DeepLearning/Notebooks/Notebook-3b.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "396d9e19-0fe3-41ee-b144-71f17fbf696b",
   "metadata": {},
   "source": [
    "# Practical-3b (Convolutional Neural Network - MNIST Fashion Dataset)\n",
    "\n",
    "---\n",
    "\n",
    "Problem Statement: Convolutional neural network (CNN): Use MNIST Fashion Dataset and create a classifier to classify fashion clothing into\n",
    "categories.\n",
    "\n",
    "- Dataset link: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset/data\n",
    "- Dataset available in [Datasets](https://git.kska.io/sppu-be-comp-content/DeepLearning/src/branch/main/Datasets/) directory.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd8945d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1777821874.395594   86996 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n",
      "I0000 00:00:1777821876.310503   86996 cpu_feature_guard.cc:227] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1777821879.617357   86996 cudart_stub.cc:31] Could not find cuda drivers on your machine, GPU will not be used.\n"
     ]
    }
   ],
   "source": [
    "# 1. Import Libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Input, Conv2D, AvgPool2D, GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "859cbc0f",
   "metadata": {},
   "outputs": [],
   "source": "# 2. Load Dataset\n# Fashion MNIST is built into Keras — downloads automatically on first run\n(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n\n# --- Offline alternative (comment out tf.keras line above and use this instead) ---\n# import pandas as pd\n# train_df = pd.read_csv('fashion-mnist_train.csv')\n# test_df  = pd.read_csv('fashion-mnist_test.csv')\n# y_train = train_df['label'].values\n# y_test  = test_df['label'].values\n# X_train = train_df.drop('label', axis=1).values.reshape(-1, 28, 28)  # unflatten pixels to 28x28\n# X_test  = test_df.drop('label', axis=1).values.reshape(-1, 28, 28)\n\nprint(\"Training set shape:\", X_train.shape)   # (60000, 28, 28)\nprint(\"Test set shape:    \", X_test.shape)    # (10000, 28, 28)\nprint(\"Classes:\", np.unique(y_train))"
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "763c59ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1000x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3. Exploratory Data Analysis (EDA)\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "# Class distribution\n",
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "plt.figure(figsize=(10, 4))\n",
    "plt.bar([class_names[i] for i in unique], counts)\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.title(\"Training Set Class Distribution\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Sample images (one per class)\n",
    "plt.figure(figsize=(15, 3))\n",
    "for i, cls in enumerate(class_names):\n",
    "    idx = np.where(y_train == i)[0][0]   # index of first image for this class\n",
    "    plt.subplot(1, 10, i + 1)\n",
    "    plt.imshow(X_train[idx], cmap='gray')\n",
    "    plt.title(cls, fontsize=7)\n",
    "    plt.axis('off')\n",
    "plt.suptitle(\"Sample Image per Class\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d9b48211",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train shape: (60000, 28, 28, 1)\n",
      "y_train_cat shape: (60000, 10)\n"
     ]
    }
   ],
   "source": [
    "# 4. Preprocess Data\n",
    "# Reshape to add channel dimension: (samples, 28, 28) -> (samples, 28, 28, 1)\n",
    "X_train = X_train.reshape(-1, 28, 28, 1).astype('float32') / 255.0  # normalize to [0,1]\n",
    "X_test  = X_test.reshape(-1, 28, 28, 1).astype('float32') / 255.0\n",
    "\n",
    "# One-hot encode labels: e.g. class 3 of 10 -> [0,0,0,1,0,0,0,0,0,0]\n",
    "y_train_cat = to_categorical(y_train, num_classes=10)\n",
    "y_test_cat  = to_categorical(y_test,  num_classes=10)\n",
    "\n",
    "print(\"X_train shape:\", X_train.shape)\n",
    "print(\"y_train_cat shape:\", y_train_cat.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44799339",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E0000 00:00:1777822594.307817   86996 cuda_platform.cc:52] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
     ]
    },
    {
     "data": {
      "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"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │           <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ average_pooling2d               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AveragePooling2D</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\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,464</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ average_pooling2d_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">AveragePooling2D</span>)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d        │ (<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\">0</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</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\">10</span>)             │           <span style=\"color: #00af00; text-decoration-color: #00af00\">330</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "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;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │           \u001b[38;5;34m640\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ average_pooling2d               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │        \u001b[38;5;34m18,464\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ average_pooling2d_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m32\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mAveragePooling2D\u001b[0m)              │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ global_average_pooling2d        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m)        │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m)             │           \u001b[38;5;34m330\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">19,434</span> (75.91 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,434\u001b[0m (75.91 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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\">19,434</span> (75.91 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,434\u001b[0m (75.91 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "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"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5. Build the CNN Model\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Input(shape=(28, 28, 1)))                                          # input: 28x28 grayscale image\n",
    "model.add(Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same')) # 64 filters, extract features\n",
    "model.add(AvgPool2D(pool_size=(2, 2)))                                        # downsample to 14x14\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same')) # 32 filters, refine features\n",
    "model.add(AvgPool2D(pool_size=(2, 2)))                                        # downsample to 7x7\n",
    "\n",
    "model.add(GlobalAveragePooling2D())                                           # average each feature map to single value\n",
    "model.add(Dense(10, activation='softmax'))                                    # output: probability for each of 10 classes\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0fbd8434",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6. Compile the Model\n",
    "# categorical_crossentropy: standard loss for multi-class one-hot classification\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "faf7009e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0000 00:00:1777822603.025974   86996 cpu_allocator_impl.cc:82] Allocation of 188160000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.5500 - loss: 1.2463 - val_accuracy: 0.6725 - val_loss: 0.9472\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.6951 - loss: 0.8683 - val_accuracy: 0.7125 - val_loss: 0.8282\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.7328 - loss: 0.7643 - val_accuracy: 0.7235 - val_loss: 0.7729\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.7576 - loss: 0.6995 - val_accuracy: 0.7400 - val_loss: 0.7323\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 18ms/step - accuracy: 0.7735 - loss: 0.6508 - val_accuracy: 0.7721 - val_loss: 0.6608\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 18ms/step - accuracy: 0.7856 - loss: 0.6153 - val_accuracy: 0.7830 - val_loss: 0.6361\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 19ms/step - accuracy: 0.7946 - loss: 0.5890 - val_accuracy: 0.7875 - val_loss: 0.6060\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 18ms/step - accuracy: 0.8011 - loss: 0.5678 - val_accuracy: 0.8054 - val_loss: 0.5709\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m33s\u001b[0m 18ms/step - accuracy: 0.8078 - loss: 0.5480 - val_accuracy: 0.8115 - val_loss: 0.5513\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m31s\u001b[0m 16ms/step - accuracy: 0.8132 - loss: 0.5339 - val_accuracy: 0.8146 - val_loss: 0.5401\n"
     ]
    }
   ],
   "source": [
    "# 7. Train the Model\n",
    "# validation_data uses test set to monitor performance after each epoch\n",
    "history = model.fit(X_train, y_train_cat, epochs=10, validation_data=(X_test, y_test_cat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6c522de6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step - accuracy: 0.8146 - loss: 0.5401\n",
      "Test Loss:     0.5401\n",
      "Test Accuracy: 81.46%\n"
     ]
    }
   ],
   "source": [
    "# 8. Evaluate the Model on Test Data\n",
    "loss, accuracy = model.evaluate(X_test, y_test_cat)\n",
    "print(f\"Test Loss:     {loss:.4f}\")\n",
    "print(f\"Test Accuracy: {accuracy*100:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "66a6f583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 9. Plot Training vs Validation Accuracy\n",
    "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "plt.title('Model Accuracy Over Epochs')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "# 10. Plot Training vs Validation Loss\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.title('Model Loss Over Epochs')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7feeda9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      " T-shirt/top       0.75      0.79      0.77      1000\n",
      "     Trouser       0.98      0.94      0.96      1000\n",
      "    Pullover       0.73      0.74      0.74      1000\n",
      "       Dress       0.73      0.87      0.79      1000\n",
      "        Coat       0.73      0.65      0.69      1000\n",
      "      Sandal       0.97      0.89      0.93      1000\n",
      "       Shirt       0.53      0.47      0.50      1000\n",
      "     Sneaker       0.83      0.97      0.89      1000\n",
      "         Bag       0.94      0.95      0.94      1000\n",
      "  Ankle boot       0.96      0.88      0.92      1000\n",
      "\n",
      "    accuracy                           0.81     10000\n",
      "   macro avg       0.82      0.81      0.81     10000\n",
      "weighted avg       0.82      0.81      0.81     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 11. Confusion Matrix and Classification Report\n",
    "y_pred = np.argmax(model.predict(X_test), axis=1)  # predicted class index\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=class_names, yticklabels=class_names)\n",
    "plt.title('Confusion Matrix')\n",
    "plt.ylabel('Actual')\n",
    "plt.xlabel('Predicted')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "print(\"\\nClassification Report:\\n\")\n",
    "print(classification_report(y_test, y_pred, target_names=class_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3ff29f35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x400 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 12. Visualize Sample Predictions\n",
    "# batch predict all test images, then pick 10 random ones to display\n",
    "all_preds = np.argmax(model.predict(X_test), axis=1)\n",
    "random_indices = np.random.choice(len(X_test), 10, replace=False)\n",
    "\n",
    "plt.figure(figsize=(20, 4))\n",
    "for i, idx in enumerate(random_indices):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    plt.imshow(X_test[idx].reshape(28, 28), cmap='gray')\n",
    "    predicted = class_names[all_preds[idx]]\n",
    "    actual    = class_names[y_test[idx]]\n",
    "    color = 'green' if predicted == actual else 'red'   # green = correct, red = wrong\n",
    "    plt.title(f\"P: {predicted}\\nA: {actual}\", fontsize=8, color=color)\n",
    "    plt.axis('off')\n",
    "plt.suptitle(\"Sample Predictions (Green=Correct, Red=Wrong)\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c717de5-87a3-4a7b-85ed-9dcac1bb9cd5",
   "metadata": {},
   "source": [
    "---"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.13"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}