqlX/tj09PZ309HTH7ZSUFCIiIkhOTiYgICB3L9BV7PgCFg42vzmENoA+n5mrPkvhO3UAJjczz4P3/x4ibinY42VdCjS7vzZPQz28ECJvLdhjihRXqSdgSjM4/4fZjyb66cI79sU0M9AkrgXfYHj0WygXdf3HFQEpKSkEBgbm6vO70FpqKleuTL169Rg4cCAjRoxg7NixV923VKlS1KhRg/379191n+bNmwNcdR9vb28CAgKyXYqtut3g0a/NX/SjW+G/7SFph9VVFU8/vmoGmuodCz7QgNk83eNDsxk84xzM7glH4gv+uCLF0dJ/mYEmpB7cGlO4x/byMydkDWtkzn018z7zS1QxY8k8NXa7PVsryl+lpqZy4MABQkOv3poQHx8PcM195E8ibjFHRpWpASmHzFW+96m3fKE69gvsWGBev2N04R3Xw9tc4T2yJaSnwKz74fiuwju+SHGwfzls/xxsbtDl7Zubd+pGlQgwF7stV8dcG3DmfeZCmsVInkNNamoq8fHxjlCRkJBAfHy8o2NvbGwsjzxyZbGuKVOm8NVXX7Fv3z727dvH9OnTmTBhQrb+MM888wwrVqzgt99+Y82aNdx///24u7s7Tk8dOHCA//znP2zatInffvuNxYsX88gjj9CqVSvq169/M6+/eAmqDP3/Z/bluHgW5vQy1x2SwvHDK4ABtbtCaCH/3nr5mgtghjWG86dhZtdi+S1OpEBcPAdfjzCv3zLIHNVoFd8geGSROYNx8kEz2JxNsq6eQpbnULNx48Zsw7RHjhxJo0aNeOGFFwA4evRotpFLdrud2NhYGjZsSNOmTZkyZQrjx4/npZeujNs/dOgQffr0oWbNmvTq1Yvg4GDWrVtH2bJlAfDy8mLZsmV06NCBqKgo/vGPf9C9e3e++uqrm3rxxZJPaTPJN+xrngb5ZiQs/bcWQixohzbBnm/Mb3Ft/21NDSUCzFFxf/4Wl3zImlpEXMmK8XDmdwioAHdY9Pf9ZyXLwSNfQqmKcPpX82897aTVVRWKm+ooXJTkpaNRsWAYsHIC/HBpBtqoe8yhgV6+1tblqmZ2hV9/NMNk13etrSX1uHn68fQBCKoKjy0B//LW1iRSVCXtgPdbmV8SH/gUou6yuqIr/vjNnIT17BEIqQ/9vjKn/ChinLKjsDgZmw1aPQvdp4O7lzk6Zsbd5ky3kr9+W2UGGjdPcyVuq5UsB/0WQ2CkGWxm3W+uQyUieWPPgq+GmYGm1r3OFWgASlcy/9b9ykLSNpjdw+UXPFaoKe7q9YBHFoNPEBzZDB92MHvvS/4wDFh+aRX1Jv3MGZ+dQWC4ed69ZHk4/os5FPRCMZqgUiQ/bJgOhzeBdwB0dtJle8pUh4cXmV0PDm2AT/uYfYBclEKNQMUWMGAZlIo0myu/Gm5+GMvN2/c9HFxnrqB++zNWV5NdcFXzvPvlQDunt0u/2Ynkq+TDsPxF83r7Mc4991dIXbMvpXeAOTHgZw9B5tVHIBdlCjViCq4KPWeAm4e5jsiWWVZXVPTZ7eZq2QC3DHTON71ytcwJ+bwDIHGNS7/ZieSrJf80Z/ANvwWaPG51NddXoTH0nWdOwnlgOcx/HLIyrK4q3ynUyBUVmlyZP2XJc3Bir7X1FHW7Fpvnsb38IXqE1dVcXVjDHN7sMq2uSsR57frK7Ifo5mHOSeNWRD5KI2+FPp+Cu7dZ/8LBLrcmYBH5SUihafk0VG5tzj674HF9a79R9ixz9mCAFk+BX7C19VxP5K3wwJwrnca/fErD/EVyciEFvn3WvB79NJSvbW09eVWlDfSeZQayHfPhq6dd6m9doUayc3OD+983+1kkbYdlL1pdUdG07XM4ucfsnNeikKdLv1FV20Kvmeab3bbPzDmM1LdKJLvlL8HZoxBUxRxBWhTV6Ajd/2vOm7VlFnw3ymX+1hVq5O8CQq/MpbJuipZTyKvMi/DTOPN69HAoEWhpOXlSs7MZarHBpo/gf6Nd5s1O5KYd3AAb/mtev2ciePpYW8/NqHM/3HfpfX79+2anZxf4W1eokZzV7AzNBprXFw02J2yT3Nky05xdtGR5uOUJq6vJu3o94N7/M6+vnWzOlipS3GVlmHPSYECDB83TOEVdwz5w91vm9VUTIW6CtfXkA4UauboO/4FytSHtBCx60qXOuxaYjPNX3hhuf6boztDc+GHodCnM/DQO1vyftfWIWG3N/8Hxneap+Q4vW11N/mnWHzq8Yl7/8WVYO8Xaem6SQo1cnacP9PjQnGNl/zL4earVFTm/Df81z7cHRpiT7RVltw6GO543r/9vtBY/leLr9K9XWiw7vur8Hf/zquWQK2vSLf1Xkf5bV6iRaytXCzpeSvHfj4GjW62tx5mlnzWbcMFcDsHD29p68kOrZ+C2S8PRv/kHbP3M2npECpthmCtwZ14wR4Y2eMDqigpGq2f/9Lc+EuI/tbaeG6RQI9fXtL+54KU9A+b3h4tpVlfknNZNhXOnILgaNOhjdTX5p92YS32DDPM05M7FVlckUni2fQ6//mS2WN8z0Vw3zxXZbObfevPB5u0vn4JfFlpb0w1QqJHrs9nMjqP+YXBqnzkxn2R37vSVfidt/wXuHtbWk59sNrN/TcO+5sJ98x/XiDgpHtJOwdJY83rrf5ozr7symw06joNGD4NhhwUDYM93VleVJwo1kju+QdDt0lDfLbOKZIIvUGvegfQUKF8Pat9vdTX5z83NDLa1u5otdp/1NVcfF3Fl3z9vtr6Wqw0th1ldTeFwczNnSa7XE+yZ8PkjcOBHq6vKNYUayb3KreD2keb1xU/DmURr63EWZ4/BuvfM63f8u+hMmZ5Xbu7Q7QOo3tHsXzCnNxzaZHVVIgXj1xUQPxuwmR/y7p5WV1R43Nyh61Sz20FWOsx9EH5fa3VVueKi775SYNrEQoWmkJ4MCwZqjSCAlW9C5nnz/6VGJ6urKVgeXtDrY6h0u7mY3yfdIGmH1VWJ5K+M8/D1cPN6s/4QcYul5VjC3dMc/VqtvblszuyecHiz1VVdl0KN5I27pzm9tpc/HFwHcW9YXZG1zhw0Z94FaPe863Yi/DNPH+gz11yd+MIZmNUVTu63uiqR/BM3wRzG7R8K7V6wuhrreHhDr1lQ8Ta4eNb8EnPsF6uruiaFGsm7oMrmKACAuNfh9zXW1mOlFeMh66LZcuEKM4zmlndJc2XvkHrm5Iwz74U/fre6KpGbd2wnrJ5kXu/8etFa5qQgePnCg3MhvBmc/wNm3gcn91ld1VUp1MiNqd/THLZs2M3TUOf/sLqiwnfqAMTPMa8Xx29zPqXg4UVQpiakHDbf7FKOWl2VyI2z283TTvZMqHk31OpidUXOwdsf+s6/8iXm43vhj9+sripHCjVy4+56w1ypNuWQuXy9CyyGlic/vmoOca7RqXiecwfwKwOPLIJSFeGPBPNUVNopq6sSuTGbPoKDP4NXSbjr9eJxOjm3Ln+JKRsFZ4/Ax10g+bDVVf2NQo3cOG9/s3+Nmwfs/NIc6l1cJO2AHQvM65enFy+uAsKg32JzHqMTu81gc/6M1VWJ5E3KUVg21rze7gUIDLe0HKfkVwYe+dL8Mnsm0Tzt7GSLHSvUyM2p0OTK+kBLnoMTe62tp7D8+ApgQJ37IbS+1dVYr3Ql883OtwwkbYM5vTTztBQt3z1nzjVVoQk0G2B1Nc7LPwQeWWyub3dqP8zsak4+6iQUauTmtRxmromScQ4WPA6Z6VZXVLAObYQ934LNDdr8y+pqnEfZGuapqBKBZhP+p30g44LVVYlc354lZmuzzd2ck8bN3eqKnFupCPNLTMkQOP6LOSrqQrLVVQEKNZIf3Nzg/vfBJwiStsOyF62uqGD98B/z3wZ9zA9yuSKkHjz0hdknIWEFzHsUsjKsrkrk6tJT4ZtnzOsth5i/w3J9wVUvtc4Gw5EtMNs5WmcVaiR/BIRC13fN6+umuO7aQAkrzcXt3DzNlbjl78KbmvPYeJSAvUvgiyfAnmV1VSI5+/EVc7BDqYrQepTV1RQt5aLg4YWXWmfXmTMPW9w6azOM4jFkJSUlhcDAQJKTkwkICLC6HNf17bOwfhr4lYUn10DJclZXlH8MAz7saJ5aaTYA7n7T6oqc277vzVNQ9gxzHZkancxmfTdPs3O5m4e58Kebx5X7st12v3Tfn/b/622NTpGbcXgz/LedOTXFQwvM2XMl7w5uMAcIXEw1/857zTJnH88nefn8dqGlhMUp3Pkf+G21eZ514WBzbgNXWQtp3/dmoPHwgVbPWl2N86t+J/SYbp6C2j7PvOQ3m/ufQs6fApO7519u/ykUXQ5Mjsdd5XZOgcqnlLlauW9Q/r8WKVxZmfDVMDPQ1OupQHMzIprBg5/BJ93NTsOZF/I11OSFWmok/x3fDdNam7/YHV4xz1MXdXY7TGtl9hlqOQw6/MfqioqOPUtg44fm70NWpjmxmT3TbMGxZ5l9bux/ut9xO+vSPpfudxa+ZaDTa1Cvh1qKirLV75ircJcoBUM2QsmyVldU9CX+DOXrmDOO56O8fH4r1EjB2DAdvhlpfusdsAzCGlpd0c35ZaHZ4uDlD8O36Zt6YTOMv4ecrD+HoxxuZwtMVwlQ2UJU5vWfOyEOTuwya6rWHu5+C0pXtPb/RvLuj9/g3RbmiM17J0Pjh62uSK5Bp5/Eek0fhwM/wO6vYUF/eGJFvqf3QmPPMmcPBmgRo0BjBZvNPIXkbvFbVuZFWPM2rHgD9i+Dd281J19sPtj62iR3DAO++YcZaCrdDo0esroiyUcu0tlBnI7NBvf+nznL7Kn95sRWRdW2z+DkXvApbYYaKb48vMz+VE+uMVcuzjgH//u32dn06Farq5Pc2LHADKTuXubCvDqF6FIUaqTg+AZBt2mADbZ8Aju+sLqivMu8CD+NM69HD4cSOnUpQJlq0O8r6PKOOZz1aDxMawv/ex4unrO6Ormac6fhu0vDtls9C2WqW1uP5DuFGilYlW+H2/9hXv9qOPzxu6Xl5NmWmeYaJyXLwy1PWF2NOBM3N2jSD2I2mMtlGFmw5h3zlNSBH6yuTnKybIy5ynSZmuaXFHE5eQ41cXFxdOnShbCwMGw2G4sWLbrm/qtWrSI6Oprg4GB8fHyIiopi4sSJ2fYZO3YsNpst2yUqKirbPhcuXCAmJobg4GBKlixJ9+7dOXbsWF7LFyu0GQXhzSA92ZyILcuJRrJcS8Z5s+8EmN/qvHytrUeck3956DkD+nwGAeFw5neYdT98MUgrljuT31bD5pnm9S5vWzbkWApWnkNNWloaDRo0YMqUKbna38/PjyFDhhAXF8euXbsYPXo0o0ePZtq0adn2q1OnDkePHnVcVq1alW37iBEj+Oqrr5g3bx4rVqzgyJEjdOvWLa/lixXcPc3VvL0DzFkn496wuqLcWf8BpCZBYCQ07md1NeLsanaCmHVmp2FssG0uTG4KW+eanVPFOpnp8NXT5vUmj0LFFpaWIwXnpoZ022w2Fi5cSNeuXfP0uG7duuHn58esWbMAs6Vm0aJFxMfH57h/cnIyZcuWZc6cOfTo0QOA3bt3U6tWLdauXcutt9563WNqSLcT2D7fHAllc4NHv4GKLa2u6OoupMDbDeD8abhvikZISN4c2giLh5mTUAJUvcMc/h1U2dq6iqufXjP7xvmVgyHrzU7/UmTk5fO70PvUbNmyhTVr1tC6dets9+/bt4+wsDCqVKlC3759SUxMdGzbtGkTGRkZtG9/ZcbHqKgoIiMjWbt2bY7HSU9PJyUlJdtFLFavBzR40JzBc8FAOP+H1RVd3bqpZqAJrg71H7C6GilqwpvCoBXQ7gVw9zb72LzbwpzwraicfnUVJ/bCyktLmnQer0Dj4got1ISHh+Pt7U3Tpk2JiYlhwIABjm3NmzdnxowZfPfdd0ydOpWEhARuv/12zp49C0BSUhJeXl6UKlUq23OWL1+epKSkHI83btw4AgMDHZeIiIgCe22SB3e9DkFVzAXkvnraOZvlz52GtZPN621jNf+I3Bh3T7OT/FNrzflQMs+bM9h+0NZc1VgKnt1uvs9kXYTqHcwO3eLSCi3UrFy5ko0bN/Lee+8xadIkPv30U8e2zp0707NnT+rXr0/Hjh359ttvOXPmDJ9//vkNHy82Npbk5GTH5eDBg/nxMuRmeftD9+nmOjo7v7zScc+ZrH4b0lOgfD2orTdBuUnBVc3h3/dNMafkT9oGH9wBS/8NF9Osrs61bZkFiWvA09dcgFZz0ri8Qgs1lStXpl69egwcOJARI0YwduzYq+5bqlQpatSowf79+wEICQnh4sWLnDlzJtt+x44dIyQkJMfn8Pb2JiAgINtFnESFxnDH8+b170aZzcPO4uwx+Pl98/odo11nMU6xls1m9ssasgHqdjdPwa6dbA7/3r/M6upcU+pxs2UMzFmfS0VaW48UCkvese12O+np6VfdnpqayoEDBwgNDQWgSZMmeHp6snz5csc+e/bsITExkRYt1Iu9SGo5DKq0MWdkXfC4OTrBGax80zxNEN4ManS0uhpxNSXLQY8P4cF5EBhhzoH0SXezj1naSaurcy3fjYILyRDa4NKINCkO8hxqUlNTiY+Pd4xUSkhIID4+3tGxNzY2lkceecSx/5QpU/jqq6/Yt28f+/btY/r06UyYMIGHHroymuSZZ55hxYoV/Pbbb6xZs4b7778fd3d3+vTpA0BgYCD9+/dn5MiR/Pjjj2zatInHHnuMFi1a5GrkkzghNze4/33wDTZXvl421uqKzA+YjR+a1+94Xk3VUnBqdICn1sGtT5mjAbd/bg7/jp/jnP3Mipp935vLIdjczFmf1S+u2MjzT3rjxo20bdvWcXvkyJEA9OvXjxkzZnD06NFsI5fsdjuxsbEkJCTg4eFB1apVGT9+PIMGDXLsc+jQIfr06cOpU6coW7Yst912G+vWraNs2StLwU+cOBE3Nze6d+9Oeno6HTt25N13372hFy1Owj8E7nsXPu0N6941h71Wv9O6elaMN1dlrtwKqrS+/v4iN8O7JHQaZ44KXPw0HNsOi5401xq7Z6LZoV7y7mIafG1+LnHrUxDW0NJypHDd1Dw1RYnmqXFi3/4T1r8PvmXMhQL9yxd+DSf3w5RbzKnu+y+DiGaFX4MUX1kZZh+bn16DzAvg4WPOxN0ixhxFJbn3v9Gw5v/M03tPrTPDoxRpTj1Pjcjf3PkSlK8L507CosHmMMzC9tOrZqCp0UmBRgqfuyfcNsIc/l25tdmva9kYc5HMw5utrq7oOLoV1l5qwb/7TQWaYkihRqznWcIc5u3hY05Sti53S3Dkm6Qd5vl3MEdJiFglqAo88iV0nWpOEndsO/y3HXz3L0hPtbo652bPujT3VZY5H406+hdLCjXiHMpFQadXzevLXizcycl+fMX8t043CK1feMcVyYnNBg0fNFf/rtfLHP69boo5I/G+762uznmtn2a+b3gHQqfxVlcjFlGoEefR5DGo1cXsrDu/f+F8Mz24AfZ8a46SaPuvgj+eSG6VLAvdP4C+C8w5VpITYXYP828j9YTV1TmXMwdh+X/M63e+aE2/PHEKCjXiPGw2c/hlQAU4fQC+e67gj/nDpTfCBg9CmeoFfzyRvKre3uzw2mKIGb53zDeHf2/5RMO/wfw/+PYZyEiDiFuhcT+rKxILKdSIc/ENgm7TAJv5pr3ji4I7VkIcJKwAN09o/c+CO47IzfLyg46vwMAfIKQ+XDgDX8bAzHvh1AGrq7PWzi9h73fm33GXtzULeDGnn744n0q3QatnzOtfDYc/fs//YxjGlebqJo9C6Yr5fwyR/BbWCAb+aI4Y9PAxg/nUluZM2FkZVldX+M6fgSWXWnRvG2H2zZNiTaFGnFPrURB+C6QnwxcDISszf59/3//g0Hrzg+FygBIpCtw9IPppc/h3lbbmvDbLX4JpbeDQJqurK1zLX4TUJAiuZq6ILsWeJt8T5/XH7/DebeaK2a2fy7+OvHY7TGtlLs/Qchh0+E/+PK9IYTMMcwbi72Lh/GnAZq5zdMe/wdvf6uryR1YGJB8ylzG5fEk+aP77+2pzn35fQ+Xbra1TCkxePr8VasS5bZ8PC/qbHSQf/QYqtrz55/xlIcx7FLz8Yfg2sx+PSFGWdhKW/hu2zTVvB4TDPW8VjblaMi9Cyl9Ci+NyEM4eMYe1X02zgXD3hMKrVwqdQk0OFGqKsEVPQfxs8436yVXmpGQ3KisT3r0VTu2DNrHmVPQirmL/cvh6BJy51A+tTjfoPN5cHdwqmel/b2n58+XsUeA6H0Pu3uaw9r9egipDWGMtPuviFGpyoFBThKWfhfdbwelfoda90Gvmjb+JbZkNXz4FPkHw9FYood8FcTEX08w1pNZOMWfXLREIHV6GRg8XzId/xoVLoeX37KeGHKElieuGFg8fKBWRQ3CpaK7h5FdWo5qKMYWaHCjUFHGHN8P0DubEfF3eNkcs5VXmRZjcxHyjvfMls7OliKs6uhUWDzX/Bah0O9wzCcpUy9vzZJzPHlounxa6fD016frP4el7JagERmQPLaUiwa+MWlvkqhRqcqBQ4wJWvw3fv2B+qxu0AsrWzNvj139gTtJVsjwMiwcv3wIpU8RpZGXCz1Phx1ch45x5Gqf1s9DyafDwMvfJOP+nkPL73zvjph67/nE8/f7SwvKX4OIbrNAiN0yhJgcKNS7AbodPusGvP0L5ejBgmbkYZm5cPAfvNDK/Vd41AW4ZWLC1ijiTP36Dr0fCgeXm7eBq5mmpM4mQloslF7xK5tynJTDiUmgJUmiRApOXz2+PQqpJ5Oa5ucH975mTjR3bDsvGQufXcvfYDf81A01gpKZRl+KndCV4aAFsnwffjYJT+7Nv9/I3J6AMzKlfS6TZOV+hRYoAhRopWvxDoOtUmNPLbFavegfU6HDtx1xIgVVvmdfbjLrS7C5SnNhsUL8XVGsPe5aYneQvh5YSpRRaxCWoO7kUPTU6mhOMASx6Es5e55z/unfh/B8QXB3q9y74+kScmW8QNOoLtbpAaAO1wohLUaiRoqn9i2a/mnMnYdFgs79NTs6dhjWTzett/2VOMS8iIi5JoUaKJs8S0GO6ORLqwA+wbkrO+62eBBfPmgGodtfCrFBERAqZQo0UXWVrQqdx5vVlL8KRLdm3n02Cn6eZ1+8Yrcm7RERcnN7lpWhr8qjZN8CeAfP7Q3rqlW0r34TM8xDerGisgSMiIjdFoUaKNpsNurwDARXg9AFY8px5/5lE2PiRef2O59URUkSkGFCokaLPNwi6fWCu5B3/CexYAD+NN1tvKreGKq2trlBERAqBQo24hkrRcPsz5vXFT8PWOeb1di9YV5OIiBQqhRpxHa2fg4jm5mgnww41OkN4U6urEhGRQqJQI67D3cM8DeUdCDZ3uOPfVlckIiKFSDORiWspXREG/WQujRBSz+pqRESkECnUiOsJqmJ1BSIiYgGdfhIRERGXoFAjIiIiLkGhRkRERFyCQo2IiIi4BIUaERERcQl5DjVxcXF06dKFsLAwbDYbixYtuub+q1atIjo6muDgYHx8fIiKimLixIlX3f+1117DZrMxfPjwbPe3adMGm82W7TJ48OC8li8iIiIuKs9DutPS0mjQoAGPP/443bp1u+7+fn5+DBkyhPr16+Pn58eqVasYNGgQfn5+PPHEE9n23bBhA++//z7169fP8bkGDhzISy+95Ljt6+ub1/JFRETEReU51HTu3JnOnTvnev9GjRrRqFEjx+1KlSrxxRdfsHLlymyhJjU1lb59+/LBBx/w8ssv5/hcvr6+hISE5LVkERERKQYKvU/Nli1bWLNmDa1bZ185OSYmhrvvvpv27dtf9bGzZ8+mTJky1K1bl9jYWM6dO3fVfdPT00lJScl2EREREddVaDMKh4eHc+LECTIzMxk7diwDBgxwbJs7dy6bN29mw4YNV338gw8+SMWKFQkLC2Pbtm0899xz7Nmzhy+++CLH/ceNG8eLL76Y769DREREnFOhhZqVK1eSmprKunXrGDVqFNWqVaNPnz4cPHiQp59+mu+//54SJUpc9fF/PlVVr149QkNDadeuHQcOHKBq1ap/2z82NpaRI0c6bqekpBAREZG/L0pEREScRqGFmsqVKwNmIDl27Bhjx46lT58+bNq0iePHj9O4cWPHvllZWcTFxTF58mTS09Nxd3f/2/M1b94cgP379+cYary9vfH29i6gVyMiIiLOxpIFLe12O+np6QC0a9eO7du3Z9v+2GOPERUVxXPPPZdjoAGIj48HIDQ0tEBrFRERkaIhz6EmNTWV/fv3O24nJCQQHx9PUFAQkZGRxMbGcvjwYWbOnAnAlClTiIyMJCoqCjDnuZkwYQLDhg0DwN/fn7p162Y7hp+fH8HBwY77Dxw4wJw5c7jrrrsIDg5m27ZtjBgxglatWl11+LeIiIgUL3kONRs3bqRt27aO25f7rfTr148ZM2Zw9OhREhMTHdvtdjuxsbEkJCTg4eFB1apVGT9+PIMGDcr1Mb28vFi2bBmTJk0iLS2NiIgIunfvzujRo/NavoiIiLgom2EYhtVFFIaUlBQCAwNJTk4mICDA6nJEREQkF/Ly+a21n0RERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS8hzqImLi6NLly6EhYVhs9lYtGjRNfdftWoV0dHRBAcH4+PjQ1RUFBMnTrzq/q+99ho2m43hw4dnu//ChQvExMQQHBxMyZIl6d69O8eOHctr+SIiIuKi8hxq0tLSaNCgAVOmTMnV/n5+fgwZMoS4uDh27drF6NGjGT16NNOmTfvbvhs2bOD999+nfv36f9s2YsQIvvrqK+bNm8eKFSs4cuQI3bp1y2v5IiIi4qJshmEYN/xgm42FCxfStWvXPD2uW7du+Pn5MWvWLMd9qampNG7cmHfffZeXX36Zhg0bMmnSJACSk5MpW7Ysc+bMoUePHgDs3r2bWrVqsXbtWm699da/HSM9PZ309HTH7ZSUFCIiIkhOTiYgICDvL1ZEREQKXUpKCoGBgbn6/C70PjVbtmxhzZo1tG7dOtv9MTEx3H333bRv3/5vj9m0aRMZGRnZtkVFRREZGcnatWtzPM64ceMIDAx0XCIiIvL3hYiIiIhTKbRQEx4ejre3N02bNiUmJoYBAwY4ts2dO5fNmzczbty4HB+blJSEl5cXpUqVynZ/+fLlSUpKyvExsbGxJCcnOy4HDx7Mt9ciIiIizsejsA60cuVKUlNTWbduHaNGjaJatWr06dOHgwcP8vTTT/P9999TokSJfDuet7c33t7e+fZ8IiIi4twKLdRUrlwZgHr16nHs2DHGjh1Lnz592LRpE8ePH6dx48aOfbOysoiLi2Py5Mmkp6cTEhLCxYsXOXPmTLbWmmPHjhESElJYL0FEREScmCXz1Njtdkcn3nbt2rF9+3bi4+Mdl6ZNm9K3b1/i4+Nxd3enSZMmeHp6snz5csdz7Nmzh8TERFq0aGHFS8jmyJnznL+YZXUZIiIixVqeW2pSU1PZv3+/43ZCQgLx8fEEBQURGRlJbGwshw8fZubMmQBMmTKFyMhIoqKiAHOemwkTJjBs2DAA/P39qVu3brZj+Pn5ERwc7Lg/MDCQ/v37M3LkSIKCgggICGDo0KG0aNEix5FPheng6XM8MG0dkUG+fPhoM3y83C2tR0REpLjKc6jZuHEjbdu2ddweOXIkAP369WPGjBkcPXqUxMREx3a73U5sbCwJCQl4eHhQtWpVxo8fz6BBg/J03IkTJ+Lm5kb37t1JT0+nY8eOvPvuu3ktP9+dSE3nzLmLHD5znsdnbFCwERERschNzVNTlORlnHtebfr9NI9MX0/axSxaVAlWsBEREcknTj1PjStqUjGImf1vwc/LnbW/nuLxGRvUx0ZERKSQKdTkEwUbERERaynU5KO/Bpv+HyvYiIiIFBaFmnzWpGIQHz9uBps1BxRsRERECotCTQFoWknBRkREpLAp1BQQBRsREZHCpVBTgP4abAbMVLAREREpKAo1BezPwWb1fgUbERGRgqJQUwiaVgpihoKNiIhIgVKoKSTNFGxEREQKlEJNIVKwERERKTgKNYXscrDxvRRsBs7cqGAjIiKSDxRqLNDsUudhXy93Vu0/qWAjIiKSDxRqLKJgIyIikr8UaiykYCMiIpJ/FGosllOwuZChYCMiIpJXCjVO4K/BZsDHCjYiIiJ5pVDjJJpVCmLGYwo2IiIiN0qhxoncUlnBRkRE5EYp1DgZBRsREZEbo1DjhP4abNR5WERE5PoUapzUn4PNyn0KNiIiItejUOPEbqkcxEePNlOwERERyQWFGifXvEqwgo2IiEguKNQUAQo2IiIi16dQU0Qo2IiIiFybQk0RcjnY+Hgq2IiIiPyVQk0R07xKMDMeU7ARERH5K4WaIkjBRkRE5O8UaoqovwabJ2ZtUrAREZFiTaGmCPtzsInbe0LBRkREijWFmiKueZVgPlKwERERUahxBbcq2IiIiCjUuAoFGxERKe7yHGri4uLo0qULYWFh2Gw2Fi1adM39V61aRXR0NMHBwfj4+BAVFcXEiROz7TN16lTq169PQEAAAQEBtGjRgiVLlmTbp02bNthstmyXwYMH57V8l6ZgIyIixVmeQ01aWhoNGjRgypQpudrfz8+PIUOGEBcXx65duxg9ejSjR49m2rRpjn3Cw8N57bXX2LRpExs3buSOO+7gvvvu45dffsn2XAMHDuTo0aOOy+uvv57X8l2ego2IiBRXNsMwjBt+sM3GwoUL6dq1a54e161bN/z8/Jg1a9ZV9wkKCuKNN96gf//+gNlS07BhQyZNmpSrY6Snp5Oenu64nZKSQkREBMnJyQQEBOSp3qJo7YFTPD5jA+czsmhVoyzTHm5CCU93q8sSERHJk5SUFAIDA3P1+V3ofWq2bNnCmjVraN26dY7bs7KymDt3LmlpabRo0SLbttmzZ1OmTBnq1q1LbGws586du+pxxo0bR2BgoOMSERGRr6/D2bWoGsyHj6rFRkREio9CCzXh4eF4e3vTtGlTYmJiGDBgQLbt27dvp2TJknh7ezN48GAWLlxI7dq1HdsffPBBPvnkE3788UdiY2OZNWsWDz300FWPFxsbS3JysuNy8ODBAnttzkrBRkREipNCO/2UkJBAamoq69atY9SoUUyePJk+ffo4tl+8eJHExESSk5OZP38+//3vf1mxYkW2YPNnP/zwA+3atWP//v1UrVr1usfPS/OVq/nzqajWNcryvk5FiYhIEeGUp58qV65MvXr1GDhwICNGjGDs2LHZtnt5eVGtWjWaNGnCuHHjaNCgAW+//fZVn6958+YA7N+/vyDLdgmXW2xKeLqxYu8JBqnFRkREXJAl89TY7fZsnXhvZJ/4+HgAQkND87M0l9WiajAfPXqLgo2IiLgsj7w+IDU1NVvrSEJCAvHx8QQFBREZGUlsbCyHDx9m5syZAEyZMoXIyEiioqIAc56bCRMmMGzYMMdzxMbG0rlzZyIjIzl79ixz5szhp59+YunSpQAcOHCAOXPmcNdddxEcHMy2bdsYMWIErVq1on79+jf1H1CcXA42j81Y7wg2OhUlIiKuIs+hZuPGjbRt29Zxe+TIkQD069ePGTNmcPToURITEx3b7XY7sbGxJCQk4OHhQdWqVRk/fjyDBg1y7HP8+HEeeeQRjh49SmBgIPXr12fp0qXceeedgHlqatmyZUyaNIm0tDQiIiLo3r07o0ePvuEXXlz9NdgM/mQT7z2kYCMiIkXfTXUULkqKc0fhnKw9cIrHZqznQoadNjXLKtiIiIhTcsqOwuJc/tx5+Kc9ZouN+tiIiEhRplBTjLWsWkbBRkREXIZCTTH312Dz1OzNZGbZrS5LREQkzxRqJFuw+WH3cV5fusfqkkRERPJMoUYAM9i82bMhANPifuXL+MPWFiQiIpJHCjXicHf9UJ5sYy458dyCbfxyJNniikRERHJPoUayeaZDTVrXKMuFDDuDZm3ij7SLVpckIiKSKwo1ko27m413HmhExWBfDv1xniGfquOwiIgUDQo18jeBvp5Me7gpvl7urN5/iteW7La6JBERketSqJEc1QzxZ0LPBgD8d1WCOg6LiIjTU6iRq7qrXihPXeo4/M/529hxWB2HRUTEeSnUyDX9o0NN2tQsS3qm2XH4tDoOi4iIk1KokWtyd7Px9gONqBTsy+Ez5xkyRx2HRUTEOSnUyHUF+ngy7ZGm+Hm5s+bAKcap47CIiDghhRrJlRrl/Xmzl9lxePqqBBZuOWRxRSIiItkp1EiudaobypC21QAYtWC7Og6LiIhTUaiRPBlxZw3a/qnj8KnUdKtLEhERARRqJI/c3WxMeqARlcv4cfjMeWLmbCZDHYdFRMQJKNRIngX6eDLt4Sb4ebmz7tfTvPrtLqtLEhERUaiRG1O9vD9v9moIwEerf+OLzeo4LCIi1lKokRvWqW4Iw+4wOw7HfrGd7YfUcVhERKyjUCM3ZXj7GrSLKnep4/BGTqrjsIiIWEShRm6Km5uNiQ80pEoZP44kXyBmtjoOi4iINRRq5KYFlPBk2iNNKOntwc8Jp3nlG3UcFhGRwqdQI/miWjl/3ro04/CMNb+xYJM6DouISOFSqJF806FOCMPaVQcgduF2th06Y21BIiJSrCjUSL4a3q467WuV4+KlGYdPnFXHYRERKRwKNZKv3NxsvNW7IVXK+nE0+YJmHBYRkUKjUCP5LqCEJ9MebkpJbw/WJ5zm5a93Wl2SiIgUAwo1UiCqlSvJxN4NAfh47e/M23jQ2oJERMTlKdRIgbmzdnmGtzc7Dv970Q62HjxjbUEiIuLSFGqkQA27ozp31i6vjsMiIlLgFGqkQLm52XirVwOqlvUjKcWccfhipjoOi4hI/stzqImLi6NLly6EhYVhs9lYtGjRNfdftWoV0dHRBAcH4+PjQ1RUFBMnTsy2z9SpU6lfvz4BAQEEBATQokULlixZkm2fCxcuEBMTQ3BwMCVLlqR79+4cO3Ysr+WLBfxLeDLtkab4e3uw/rfTvPyNOg6LiEj+y3OoSUtLo0GDBkyZMiVX+/v5+TFkyBDi4uLYtWsXo0ePZvTo0UybNs2xT3h4OK+99hqbNm1i48aN3HHHHdx333388ssvjn1GjBjBV199xbx581ixYgVHjhyhW7dueS1fLFK17JWOwzPX/s7n6jgsIiL5zGYYhnHDD7bZWLhwIV27ds3T47p164afnx+zZs266j5BQUG88cYb9O/fn+TkZMqWLcucOXPo0aMHALt376ZWrVqsXbuWW2+99brHTElJITAwkOTkZAICAvJUr+Sft5ftY+KyvXi5u/HZoFtpFFna6pJERMSJ5eXzu9D71GzZsoU1a9bQunXrHLdnZWUxd+5c0tLSaNGiBQCbNm0iIyOD9u3bO/aLiooiMjKStWvX5vg86enppKSkZLuI9YbeUY0OtctzMcvO4E82cfzsBatLEhERF1FooSY8PBxvb2+aNm1KTEwMAwYMyLZ9+/btlCxZEm9vbwYPHszChQupXbs2AElJSXh5eVGqVKlsjylfvjxJSUk5Hm/cuHEEBgY6LhEREQXyuiRv3NxsvHmp4/CxlHSe+kQdh0VEJH8UWqhZuXIlGzdu5L333mPSpEl8+umn2bbXrFmT+Ph4fv75Z5588kn69evHzp033qE0NjaW5ORkx+XgQfXhcBb+JTz54FLH4Y2//8FLX/9y/QeJiIhch0dhHahy5coA1KtXj2PHjjF27Fj69Onj2O7l5UW1atUAaNKkCRs2bODtt9/m/fffJyQkhIsXL3LmzJlsrTXHjh0jJCQkx+N5e3vj7e1dcC9IbkqVsiV5u09D+n+8kU/WJVKvQiC9m0VaXZaIiBRhlsxTY7fbSU+/9iRsf96nSZMmeHp6snz5csf2PXv2kJiY6Oh3I0XPHVHlGdm+BgDPL/qFzYl/WFyRiIgUZXluqUlNTWX//v2O2wkJCcTHxxMUFERkZCSxsbEcPnyYmTNnAjBlyhQiIyOJiooCzHluJkyYwLBhwxzPERsbS+fOnYmMjOTs2bPMmTOHn376iaVLlwIQGBhI//79GTlyJEFBQQQEBDB06FBatGiRq5FP4rxi2lZjx5Fklv5yjCc/2cRXQ2+jnH8Jq8sSEZEiKM+hZuPGjbRt29Zxe+TIkQD069ePGTNmcPToURITEx3b7XY7sbGxJCQk4OHhQdWqVRk/fjyDBg1y7HP8+HEeeeQRjh49SmBgIPXr12fp0qXceeedjn0mTpyIm5sb3bt3Jz09nY4dO/Luu+/e0IsW52F2HG7Ir1NWs+94Kk99spk5A2/Fy0OTXYuISN7c1Dw1RYnmqXFuCSfTuHfyKs5eyKRv80heub+e1SWJiIgTcOp5akRyUrmMH+880AibDWb/nMjc9YnXf5CIiMifKNSI02gbVY5/3Gl2HH7hy1/Y9Ls6DouISO4p1IhTiWlbjU51QriYZefJTzZxLEUzDouISO4o1IhTsdlsTOjVgBrlS3L8bDpPfrKJ9Mwsq8sSEZEiQKFGnE5Jbw+mPdyUgBIebE48w9jFNz6ztIiIFB8KNeKUKpXx450+ZsfhT9cnMudndRwWEZFrU6gRp9WmZjme6VATgDGLd7Dp99MWVyQiIs5MoUac2lNtqnJXvRAysgwGf7JZHYdFROSqFGrEqdlsNt7o0YCa5f05cTadweo4LCIiV6FQI07Pz9uDaY80IaCEB1sSzzB28S9WlyQiIk5IoUaKhIrBf+44fJDZP/9udUkiIuJkFGqkyGhTsxzPdjQ7Do9d/Asbf1PHYRERuUKhRoqUJ1tX5e56oY6Ow0nJ6jgsIiImhRopUmw2G2/0rE9UiD8nU9Vx2FldzLSTeOoc6349xRebD7Foy2H9nESkwNkMwzCsLqIw5GXpcnF+iafO0WXyKpLPZ9C7aQSvda+HzWazuqxiwW43OJmWzpEzFzh65jyHz5znaPIFjpw5z5FL/55MTeev7yzVy5XkjZ4NaBhRypK6RaRoysvnt0KNFFlxe0/w6EfrsRvwn651efjWilaX5BLOXsjgyJkLHEk+z5Ez5zl65nJgOc+RMxdISr7AxSz7dZ/Hy8ONCqV8CA0swd5jZzmZehE3GwxsVYUR7WtQwtO9EF6NiBR1CjU5UKhxTe+tOMBrS3bj4Wbj0ydupVmlIKtLcmoXM+0kJV+41LpyPlvryuXwcjY987rP42aDcv4lCCtVgtBSPo7wElbKh7BAH8JKlSDIz8vRevZH2kXGfvULX8YfAaBqWT/e6NmAxpGlC/T1ikjRp1CTA4Ua12QYBkM/3cLX245SpqQ3Xw2NJjTQx+qyLJGb00Inzqbn6rkCfTwvBRQzqISWKnEpuJiBpXxACTzd894l73+/JPHvRTs4cTYdNxv0v60y/+hQU602InJVCjU5UKhxXecuZtLt3TXsTjpLg4hSfPbErS75IZlyIeMvp4LM1pXL4eVGTguFlfLJFl7CSpUgNNAHP2+PAnsdZ85d5KWvd/LF5sMAVCnjx+s96tNUrWwikgOFmhwo1Li2P3cc7tU0nPHd6xdKx+HMLDsXs+xczDQv6ZnZb2fflmVu/+s+f7qd/pfrZ/8UZAritJCVlu86xr8WbudYSjo2GzzWsjLPdqyJj5frBVIRuXEKNTlQqHF9K/edoN+HZsfhYXdUo3ZYAOmXg0JOYeLPQeLS7fSMrGuEkr+HFHsh//UU1GkhqySfy+A/3+xk/qZDAFQK9mV89/o0rxJscWUi4iwUanKgUFM8TIs7wKvf7rbk2DYbeLm74eXhhreHm+O6edvdvP6n+7w83PD+8+0c9vfzci+000JW+nHPcWIXbCfp0irsj7asxD871cTXyzVfr4jknkJNDhRqigfDMJj8w36W7TqG51UDw/UDx1/3+ev+3jnc7+Fmc4rTOkVVyoUMXv1mF3M3HAQgMshstWlRVa02IsWZQk0OFGpEioa4vScYtWAbRy4tgfHwrRUZ1TnKZVupROTa8vL5XXROvotIsdCqRlmWjmjFg80jAZi17nc6Topj9f6TFlcmIs5OoUZEnI5/CU9evb8eswc0p0IpHw79cZ6+//2Zfy3cztkLGVaXJyJOSqFGRJxWdLUyLB3RyrEExpyfE+k4MY64vScsrkxEnJFCjYg4tZLeHvyna13mDGxORJAPR5Iv8MiH63lu/jZS1GpzQ7RiurgqhRoRKRJaVi3D0uGteLRlJQA+23iQjhPj+HHPcWsLKyKSz2cw++ffuf/d1UQ9/x3/+XonWYU90ZJIAdPoJxEpcn7+9RT/XLCN30+dA6Bnk3BG31ObQB9PiytzLll2g9X7TzJ/0yGW/pJEemb2ZTTa1CzL//VphH8J/b+J89KQ7hwo1Ii4lvMXs3hj6R4+WpOAYUD5AG9evb8e7WqVt7o0y/16IpX5mw7xxebDjgkNAWqUL0nPJhGU8vXk+S93cCHDTvVyJfnw0WZEBPlaWLHI1SnU5EChRsQ1bfztNM/O30bCyTQAujWqwAtdalPK18viygpXyoUMvtl2lPmbDrHp9z8c9wf6eHJfwzB6NomgboUAxwSR2w6dYeDMjRxLSSfIz4v3H25CMy0qKk5IoSYHCjUirutCRhZv/m8P/11lttqU9Tdbbe6s7dqtNll2g7UHTjFv00G+23Hl9JKbDdrULEePJuG0q1UOb4+cFwlNSr7AgJkb2HE4BU93G6/eX4+eTSMK8yWIXJdCTQ4UakRc36bf/+Cf87dy4ITZanNfwzDGdqlDaT/XarVJOJnGgk2H+GLzIcfMywDVy5WkR5Nw7m9UgXIBJXL1XOcvZvGPefF8uz0JgEGtq/Bcxyjc3LTkhziHAg01cXFxvPHGG2zatImjR4+ycOFCunbtetX9V61axXPPPcfu3bs5d+4cFStWZNCgQYwYMcKxz7hx4/jiiy/YvXs3Pj4+tGzZkvHjx1OzZk3HPm3atGHFihXZnnvQoEG89957uao7t/8pWVlZZGRomKjkjqenJ+7uOX8LFmtcyMhi0rJ9TIs7gN2AMiW9eLlrXTrVDbW6tJty9kIG324/yryNh9j4p9NLASU8uK9hBXo0Cad+eOANrT9mtxtMXLaX//thPwB31i7PpN4NtTSFOIW8hJo8/8ampaXRoEEDHn/8cbp163bd/f38/BgyZAj169fHz8+PVatWMWjQIPz8/HjiiScAWLFiBTExMTRr1ozMzEz+9a9/0aFDB3bu3Imfn5/juQYOHMhLL73kuO3rm38d2wzDICkpiTNnzuTbc0rxUKpUKUJCQrSYpZMo4enOqM5RdKobwrPztrLveCqDP9nMPfVDefHeOgSX9La6xFyz2w3W/nqK+ZsOsWTHUS5kXDm91KpGWXo0Cad9rfKU8Ly5YO3mZuMfHWpSrVxJnp2/je93HqPHe2v5b7+mVCjlkx8vRaRQ3NTpJ5vNdt2Wmpx069YNPz8/Zs2aleP2EydOUK5cOVasWEGrVq0As6WmYcOGTJo06YZqvV7SO3r0KGfOnKFcuXL4+vrqA0quyzAMzp07x/HjxylVqhShoUW7JcAVpWdm8c7yfby34ley7AbBfl68dF9d7q7v3D+r30+Zp5cWbD7M4TPnHfdXLetHz6YR3N+oAuVzeXoprzb9/geDZm3kZOpFypT0ZtojTWgcWbpAjiWSGwXaUnOztmzZwpo1a3j55Zevuk9ycjIAQUHZe+LPnj2bTz75hJCQELp06cLzzz9/1daa9PR00tPTHbdTUlKuerysrCxHoAkODs7Ly5FizsfH/BZ7/PhxypUrp1NRTsbbw51nO0bRqU4oz8zbyp5jZ4mZs5lvtofw0n11KeNErTap6Zl8e2n00vrfTjvu9y/hwb0NwujRJJyGEaUK/AtXk4qlWRQTzYCPN7I76SwPTFvHGz3qc1/DCgV6XJH8UGihJjw8nBMnTpCZmcnYsWMZMGBAjvvZ7XaGDx9OdHQ0devWddz/4IMPUrFiRcLCwti2bRvPPfcce/bs4YsvvsjxecaNG8eLL76Yq9ou96HJz9NZUnxc/r3JyMhQqHFS9cIDWTw0mik/7Ofdnw7w7fYk1h44xYv31aVL/VDLWmbtdoN1CZdOL21P4nyGuXyBmw1ur26eXrqz9s2fXsqr8NK+LHiyJU/PjWfZrmM8PTeeA8dTGd6+hjoQi1MrtNNPCQkJpKamsm7dOkaNGsXkyZPp06fP3/Z78sknWbJkCatWrSI8PPyqz/fDDz/Qrl079u/fT9WqVf+2PaeWmoiIiBybry5cuEBCQgKVK1emRImCadIV16Xfn6Jlx+Fknp2/jV1HzdbbjnXK85+udSnnX3g/u8RT55i/+RALNh3KdnqpSlk/ejQJp1ujcEICrf9dyrIbvP7dbt6P+xWAu+qF8GbPhvh4KbxL4XHK00+VK1cGoF69ehw7doyxY8f+LdQMGTKEr7/+mri4uGsGGoDmzZsDXDXUeHt74+3tPE3LIuIc6lYI5MuYaN79aT+Tf9jP0l+O8XPCacZ2qcN9DcMKrNUmLT2Tb7ebp5d+TvjT6SVvD+5pEEbPpuE0KoTTS3nh7mYj9q5aVC1Xkn8v3M6325M4eHotHzzS1ClCl8hfWTJez263Z2tFMQyDoUOHsnDhQn766SdHALqW+Ph4AHXOdBGPPvooZ86cYdGiRZY+hxQPXh5uDG9fgw61Q3h2/lZ+OZLC8M/i+XrbEV65v16+dcK12w3W/3aaeRvN0UvnLpqnl2w2uK1aGXo0CadjnZBCP72UV72aRlAp2I9Bszay/XAy901ZxX8faUa98ECrSxPJJs+hJjU1lf379ztuJyQkEB8fT1BQEJGRkcTGxnL48GFmzpwJwJQpU4iMjCQqKgow57mZMGECw4YNczxHTEwMc+bM4csvv8Tf35+kJHMSqMDAQHx8fDhw4ABz5szhrrvuIjg4mG3btjFixAhatWpF/fr1b+o/wFWsXbuW2267jU6dOvHNN98U+PHGjh3LokWLHOHyZr399tsUk3kgxYnUDgtgUUw07/10gHd+2MeyXcdZn7CCMV3q0K1xhRtuNTl4+hwLNh9iweZDHDx95fRS5TKXTi81rkBoYNEaKn1L5SC+jLmN/h9vYN/xVHq+v4a3ejXkrnr6YinOI8+hZuPGjbRt29Zxe+TIkQD069ePGTNmcPToURITEx3b7XY7sbGxJCQk4OHhQdWqVRk/fjyDBg1y7DN16lTAHLb9Zx999BGPPvooXl5eLFu2jEmTJpGWlkZERATdu3dn9OjReS3fZU2fPp2hQ4cyffp0jhw5QlhYmNUlAWbnWU/P668AHBiob3xiDU93N4a2q86ddcrz7LxtbD+czD/mbeXrbUcY161+rk+znLuYyZLtSczfdIi1v55y3F/S24MuDULp0SScxpGlner0Ul5FBvuy4KmWDJ2zhRV7T/DU7M38484aDLmjWpF+XeJCjGIiOTnZAIzk5OS/bTt//ryxc+dO4/z584777Ha7kZaeYcnFbrfn6bWdPXvWKFmypLF7926jd+/exiuvvOLY1qdPH6NXr17Z9r948aIRHBxsfPzxx4ZhGEZKSorx4IMPGr6+vkZISIjx1ltvGa1btzaefvrpHI/30UcfGUC2y0cffWQYhmEAxrvvvmt06dLF8PX1NcaMGWNkZmYajz/+uFGpUiWjRIkSRo0aNYxJkyZle85+/foZ9913n+N269atjaFDhxrPPvusUbp0aaN8+fLGmDFjrvn/8NfnuHDhgjF06FCjbNmyhre3txEdHW2sX7/esf306dPGgw8+aJQpU8YoUaKEUa1aNePDDz80DMMw0tPTjZiYGCMkJMTw9vY2IiMjjVdffTXH4+b0+yNFU0ZmljHlx31G9X99a1R87muj7pjvjM82JF71b9JutxvrDpw0nvk83qj9/BKj4nNfGxWf+9qoNOpro+8H64yFmw8Z59IzC/lVFLyMzCzjxcW/OF7vsE83G+cvut7rFOdwrc/vv9Ic2FdxPiOL2i8steTYO1/qiK9X7n80n3/+OVFRUdSsWZOHHnqI4cOHExsbi81mo2/fvvTs2ZPU1FRKliwJwNKlSzl37hz3338/YLa2rV69msWLF1O+fHleeOEFNm/eTMOGDXM8Xu/evdmxYwffffcdy5YtA7K3tIwdO5bXXnuNSZMm4eHhgd1uJzw8nHnz5hEcHMyaNWt44oknCA0NpVevXld9XR9//DEjR47k559/Zu3atTz66KNER0dz55135ur/5Z///CcLFizg448/pmLFirz++ut07NiR/fv3ExQUxPPPP8/OnTtZsmQJZcqUYf/+/Zw/b54qeOedd1i8eDGff/45kZGRHDx4kIMHD+bquFJ0ebi78VSbatxZqzzPzN/G1oNn+Of8bXy97SivdatH2KXZdQ/9cY4vNh9m/qZDJJ4+53h8pWBfc+2lxuEuPROvh7sbL3SpTdVyfoz58he+jD9C4ulzTHu4KWX9NUBDrKNQ4wKmT5/OQw89BECnTp1ITk5mxYoVtGnTho4dO+Ln58fChQt5+OGHAZgzZw733nsv/v7+nD17lo8//pg5c+bQrl07wDztd63TVz4+PpQsWRIPDw9CQkL+tv3BBx/ksccey3bfn+cMqly5MmvXruXzzz+/ZqipX78+Y8aMAaB69epMnjyZ5cuX5yrUpKWlMXXqVGbMmEHnzp0B+OCDD/j++++ZPn06zz77LImJiTRq1IimTZsCUKlSJcfjExMTqV69Orfddhs2m42KFSte95jiOqqX92fB4BZMX5XAm9/vJW7vCTpMjOPx6Eps/P0P1hzIfnrp7nqh9GgaTtOKRfv0Ul71bV6RysF+PDl7M1sSz3Df5FX8t18zaodp0WCxhkLNVfh4urPzpY6WHTu39uzZw/r161m4cCEAHh4e9O7dm+nTp9OmTRs8PDzo1asXs2fP5uGHHyYtLY0vv/ySuXPnAvDrr7+SkZHBLbfc4njOwMDAbIuJ5tXlkPBnU6ZM4cMPPyQxMZHz589z8eLFq7YEXfbXTuChoaEcP348VzUcOHCAjIwMoqOjHfd5enpyyy23sGvXLsCcE6l79+5s3ryZDh060LVrV1q2bAmYI6nuvPNOatasSadOnbjnnnvo0KFDro4trsHD3Y1BravSrlZ5np2/lS2JZ3jnhyuDJKKrBTtGL+WlZdXVtKxWhoVPtWTAxxv59WQaPd5bw9sPNOLO2uWtLk2KoeL7l3gdNputSLxRTZ8+nczMzGwtK4Zh4O3tzeTJkwkMDKRv3760bt2a48eP8/333+Pj40OnTp0KrKY/L0IKMHfuXJ555hnefPNNWrRogb+/P2+88QY///zzNZ/nrx2MbTYbdrs93+rs3Lkzv//+O99++y3ff/897dq1IyYmhgkTJtC4cWMSEhJYsmQJy5Yto1evXrRv35758+fn2/GlaKhWriTzB7fko9UJfLv9KG1qlqNb4wqEl9YM5JdVKVuShU9F89ScTazef4onZm1kVKconmhVpVi1XIn13KwuQG5cZmYmM2fO5M033yQ+Pt5x2bp1K2FhYXz66acAtGzZkoiICD777DNmz55Nz549HYGhSpUqeHp6smHDBsfzJicns3fv3mse28vLi6ysrFzVuXr1alq2bMlTTz1Fo0aNqFatGgcOHLjBV507VatWxcvLi9WrVzvuy8jIYMOGDdSuXdtxX9myZenXrx+ffPIJkyZNYtq0aY5tAQEB9O7dmw8++IDPPvuMBQsWcPr0aaT4cXezMeD2KnzxVDTD2lVXoMlBoK8nMx67hb7NIzEMGLdkN8/O30Z6Zu7eJ0Tyg/M3RchVff311/zxxx/079//b0Oiu3fvzvTp0xk8eDBg9nN577332Lt3Lz/++KNjP39/f/r168ezzz5LUFAQ5cqVY8yYMbi5uV3zG1alSpUccxSFh4fj7+9/1Rmcq1evzsyZM1m6dCmVK1dm1qxZbNiwIVeTLN4oPz8/nnzyScfrioyM5PXXX+fcuXP0798fgBdeeIEmTZpQp04d0tPT+frrr6lVqxYAb731FqGhoTRq1Ag3NzfmzZtHSEgIpUqVKrCaRYo6T3c3Xu5alxrl/Xnxq1/MjtSnzvHew00I8vOyujwpBtRSU4RNnz6d9u3b5zjHS/fu3dm4cSPbtm0DoG/fvuzcuZMKFSpk62cC5gd4ixYtuOeee2jfvj3R0dHUqlXrmusYde/enU6dOtG2bVvKli3raBXKyaBBg+jWrRu9e/emefPmnDp1iqeeeuoGX3Xuvfbaa3Tv3p2HH36Yxo0bs3//fpYuXUrp0qUBs7UpNjaW+vXr06pVK9zd3R19jfz9/Xn99ddp2rQpzZo147fffuPbb7/FzU1/MiLXYrPZ6NeyEh89dgv+3h6s/+00901Zxb5jZ60uTYqBm1rQsii51oJYWpAwu7S0NCpUqMCbb77paNWQq9Pvj0jO9h07S/+PN5J4+hz+3h6882Aj2tYsZ3VZUsTkZUFLfe0UtmzZwqeffsqBAwfYvHkzffv2BeC+++6zuDIRKcqql/dnUUw0t1QO4mx6Jv1nbODDVQlaEkUKjEKNADBhwgQaNGhA+/btSUtLY+XKlZQpU8bqskSkiAvy8+KT/s3p1TQcuwEvfb2Tfy/aQUZW/o1kFOsZhkGW3eBiprU/V3UUFho1asSmTZusLkNEXJSXhxvju9enejl/Xl2yizk/J/LbyTTe7duYUr7qQJyemcX5i1lkZBlk2u1kZhlkZNnJtJv/ZtkNc9uf7su8tG/Gn//Ndt3cN6f7Lj8+w26//mP+Us9fa/vzvgDVy5Xk+5GtLfu/VKgREZECZ7PZGNiqClXK+jHs0y2sOXCK+99dw/R+TalStqTV5RUqu91g++Fk4vaeIG7fCTYnniHL7hqn5DItfh0KNSIiUmja1SrP/CfNGYgTTqbRdcpqpj7UhOhqrn26+3jKBeL2nWTF3hOs2neCP85l5Lifp7sNDzc3PNxteLq74eF26V93Gx5uV7Z5uLvh6WbLtp+Hu1v2x7u54e5uu7Tflfuu+Rh3G+5ubld5TM61uV+uw80NTw9re7Uo1IiISKGqFRrAophoBs3ayObEMzzy4XpevLcOD93qOmuspWdmsfG3P4jbe4IVe0+wOyn7kHZ/bw9aVgumVY2ytKpeltDAEri72TQD801SqBERkUJX1t+bOQNvZdSCbSyKP8LoRTvYfzyV0XfXwsO96I1hMQyDhJNpl04pnWTtgVOcz7gym7LNBvUqBNKqella1ShLo8hSeBbB1+nsFGpERMQSJTzdmdi7IdXL+/PG0j3MWPMbv55MY/KDjQgo4Xn9J7DY2QsZrDlwytE35uDp89m2l/X35vbqZWhdoyy3VStDcMmcZ12X/KNQIyIilrHZbMS0rUbVsn6M+GwrcXtP0O1SB+KKwX7Xf4JCZLcb/HIkhbh9J1ix5wSbE//I1jHW091Gs0pBjlNKtUL9dTqpkCnUSJEzduxYFi1aRHx8vNWliEg+6VQ3lPDSvgz4eCP7j6fSdcpq3nuoCc2rBFta1/GzF1i59yRx+06wat9JTqVdzLa9chk/WlUvQ6saZbm1SjB+3vpYtZL+913E2rVrue222+jUqRPffPNNgR+vIILFjBkzGD58OGfOnMm35xSRoqNuhUC+HBLNwJkb2XYomYem/8wrXevRq1lEodVwMdPOxt9PE7f3JHF7T7DzaEq27SW9PWhR1ezg27p6WSKDtWK7M1GocRHTp09n6NChTJ8+nSNHjhAWFmZ1SSIieVY+oASfPdGCZ+Zv5ZttR/nngm3sP5HKc52icHcrmFM5v51MI27fCeL2nmDNgVOcu5iVbXu9CoG0qlGGVtXL0rhiaXXwdWL6yVyNYcDFNGsueVwXJTU1lc8++4wnn3ySu+++mxkzZji2Pfjgg/Tu3Tvb/hkZGZQpU4aZM2cCcPbsWfr27Yufnx+hoaFMnDiRNm3aMHz48ByPN2PGDF588UW2bt2KzWYOQbx8zDNnzjBgwADKli1LQEAAd9xxB1u3bnU8duvWrbRt2xZ/f38CAgJo0qQJGzdu5KeffuKxxx4jOTnZ8Zxjx47N1eu32+289NJLhIeH4+3tTcOGDfnuu+8c2y9evMiQIUMIDQ2lRIkSVKxYkXHjxgHmiIWxY8cSGRmJt7c3YWFhDBs2LFfHFZGC4ePlzuQ+jXi6XXUApsX9yqBZG0lNz8yX509Nz+T7nccYvWg7rV7/kTYTfuKFL39h2a7jnLuYRZmS3nRrVIFJvRuycXR7vhp6G892jKJ5lWAFGienlpqryTgHr1rU2vGvI+CV+w5yn3/+OVFRUdSsWZOHHnqI4cOHExsbi81mo2/fvvTs2ZPU1FRKljRn7Vy6dCnnzp3j/vvvB2DkyJGsXr2axYsXU758eV544QU2b95Mw4YNczxe79692bFjB9999x3Lli0DIDAwEICePXvi4+PDkiVLCAwM5P3336ddu3bs3buXoKAg+vbtS6NGjZg6dSru7u7Ex8fj6elJy5YtmTRpEi+88AJ79uwBcNR7PW+//TZvvvkm77//Po0aNeLDDz/k3nvv5ZdffqF69eq88847LF68mM8//5zIyEgOHjzIwYMHAViwYAETJ05k7ty51KlTh6SkpGwhTESsYbPZGHFnDaqWK8kz87aybNdxekxdw3/7NSW8dN5O+djtBjuPprBir9kas+n3v3fwbVKxtKODb+3QANwKqFVICpZCjQuYPn06Dz30EACdOnUiOTmZFStW0KZNGzp27Iifnx8LFy7k4YcfBmDOnDnce++9+Pv7c/bsWT7++GPmzJlDu3btAPjoo4+uefrKx8eHkiVL4uHhQUhIiOP+VatWsX79eo4fP463tzl0ccKECSxatIj58+fzxBNPkJiYyLPPPktUVBQA1atXdzw+MDAQm82W7TlzY8KECTz33HM88MADAIwfP54ff/yRSZMmMWXKFBITE6levTq33XYbNpuNihWvTPCVmJhISEgI7du3x9PTk8jISG655ZY8HV9ECs69DcKIKO3DE7M2sTvpLF2nrOb9h5vQpGLQNR93MjWdlftOELf3JCv3neBkavYOvhWDfWlVvSyta5Tl1qrBlFQHX5egn+LVePqaLSZWHTuX9uzZw/r161m4cCEAHh4e9O7dm+nTp9OmTRs8PDzo1asXs2fP5uGHHyYtLY0vv/ySuXPnAvDrr7+SkZGR7YM8MDCQmjVr5rnsrVu3kpqaSnBw9tEK58+f58CBA4DZKjRgwABmzZpF+/bt6dmzJ1WrVs3zsS5LSUnhyJEjREdHZ7s/Ojra0eLy6KOPcuedd1KzZk06derEPffcQ4cOHQCzZWnSpElUqVKFTp06cdddd9GlSxc8PPSnIeIsGkWW5suYaAZ8vJGdR1PoM+1nxveox/2Nwh37XMy0sznxD8ecMTsOZ+/g6+vlTsuqZWhdwxyp5GzDxSV/6J37amy2PJ0Cssr06dPJzMzM1rJiGAbe3t5MnjyZwMBA+vbtS+vWrTl+/Djff/89Pj4+dOrUKd9rSU1NJTQ0lJ9++ulv20qVKgWYo6YefPBBvvnmG5YsWcKYMWOYO3eu41RYQWjcuDEJCQksWbKEZcuW0atXL9q3b8/8+fOJiIhgz549LFu2jO+//56nnnqKN954gxUrVuDp6fyTf4kUF2GlfJg3uAUjPovnfzuPMeKzrexJSqVCaR9W7DnB2gMnSftLB986YQGOU0pNKpbGy+J1iaTgKdQUYZmZmcycOZM333zT0fJwWdeuXfn0008ZPHgwLVu2JCIigs8++4wlS5bQs2dPxwd2lSpV8PT0ZMOGDURGRgKQnJzM3r17adWq1VWP7eXlRVZW9jeQxo0bk5SUhIeHB5UqVbrqY2vUqEGNGjUYMWIEffr04aOPPuL+++/P8TmvJyAggLCwMFavXk3r1leWu1+9enW21qeAgAB69+5N79696dGjB506deL06dMEBQXh4+NDly5d6NKlCzExMURFRbF9+3YaN26cp1pEpGD5eXvw3kNNmPC/Pbz70wHeW3Eg2/ZgPy9uvzRnzO3Vy1LWXzP4FjcKNUXY119/zR9//EH//v0dHXUv6969O9OnT2fw4MGAOQrqvffeY+/evfz444+O/fz9/enXrx/PPvssQUFBlCtXjjFjxuDm5nbNmTArVapEQkIC8fHxhIeH4+/vT/v27WnRogVdu3bl9ddfp0aNGhw5coRvvvmG+++/nzp16vDss8/So0cPKleuzKFDh9iwYQPdu3d3PGdqairLly+nQYMG+Pr64ut7/VNxzz77LGPGjKFq1ao0bNiQjz76iPj4eGbPng3AW2+9RWhoKI0aNcLNzY158+YREhJCqVKlmDFjBllZWTRv3hxfX18++eQTfHx8svW7ERHn4eZm45+doqhatiTjv9tNpTJ+tK5h9o1RB1/BKCaSk5MNwEhOTv7btvPnzxs7d+40zp8/b0FlN+6ee+4x7rrrrhy3/fzzzwZgbN261TAMw9i5c6cBGBUrVjTsdnu2fVNSUowHH3zQ8PX1NUJCQoy33nrLuOWWW4xRo0Zd9dgXLlwwunfvbpQqVcoAjI8++sjxXEOHDjXCwsIMT09PIyIiwujbt6+RmJhopKenGw888IARERFheHl5GWFhYcaQIUOy/b8PHjzYCA4ONgBjzJgxOR57zJgxRoMGDRy3s7KyjLFjxxoVKlQwPD09jQYNGhhLlixxbJ82bZrRsGFDw8/PzwgICDDatWtnbN682TAMw1i4cKHRvHlzIyAgwPDz8zNuvfVWY9myZVd93Tkpqr8/IiJFwbU+v//KZhh5nBSliEpJSSEwMJDk5GQCAgKybbtw4QIJCQlUrlyZEiVKWFSh80hLS6NChQq8+eab9O/f3+pynJ5+f0RECs61Pr//SqefhC1btrB7925uueUWkpOTeemllwC47777LK5MREQk9xRqBDDnetmzZw9eXl40adKElStXUqZMGavLEhERyTWFGqFRo0Zs2rTJ6jJERERuigbti4iIiEtQqPmTYtJnWvKZfm9ERJxDnkNNXFwcXbp0ISwsDJvNxqJFi665/6pVq4iOjiY4OBgfHx+ioqKYOHFitn3GjRtHs2bN8Pf3p1y5cnTt2tWxqOFlFy5cICYmhuDgYEqWLEn37t05duxYXsvP0eWJ6M6dO5cvzyfFy+XfG81ALCJirTz3qUlLS6NBgwY8/vjjdOvW7br7+/n5MWTIEOrXr4+fnx+rVq1i0KBB+Pn58cQTTwCwYsUKYmJiaNasGZmZmfzrX/+iQ4cO7Ny5Ez8/c6mCESNG8M033zBv3jwCAwMZMmQI3bp1Y/Xq1Xl9CX/j7u5OqVKlOH78OAC+vr7XnHhOBMwWmnPnznH8+HFKlSqFu7u71SWJiBRrNzVPjc1mY+HChXTt2jVPj+vWrRt+fn7MmjUrx+0nTpygXLlyrFixglatWpGcnEzZsmWZM2cOPXr0AGD37t3UqlWLtWvXcuutt/7tOdLT00lPT3fcTklJISIi4qrj3A3DICkpiTNnzuTptYiUKlWKkJAQBWERkQLg1PPUbNmyhTVr1vDyyy9fdZ/k5GQAgoLMpeU3bdpERkYG7du3d+wTFRVFZGTkVUPNuHHjePHFF3Ndl81mIzQ0lHLlypGRkZHrx0nx5unpqRYaEREnUWihJjw8nBMnTpCZmcnYsWMZMGBAjvvZ7XaGDx9OdHQ0devWBSApKQkvLy/HSs+XlS9fnqSkpByfJzY2lpEjRzpuX26puR53d3d9SImIiBRBhRZqVq5cSWpqKuvWrWPUqFFUq1aNPn36/G2/mJgYduzYwapVq27qeN7e3nh7a4VWERGR4qLQQk3lypUBqFevHseOHWPs2LF/CzVDhgzh66+/Ji4ujvDwcMf9ISEhXLx4kTNnzmRrrTl27BghISGFUr+IiIg4N0vmqbHb7dk68RqGwZAhQ1i4cCE//PCDIwBd1qRJEzw9PVm+fLnjvj179pCYmEiLFi0KrW4RERFxXnluqUlNTWX//v2O2wkJCcTHxxMUFERkZCSxsbEcPnyYmTNnAjBlyhQiIyOJiooCzHluJkyYwLBhwxzPERMTw5w5c/jyyy/x9/d39JMJDAzEx8eHwMBA+vfvz8iRIwkKCiIgIIChQ4fSokWLHDsJ5+TyIK+UlJS8vmQRERGxyOXP7VwN1jby6McffzSAv1369etnGIZh9OvXz2jdurVj/3feeceoU6eO4evrawQEBBiNGjUy3n33XSMrK8uxT07PBxgfffSRY5/z588bTz31lFG6dGnD19fXuP/++42jR4/muu6DBw9e9Ti66KKLLrroootzXw4ePHjdz/qbmqemKLHb7Rw5cgR/f/98n0/k8siqgwcPXncMvRQ8/Tyci34ezkU/D+ejn8m1GYbB2bNnCQsLw83t2r1mis0q3W5ubtk6HxeEgIAA/UI6Ef08nIt+Hs5FPw/no5/J1QUGBuZqPy1oKSIiIi5BoUZERERcgkJNPvD29mbMmDGa7M9J6OfhXPTzcC76eTgf/UzyT7HpKCwiIiKuTS01IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUHOTpkyZQqVKlShRogTNmzdn/fr1VpdUbI0bN45mzZrh7+9PuXLl6Nq1K3v27LG6LLnktddew2azMXz4cKtLKbYOHz7MQw89RHBwMD4+PtSrV4+NGzdaXVaxlJWVxfPPP0/lypXx8fGhatWq/Oc//8nd+kZyVQo1N+Gzzz5j5MiRjBkzhs2bN9OgQQM6duzI8ePHrS6tWFqxYgUxMTGsW7eO77//noyMDDp06EBaWprVpRV7GzZs4P3336d+/fpWl1Js/fHHH0RHR+Pp6cmSJUvYuXMnb775JqVLl7a6tGJp/PjxTJ06lcmTJ7Nr1y7Gjx/P66+/zv/93/9ZXVqRpiHdN6F58+Y0a9aMyZMnA+b6UhEREQwdOpRRo0ZZXJ2cOHGCcuXKsWLFClq1amV1OcVWamoqjRs35t133+Xll1+mYcOGTJo0yeqyip1Ro0axevVqVq5caXUpAtxzzz2UL1+e6dOnO+7r3r07Pj4+fPLJJxZWVrSppeYGXbx4kU2bNtG+fXvHfW5ubrRv3561a9daWJlclpycDEBQUJDFlRRvMTEx3H333dn+VqTwLV68mKZNm9KzZ0/KlStHo0aN+OCDD6wuq9hq2bIly5cvZ+/evQBs3bqVVatW0blzZ4srK9qKzYKW+e3kyZNkZWVRvnz5bPeXL1+e3bt3W1SVXGa32xk+fDjR0dHUrVvX6nKKrblz57J582Y2bNhgdSnF3q+//srUqVMZOXIk//rXv9iwYQPDhg3Dy8uLfv36WV1esTNq1ChSUlKIiorC3d2drKwsXnnlFfr27Wt1aUWaQo24pJiYGHbs2MGqVausLqXYOnjwIE8//TTff/89JUqUsLqcYs9ut9O0aVNeffVVABo1asSOHTt47733FGos8PnnnzN79mzmzJlDnTp1iI+PZ/jw4YSFhenncRMUam5QmTJlcHd359ixY9nuP3bsGCEhIRZVJQBDhgzh66+/Ji4ujvDwcKvLKbY2bdrE8ePHady4seO+rKws4uLimDx5Munp6bi7u1tYYfESGhpK7dq1s91Xq1YtFixYYFFFxduzzz7LqFGjeOCBBwCoV68ev//+O+PGjVOouQnqU3ODvLy8aNKkCcuXL3fcZ7fbWb58OS1atLCwsuLLMAyGDBnCwoUL+eGHH6hcubLVJRVr7dq1Y/v27cTHxzsuTZs2pW/fvsTHxyvQFLLo6Oi/TXGwd+9eKlasaFFFxdu5c+dwc8v+Eezu7o7dbreoIteglpqbMHLkSPr160fTpk255ZZbmDRpEmlpaTz22GNWl1YsxcTEMGfOHL788kv8/f1JSkoCIDAwEB8fH4urK378/f3/1p/Jz8+P4OBg9XOywIgRI2jZsiWvvvoqvXr1Yv369UybNo1p06ZZXVqx1KVLF1555RUiIyOpU6cOW7Zs4a233uLxxx+3urSizZCb8n//939GZGSk4eXlZdxyyy3GunXrrC6p2AJyvHz00UdWlyaXtG7d2nj66aetLqPY+uqrr4y6desa3t7eRlRUlDFt2jSrSyq2UlJSjKefftqIjIw0SpQoYVSpUsX497//baSnp1tdWpGmeWpERETEJahPjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIuQaFGREREXIJCjYgUWzabjUWLFlldhojkE4UaEbHEo48+is1m+9ulU6dOVpcmIkWUFrQUEct06tSJjz76KNt93t7eFlUjIkWdWmpExDLe3t6EhIRku5QuXRowTw1NnTqVzp074+PjQ5UqVZg/f362x2/fvp077rgDHx8fgoODeeKJJ0hNTc22z4cffkidOnXw9vYmNDSUIUOGZNt+8uRJ7r//fnx9falevTqLFy8u2BctIgVGoUZEnNbzzz9P9+7d2bp1K3379uWBBx5g165dAKSlpdGxY0dKly7Nhg0bmDdvHsuWLcsWWqZOnUpMTAxPPPEE27dvZ/HixVSrVi3bMV588UV69erFtm3buOuuu+jbty+nT58u1NcpIvnE6mXCRaR46tevn+Hu7m74+fllu7zyyiuGYRgGYAwePDjbY5o3b248+eSThmEYxrRp04zSpUsbqampju3ffPON4ebmZiQlJRmGYRhhYWHGv//976vWABijR4923E5NTTUAY8mSJfn2OkWk8KhPjYhYpm3btkydOjXbfUFBQY7rLVq0yLatRYsWxMfHA7Br1y4aNGiAn5+fY3t0dDR2u509e/Zgs9k4cuQI7dq1u2YN9evXd1z38/MjICCA48eP3+hLEhELKdSIiGX8/Pz+djoov/j4+ORqP09Pz2y3bTYbdru9IEoSkQKmPjUi4rTWrVv3t9u1atUCoFatWmzdupW0tDTH9tWrV+Pm5kbNmjXx9/enUqVKLF++vFBrFhHrqKVGRCyTnp5OUlJStvs8PDwoU6YMAPPmzaNp06bcdtttzJ49m/Xr1zN9+nQA+vbty5gxY+jXrx9jx47lxIkTDB06lIcffpjy5csDMHbsWAYPHky5cuXo3LkzZ8+eZfXq1QwdOrRwX6iIFAqFGhGxzHfffUdoaGi2+2rWrMnu3bsBc2TS3LlzeeqppwgNDeXTTz+ldu3aAPj6+rJ06VKefvppmjVrhq+vL927d+ett95yPFe/fv24cOECEydO5JlnnqFMmTL06NGj8F6giBQqm2EYhtVFiIj8lc1mY+HChXTt2tXqUkSkiFCfGhEREXEJCjUiIiLiEtSnRkScks6Mi0heqaVGREREXIJCjYiIiLgEhRoRERFxCQo1IiIi4hIUakRERMQlKNSIiIiIS1CoEREREZegUCMiIiIu4f8BqzJDmqFVq2IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_loss, label=\"Avg train loss\")\n",
    "plt.plot(test_loss, label=\"Avg test loss\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Epoch\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "0f75c0c8c2d7cdc5b7009fb59c6c39d41e1dc7738cb4764e7938041d68d9b104"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}