from __future__ import print_function
import keras
from keras.datasets import fashion_mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
from keras.utils import plot_model
import matplotlib.pyplot as plt

batch_size = 128
num_classes = 10
epochs = 12
#epochs = 2

# input image dimensions
img_rows, img_cols = 28, 28

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

if K.image_data_format() == \’channels_first\’:
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype(\’float32\’)
x_test = x_test.astype(\’float32\’)
x_train /= 255
x_test /= 255
print(\’x_train shape:\’, x_train.shape)
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(Conv2D(32, kernel_size=(3, 3),
                 activation=\’relu\’,
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation=\’relu\’))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation=\’relu\’))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation=\’softmax\’))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=[\’accuracy\’])

#log = model.fit(X_train, Y_train,   
#          batch_size=batch_size, nb_epoch=num_epochs,  
#          verbose=1, validation_split=0.1)  

log = model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print(\’Test loss:\’, score[0])
print(\’Test accuracy:\’, score[1])

plt.figure(\’acc\’)  
plt.subplot(2, 1, 1)  
plt.plot(log.history[\’acc\’],\’r–\’,label=\’Training Accuracy\’)  
plt.plot(log.history[\’val_acc\’],\’r-\’,label=\’Validation Accuracy\’)  
plt.legend(loc=\’best\’)  
plt.xlabel(\’Epochs\’)  
plt.axis([0, epochs, 0.9, 1])  
plt.figure(\’loss\’)  
plt.subplot(2, 1, 2)  
plt.plot(log.history[\’loss\’],\’b–\’,label=\’Training Loss\’)  
plt.plot(log.history[\’val_loss\’],\’b-\’,label=\’Validation Loss\’)  
plt.legend(loc=\’best\’)  
plt.xlabel(\’Epochs\’)  
plt.axis([0, epochs, 0, 1])  
plt.show()