06-DEEP-LEARNING/6.2-keras-cnn-mnist.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convolutional Neural Networks for Image Classification\n",
    "\n",
    "The Convolutional Neural Network (CNN or ConvNet) is a subtype of Neural Networks that is mainly used for applications in image and speech recognition. Its built-in convolutional layer reduces the high dimensionality of images without losing its information. That is why CNNs are especially suited for this use case."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing MNIST Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.datasets import mnist\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing Data\n",
    "\n",
    "We first need to make sure the labels will be understandable by our CNN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### X Values\n",
    "We should normalize the X data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train / 255\n",
    "x_test = x_test / 255"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Y values\n",
    "We need to translate this to be \"one hot encoded\" so our CNN can understand, otherwise it will think this is some sort of regression problem on a continuous axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_cat_train = to_categorical(y_train, 10)\n",
    "y_cat_test = to_categorical(y_test, 10)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reshaping the Data\n",
    "\n",
    "Right now our data is 60,000 images stored in 28 by 28 pixel array formation. \n",
    "\n",
    "This is correct for a CNN, but we need to add one more dimension to show we're dealing with 1 RGB channel (since technically the images are in black and white, only showing values from 0-255 on a single channel), an color image would have 3 dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = x_train.reshape(60000, 28, 28, 1)\n",
    "x_test = x_test.reshape(10000, 28, 28, 1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-08 13:47:35.060798: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.1427 - accuracy: 0.9567\n",
      "Epoch 2/2\n",
      "1875/1875 [==============================] - 12s 6ms/step - loss: 0.0500 - accuracy: 0.9852\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x17ed2fe80>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(28, 28, 1), activation=\"relu\",))\n",
    "\n",
    "model.add(MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "\n",
    "model.add(Dense(128, activation=\"relu\"))\n",
    "model.add(Dense(10, activation=\"softmax\"))\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(x_train, y_cat_train, epochs=2)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.0430 - accuracy: 0.9870\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.04304957762360573, 0.9869999885559082]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_test, y_cat_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.99      0.99       980\n",
      "           1       1.00      1.00      1.00      1135\n",
      "           2       0.98      0.99      0.98      1032\n",
      "           3       1.00      0.99      0.99      1010\n",
      "           4       0.98      0.99      0.99       982\n",
      "           5       0.99      0.99      0.99       892\n",
      "           6       0.99      0.99      0.99       958\n",
      "           7       0.99      0.98      0.98      1028\n",
      "           8       0.97      0.99      0.98       974\n",
      "           9       1.00      0.96      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"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "\n",
    "predictions = np.argmax(model.predict(x_test), axis=-1)\n",
    "print(classification_report(y_test, predictions))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cv-env",
   "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"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "140c02b74bade385498b1db38246b562af42a2db82208b699a6aa206525ab17b"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}