MNIST_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b67fab3b-def1-4982-b0e0-edc5836aa59c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(42)\n",
    "\n",
    "import idx2numpy as idx2np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import tensorflow as tf\n",
    "tf.random.set_seed(42)\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten, Input\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0901b261-629d-4016-a22d-eadedcc6e0ca",
   "metadata": {},
   "source": [
    "# MNIST Modeling - June 2024\n",
    "In Fall 2021, I participated in a General Assembly bootcamp for Data Science. During the class, one of the projects tackled was classifying handwritten digits from the MNIST dataset.\n",
    "\n",
    "This analysis, here, re-visits that project. I will build a neural network model to classify the digits, seeing how accurate these predictions could be."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156ae1e9-9445-40cf-9b2b-ac9a234ec3da",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bd98302b-8afd-41b9-8871-48bd878361a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# File paths\n",
    "train_images_path = './archive/train-images-idx3-ubyte/train-images-idx3-ubyte'\n",
    "train_labels_path = './archive/train-labels-idx1-ubyte/train-labels-idx1-ubyte'\n",
    "test_images_path = './archive/t10k-images-idx3-ubyte/t10k-images-idx3-ubyte'\n",
    "test_labels_path = './archive/t10k-labels-idx1-ubyte/t10k-labels-idx1-ubyte'\n",
    "\n",
    "# Loading data\n",
    "X_train = idx2np.convert_from_file(train_images_path)\n",
    "y_train = idx2np.convert_from_file(train_labels_path)\n",
    "X_test = idx2np.convert_from_file(test_images_path)\n",
    "y_test = idx2np.convert_from_file(test_labels_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "096a7178-9278-43c4-abff-708b60e6cb66",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(None, None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(X_train.shape), print(y_train.shape)\n",
    "print(X_test.shape), print(y_test.shape)\n",
    "#why does (None, None) return?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ea26d65-cc31-4977-958f-7a5cac95cdba",
   "metadata": {},
   "source": [
    "A quick look at the images being examined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1da4496a-071a-4cf9-b321-9e11244f65b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Convert one-hot encoded labels back to digit values\n",
    "num_samples = 5\n",
    "\n",
    "# Select random indices to sample from the dataset\n",
    "random_indices = np.random.choice(X_train.shape[0]\n",
    "                                  , num_samples\n",
    "                                  , replace = False)\n",
    "\n",
    "# Plot the sampled images\n",
    "for i, idx in enumerate(random_indices):\n",
    "    plt.subplot(1, num_samples, i + 1)\n",
    "    plt.imshow(X_train[idx], cmap = 'gray')  # Assuming images are grayscale\n",
    "    plt.title(f\"Label: {y_train[idx]}\")  # Use the digit label instead of the one-hot encoded array\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6984f07b-f4ad-4926-aa75-0f459c06183e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "X_train = X_train / 255.0\n",
    "X_test = X_test / 255.0\n",
    "\n",
    "X_train = X_train.reshape(-1, 28, 28, 1)\n",
    "X_test = X_test.reshape(-1, 28, 28, 1)\n",
    "\n",
    "# Convert labels to categorical format\n",
    "y_train = to_categorical(y_train)\n",
    "y_test = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4b6bf79-c781-416a-8ed3-b0e677ee788e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5187b62c-54a1-405f-a2af-eeff73d9486b",
   "metadata": {},
   "source": [
    "### Feed-Forward Neural Network\n",
    "Building a simple, feed-forward neural network to set baseline of performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "11e9e85c-9522-4ff9-89d4-0101fc72d060",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1875/1875 - 4s - 2ms/step - accuracy: 0.9401 - loss: 0.1950 - val_accuracy: 0.9670 - val_loss: 0.1093\n",
      "Epoch 2/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9736 - loss: 0.0845 - val_accuracy: 0.9730 - val_loss: 0.0954\n",
      "Epoch 3/20\n",
      "1875/1875 - 4s - 2ms/step - accuracy: 0.9821 - loss: 0.0575 - val_accuracy: 0.9705 - val_loss: 0.1050\n",
      "Epoch 4/20\n",
      "1875/1875 - 4s - 2ms/step - accuracy: 0.9855 - loss: 0.0467 - val_accuracy: 0.9778 - val_loss: 0.0839\n",
      "Epoch 5/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9888 - loss: 0.0361 - val_accuracy: 0.9760 - val_loss: 0.0990\n",
      "Epoch 6/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9898 - loss: 0.0323 - val_accuracy: 0.9789 - val_loss: 0.0832\n",
      "Epoch 7/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9906 - loss: 0.0279 - val_accuracy: 0.9778 - val_loss: 0.1038\n",
      "Epoch 8/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9918 - loss: 0.0241 - val_accuracy: 0.9774 - val_loss: 0.1124\n",
      "Epoch 9/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9931 - loss: 0.0215 - val_accuracy: 0.9814 - val_loss: 0.0987\n",
      "Epoch 10/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9939 - loss: 0.0195 - val_accuracy: 0.9780 - val_loss: 0.0970\n",
      "Epoch 11/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9945 - loss: 0.0170 - val_accuracy: 0.9825 - val_loss: 0.0964\n",
      "Epoch 12/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9947 - loss: 0.0169 - val_accuracy: 0.9786 - val_loss: 0.1037\n",
      "Epoch 13/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9944 - loss: 0.0182 - val_accuracy: 0.9800 - val_loss: 0.1148\n",
      "Epoch 14/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9954 - loss: 0.0160 - val_accuracy: 0.9820 - val_loss: 0.0996\n",
      "Epoch 15/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9955 - loss: 0.0161 - val_accuracy: 0.9800 - val_loss: 0.0988\n",
      "Epoch 16/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9963 - loss: 0.0130 - val_accuracy: 0.9806 - val_loss: 0.1046\n",
      "Epoch 17/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9962 - loss: 0.0137 - val_accuracy: 0.9810 - val_loss: 0.1008\n",
      "Epoch 18/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9962 - loss: 0.0145 - val_accuracy: 0.9790 - val_loss: 0.1191\n",
      "Epoch 19/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9964 - loss: 0.0120 - val_accuracy: 0.9817 - val_loss: 0.1126\n",
      "Epoch 20/20\n",
      "1875/1875 - 3s - 2ms/step - accuracy: 0.9974 - loss: 0.0092 - val_accuracy: 0.9844 - val_loss: 0.0991\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([Input(shape = (28, 28, 1)) # Specify the input shape here\n",
    "                    , Flatten()\n",
    "                    , Dense(512, activation = 'relu')\n",
    "                    , Dense(256, activation = 'relu')\n",
    "                    , Dense(128, activation = 'relu')\n",
    "                    , Dense(10, activation = 'softmax')\n",
    "                   ])\n",
    "\n",
    "model.compile(optimizer = 'adam'\n",
    "              , loss = 'categorical_crossentropy'\n",
    "              , metrics = ['accuracy']\n",
    "             )\n",
    "\n",
    "# Train the model for more epochs\n",
    "history = model.fit(X_train\n",
    "                    , y_train\n",
    "                    , epochs = 20\n",
    "                    , verbose = 2\n",
    "                    , validation_data = (X_test, y_test)\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "476d3b5f-9dfd-4aab-9064-c001ec0d36c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 652us/step - accuracy: 0.9806 - loss: 0.1299\n",
      "Test Accuracy: 0.9843999743461609\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a21a7fbe-259f-4191-a24d-f065c6c89a3e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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",
       "│ 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\">784</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">401,920</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\">256</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">131,328</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\">128</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">32,896</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\">1,290</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",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m512\u001b[0m)            │       \u001b[38;5;34m401,920\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m256\u001b[0m)            │       \u001b[38;5;34m131,328\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m)            │        \u001b[38;5;34m32,896\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;34m1,290\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\">1,702,304</span> (6.49 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,702,304\u001b[0m (6.49 MB)\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\">567,434</span> (2.16 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m567,434\u001b[0m (2.16 MB)\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"
    },
    {
     "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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,134,870</span> (4.33 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m1,134,870\u001b[0m (4.33 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30488e62-f883-4604-8d1e-21c6b0c8bf07",
   "metadata": {},
   "source": [
    "#### Results\n",
    "Model performed with 98.44% accuracy. *Consider* Can we do better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d7643a5-0784-4ccb-b5a7-11de007cfb61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 826us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      0.99      0.99      1135\n",
      "           2       0.98      0.99      0.98      1032\n",
      "           3       0.98      0.99      0.98      1010\n",
      "           4       0.99      0.98      0.98       982\n",
      "           5       0.99      0.98      0.98       892\n",
      "           6       0.99      0.99      0.99       958\n",
      "           7       0.98      0.98      0.98      1028\n",
      "           8       0.98      0.98      0.98       974\n",
      "           9       0.98      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.98     10000\n",
      "   macro avg       0.98      0.98      0.98     10000\n",
      "weighted avg       0.98      0.98      0.98     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = np.argmax(y_pred, axis = 1)\n",
    "y_true = np.argmax(y_test, axis = 1)\n",
    "\n",
    "# Classification report\n",
    "print(classification_report(y_true, y_pred_classes))\n",
    "\n",
    "# Confusion matrix\n",
    "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
    "plt.figure(figsize = (10, 8))\n",
    "sns.heatmap(conf_matrix, annot = True, fmt = 'd')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "04e26031-0849-4b36-acb6-f15aa4932cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "867a5926-0e3e-4612-931a-cde30d2aaf33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "935fb781-c351-4d2e-a3d9-bc8d3475b15e",
   "metadata": {},
   "source": [
    "### Feed-Forward with Data Augmentation\n",
    "Transforming images to improve model's generalizability, will this also improve performance?\n",
    "\n",
    "Randomly, using keras's ImageDataGenerator() the images will be:\n",
    "- Rotated by up to 10 degrees\n",
    "- Zoomed in by up to 10%\n",
    "- Shifted horizontally by up to 10% of the image's width\n",
    "- Shifted vertically up to 10% of the image's height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "90289ae5-7e15-40b3-bac6-179c9ed977ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001b[1m  31/1875\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.8024 - loss: 1.5606  "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\missm\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 5ms/step - accuracy: 0.9033 - loss: 0.3875 - val_accuracy: 0.9797 - val_loss: 0.0671\n",
      "Epoch 2/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9556 - loss: 0.1491 - val_accuracy: 0.9819 - val_loss: 0.0582\n",
      "Epoch 3/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9630 - loss: 0.1242 - val_accuracy: 0.9831 - val_loss: 0.0571\n",
      "Epoch 4/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9651 - loss: 0.1164 - val_accuracy: 0.9827 - val_loss: 0.0568\n",
      "Epoch 5/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9687 - loss: 0.1043 - val_accuracy: 0.9846 - val_loss: 0.0528\n",
      "Epoch 6/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9696 - loss: 0.1015 - val_accuracy: 0.9852 - val_loss: 0.0496\n",
      "Epoch 7/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9702 - loss: 0.0967 - val_accuracy: 0.9835 - val_loss: 0.0542\n",
      "Epoch 8/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9730 - loss: 0.0906 - val_accuracy: 0.9835 - val_loss: 0.0556\n",
      "Epoch 9/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9731 - loss: 0.0897 - val_accuracy: 0.9838 - val_loss: 0.0555\n",
      "Epoch 10/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9744 - loss: 0.0835 - val_accuracy: 0.9856 - val_loss: 0.0492\n",
      "Epoch 11/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9747 - loss: 0.0818 - val_accuracy: 0.9847 - val_loss: 0.0535\n",
      "Epoch 12/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9750 - loss: 0.0821 - val_accuracy: 0.9820 - val_loss: 0.0634\n",
      "Epoch 13/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9767 - loss: 0.0813 - val_accuracy: 0.9850 - val_loss: 0.0502\n",
      "Epoch 14/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9768 - loss: 0.0770 - val_accuracy: 0.9873 - val_loss: 0.0466\n",
      "Epoch 15/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9781 - loss: 0.0727 - val_accuracy: 0.9847 - val_loss: 0.0507\n",
      "Epoch 16/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9784 - loss: 0.0732 - val_accuracy: 0.9866 - val_loss: 0.0422\n",
      "Epoch 17/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9792 - loss: 0.0718 - val_accuracy: 0.9870 - val_loss: 0.0435\n",
      "Epoch 18/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9791 - loss: 0.0704 - val_accuracy: 0.9837 - val_loss: 0.0542\n",
      "Epoch 19/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9786 - loss: 0.0689 - val_accuracy: 0.9857 - val_loss: 0.0448\n",
      "Epoch 20/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 6ms/step - accuracy: 0.9791 - loss: 0.0663 - val_accuracy: 0.9871 - val_loss: 0.0479\n"
     ]
    }
   ],
   "source": [
    "# Create an instance of the ImageDataGenerator class with the desired transformations\n",
    "datagen = ImageDataGenerator(rotation_range = 10          # Randomly rotate images by up to 10 degrees\n",
    "                             , zoom_range = 0.10          # Randomly zoom in on images by up to 10%\n",
    "                             , width_shift_range = 0.10    # Randomly shift images horizontally by up to 10% of the width\n",
    "                             , height_shift_range = 0.10   # Randomly shift images vertically by up to 10% of the height\n",
    "                            )\n",
    "\n",
    "# Fit the ImageDataGenerator to the training data\n",
    "datagen.fit(X_train)\n",
    "\n",
    "# Use the augmented data generator to train the model\n",
    "history = model.fit(datagen.flow(X_train, y_train, batch_size = 32)\n",
    "                    , epochs = 20\n",
    "                    , validation_data = (X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b65f6bd-5ff4-4e9b-a736-0acf17e44695",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 575us/step - accuracy: 0.9845 - loss: 0.0625\n",
      "Test Accuracy: 0.9871000051498413\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "169b53a2-2f67-49c4-ade7-97a3c0ce3384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure to display the images\n",
    "plt.figure(figsize = (10, 10))\n",
    "\n",
    "# Select a random image from the training set\n",
    "sample_image = X_train[0]\n",
    "\n",
    "# Generate augmented images and display them\n",
    "for i in range(9):\n",
    "    augmented_image = datagen.random_transform(sample_image)\n",
    "    plt.suptitle('Manipulated Digit Images')\n",
    "    plt.subplot(3, 3, i + 1)\n",
    "    plt.imshow(augmented_image.squeeze(), cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2d92a8e-28a7-4b1d-8949-92dff8059763",
   "metadata": {},
   "source": [
    "#### Results\n",
    "Model with data augmentation performed with 98.68% accuracy, better than a basic feed-forward neural network and likely more generalizable. Would a Convolutional Neural Network, ideal for image processing, perform better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6228b1fe-0362-4c2d-9725-7f932f8905c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 662us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      1.00      0.99      1135\n",
      "           2       0.98      0.99      0.99      1032\n",
      "           3       0.98      0.99      0.99      1010\n",
      "           4       0.99      0.98      0.99       982\n",
      "           5       0.98      0.98      0.98       892\n",
      "           6       1.00      0.98      0.99       958\n",
      "           7       0.98      0.99      0.98      1028\n",
      "           8       1.00      0.98      0.99       974\n",
      "           9       0.98      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.99     10000\n",
      "   macro avg       0.99      0.99      0.99     10000\n",
      "weighted avg       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = np.argmax(y_pred, axis = 1)\n",
    "y_true = np.argmax(y_test, axis = 1)\n",
    "\n",
    "# Classification report\n",
    "print(classification_report(y_true, y_pred_classes))\n",
    "\n",
    "# Confusion matrix\n",
    "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
    "plt.figure(figsize = (10, 8))\n",
    "sns.heatmap(conf_matrix, annot = True, fmt = 'd')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "723c9e4e-1415-4d9b-8a23-5d48cfc6ccea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f900274d-c44d-4a35-a055-991d90b8443f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f9328ee-3308-4d73-9561-7e9daf050bc3",
   "metadata": {},
   "source": [
    "### Convolutional Neural Network\n",
    "Can we further improve my predictions by using a convolutional neural network?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2349ad9a-94fe-41fe-a4f4-acd1655f370c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\missm\\anaconda3\\Lib\\site-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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m  20/1875\u001b[0m \u001b[37m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m10s\u001b[0m 6ms/step - accuracy: 0.1281 - loss: 2.2858"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\missm\\anaconda3\\Lib\\site-packages\\keras\\src\\trainers\\data_adapters\\py_dataset_adapter.py:121: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
      "  self._warn_if_super_not_called()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 5ms/step - accuracy: 0.7664 - loss: 0.7030 - val_accuracy: 0.9858 - val_loss: 0.0430\n",
      "Epoch 2/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9580 - loss: 0.1451 - val_accuracy: 0.9885 - val_loss: 0.0366\n",
      "Epoch 3/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9704 - loss: 0.0990 - val_accuracy: 0.9888 - val_loss: 0.0327\n",
      "Epoch 4/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9770 - loss: 0.0792 - val_accuracy: 0.9918 - val_loss: 0.0271\n",
      "Epoch 5/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9826 - loss: 0.0598 - val_accuracy: 0.9925 - val_loss: 0.0242\n",
      "Epoch 6/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9829 - loss: 0.0582 - val_accuracy: 0.9906 - val_loss: 0.0301\n",
      "Epoch 7/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9845 - loss: 0.0548 - val_accuracy: 0.9935 - val_loss: 0.0217\n",
      "Epoch 8/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9848 - loss: 0.0510 - val_accuracy: 0.9938 - val_loss: 0.0200\n",
      "Epoch 9/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9867 - loss: 0.0460 - val_accuracy: 0.9915 - val_loss: 0.0254\n",
      "Epoch 10/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9872 - loss: 0.0455 - val_accuracy: 0.9936 - val_loss: 0.0234\n",
      "Epoch 11/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9870 - loss: 0.0443 - val_accuracy: 0.9934 - val_loss: 0.0235\n",
      "Epoch 12/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9883 - loss: 0.0389 - val_accuracy: 0.9913 - val_loss: 0.0315\n",
      "Epoch 13/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9888 - loss: 0.0378 - val_accuracy: 0.9932 - val_loss: 0.0238\n",
      "Epoch 14/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9897 - loss: 0.0372 - val_accuracy: 0.9913 - val_loss: 0.0259\n",
      "Epoch 15/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9897 - loss: 0.0376 - val_accuracy: 0.9935 - val_loss: 0.0233\n",
      "Epoch 16/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9909 - loss: 0.0344 - val_accuracy: 0.9922 - val_loss: 0.0242\n",
      "Epoch 17/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9902 - loss: 0.0352 - val_accuracy: 0.9934 - val_loss: 0.0223\n",
      "Epoch 18/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9898 - loss: 0.0359 - val_accuracy: 0.9925 - val_loss: 0.0241\n",
      "Epoch 19/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 5ms/step - accuracy: 0.9901 - loss: 0.0353 - val_accuracy: 0.9946 - val_loss: 0.0202\n",
      "Epoch 20/20\n",
      "\u001b[1m1875/1875\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 5ms/step - accuracy: 0.9907 - loss: 0.0334 - val_accuracy: 0.9943 - val_loss: 0.0198\n"
     ]
    }
   ],
   "source": [
    "# Define the CNN model\n",
    "cnn_model = Sequential([Conv2D(32, (3, 3), activation = 'relu', input_shape = (28, 28, 1))\n",
    "                        , MaxPooling2D((2, 2))\n",
    "                        , Conv2D(64, (3, 3), activation = 'relu')\n",
    "                        , MaxPooling2D((2, 2))\n",
    "                        , Conv2D(64, (3, 3), activation = 'relu')\n",
    "                        , Flatten()\n",
    "                        , Dense(128, activation = 'relu')\n",
    "                        , Dropout(0.5) # Regularization to prevent overfitting\n",
    "                        , Dense(10, activation = 'softmax')\n",
    "                       ])\n",
    "\n",
    "# Compile the model\n",
    "cnn_model.compile(optimizer = 'adam'\n",
    "                  , loss = 'categorical_crossentropy'\n",
    "                  , metrics = ['accuracy']\n",
    "                 )\n",
    "\n",
    "# Use the augmented data generator to train the model\n",
    "history = cnn_model.fit(datagen.flow(X_train, y_train, batch_size = 32)\n",
    "                        , epochs = 20\n",
    "                        , validation_data = (X_test, y_test)\n",
    "                       )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d70e8815-c1cd-44a5-ad08-a1ac90102c5e",
   "metadata": {},
   "outputs": [
    {
     "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 - accuracy: 0.9928 - loss: 0.0248\n",
      "Test Accuracy: 0.9943000078201294\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = cnn_model.evaluate(X_test, y_test)\n",
    "print(f'Test Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "894c37e3-53d6-48d4-843c-20da3fa8b9f8",
   "metadata": {},
   "source": [
    "#### Results\n",
    "The CNN performed with 99.35% accuracy, better than the basic feed-forward neural network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "98c930e9-3ad4-497b-a355-ff76c3826f12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 640us/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       0.99      1.00      0.99      1135\n",
      "           2       0.98      0.99      0.99      1032\n",
      "           3       0.98      0.99      0.99      1010\n",
      "           4       0.99      0.98      0.99       982\n",
      "           5       0.98      0.98      0.98       892\n",
      "           6       1.00      0.98      0.99       958\n",
      "           7       0.98      0.99      0.98      1028\n",
      "           8       1.00      0.98      0.99       974\n",
      "           9       0.98      0.98      0.98      1009\n",
      "\n",
      "    accuracy                           0.99     10000\n",
      "   macro avg       0.99      0.99      0.99     10000\n",
      "weighted avg       0.99      0.99      0.99     10000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred_classes = np.argmax(y_pred, axis = 1)\n",
    "y_true = np.argmax(y_test, axis = 1)\n",
    "\n",
    "# Classification report\n",
    "print(classification_report(y_true, y_pred_classes))\n",
    "\n",
    "# Confusion matrix\n",
    "conf_matrix = confusion_matrix(y_true, y_pred_classes)\n",
    "plt.figure(figsize = (10, 8))\n",
    "sns.heatmap(conf_matrix, annot = True, fmt = 'd')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ccd432a1-2aa7-45c3-9068-f34044519894",
   "metadata": {},
   "source": [
    "A quick look at the model's incorrect predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fee31538-709e-4103-849d-af469bdc01a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Find indices where the predictions don't match the true labels\n",
    "incorrect_indices = np.where(y_pred_classes != y_true)[0]\n",
    "\n",
    "# Select a sample of inaccurately predicted images\n",
    "num_samples = 5\n",
    "sample_indices = np.random.choice(incorrect_indices, num_samples, replace=False)\n",
    "\n",
    "# Plot the sampled images stacked vertically\n",
    "plt.figure(figsize=(8, 10))\n",
    "\n",
    "for i, idx in enumerate(sample_indices):\n",
    "    plt.subplot(num_samples, 1, i + 1)\n",
    "    plt.imshow(X_test[idx].reshape(28, 28), cmap='gray')  # Assuming images are grayscale\n",
    "    plt.title(f\"True: {y_true[idx]}, Predicted: {y_pred_classes[idx]}\")\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e475955f-23c7-4d4b-ab96-4ba1e449e2d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b19e0d7790>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd7a9961-011e-4fee-8f57-8b1ea4e97d91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Validation'], loc = 'upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "261cbd7c-3ba7-445e-a71a-5266b21021ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}