Keras_Mnist.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.models import Model\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.optimizers import RMSprop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "img_rows, img_cols = 28, 28\n",
    "x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n",
    "input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "\n",
    "\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "\n",
    "y_train = keras.utils.to_categorical(y_train, 10)\n",
    "y_test = keras.utils.to_categorical(y_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_9 (Conv2D)            (None, 27, 27, 32)        160       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 12, 12, 64)        8256      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1024)              2360320   \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 2,378,986.0\n",
      "Trainable params: 2,378,986.0\n",
      "Non-trainable params: 0.0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(32, kernel_size=(2, 2), activation='relu',input_shape=(28,28,1)))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Conv2D(64, (2, 2), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024, activation='relu'))\n",
    "model.add(Dropout(0.8))\n",
    "model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "# model.add(Dense(64, activation='relu', input_shape=(784,)))\n",
    "# # Dropout layers remove features and fight overfitting\n",
    "# model.add(Dropout(0.1))\n",
    "# model.add(Dense(64, activation='relu'))\n",
    "# model.add(Dropout(0.1))\n",
    "# # End with a number of units equal to the number of classes we have for our outcome\n",
    "# model.add(Dense(10, activation='softmax'))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "# Compile the model to put it all together.\n",
    "model.compile(loss='categorical_crossentropy',optimizer=RMSprop(), metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 98s - loss: 0.2565 - acc: 0.9199 - val_loss: 0.0745 - val_acc: 0.9755\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 113s - loss: 0.0913 - acc: 0.9720 - val_loss: 0.0456 - val_acc: 0.9846\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 107s - loss: 0.0698 - acc: 0.9790 - val_loss: 0.0418 - val_acc: 0.9846\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 108s - loss: 0.0581 - acc: 0.9825 - val_loss: 0.0395 - val_acc: 0.9864\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 104s - loss: 0.0510 - acc: 0.9846 - val_loss: 0.0354 - val_acc: 0.9882\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 112s - loss: 0.0461 - acc: 0.9858 - val_loss: 0.0320 - val_acc: 0.9900\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 149s - loss: 0.0435 - acc: 0.9865 - val_loss: 0.0340 - val_acc: 0.9887\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 127s - loss: 0.0401 - acc: 0.9886 - val_loss: 0.0333 - val_acc: 0.9894\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 105s - loss: 0.0404 - acc: 0.9878 - val_loss: 0.0369 - val_acc: 0.9887\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 104s - loss: 0.0377 - acc: 0.9888 - val_loss: 0.0331 - val_acc: 0.9909\n",
      "Test loss: 0.033109523836940935\n",
      "Test accuracy: 0.9909\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train,\n",
    "                    batch_size=128,\n",
    "                    epochs=10,\n",
    "                    verbose=1,\n",
    "                    validation_data=(x_test, y_test))\n",
    "score = model.evaluate(x_test, y_test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x263c4cbb2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 7\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "\n",
    "def print_test_image(image_index):\n",
    "    image = x_test[image_index]\n",
    "    image = np.array(image, dtype='float')\n",
    "    pixels = image.reshape((28, 28))\n",
    "    plt.imshow(pixels, cmap='gray')\n",
    "    plt.show()\n",
    "\n",
    "image_index = 0\n",
    "print_test_image(image_index)\n",
    "print(\"Predicted:\",np.argmax(model.predict(x_test[image_index].reshape((1,28,28,1)))[0]))"
   ]
  }
 ],
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