Assignment2-3CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.layers import Conv2D, MaxPooling2D, Flatten,Dense\n",
    "from keras.models import Sequential\n",
    "from keras.datasets import mnist\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n",
      "(60000,)\n",
      "(10000, 28, 28)\n",
      "(10000,)\n"
     ]
    }
   ],
   "source": [
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "epochs = 3\n",
    "\n",
    "X_train = X_train.reshape(60000,28,28,1)\n",
    "X_test = X_test.reshape(10000,28,28,1)\n",
    "X_train = X_train.astype('float32')\n",
    "X_test = X_test.astype('float32')\n",
    "X_train /= 255.0\n",
    "X_test /= 255.0\n",
    "y_train = to_categorical(y_train,num_classes)\n",
    "y_test = to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28, 1)\n",
      "(60000, 10)\n",
      "(10000, 28, 28, 1)\n",
      "(10000, 10)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(Conv2D(32, kernel_size=(5,5), input_shape=(28,28,1), padding='same', activation='relu'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(MaxPooling2D())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 28, 28, 32)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 14, 14, 64)        51264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 3136)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 1024)              3212288   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 3,274,634\n",
      "Trainable params: 3,274,634\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "cnn.add(Conv2D(64, kernel_size=(5,5), padding='same', activation='relu'))\n",
    "cnn.add(MaxPooling2D())\n",
    "cnn.add(Flatten())\n",
    "cnn.add(Dense(1024, activation='relu'))\n",
    "cnn.add(Dense(10,activation='softmax'))\n",
    "cnn.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "print(cnn.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 263s 4ms/step - loss: 0.0963 - accuracy: 0.9700 - val_loss: 0.0363 - val_accuracy: 0.9886\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 257s 4ms/step - loss: 0.0352 - accuracy: 0.9892 - val_loss: 0.0268 - val_accuracy: 0.9914\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 285s 5ms/step - loss: 0.0250 - accuracy: 0.9923 - val_loss: 0.0134 - val_accuracy: 0.9958\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 252s 4ms/step - loss: 0.0177 - accuracy: 0.9943 - val_loss: 0.0134 - val_accuracy: 0.9958\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 258s 4ms/step - loss: 0.0148 - accuracy: 0.9955 - val_loss: 0.0074 - val_accuracy: 0.9977\n"
     ]
    }
   ],
   "source": [
    "history_cnn = cnn.fit(X_train,y_train,epochs=5,verbose=1,validation_data=(X_train,y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "loss=[0.0963,0.0352,0.0250,0.0177,0.0148]\n",
    "acc=[0.9700,0.9892,0.9923,0.9943,0.9955]\n",
    "epochs=[1,2,3,4,5]\n",
    "plt.plot(epochs,acc)\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epochs')\n",
    "#plt.plot(history_cnn.history['loss'])\n",
    "#plt.plot(history_cnn.history['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Epochs')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(epochs,loss)\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epochs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}