Autoencoder: Layer "model_3" expects 1 input(s), but it received 64 input tensors-CodePudding

I have two batches of length 64. Each index is an ndarray of size size (128, 128, 3).

My Code:

ae_encoder = Conv2D(32, (2,2), padding='same')(input)
ae_encoder = LeakyReLU()(ae_encoder)
ae_encoder = Conv2D(64, (3, 3), padding='same',strides =(2,2))(ae_encoder)
ae_encoder = LeakyReLU()(ae_encoder)
ae_encoder = Conv2D(128, (3, 3), padding='same',strides =(2,2))(ae_encoder)
ae_encoder = LeakyReLU()(ae_encoder)
ae_encoder = Conv2D(256, (3, 3), padding='same',strides =(2,2))(ae_encoder)
ae_encoder = LeakyReLU()(ae_encoder)
ae_encoder = Conv2D(512, (3, 3), padding='same',strides =(2,2))(ae_encoder)
ae_encoder = LeakyReLU()(ae_encoder)
ae_encoder = Conv2D(1024, (3, 3), padding='same',strides =(2,2))(ae_encoder)
ae_encoder = LeakyReLU()(ae_encoder)
#Flattening for the bottleneck
vol = ae_encoder.shape
ae_encoder = Flatten()(ae_encoder)
ae_encoder_output = Dense(Z_DIM, activation='relu')(ae_encoder)

I can't seem to find why it is treating the entire batch of size 64) as different channels. Shouldn't it is supposed to be iterating over the ndarray inside these batches?

Error:

ValueError: Layer "model_3" expects 1 input(s), but it received 64 input tensors.

Update-1 x_train and y_train are both lists of length 64 and each index is of shape (128, 128, 3).

Sample input (Input is quite large so can't copy it entirely)

CodePudding user response：

If you are trying to implement a vanilla Autoencoder, where the input shape should equals the output shape then you have to change the last decoder layer to this:

ae_decoder_output = tf.keras.layers.Conv2D(3, (3,3), activation='sigmoid', padding='same',strides=(1,1))(ae_decoder)

resulting in the output shape (None, 128, 128, 3). Also, you need to make sure that your data has the shape (samples, 128, 128, 3).