keras mnsit 99% .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (60000, 28, 28, 1)\n",
      "60000 train samples\n",
      "10000 test samples\n",
      "Train on 60000 samples, validate on 10000 samples\n",
      "Epoch 1/12\n",
      "60000/60000 [==============================] - 145s 2ms/step - loss: 0.2710 - acc: 0.9167 - val_loss: 0.0670 - val_acc: 0.9789\n",
      "Epoch 2/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0892 - acc: 0.9740 - val_loss: 0.0424 - val_acc: 0.9857\n",
      "Epoch 3/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0672 - acc: 0.9797 - val_loss: 0.0389 - val_acc: 0.9860\n",
      "Epoch 4/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0566 - acc: 0.9832 - val_loss: 0.0327 - val_acc: 0.9889\n",
      "Epoch 5/12\n",
      "60000/60000 [==============================] - 140s 2ms/step - loss: 0.0475 - acc: 0.9853 - val_loss: 0.0305 - val_acc: 0.9894\n",
      "Epoch 6/12\n",
      "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0425 - acc: 0.9868 - val_loss: 0.0275 - val_acc: 0.9911\n",
      "Epoch 7/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0380 - acc: 0.9885 - val_loss: 0.0315 - val_acc: 0.9900\n",
      "Epoch 8/12\n",
      "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0343 - acc: 0.9895 - val_loss: 0.0285 - val_acc: 0.9914\n",
      "Epoch 9/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0314 - acc: 0.9900 - val_loss: 0.0287 - val_acc: 0.9908\n",
      "Epoch 10/12\n",
      "60000/60000 [==============================] - 138s 2ms/step - loss: 0.0312 - acc: 0.9902 - val_loss: 0.0278 - val_acc: 0.9909\n",
      "Epoch 11/12\n",
      "60000/60000 [==============================] - 140s 2ms/step - loss: 0.0288 - acc: 0.9911 - val_loss: 0.0389 - val_acc: 0.9897\n",
      "Epoch 12/12\n",
      "60000/60000 [==============================] - 137s 2ms/step - loss: 0.0262 - acc: 0.9920 - val_loss: 0.0285 - val_acc: 0.9909\n",
      "Test loss: 0.028455203669045658\n",
      "Test accuracy: 0.9909\n"
     ]
    }
   ],
   "source": [
    "'''Trains a simple convnet on the MNIST dataset.\n",
    "\n",
    "Gets to 99.25% test accuracy after 12 epochs\n",
    "(there is still a lot of margin for parameter tuning).\n",
    "16 seconds per epoch on a GRID K520 GPU.\n",
    "'''\n",
    "\n",
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras import backend as K\n",
    "\n",
    "batch_size = 128\n",
    "num_classes = 10\n",
    "epochs = 12\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "\n",
    "# the data, split between train and test sets\n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "if K.image_data_format() == 'channels_first':\n",
    "    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n",
    "    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n",
    "    input_shape = (1, img_rows, img_cols)\n",
    "else:\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",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "                 activation='relu',\n",
    "                 input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,\n",
    "              optimizer=keras.optimizers.Adadelta(),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train, y_train,\n",
    "          batch_size=batch_size,\n",
    "          epochs=epochs,\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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbecd5c8710>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_test[0].reshape(28,28), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model.save('mldata\\keras_digits.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict_classes(x_test[0].reshape(1,28,28,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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