test_keras.py

"""
Trains a simple deep NN on the MNIST dataset.

Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
"""

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import tensorflow as tf

GRAPH = tf.get_default_graph()


def run():
    with GRAPH.as_default():
        batch_size = 128
        num_classes = 10
        # epochs = 20
        epochs = 4

        # the data, split between train and test sets
        (x_train, y_train), (x_test, y_test) = mnist.load_data()

        x_train = x_train.reshape(60000, 784)
        x_test = x_test.reshape(10000, 784)
        x_train = x_train.astype('float32')
        x_test = x_test.astype('float32')
        x_train /= 255
        x_test /= 255
        print(x_train.shape[0], 'train samples')
        print(x_test.shape[0], 'test samples')

        # convert class vectors to binary class matrices
        y_train = keras.utils.to_categorical(y_train, num_classes)
        y_test = keras.utils.to_categorical(y_test, num_classes)

        model = Sequential()
        model.add(Dense(512, activation='relu', input_shape=(784,)))
        model.add(Dropout(0.2))
        model.add(Dense(512, activation='relu'))
        model.add(Dropout(0.2))
        model.add(Dense(num_classes, activation='softmax'))

        model.summary()

        print('before compiling')

        model.compile(loss='categorical_crossentropy',
                      optimizer=RMSprop(),
                      metrics=['accuracy'])

        print('after compiling')

        history = model.fit(x_train, y_train,
                            batch_size=batch_size,
                            epochs=epochs,
                            verbose=1,
                            validation_data=(x_test, y_test))

        print('after training')

        score = model.evaluate(x_test, y_test, verbose=0)

        print('after evaluating')

        print('Test loss:', score[0])
        print('Test accuracy:', score[1])