Advanced Deep Learning with Keras

Chapter 8
The solution to this problem is to push out the Sampling process as the input
as shown on the right side of Figure 8.1.1. Then, compute the sample as:
Sample = µ + ∈ σ (Equation 8.1.13)
If ∈ and σ are expressed in vector format, then ∈ σ is element-wise multiplication.
Using Equation 8.1.13, it appears as if sampling is directly coming from the
latent space as originally intended. This technique is better known as the
Reparameterization Trick.
With Sampling now happening at the input, the VAE network can be trained
using the familiar optimization algorithms such as SGD, Adam, or RMSProp.
Decoder testing
After training the VAE network, the inference model including the addition and
multiplication operator can be discarded. To generate new meaningful outputs,
samples are taken from the Gaussian distribution used in generating ∈ . Following
figure shows us how to test the decoder:
Figure 8.1.2: Decoder testing setup
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