CNN_for_Mnistdata.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test)= tf.keras.datasets.mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000,)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,7))\n",
    "for x in range(20):\n",
    "    plt.subplot(5, 4, x+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(x_train[x])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test=x_test.reshape((x_test.shape[0],28, 28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=x_train.reshape((x_train.shape[0],28, 28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Conv2D(32,(3,3), activation='relu', input_shape=(28, 28, 1)),\n",
    "    tf.keras.layers.MaxPool2D((2,2)),\n",
    "    tf.keras.layers.Conv2D(64,(3,3), activation='relu'),\n",
    "    tf.keras.layers.MaxPool2D((2,2)),\n",
    "    tf.keras.layers.Conv2D(64,(3,3), activation='relu'),\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(64, activation='relu'),\n",
    "    tf.keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 26, 26, 32)        320       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 11, 11, 64)        18496     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 3, 3, 64)          36928     \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 576)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 64)                36928     \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                650       \n",
      "=================================================================\n",
      "Total params: 93,322\n",
      "Trainable params: 93,322\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=model.compile(optimizer='adam',\n",
    "             loss='sparse_categorical_crossentropy',\n",
    "             metrics=['acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 87s 1ms/sample - loss: 0.2249 - acc: 0.9458 - val_loss: 0.0764 - val_acc: 0.9763\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 81s 1ms/sample - loss: 0.0630 - acc: 0.9808 - val_loss: 0.0704 - val_acc: 0.9801\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 82s 1ms/sample - loss: 0.0504 - acc: 0.9843 - val_loss: 0.0474 - val_acc: 0.9859\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 80s 1ms/sample - loss: 0.0438 - acc: 0.9864 - val_loss: 0.0488 - val_acc: 0.9866\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 78s 1ms/sample - loss: 0.0367 - acc: 0.9887 - val_loss: 0.0542 - val_acc: 0.9840\n"
     ]
    }
   ],
   "source": [
    "history=model.fit(x_train, y_train, epochs=5, validation_data=(x_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\ANACONDA\\lib\\site-packages\\ipykernel_launcher.py:6: MatplotlibDeprecationWarning: Unrecognized location 'upper_right'. Falling back on 'best'; valid locations are\n",
      "\tbest\n",
      "\tupper right\n",
      "\tupper left\n",
      "\tlower left\n",
      "\tlower right\n",
      "\tright\n",
      "\tcenter left\n",
      "\tcenter right\n",
      "\tlower center\n",
      "\tupper center\n",
      "\tcenter\n",
      "This will raise an exception in 3.3.\n",
      "  \n"
     ]
    },
    {
     "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(history.history['acc'], label='Accuracy')\n",
    "plt.plot(history.history['val_acc'], label='Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.ylim(0.8,1.2)\n",
    "plt.legend(loc='upper_right')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 4s - loss: 0.0324 - acc: 0.9840\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc= model.evaluate(x_test, y_test, verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss:  0.05415225564179127\n",
      "Test Accuracy:  0.984\n"
     ]
    }
   ],
   "source": [
    "print('Test Loss: ', test_loss)\n",
    "print('Test Accuracy: ', test_acc)"
   ]
  },
  {
   "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
}