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I am implementing a 3D CNN based autoencoder.

I want to fix the error

conv3D_encoder = keras.models.Sequential([
 keras.layers.Conv3D(filters = 16, kernel_size = (3,3,3), padding = "same", input_shape=[8, 160, 160, 3], activation='relu'),
 keras.layers.MaxPooling3D(pool_size = (2,2,2)),
 keras.layers.Conv3D(filters = 32, kernel_size = (3,3,3), padding = "same", activation='relu'),
 keras.layers.MaxPooling3D(pool_size = (2,2,2)),
 keras.layers.Conv3D(filters = 64, kernel_size = (3,3,3), padding = "same", activation='relu'),
 keras.layers.MaxPooling3D(pool_size = (2,2,2))])

conv3D_decoder = keras.models.Sequential([ 
 keras.layers.Conv3DTranspose(filters = 32, kernel_size=(3,3,3), strides=2, padding="valid", activation="relu", input_shape=[None,3,3,64]), 
 keras.layers.Conv3DTranspose(filters = 16, kernel_size=(3,3,3), strides=2, padding="same", activation="relu"), 
 keras.layers.Conv3DTranspose(filters = 1, kernel_size=(3,3,3), strides=2, padding="same", activation="sigmoid"), ])

conv3D_ae = keras.models.Sequential([conv3D_encoder,conv3D_decoder])

conv3D_ae.compile(loss="binary_crossentropy", optimizer=keras.optimizers.SGD(learning_rate=1.5))
history = conv3D_ae.fit(X_train, X_train, epochs=10, validation_data=(X_valid,X_valid))

error Message:

WARNING:tensorflow:Model was constructed with shape (None, None, 3, 3, 64) for input Tensor("conv3d_transpose_63_input:0", shape=(None, None, 3, 3, 64), dtype=float32), but it was called on an input with incompatible shape (None, 1, 20, 20, 64).

ValueError: logits and labels must have the same shape ((None, 12, 164, 164, 1) vs (None, 8, 160, 160, 3))
Progman
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Jongmin Lim
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1 Answers1

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The input of the encoder and the output of the decoder should be the same in an autoencoder. The input of the decoder will be the output of the encoder. Changing the architecture of the decoder will help.

conv3D_decoder = keras.models.Sequential([ 
 keras.layers.Conv3DTranspose(filters = 32, kernel_size=(3,3,3), strides=2, padding="valid", activation="relu", input_shape=[1, 20, 20, 64]), 
 keras.layers.Conv3DTranspose(filters = 16, kernel_size=(3,3,3), strides=2, padding="same", activation="relu"), 
 keras.layers.Conv3DTranspose(filters = 16, kernel_size=(3,3,3), strides=2, padding="same", activation="sigmoid"), 
 keras.layers.Conv3D(filters = 3, kernel_size = (5,5,5), padding = "valid", activation='relu'),])

Please go through this gist, where I was able to replicate the error with random data.