model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3,3),padding='SAME', input_shape=X[0].shape))

model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), dim_ordering='th'))

model.add(Conv2D(filters=64, kernel_size=(3,3), padding='SAME'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), dim_ordering='th'))
model.add(Dropout(rate=0.4))

model.add(Conv2D(filters=128, kernel_size=(3,3), padding='SAME'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2), dim_ordering='th'))
model.add(Dropout(rate=0.35))

#model.add(Conv2D(filters=64, kernel_size=(3,3), padding='SAME'))
#model.add(Activation('relu'))
#model.add(MaxPooling2D(pool_size=(2,2), dim_ordering='th'))

model.add(Flatten())
model.add(Dense(1024))
model.add(Activation('relu'))
model.add(Dropout(rate=0.3))

model.add(Dense(2))
model.add(Activation('softmax'))

opt = keras.optimizers.SGD(lr=0.0001, decay=0.0)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics['accuracy'])
print(model.summary())

model.fit(X, np.array(Y), validation_data=(test_x, np.array(test_y)), epochs=30, verbose=2)