# input layer
input_layer = Input(shape=(28, 28, 1))

# encoding architecture
encoded_layer1 = Conv2D(64, (3, 3), activation='relu', padding='same')(input_layer)
encoded_layer1 = MaxPool2D( (2, 2), padding='same')(encoded_layer1)
encoded_layer2 = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded_layer1)
encoded_layer2 = MaxPool2D( (2, 2), padding='same')(encoded_layer2)
encoded_layer3 = Conv2D(16, (3, 3), activation='relu', padding='same')(encoded_layer2)
latent_view    = MaxPool2D( (2, 2), padding='same')(encoded_layer3)

# decoding architecture
decoded_layer1 = Conv2D(16, (3, 3), activation='relu', padding='same')(latent_view)
decoded_layer1 = UpSampling2D((2, 2))(decoded_layer1)
decoded_layer2 = Conv2D(32, (3, 3), activation='relu', padding='same')(decoded_layer1)
decoded_layer2 = UpSampling2D((2, 2))(decoded_layer2)
decoded_layer3 = Conv2D(64, (3, 3), activation='relu')(decoded_layer2)
decoded_layer3 = UpSampling2D((2, 2))(decoded_layer3)
output_layer   = Conv2D(1, (3, 3), padding='same', activation='sigmoid')(decoded_layer3)

# compile the model
model = Model(input_layer, output_layer)
model.compile(optimizer='adam', loss='mse')

# run the model
early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=5, mode='auto')
history = model.fit(train_x_n, x_train, epochs=20, batch_size=2048, validation_data=(val_x_n, x_test), callbacks=[early_stopping])