"""
Ejecutado en GPU de Floyd
Resultados:
2017-08-15 07:56:28,354 INFO - Test loss: 0.0185990586012
2017-08-15 07:56:28,354 INFO - Test accuracy: 0.9943
"""

from __future__ import print_function
import keras
from keras.datasets import 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

batch_size = 128
num_classes = 10
epochs = 12

# 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) = 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 = (5, 5),
	activation = 'relu',
	input_shape = input_shape))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64,
	(5, 5),
	activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dropout(0.2))
model.add(Dense(1024,
	activation = 'relu'))

model.add(Dropout(0.4))
model.add(Dense(num_classes,
	activation = 'softmax'))

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

model.fit(x_train, y_train,
	batch_size = batch_size,
	epochs = epochs,
	verbose = 0,
	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])

