How can I resolve shape mismatch errors when feeding data into a Keras VAE

Question

With the help of Python programming, can you explain How can I resolve shape mismatch errors when feeding data into a Keras VAE?

Answer 1

To resolve shape mismatch errors when feeding data into a Keras Variational Autoencoder (VAE), ensure that the input and output shapes of the encoder and decoder are consistent and that the data is properly reshaped and normalized.

Here is the code snippet showing how it is done:

In the above code, we are using the following:

• Consistent Shapes: Ensure encoder and decoder have matching input and output shapes by using Flatten and Reshape layers appropriately.
• Latent Space: Ensure the latent space (e.g., (2,)) is compatible with the decoder's input.
• Data Normalization: Normalize input data to the same range as the model's expected input (e.g., [0, 1] for image data).

Hence, by referring to the above, you can resolve shape mismatch errors when feeding data into a Keras VAE.