model_architecture_ae.py

Input_img = Input(shape=(80, 80, 3))  
    
#encoding architecture
x1 = Conv2D(256, (3, 3), activation='relu', padding='same')(Input_img)
x2 = Conv2D(128, (3, 3), activation='relu', padding='same')(x1)
x2 = MaxPool2D( (2, 2))(x2)
encoded = Conv2D(64, (3, 3), activation='relu', padding='same')(x2)

# decoding architecture
x3 = Conv2D(64, (3, 3), activation='relu', padding='same')(encoded)
x3 = UpSampling2D((2, 2))(x3)
x2 = Conv2D(128, (3, 3), activation='relu', padding='same')(x3)
x1 = Conv2D(256, (3, 3), activation='relu', padding='same')(x2)
decoded = Conv2D(3, (3, 3), padding='same')(x1)

autoencoder = Model(Input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')