{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "eb28146d",
   "metadata": {},
   "source": [
    "# PyTorch fine-tuning of ResNet50\n",
    "\n",
    "**Note**: this notebook is part of the [`gperdrizet/CIFAR10`](https://github.com/gperdrizet/CIFAR10) repository and is meant to run in the development container environment specified in that repository. If you are attempting to run in somewhere else you may have to install some dependencies. Start with:\n",
    "\n",
    "```\n",
    "pip install image-classification-tools\n",
    "```\n",
    "\n",
    "Also install anything else that causes a 'module not found' error the same way.\n",
    "\n",
    "## Notebook set-up\n",
    "\n",
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc6f4eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "\n",
    "# Third party imports\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms, models\n",
    "\n",
    "# Package imports\n",
    "import image_classification_tools.pytorch.data as data_utils\n",
    "import image_classification_tools.pytorch.evaluation as eval_utils\n",
    "import image_classification_tools.pytorch.plotting as plots\n",
    "import image_classification_tools.pytorch.training as training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912e6f11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set random seeds for reproducibility\n",
    "torch.manual_seed(315)\n",
    "np.random.seed(315)\n",
    "\n",
    "# Device configuration\n",
    "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Using device: {DEVICE}')\n",
    "\n",
    "# Paths\n",
    "MODELS_DIR = Path('../models/pytorch')\n",
    "RESULTS_DIR = Path('../data/pytorch/performance_results')\n",
    "DATA_DIR = Path('../data/pytorch/cifar10')\n",
    "\n",
    "# Make sure directories exist\n",
    "MODELS_DIR.mkdir(parents=True, exist_ok=True)\n",
    "RESULTS_DIR.mkdir(parents=True, exist_ok=True)\n",
    "DATA_DIR.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# CIFAR-10 class names in class order\n",
    "CLASS_NAMES = [\n",
    "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be9c1f7b",
   "metadata": {},
   "source": [
    "### Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27433cdd",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "92f9ee0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "validation_size = 10000\n",
    "batch_size = 64\n",
    "learning_rate = 1e-3\n",
    "epochs = 100  # Early stopping will halt if validation accuracy stops improving\n",
    "print_every = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1775dfbf",
   "metadata": {},
   "source": [
    "## 1. Load and preprocess CIFAR-10 data\n",
    "\n",
    "### 1.1. Visualize sample images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10473ed2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get a sample dataset for visualization\n",
    "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\n",
    "sample_dataset = datasets.CIFAR10(root=DATA_DIR, transform=transform)\n",
    "                                    \n",
    "# Plot first 10 images from the training dataset\n",
    "fig, axes = plots.plot_sample_images(sample_dataset, CLASS_NAMES)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efe7b605",
   "metadata": {},
   "source": [
    "### 1.2. Define preprocessing transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c9e5f9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define basic input transforms (RGB) - no resize needed, we'll adapt the model\n",
    "# to the CIFAR-10 image size later\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "456474dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = data_utils.load_dataset(\n",
    "    data_source=datasets.CIFAR10,\n",
    "    transform=transform,\n",
    "    root=DATA_DIR,\n",
    "    train=True\n",
    ")\n",
    "\n",
    "test_dataset = data_utils.load_dataset(\n",
    "    data_source=datasets.CIFAR10,\n",
    "    transform=transform,\n",
    "    root=DATA_DIR,\n",
    "    train=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "172fe439",
   "metadata": {},
   "source": [
    "### 1.4. Make training, validation split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "58a5ba1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset, val_dataset, test_dataset = data_utils.prepare_splits(\n",
    "    train_dataset=train_dataset,\n",
    "    test_dataset=test_dataset,\n",
    "    val_size=validation_size\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79ab9647",
   "metadata": {},
   "source": [
    "### 1.5. Create dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec1468bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training batches: 625\n",
      "Validation batches: 157\n",
      "Test batches: 157\n"
     ]
    }
   ],
   "source": [
    "train_loader, val_loader, test_loader = data_utils.create_dataloaders(\n",
    "    train_dataset, val_dataset, test_dataset,\n",
    "    batch_size=batch_size,\n",
    "    preload_to_memory=True,\n",
    "    device=DEVICE\n",
    ")\n",
    "\n",
    "print(f'Training batches: {len(train_loader)}')\n",
    "print(f'Validation batches: {len(val_loader)}')\n",
    "print(f'Test batches: {len(test_loader)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d86b258",
   "metadata": {},
   "source": [
    "## 2. ResNet50\n",
    "\n",
    "### 2.1. Load model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "894271d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Model layers:\n",
      "\n",
      "Name: , Module Type: ResNet\n",
      "Name: conv1, Module Type: Conv2d\n",
      "Name: bn1, Module Type: BatchNorm2d\n",
      "Name: relu, Module Type: ReLU\n",
      "Name: maxpool, Module Type: MaxPool2d\n",
      "Name: layer1, Module Type: Sequential\n",
      "Name: layer1.0, Module Type: Bottleneck\n",
      "Name: layer1.0.conv1, Module Type: Conv2d\n",
      "Name: layer1.0.bn1, Module Type: BatchNorm2d\n",
      "Name: layer1.0.conv2, Module Type: Conv2d\n",
      "Name: layer1.0.bn2, Module Type: BatchNorm2d\n",
      "Name: layer1.0.conv3, Module Type: Conv2d\n",
      "Name: layer1.0.bn3, Module Type: BatchNorm2d\n",
      "Name: layer1.0.relu, Module Type: ReLU\n",
      "Name: layer1.0.downsample, Module Type: Sequential\n",
      "Name: layer1.0.downsample.0, Module Type: Conv2d\n",
      "Name: layer1.0.downsample.1, Module Type: BatchNorm2d\n",
      "Name: layer1.1, Module Type: Bottleneck\n",
      "Name: layer1.1.conv1, Module Type: Conv2d\n",
      "Name: layer1.1.bn1, Module Type: BatchNorm2d\n",
      "Name: layer1.1.conv2, Module Type: Conv2d\n",
      "Name: layer1.1.bn2, Module Type: BatchNorm2d\n",
      "Name: layer1.1.conv3, Module Type: Conv2d\n",
      "Name: layer1.1.bn3, Module Type: BatchNorm2d\n",
      "Name: layer1.1.relu, Module Type: ReLU\n",
      "Name: layer1.2, Module Type: Bottleneck\n",
      "Name: layer1.2.conv1, Module Type: Conv2d\n",
      "Name: layer1.2.bn1, Module Type: BatchNorm2d\n",
      "Name: layer1.2.conv2, Module Type: Conv2d\n",
      "Name: layer1.2.bn2, Module Type: BatchNorm2d\n",
      "Name: layer1.2.conv3, Module Type: Conv2d\n",
      "Name: layer1.2.bn3, Module Type: BatchNorm2d\n",
      "Name: layer1.2.relu, Module Type: ReLU\n",
      "Name: layer2, Module Type: Sequential\n",
      "Name: layer2.0, Module Type: Bottleneck\n",
      "Name: layer2.0.conv1, Module Type: Conv2d\n",
      "Name: layer2.0.bn1, Module Type: BatchNorm2d\n",
      "Name: layer2.0.conv2, Module Type: Conv2d\n",
      "Name: layer2.0.bn2, Module Type: BatchNorm2d\n",
      "Name: layer2.0.conv3, Module Type: Conv2d\n",
      "Name: layer2.0.bn3, Module Type: BatchNorm2d\n",
      "Name: layer2.0.relu, Module Type: ReLU\n",
      "Name: layer2.0.downsample, Module Type: Sequential\n",
      "Name: layer2.0.downsample.0, Module Type: Conv2d\n",
      "Name: layer2.0.downsample.1, Module Type: BatchNorm2d\n",
      "Name: layer2.1, Module Type: Bottleneck\n",
      "Name: layer2.1.conv1, Module Type: Conv2d\n",
      "Name: layer2.1.bn1, Module Type: BatchNorm2d\n",
      "Name: layer2.1.conv2, Module Type: Conv2d\n",
      "Name: layer2.1.bn2, Module Type: BatchNorm2d\n",
      "Name: layer2.1.conv3, Module Type: Conv2d\n",
      "Name: layer2.1.bn3, Module Type: BatchNorm2d\n",
      "Name: layer2.1.relu, Module Type: ReLU\n",
      "Name: layer2.2, Module Type: Bottleneck\n",
      "Name: layer2.2.conv1, Module Type: Conv2d\n",
      "Name: layer2.2.bn1, Module Type: BatchNorm2d\n",
      "Name: layer2.2.conv2, Module Type: Conv2d\n",
      "Name: layer2.2.bn2, Module Type: BatchNorm2d\n",
      "Name: layer2.2.conv3, Module Type: Conv2d\n",
      "Name: layer2.2.bn3, Module Type: BatchNorm2d\n",
      "Name: layer2.2.relu, Module Type: ReLU\n",
      "Name: layer2.3, Module Type: Bottleneck\n",
      "Name: layer2.3.conv1, Module Type: Conv2d\n",
      "Name: layer2.3.bn1, Module Type: BatchNorm2d\n",
      "Name: layer2.3.conv2, Module Type: Conv2d\n",
      "Name: layer2.3.bn2, Module Type: BatchNorm2d\n",
      "Name: layer2.3.conv3, Module Type: Conv2d\n",
      "Name: layer2.3.bn3, Module Type: BatchNorm2d\n",
      "Name: layer2.3.relu, Module Type: ReLU\n",
      "Name: layer3, Module Type: Sequential\n",
      "Name: layer3.0, Module Type: Bottleneck\n",
      "Name: layer3.0.conv1, Module Type: Conv2d\n",
      "Name: layer3.0.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.0.conv2, Module Type: Conv2d\n",
      "Name: layer3.0.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.0.conv3, Module Type: Conv2d\n",
      "Name: layer3.0.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.0.relu, Module Type: ReLU\n",
      "Name: layer3.0.downsample, Module Type: Sequential\n",
      "Name: layer3.0.downsample.0, Module Type: Conv2d\n",
      "Name: layer3.0.downsample.1, Module Type: BatchNorm2d\n",
      "Name: layer3.1, Module Type: Bottleneck\n",
      "Name: layer3.1.conv1, Module Type: Conv2d\n",
      "Name: layer3.1.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.1.conv2, Module Type: Conv2d\n",
      "Name: layer3.1.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.1.conv3, Module Type: Conv2d\n",
      "Name: layer3.1.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.1.relu, Module Type: ReLU\n",
      "Name: layer3.2, Module Type: Bottleneck\n",
      "Name: layer3.2.conv1, Module Type: Conv2d\n",
      "Name: layer3.2.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.2.conv2, Module Type: Conv2d\n",
      "Name: layer3.2.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.2.conv3, Module Type: Conv2d\n",
      "Name: layer3.2.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.2.relu, Module Type: ReLU\n",
      "Name: layer3.3, Module Type: Bottleneck\n",
      "Name: layer3.3.conv1, Module Type: Conv2d\n",
      "Name: layer3.3.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.3.conv2, Module Type: Conv2d\n",
      "Name: layer3.3.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.3.conv3, Module Type: Conv2d\n",
      "Name: layer3.3.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.3.relu, Module Type: ReLU\n",
      "Name: layer3.4, Module Type: Bottleneck\n",
      "Name: layer3.4.conv1, Module Type: Conv2d\n",
      "Name: layer3.4.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.4.conv2, Module Type: Conv2d\n",
      "Name: layer3.4.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.4.conv3, Module Type: Conv2d\n",
      "Name: layer3.4.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.4.relu, Module Type: ReLU\n",
      "Name: layer3.5, Module Type: Bottleneck\n",
      "Name: layer3.5.conv1, Module Type: Conv2d\n",
      "Name: layer3.5.bn1, Module Type: BatchNorm2d\n",
      "Name: layer3.5.conv2, Module Type: Conv2d\n",
      "Name: layer3.5.bn2, Module Type: BatchNorm2d\n",
      "Name: layer3.5.conv3, Module Type: Conv2d\n",
      "Name: layer3.5.bn3, Module Type: BatchNorm2d\n",
      "Name: layer3.5.relu, Module Type: ReLU\n",
      "Name: layer4, Module Type: Sequential\n",
      "Name: layer4.0, Module Type: Bottleneck\n",
      "Name: layer4.0.conv1, Module Type: Conv2d\n",
      "Name: layer4.0.bn1, Module Type: BatchNorm2d\n",
      "Name: layer4.0.conv2, Module Type: Conv2d\n",
      "Name: layer4.0.bn2, Module Type: BatchNorm2d\n",
      "Name: layer4.0.conv3, Module Type: Conv2d\n",
      "Name: layer4.0.bn3, Module Type: BatchNorm2d\n",
      "Name: layer4.0.relu, Module Type: ReLU\n",
      "Name: layer4.0.downsample, Module Type: Sequential\n",
      "Name: layer4.0.downsample.0, Module Type: Conv2d\n",
      "Name: layer4.0.downsample.1, Module Type: BatchNorm2d\n",
      "Name: layer4.1, Module Type: Bottleneck\n",
      "Name: layer4.1.conv1, Module Type: Conv2d\n",
      "Name: layer4.1.bn1, Module Type: BatchNorm2d\n",
      "Name: layer4.1.conv2, Module Type: Conv2d\n",
      "Name: layer4.1.bn2, Module Type: BatchNorm2d\n",
      "Name: layer4.1.conv3, Module Type: Conv2d\n",
      "Name: layer4.1.bn3, Module Type: BatchNorm2d\n",
      "Name: layer4.1.relu, Module Type: ReLU\n",
      "Name: layer4.2, Module Type: Bottleneck\n",
      "Name: layer4.2.conv1, Module Type: Conv2d\n",
      "Name: layer4.2.bn1, Module Type: BatchNorm2d\n",
      "Name: layer4.2.conv2, Module Type: Conv2d\n",
      "Name: layer4.2.bn2, Module Type: BatchNorm2d\n",
      "Name: layer4.2.conv3, Module Type: Conv2d\n",
      "Name: layer4.2.bn3, Module Type: BatchNorm2d\n",
      "Name: layer4.2.relu, Module Type: ReLU\n",
      "Name: avgpool, Module Type: AdaptiveAvgPool2d\n",
      "Name: fc, Module Type: Linear\n"
     ]
    }
   ],
   "source": [
    "model = models.resnet50(weights=models.ResNet50_Weights.IMAGENET1K_V2)\n",
    "\n",
    "print('\\nModel layers:\\n')\n",
    "\n",
    "for name, module in model.named_modules():\n",
    "    print(f'Name: {name}, Module Type: {module.__class__.__name__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "391e180d",
   "metadata": {},
   "source": [
    "### 2.2. Replace input\n",
    "\n",
    "The the first four layers in ResNet50 are a 7×7 convolution with a stride of 2 + max pooling with stride 2. This aggressively downsamples by 4x and was designed for 224×224 images. For 32×32 CIFAR images we can replace it with a 3×3, stride 1 convolution and remove the maxpool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a33d9ea7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace the stem (conv1 + maxpool) to work with 32x32 CIFAR images\n",
    "# Original stem: 7x7 conv stride 2 + 3x3 maxpool stride 2 = 4x downsampling\n",
    "# New stem: 3x3 conv stride 1, no maxpool = preserves spatial resolution\n",
    "model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)\n",
    "model.maxpool = nn.Identity()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4826e4ff",
   "metadata": {},
   "source": [
    "### 2.3. Replace output layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "68f5c80e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original final layer input features: 2048\n",
      "Original final layer output: 1000 classes\n"
     ]
    }
   ],
   "source": [
    "# Check the number of input/output features to the final layer\n",
    "num_inputs = model.fc.in_features\n",
    "num_outputs = model.fc.out_features\n",
    "print(f'Original final layer input features: {num_inputs}')\n",
    "print(f'Original final layer output: {num_outputs} classes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73e21e03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New final layer: Dropout(0.5) -> Linear(2048, 10)\n"
     ]
    }
   ],
   "source": [
    "# Replace the final layer with dropout + linear\n",
    "# Dropout helps prevent overfitting during fine-tuning\n",
    "model.fc = nn.Sequential(\n",
    "    nn.Dropout(0.5),\n",
    "    nn.Linear(num_inputs, len(CLASS_NAMES))\n",
    ")\n",
    "\n",
    "new_num_outputs = len(CLASS_NAMES)\n",
    "print(f'New final layer: Dropout(0.5) -> Linear({num_inputs}, {new_num_outputs})')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c11096c6",
   "metadata": {},
   "source": [
    "### 2.4. Freeze layers\n",
    "\n",
    "We freeze everything except:\n",
    "- The new stem (conv1) - since it's a new untrained layer\n",
    "- Layer 4 - the last set of 3 blocks of 3 convolutional layers\n",
    "- The fully connected output layer (fc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6793089",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total parameters: 23,520,906\n",
      "Trainable parameters: 16,695,050\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "\n",
    "    # Freeze everything except conv1, layer4, and fc\n",
    "    if 'conv1' not in name and 'layer4' not in name and 'fc' not in name:\n",
    "        param.requires_grad = False\n",
    "\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'Total parameters: {total_params:,}')\n",
    "print(f'Trainable parameters: {trainable_params:,}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "082eb310",
   "metadata": {},
   "source": [
    "## 3. Fine-tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "026dab17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define criterion and optimizer for training\n",
    "# Weight decay (L2 regularization) helps prevent overfitting\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b96b3b19",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch   1/100 - loss: 0.7090 - acc: 75.38% - val_loss: 0.3274 - val_acc: 89.19%\n",
      "Epoch   5/100 - loss: 0.1175 - acc: 96.03% - val_loss: 0.2772 - val_acc: 91.41%\n",
      "Epoch  10/100 - loss: 0.0758 - acc: 97.46% - val_loss: 0.2932 - val_acc: 91.42%\n",
      "\n",
      "Early stopping at epoch 11\n",
      "Best val_loss: 0.2366 at epoch 8\n",
      "Restored best model weights\n",
      "\n",
      "CPU times: user 13min 34s, sys: 498 ms, total: 13min 35s\n",
      "Wall time: 13min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Do the run, moving the model to the selected device\n",
    "# Early stopping will halt training when validation accuracy stops improving\n",
    "history = training.train_model(\n",
    "    model=model.to(DEVICE), \n",
    "    train_loader=train_loader,\n",
    "    val_loader=val_loader,\n",
    "    criterion=criterion,\n",
    "    optimizer=optimizer,\n",
    "    lazy_loading=False,\n",
    "    device=DEVICE,\n",
    "    epochs=epochs,\n",
    "    print_every=print_every,\n",
    "    enable_early_stopping=True,\n",
    "    early_stopping_patience=3\n",
    ")\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da355d78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plots.plot_learning_curves(history)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da34565",
   "metadata": {},
   "source": [
    "## 3. Evaluate model on test set\n",
    "\n",
    "### 3.1. Calculate test accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0323b059",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 92.50%\n"
     ]
    }
   ],
   "source": [
    "test_accuracy, predictions, true_labels = eval_utils.evaluate_model(model, test_loader)\n",
    "print(f'Test accuracy: {test_accuracy:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf993d13",
   "metadata": {},
   "source": [
    "### 3.2. Per-class accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af3fea3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Per-class accuracy:\n",
      "------------------------------\n",
      "airplane    : 93.80%\n",
      "automobile  : 97.40%\n",
      "bird        : 88.50%\n",
      "cat         : 83.40%\n",
      "deer        : 95.30%\n",
      "dog         : 88.60%\n",
      "frog        : 97.60%\n",
      "horse       : 93.80%\n",
      "ship        : 94.20%\n",
      "truck       : 92.40%\n"
     ]
    }
   ],
   "source": [
    "# Calculate per-class accuracy\n",
    "class_correct = {name: 0 for name in CLASS_NAMES}\n",
    "class_total = {name: 0 for name in CLASS_NAMES}\n",
    "\n",
    "for pred, true in zip(predictions, true_labels):\n",
    "\n",
    "    class_name = CLASS_NAMES[true]\n",
    "    class_total[class_name] += 1\n",
    "\n",
    "    if pred == true:\n",
    "        class_correct[class_name] += 1\n",
    "\n",
    "print('Per-class accuracy:')\n",
    "print('-' * 30)\n",
    "\n",
    "for name in CLASS_NAMES:\n",
    "    acc = 100 * class_correct[name] / class_total[name]\n",
    "    print(f'{name:12s}: {acc:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c6aa2d",
   "metadata": {},
   "source": [
    "### 3.3. Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fa8603b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plots.plot_confusion_matrix(true_labels, predictions, CLASS_NAMES)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51a080ec",
   "metadata": {},
   "source": [
    "### 3.4. Predicted class probability distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11b5023e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tWbNGxYsXV2xsrP75z3/Kw+N/n2ewZs0aPf3009q1a5cqV66sf/zjH+rXr1+Ox0ku4UzOyObtgmzCWchl9sglnIlsZu3GXEpkE4WnoHJJ0SmPOFHDWThJZ49cwpnIZvbIJpyFXGaPXMKZyGbWKDrBmQoqly79IHEAAAAAAADcnig6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOEoOgEAAAAAAMDhKDoBAAAAAADA4Sg6AQAAAAAAwOE8nD0AAABuZxaLxdlDAAAAAFwSRScAAAAAAIAiqKD/gErRCbhNcDUFAAAAAOB2QtEJAAAAdxT+UAMAgGug6AQAAAAAKFAUg4GiiU+vAwAAAAAAgMNRdAIAAAAAAIDDFemi0+zZsxUaGiofHx9FRERo06ZNzh4SAJFNwBWRS8D1kEvANZFN4H+KbNHp448/1qhRoxQXF6ctW7YoPDxc0dHROnnypLOHBhRpZBNwPeQScD3kEnBNZBOwZTHGGGcPwhkiIiLUuHFjzZo1S5KUkZGh4OBgjRgxQuPHj7frf+XKFV25csX6PjU1VVWqVMly2ampqQUzaBRpAQEBNu9TUlLs2u4EucnmrXJJFlEYikI2OWfidkMu85fLG5FTOEJWGSzq2cxtLskiCkKBnzNNEXTlyhXj7u5uPv30U5v2vn37mgcffDDLeeLi4owkXrxc5rV///5CSEvhym02ySUvV3zdadnknMnrTniRS3LJyzVfRT2b5JKXK74cnUsPFUGnT59Wenq6ypUrZ9Nerlw5/frrr1nO88wzz2jUqFHW9ykpKQoJCdGhQ4fuuOp8YTh37pyCg4N1+PBh+fv7O3s4t5XMv4CUKlXK2UNxuNxmk1w6FrnMnzs1m5wznYtc5g+5/B9y6VhkM3/I5l/IpWORy/wpqFwWyaJTXnh7e8vb29uuPSAggAM6H/z9/dl/eeTmVmQfyWZFLgsGucwfskk2CwK5zB9ySS4LCtnMn6KeTXJZMMhl/jg6l0Uy5WXKlJG7u7tOnDhh037ixAmVL1/eSaMCQDYB10MuAddDLgHXRDYBe0Wy6OTl5aVGjRpp9erV1raMjAytXr1akZGRThwZULSRTcD1kEvA9ZBLwDWRTcBekb29btSoUYqNjdW9996rJk2aaPr06bpw4YL69++fo/m9vb0VFxeX5eWQuDX2X97d6fsuP9m80/dNQWP/5c+dvP84ZzoP+y5/7uT9Ry6di/2XP3fy/uNnWedh/+VPQe0/izHGOHSJt5FZs2bp1Vdf1fHjx9WwYUO98cYbioiIcPawgCKPbAKuh1wCrodcAq6JbAL/U6SLTgAAAAAAACgYRfKZTgAAAAAAAChYFJ0AAAAAAADgcHkuOrVp08bmqfw3SkxMVJs2bfK6eAAAAAAAANzG8lx0WrNmjU6cOJHt9JMnT2rt2rV5XTwAAAAAAABuY/m6vc5isWQ7bd++fSpRokR+Fu90s2fPVmhoqHx8fBQREaFNmzbdtP+iRYtUu3Zt+fj4qH79+lq2bFkhjdQ15Wb/JSQkyGKx2Lx8fHwKcbSuY926derUqZMqVqwoi8WiJUuW3HKeNWvW6J577pG3t7eqV6+uhISEAh+ns5DL/CGXeUMub41s5h25zDuyeXPkMn/IZt6Qy5sjl/lDLvPOWdnMVdFp3rx5atOmjfW2uZdeesn6/vrXPffco7i4OLVs2TLXA3IVH3/8sUaNGqW4uDht2bJF4eHhio6O1smTJ7Psv379evXu3VsDBgzQ1q1b1blzZ3Xu3Fk7duwo5JG7htzuP0ny9/fXsWPHrK/k5ORCHLHruHDhgsLDwzV79uwc9U9KSlJMTIxat26tbdu2aeTIkRo4cKBWrFhRwCMtfOQyf8hl3pHLmyObeUcu84dsZo9c5g/ZzDtymT1ymT/kMn+clk2TC3PmzDH16tUz9erVMxaLxQQHB1vfZ77q169vmjZtaoYNG2ZOnDiRm8W7lCZNmphhw4ZZ36enp5uKFSuaKVOmZNm/R48eJiYmxqYtIiLCDB48uEDH6apyu//i4+NNQEBAIY3u9iHJfPrppzftM27cOFO3bl2btp49e5ro6OgCHJlzkMv8IZeOQS7tkc28I5eOQzZtkcv8IZuOQS5tkcv8IZeOU5jZzNWVTk888YS2b9+u7du3KyQkRLNmzbK+z3z98ssv2rBhg2bNmqWyZcvmrgLmIq5evarNmzerbdu21jY3Nze1bdtWGzZsyHKeDRs22PSXpOjo6Gz738nysv8k6c8//1RISIiCg4P10EMPaefOnYUx3NteUTn2yGX+kMvCVZSOPbKZd+Sy8BWVY49c5g/ZLFxF5dgjl/lDLgufo46/PD/TKSkpSQ8++GBeZ3dpp0+fVnp6usqVK2fTXq5cOR0/fjzLeY4fP56r/neyvOy/WrVq6b333tNnn32mDz74QBkZGWrWrJl+//33whjybS27Y+/cuXO6dOmSk0bleOQyf8hl4SoquZTIZn6Qy8JXVLJJLvOHbBYuckkuc4JcFj5HZdMjvwM5f/68kpOTdfbsWf11lZat2/m5Tig8kZGRioyMtL5v1qyZ6tSpo7feeksvvviiE0cGFF3kEnA95BJwTWQTcD3k0jXkueh0+vRpjRgxQp988onS09PtphtjZLFYspzm6sqUKSN3d3edOHHCpv3EiRMqX758lvOUL18+V/3vZHnZfzfy9PTU3XffrX379hXEEO8o2R17/v7+KlasmJNG5XjkMn/IZeEqKrmUyGZ+kMvCV1SySS7zh2wWLnJJLnOCXBY+R2Uzz7fXDRo0SIsXL7YWnr755hubV2Jior755pu8Lt6pvLy81KhRI61evdralpGRodWrV9tUSq8XGRlp01+SVq5cmW3/O1le9t+N0tPTtX37dlWoUKGghnnHKCrHHrnMH3JZuIrSsUc2845cFr6icuyRy/whm4WrqBx75DJ/yGXhc9jxl7tnnP9P8eLFzdixY/M6u8v76KOPjLe3t0lISDC7du0ygwYNMoGBgeb48ePGGGP69Oljxo8fb+3//fffGw8PDzN16lSze/duExcXZzw9Pc327dudtQlOldv9N3HiRLNixQqzf/9+s3nzZtOrVy/j4+Njdu7c6axNcJrz58+brVu3mq1btxpJZtq0aWbr1q0mOTnZGGPM+PHjTZ8+faz9Dxw4YHx9fc3YsWPN7t27zezZs427u7tZvny5szahwJDL/CGXeUcub45s5h25zB+ymT1ymT9kM+/IZfbIZf6Qy/xxVjbzXHQKCgoys2fPzuvst4WZM2eaKlWqGC8vL9OkSROzceNG67SoqCgTGxtr03/hwoWmZs2axsvLy9StW9d8+eWXhTxi15Kb/Tdy5Ehr33LlypkOHTqYLVu2OGHUzpeYmGgk2b0y91dsbKyJioqym6dhw4bGy8vLVK1a1cTHxxf6uAsLucwfcpk35PLWyGbekcu8I5s3Ry7zh2zmDbm8OXKZP+Qy75yVTYsxWTz9OwdGjRql7du3a+XKlXmZHQAAAAAAAHewPBed1q9frxEjRigoKEiDBg1ScHCw3N3d7frdc889+R4kAAAAAAAAbi95Ljq5uf3vGeQWi8VuurmNP70OAAAAAAAA+eOR1xnj4+MdOQ4AAAAAAADcQfJ8pRMAAAAAAACQHbdbdwEAAAAAAAByJ8+31z322GO37GOxWPTuu+/mdRUAAAAAAAC4TeW56PTNN9/YPUA8PT1dx44dU3p6uoKCglS8ePF8DxAAAAAAAAC3nzwXnQ4ePJhl+7Vr1/TWW29p+vTpWrlyZV4XDwAAAAAAgNtYgT1IfOjQoUpOTtaXX35ZEIsHAAAAAACACyuwB4mHh4dr3bp1BbV4AAAAAAAAuLACKzqtXLlSvr6+BbV4AAAAAAAAuLA8P9Np0qRJWbanpKRo3bp12rJli8aPH5/ngQEAAAAAAOD2lednOrm5ZX2RVMmSJVWtWjUNHDhQjz/+uN0n3AEAAAAAAODOV2APEgcAAAAAAEDRVWDPdAIAAAAAAEDRlednOmVau3atvvzySyUnJ0uSQkJCFBMTo6ioqHwPDgAAAAAAALenPN9ed/XqVfXu3VtLliyRMUaBgYGS/nqQuMViUZcuXfThhx/K09PTkeMFAAAAAADAbSDPt9dNnDhRn376qUaPHq1jx47pjz/+0B9//KHjx49rzJgx+u9//5vtJ9yhaJgwYQIPkgec5Mcff1SzZs1UvHhxWSwWbdu2zdlDAoqEzHPf6dOnnT0UAA7QqlUr1atX75b9Dh48KIvFooSEhIIfFACH4bxd8PJ8e91//vMfxcbG6pVXXrFpL1u2rP71r3/pxIkTev/99/Xiiy/me5AAgJy7du2aunfvLh8fH73++uvy9fVVSEiIs4cFAAAA2Fi/fr2+/vprjRw50nr3FO4seS46HTt2TBEREdlOj4iI0EcffZTXxQMA8mj//v1KTk7Wv//9bw0cONDZwwEA4I4XEhKiS5cu8WgRIJfWr1+viRMnql+/fhSd7lB5vr2ucuXKWrNmTbbT165dq8qVK+d18QCAPDp58qQk3fLEfeHChUIYDQBHMsbo0qVLzh4GgBtYLBb5+PjI3d3d2UMB7kgZGRm6fPmys4eBPMhz0Sk2NlYLFy7UkCFDtGfPHqWnpysjI0N79uzRE088oUWLFqlfv34OHCpc2XfffafGjRvLx8dH1apV01tvvWXXJy0tTS+++KKqVasmb29vhYaG6tlnn9WVK1ds+mVkZGjChAmqWLGifH191bp1a+3atUuhoaEcU8At9OvXz/rpod27d5fFYlGrVq3Ur18/+fn5af/+/erQoYNKlCihv//975L+Kj6NHj1awcHB8vb2Vq1atTR16lTd+DkTly5d0pNPPqkyZcqoRIkSevDBB3XkyBFZLBZNmDChsDcVcGkpKSnWv9oGBASof//+unjxonV6Ts+JoaGh6tixo1asWKF7771XxYoVs55jV65cqRYtWigwMFB+fn6qVauWnn32WZv5r1y5ori4OFWvXl3e3t4KDg7WuHHj7NYDFFXnz5/XyJEjFRoaKm9vb5UtW1b333+/tmzZYtNv165dat26tXx9fVWpUiW7R4xk9UynzHPvgQMHFB0dreLFi6tixYqaNGmS3TkWKIomTJigsWPHSpLCwsJksVhksViseRo+fLgWLFigunXrytvbW8uXL9eaNWtksVjsLoDJ7rlqv/76q3r06KGgoCAVK1ZMtWrV0nPPPXfTcSUnJ6t69eqqV6+eTpw44chNLpLyfHvds88+q/379+vtt9/Wv//9b7m5/VW/ysjIkDFGsbGxdj/44M60fft2tWvXTkFBQZowYYLS0tIUFxencuXK2fQbOHCg5s2bp27dumn06NH64YcfNGXKFO3evVuffvqptd8zzzyjV155RZ06dVJ0dLR+/vlnRUdHU9kGcmDw4MGqVKmSJk+erCeffFKNGzdWuXLltGDBAqWlpSk6OlotWrTQ1KlT5evrK2OMHnzwQSUmJmrAgAFq2LChVqxYobFjx+rIkSN6/fXXrcvu16+fFi5cqD59+qhp06Zau3atYmJinLi1gOvq0aOHwsLCNGXKFG3ZskXvvPOO9bmXUs7PiZK0Z88e9e7dW4MHD9bjjz+uWrVqaefOnerYsaMaNGigSZMmydvbW/v27dP3339vnS8jI0MPPvigvvvuOw0aNEh16tTR9u3b9frrr+u3337TkiVLCnOXAC5pyJAhWrx4sYYPH6677rpLZ86c0Xfffafdu3frnnvukSSdPXtWDzzwgB5++GH16NFDixcv1v/93/+pfv36at++/U2Xn56ergceeEBNmzbVK6+8ouXLlysuLk5paWl86BKKvIcffli//fabPvzwQ73++usqU6aMJCkoKEiS9M0332jhwoUaPny4ypQpo9DQUKWkpOR4+b/88ovuu+8+eXp6atCgQQoNDdX+/fu1dOlSvfzyy1nOs3//frVp00alSpXSypUrrWNCPph8+vnnn82UKVPMkCFDzJAhQ8yUKVPMzz//nN/F4jbSuXNn4+PjY5KTk61tu3btMu7u7ibzENu2bZuRZAYOHGgz75gxY4wk88033xhjjDl+/Ljx8PAwnTt3tuk3YcIEI8nExsYW7MYAd4DExEQjySxatMjaFhsbaySZ8ePH2/RdsmSJkWReeuklm/Zu3boZi8Vi9u3bZ4wxZvPmzUaSGTlypE2/fv36GUkmLi6uYDYGuM3ExcUZSeaxxx6zae/SpYspXbq0MSbn50RjjAkJCTGSzPLly236vv7660aSOXXqVLZjef/9942bm5v59ttvbdrffPNNI8l8//33edpG4E4SEBBghg0blu30qKgoI8nMnz/f2nblyhVTvnx507VrV2tbUlKSkWTi4+OtbZnn3hEjRljbMjIyTExMjPHy8rppfoGi4tVXXzWSTFJSkk27JOPm5mZ27txp0575c25iYqJNe1YZbNmypSlRooTN76nG/JXDTJnn7VOnTpndu3ebihUrmsaNG5s//vjDIdsHY3J1e93ly5c1ZMgQzZw509rWoEEDjR8/XnPnztXcuXM1fvx4rVmzRkOHDtW1a9fyXg3DbSE9PV0rVqxQ586dVaVKFWt7nTp1FB0dbX2/bNkySdKoUaNs5h89erQk6csvv5QkrV69WmlpaRo6dKhNvxEjRhTI+IGi5oknnrB5v2zZMrm7u+vJJ5+0aR89erSMMfrqq68kScuXL5cksgnk0JAhQ2ze33fffTpz5ozOnTuX43NiprCwMJtzqvS/Z7Z99tlnysjIyHIMixYtUp06dVS7dm2dPn3a+mrTpo0kKTExMW8bB9xBAgMD9cMPP+jo0aPZ9vHz89Ojjz5qfe/l5aUmTZrowIEDOVrH8OHDrf/PvGXo6tWrWrVqVd4HDhQBUVFRuuuuu/I076lTp7Ru3To99thjNr+nSn/l8EY7duxQVFSUQkNDtWrVKpUsWTJP64W9XBWd3n77bSUkJNzydoqYmBi99957euedd/I1OLi+U6dO6dKlS6pRo4bdtFq1aln/n5ycLDc3N1WvXt2mT/ny5RUYGKjk5GRrP0l2/UqVKkXwgXzy8PCw+4CH5ORkVaxYUSVKlLBpr1OnjnV65r9ubm4KCwuz6XdjVgH85cYfcDPPYWfPns3xOTHTjbmTpJ49e6p58+YaOHCgypUrp169emnhwoU2Bai9e/dq586dCgoKsnnVrFlT0v8+dAAoyl555RXt2LFDwcHBatKkiSZMmGBXTKpcubLdL6klS5bU2bNnb7l8Nzc3Va1a1aYtM4MHDx7M3+CBO1xW57+cysxxvXr1ctS/U6dOKlGihFasWCF/f/88rxf2clV0Wrhwobp27Wr3jfNG1apVU/fu3fXhhx/ma3C482RVVQZQOLy9va3P3wNQsLL7BCtz3cODc3pOLFasWJZt69at06pVq9SnTx/98ssv6tmzp+6//36lp6dL+uuZTvXr19fKlSuzfN145SJQFPXo0UMHDhzQzJkzVbFiRb366quqW7eu9UpfKWd5BuB4WZ3/sjt3Zp778qpr167av3+/FixYkK/lwF6ufvvYvn27WrRokaO+zZo10y+//JKnQeH2kfkpAHv37rWbtmfPHuv/Q0JClJGRYdfvxIkTSklJUUhIiLWfJO3bt8+m35kzZ3L01yQAuRMSEqKjR4/q/PnzNu2//vqrdXrmvxkZGUpKSrLpd2NWAdxaTs+Jt+Lm5qa//e1vmjZtmnbt2qWXX35Z33zzjfW2uWrVqumPP/7Q3/72N7Vt29budf0VyUBRVqFCBQ0dOlRLlixRUlKSSpcune1DhnMrIyPD7sqp3377TdJfn04JFHW5vSgh88rhGx8ofuNVwpkXyuzYsSNHy3311Vc1YMAADR06VP/5z39yNSbcXK6KTlevXpWXl1eO+np5efFxvEWAu7u7oqOjtWTJEh06dMjavnv3bq1YscL6vkOHDpKk6dOn28w/bdo0SbLesvm3v/1NHh4emjt3rk2/WbNmFcTwgSKvQ4cOSk9Pt8vY66+/LovFYv1UnsznycyZM8em3/XP+AOQMzk9J97MH3/8YdfWsGFDSbL+/NWjRw8dOXJE//73v+36Xrp0SRcuXMjNsIE7Tnp6ulJTU23aypYtq4oVKzr095jrz7HGGM2aNUuenp7629/+5rB1ALer4sWLS7IvImUnJCRE7u7uWrdunU37jT+jBgUFqWXLlnrvvfdsfk+Vsr5K0WKx6O2331a3bt0UGxurzz//PBdbgZvxyE3nihUr5rhSuGPHDlWsWDFPg8LtZeLEiVq+fLnuu+8+DR06VGlpaZo5c6bq1q1rvdotPDxcsbGxevvtt5WSkqKoqCht2rRJ8+bNU+fOndW6dWtJUrly5fTUU0/ptdde04MPPqgHHnhAP//8s7766iuVKVOG2/MAB+vUqZNat26t5557TgcPHlR4eLi+/vprffbZZxo5cqSqVasmSWrUqJG6du2q6dOn68yZM2ratKnWrl1r/Wst2QRyLqfnxJuZNGmS1q1bp5iYGIWEhOjkyZOaM2eOKleubL0qvU+fPlq4cKGGDBmixMRENW/eXOnp6fr111+1cOFCrVixQvfee29Bby7gss6fP6/KlSurW7duCg8Pl5+fn1atWqUff/xRr732mkPW4ePjo+XLlys2NlYRERH66quv9OWXX+rZZ5+1fiw8UJQ1atRIkvTcc8+pV69e8vT0VKdOnbLtHxAQoO7du2vmzJmyWCyqVq2avvjiiyyfU/jGG2+oRYsWuueeezRo0CCFhYXp4MGD+vLLL7Vt2za7/m5ubvrggw/UuXNn9ejRQ8uWLbN++AbyITcfdTdo0CBTqlQpc+LEiZv2O3HihClVqpQZPHhwXj9VD7eZtWvXmkaNGhkvLy9TtWpV8+abb1o/fjLTtWvXzMSJE01YWJjx9PQ0wcHB5plnnjGXL1+2WVZaWpr5xz/+YcqXL2+KFStm2rRpY3bv3m1Kly5thgwZUtibBtx2Mj9KdtGiRda22NhYU7x48Sz7nz9/3jz99NOmYsWKxtPT09SoUcO8+uqrNh8na4wxFy5cMMOGDTOlSpUyfn5+pnPnzmbPnj1GkvnnP/9ZoNsE3C6u/+jl68XHx9t8JHROz4khISEmJibGbj2rV682Dz30kKlYsaLx8vIyFStWNL179za//fabTb+rV6+af/3rX6Zu3brG29vblCxZ0jRq1MhMnDjRpKamOnbjgdvMlStXzNixY014eLgpUaKEKV68uAkPDzdz5syx9omKijJ169a1mzc2NtaEhIRY32f1ce2Z5979+/ebdu3aGV9fX1OuXDkTFxdn0tPTC3LTgNvKiy++aCpVqmTc3Nys50pJZtiwYVn2P3XqlOnatavx9fU1JUuWNIMHDzY7duywy6AxxuzYscN06dLFBAYGGh8fH1OrVi3zj3/8wzo9q/P2xYsXTVRUlPHz8zMbN24skG0uSizG5PwJeAcOHFD9+vUVFhamd999VxEREXZ9fvjhBw0cOFAHDhzQL7/8Yv0rOZAfKSkpKlmypF566SU999xzzh4OgP9v27Ztuvvuu/XBBx/o73//u7OHAwCAy+jXr58WL16sP//809lDAQCnydXtdVW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      "text/plain": [
       "<Figure size 1200x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get predicted probabilities for all test samples\n",
    "model.eval()\n",
    "all_probs = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for images, _ in test_loader:\n",
    "        outputs = model(images)\n",
    "        probs = torch.softmax(outputs, dim=1)\n",
    "        all_probs.append(probs.cpu().numpy())\n",
    "\n",
    "all_probs = np.concatenate(all_probs, axis=0)\n",
    "\n",
    "# Plot probability distributions\n",
    "fig, axes = plots.plot_class_probability_distributions(all_probs, CLASS_NAMES)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3109dd90",
   "metadata": {},
   "source": [
    "### 3.5. Evaluation curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8420d418",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plots.plot_evaluation_curves(true_labels, all_probs, CLASS_NAMES)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a44452fa",
   "metadata": {},
   "source": [
    "## 4. Save model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4cc7d40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved to: ../models/pytorch/resnet50.pth\n",
      "Test accuracy: 92.50%\n"
     ]
    }
   ],
   "source": [
    "# Save trained model\n",
    "model_path = MODELS_DIR / 'resnet50.pth'\n",
    "torch.save(model, model_path)\n",
    "\n",
    "print(f'Model saved to: {model_path}')\n",
    "print(f'Test accuracy: {test_accuracy:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86b87786",
   "metadata": {},
   "source": [
    "## 5. Save test results for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6155613c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test results saved to: ../data/pytorch/performance_results/resnet50_results.pkl\n",
      "  - Test accuracy: 92.50%\n",
      "  - Trainable parameters: 16,695,050\n",
      "  - Total parameters: 23,520,906\n"
     ]
    }
   ],
   "source": [
    "# Save results path\n",
    "results_path = RESULTS_DIR / 'resnet50_results.pkl'\n",
    "\n",
    "# Count model parameters\n",
    "total_params = sum(p.numel() for p in model.parameters())\n",
    "trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "# Create results dictionary\n",
    "results_dict = {\n",
    "    'true_labels': true_labels,\n",
    "    'predictions': predictions,\n",
    "    'all_probs': all_probs,\n",
    "    'test_accuracy': test_accuracy,\n",
    "    'total_params': total_params,\n",
    "    'trainable_params': trainable_params\n",
    "}\n",
    "\n",
    "# Save results\n",
    "with open(results_path, 'wb') as f:\n",
    "    pickle.dump(results_dict, f)\n",
    "\n",
    "print(f'Test results saved to: {results_path}')\n",
    "\n",
    "print(f'  - Test accuracy: {test_accuracy:.2f}%')\n",
    "print(f'  - Trainable parameters: {trainable_params:,}')\n",
    "print(f'  - Total parameters: {total_params:,}')\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}