{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "# --- Step 1: Import Libraries ---\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers, models\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "print(\"TensorFlow version:\", tf.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FSIi-kVm-CKK",
        "outputId": "cef9e014-8ac4-4765-a331-8115a145549b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow version: 2.19.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 2: Load CIFAR-10 Dataset ---\n",
        "\n",
        "# Load data\n",
        "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
        "\n",
        "# Normalize pixel values to [0, 1]\n",
        "x_train = x_train.astype(\"float32\") / 255.0\n",
        "x_test  = x_test.astype(\"float32\") / 255.0\n",
        "\n",
        "# Convert labels to 1D\n",
        "y_train = y_train.flatten()\n",
        "y_test  = y_test.flatten()\n",
        "\n",
        "print(\"Training data:\", x_train.shape, y_train.shape)\n",
        "print(\"Test data:\", x_test.shape, y_test.shape)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6qYUbfd5-C6n",
        "outputId": "432985af-8067-4384-8c0a-3706e9b0ba94"
      },
      "execution_count": 3,
      "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 data: (50000, 32, 32, 3) (50000,)\n",
            "Test data: (10000, 32, 32, 3) (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 2.5: Visualise Some Sample Images ---\n",
        "\n",
        "class_names = [\n",
        "    \"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\",\n",
        "    \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"\n",
        "]\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "for i in range(12):   # show 12 images\n",
        "    plt.subplot(3, 4, i + 1)\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.title(class_names[y_train[i]])\n",
        "    plt.axis(\"off\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "fhmvDRzF-FxU",
        "outputId": "41801c6d-b858-4649-a582-6e860b32c5bd"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 3: Build CNN Model ---\n",
        "\n",
        "model = models.Sequential([\n",
        "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "    layers.MaxPooling2D((2, 2)),\n",
        "\n",
        "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(64, activation='relu'),\n",
        "    layers.Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "XR52G_tA-Mr1",
        "outputId": "c6c6fa79-5e17-4024-9396-47b00030820c"
      },
      "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"
          ]
        },
        {
          "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",
              "│ 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",
              "│ 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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 4: Compile & Train ---\n",
        "\n",
        "model.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    x_train, y_train,\n",
        "    epochs=15,\n",
        "    batch_size=64,\n",
        "    validation_split=0.2,\n",
        "    verbose=1\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lMK2EC1n_-_9",
        "outputId": "91b727a9-19fc-464f-874a-3c52d725eb20"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 85ms/step - accuracy: 0.3195 - loss: 1.8377 - val_accuracy: 0.4999 - val_loss: 1.3921\n",
            "Epoch 2/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 90ms/step - accuracy: 0.5249 - loss: 1.3293 - val_accuracy: 0.5742 - val_loss: 1.1931\n",
            "Epoch 3/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 87ms/step - accuracy: 0.5980 - loss: 1.1370 - val_accuracy: 0.6235 - val_loss: 1.0754\n",
            "Epoch 4/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 87ms/step - accuracy: 0.6436 - loss: 1.0176 - val_accuracy: 0.6413 - val_loss: 1.0211\n",
            "Epoch 5/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 87ms/step - accuracy: 0.6735 - loss: 0.9373 - val_accuracy: 0.6554 - val_loss: 0.9816\n",
            "Epoch 6/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 87ms/step - accuracy: 0.6957 - loss: 0.8792 - val_accuracy: 0.6612 - val_loss: 0.9653\n",
            "Epoch 7/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 88ms/step - accuracy: 0.7165 - loss: 0.8175 - val_accuracy: 0.6923 - val_loss: 0.8954\n",
            "Epoch 8/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 89ms/step - accuracy: 0.7344 - loss: 0.7635 - val_accuracy: 0.6901 - val_loss: 0.8860\n",
            "Epoch 9/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m57s\u001b[0m 90ms/step - accuracy: 0.7529 - loss: 0.7088 - val_accuracy: 0.6986 - val_loss: 0.8812\n",
            "Epoch 10/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m56s\u001b[0m 90ms/step - accuracy: 0.7671 - loss: 0.6664 - val_accuracy: 0.7037 - val_loss: 0.8694\n",
            "Epoch 11/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 87ms/step - accuracy: 0.7736 - loss: 0.6389 - val_accuracy: 0.6957 - val_loss: 0.8829\n",
            "Epoch 12/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 89ms/step - accuracy: 0.7875 - loss: 0.6053 - val_accuracy: 0.6976 - val_loss: 0.8940\n",
            "Epoch 13/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 87ms/step - accuracy: 0.8040 - loss: 0.5663 - val_accuracy: 0.7021 - val_loss: 0.8897\n",
            "Epoch 14/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 85ms/step - accuracy: 0.8114 - loss: 0.5406 - val_accuracy: 0.7015 - val_loss: 0.9011\n",
            "Epoch 15/15\n",
            "\u001b[1m625/625\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m55s\u001b[0m 88ms/step - accuracy: 0.8182 - loss: 0.5111 - val_accuracy: 0.7074 - val_loss: 0.8990\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 5: Visualise Training Results ---\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "# Accuracy\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label=\"Train Acc\")\n",
        "plt.plot(history.history['val_accuracy'], label=\"Val Acc\")\n",
        "plt.legend()\n",
        "plt.title(\"Accuracy\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "\n",
        "# Loss\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.legend()\n",
        "plt.title(\"Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "uyNzEw1yAD2c",
        "outputId": "3f1c96e5-f6af-489e-f322-505a8a5903fa"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 6: Evaluate Model ---\n",
        "\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)\n",
        "print(\"Test loss:\", test_loss)\n",
        "print(\"Test accuracy:\", test_acc)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P3RTxFaRDrob",
        "outputId": "64619c8c-6506-4026-9750-b4feb132a9a3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 0.9173159003257751\n",
            "Test accuracy: 0.7074000239372253\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# --- Step 7: Metrics & Reports ---\n",
        "\n",
        "# Predictions\n",
        "y_pred_prob = model.predict(x_test)\n",
        "y_pred = np.argmax(y_pred_prob, axis=1)\n",
        "\n",
        "# Confusion matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(\n",
        "    cm,\n",
        "    annot=False,\n",
        "    cmap=\"Blues\",\n",
        "    xticklabels=class_names,\n",
        "    yticklabels=class_names\n",
        ")\n",
        "plt.xlabel(\"Predicted label\")\n",
        "plt.ylabel(\"True label\")\n",
        "plt.title(\"Confusion Matrix\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Classification report\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred, target_names=class_names))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "h4m3-6RkDv3x",
        "outputId": "cb778ff7-d404-411e-ef69-d7825fc5398f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 31ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    airplane       0.68      0.81      0.74      1000\n",
            "  automobile       0.78      0.83      0.81      1000\n",
            "        bird       0.63      0.58      0.60      1000\n",
            "         cat       0.57      0.49      0.53      1000\n",
            "        deer       0.65      0.67      0.66      1000\n",
            "         dog       0.64      0.56      0.60      1000\n",
            "        frog       0.73      0.83      0.78      1000\n",
            "       horse       0.76      0.75      0.75      1000\n",
            "        ship       0.84      0.76      0.80      1000\n",
            "       truck       0.77      0.79      0.78      1000\n",
            "\n",
            "    accuracy                           0.71     10000\n",
            "   macro avg       0.71      0.71      0.70     10000\n",
            "weighted avg       0.71      0.71      0.70     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o7cNGCB2D1yN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}