{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# 🧠 Convolutional Neural Network (CNN) for Image Classification\n",
        "\n",
        "This notebook demonstrates a **Convolutional Neural Network (CNN)** for image classification using **TensorFlow/Keras**.  \n",
        "\n",
        "### **Project Workflow**\n",
        "1. Import libraries & load dataset (CIFAR-10)\n",
        "2. Explore & preprocess data\n",
        "3. Build a CNN model\n",
        "4. Train & evaluate the model\n",
        "5. Visualize performance metrics\n"
      ],
      "metadata": {
        "id": "nauENUcFWcij"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\"**Import libraries & load dataset (CIFAR-10)**"
      ],
      "metadata": {
        "id": "KfNfBqqQWkv0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Import Required Libraries\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets, layers, models\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "print(\"TensorFlow Version:\", tf.__version__)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ajQn_IwKWgBD",
        "outputId": "3a08e702-6d4f-4933-9eba-bd22f9f6a92e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TensorFlow Version: 2.18.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "30cab486"
      },
      "source": [
        "## 📂 Step 2: Load the CIFAR-10 Dataset\n",
        "\n",
        "- CIFAR-10 contains **60,000 images** of size **32x32x3 (RGB)**.\n",
        "- Divided into **50,000 training** and **10,000 testing images**.\n",
        "- **10 Classes:** Airplane, Automobile, Bird, Cat, Deer, Dog, Frog, Horse, Ship, Truck"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load CIFAR-10 Dataset (Directly from TensorFlow)\n",
        "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()\n",
        "\n",
        "# Normalize images to [0,1]\n",
        "x_train, x_test = x_train / 255.0, x_test / 255.0\n",
        "\n",
        "# Flatten labels (from shape (n,1) to (n,))\n",
        "y_train, y_test = y_train.flatten(), y_test.flatten()\n",
        "\n",
        "print(\"Training Data Shape:\", x_train.shape)\n",
        "print(\"Test Data Shape:\", x_test.shape)\n",
        "print(\"Sample Label:\", y_train[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p22FktZ8XE8A",
        "outputId": "9ad64161-891b-4ad4-e3f8-e591478c15db"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data Shape: (50000, 32, 32, 3)\n",
            "Test Data Shape: (10000, 32, 32, 3)\n",
            "Sample Label: 6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 👀 Step 3: Visualize Sample Images\n",
        "\n",
        "Before training, let's look at a few **sample images** to understand the dataset visually.\n"
      ],
      "metadata": {
        "id": "Ojo1JG0OXKFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Class labels for CIFAR-10\n",
        "class_names = ['Airplane','Automobile','Bird','Cat','Deer',\n",
        "               'Dog','Frog','Horse','Ship','Truck']\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "for i in range(10):\n",
        "    plt.subplot(2,5,i+1)\n",
        "    plt.xticks([]); plt.yticks([])\n",
        "    plt.imshow(x_train[i])\n",
        "    plt.title(class_names[y_train[i]])\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "Yw8jWeXmXIwK",
        "outputId": "78142d0b-1d17-4223-90d1-4282418ae0ac"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🏗️ Step 4: Build Convolutional Neural Network (CNN)\n",
        "\n",
        "Our CNN will consist of the following layers:\n",
        "\n",
        "1. **Input Layer:** 32×32 RGB image\n",
        "2. **Convolution Layer 1:** 32 filters, 3×3 kernel, ReLU activation\n",
        "3. **Max Pooling Layer 1:** 2×2 pooling\n",
        "4. **Convolution Layer 2:** 64 filters, 3×3 kernel, ReLU activation\n",
        "5. **Max Pooling Layer 2:** 2×2 pooling\n",
        "6. **Convolution Layer 3:** 64 filters, 3×3 kernel, ReLU activation\n",
        "7. **Flatten Layer:** Convert 2D feature maps to 1D vector\n",
        "8. **Fully Connected Layer:** 64 neurons, ReLU activation\n",
        "9. **Output Layer:** 10 neurons, Softmax activation (for 10 classes)\n",
        "\n",
        "> **Optimizer:** Adam  \n",
        "> **Loss Function:** Sparse Categorical Crossentropy  \n",
        "> **Metric:** Accuracy\n"
      ],
      "metadata": {
        "id": "r8Y53kmTXjb1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Build CNN Model using Keras Sequential API\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') # Output layer for 10 classes\n",
        "])\n",
        "\n",
        "# Display Model Summary\n",
        "model.summary()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "id": "YNLINJ65Xljq",
        "outputId": "469cd88e-85a7-4067-96ac-75da1d1c86b2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/keras/src/layers/convolutional/base_conv.py:107: 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_1\"\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_1\"</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_3 (\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_2 (\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_4 (\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_3 (\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_5 (\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_1 (\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_2 (\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_3 (\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_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\">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_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\">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_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\">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_3 (<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_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\">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_1 (<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_2 (<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_3 (<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": "markdown",
      "source": [
        "## ⚙️ Step 5: Compile the Model\n",
        "\n",
        "- **Optimizer:** Adam (Adaptive learning rate optimization)\n",
        "- **Loss Function:** Sparse Categorical Crossentropy (multi-class classification with integer labels)\n",
        "- **Metric:** Accuracy (to evaluate model performance)\n"
      ],
      "metadata": {
        "id": "zgml9DJeXrH7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the CNN model\n",
        "model.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=['accuracy'])\n"
      ],
      "metadata": {
        "id": "zJISA8MlXscH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 🏋️ Step 6: Train the CNN Model\n",
        "\n",
        "We will train the model for **10 epochs** with a **batch size of 64**.\n",
        "\n",
        "- **Training Data:** 50,000 images  \n",
        "- **Validation Data:** 10,000 images  \n",
        "- **Output:** Training & Validation Accuracy/Loss per epoch\n"
      ],
      "metadata": {
        "id": "yahS94PfXvv7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model and store training history\n",
        "history = model.fit(x_train, y_train,\n",
        "                    epochs=10,\n",
        "                    batch_size=64,\n",
        "                    validation_data=(x_test, y_test))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "95vBWA_BXw6j",
        "outputId": "c6c75eec-f36d-46a7-cffe-7e310f23f15a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m68s\u001b[0m 84ms/step - accuracy: 0.3437 - loss: 1.7766 - val_accuracy: 0.5247 - val_loss: 1.3203\n",
            "Epoch 2/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 83ms/step - accuracy: 0.5532 - loss: 1.2562 - val_accuracy: 0.5905 - val_loss: 1.1609\n",
            "Epoch 3/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m64s\u001b[0m 81ms/step - accuracy: 0.6196 - loss: 1.0757 - val_accuracy: 0.6332 - val_loss: 1.0284\n",
            "Epoch 4/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 84ms/step - accuracy: 0.6621 - loss: 0.9665 - val_accuracy: 0.6617 - val_loss: 0.9664\n",
            "Epoch 5/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 83ms/step - accuracy: 0.6889 - loss: 0.8886 - val_accuracy: 0.6807 - val_loss: 0.9205\n",
            "Epoch 6/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 84ms/step - accuracy: 0.7138 - loss: 0.8156 - val_accuracy: 0.6885 - val_loss: 0.9048\n",
            "Epoch 7/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 81ms/step - accuracy: 0.7344 - loss: 0.7630 - val_accuracy: 0.6974 - val_loss: 0.8848\n",
            "Epoch 8/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 83ms/step - accuracy: 0.7531 - loss: 0.7109 - val_accuracy: 0.7014 - val_loss: 0.8778\n",
            "Epoch 9/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 81ms/step - accuracy: 0.7658 - loss: 0.6707 - val_accuracy: 0.7013 - val_loss: 0.8894\n",
            "Epoch 10/10\n",
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 84ms/step - accuracy: 0.7771 - loss: 0.6362 - val_accuracy: 0.6926 - val_loss: 0.9035\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 📊 Step 7: Visualize Training and Validation Performance\n",
        "\n",
        "Plot **Accuracy** and **Loss** curves to understand:\n",
        "- If the model is learning properly\n",
        "- If there is overfitting or underfitting\n"
      ],
      "metadata": {
        "id": "G4sAPnZwa_Ja"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot Accuracy and Loss curves\n",
        "plt.figure(figsize=(12,5))\n",
        "\n",
        "# Accuracy Plot\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('Training vs Validation Accuracy')\n",
        "plt.xlabel('Epochs'); plt.ylabel('Accuracy')\n",
        "plt.legend(); plt.grid(True)\n",
        "\n",
        "# Loss Plot\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('Training vs Validation Loss')\n",
        "plt.xlabel('Epochs'); plt.ylabel('Loss')\n",
        "plt.legend(); plt.grid(True)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "0VQm1iqvbAef",
        "outputId": "754ea74f-5a97-4420-b0bb-9c701fdffcad"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ✅ Step 8: Evaluate Model Performance on Test Dataset\n",
        "\n",
        "We will check:\n",
        "1. **Final Test Accuracy**\n",
        "2. **Confusion Matrix**\n",
        "3. **Classification Report (Precision, Recall, F1-score)**\n",
        "\n",
        "> This helps to identify which classes are predicted well and where the model struggles.\n"
      ],
      "metadata": {
        "id": "Pzub5UEddrYD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "import seaborn as sns\n",
        "\n",
        "# Step 8.1: Evaluate Accuracy on Test Set\n",
        "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
        "print(f\"\\n🎯 Test Accuracy: {test_acc*100:.2f}%\")\n",
        "\n",
        "# Step 8.2: Predictions\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "\n",
        "# Step 8.3: Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_classes)\n",
        "\n",
        "plt.figure(figsize=(10,7))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n",
        "\n",
        "# Step 8.4: Classification Report\n",
        "print(\"\\n📋 Classification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred_classes, target_names=class_names))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "D4IIJfcndsmv",
        "outputId": "0c3f7e8f-d9de-4ddb-9fcd-bde27facc97b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 28ms/step - accuracy: 0.6942 - loss: 0.8914\n",
            "\n",
            "🎯 Test Accuracy: 69.26%\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 15ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "📋 Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Airplane       0.74      0.74      0.74      1000\n",
            "  Automobile       0.87      0.79      0.83      1000\n",
            "        Bird       0.63      0.57      0.60      1000\n",
            "         Cat       0.45      0.57      0.50      1000\n",
            "        Deer       0.75      0.50      0.60      1000\n",
            "         Dog       0.49      0.74      0.59      1000\n",
            "        Frog       0.87      0.66      0.75      1000\n",
            "       Horse       0.79      0.70      0.75      1000\n",
            "        Ship       0.81      0.84      0.82      1000\n",
            "       Truck       0.78      0.81      0.79      1000\n",
            "\n",
            "    accuracy                           0.69     10000\n",
            "   macro avg       0.72      0.69      0.70     10000\n",
            "weighted avg       0.72      0.69      0.70     10000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 💾 Step 9: Save & Load Trained Model\n",
        "\n",
        "We save the trained model so we can:\n",
        "- Deploy it later without retraining\n",
        "- Share it or upload to GitHub\n"
      ],
      "metadata": {
        "id": "s5N59oZaeBas"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model in HDF5 format\n",
        "model.save(\"cnn_cifar10_model.h5\")\n",
        "print(\"✅ Model saved as cnn_cifar10_model.h5\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgxtkC5xeDmr",
        "outputId": "c7c4c20f-1a41-4a70-c702-ab1b52ee1f89"
      },
      "execution_count": 11,
      "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 as cnn_cifar10_model.h5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "\n",
        "# Load the saved model\n",
        "loaded_model = load_model(\"cnn_cifar10_model.h5\")\n",
        "\n",
        "# Verify by evaluating on test data\n",
        "loss, acc = loaded_model.evaluate(x_test, y_test)\n",
        "print(f\"Loaded Model Accuracy: {acc*100:.2f}%\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h17a2noQeHf5",
        "outputId": "d3e3fe6f-70c8-4f07-e170-1f17c9379fbc"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 16ms/step - accuracy: 0.6942 - loss: 0.8914\n",
            "Loaded Model Accuracy: 69.26%\n"
          ]
        }
      ]
    }
  ]
}