potential bug in the encoder

[xmax1]

for level in range(1, self._levels): for i_dl in range(self._dense_layers-1): hidden = self.get('h{}_dense'.format(5+(level-1)*self._dense_layers+i_dl), tfkl.Dense, self._embed_size, activation=tf.nn.relu)(hidden) if self._dense_layers > 0: hidden = self.get('h{}_dense'.format(4+level*self._dense_layers), tfkl.Dense, feat_size, activation=None)(hidden) layer = hidden

line 39 onwards in the cnn.py Encoder(), the depth of these layers increases with the level as the hidden variables is overwritten. At large n_levels and n_enc_dense_layers this will result in a very deep network mapping from the observation embedding to the latent space. Not sure it's intentional, doesn't seem to have a purpose, ie is there a reason the higher latent spaces need a deeper function to map from the embedding?

[xmax1]

Ah, it's intentional for a ladder vae type structure. Closed.