Advanced Deep Learning with Keras

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Chapter 6
enc1.trainable = False
# discriminator1 weights frozen
dis1.trainable = False
gen1_inputs = [labels, z1]
gen1_outputs = gen1(gen1_inputs)
adv1_outputs = dis1(gen1_outputs) + [enc1(gen1_outputs)]
# labels + z1 to prob labels are real + z1 recon + feature1 recon
adv1 = Model(gen1_inputs, adv1_outputs, name="adv1")
# loss functions: 1) prob labels are real (adversarial1 loss)
# 2) Q network 1 loss (entropy1 loss)
# 3) conditional1 loss (classifier error)
loss_weights = [1.0, 1.0, 1.0]
loss = ['binary_crossentropy', 'mse', 'categorical_crossentropy']
adv1.compile(loss=loss,
loss_weights=loss_weights,
optimizer=optimizer,
metrics=['accuracy'])
adv1.summary()
# train discriminator and adversarial networks
models = (enc0, enc1, gen0, gen1, dis0, dis1, adv0, adv1)
params = (batch_size, train_steps, num_labels, z_dim, model_name)
train(models, data, params)
Finally, the training function bears a resemblance to a typical GAN training except
that we only train one GAN at a time (that is, GAN 1
then GAN 0
). The code is shown
in Listing 6.2.5. It's worth noting that the training sequence is:
1. Discriminator 1
and Q 1
networks by minimizing the discriminator and entropy
losses
2. Discriminator 0
and Q 0
networks by minimizing the discriminator and entropy
losses
3. Adversarial 1
network by minimizing the adversarial, entropy, and conditional
losses
4. Adversarial 0
network by minimizing the adversarial, entropy, and conditional
losses
Listing 6.2.5, stackedgan-mnist-6.2.1.py shows us training the StackedGAN
in Keras:
def train(models, data, params):
"""Train the discriminator and adversarial Networks
Alternately train discriminator and adversarial networks by batch.