There is one output in the decoder class:
print("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape)))
Here the letter z seems to represent the latent vector by convention. But actually in the encoder class h stands for latent vector.
What I don't understand is: why is z the bottleneck, but h is used as the latent vector?
Hi.
There is one output in the decoder class:
print("Working with z of shape {} = {} dimensions.".format(self.z_shape, np.prod(self.z_shape)))Here the letter z seems to represent the latent vector by convention. But actually in the encoder class h stands for latent vector. What I don't understand is: why is z the bottleneck, but h is used as the latent vector?Looking forward to your response Best