Implementation of Convolutional Neural Network using LeNet Architecture.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import mnist\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "labels = []\n",
    "for row in open('A_Z Handwritten Data.csv'):\n",
    "    \n",
    "    row = row.split(\",\")\n",
    "    label = int(row[0])\n",
    "    image = np.array([int(x) for x in row[1:]], dtype=\"uint8\")\n",
    "    image = image.reshape((28, 28))\n",
    "    data.append(image)\n",
    "    labels.append(label)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "azdata = np.array(data, dtype=\"float32\")\n",
    "azlabels = np.array(labels, dtype=\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "    \n",
    "((trainData, trainLabels), (testData, testLabels)) = mnist.load_data()\n",
    "digitsdata = np.vstack([trainData, testData])\n",
    "digitslabels = np.hstack([trainLabels, testLabels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense , Dropout , Flatten , Conv2D , MaxPooling2D\n",
    "from tensorflow.keras.optimizers import SGD\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "azlabels += 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.vstack([azdata, digitsdata])\n",
    "labels = np.hstack([azlabels, digitslabels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "(trainX, testX, trainY, testY) = train_test_split(data,labels, test_size=0.20, stratify=labels, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX=trainX.reshape(trainX.shape[0],28,28,1)\n",
    "testX=testX.reshape(testX.shape[0],28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape=(28,28,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainX=trainX.astype('float32')\n",
    "testX=testX.astype('float32')\n",
    "trainX/=255\n",
    "testX/=255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainY=keras.utils.to_categorical(trainY,36)\n",
    "testY=keras.utils.to_categorical(testY,36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(28, 28, 1..., activation=\"relu\")`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (3, 3), activation=\"relu\")`\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=128)`\n",
      "  import sys\n",
      "C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=36)`\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "model.add(Conv2D(32,3,3,input_shape=(28,28,1),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(64,3,3,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(output_dim=128,activation='relu'))\n",
    "\n",
    "model.add(Dense(output_dim=36,activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.categorical_crossentropy,optimizer='adam',\n",
    "             metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\kevin\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 283168 samples, validate on 70792 samples\n",
      "Epoch 1/3\n",
      "283168/283168 [==============================] - 226s 800us/step - loss: 0.3643 - accuracy: 0.8582 - val_loss: 0.1361 - val_accuracy: 0.9622\n",
      "Epoch 2/3\n",
      "283168/283168 [==============================] - 227s 803us/step - loss: 0.1140 - accuracy: 0.9674 - val_loss: 0.1029 - val_accuracy: 0.9712\n",
      "Epoch 3/3\n",
      "283168/283168 [==============================] - 226s 798us/step - loss: 0.0852 - accuracy: 0.9757 - val_loss: 0.1018 - val_accuracy: 0.9719\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x25787715780>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(trainX,trainY,batch_size=128,epochs=3,verbose=1,validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_file=\"Pickle_Model.pkl\" \n",
    "pickle.dump(model, open(model_file, 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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