{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oLQPomFbzVGl"
      },
      "source": [
        "machine learning"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KePjtB-xwus8",
        "outputId": "2bd0b06a-bf3e-4589-f040-b54f30978516"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.19.0\n"
          ]
        }
      ],
      "source": [
        "# Cell 1 – Import required libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"TensorFlow version:\", tf.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGW1dvTCy4-D",
        "outputId": "80c4572e-e5da-484a-bb6b-e674acb157ca"
      },
      "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[1m3s\u001b[0m 0us/step\n",
            "Training set: (50000, 32, 32, 3) Testing set: (10000, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "# Cell 2 – Load CIFAR-10 dataset\n",
        "from tensorflow.keras import datasets # Added import\n",
        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "# Normalize pixel values (0–255) → (0–1)\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# Flatten label arrays\n",
        "y_train = y_train.flatten()\n",
        "y_test = y_test.flatten()\n",
        "\n",
        "print(\"Training set:\", x_train.shape, \"Testing set:\", x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "7dVbDw3_y8GB",
        "outputId": "56872d39-c8fe-4abc-d7a8-f256dbe76ddc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 3 – Display sample images\n",
        "class_names = ['airplane','car','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','lorry']\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.xlabel(class_names[y_train[i]])\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yDJDXtZ8y_cA",
        "outputId": "e4861950-8c75-46da-cc97-8d367c450fe6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\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: \"sequential\"</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",
              "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m15\u001b[0m, \u001b[38;5;34m15\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;34m13\u001b[0m, \u001b[38;5;34m13\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m64\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;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m64\u001b[0m)       │        \u001b[38;5;34m36,928\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1024\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;34m64\u001b[0m)             │        \u001b[38;5;34m65,600\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\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;34m650\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",
              "│ 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\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</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\">15</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">15</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\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">13</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</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\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</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\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)       │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</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\">1024</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\">64</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</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\">64</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\">650</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m122,570\u001b[0m (478.79 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\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,570</span> (478.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m122,570\u001b[0m (478.79 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\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">122,570</span> (478.79 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\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\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 4 – Define the CNN model\n",
        "model = models.Sequential([\n",
        "    layers.Conv2D(32, (3,3), activation='relu', input_shape=(32,32,3)),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    layers.MaxPooling2D((2,2)),\n",
        "    layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dropout(0.5),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nDffiTRizDcB"
      },
      "outputs": [],
      "source": [
        "# Cell 5 – Compile model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DYMvumNzG7H",
        "outputId": "18065487-eff6-4372-e9a7-a77ce7f47034"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 98ms/step - accuracy: 0.2674 - loss: 1.9535 - val_accuracy: 0.5118 - val_loss: 1.3444\n",
            "Epoch 2/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 95ms/step - accuracy: 0.4789 - loss: 1.4273 - val_accuracy: 0.5758 - val_loss: 1.1625\n",
            "Epoch 3/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 96ms/step - accuracy: 0.5503 - loss: 1.2616 - val_accuracy: 0.5794 - val_loss: 1.2327\n",
            "Epoch 4/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 100ms/step - accuracy: 0.5894 - loss: 1.1671 - val_accuracy: 0.6112 - val_loss: 1.1250\n",
            "Epoch 5/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 97ms/step - accuracy: 0.6127 - loss: 1.0978 - val_accuracy: 0.6705 - val_loss: 0.9452\n",
            "Epoch 6/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 96ms/step - accuracy: 0.6441 - loss: 1.0115 - val_accuracy: 0.6709 - val_loss: 0.9446\n",
            "Epoch 7/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 97ms/step - accuracy: 0.6603 - loss: 0.9621 - val_accuracy: 0.6837 - val_loss: 0.8956\n",
            "Epoch 8/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 96ms/step - accuracy: 0.6773 - loss: 0.9186 - val_accuracy: 0.6981 - val_loss: 0.8588\n",
            "Epoch 9/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 98ms/step - accuracy: 0.6927 - loss: 0.8837 - val_accuracy: 0.7060 - val_loss: 0.8500\n",
            "Epoch 10/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 97ms/step - accuracy: 0.7060 - loss: 0.8452 - val_accuracy: 0.6808 - val_loss: 0.9174\n",
            "Epoch 11/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 99ms/step - accuracy: 0.7140 - loss: 0.8222 - val_accuracy: 0.7223 - val_loss: 0.8092\n",
            "Epoch 12/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 97ms/step - accuracy: 0.7290 - loss: 0.7829 - val_accuracy: 0.7198 - val_loss: 0.8133\n",
            "Epoch 13/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 96ms/step - accuracy: 0.7314 - loss: 0.7632 - val_accuracy: 0.7182 - val_loss: 0.8246\n",
            "Epoch 14/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 95ms/step - accuracy: 0.7364 - loss: 0.7478 - val_accuracy: 0.7227 - val_loss: 0.8234\n",
            "Epoch 15/15\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 99ms/step - accuracy: 0.7463 - loss: 0.7250 - val_accuracy: 0.7254 - val_loss: 0.8325\n"
          ]
        }
      ],
      "source": [
        "# Cell 6 – Train the CNN\n",
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    epochs=15,\n",
        "    validation_data=(x_test, y_test),\n",
        "    batch_size=64\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "uoXR1JHYzJfQ",
        "outputId": "c4ba0df5-b385-4e68-c5de-8344059f252b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 7 – Plot accuracy and loss\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Model Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Model Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNJ9bk1z3i29",
        "outputId": "c2ac2d67-0540-45f0-8683-e7ef467599fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 6s - 19ms/step - accuracy: 0.7254 - loss: 0.8325\n",
            "\n",
            "Test accuracy: 72.54 %\n"
          ]
        }
      ],
      "source": [
        "# Cell 8 – Evaluate model performance\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
        "print('\\nTest accuracy:', round(test_acc*100, 2), '%')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UBNlKIhm3oSi",
        "outputId": "93bdda85-a620-4dfd-998f-64a8d66ff372"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 18ms/step\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.76      0.74      0.75      1000\n",
            "         car       0.89      0.84      0.86      1000\n",
            "        bird       0.69      0.51      0.59      1000\n",
            "         cat       0.57      0.54      0.56      1000\n",
            "        deer       0.57      0.78      0.66      1000\n",
            "         dog       0.69      0.55      0.61      1000\n",
            "        frog       0.75      0.82      0.78      1000\n",
            "       horse       0.73      0.80      0.76      1000\n",
            "        ship       0.82      0.85      0.83      1000\n",
            "       lorry       0.82      0.83      0.83      1000\n",
            "\n",
            "    accuracy                           0.73     10000\n",
            "   macro avg       0.73      0.73      0.72     10000\n",
            "weighted avg       0.73      0.73      0.72     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Cell 9 – Confusion Matrix and Classification Report\n",
        "y_pred = np.argmax(model.predict(x_test), axis=1)\n",
        "\n",
        "print(\"\\nClassification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred, target_names=class_names))\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q4rdDjbL3xWH",
        "outputId": "be589572-e573-440f-b509-36df908cb5f5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model saved successfully!\n"
          ]
        }
      ],
      "source": [
        "# Cell 10 – Save trained model\n",
        "model.save('CIFAR10_CNN_Model.h5')\n",
        "print(\"Model saved successfully!\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gMamY4lWvhEb"
      },
      "outputs": [],
      "source": [
        "# model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005),\n",
        "#               loss='sparse_categorical_crossentropy',\n",
        "#               metrics=['accuracy'])\n",
        "# Try changing dropout rate (e.g., 0.3) or batch size (e.g., 128)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGSfwMMp4PQb"
      },
      "source": [
        "Define a Model-Building Function -  clean colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KvuCTQUyj06W",
        "outputId": "a00e8154-9663-4327-84f7-66e058575331"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.19.0\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "print(\"TensorFlow version:\", tf.__version__)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cz-p6ElOj42u",
        "outputId": "3e78047d-5153-4018-c3da-837caefad693"
      },
      "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[1m4s\u001b[0m 0us/step\n",
            "Training data shape: (50000, 32, 32, 3)\n",
            "Testing data shape: (10000, 32, 32, 3)\n"
          ]
        }
      ],
      "source": [
        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "x_train = x_train.astype(\"float32\") / 255.0\n",
        "x_test = x_test.astype(\"float32\") / 255.0\n",
        "\n",
        "y_train = y_train.flatten()\n",
        "y_test = y_test.flatten()\n",
        "\n",
        "class_names = ['airplane','automobile','bird','cat','deer',\n",
        "               'dog','frog','horse','ship','truck']\n",
        "\n",
        "print(\"Training data shape:\", x_train.shape)\n",
        "print(\"Testing data shape:\", x_test.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "J9uPjWj5kA55",
        "outputId": "5414d146-55f3-4b3f-ea22-4567e88b441a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 25 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.xlabel(class_names[y_train[i]])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5R9cB40SkIO9"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=10,\n",
        "    width_shift_range=0.1,\n",
        "    height_shift_range=0.1,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "\n",
        "datagen.fit(x_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1xRu8jCMkSg3"
      },
      "outputs": [],
      "source": [
        "def build_optimised_cnn(dropout_rate=0.5, filters_block1=32, filters_block2=64, filters_block3=128):\n",
        "    model = models.Sequential([\n",
        "\n",
        "        # Block 1\n",
        "        layers.Conv2D(filters_block1, (3,3), padding='same', activation='relu', input_shape=(32,32,3)),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(filters_block1, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 2\n",
        "        layers.Conv2D(filters_block2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(filters_block2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 3\n",
        "        layers.Conv2D(filters_block3, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Dense layers\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dropout(dropout_rate),\n",
        "        layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "    return model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fXxJZFuhkVTz",
        "outputId": "0b69d5f6-dfb4-48f4-e533-45657591e093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 - 46s - 58ms/step - accuracy: 0.3633 - loss: 1.7457 - val_accuracy: 0.4505 - val_loss: 1.5763\n",
            "Epoch 2/25\n",
            "782/782 - 31s - 39ms/step - accuracy: 0.5128 - loss: 1.3525 - val_accuracy: 0.5819 - val_loss: 1.2392\n",
            "Epoch 3/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.5962 - loss: 1.1572 - val_accuracy: 0.6089 - val_loss: 1.1522\n",
            "Epoch 4/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.6491 - loss: 1.0113 - val_accuracy: 0.7050 - val_loss: 0.8890\n",
            "Epoch 5/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.6835 - loss: 0.9256 - val_accuracy: 0.6628 - val_loss: 1.0753\n",
            "Epoch 6/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7088 - loss: 0.8582 - val_accuracy: 0.7545 - val_loss: 0.7333\n",
            "Epoch 7/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7285 - loss: 0.8067 - val_accuracy: 0.7559 - val_loss: 0.7193\n",
            "Epoch 8/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7461 - loss: 0.7660 - val_accuracy: 0.7370 - val_loss: 0.7839\n",
            "Epoch 9/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7590 - loss: 0.7262 - val_accuracy: 0.7840 - val_loss: 0.6449\n",
            "Epoch 10/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7674 - loss: 0.6946 - val_accuracy: 0.7794 - val_loss: 0.6608\n",
            "Epoch 11/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7758 - loss: 0.6712 - val_accuracy: 0.7713 - val_loss: 0.6962\n",
            "Epoch 12/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7875 - loss: 0.6397 - val_accuracy: 0.8038 - val_loss: 0.5850\n",
            "Epoch 13/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7907 - loss: 0.6239 - val_accuracy: 0.8058 - val_loss: 0.6123\n",
            "Epoch 14/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7995 - loss: 0.6029 - val_accuracy: 0.8190 - val_loss: 0.5426\n",
            "Epoch 15/25\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8077 - loss: 0.5823 - val_accuracy: 0.7988 - val_loss: 0.6126\n",
            "Epoch 16/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8129 - loss: 0.5625 - val_accuracy: 0.8114 - val_loss: 0.5617\n",
            "Epoch 17/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8154 - loss: 0.5578 - val_accuracy: 0.8075 - val_loss: 0.5821\n",
            "Epoch 18/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8187 - loss: 0.5452 - val_accuracy: 0.7930 - val_loss: 0.6276\n",
            "Epoch 19/25\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8240 - loss: 0.5287 - val_accuracy: 0.8004 - val_loss: 0.5862\n",
            "Epoch 20/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8296 - loss: 0.5123 - val_accuracy: 0.8197 - val_loss: 0.5442\n",
            "Epoch 21/25\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8322 - loss: 0.5082 - val_accuracy: 0.8281 - val_loss: 0.5325\n",
            "Epoch 22/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8340 - loss: 0.4995 - val_accuracy: 0.8216 - val_loss: 0.5538\n",
            "Epoch 23/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8355 - loss: 0.4902 - val_accuracy: 0.8337 - val_loss: 0.5175\n",
            "Epoch 24/25\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8395 - loss: 0.4800 - val_accuracy: 0.8270 - val_loss: 0.5309\n",
            "Epoch 25/25\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8419 - loss: 0.4731 - val_accuracy: 0.8359 - val_loss: 0.5014\n"
          ]
        }
      ],
      "source": [
        "model = build_optimised_cnn(dropout_rate=0.5)\n",
        "\n",
        "history = model.fit(\n",
        "    datagen.flow(x_train, y_train, batch_size=64),\n",
        "    epochs=25,\n",
        "    validation_data=(x_test, y_test),\n",
        "    verbose=2\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oOhU7aCHCMV",
        "outputId": "5540d034-20a2-404f-f3d7-ee2931bd8a15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 1s - 3ms/step - accuracy: 0.8359 - loss: 0.5014\n",
            "Baseline Model Test Accuracy: 83.59000086784363\n"
          ]
        }
      ],
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)\n",
        "print(\"Baseline Model Test Accuracy:\", test_acc * 100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "uMkkzpb_HQP7",
        "outputId": "d2f181cd-8d0c-4b7f-f14a-854e5b32e2e5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Val Accuracy')\n",
        "plt.title('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Val Loss')\n",
        "plt.title('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "gZyIlkZFHVsn",
        "outputId": "a521319c-c870-49d1-c35a-fdf0c42647cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.81      0.89      0.85      1000\n",
            "  automobile       0.89      0.96      0.92      1000\n",
            "        bird       0.81      0.71      0.75      1000\n",
            "         cat       0.72      0.70      0.71      1000\n",
            "        deer       0.82      0.82      0.82      1000\n",
            "         dog       0.81      0.72      0.76      1000\n",
            "        frog       0.86      0.89      0.87      1000\n",
            "       horse       0.78      0.91      0.84      1000\n",
            "        ship       0.92      0.91      0.91      1000\n",
            "       truck       0.94      0.86      0.90      1000\n",
            "\n",
            "    accuracy                           0.84     10000\n",
            "   macro avg       0.84      0.84      0.83     10000\n",
            "weighted avg       0.84      0.84      0.83     10000\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "y_pred = np.argmax(model.predict(x_test), axis=1)\n",
        "\n",
        "print(classification_report(y_test, y_pred, target_names=class_names))\n",
        "\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('True')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uJSzo_f6Hd6Z"
      },
      "outputs": [],
      "source": [
        "experiment_configs = [\n",
        "    {\"name\": \"A_default\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"B_dropout_0.3\", \"dropout\": 0.3, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"C_filters_high\", \"dropout\": 0.5, \"f1\": 64, \"f2\": 128, \"f3\": 256, \"batch\": 64},\n",
        "    {\"name\": \"D_batch_128\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 128},\n",
        "]\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ww_fJ7M1Hj1I",
        "outputId": "4d02651b-549d-465f-c018-3110eb9c43a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===== Running: A_default =====\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n",
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "782/782 - 42s - 54ms/step - accuracy: 0.3529 - loss: 1.7731 - val_accuracy: 0.4805 - val_loss: 1.4787\n",
            "Epoch 2/20\n",
            "782/782 - 31s - 39ms/step - accuracy: 0.5028 - loss: 1.3764 - val_accuracy: 0.5902 - val_loss: 1.2110\n",
            "Epoch 3/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.5835 - loss: 1.1771 - val_accuracy: 0.6783 - val_loss: 0.9072\n",
            "Epoch 4/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.6353 - loss: 1.0507 - val_accuracy: 0.7084 - val_loss: 0.8528\n",
            "Epoch 5/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.6745 - loss: 0.9515 - val_accuracy: 0.6888 - val_loss: 0.9602\n",
            "Epoch 6/20\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.6987 - loss: 0.8816 - val_accuracy: 0.6990 - val_loss: 0.9194\n",
            "Epoch 7/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7205 - loss: 0.8281 - val_accuracy: 0.6585 - val_loss: 1.1129\n",
            "Epoch 8/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7381 - loss: 0.7755 - val_accuracy: 0.6537 - val_loss: 1.1337\n",
            "Epoch 9/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.7528 - loss: 0.7358 - val_accuracy: 0.7502 - val_loss: 0.7427\n",
            "Epoch 10/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7639 - loss: 0.7068 - val_accuracy: 0.7554 - val_loss: 0.7435\n",
            "Epoch 11/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7758 - loss: 0.6744 - val_accuracy: 0.7659 - val_loss: 0.7009\n",
            "Epoch 12/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7802 - loss: 0.6518 - val_accuracy: 0.7800 - val_loss: 0.6536\n",
            "Epoch 13/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7900 - loss: 0.6287 - val_accuracy: 0.7645 - val_loss: 0.7666\n",
            "Epoch 14/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7932 - loss: 0.6146 - val_accuracy: 0.7420 - val_loss: 0.8421\n",
            "Epoch 15/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8010 - loss: 0.5928 - val_accuracy: 0.7826 - val_loss: 0.6749\n",
            "Epoch 16/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8069 - loss: 0.5783 - val_accuracy: 0.7582 - val_loss: 0.8199\n",
            "Epoch 17/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8153 - loss: 0.5555 - val_accuracy: 0.7994 - val_loss: 0.6270\n",
            "Epoch 18/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8162 - loss: 0.5459 - val_accuracy: 0.7835 - val_loss: 0.7084\n",
            "Epoch 19/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8205 - loss: 0.5353 - val_accuracy: 0.8219 - val_loss: 0.5575\n",
            "Epoch 20/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8228 - loss: 0.5252 - val_accuracy: 0.8233 - val_loss: 0.5438\n",
            "A_default Accuracy: 82.33%\n",
            "\n",
            "===== Running: B_dropout_0.3 =====\n",
            "Epoch 1/20\n",
            "782/782 - 47s - 59ms/step - accuracy: 0.4420 - loss: 1.5594 - val_accuracy: 0.5076 - val_loss: 1.4213\n",
            "Epoch 2/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.5924 - loss: 1.1527 - val_accuracy: 0.6406 - val_loss: 1.0438\n",
            "Epoch 3/20\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.6571 - loss: 0.9819 - val_accuracy: 0.6463 - val_loss: 1.0236\n",
            "Epoch 4/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.6954 - loss: 0.8797 - val_accuracy: 0.7261 - val_loss: 0.8087\n",
            "Epoch 5/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.7188 - loss: 0.8114 - val_accuracy: 0.7345 - val_loss: 0.7721\n",
            "Epoch 6/20\n",
            "782/782 - 40s - 51ms/step - accuracy: 0.7430 - loss: 0.7501 - val_accuracy: 0.7527 - val_loss: 0.7434\n",
            "Epoch 7/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7570 - loss: 0.7097 - val_accuracy: 0.7620 - val_loss: 0.7049\n",
            "Epoch 8/20\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.7736 - loss: 0.6657 - val_accuracy: 0.7543 - val_loss: 0.7507\n",
            "Epoch 9/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.7829 - loss: 0.6381 - val_accuracy: 0.7821 - val_loss: 0.6335\n",
            "Epoch 10/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.7925 - loss: 0.6121 - val_accuracy: 0.7896 - val_loss: 0.6320\n",
            "Epoch 11/20\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8033 - loss: 0.5853 - val_accuracy: 0.8146 - val_loss: 0.5431\n",
            "Epoch 12/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8081 - loss: 0.5667 - val_accuracy: 0.7655 - val_loss: 0.7370\n",
            "Epoch 13/20\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8194 - loss: 0.5379 - val_accuracy: 0.8032 - val_loss: 0.6076\n",
            "Epoch 14/20\n",
            "782/782 - 31s - 39ms/step - accuracy: 0.8185 - loss: 0.5330 - val_accuracy: 0.8023 - val_loss: 0.6210\n",
            "Epoch 15/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8246 - loss: 0.5158 - val_accuracy: 0.7937 - val_loss: 0.6384\n",
            "Epoch 16/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8304 - loss: 0.5038 - val_accuracy: 0.8230 - val_loss: 0.5423\n",
            "Epoch 17/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8371 - loss: 0.4861 - val_accuracy: 0.8026 - val_loss: 0.6222\n",
            "Epoch 18/20\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8383 - loss: 0.4799 - val_accuracy: 0.8151 - val_loss: 0.5656\n",
            "Epoch 19/20\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8434 - loss: 0.4620 - val_accuracy: 0.7963 - val_loss: 0.6439\n",
            "Epoch 20/20\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8454 - loss: 0.4569 - val_accuracy: 0.8319 - val_loss: 0.5202\n",
            "B_dropout_0.3 Accuracy: 83.19%\n",
            "\n",
            "===== Running: C_filters_high =====\n",
            "Epoch 1/20\n",
            "782/782 - 48s - 62ms/step - accuracy: 0.3248 - loss: 1.8307 - val_accuracy: 0.4428 - val_loss: 1.5433\n",
            "Epoch 2/20\n",
            "782/782 - 33s - 42ms/step - accuracy: 0.4886 - loss: 1.4060 - val_accuracy: 0.5793 - val_loss: 1.1819\n",
            "Epoch 3/20\n",
            "782/782 - 33s - 43ms/step - accuracy: 0.5834 - loss: 1.1800 - val_accuracy: 0.6532 - val_loss: 1.0005\n",
            "Epoch 4/20\n",
            "782/782 - 32s - 41ms/step - accuracy: 0.6400 - loss: 1.0427 - val_accuracy: 0.6699 - val_loss: 1.0103\n",
            "Epoch 5/20\n",
            "782/782 - 33s - 42ms/step - accuracy: 0.6848 - loss: 0.9268 - val_accuracy: 0.6698 - val_loss: 1.0896\n",
            "Epoch 6/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.7131 - loss: 0.8551 - val_accuracy: 0.7211 - val_loss: 0.8622\n",
            "Epoch 7/20\n",
            "782/782 - 32s - 40ms/step - accuracy: 0.7409 - loss: 0.7801 - val_accuracy: 0.7771 - val_loss: 0.6880\n",
            "Epoch 8/20\n",
            "782/782 - 32s - 41ms/step - accuracy: 0.7552 - loss: 0.7398 - val_accuracy: 0.7711 - val_loss: 0.7005\n",
            "Epoch 9/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.7722 - loss: 0.6919 - val_accuracy: 0.7981 - val_loss: 0.6079\n",
            "Epoch 10/20\n",
            "782/782 - 33s - 42ms/step - accuracy: 0.7861 - loss: 0.6555 - val_accuracy: 0.8016 - val_loss: 0.6030\n",
            "Epoch 11/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.7976 - loss: 0.6193 - val_accuracy: 0.8183 - val_loss: 0.5480\n",
            "Epoch 12/20\n",
            "782/782 - 32s - 40ms/step - accuracy: 0.8065 - loss: 0.5846 - val_accuracy: 0.7787 - val_loss: 0.7562\n",
            "Epoch 13/20\n",
            "782/782 - 32s - 41ms/step - accuracy: 0.8134 - loss: 0.5669 - val_accuracy: 0.8168 - val_loss: 0.5693\n",
            "Epoch 14/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.8233 - loss: 0.5381 - val_accuracy: 0.7852 - val_loss: 0.7024\n",
            "Epoch 15/20\n",
            "782/782 - 32s - 41ms/step - accuracy: 0.8284 - loss: 0.5275 - val_accuracy: 0.7907 - val_loss: 0.7142\n",
            "Epoch 16/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.8369 - loss: 0.5028 - val_accuracy: 0.8094 - val_loss: 0.6106\n",
            "Epoch 17/20\n",
            "782/782 - 33s - 43ms/step - accuracy: 0.8419 - loss: 0.4849 - val_accuracy: 0.8192 - val_loss: 0.6194\n",
            "Epoch 18/20\n",
            "782/782 - 32s - 40ms/step - accuracy: 0.8454 - loss: 0.4728 - val_accuracy: 0.8134 - val_loss: 0.5988\n",
            "Epoch 19/20\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.8503 - loss: 0.4586 - val_accuracy: 0.8411 - val_loss: 0.4860\n",
            "Epoch 20/20\n",
            "782/782 - 32s - 41ms/step - accuracy: 0.8548 - loss: 0.4455 - val_accuracy: 0.8533 - val_loss: 0.4642\n",
            "C_filters_high Accuracy: 85.33%\n",
            "\n",
            "===== Running: D_batch_128 =====\n",
            "Epoch 1/20\n",
            "391/391 - 43s - 109ms/step - accuracy: 0.3705 - loss: 1.7446 - val_accuracy: 0.2201 - val_loss: 2.3341\n",
            "Epoch 2/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.5104 - loss: 1.3647 - val_accuracy: 0.5968 - val_loss: 1.1270\n",
            "Epoch 3/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.5914 - loss: 1.1615 - val_accuracy: 0.6388 - val_loss: 1.0886\n",
            "Epoch 4/20\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.6391 - loss: 1.0370 - val_accuracy: 0.6426 - val_loss: 1.0798\n",
            "Epoch 5/20\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.6741 - loss: 0.9440 - val_accuracy: 0.6853 - val_loss: 0.9158\n",
            "Epoch 6/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.7023 - loss: 0.8715 - val_accuracy: 0.7253 - val_loss: 0.7972\n",
            "Epoch 7/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.7224 - loss: 0.8107 - val_accuracy: 0.7553 - val_loss: 0.7542\n",
            "Epoch 8/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.7383 - loss: 0.7744 - val_accuracy: 0.7381 - val_loss: 0.8127\n",
            "Epoch 9/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.7552 - loss: 0.7308 - val_accuracy: 0.7264 - val_loss: 0.8348\n",
            "Epoch 10/20\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.7646 - loss: 0.6991 - val_accuracy: 0.7785 - val_loss: 0.6617\n",
            "Epoch 11/20\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.7770 - loss: 0.6675 - val_accuracy: 0.7889 - val_loss: 0.6240\n",
            "Epoch 12/20\n",
            "391/391 - 41s - 104ms/step - accuracy: 0.7831 - loss: 0.6478 - val_accuracy: 0.7681 - val_loss: 0.7132\n",
            "Epoch 13/20\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.7917 - loss: 0.6212 - val_accuracy: 0.7732 - val_loss: 0.6951\n",
            "Epoch 14/20\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.7959 - loss: 0.6091 - val_accuracy: 0.7736 - val_loss: 0.6954\n",
            "Epoch 15/20\n",
            "391/391 - 41s - 105ms/step - accuracy: 0.8036 - loss: 0.5857 - val_accuracy: 0.7601 - val_loss: 0.7885\n",
            "Epoch 16/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.8091 - loss: 0.5700 - val_accuracy: 0.8121 - val_loss: 0.5832\n",
            "Epoch 17/20\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.8174 - loss: 0.5572 - val_accuracy: 0.8147 - val_loss: 0.5781\n",
            "Epoch 18/20\n",
            "391/391 - 29s - 74ms/step - accuracy: 0.8213 - loss: 0.5378 - val_accuracy: 0.8191 - val_loss: 0.5503\n",
            "Epoch 19/20\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.8212 - loss: 0.5326 - val_accuracy: 0.8221 - val_loss: 0.5398\n",
            "Epoch 20/20\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.8277 - loss: 0.5141 - val_accuracy: 0.8143 - val_loss: 0.6007\n",
            "D_batch_128 Accuracy: 81.43%\n"
          ]
        }
      ],
      "source": [
        "def build_optimised_cnn(dropout_rate=0.5, filters_block1=32, filters_block2=64, filters_block3=128):\n",
        "    model = models.Sequential([\n",
        "\n",
        "        # Block 1\n",
        "        layers.Conv2D(filters_block1, (3,3), padding='same', activation='relu', input_shape=(32,32,3)),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(filters_block1, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 2\n",
        "        layers.Conv2D(filters_block2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(filters_block2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 3\n",
        "        layers.Conv2D(filters_block3, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Dense layers\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dropout(dropout_rate),\n",
        "        layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "    return model\n",
        "\n",
        "experiment_configs = [\n",
        "    {\"name\": \"A_default\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"B_dropout_0.3\", \"dropout\": 0.3, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"C_filters_high\", \"dropout\": 0.5, \"f1\": 64, \"f2\": 128, \"f3\": 256, \"batch\": 64},\n",
        "    {\"name\": \"D_batch_128\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 128},\n",
        "]\n",
        "\n",
        "experiment_results = []\n",
        "EPOCHS = 20\n",
        "\n",
        "for cfg in experiment_configs:\n",
        "    print(f\"\\n===== Running: {cfg['name']} =====\")\n",
        "\n",
        "    model = build_optimised_cnn(\n",
        "        dropout_rate=cfg[\"dropout\"],\n",
        "        filters_block1=cfg[\"f1\"],\n",
        "        filters_block2=cfg[\"f2\"],\n",
        "        filters_block3=cfg[\"f3\"]\n",
        "    )\n",
        "\n",
        "    history = model.fit(\n",
        "        datagen.flow(x_train, y_train, batch_size=cfg[\"batch\"]),\n",
        "        epochs=EPOCHS,\n",
        "        validation_data=(x_test, y_test),\n",
        "        verbose=2\n",
        "    )\n",
        "\n",
        "    test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
        "    print(f\"{cfg['name']} Accuracy: {test_acc*100:.2f}%\")\n",
        "\n",
        "    experiment_results.append({\n",
        "        \"name\": cfg[\"name\"],\n",
        "        \"history\": history.history,\n",
        "        \"test_acc\": test_acc,\n",
        "        \"test_loss\": test_loss,\n",
        "        \"dropout\": cfg[\"dropout\"],\n",
        "        \"filters\": (cfg[\"f1\"], cfg[\"f2\"], cfg[\"f3\"]),\n",
        "        \"batch\": cfg[\"batch\"]\n",
        "    })\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cell 12 – Summary of tuning\n",
        "print(\"\\n=== Experiment Accuracy Summary ===\")\n",
        "for r in experiment_results:\n",
        "    print(f\"{r['name']}: {r['test_acc']*100:.2f}%\")\n"
      ],
      "metadata": {
        "id": "yUd4ydX6lX1I",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52d363e2-5511-4fd7-a9e5-a56af6e9c259"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "=== Experiment Accuracy Summary ===\n",
            "A_default: 82.33%\n",
            "B_dropout_0.3: 83.19%\n",
            "C_filters_high: 85.33%\n",
            "D_batch_128: 81.43%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Repeat test for increased accuracy"
      ],
      "metadata": {
        "id": "sce3CJIIoWe7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "experiment_configs = [\n",
        "    {\"name\": \"A_default_revised\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"B_lower_dropout\", \"dropout\": 0.3, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 64},\n",
        "    {\"name\": \"C_filters_boost\", \"dropout\": 0.5, \"f1\": 64, \"f2\": 128, \"f3\": 384, \"batch\": 64},\n",
        "    {\"name\": \"D_batch_128_revised\", \"dropout\": 0.5, \"f1\": 32, \"f2\": 64, \"f3\": 128, \"batch\": 128},\n",
        "]"
      ],
      "metadata": {
        "id": "pGjg9bqmpvNJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
        "\n",
        "callbacks = [\n",
        "    EarlyStopping(monitor='val_accuracy', patience=5, restore_best_weights=True),\n",
        "    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3)\n",
        "]\n",
        "\n",
        "history = model.fit(\n",
        "    datagen.flow(x_train, y_train, batch_size=cfg[\"batch\"]),\n",
        "    epochs=35,   # increased for augmentation\n",
        "    validation_data=(x_test, y_test),\n",
        "    verbose=2,\n",
        "    callbacks=callbacks\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuhECKeCocCm",
        "outputId": "0bdf7b11-acf3-47eb-fe00-8c4277c1d3d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/35\n",
            "391/391 - 29s - 75ms/step - accuracy: 0.8788 - loss: 0.3534 - val_accuracy: 0.8639 - val_loss: 0.4209 - learning_rate: 2.5000e-04\n",
            "Epoch 2/35\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.8816 - loss: 0.3472 - val_accuracy: 0.8560 - val_loss: 0.4417 - learning_rate: 2.5000e-04\n",
            "Epoch 3/35\n",
            "391/391 - 28s - 71ms/step - accuracy: 0.8827 - loss: 0.3436 - val_accuracy: 0.8659 - val_loss: 0.4138 - learning_rate: 2.5000e-04\n",
            "Epoch 4/35\n",
            "391/391 - 29s - 75ms/step - accuracy: 0.8854 - loss: 0.3396 - val_accuracy: 0.8682 - val_loss: 0.3986 - learning_rate: 2.5000e-04\n",
            "Epoch 5/35\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.8847 - loss: 0.3395 - val_accuracy: 0.8635 - val_loss: 0.4328 - learning_rate: 2.5000e-04\n",
            "Epoch 6/35\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.8855 - loss: 0.3336 - val_accuracy: 0.8535 - val_loss: 0.4577 - learning_rate: 2.5000e-04\n",
            "Epoch 7/35\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.8875 - loss: 0.3336 - val_accuracy: 0.8637 - val_loss: 0.4289 - learning_rate: 2.5000e-04\n",
            "Epoch 8/35\n",
            "391/391 - 29s - 73ms/step - accuracy: 0.8909 - loss: 0.3214 - val_accuracy: 0.8653 - val_loss: 0.4255 - learning_rate: 1.2500e-04\n",
            "Epoch 9/35\n",
            "391/391 - 28s - 72ms/step - accuracy: 0.8927 - loss: 0.3154 - val_accuracy: 0.8651 - val_loss: 0.4342 - learning_rate: 1.2500e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_lr = 0.0001  # instead of 0.001 or 0.00025\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate=initial_lr)\n"
      ],
      "metadata": {
        "id": "-_MbjJ7gryGp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(\n",
        "    optimizer=optimizer,\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "lCr119Onr0d7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "EPOCHS = 40\n"
      ],
      "metadata": {
        "id": "po9AmHrVr4TQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Block3: 256\n"
      ],
      "metadata": {
        "id": "gNgjMIHHr8RT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "384 or 512"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NR7Jdk6MsA2Z",
        "outputId": "fae2a5e2-6434-4225-9c63-65c4f45e39d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "384"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filters_block3=512\n"
      ],
      "metadata": {
        "id": "o534GP8UsCmO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "experiment_configs = [\n",
        "    {\"name\": \"C_filters_boost_v2\", \"dropout\": 0.4, \"f1\": 64, \"f2\": 128, \"f3\": 512, \"batch\": 64},\n",
        "]\n"
      ],
      "metadata": {
        "id": "cT1X7rvBsK3A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "    EarlyStopping(monitor='val_accuracy', patience=8, restore_best_weights=True),\n",
        "    ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4)\n",
        "]\n"
      ],
      "metadata": {
        "id": "0jff1_X3sQom"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    datagen.flow(x_train, y_train, batch_size=64),\n",
        "    epochs=40,\n",
        "    validation_data=(x_test, y_test),\n",
        "    callbacks=callbacks,\n",
        "    verbose=2\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_sFJzkZsSvq",
        "outputId": "cc5ac5f7-8db8-4746-9481-a86d58970986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 - 43s - 55ms/step - accuracy: 0.8837 - loss: 0.3462 - val_accuracy: 0.8648 - val_loss: 0.4249 - learning_rate: 1.0000e-04\n",
            "Epoch 2/40\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8829 - loss: 0.3430 - val_accuracy: 0.8651 - val_loss: 0.4201 - learning_rate: 1.0000e-04\n",
            "Epoch 3/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8862 - loss: 0.3390 - val_accuracy: 0.8634 - val_loss: 0.4123 - learning_rate: 1.0000e-04\n",
            "Epoch 4/40\n",
            "782/782 - 31s - 39ms/step - accuracy: 0.8865 - loss: 0.3320 - val_accuracy: 0.8646 - val_loss: 0.4145 - learning_rate: 1.0000e-04\n",
            "Epoch 5/40\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.8872 - loss: 0.3310 - val_accuracy: 0.8619 - val_loss: 0.4318 - learning_rate: 1.0000e-04\n",
            "Epoch 6/40\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8895 - loss: 0.3290 - val_accuracy: 0.8630 - val_loss: 0.4247 - learning_rate: 1.0000e-04\n",
            "Epoch 7/40\n",
            "782/782 - 31s - 40ms/step - accuracy: 0.8874 - loss: 0.3302 - val_accuracy: 0.8666 - val_loss: 0.4091 - learning_rate: 1.0000e-04\n",
            "Epoch 8/40\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8881 - loss: 0.3296 - val_accuracy: 0.8676 - val_loss: 0.4049 - learning_rate: 1.0000e-04\n",
            "Epoch 9/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8896 - loss: 0.3269 - val_accuracy: 0.8663 - val_loss: 0.4235 - learning_rate: 1.0000e-04\n",
            "Epoch 10/40\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8909 - loss: 0.3234 - val_accuracy: 0.8680 - val_loss: 0.4165 - learning_rate: 1.0000e-04\n",
            "Epoch 11/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8903 - loss: 0.3250 - val_accuracy: 0.8689 - val_loss: 0.4071 - learning_rate: 1.0000e-04\n",
            "Epoch 12/40\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8921 - loss: 0.3228 - val_accuracy: 0.8689 - val_loss: 0.4052 - learning_rate: 1.0000e-04\n",
            "Epoch 13/40\n",
            "782/782 - 29s - 37ms/step - accuracy: 0.8933 - loss: 0.3150 - val_accuracy: 0.8727 - val_loss: 0.3971 - learning_rate: 5.0000e-05\n",
            "Epoch 14/40\n",
            "782/782 - 30s - 39ms/step - accuracy: 0.8940 - loss: 0.3125 - val_accuracy: 0.8677 - val_loss: 0.4071 - learning_rate: 5.0000e-05\n",
            "Epoch 15/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8955 - loss: 0.3107 - val_accuracy: 0.8704 - val_loss: 0.4049 - learning_rate: 5.0000e-05\n",
            "Epoch 16/40\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8941 - loss: 0.3088 - val_accuracy: 0.8699 - val_loss: 0.4051 - learning_rate: 5.0000e-05\n",
            "Epoch 17/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8948 - loss: 0.3062 - val_accuracy: 0.8689 - val_loss: 0.4131 - learning_rate: 5.0000e-05\n",
            "Epoch 18/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8953 - loss: 0.3058 - val_accuracy: 0.8700 - val_loss: 0.4086 - learning_rate: 2.5000e-05\n",
            "Epoch 19/40\n",
            "782/782 - 30s - 38ms/step - accuracy: 0.8955 - loss: 0.3034 - val_accuracy: 0.8693 - val_loss: 0.4123 - learning_rate: 2.5000e-05\n",
            "Epoch 20/40\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8966 - loss: 0.3019 - val_accuracy: 0.8693 - val_loss: 0.4071 - learning_rate: 2.5000e-05\n",
            "Epoch 21/40\n",
            "782/782 - 29s - 38ms/step - accuracy: 0.8952 - loss: 0.3053 - val_accuracy: 0.8722 - val_loss: 0.4034 - learning_rate: 2.5000e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "round 4 attempted.\n"
      ],
      "metadata": {
        "id": "oPuP5_lk3EaZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "layers.Dense(256, activation='relu'),\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cn6-HxAH3POa",
        "outputId": "e0393c26-707d-4705-ef5e-bbdd558e4ebb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(<Dense name=dense_11, built=False>,)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ImageDataGenerator(\n",
        "    horizontal_flip=True,\n",
        "    width_shift_range=0.05,\n",
        "    height_shift_range=0.05,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2-5aL0Js3Uy7",
        "outputId": "84ccf54a-078e-4396-ebae-9896d918169c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.legacy.preprocessing.image.ImageDataGenerator at 0x79433012fdd0>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "layers.Dense(256, activation='relu'),\n",
        "layers.Dense(128, activation='relu'),  # NEW\n",
        "layers.Dropout(dropout_rate),\n",
        "layers.Dense(10, activation='softmax')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o2HTF7163rww",
        "outputId": "1a7df9d1-fdcd-43b3-b3ca-f445fd6713de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Dense name=dense_22, built=False>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f9f2963d"
      },
      "source": [
        "dropout_rate = 0.5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 32\n"
      ],
      "metadata": {
        "id": "nJS7RHKh4x7w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_optimised_cnn(dropout_rate=0.4, f1=64, f2=128, f3=512):\n",
        "\n",
        "    model = models.Sequential([\n",
        "\n",
        "        # Block 1\n",
        "        layers.Conv2D(f1, (3,3), padding='same', activation='relu', input_shape=(32,32,3)),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(f1, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 2\n",
        "        layers.Conv2D(f2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.Conv2D(f2, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Block 3\n",
        "        layers.Conv2D(f3, (3,3), padding='same', activation='relu'),\n",
        "        layers.BatchNormalization(),\n",
        "        layers.MaxPooling2D((2,2)),\n",
        "\n",
        "        # Dense layers\n",
        "        layers.Flatten(),\n",
        "        layers.Dense(256, activation='relu'),\n",
        "        layers.Dense(128, activation='relu'),\n",
        "        layers.Dropout(dropout_rate),\n",
        "        layers.Dense(10, activation='softmax')\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
        "        loss='sparse_categorical_crossentropy',\n",
        "        metrics=['accuracy']\n",
        "    )\n",
        "    return model\n"
      ],
      "metadata": {
        "id": "FJ4jKgwe44mT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = ImageDataGenerator(\n",
        "    horizontal_flip=True,\n",
        "    width_shift_range=0.05,\n",
        "    height_shift_range=0.05\n",
        ")\n",
        "\n",
        "datagen.fit(x_train)\n"
      ],
      "metadata": {
        "id": "u_3rhNbm49Fg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    datagen.flow(x_train, y_train, batch_size=32),\n",
        "    epochs=40,\n",
        "    validation_data=(x_test, y_test),\n",
        "    callbacks=callbacks,\n",
        "    verbose=2\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSE-R73U5A7d",
        "outputId": "24842963-7069-4caf-b66a-ababb91d53da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/40\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
            "  self._warn_if_super_not_called()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1563/1563 - 36s - 23ms/step - accuracy: 0.9038 - loss: 0.2792 - val_accuracy: 0.8730 - val_loss: 0.3911 - learning_rate: 1.2500e-05\n",
            "Epoch 2/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9036 - loss: 0.2802 - val_accuracy: 0.8727 - val_loss: 0.3954 - learning_rate: 1.2500e-05\n",
            "Epoch 3/40\n",
            "1563/1563 - 34s - 22ms/step - accuracy: 0.9058 - loss: 0.2740 - val_accuracy: 0.8714 - val_loss: 0.4001 - learning_rate: 1.2500e-05\n",
            "Epoch 4/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9046 - loss: 0.2770 - val_accuracy: 0.8719 - val_loss: 0.3938 - learning_rate: 1.2500e-05\n",
            "Epoch 5/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9077 - loss: 0.2707 - val_accuracy: 0.8710 - val_loss: 0.3974 - learning_rate: 1.2500e-05\n",
            "Epoch 6/40\n",
            "1563/1563 - 32s - 20ms/step - accuracy: 0.9057 - loss: 0.2727 - val_accuracy: 0.8732 - val_loss: 0.3924 - learning_rate: 6.2500e-06\n",
            "Epoch 7/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9063 - loss: 0.2732 - val_accuracy: 0.8742 - val_loss: 0.3881 - learning_rate: 6.2500e-06\n",
            "Epoch 8/40\n",
            "1563/1563 - 34s - 22ms/step - accuracy: 0.9087 - loss: 0.2681 - val_accuracy: 0.8741 - val_loss: 0.3912 - learning_rate: 6.2500e-06\n",
            "Epoch 9/40\n",
            "1563/1563 - 33s - 21ms/step - accuracy: 0.9068 - loss: 0.2718 - val_accuracy: 0.8745 - val_loss: 0.3890 - learning_rate: 6.2500e-06\n",
            "Epoch 10/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9072 - loss: 0.2701 - val_accuracy: 0.8753 - val_loss: 0.3876 - learning_rate: 6.2500e-06\n",
            "Epoch 11/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9070 - loss: 0.2709 - val_accuracy: 0.8740 - val_loss: 0.3888 - learning_rate: 6.2500e-06\n",
            "Epoch 12/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9063 - loss: 0.2694 - val_accuracy: 0.8749 - val_loss: 0.3876 - learning_rate: 6.2500e-06\n",
            "Epoch 13/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9083 - loss: 0.2685 - val_accuracy: 0.8738 - val_loss: 0.3881 - learning_rate: 6.2500e-06\n",
            "Epoch 14/40\n",
            "1563/1563 - 32s - 20ms/step - accuracy: 0.9074 - loss: 0.2721 - val_accuracy: 0.8748 - val_loss: 0.3886 - learning_rate: 6.2500e-06\n",
            "Epoch 15/40\n",
            "1563/1563 - 30s - 19ms/step - accuracy: 0.9083 - loss: 0.2688 - val_accuracy: 0.8745 - val_loss: 0.3907 - learning_rate: 3.1250e-06\n",
            "Epoch 16/40\n",
            "1563/1563 - 30s - 19ms/step - accuracy: 0.9072 - loss: 0.2703 - val_accuracy: 0.8755 - val_loss: 0.3864 - learning_rate: 3.1250e-06\n",
            "Epoch 17/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9090 - loss: 0.2652 - val_accuracy: 0.8751 - val_loss: 0.3880 - learning_rate: 3.1250e-06\n",
            "Epoch 18/40\n",
            "1563/1563 - 30s - 19ms/step - accuracy: 0.9075 - loss: 0.2708 - val_accuracy: 0.8767 - val_loss: 0.3844 - learning_rate: 3.1250e-06\n",
            "Epoch 19/40\n",
            "1563/1563 - 30s - 19ms/step - accuracy: 0.9088 - loss: 0.2726 - val_accuracy: 0.8752 - val_loss: 0.3886 - learning_rate: 3.1250e-06\n",
            "Epoch 20/40\n",
            "1563/1563 - 32s - 20ms/step - accuracy: 0.9092 - loss: 0.2633 - val_accuracy: 0.8746 - val_loss: 0.3870 - learning_rate: 3.1250e-06\n",
            "Epoch 21/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9077 - loss: 0.2688 - val_accuracy: 0.8759 - val_loss: 0.3871 - learning_rate: 3.1250e-06\n",
            "Epoch 22/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9086 - loss: 0.2666 - val_accuracy: 0.8761 - val_loss: 0.3869 - learning_rate: 3.1250e-06\n",
            "Epoch 23/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9083 - loss: 0.2652 - val_accuracy: 0.8755 - val_loss: 0.3890 - learning_rate: 1.5625e-06\n",
            "Epoch 24/40\n",
            "1563/1563 - 30s - 19ms/step - accuracy: 0.9086 - loss: 0.2644 - val_accuracy: 0.8751 - val_loss: 0.3891 - learning_rate: 1.5625e-06\n",
            "Epoch 25/40\n",
            "1563/1563 - 31s - 20ms/step - accuracy: 0.9100 - loss: 0.2643 - val_accuracy: 0.8752 - val_loss: 0.3889 - learning_rate: 1.5625e-06\n",
            "Epoch 26/40\n",
            "1563/1563 - 32s - 21ms/step - accuracy: 0.9087 - loss: 0.2633 - val_accuracy: 0.8756 - val_loss: 0.3871 - learning_rate: 1.5625e-06\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val_accuracy: 0.9000\n"
      ],
      "metadata": {
        "id": "j9r0IhTc5Fi8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
        "print(\"Final Test Accuracy:\", test_acc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s8tNqXOs-IqR",
        "outputId": "572efabe-10a6-47a1-bf22-be84b08c8ee2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.8759 - loss: 0.3790\n",
            "Final Test Accuracy: 0.8766999840736389\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}