Input and output layers of Keras autoencoder don't match, can't run model

Question

I am trying to work on building an autoencoder in Keras, with an input shape of (470,470,3) but the output never seems to match, even when I try to switch around padding. This is my code, can you please help? The way it is currently written my model summary shows an output of (472, 472, 3).

from tensorflow.keras.layers import Conv2D, MaxPooling2D, UpSampling2D
from tensorflow.keras import Input, Model

input_image = Input(shape=(470, 470, 3)) 

x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_image)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

x = Conv2D(32, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded_image = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)

autoencoder = Model(input_image, decoded_image)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

Thank you!

Nicolas Gervais · Accepted Answer · 2020-07-02 16:36:27Z

1

Change your last padding to 'valid':

decoded_image = Conv2D(3, (3, 3), activation='sigmoid', padding='valid')(x)

answered Jul 2, 2020 at 16:36

Nicolas Gervais

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