{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGmh_7RRUARN",
        "outputId": "9873cd4a-57f4-457c-937f-59d45e4438ec"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.19.0\n",
            "GPUs: [PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "print(\"TensorFlow version:\", tf.__version__)\n",
        "print(\"GPUs:\", tf.config.list_physical_devices('GPU'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras import layers, models\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "# load CIFAR-10\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n",
        "print(\"x_train:\", x_train.shape)\n",
        "print(\"y_train:\", y_train.shape)\n",
        "print(\"x_test:\", x_test.shape)\n",
        "print(\"y_test:\", y_test.shape)\n",
        "\n",
        "labels = ['airplane','automobile','bird','cat','deer',\n",
        "          'dog','frog','horse','ship','truck']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oEemZGemVMKX",
        "outputId": "8dc1e8dc-01cc-45aa-fd7b-ee1a9559a7f8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "\u001b[1m170498071/170498071\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 0us/step\n",
            "x_train: (50000, 32, 32, 3)\n",
            "y_train: (50000, 1)\n",
            "x_test: (10000, 32, 32, 3)\n",
            "y_test: (10000, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "for i in range(20):\n",
        "    plt.subplot(4,5,i+1)\n",
        "    idx = np.random.randint(0, x_train.shape[0])\n",
        "    plt.imshow(x_train[idx])\n",
        "    plt.title(labels[int(y_train[idx])], fontsize=8)\n",
        "    plt.axis('off')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 662
        },
        "id": "LQpMusyyVrMn",
        "outputId": "c785a334-f0f8-4fc5-b86d-55df88e1c7a2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3793096579.py:6: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
            "  plt.title(labels[int(y_train[idx])], fontsize=8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 20 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalise to [0,1]\n",
        "x_train = x_train.astype('float32') / 255.0\n",
        "x_test  = x_test.astype('float32')  / 255.0\n",
        "\n",
        "# one-hot encode labels\n",
        "y_train_cat = to_categorical(y_train, num_classes=10)\n",
        "y_test_cat  = to_categorical(y_test,  num_classes=10)\n",
        "\n",
        "print(\"x_train min/max:\", x_train.min(), x_train.max())\n",
        "print(\"y_train_cat shape:\", y_train_cat.shape)\n",
        "print(\"y_test_cat shape:\", y_test_cat.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBeSHitXV6IP",
        "outputId": "a91edefd-4a0a-48e5-8701-d4198eb43e19"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train min/max: 0.0 1.0\n",
            "y_train_cat shape: (50000, 10)\n",
            "y_test_cat shape: (10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_augment = tf.keras.Sequential(\n",
        "    [\n",
        "        layers.RandomFlip(\"horizontal\"),\n",
        "        layers.RandomRotation(0.08),\n",
        "        layers.RandomTranslation(0.08, 0.08),\n",
        "    ],\n",
        "    name=\"data_augment\"\n",
        ")\n",
        "\n",
        "def build_model(input_shape=(32,32,3), num_classes=10, dropout_rate=0.4):\n",
        "    inp = layers.Input(shape=input_shape)\n",
        "\n",
        "    x = data_augment(inp)\n",
        "\n",
        "    # Block 1\n",
        "    x = layers.Conv2D(32, (3,3), padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.Conv2D(32, (3,3), padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.MaxPooling2D((2,2))(x)\n",
        "    x = layers.Dropout(dropout_rate)(x)\n",
        "\n",
        "    # Block 2\n",
        "    x = layers.Conv2D(64, (3,3), padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.Conv2D(64, (3,3), padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.MaxPooling2D((2,2))(x)\n",
        "    x = layers.Dropout(dropout_rate)(x)\n",
        "\n",
        "    # Block 3\n",
        "    x = layers.Conv2D(128, (3,3), padding='same')(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.MaxPooling2D((2,2))(x)\n",
        "    x = layers.Dropout(dropout_rate)(x)\n",
        "\n",
        "    # Dense head\n",
        "    x = layers.Flatten()(x)\n",
        "    x = layers.Dense(256)(x)\n",
        "    x = layers.BatchNormalization()(x)\n",
        "    x = layers.ReLU()(x)\n",
        "    x = layers.Dropout(0.5)(x)\n",
        "    out = layers.Dense(num_classes, activation='softmax')(x)\n",
        "\n",
        "    model = models.Model(inputs=inp, outputs=out, name=\"simple_cifar10_cnn\")\n",
        "    return model"
      ],
      "metadata": {
        "id": "WPTSL4sQWCKn"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = build_model(dropout_rate=0.4)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "42axD3ZlWI3D",
        "outputId": "d0fb8171-5e01-4dfb-a2d6-aaa8c769568b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"simple_cifar10_cnn\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"simple_cifar10_cnn\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (\u001b[38;5;33mInputLayer\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ data_augment (\u001b[38;5;33mSequential\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu (\u001b[38;5;33mReLU\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │         \u001b[38;5;34m9,248\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m128\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_1 (\u001b[38;5;33mReLU\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_2 (\u001b[38;5;33mReLU\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m256\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_3 (\u001b[38;5;33mReLU\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │        \u001b[38;5;34m73,856\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │           \u001b[38;5;34m512\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_4 (\u001b[38;5;33mReLU\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m128\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m524,544\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │         \u001b[38;5;34m1,024\u001b[0m │\n",
              "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_5 (\u001b[38;5;33mReLU\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │         \u001b[38;5;34m2,570\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ input_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ data_augment (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Sequential</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">524,544</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ batch_normalization_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,024</span> │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)            │                        │               │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ re_lu_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">ReLU</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,570</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m668,842\u001b[0m (2.55 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">668,842</span> (2.55 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m667,690\u001b[0m (2.55 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">667,690</span> (2.55 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m1,152\u001b[0m (4.50 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,152</span> (4.50 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 9: compile baseline model and set callbacks\n",
        "\n",
        "baseline_lr = 1e-3\n",
        "\n",
        "model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=baseline_lr),\n",
        "    loss='categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "es = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    patience=8,\n",
        "    restore_best_weights=True\n",
        ")\n",
        "\n",
        "rlr = tf.keras.callbacks.ReduceLROnPlateau(\n",
        "    monitor='val_loss',\n",
        "    factor=0.5,\n",
        "    patience=4,\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "print(\"Compiled baseline model with Adam(lr=1e-3) and callbacks.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "41iqH1bLWzV_",
        "outputId": "cec3a6f1-033f-41f3-edf4-f5c86bf64c4c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Compiled baseline model with Adam(lr=1e-3) and callbacks.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 10: train the baseline model\n",
        "\n",
        "history_baseline = model.fit(\n",
        "    x_train, y_train_cat,\n",
        "    epochs=30,\n",
        "    batch_size=64,\n",
        "    validation_split=0.1,\n",
        "    callbacks=[es, rlr],\n",
        "    verbose=2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8obC0W9gXdQ1",
        "outputId": "106921e7-3d3d-4011-ac60-3c1ace6c76ee"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "704/704 - 22s - 32ms/step - accuracy: 0.3620 - loss: 1.7877 - val_accuracy: 0.4404 - val_loss: 1.5077 - learning_rate: 1.0000e-03\n",
            "Epoch 2/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.4828 - loss: 1.4263 - val_accuracy: 0.4914 - val_loss: 1.4218 - learning_rate: 1.0000e-03\n",
            "Epoch 3/30\n",
            "704/704 - 21s - 29ms/step - accuracy: 0.5353 - loss: 1.2884 - val_accuracy: 0.5418 - val_loss: 1.2817 - learning_rate: 1.0000e-03\n",
            "Epoch 4/30\n",
            "704/704 - 13s - 19ms/step - accuracy: 0.5715 - loss: 1.2022 - val_accuracy: 0.6222 - val_loss: 1.0450 - learning_rate: 1.0000e-03\n",
            "Epoch 5/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5922 - loss: 1.1453 - val_accuracy: 0.5748 - val_loss: 1.2339 - learning_rate: 1.0000e-03\n",
            "Epoch 6/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6091 - loss: 1.0999 - val_accuracy: 0.6406 - val_loss: 1.0277 - learning_rate: 1.0000e-03\n",
            "Epoch 7/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6244 - loss: 1.0603 - val_accuracy: 0.5866 - val_loss: 1.2505 - learning_rate: 1.0000e-03\n",
            "Epoch 8/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6412 - loss: 1.0226 - val_accuracy: 0.6610 - val_loss: 0.9928 - learning_rate: 1.0000e-03\n",
            "Epoch 9/30\n",
            "704/704 - 13s - 19ms/step - accuracy: 0.6497 - loss: 0.9989 - val_accuracy: 0.6860 - val_loss: 0.9088 - learning_rate: 1.0000e-03\n",
            "Epoch 10/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6576 - loss: 0.9735 - val_accuracy: 0.6656 - val_loss: 0.9695 - learning_rate: 1.0000e-03\n",
            "Epoch 11/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6662 - loss: 0.9537 - val_accuracy: 0.6328 - val_loss: 1.1167 - learning_rate: 1.0000e-03\n",
            "Epoch 12/30\n",
            "704/704 - 21s - 29ms/step - accuracy: 0.6729 - loss: 0.9399 - val_accuracy: 0.6600 - val_loss: 0.9667 - learning_rate: 1.0000e-03\n",
            "Epoch 13/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6781 - loss: 0.9261 - val_accuracy: 0.6988 - val_loss: 0.8808 - learning_rate: 1.0000e-03\n",
            "Epoch 14/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6803 - loss: 0.9186 - val_accuracy: 0.6978 - val_loss: 0.8784 - learning_rate: 1.0000e-03\n",
            "Epoch 15/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6879 - loss: 0.9021 - val_accuracy: 0.6946 - val_loss: 0.9020 - learning_rate: 1.0000e-03\n",
            "Epoch 16/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6911 - loss: 0.8901 - val_accuracy: 0.6914 - val_loss: 0.9237 - learning_rate: 1.0000e-03\n",
            "Epoch 17/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6977 - loss: 0.8762 - val_accuracy: 0.7518 - val_loss: 0.7224 - learning_rate: 1.0000e-03\n",
            "Epoch 18/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6993 - loss: 0.8690 - val_accuracy: 0.7372 - val_loss: 0.7678 - learning_rate: 1.0000e-03\n",
            "Epoch 19/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7014 - loss: 0.8546 - val_accuracy: 0.7356 - val_loss: 0.7630 - learning_rate: 1.0000e-03\n",
            "Epoch 20/30\n",
            "704/704 - 21s - 29ms/step - accuracy: 0.7074 - loss: 0.8520 - val_accuracy: 0.7274 - val_loss: 0.8029 - learning_rate: 1.0000e-03\n",
            "Epoch 21/30\n",
            "\n",
            "Epoch 21: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "704/704 - 20s - 29ms/step - accuracy: 0.7068 - loss: 0.8451 - val_accuracy: 0.7416 - val_loss: 0.7537 - learning_rate: 1.0000e-03\n",
            "Epoch 22/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7198 - loss: 0.8096 - val_accuracy: 0.7598 - val_loss: 0.6803 - learning_rate: 5.0000e-04\n",
            "Epoch 23/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7224 - loss: 0.7980 - val_accuracy: 0.7466 - val_loss: 0.7395 - learning_rate: 5.0000e-04\n",
            "Epoch 24/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7277 - loss: 0.7875 - val_accuracy: 0.7460 - val_loss: 0.7542 - learning_rate: 5.0000e-04\n",
            "Epoch 25/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7263 - loss: 0.7893 - val_accuracy: 0.7614 - val_loss: 0.6988 - learning_rate: 5.0000e-04\n",
            "Epoch 26/30\n",
            "\n",
            "Epoch 26: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7300 - loss: 0.7714 - val_accuracy: 0.7440 - val_loss: 0.7469 - learning_rate: 5.0000e-04\n",
            "Epoch 27/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7356 - loss: 0.7647 - val_accuracy: 0.7478 - val_loss: 0.7577 - learning_rate: 2.5000e-04\n",
            "Epoch 28/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7362 - loss: 0.7619 - val_accuracy: 0.7408 - val_loss: 0.7789 - learning_rate: 2.5000e-04\n",
            "Epoch 29/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7381 - loss: 0.7557 - val_accuracy: 0.7644 - val_loss: 0.6808 - learning_rate: 2.5000e-04\n",
            "Epoch 30/30\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7424 - loss: 0.7441 - val_accuracy: 0.7678 - val_loss: 0.6799 - learning_rate: 2.5000e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 11: plot baseline training history (loss and accuracy)\n",
        "\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history_baseline.history['loss'], label='train_loss')\n",
        "plt.plot(history_baseline.history['val_loss'], label='val_loss')\n",
        "plt.xlabel('Epoch'); plt.ylabel('Loss')\n",
        "plt.title('Baseline – Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history_baseline.history['accuracy'], label='train_acc')\n",
        "plt.plot(history_baseline.history['val_accuracy'], label='val_acc')\n",
        "plt.xlabel('Epoch'); plt.ylabel('Accuracy')\n",
        "plt.title('Baseline – Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "Le1vHERkZVU3",
        "outputId": "107fd87e-682d-4390-d9d0-ccbb82239605"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 12: evaluate baseline on the test set\n",
        "\n",
        "test_loss_base, test_acc_base = model.evaluate(x_test, y_test_cat, verbose=2)\n",
        "print(f\"Baseline test loss: {test_loss_base:.4f}\")\n",
        "print(f\"Baseline test accuracy: {test_acc_base:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wngBDhZZo2k",
        "outputId": "c5beb539-6e75-4aee-cd13-9101d20aca62"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 2s - 5ms/step - accuracy: 0.7618 - loss: 0.7076\n",
            "Baseline test loss: 0.7076\n",
            "Baseline test accuracy: 0.7618\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 13: classification report and confusion matrix for baseline\n",
        "\n",
        "y_pred_probs = model.predict(x_test)\n",
        "y_pred = np.argmax(y_pred_probs, axis=1)\n",
        "y_true = y_test.flatten()\n",
        "\n",
        "print(\"Classification report (baseline):\\n\")\n",
        "print(classification_report(y_true, y_pred, target_names=labels))\n",
        "\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "plt.figure(figsize=(9,7))\n",
        "sns.heatmap(cm, annot=True, fmt='d',\n",
        "            xticklabels=labels, yticklabels=labels)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix – Baseline')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lA__bAQJZ6xu",
        "outputId": "daebdf49-6dff-4051-b23a-e162c2701e99"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 4ms/step\n",
            "Classification report (baseline):\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.84      0.75      0.79      1000\n",
            "  automobile       0.90      0.93      0.91      1000\n",
            "        bird       0.86      0.51      0.64      1000\n",
            "         cat       0.73      0.48      0.58      1000\n",
            "        deer       0.72      0.73      0.73      1000\n",
            "         dog       0.80      0.63      0.71      1000\n",
            "        frog       0.51      0.96      0.67      1000\n",
            "       horse       0.88      0.79      0.83      1000\n",
            "        ship       0.88      0.90      0.89      1000\n",
            "       truck       0.78      0.93      0.85      1000\n",
            "\n",
            "    accuracy                           0.76     10000\n",
            "   macro avg       0.79      0.76      0.76     10000\n",
            "weighted avg       0.79      0.76      0.76     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 14: define hyperparameter sets and training helper\n",
        "\n",
        "hyperparameter_sets = {\n",
        "    \"A_baseline\": {\n",
        "        \"learning_rate\": 1e-3,\n",
        "        \"dropout_rate\": 0.4,\n",
        "        \"batch_size\": 64,\n",
        "        \"epochs\": 20,\n",
        "    },\n",
        "    \"B_lowerLR\": {\n",
        "        \"learning_rate\": 5e-4,\n",
        "        \"dropout_rate\": 0.4,\n",
        "        \"batch_size\": 64,\n",
        "        \"epochs\": 20,\n",
        "    },\n",
        "    \"C_moreDrop_bigBatch\": {\n",
        "        \"learning_rate\": 5e-4,\n",
        "        \"dropout_rate\": 0.6,\n",
        "        \"batch_size\": 128,\n",
        "        \"epochs\": 20,\n",
        "    },\n",
        "}\n",
        "\n",
        "print(\"Hyperparameter configurations:\")\n",
        "for name, cfg in hyperparameter_sets.items():\n",
        "    print(name, \":\", cfg)\n",
        "\n",
        "\n",
        "from tensorflow.keras import callbacks\n",
        "\n",
        "def train_one_config(name, cfg):\n",
        "    \"\"\"Build, compile and train a model with given hyperparameters.\"\"\"\n",
        "    print(f\"\\n======================\")\n",
        "    print(f\"Training config: {name}\")\n",
        "    print(cfg)\n",
        "    print(f\"======================\")\n",
        "\n",
        "    # build a fresh model with chosen dropout\n",
        "    model_cfg = build_model(dropout_rate=cfg[\"dropout_rate\"])\n",
        "\n",
        "    # compile with chosen learning rate\n",
        "    model_cfg.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=cfg[\"learning_rate\"]),\n",
        "        loss='categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "\n",
        "    es_cfg = callbacks.EarlyStopping(\n",
        "        monitor='val_loss', patience=5, restore_best_weights=True\n",
        "    )\n",
        "    rlr_cfg = callbacks.ReduceLROnPlateau(\n",
        "        monitor='val_loss', factor=0.5, patience=3, verbose=1\n",
        "    )\n",
        "\n",
        "    history_cfg = model_cfg.fit(\n",
        "        x_train, y_train_cat,\n",
        "        epochs=cfg[\"epochs\"],\n",
        "        batch_size=cfg[\"batch_size\"],\n",
        "        validation_split=0.1,\n",
        "        callbacks=[es_cfg, rlr_cfg],\n",
        "        verbose=2\n",
        "    )\n",
        "\n",
        "    test_loss_cfg, test_acc_cfg = model_cfg.evaluate(x_test, y_test_cat, verbose=0)\n",
        "    print(f\"{name} -> Test loss: {test_loss_cfg:.4f}, Test accuracy: {test_acc_cfg:.4f}\")\n",
        "\n",
        "    return model_cfg, history_cfg, test_loss_cfg, test_acc_cfg"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTqCpUnJaYhs",
        "outputId": "5ef53b17-0c2f-44fb-af78-5deb04208db2"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hyperparameter configurations:\n",
            "A_baseline : {'learning_rate': 0.001, 'dropout_rate': 0.4, 'batch_size': 64, 'epochs': 20}\n",
            "B_lowerLR : {'learning_rate': 0.0005, 'dropout_rate': 0.4, 'batch_size': 64, 'epochs': 20}\n",
            "C_moreDrop_bigBatch : {'learning_rate': 0.0005, 'dropout_rate': 0.6, 'batch_size': 128, 'epochs': 20}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 15: run experiments for each hyperparameter configuration\n",
        "\n",
        "all_models = {}\n",
        "all_histories = {}\n",
        "all_results = {}\n",
        "\n",
        "for name, cfg in hyperparameter_sets.items():\n",
        "    tf.keras.backend.clear_session()\n",
        "    model_cfg, history_cfg, loss_cfg, acc_cfg = train_one_config(name, cfg)\n",
        "    all_models[name] = model_cfg\n",
        "    all_histories[name] = history_cfg\n",
        "    all_results[name] = {\n",
        "        \"test_loss\": loss_cfg,\n",
        "        \"test_acc\":  acc_cfg\n",
        "    }\n",
        "\n",
        "print(\"\\nFinal comparison (test performance):\")\n",
        "for name, res in all_results.items():\n",
        "    print(f\"{name:20s}  acc={res['test_acc']:.4f}  loss={res['test_loss']:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nU9ZBkNqa4wJ",
        "outputId": "ce2336fc-099d-4f8b-f0ea-b68889ed597e"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================\n",
            "Training config: A_baseline\n",
            "{'learning_rate': 0.001, 'dropout_rate': 0.4, 'batch_size': 64, 'epochs': 20}\n",
            "======================\n",
            "Epoch 1/20\n",
            "704/704 - 17s - 25ms/step - accuracy: 0.3612 - loss: 1.7887 - val_accuracy: 0.4702 - val_loss: 1.4970 - learning_rate: 1.0000e-03\n",
            "Epoch 2/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.4720 - loss: 1.4501 - val_accuracy: 0.5488 - val_loss: 1.2298 - learning_rate: 1.0000e-03\n",
            "Epoch 3/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5240 - loss: 1.3166 - val_accuracy: 0.5788 - val_loss: 1.1712 - learning_rate: 1.0000e-03\n",
            "Epoch 4/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5610 - loss: 1.2229 - val_accuracy: 0.6094 - val_loss: 1.0865 - learning_rate: 1.0000e-03\n",
            "Epoch 5/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5842 - loss: 1.1612 - val_accuracy: 0.6448 - val_loss: 1.0274 - learning_rate: 1.0000e-03\n",
            "Epoch 6/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6050 - loss: 1.1081 - val_accuracy: 0.6462 - val_loss: 0.9909 - learning_rate: 1.0000e-03\n",
            "Epoch 7/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6228 - loss: 1.0703 - val_accuracy: 0.6264 - val_loss: 1.0936 - learning_rate: 1.0000e-03\n",
            "Epoch 8/20\n",
            "704/704 - 14s - 19ms/step - accuracy: 0.6346 - loss: 1.0321 - val_accuracy: 0.6544 - val_loss: 0.9761 - learning_rate: 1.0000e-03\n",
            "Epoch 9/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6415 - loss: 1.0137 - val_accuracy: 0.6318 - val_loss: 1.1032 - learning_rate: 1.0000e-03\n",
            "Epoch 10/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6551 - loss: 0.9836 - val_accuracy: 0.7184 - val_loss: 0.8059 - learning_rate: 1.0000e-03\n",
            "Epoch 11/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6651 - loss: 0.9578 - val_accuracy: 0.7092 - val_loss: 0.8493 - learning_rate: 1.0000e-03\n",
            "Epoch 12/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6710 - loss: 0.9408 - val_accuracy: 0.6486 - val_loss: 1.0055 - learning_rate: 1.0000e-03\n",
            "Epoch 13/20\n",
            "\n",
            "Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257.\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6778 - loss: 0.9282 - val_accuracy: 0.6828 - val_loss: 0.9526 - learning_rate: 1.0000e-03\n",
            "Epoch 14/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6905 - loss: 0.8883 - val_accuracy: 0.7298 - val_loss: 0.7675 - learning_rate: 5.0000e-04\n",
            "Epoch 15/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6996 - loss: 0.8637 - val_accuracy: 0.7658 - val_loss: 0.6812 - learning_rate: 5.0000e-04\n",
            "Epoch 16/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7011 - loss: 0.8569 - val_accuracy: 0.7400 - val_loss: 0.7649 - learning_rate: 5.0000e-04\n",
            "Epoch 17/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7061 - loss: 0.8447 - val_accuracy: 0.7406 - val_loss: 0.7479 - learning_rate: 5.0000e-04\n",
            "Epoch 18/20\n",
            "\n",
            "Epoch 18: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "704/704 - 13s - 19ms/step - accuracy: 0.7072 - loss: 0.8445 - val_accuracy: 0.7242 - val_loss: 0.8144 - learning_rate: 5.0000e-04\n",
            "Epoch 19/20\n",
            "704/704 - 13s - 19ms/step - accuracy: 0.7185 - loss: 0.8182 - val_accuracy: 0.7438 - val_loss: 0.7469 - learning_rate: 2.5000e-04\n",
            "Epoch 20/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.7204 - loss: 0.8083 - val_accuracy: 0.7524 - val_loss: 0.7131 - learning_rate: 2.5000e-04\n",
            "A_baseline -> Test loss: 0.7191, Test accuracy: 0.7466\n",
            "\n",
            "======================\n",
            "Training config: B_lowerLR\n",
            "{'learning_rate': 0.0005, 'dropout_rate': 0.4, 'batch_size': 64, 'epochs': 20}\n",
            "======================\n",
            "Epoch 1/20\n",
            "704/704 - 18s - 25ms/step - accuracy: 0.3281 - loss: 1.9076 - val_accuracy: 0.4276 - val_loss: 1.5348 - learning_rate: 5.0000e-04\n",
            "Epoch 2/20\n",
            "704/704 - 13s - 19ms/step - accuracy: 0.4417 - loss: 1.5364 - val_accuracy: 0.3904 - val_loss: 1.8046 - learning_rate: 5.0000e-04\n",
            "Epoch 3/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.4944 - loss: 1.3952 - val_accuracy: 0.5100 - val_loss: 1.4086 - learning_rate: 5.0000e-04\n",
            "Epoch 4/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5295 - loss: 1.3063 - val_accuracy: 0.4308 - val_loss: 1.7505 - learning_rate: 5.0000e-04\n",
            "Epoch 5/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5587 - loss: 1.2365 - val_accuracy: 0.5236 - val_loss: 1.3600 - learning_rate: 5.0000e-04\n",
            "Epoch 6/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5752 - loss: 1.1830 - val_accuracy: 0.5544 - val_loss: 1.3881 - learning_rate: 5.0000e-04\n",
            "Epoch 7/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.5967 - loss: 1.1343 - val_accuracy: 0.6472 - val_loss: 1.0007 - learning_rate: 5.0000e-04\n",
            "Epoch 8/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6133 - loss: 1.0987 - val_accuracy: 0.6104 - val_loss: 1.1653 - learning_rate: 5.0000e-04\n",
            "Epoch 9/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6219 - loss: 1.0658 - val_accuracy: 0.6464 - val_loss: 1.0497 - learning_rate: 5.0000e-04\n",
            "Epoch 10/20\n",
            "\n",
            "Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6333 - loss: 1.0389 - val_accuracy: 0.6268 - val_loss: 1.0597 - learning_rate: 5.0000e-04\n",
            "Epoch 11/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6479 - loss: 0.9966 - val_accuracy: 0.6384 - val_loss: 1.0679 - learning_rate: 2.5000e-04\n",
            "Epoch 12/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6524 - loss: 0.9838 - val_accuracy: 0.6728 - val_loss: 0.9328 - learning_rate: 2.5000e-04\n",
            "Epoch 13/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6587 - loss: 0.9650 - val_accuracy: 0.6768 - val_loss: 0.9493 - learning_rate: 2.5000e-04\n",
            "Epoch 14/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6646 - loss: 0.9498 - val_accuracy: 0.6868 - val_loss: 0.8947 - learning_rate: 2.5000e-04\n",
            "Epoch 15/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6688 - loss: 0.9410 - val_accuracy: 0.6668 - val_loss: 0.9727 - learning_rate: 2.5000e-04\n",
            "Epoch 16/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6720 - loss: 0.9378 - val_accuracy: 0.7136 - val_loss: 0.8141 - learning_rate: 2.5000e-04\n",
            "Epoch 17/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6748 - loss: 0.9303 - val_accuracy: 0.6600 - val_loss: 0.9684 - learning_rate: 2.5000e-04\n",
            "Epoch 18/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6794 - loss: 0.9114 - val_accuracy: 0.6892 - val_loss: 0.8850 - learning_rate: 2.5000e-04\n",
            "Epoch 19/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6860 - loss: 0.9025 - val_accuracy: 0.7190 - val_loss: 0.7953 - learning_rate: 2.5000e-04\n",
            "Epoch 20/20\n",
            "704/704 - 13s - 18ms/step - accuracy: 0.6869 - loss: 0.9004 - val_accuracy: 0.6958 - val_loss: 0.8847 - learning_rate: 2.5000e-04\n",
            "B_lowerLR -> Test loss: 0.8293, Test accuracy: 0.7055\n",
            "\n",
            "======================\n",
            "Training config: C_moreDrop_bigBatch\n",
            "{'learning_rate': 0.0005, 'dropout_rate': 0.6, 'batch_size': 128, 'epochs': 20}\n",
            "======================\n",
            "Epoch 1/20\n",
            "352/352 - 15s - 43ms/step - accuracy: 0.2437 - loss: 2.1739 - val_accuracy: 0.1018 - val_loss: 3.1751 - learning_rate: 5.0000e-04\n",
            "Epoch 2/20\n",
            "352/352 - 10s - 28ms/step - accuracy: 0.3530 - loss: 1.7537 - val_accuracy: 0.3610 - val_loss: 1.7240 - learning_rate: 5.0000e-04\n",
            "Epoch 3/20\n",
            "352/352 - 10s - 29ms/step - accuracy: 0.4017 - loss: 1.6162 - val_accuracy: 0.3344 - val_loss: 1.8135 - learning_rate: 5.0000e-04\n",
            "Epoch 4/20\n",
            "352/352 - 10s - 27ms/step - accuracy: 0.4379 - loss: 1.5392 - val_accuracy: 0.3770 - val_loss: 1.7332 - learning_rate: 5.0000e-04\n",
            "Epoch 5/20\n",
            "\n",
            "Epoch 5: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.\n",
            "352/352 - 10s - 28ms/step - accuracy: 0.4599 - loss: 1.4777 - val_accuracy: 0.3448 - val_loss: 2.0471 - learning_rate: 5.0000e-04\n",
            "Epoch 6/20\n",
            "352/352 - 10s - 28ms/step - accuracy: 0.4861 - loss: 1.4127 - val_accuracy: 0.4090 - val_loss: 1.6733 - learning_rate: 2.5000e-04\n",
            "Epoch 7/20\n",
            "352/352 - 10s - 27ms/step - accuracy: 0.4931 - loss: 1.3893 - val_accuracy: 0.2924 - val_loss: 2.0763 - learning_rate: 2.5000e-04\n",
            "Epoch 8/20\n",
            "352/352 - 10s - 27ms/step - accuracy: 0.5066 - loss: 1.3656 - val_accuracy: 0.3328 - val_loss: 1.9842 - learning_rate: 2.5000e-04\n",
            "Epoch 9/20\n",
            "\n",
            "Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0001250000059371814.\n",
            "352/352 - 10s - 27ms/step - accuracy: 0.5139 - loss: 1.3454 - val_accuracy: 0.3358 - val_loss: 2.0350 - learning_rate: 2.5000e-04\n",
            "Epoch 10/20\n",
            "352/352 - 10s - 28ms/step - accuracy: 0.5230 - loss: 1.3240 - val_accuracy: 0.3328 - val_loss: 2.0525 - learning_rate: 1.2500e-04\n",
            "Epoch 11/20\n",
            "352/352 - 10s - 28ms/step - accuracy: 0.5303 - loss: 1.3072 - val_accuracy: 0.3686 - val_loss: 1.8511 - learning_rate: 1.2500e-04\n",
            "C_moreDrop_bigBatch -> Test loss: 1.6916, Test accuracy: 0.4061\n",
            "\n",
            "Final comparison (test performance):\n",
            "A_baseline            acc=0.7466  loss=0.7191\n",
            "B_lowerLR             acc=0.7055  loss=0.8293\n",
            "C_moreDrop_bigBatch   acc=0.4061  loss=1.6916\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 16: plot validation accuracy for each configuration\n",
        "\n",
        "plt.figure(figsize=(8,5))\n",
        "for name, history in all_histories.items():\n",
        "    plt.plot(history.history['val_accuracy'], label=name)\n",
        "\n",
        "plt.title(\"Validation accuracy for different hyperparameters\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Validation accuracy\")\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "EQ1XbJEpe_yK",
        "outputId": "430529c9-6d5f-4d55-8bc3-d1b698484cb5"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 17: bar chart comparison of test accuracy\n",
        "\n",
        "names = list(all_results.keys())\n",
        "accs  = [all_results[n]['test_acc'] for n in names]\n",
        "\n",
        "plt.figure(figsize=(6,4))\n",
        "plt.bar(names, accs)\n",
        "plt.ylabel(\"Test accuracy\")\n",
        "plt.title(\"Test accuracy for different hyperparameter settings\")\n",
        "plt.ylim(0, 1.0)\n",
        "plt.xticks(rotation=20)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "dYlzMBPVfSpI",
        "outputId": "ed0b7521-b357-4161-a252-ab0688840860"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 18: detailed evaluation of the best configuration\n",
        "\n",
        "best_name = max(all_results, key=lambda n: all_results[n]['test_acc'])\n",
        "best_model = all_models[best_name]\n",
        "print(\"Best configuration:\", best_name, all_results[best_name])\n",
        "\n",
        "y_pred_probs_best = best_model.predict(x_test)\n",
        "y_pred_best = np.argmax(y_pred_probs_best, axis=1)\n",
        "y_true = y_test.flatten()\n",
        "\n",
        "print(\"\\nClassification report for best configuration:\\n\")\n",
        "print(classification_report(y_true, y_pred_best, target_names=labels))\n",
        "\n",
        "cm_best = confusion_matrix(y_true, y_pred_best)\n",
        "\n",
        "plt.figure(figsize=(9,7))\n",
        "sns.heatmap(cm_best, annot=True, fmt='d',\n",
        "            xticklabels=labels, yticklabels=labels)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title(f'Confusion Matrix – Best config: {best_name}')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rg_lngqWfkRo",
        "outputId": "25b0e767-5ca5-426e-a83a-f5cc1d9c88de"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best configuration: A_baseline {'test_loss': 0.7191404104232788, 'test_acc': 0.7465999722480774}\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 3ms/step\n",
            "\n",
            "Classification report for best configuration:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.73      0.83      0.78      1000\n",
            "  automobile       0.84      0.94      0.89      1000\n",
            "        bird       0.81      0.49      0.61      1000\n",
            "         cat       0.67      0.49      0.57      1000\n",
            "        deer       0.74      0.63      0.68      1000\n",
            "         dog       0.79      0.60      0.68      1000\n",
            "        frog       0.57      0.93      0.71      1000\n",
            "       horse       0.79      0.82      0.80      1000\n",
            "        ship       0.90      0.84      0.87      1000\n",
            "       truck       0.75      0.90      0.82      1000\n",
            "\n",
            "    accuracy                           0.75     10000\n",
            "   macro avg       0.76      0.75      0.74     10000\n",
            "weighted avg       0.76      0.75      0.74     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}