Advanced Deep Learning with Keras

Variational Autoencoders (VAEs)
VAEs in Keras
The structure of VAE bears a resemblance to a typical autoencoder. The
difference is mainly on the sampling of the Gaussian random variables in the
reparameterization trick. Listing 8.1.1 shows the encoder, decoder, and VAE which
are implemented using MLP. This code has also been contributed to the official
Keras GitHub repository. For simplicity of the discussion, the latent vector z is 2-dim.
The encoder is just a two-layer MLP with the second layer generating the mean and
log variance. The use of log variance is for simplicity in the computation of KL Loss
and reparameterization trick. The third output of the encoder is the sampling of z
using the reparameterization trick. We should note that in the sampling function,
2 2
e0.5logσ = σ = σ since σ > 0 given that it's the standard deviation of the Gaussian
distribution.
The decoder is also a two-layer MLP that takes samples of z to approximate
the inputs. Both the encoder and the decoder use an intermediate dimension
with a size of 512.
The VAE network is simply both the encoder and the decoder joined together.
Figures 8.1.3 to 8.1.5 show the encoder, decoder, and VAE models. The loss function
is the sum of both the Reconstruction Loss and KL Loss. The VAE network has good
results on the default Adam optimizer. The total number of parameters of the VAE
network is 807,700.
The Keras code for VAE MLP has pretrained weights. To test, we need to run:
$ python3 vae-mlp-mnist-8.1.1.py --weights=vae_mlp_mnist.h5
The complete code can be found on the following link: https://
github.com/PacktPublishing/Advanced-Deep-Learningwith-Keras.
Listing 8.1.1, vae-mlp-mnist-8.1.1.py shows us the Keras code of VAE using MLP
layers:
# reparameterization trick
# instead of sampling from Q(z|X), sample eps = N(0,I)
# z = z_mean + sqrt(var)*eps
def sampling(args):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
# K is the keras backend
dim = K.int_shape(z_mean)[1]
# by default, random_normal has mean=0 and std=1.0
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