real_loss = cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
total_loss = real_loss + fake_loss
return total_loss
def generator_loss(fake_output):
return cross_entropy(tf.ones_like(fake_output), fake_output)
generator_optimizer, discriminator_optimizer = model_builder.get_optimizers()
checkpoint_dir = './training_checkpoints'
checkpoint_prefix = 'ckpt'
checkpoint = model_builder.get_checkpoint(checkpoint_dir, checkpoint_prefix,
generator_optimizer,
discriminator_optimizer, generator,
discriminator)
checkpoint_manager = tf.train.CheckpointManager(checkpoint,
checkpoint_dir,
max_to_keep=3)
if (args.checkpoint is not None):
checkpoint.restore(args.checkpoint)
seed = tf.random.normal([num_examples_to_generate, noise_dim])
rand_gen = tf.random.Generator.from_seed(1234)
rand = rand_gen.uniform([num_examples_to_generate],
minval=0,
maxval=num_words - 1,
dtype=tf.dtypes.int32)
