{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e1e6687",
   "metadata": {},
   "source": [
    "# Lesson 30 demo part 1: optimizer comparison\n",
    "\n",
    "**TLDR**: see [optimizers summary](https://gperdrizet.github.io/FSA_devops/resource_pages/optimizer_summary.html)\n",
    "\n",
    "This notebook compares four common optimizers used in deep learning:\n",
    "\n",
    "1. **SGD** (Stochastic Gradient Descent) - vanilla, no momentum\n",
    "2. **SGD + Momentum** - SGD with momentum=0.9\n",
    "3. **RMSprop** - Adaptive learning rate per parameter\n",
    "4. **Adam** - Adaptive learning rate + momentum (most popular)\n",
    "\n",
    "We'll see how they differ in:\n",
    "- **Part 1**: Batch size effect (true SGD vs mini-batch)\n",
    "- **Part 2**: Momentum effect (SGD with different momentum values)\n",
    "- **Part 3**: Convergence speed at the same learning rate\n",
    "- **Part 4**: Sensitivity to learning rate choice\n",
    "\n",
    "> **Note**: PyTorch's \"SGD\" optimizer is a bit of a misnomer—it's only true *stochastic* gradient descent when batch_size=1. With larger batches, it's technically *mini-batch* gradient descent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a0a84698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "# Standard library imports\n",
    "import time\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\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "# Custom package import\n",
    "from cifar10_tools.pytorch import train_model\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(315)\n",
    "np.random.seed(315)\n",
    "\n",
    "# Check for GPU\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3c65784d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration\n",
    "batch_size = 10000  # Default for validation loader\n",
    "print_every = 10\n",
    "\n",
    "# Part 1 settings (batch size comparison)\n",
    "batch_sizes_part1 = [1, 4, 16, 64, 256, 1024]\n",
    "n_samples_part1 = 1024  # Full dataset -validation = 40000\n",
    "n_epochs_part1 = 50\n",
    "lr_part1 = 0.01\n",
    "\n",
    "# Part 2 settings (momentum comparison)\n",
    "momentum_values = [0.0, 0.5, 0.9, 0.95, 0.99]\n",
    "batch_size_part2 = 16\n",
    "n_epochs_part2 = 50\n",
    "lr_part2 = 0.01\n",
    "\n",
    "# Part 3 settings (optimizer comparison)\n",
    "n_epochs_part3 = 50\n",
    "lr_part3 = 0.001\n",
    "\n",
    "# Part 4 settings (learning rate sensitivity)\n",
    "n_epochs_part4 = 10\n",
    "learning_rates = [0.1, 0.01, 0.001, 0.0001]\n",
    "\n",
    "# Optimizer configurations\n",
    "optimizer_configs = {\n",
    "    'SGD': lambda params, lr: optim.SGD(params, lr=lr),\n",
    "    'SGD+Momentum': lambda params, lr: optim.SGD(params, lr=lr, momentum=0.9),\n",
    "    'RMSprop': lambda params, lr: optim.RMSprop(params, lr=lr),\n",
    "    'Adam': lambda params, lr: optim.Adam(params, lr=lr)\n",
    "}\n",
    "\n",
    "class_names = [\n",
    "    'airplane', 'automobile', 'bird', 'cat', 'deer',\n",
    "    'dog', 'frog', 'horse', 'ship', 'truck'\n",
    "]\n",
    "\n",
    "num_classes = len(class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb6666cf",
   "metadata": {},
   "source": [
    "## 1. Load CIFAR-10 data (grayscale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "37bddf2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 170M/170M [00:02<00:00, 59.2MB/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 50000\n",
      "Test samples: 10000\n"
     ]
    }
   ],
   "source": [
    "# Data directory\n",
    "data_dir = Path('../data')\n",
    "data_dir.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Grayscale transform for faster training\n",
    "transform = transforms.Compose([\n",
    "    transforms.Grayscale(num_output_channels=1),\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "# Load datasets\n",
    "train_dataset = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=True,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "test_dataset = datasets.CIFAR10(\n",
    "    root=data_dir,\n",
    "    train=False,\n",
    "    download=True,\n",
    "    transform=transform\n",
    ")\n",
    "\n",
    "print(f'Training samples: {len(train_dataset)}')\n",
    "print(f'Test samples: {len(test_dataset)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "508f6b43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: torch.Size([40000, 1, 32, 32])\n",
      "X_val: torch.Size([10000, 1, 32, 32])\n"
     ]
    }
   ],
   "source": [
    "# Preload to GPU\n",
    "X_train_full = torch.stack([img for img, _ in train_dataset]).to(device)\n",
    "y_train_full = torch.tensor([label for _, label in train_dataset]).to(device)\n",
    "X_test = torch.stack([img for img, _ in test_dataset]).to(device)\n",
    "y_test = torch.tensor([label for _, label in test_dataset]).to(device)\n",
    "\n",
    "# Train/val split (80/20)\n",
    "n_train = int(0.8 * len(X_train_full))\n",
    "indices = torch.randperm(len(X_train_full))\n",
    "\n",
    "X_train = X_train_full[indices[:n_train]]\n",
    "y_train = y_train_full[indices[:n_train]]\n",
    "X_val = X_train_full[indices[n_train:]]\n",
    "y_val = y_train_full[indices[n_train:]]\n",
    "\n",
    "# Validation loader (fixed batch size)\n",
    "val_loader = DataLoader(\n",
    "    torch.utils.data.TensorDataset(X_val, y_val), \n",
    "    batch_size=batch_size, \n",
    "    shuffle=False\n",
    ")\n",
    "\n",
    "print(f'X_train: {X_train.shape}')\n",
    "print(f'X_val: {X_val.shape}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f1be871",
   "metadata": {},
   "source": [
    "## 2. Variable batch size training data loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a1eee363",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_train_loader(batch_size: int, n_samples: int = None) -> DataLoader:\n",
    "    '''Create a training DataLoader with specified batch size and optional sample limit.'''\n",
    "    \n",
    "    if n_samples is not None and n_samples < len(X_train):\n",
    "\n",
    "        # Use a random subset\n",
    "        indices = torch.randperm(len(X_train))[:n_samples]\n",
    "        X_subset = X_train[indices]\n",
    "        y_subset = y_train[indices]\n",
    "        dataset = torch.utils.data.TensorDataset(X_subset, y_subset)\n",
    "\n",
    "    else:\n",
    "        dataset = torch.utils.data.TensorDataset(X_train, y_train)\n",
    "    \n",
    "    return DataLoader(dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4be5e40b",
   "metadata": {},
   "source": [
    "## 3. Define CNN architecture\n",
    "\n",
    "We use a simple 1-block grayscale CNN for fast training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8bdcb94a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model parameters: 273,258\n"
     ]
    }
   ],
   "source": [
    "def create_cnn():\n",
    "    '''Create a fresh 1-block grayscale CNN.'''\n",
    "\n",
    "    return nn.Sequential(\n",
    "    \n",
    "        # Conv block: 1 -> 32 channels, 32x32 -> 8x8\n",
    "        nn.Conv2d(1, 32, kernel_size=3, padding=1),\n",
    "        nn.BatchNorm2d(32),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(2, 2),\n",
    "        nn.Dropout(0.5),\n",
    "        nn.Conv2d(32, 32, kernel_size=3, padding=1),\n",
    "        nn.BatchNorm2d(32),\n",
    "        nn.ReLU(),\n",
    "        nn.MaxPool2d(2, 2),\n",
    "        nn.Dropout(0.5),\n",
    "        \n",
    "        # Classifier\n",
    "        nn.Flatten(),\n",
    "        nn.Linear(32 * 8 * 8, 128),\n",
    "        nn.ReLU(),\n",
    "        nn.Dropout(0.5),\n",
    "        nn.Linear(128, num_classes)\n",
    "    )\n",
    "\n",
    "# Show model summary\n",
    "model = create_cnn().to(device)\n",
    "params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "print(f'Model parameters: {params:,}')\n",
    "del model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6444f57",
   "metadata": {},
   "source": [
    "## 4. Training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1eda330c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_with_optimizer(\n",
    "        optimizer_name: str,\n",
    "        learning_rate: float,\n",
    "        n_epochs: int,\n",
    "        print_every: int,\n",
    "        train_loader: DataLoader = None,\n",
    "        batch_size: int = 10000,\n",
    "        n_samples: int = None\n",
    ") -> dict:\n",
    "    '''Train a fresh model with the specified optimizer and return history.'''\n",
    "    \n",
    "    # Create train loader if not provided\n",
    "    if train_loader is None:\n",
    "        train_loader = create_train_loader(batch_size, n_samples)\n",
    "    \n",
    "    # Create fresh model with same initialization\n",
    "    torch.manual_seed(315)\n",
    "    model = create_cnn().to(device)\n",
    "    \n",
    "    # Create optimizer and criterion\n",
    "    optimizer = optimizer_configs[optimizer_name](model.parameters(), learning_rate)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "    # Train the model\n",
    "    history = train_model(\n",
    "        model=model,\n",
    "        train_loader=train_loader,\n",
    "        val_loader=val_loader,\n",
    "        criterion=criterion,\n",
    "        optimizer=optimizer,\n",
    "        epochs=n_epochs,\n",
    "        print_every=print_every\n",
    "    )\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4449f0aa",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 1: Batch size effect (true SGD vs mini-batch)\n",
    "\n",
    "PyTorch's `SGD` optimizer is only *true* stochastic gradient descent when `batch_size=1`. \n",
    "With larger batches, it's mini-batch gradient descent.\n",
    "\n",
    "Let's see how batch size affects training with vanilla SGD (no momentum):\n",
    "- **batch_size=1**: True SGD - update after every sample (noisy but can escape local minima)\n",
    "- **batch_size=4, 16, 64**: Mini-batch - smoother gradients, faster per epoch\n",
    "\n",
    "> We use a subset of the training data (`n_samples_part1`) to keep runtime reasonable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e3245fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 1024 samples\n",
      "\n",
      "Training with batch_size=1 (1024 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.3429 - accuracy: 8.98% - val_loss: 2.3029 - val_accuracy: 9.78%\n",
      "Epoch 10/50 - loss: 2.2732 - accuracy: 12.70% - val_loss: 2.2744 - val_accuracy: 11.44%\n",
      "Epoch 20/50 - loss: 2.0004 - accuracy: 27.05% - val_loss: 2.1402 - val_accuracy: 21.45%\n",
      "Epoch 30/50 - loss: 1.6697 - accuracy: 38.48% - val_loss: 2.1236 - val_accuracy: 24.86%\n",
      "Epoch 40/50 - loss: 1.4528 - accuracy: 48.93% - val_loss: 2.0882 - val_accuracy: 26.10%\n",
      "Epoch 50/50 - loss: 1.1534 - accuracy: 58.89% - val_loss: 2.1450 - val_accuracy: 25.86%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 25.86% (took 311.0s)\n",
      "\n",
      "Training with batch_size=4 (256 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.3047 - accuracy: 13.67% - val_loss: 2.2028 - val_accuracy: 19.43%\n",
      "Epoch 10/50 - loss: 1.8809 - accuracy: 31.05% - val_loss: 1.8670 - val_accuracy: 33.27%\n",
      "Epoch 20/50 - loss: 1.5914 - accuracy: 43.26% - val_loss: 1.7615 - val_accuracy: 36.05%\n",
      "Epoch 30/50 - loss: 1.3975 - accuracy: 49.51% - val_loss: 1.7388 - val_accuracy: 39.93%\n",
      "Epoch 40/50 - loss: 1.2056 - accuracy: 56.35% - val_loss: 1.6652 - val_accuracy: 41.24%\n",
      "Epoch 50/50 - loss: 0.9720 - accuracy: 65.82% - val_loss: 1.7363 - val_accuracy: 41.10%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 41.10% (took 73.9s)\n",
      "\n",
      "Training with batch_size=16 (64 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.3366 - accuracy: 12.21% - val_loss: 2.2486 - val_accuracy: 21.95%\n",
      "Epoch 10/50 - loss: 1.8309 - accuracy: 32.71% - val_loss: 1.8883 - val_accuracy: 32.18%\n",
      "Epoch 20/50 - loss: 1.6355 - accuracy: 41.89% - val_loss: 1.7520 - val_accuracy: 37.19%\n",
      "Epoch 30/50 - loss: 1.4559 - accuracy: 47.17% - val_loss: 1.6848 - val_accuracy: 39.83%\n",
      "Epoch 40/50 - loss: 1.3394 - accuracy: 50.78% - val_loss: 1.6457 - val_accuracy: 40.92%\n",
      "Epoch 50/50 - loss: 1.1636 - accuracy: 59.18% - val_loss: 1.6635 - val_accuracy: 42.35%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 42.35% (took 25.1s)\n",
      "\n",
      "Training with batch_size=64 (16 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.3678 - accuracy: 9.67% - val_loss: 2.2945 - val_accuracy: 11.12%\n",
      "Epoch 10/50 - loss: 2.0736 - accuracy: 24.61% - val_loss: 2.0692 - val_accuracy: 28.39%\n",
      "Epoch 20/50 - loss: 1.8958 - accuracy: 30.96% - val_loss: 1.9397 - val_accuracy: 31.50%\n",
      "Epoch 30/50 - loss: 1.7875 - accuracy: 35.25% - val_loss: 1.8727 - val_accuracy: 33.19%\n",
      "Epoch 40/50 - loss: 1.7083 - accuracy: 40.53% - val_loss: 1.8175 - val_accuracy: 35.35%\n",
      "Epoch 50/50 - loss: 1.6270 - accuracy: 42.09% - val_loss: 1.7710 - val_accuracy: 37.06%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 37.06% (took 16.6s)\n",
      "\n",
      "Training with batch_size=256 (4 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.4081 - accuracy: 9.47% - val_loss: 2.3031 - val_accuracy: 8.59%\n",
      "Epoch 10/50 - loss: 2.2538 - accuracy: 16.50% - val_loss: 2.2565 - val_accuracy: 21.99%\n",
      "Epoch 20/50 - loss: 2.1576 - accuracy: 21.48% - val_loss: 2.1724 - val_accuracy: 25.50%\n",
      "Epoch 30/50 - loss: 2.0737 - accuracy: 26.46% - val_loss: 2.1047 - val_accuracy: 27.56%\n",
      "Epoch 40/50 - loss: 2.0175 - accuracy: 28.03% - val_loss: 2.0509 - val_accuracy: 29.27%\n",
      "Epoch 50/50 - loss: 1.9436 - accuracy: 30.86% - val_loss: 2.0041 - val_accuracy: 30.12%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 30.12% (took 13.5s)\n",
      "\n",
      "Training with batch_size=1024 (1 batches/epoch)\n",
      "Epoch 1/50 - loss: 2.4103 - accuracy: 9.77% - val_loss: 2.3049 - val_accuracy: 7.69%\n",
      "Epoch 10/50 - loss: 2.3206 - accuracy: 11.62% - val_loss: 2.2998 - val_accuracy: 9.42%\n",
      "Epoch 20/50 - loss: 2.3107 - accuracy: 14.06% - val_loss: 2.2905 - val_accuracy: 11.93%\n",
      "Epoch 30/50 - loss: 2.2731 - accuracy: 14.45% - val_loss: 2.2746 - val_accuracy: 17.99%\n",
      "Epoch 40/50 - loss: 2.2393 - accuracy: 18.95% - val_loss: 2.2538 - val_accuracy: 23.24%\n",
      "Epoch 50/50 - loss: 2.2306 - accuracy: 16.80% - val_loss: 2.2317 - val_accuracy: 24.71%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 24.71% (took 12.9s)\n",
      "\n",
      "CPU times: user 7min 31s, sys: 1.66 s, total: 7min 33s\n",
      "Wall time: 7min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "print(f'Using {n_samples_part1} samples')\n",
    "\n",
    "# Select data sample once for fair comparison across batch sizes\n",
    "subset_indices = torch.randperm(len(X_train))[:n_samples_part1]\n",
    "X_train_subset = X_train[subset_indices]\n",
    "y_train_subset = y_train[subset_indices]\n",
    "\n",
    "results_part1 = {}\n",
    "\n",
    "for bs in batch_sizes_part1:\n",
    "    \n",
    "    # Create loader from the same subset with different batch size\n",
    "    train_loader_part1 = DataLoader(\n",
    "        torch.utils.data.TensorDataset(X_train_subset, y_train_subset),\n",
    "        batch_size=bs,\n",
    "        shuffle=True\n",
    "    )\n",
    "\n",
    "    print(f'\\nTraining with batch_size={bs} ({len(train_loader_part1)} batches/epoch)')\n",
    "    \n",
    "    start = time.time()\n",
    "    results_part1[bs] = train_with_optimizer(\n",
    "        'SGD', lr_part1, n_epochs_part1, \n",
    "        print_every=print_every, \n",
    "        train_loader=train_loader_part1\n",
    "    )\n",
    "    elapsed = time.time() - start\n",
    "    \n",
    "    final_acc = results_part1[bs]['val_accuracy'][-1]\n",
    "    print(f'-> Final val acc: {final_acc:.2f}% (took {elapsed:.1f}s)')\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42c18f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot part 1 results\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "fig.suptitle('Effect of batch size')\n",
    "\n",
    "# Validation accuracy over epochs\n",
    "axes[0].set_title('Validation accuracy')\n",
    "\n",
    "for bs, history in results_part1.items():\n",
    "    axes[0].plot(history['val_accuracy'], label=f'batch_size={bs}')\n",
    "\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Accuracy (%)')\n",
    "axes[0].legend()\n",
    "\n",
    "# Final accuracy bar chart\n",
    "axes[1].set_title('Final validation accuracy')\n",
    "\n",
    "batch_sizes = list(results_part1.keys())\n",
    "final_accs = [results_part1[bs]['val_accuracy'][-1] for bs in batch_sizes]\n",
    "\n",
    "bars = axes[1].bar([str(bs) for bs in batch_sizes], final_accs)\n",
    "axes[1].set_xlabel('Batch size')\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].set_ylim(0, 100)\n",
    "\n",
    "# Add value labels\n",
    "for bar, acc in zip(bars, final_accs):\n",
    "    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,\n",
    "                 f'{acc:.1f}%', ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52303945",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 2: Momentum effect (SGD with different momentum values)\n",
    "\n",
    "Momentum helps SGD accelerate in relevant directions and dampen oscillations. Let's see how different momentum values affect training:\n",
    "- **momentum=0.0**: Vanilla SGD (no momentum)\n",
    "- **momentum=0.5**: Light momentum\n",
    "- **momentum=0.9**: Standard momentum (commonly used)\n",
    "- **momentum=0.95**: High momentum\n",
    "- **momentum=0.99**: Very high momentum (can overshoot)\n",
    "\n",
    "We use batch_size=16 and lr=0.01 (from Part 1's good settings)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "84a03134",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training with momentum=0.0\n",
      "Epoch 1/50 - loss: 1.8974 - accuracy: 30.34% - val_loss: 1.5768 - val_accuracy: 45.38%\n",
      "Epoch 10/50 - loss: 1.2988 - accuracy: 54.41% - val_loss: 1.0809 - val_accuracy: 62.65%\n",
      "Epoch 20/50 - loss: 1.1817 - accuracy: 58.90% - val_loss: 0.9931 - val_accuracy: 66.75%\n",
      "Epoch 30/50 - loss: 1.1266 - accuracy: 60.63% - val_loss: 0.9447 - val_accuracy: 67.91%\n",
      "Epoch 40/50 - loss: 1.0869 - accuracy: 61.84% - val_loss: 0.9152 - val_accuracy: 69.29%\n",
      "Epoch 50/50 - loss: 1.0588 - accuracy: 63.05% - val_loss: 0.8787 - val_accuracy: 69.78%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 69.78% (took 540.5s)\n",
      "\n",
      "Training with momentum=0.5\n",
      "Epoch 1/50 - loss: 1.8950 - accuracy: 30.20% - val_loss: 1.5671 - val_accuracy: 45.09%\n",
      "Epoch 10/50 - loss: 1.2890 - accuracy: 55.05% - val_loss: 1.0821 - val_accuracy: 64.35%\n",
      "Epoch 20/50 - loss: 1.1827 - accuracy: 58.89% - val_loss: 0.9976 - val_accuracy: 66.27%\n",
      "Epoch 30/50 - loss: 1.1301 - accuracy: 60.95% - val_loss: 0.9327 - val_accuracy: 68.93%\n",
      "Epoch 40/50 - loss: 1.0962 - accuracy: 61.61% - val_loss: 0.9242 - val_accuracy: 69.00%\n",
      "Epoch 50/50 - loss: 1.0611 - accuracy: 63.05% - val_loss: 0.8779 - val_accuracy: 70.02%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 70.02% (took 532.7s)\n",
      "\n",
      "Training with momentum=0.9\n",
      "Epoch 1/50 - loss: 2.1221 - accuracy: 19.89% - val_loss: 1.8790 - val_accuracy: 33.40%\n",
      "Epoch 10/50 - loss: 1.5119 - accuracy: 47.15% - val_loss: 1.2831 - val_accuracy: 54.36%\n",
      "Epoch 20/50 - loss: 1.4274 - accuracy: 50.13% - val_loss: 1.1829 - val_accuracy: 59.15%\n",
      "Epoch 30/50 - loss: 1.3810 - accuracy: 51.96% - val_loss: 1.1546 - val_accuracy: 60.09%\n",
      "Epoch 40/50 - loss: 1.3632 - accuracy: 52.66% - val_loss: 1.0520 - val_accuracy: 64.19%\n",
      "Epoch 50/50 - loss: 1.3403 - accuracy: 53.59% - val_loss: 1.0803 - val_accuracy: 62.85%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 62.85% (took 532.4s)\n",
      "\n",
      "Training with momentum=0.95\n",
      "Epoch 1/50 - loss: 2.3064 - accuracy: 10.37% - val_loss: 2.3033 - val_accuracy: 10.58%\n",
      "Epoch 10/50 - loss: 1.9413 - accuracy: 29.08% - val_loss: 1.8195 - val_accuracy: 32.47%\n",
      "Epoch 20/50 - loss: 1.9969 - accuracy: 26.36% - val_loss: 1.8734 - val_accuracy: 31.29%\n",
      "Epoch 30/50 - loss: 1.8711 - accuracy: 32.12% - val_loss: 1.7959 - val_accuracy: 35.36%\n",
      "Epoch 40/50 - loss: 2.0627 - accuracy: 23.20% - val_loss: 1.9938 - val_accuracy: 26.25%\n",
      "Epoch 50/50 - loss: 1.9275 - accuracy: 29.57% - val_loss: 1.7552 - val_accuracy: 37.23%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 37.23% (took 536.1s)\n",
      "\n",
      "Training with momentum=0.99\n",
      "Epoch 1/50 - loss: 2.3190 - accuracy: 10.69% - val_loss: 2.3103 - val_accuracy: 10.58%\n",
      "Epoch 10/50 - loss: 2.3164 - accuracy: 10.07% - val_loss: 2.3143 - val_accuracy: 9.43%\n",
      "Epoch 20/50 - loss: 2.3190 - accuracy: 9.86% - val_loss: 2.3285 - val_accuracy: 10.12%\n",
      "Epoch 30/50 - loss: 2.3150 - accuracy: 9.79% - val_loss: 2.3139 - val_accuracy: 10.25%\n",
      "Epoch 40/50 - loss: 2.3165 - accuracy: 10.13% - val_loss: 2.3206 - val_accuracy: 9.63%\n",
      "Epoch 50/50 - loss: 2.3184 - accuracy: 9.94% - val_loss: 2.3287 - val_accuracy: 9.63%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 9.63% (took 728.9s)\n",
      "\n",
      "CPU times: user 47min 52s, sys: 10 s, total: 48min 2s\n",
      "Wall time: 47min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Create train loader for Part 2\n",
    "train_loader_part2 = create_train_loader(batch_size_part2)\n",
    "\n",
    "# Dictionary to store results for Part 2\n",
    "results_part2 = {}\n",
    "\n",
    "for momentum in momentum_values:\n",
    "\n",
    "    print(f'\\nTraining with momentum={momentum}')\n",
    "    \n",
    "    # Create fresh model\n",
    "    torch.manual_seed(42)\n",
    "    model = create_cnn().to(device)\n",
    "    \n",
    "    # Create optimizer with specified momentum\n",
    "    optimizer = optim.SGD(model.parameters(), lr=lr_part2, momentum=momentum)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "    # Train the model\n",
    "    start = time.time()\n",
    "    results_part2[momentum] = train_model(\n",
    "        model=model,\n",
    "        train_loader=train_loader_part2,\n",
    "        val_loader=val_loader,\n",
    "        criterion=criterion,\n",
    "        optimizer=optimizer,\n",
    "        epochs=n_epochs_part2,\n",
    "        print_every=print_every\n",
    "    )\n",
    "    elapsed = time.time() - start\n",
    "    \n",
    "    final_acc = results_part2[momentum]['val_accuracy'][-1]\n",
    "    print(f'-> Final val acc: {final_acc:.2f}% (took {elapsed:.1f}s)')\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "594766c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot part 2 results\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "fig.suptitle(f'Effect of momentum')\n",
    "\n",
    "# Validation accuracy over epochs\n",
    "axes[0].set_title('Validation accuracy')\n",
    "\n",
    "for momentum, history in results_part2.items():\n",
    "    axes[0].plot(history['val_accuracy'], label=momentum)\n",
    "\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Accuracy (%)')\n",
    "axes[0].legend(title='Momentum')\n",
    "\n",
    "# Final accuracy bar chart\n",
    "axes[1].set_title('Final validation accuracy')\n",
    "\n",
    "momentums = list(results_part2.keys())\n",
    "final_accs = [results_part2[m]['val_accuracy'][-1] for m in momentums]\n",
    "\n",
    "bars = axes[1].bar([str(m) for m in momentums], final_accs)\n",
    "axes[1].set_xlabel('Momentum')\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].set_ylim(0, 100)\n",
    "\n",
    "# Add value labels\n",
    "for bar, acc in zip(bars, final_accs):\n",
    "    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,\n",
    "                 f'{acc:.1f}%', ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22df1884",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## Part 3: Optimizer comparison (same learning rate)\n",
    "\n",
    "Now we compare all four optimizers with the **same learning rate (0.001)** to see how they differ in convergence speed.\n",
    "\n",
    "We'll use a larger batch size for efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "497f2ec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training with SGD\n",
      "Epoch 1/50 - loss: 2.4251 - accuracy: 9.84% - val_loss: 2.3054 - val_accuracy: 8.12%\n",
      "Epoch 10/50 - loss: 2.3770 - accuracy: 10.64% - val_loss: 2.2986 - val_accuracy: 10.35%\n",
      "Epoch 20/50 - loss: 2.3555 - accuracy: 10.57% - val_loss: 2.2884 - val_accuracy: 13.76%\n",
      "Epoch 30/50 - loss: 2.3389 - accuracy: 11.19% - val_loss: 2.2822 - val_accuracy: 15.90%\n",
      "Epoch 40/50 - loss: 2.3264 - accuracy: 11.67% - val_loss: 2.2774 - val_accuracy: 17.11%\n",
      "Epoch 50/50 - loss: 2.3136 - accuracy: 12.22% - val_loss: 2.2732 - val_accuracy: 18.07%\n",
      "\n",
      "Training complete.\n",
      "\n",
      "Training with SGD+Momentum\n",
      "Epoch 1/50 - loss: 2.4232 - accuracy: 9.84% - val_loss: 2.3050 - val_accuracy: 8.22%\n",
      "Epoch 10/50 - loss: 2.2899 - accuracy: 13.55% - val_loss: 2.2753 - val_accuracy: 18.86%\n",
      "Epoch 20/50 - loss: 2.2389 - accuracy: 16.61% - val_loss: 2.2226 - val_accuracy: 25.73%\n",
      "Epoch 30/50 - loss: 2.1819 - accuracy: 19.33% - val_loss: 2.1598 - val_accuracy: 27.43%\n",
      "Epoch 40/50 - loss: 2.1315 - accuracy: 21.60% - val_loss: 2.1021 - val_accuracy: 28.76%\n",
      "Epoch 50/50 - loss: 2.0868 - accuracy: 23.50% - val_loss: 2.0530 - val_accuracy: 29.94%\n",
      "\n",
      "Training complete.\n",
      "\n",
      "Training with RMSprop\n",
      "Epoch 1/50 - loss: 6.5869 - accuracy: 10.21% - val_loss: 2.2995 - val_accuracy: 10.46%\n",
      "Epoch 10/50 - loss: 2.1473 - accuracy: 22.27% - val_loss: 2.1204 - val_accuracy: 27.53%\n",
      "Epoch 20/50 - loss: 2.0225 - accuracy: 26.36% - val_loss: 1.9250 - val_accuracy: 33.89%\n",
      "Epoch 30/50 - loss: 1.9469 - accuracy: 28.62% - val_loss: 1.8511 - val_accuracy: 36.35%\n",
      "Epoch 40/50 - loss: 1.8706 - accuracy: 31.05% - val_loss: 1.7352 - val_accuracy: 41.67%\n",
      "Epoch 50/50 - loss: 1.8085 - accuracy: 33.12% - val_loss: 1.6702 - val_accuracy: 42.82%\n",
      "\n",
      "Training complete.\n",
      "\n",
      "Training with Adam\n",
      "Epoch 1/50 - loss: 2.4524 - accuracy: 11.29% - val_loss: 2.2977 - val_accuracy: 12.23%\n",
      "Epoch 10/50 - loss: 2.0058 - accuracy: 25.53% - val_loss: 2.0433 - val_accuracy: 24.22%\n",
      "Epoch 20/50 - loss: 1.8264 - accuracy: 31.46% - val_loss: 1.6990 - val_accuracy: 41.38%\n",
      "Epoch 30/50 - loss: 1.7513 - accuracy: 34.42% - val_loss: 1.5873 - val_accuracy: 45.78%\n",
      "Epoch 40/50 - loss: 1.6812 - accuracy: 37.58% - val_loss: 1.4997 - val_accuracy: 48.91%\n",
      "Epoch 50/50 - loss: 1.6301 - accuracy: 39.80% - val_loss: 1.4078 - val_accuracy: 52.00%\n",
      "\n",
      "Training complete.\n",
      "\n",
      "CPU times: user 5min 40s, sys: 177 ms, total: 5min 40s\n",
      "Wall time: 5min 31s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Create train loader with default batch size for Parts 3 & 4\n",
    "train_loader = create_train_loader(batch_size)\n",
    "\n",
    "results_part3 = {}\n",
    "\n",
    "for name in optimizer_configs.keys():\n",
    "\n",
    "    print(f'\\nTraining with {name}')\n",
    "\n",
    "    results_part3[name] = train_with_optimizer(\n",
    "        name, lr_part3, n_epochs_part3, print_every, train_loader=train_loader\n",
    "    )\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c82bac14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot part 3 results\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "fig.suptitle(f'Optimizer comparison')\n",
    "\n",
    "# Validation accuracy over epochs\n",
    "axes[0].set_title('Validation accuracy')\n",
    "\n",
    "for name, history in results_part3.items():\n",
    "    axes[0].plot(history['val_accuracy'], label=name)\n",
    "\n",
    "axes[0].set_xlabel('Epoch')\n",
    "axes[0].set_ylabel('Accuracy (%)')\n",
    "axes[0].legend()\n",
    "\n",
    "# Final accuracy bar chart\n",
    "axes[1].set_title('Final validation accuracy')\n",
    "\n",
    "names = list(results_part3.keys())\n",
    "final_accs = [results_part3[n]['val_accuracy'][-1] for n in names]\n",
    "\n",
    "bars = axes[1].bar(names, final_accs)\n",
    "axes[1].set_ylabel('Accuracy (%)')\n",
    "axes[1].set_ylim(0, 100)\n",
    "\n",
    "# Add value labels\n",
    "for bar, acc in zip(bars, final_accs):\n",
    "    axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 1,\n",
    "                 f'{acc:.1f}%', ha='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e58b3594",
   "metadata": {},
   "source": [
    "## Part 4: Learning rate sensitivity\n",
    "\n",
    "Learning rates tested: `[0.1, 0.01, 0.001, 0.0001]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c8f4d0e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training with SGD, lr=0.1\n",
      "Epoch 1/10 - loss: 2.3505 - accuracy: 11.09% - val_loss: 2.2978 - val_accuracy: 9.48%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 28.21%\n",
      "\n",
      "Training with SGD, lr=0.01\n",
      "Epoch 1/10 - loss: 2.4029 - accuracy: 9.96% - val_loss: 2.3033 - val_accuracy: 8.75%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 21.02%\n",
      "\n",
      "Training with SGD, lr=0.001\n",
      "Epoch 1/10 - loss: 2.4251 - accuracy: 9.84% - val_loss: 2.3054 - val_accuracy: 8.12%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 10.35%\n",
      "\n",
      "Training with SGD, lr=0.0001\n",
      "Epoch 1/10 - loss: 2.4281 - accuracy: 9.82% - val_loss: 2.3057 - val_accuracy: 7.96%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 8.19%\n",
      "\n",
      "Training with SGD+Momentum, lr=0.1\n",
      "Epoch 1/10 - loss: 2.3397 - accuracy: 11.74% - val_loss: 2.2974 - val_accuracy: 9.48%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 35.94%\n",
      "\n",
      "Training with SGD+Momentum, lr=0.01\n",
      "Epoch 1/10 - loss: 2.3939 - accuracy: 10.13% - val_loss: 2.3013 - val_accuracy: 9.99%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 29.89%\n",
      "\n",
      "Training with SGD+Momentum, lr=0.001\n",
      "Epoch 1/10 - loss: 2.4232 - accuracy: 9.84% - val_loss: 2.3050 - val_accuracy: 8.22%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 18.86%\n",
      "\n",
      "Training with SGD+Momentum, lr=0.0001\n",
      "Epoch 1/10 - loss: 2.4279 - accuracy: 9.81% - val_loss: 2.3057 - val_accuracy: 7.98%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 10.04%\n",
      "\n",
      "Training with RMSprop, lr=0.1\n",
      "Epoch 1/10 - loss: 10.8645 - accuracy: 9.85% - val_loss: 2.3133 - val_accuracy: 9.89%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 10.12%\n",
      "\n",
      "Training with RMSprop, lr=0.01\n",
      "Epoch 1/10 - loss: 84.7240 - accuracy: 9.77% - val_loss: 2.3048 - val_accuracy: 9.54%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 12.68%\n",
      "\n",
      "Training with RMSprop, lr=0.001\n",
      "Epoch 1/10 - loss: 6.5869 - accuracy: 10.21% - val_loss: 2.2995 - val_accuracy: 10.45%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 26.14%\n",
      "\n",
      "Training with RMSprop, lr=0.0001\n",
      "Epoch 1/10 - loss: 2.4490 - accuracy: 11.58% - val_loss: 2.2940 - val_accuracy: 13.53%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 29.54%\n",
      "\n",
      "Training with Adam, lr=0.1\n",
      "Epoch 1/10 - loss: 91.5965 - accuracy: 9.90% - val_loss: 2.3141 - val_accuracy: 9.79%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 15.00%\n",
      "\n",
      "Training with Adam, lr=0.01\n",
      "Epoch 1/10 - loss: 6.8103 - accuracy: 9.98% - val_loss: 2.3430 - val_accuracy: 11.40%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 15.79%\n",
      "\n",
      "Training with Adam, lr=0.001\n",
      "Epoch 1/10 - loss: 2.4524 - accuracy: 11.29% - val_loss: 2.2977 - val_accuracy: 12.26%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 24.26%\n",
      "\n",
      "Training with Adam, lr=0.0001\n",
      "Epoch 1/10 - loss: 2.3794 - accuracy: 10.30% - val_loss: 2.3004 - val_accuracy: 10.30%\n",
      "\n",
      "Training complete.\n",
      "-> Final val acc: 29.35%\n",
      "\n",
      "CPU times: user 4min 33s, sys: 185 ms, total: 4min 33s\n",
      "Wall time: 4min 26s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "results_part4 = {name: {} for name in optimizer_configs.keys()}\n",
    "\n",
    "for name in optimizer_configs.keys():\n",
    "    for lr in learning_rates:\n",
    "\n",
    "        print(f'\\nTraining with {name}, lr={lr}')\n",
    "\n",
    "        results_part4[name][lr] = train_with_optimizer(\n",
    "            name, lr, n_epochs_part4, print_every=n_epochs_part4 + 1, train_loader=train_loader\n",
    "        )\n",
    "\n",
    "        print(f'-> Final val acc: {results_part4[name][lr][\"val_accuracy\"][-1]:.2f}%')\n",
    "\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "516b8ea9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Validation Accuracy Matrix:\n",
      "\n",
      "Optimizer             0.1      0.01     0.001    0.0001\n",
      "SGD                 28.2%     21.0%     10.3%      8.2%\n",
      "SGD+Momentum        35.9%     29.9%     18.9%     10.0%\n",
      "RMSprop             10.1%     12.7%     26.1%     29.5%\n",
      "Adam                15.0%     15.8%     24.3%     29.4%\n"
     ]
    }
   ],
   "source": [
    "# Create results matrix for heatmap\n",
    "optimizer_names = list(optimizer_configs.keys())\n",
    "accuracy_matrix = np.zeros((len(optimizer_names), len(learning_rates)))\n",
    "\n",
    "for i, name in enumerate(optimizer_names):\n",
    "    for j, lr in enumerate(learning_rates):\n",
    "        accuracy_matrix[i, j] = results_part4[name][lr]['val_accuracy'][-1]\n",
    "\n",
    "print('Final Validation Accuracy Matrix:')\n",
    "print()\n",
    "print(f'{\"Optimizer\":<15}', end='')\n",
    "\n",
    "for lr in learning_rates:\n",
    "    print(f'{lr:>10}', end='')\n",
    "\n",
    "print()\n",
    "\n",
    "for i, name in enumerate(optimizer_names):\n",
    "    print(f'{name:<15}', end='')\n",
    "\n",
    "    for j, lr in enumerate(learning_rates):\n",
    "        acc = accuracy_matrix[i, j]\n",
    "        print(f'{acc:>9.1f}%', end='')\n",
    "\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b06e9955",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot part 4 results\n",
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
    "\n",
    "plt.suptitle(f'Learning rate sensitivity')\n",
    "\n",
    "# Heatmap\n",
    "axes[0].set_title('Validation accuracy heatmap')\n",
    "\n",
    "im = axes[0].imshow(accuracy_matrix, aspect='auto', vmin=10, vmax=70)\n",
    "\n",
    "axes[0].set_xticks(range(len(learning_rates)))\n",
    "axes[0].set_xticklabels([str(lr) for lr in learning_rates])\n",
    "axes[0].set_yticks(range(len(optimizer_names)))\n",
    "axes[0].set_yticklabels(optimizer_names)\n",
    "axes[0].set_xlabel('Learning rate')\n",
    "\n",
    "# Add text annotations\n",
    "for i in range(len(optimizer_names)):\n",
    "    for j in range(len(learning_rates)):\n",
    "\n",
    "        text = f'{accuracy_matrix[i, j]:.1f}%'\n",
    "        color = 'white' if accuracy_matrix[i, j] < 40 else 'black'\n",
    "        axes[0].text(j, i, text, ha='center', va='center', color=color, fontsize=11)\n",
    "\n",
    "plt.colorbar(im, ax=axes[0], label='Validation accuracy (%)')\n",
    "\n",
    "# Line plot: accuracy vs learning rate\n",
    "axes[1].set_title('Accuracy vs learning rate')\n",
    "\n",
    "for name in optimizer_names:\n",
    "    accs = [results_part4[name][lr]['val_accuracy'][-1] for lr in learning_rates]\n",
    "    axes[1].plot(range(len(learning_rates)), accs, 'o-', label=name)\n",
    "\n",
    "axes[1].set_xticks(range(len(learning_rates)))\n",
    "axes[1].set_xticklabels([str(lr) for lr in learning_rates])\n",
    "axes[1].set_xlabel('Learning rate')\n",
    "axes[1].set_ylabel('Validation accuracy (%)')\n",
    "axes[1].legend()\n",
    "axes[1].set_ylim(0, 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "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
}
