MahsaDorri-301257460_COMP377LAB4ex1.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COMP377: AI for Software Developers\n",
    "## MIDTERM \n",
    "### Exercise 1\n",
    "\n",
    "Name: --------------MahsaDorri--------------------------------\n",
    "\n",
    "Student ID: ---------301257460--------------------------------------\n",
    "\n",
    "Date: --------------- April 1,2024----------------------------\n",
    "\n",
    "File Name: MahsaDorri_COMP377LAB4_EX1\n",
    "\n",
    "\n",
    "### Instructions\n",
    "\n",
    "1.  Complete the code in the cells provided according to the instructions given in the comments.\n",
    "2.  Ensure that your code runs without errors.\n",
    "3.  Submit your completed notebook via eCentennial.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mahsadorri/anaconda3/lib/python3.10/site-packages/pandas/core/arrays/masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.5' currently installed).\n",
      "  from pandas.core import (\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import datasets, layers, models, optimizers\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# SVM model's reported accuracy for comparison\n",
    "svm_model_accuracy_pct = 88.61"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 1: Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and preprocess the MNIST dataset\n",
    "(mnist_train_images, mnist_train_labels), (mnist_test_images, mnist_test_labels) = datasets.mnist.load_data()\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_train_images, mnist_test_images = mnist_train_images / 255.0, mnist_test_images / 255.0\n",
    "mnist_train_images = mnist_train_images.reshape((60000, 28, 28, 1))\n",
    "mnist_test_images = mnist_test_images.reshape((10000, 28, 28, 1))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 3: Create and Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mahsadorri/anaconda3/lib/python3.10/site-packages/keras/src/layers/convolutional/base_conv.py:99: 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__(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.8470 - loss: 0.4987 - val_accuracy: 0.9571 - val_loss: 0.1374\n",
      "Epoch 2/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9567 - loss: 0.1394 - val_accuracy: 0.9648 - val_loss: 0.1108\n",
      "Epoch 3/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9701 - loss: 0.0976 - val_accuracy: 0.9748 - val_loss: 0.0828\n",
      "Epoch 4/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.9798 - loss: 0.0674 - val_accuracy: 0.9780 - val_loss: 0.0692\n",
      "Epoch 5/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 3ms/step - accuracy: 0.9850 - loss: 0.0523 - val_accuracy: 0.9800 - val_loss: 0.0617\n",
      "Epoch 6/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9869 - loss: 0.0416 - val_accuracy: 0.9820 - val_loss: 0.0553\n",
      "Epoch 7/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9912 - loss: 0.0307 - val_accuracy: 0.9813 - val_loss: 0.0577\n",
      "Epoch 8/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9920 - loss: 0.0271 - val_accuracy: 0.9836 - val_loss: 0.0527\n",
      "Epoch 9/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9941 - loss: 0.0203 - val_accuracy: 0.9837 - val_loss: 0.0489\n",
      "Epoch 10/10\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.9945 - loss: 0.0181 - val_accuracy: 0.9826 - val_loss: 0.0563\n"
     ]
    }
   ],
   "source": [
    "# Define the CNN model architecture\n",
    "cnn_model = models.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    layers.AveragePooling2D((2, 2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(10, activation='softmax')\n",
    "])\n",
    "\n",
    "# Compile the CNN model\n",
    "cnn_model.compile(optimizer=optimizers.SGD(momentum=0.9),\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the CNN model\n",
    "training_history = cnn_model.fit(mnist_train_images, mnist_train_labels, epochs=10, validation_data=(mnist_test_images, mnist_test_labels))\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 4:Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 0s - 1ms/step - accuracy: 0.9826 - loss: 0.0563\n",
      "\n",
      "CNN Test Accuracy: 98.26%\n",
      "SVM Model Accuracy: 88.61%\n"
     ]
    }
   ],
   "source": [
    "cnn_test_loss, cnn_test_acc = cnn_model.evaluate(mnist_test_images, mnist_test_labels, verbose=2)\n",
    "print(f'\\nCNN Test Accuracy: {cnn_test_acc*100:.2f}%')\n",
    "print(f\"SVM Model Accuracy: {svm_model_accuracy_pct:.2f}%\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 5: Calculate Accuracy & Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The CNN model is better than the SVM model on the MNIST dataset.\n"
     ]
    }
   ],
   "source": [
    "# Compare CNN model with the SVM model\n",
    "if cnn_test_acc > svm_model_accuracy_pct / 100.0:\n",
    "    print(\"The CNN model is better than the SVM model on the MNIST dataset.\")\n",
    "else:\n",
    "    print(\"The SVM model performs comparably or better than the CNN model on the MNIST dataset.\")\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization and Display (Optional)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x300 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display predictions on test images with updated color scheme\n",
    "def display_predictions(model, images, labels, num_images=10):\n",
    "    predictions = model.predict(images)\n",
    "    plt.figure(figsize=(10, 3))\n",
    "    for i in range(num_images):\n",
    "        plt.subplot(1, num_images, i+1)\n",
    "        plot_single_image_prediction(i, predictions[i], labels, images)\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Adjusted to modify color in plot_single_image_prediction function\n",
    "def plot_single_image_prediction(i, predictions, true_labels, images):\n",
    "    true_label, img = true_labels[i], images[i].reshape(28, 28)\n",
    "    plt.grid(False)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "  \n",
    "    plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "    predicted_label = np.argmax(predictions)\n",
    "    color = 'blue' if predicted_label == true_label else 'red'\n",
    "  \n",
    "    # Change the label color to dark green for correct and dark red for incorrect\n",
    "    plt.xlabel(f\"{predicted_label} {100*np.max(predictions):2.0f}% ({true_label})\", color=color)\n",
    "\n",
    "# Function to plot training history with updated colors\n",
    "def plot_model_training_history(history):\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    \n",
    "    # Training and Validation Accuracy with updated colors\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history['accuracy'], 'go', label='Training Accuracy')  # Changed to green dots\n",
    "    plt.plot(history.history['val_accuracy'], 'r', label='Validation Accuracy')  # Changed to red line\n",
    "    plt.title('Training and Validation Accuracy')\n",
    "    plt.legend()\n",
    "\n",
    "    # Training and Validation Loss with updated colors\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history['loss'], 'go', label='Training Loss')  # Changed to green dots\n",
    "    plt.plot(history.history['val_loss'], 'r', label='Validation Loss')  # Changed to red line\n",
    "    plt.title('Training and Validation Loss')\n",
    "    plt.legend()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "plot_model_training_history(training_history)\n",
    "display_predictions(cnn_model, mnist_test_images, mnist_test_labels)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Conclusions\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CNN model trained on MNIST displays excellent performance with a final test accuracy of 98.34%, surpassing the SVM's 88.61%. The training and validation accuracy trends indicate effective learning without significant overfitting. Loss trends decrease steadily, confirming model improvement over epochs. Visual predictions demonstrate the CNN's strong ability to correctly classify handwritten digits, reinforcing its suitability for image-based tasks over the traditional SVM approach."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}