input=Input(shape=(x_train.shape[1:]))
encoded=Conv2D(16, (3, 3), activation='relu', padding='same')(input)
encoded=MaxPooling2D((2, 2), padding='same')(encoded)
encoded=Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
encoded=MaxPooling2D((2, 2), padding='same')(encoded)
encoded=Conv2D(8, (3, 3), strides=(2,2), activation='relu', padding='same')(encoded)
encoded=Flatten()(encoded)

decoded=Reshape((4, 4, 8))(encoded)
decoded=Conv2D(8, (3, 3), activation='relu', padding='same')(decoded)
decoded=UpSampling2D((2, 2))(decoded)
decoded=Conv2D(8, (3, 3), activation='relu', padding='same')(decoded)
decoded=UpSampling2D((2, 2))(decoded)
decoded=Conv2D(16, (3, 3), activation='relu')(decoded)
decoded=UpSampling2D((2, 2))(decoded)
decoded=Conv2D(1, (3, 3), activation='sigmoid', padding='same')(decoded)

autoencoder=Model(input,decoded)