train.py

from __future__ import print_function

import json
import keras
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Dense, Dropout, Input
from keras.optimizers import RMSprop

batch_size = 128
num_classes = 10
epochs = 2

# 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)

inp = Input((784,))
x = Dense(512, activation='relu')(inp)
x = Dropout(0.2)(x)
x = Dense(512, activation='relu')(x)
x = Dropout(0.2)(x)
out = Dense(num_classes, activation='softmax')(x)

model = Model(inp, out)

model.summary()

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

checkpoint = keras.callbacks.ModelCheckpoint(
    filepath='model_{epoch:02d}.hdf5', monitor='val_loss', verbose=0, save_best_only=False,
    save_weights_only=False, mode='auto', period=1)

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

with open('history.json', 'w') as f:
    json_hist = json.dumps(history.history)
    f.write(json_hist)