Advanced Deep Learning with Keras

Disentangled Representation GANs
StackedGAN starts with an Encoder. It could be a trained classifier that predicts
the correct labels. The intermediate features vector, f 1r
, is made available for
GAN training. For MNIST, we can use a CNN-based classifier similar to what
we discussed in Chapter 1, Introducing Advanced Deep Learning with Keras. Following
figure shows the Encoder and its network model implementation in Keras:
Figure 6.2.3: The encoder in StackedGAN is a simple CNN-based classifier
Listing 6.2.1 shows the Keras code for preceding figure. It is similar to the CNNbased
classifier in Chapter 1, Introducing Advanced Deep Learning with Keras except
that we use a Dense layer to extract the 256-dim feature. There are two output
models, Encoder 0
and Encoder 1
. Both will be used to train the StackedGAN.
The Encoder 0
output, f 1r
, is the 256-dim feature vector that we want Generator 1
to learn
to synthesize. It is available as an auxiliary output of Encoder 0
, E 0
. The overall Encoder
is trained to classify MNIST digits, x r
. The correct labels, y r
, are predicted by Encoder 1
,
E 1
. In the process, the intermediate set of features, f 1r
, is learned and made available
for Generator 0
training. Subscript r is used to emphasize and distinguish real data
from fake data when the GAN is trained against this encoder.
Listing 6.2.1, stackedgan-mnist-6.2.1.py shows encoder implementation in Keras:
def build_encoder(inputs, num_labels=10, feature1_dim=256):
""" Build the Classifier (Encoder) Model sub networks
Two sub networks:
1) Encoder0: Image to feature1 (intermediate latent feature)
2) Encoder1: feature1 to labels
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