def infogan():
real_images = prepare_data()
# Dimensions of the structured and unstructured noise dimensions.
cat_dim, cont_dim, noise_dims = 10, 2, 64
# (HEBI: using infogan generator)
generator_fn = functools.partial(infogan_generator,
categorical_dim=cat_dim)
discriminator_fn = functools.partial(infogan_discriminator,
categorical_dim=cat_dim,
continuous_dim=cont_dim)
unstructured_inputs, structured_inputs = util.get_infogan_noise(
batch_size, cat_dim, cont_dim, noise_dims)
infogan_model = tfgan.infogan_model(
generator_fn=generator_fn,
discriminator_fn=discriminator_fn,
real_data=real_images,
unstructured_generator_inputs=unstructured_inputs,
structured_generator_inputs=structured_inputs)
infogan_loss = tfgan.gan_loss(
infogan_model,
gradient_penalty_weight=1.0,
# (HEBI: the mutual information penalty!!)
mutual_information_penalty_weight=1.0)
# Sanity check that we can evaluate our losses.
evaluate_tfgan_loss(infogan_loss)
# train ops
generator_optimizer = tf.train.AdamOptimizer(0.001, beta1=0.5)
discriminator_optimizer = tf.train.AdamOptimizer(0.00009, beta1=0.5)
gan_train_ops = tfgan.gan_train_ops(infogan_model, infogan_loss,
generator_optimizer,
discriminator_optimizer)
# train
tfgan.gan_train(
gan_train_ops,
hooks=[tf.train.StopAtStepHook(num_steps=FLAGS.max_number_of_steps)],
logdir=FLAGS.train_log_dir)
# ### InfoGANModel Tuple
#
# The InfoGAN model requires some extra information, so we use a subclassed tuple.
# In[14]:
# Dimensions of the structured and unstructured noise dimensions.
cat_dim, cont_dim, noise_dims = 10, 2, 64
generator_fn = functools.partial(infogan_generator, categorical_dim=cat_dim)
discriminator_fn = functools.partial(infogan_discriminator,
categorical_dim=cat_dim,
continuous_dim=cont_dim)
unstructured_inputs, structured_inputs = util.get_infogan_noise(
batch_size, cat_dim, cont_dim, noise_dims)
infogan_model = tfgan.infogan_model(
generator_fn=generator_fn,
discriminator_fn=discriminator_fn,
real_data=real_images,
unstructured_generator_inputs=unstructured_inputs,
structured_generator_inputs=structured_inputs)
# <a id='infogan_loss'></a>
# ## Losses
#
# The loss will be the same as before, with the addition of the mutual information loss.
# In[17]: