{
  "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": {
        "id": "491FWCr7m-Ot"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train, y_train), (X_test, y_test) = datasets.cifar10.load_data()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkVwloy_nCWA",
        "outputId": "c837b4c8-21fa-46c0-98f1-6e8388878e9a"
      },
      "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[1m4s\u001b[0m 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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 = to_categorical(y_train, 10)\n",
        "y_test = to_categorical(y_test, 10)"
      ],
      "metadata": {
        "id": "kZHWzPbenCYn"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class_names = ['Airplane','Automobile','Bird','Cat','Deer','Dog','Frog','Horse','Ship','Truck']\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(16):\n",
        "    plt.subplot(4,4,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(X_train[i])\n",
        "    plt.xlabel(class_names[np.argmax(y_train[i])])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 826
        },
        "id": "WXhqCuKunCbg",
        "outputId": "f707070f-7949-4e2d-fd7e-5b8318b1d03c"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 16 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = models.Sequential()\n",
        "\n",
        "# Convolutional Block 1\n",
        "model.add(layers.Conv2D(32, (3,3), activation='relu', padding='same', input_shape=(32,32,3)))\n",
        "model.add(layers.Conv2D(32, (3,3), activation='relu', padding='same'))\n",
        "model.add(layers.MaxPooling2D((2,2)))\n",
        "model.add(layers.Dropout(0.25))\n",
        "\n",
        "# Convolutional Block 2\n",
        "model.add(layers.Conv2D(64, (3,3), activation='relu', padding='same'))\n",
        "model.add(layers.Conv2D(64, (3,3), activation='relu', padding='same'))\n",
        "model.add(layers.MaxPooling2D((2,2)))\n",
        "model.add(layers.Dropout(0.25))\n",
        "\n",
        "# Convolutional Block 3\n",
        "model.add(layers.Conv2D(128, (3,3), activation='relu', padding='same'))\n",
        "model.add(layers.Conv2D(128, (3,3), activation='relu', padding='same'))\n",
        "model.add(layers.MaxPooling2D((2,2)))\n",
        "model.add(layers.Dropout(0.25))\n",
        "\n",
        "# Flatten and Fully Connected Layers\n",
        "model.add(layers.Flatten())\n",
        "model.add(layers.Dense(512, activation='relu'))\n",
        "model.add(layers.Dropout(0.5))\n",
        "model.add(layers.Dense(10, activation='softmax'))  # Output layer for 10 classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwUSQzD8nCeA",
        "outputId": "6d4b2d86-d428-49a2-e39a-fa3a7ebb426c"
      },
      "execution_count": 5,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss='categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 680
        },
        "id": "TcIdHPL1nYLG",
        "outputId": "f1710f26-ee86-450d-da2a-6a227413f2e2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "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;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │           \u001b[38;5;34m896\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",
              "│ 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",
              "│ 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",
              "│ 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",
              "│ conv2d_5 (\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;34m147,584\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;34m512\u001b[0m)            │     \u001b[38;5;34m1,049,088\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\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;34m5,130\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\">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",
              "│ 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",
              "│ 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",
              "│ 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",
              "│ 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",
              "│ conv2d_5 (<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\">147,584</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\">512</span>)            │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,049,088</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\">512</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\">5,130</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,341,226\u001b[0m (5.12 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\">1,341,226</span> (5.12 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,341,226\u001b[0m (5.12 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\">1,341,226</span> (5.12 MB)\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train, y_train,\n",
        "                    epochs=30,\n",
        "                    batch_size=64,\n",
        "                    validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "cw8-5vWZnYNs",
        "outputId": "8a97728b-d4a9-4e2e-ab03-28078e245349"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 13ms/step - accuracy: 0.2682 - loss: 1.9515 - val_accuracy: 0.5035 - val_loss: 1.3567\n",
            "Epoch 2/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 10ms/step - accuracy: 0.5114 - loss: 1.3401 - val_accuracy: 0.5835 - val_loss: 1.1567\n",
            "Epoch 3/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.6024 - loss: 1.1149 - val_accuracy: 0.6800 - val_loss: 0.9071\n",
            "Epoch 4/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.6476 - loss: 0.9874 - val_accuracy: 0.6992 - val_loss: 0.8482\n",
            "Epoch 5/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 8ms/step - accuracy: 0.6846 - loss: 0.8898 - val_accuracy: 0.7234 - val_loss: 0.7846\n",
            "Epoch 6/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7145 - loss: 0.8111 - val_accuracy: 0.7430 - val_loss: 0.7377\n",
            "Epoch 7/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7263 - loss: 0.7731 - val_accuracy: 0.7463 - val_loss: 0.7241\n",
            "Epoch 8/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.7522 - loss: 0.7070 - val_accuracy: 0.7598 - val_loss: 0.6913\n",
            "Epoch 9/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7573 - loss: 0.6915 - val_accuracy: 0.7731 - val_loss: 0.6538\n",
            "Epoch 10/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.7733 - loss: 0.6455 - val_accuracy: 0.7754 - val_loss: 0.6418\n",
            "Epoch 11/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7764 - loss: 0.6255 - val_accuracy: 0.7828 - val_loss: 0.6376\n",
            "Epoch 12/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.7869 - loss: 0.6040 - val_accuracy: 0.7759 - val_loss: 0.6630\n",
            "Epoch 13/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.7962 - loss: 0.5769 - val_accuracy: 0.7849 - val_loss: 0.6236\n",
            "Epoch 14/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8037 - loss: 0.5585 - val_accuracy: 0.7919 - val_loss: 0.6150\n",
            "Epoch 15/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8090 - loss: 0.5333 - val_accuracy: 0.7982 - val_loss: 0.5916\n",
            "Epoch 16/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8120 - loss: 0.5325 - val_accuracy: 0.7895 - val_loss: 0.6351\n",
            "Epoch 17/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8182 - loss: 0.5167 - val_accuracy: 0.7884 - val_loss: 0.6244\n",
            "Epoch 18/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8184 - loss: 0.5127 - val_accuracy: 0.7680 - val_loss: 0.7018\n",
            "Epoch 19/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8221 - loss: 0.5047 - val_accuracy: 0.8059 - val_loss: 0.5928\n",
            "Epoch 20/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8307 - loss: 0.4823 - val_accuracy: 0.8024 - val_loss: 0.5886\n",
            "Epoch 21/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8336 - loss: 0.4719 - val_accuracy: 0.8056 - val_loss: 0.5831\n",
            "Epoch 22/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8326 - loss: 0.4743 - val_accuracy: 0.8013 - val_loss: 0.6126\n",
            "Epoch 23/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8401 - loss: 0.4514 - val_accuracy: 0.8009 - val_loss: 0.6175\n",
            "Epoch 24/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8416 - loss: 0.4516 - val_accuracy: 0.8051 - val_loss: 0.5793\n",
            "Epoch 25/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8449 - loss: 0.4389 - val_accuracy: 0.8103 - val_loss: 0.5933\n",
            "Epoch 26/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8434 - loss: 0.4355 - val_accuracy: 0.8027 - val_loss: 0.5969\n",
            "Epoch 27/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8485 - loss: 0.4319 - val_accuracy: 0.8027 - val_loss: 0.6002\n",
            "Epoch 28/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8447 - loss: 0.4295 - val_accuracy: 0.8080 - val_loss: 0.5842\n",
            "Epoch 29/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 9ms/step - accuracy: 0.8495 - loss: 0.4219 - val_accuracy: 0.7955 - val_loss: 0.6536\n",
            "Epoch 30/30\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 9ms/step - accuracy: 0.8498 - loss: 0.4270 - val_accuracy: 0.8090 - val_loss: 0.6064\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)\n",
        "print(f\"\\nTest Accuracy: {test_acc*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53QM2cdxnYRS",
        "outputId": "3cf722cf-36f1-4518-9bb6-707b9fd04931"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 - 2s - 5ms/step - accuracy: 0.7963 - loss: 0.6272\n",
            "\n",
            "Test Accuracy: 79.63%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5))\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",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "7ApAOn6LnCgj",
        "outputId": "02edfbe5-e57b-4c90-8600-4411976520bc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdbb07d6ea0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "epLgjKy8ni88",
        "outputId": "6476087d-8e4a-495b-8bce-1f57ca037a40"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7fdbb0610dd0>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_predictions(index):\n",
        "    img = X_test[index]\n",
        "    true_label = class_names[np.argmax(y_test[index])]\n",
        "    pred_probs = model.predict(np.expand_dims(img, axis=0))\n",
        "    pred_label = class_names[np.argmax(pred_probs)]\n",
        "\n",
        "    plt.imshow(img)\n",
        "    plt.title(f\"True: {true_label} | Pred: {pred_label}\")\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "\n",
        "# Example: Predict first 5 test images\n",
        "for i in range(5):\n",
        "    plot_predictions(i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mxa9WZPtni_b",
        "outputId": "3b8c187a-00ea-41ab-b81a-c5f0c0ecea4e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 860ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "avgnODUknjCN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}