def get_callable_acgan_model():
return namedtuples.ACGANModel(
*get_callable_gan_model(),
one_hot_labels=array_ops.one_hot([0, 1, 2], 10),
discriminator_real_classification_logits=array_ops.one_hot([0, 1, 3],
10),
discriminator_gen_classification_logits=array_ops.one_hot([0, 1, 4],
10))
def acgan_model(
# Lambdas defining models.
generator_fn,
discriminator_fn,
# Real data and conditioning.
real_data,
generator_inputs,
one_hot_labels,
# Optional scopes.
generator_scope='Generator',
discriminator_scope='Discriminator',
check_shapes=True):
"""Returns an ACGANModel contains all the pieces needed for ACGAN training.
The `acgan_model` is the same as the `gan_model` with the only difference
being that the discriminator additionally outputs logits to classify the input
(real or generated).
Therefore, an explicit field holding one_hot_labels is necessary, as well as a
discriminator_fn that outputs a 2-tuple holding the logits for real/fake and
classification.
See https://arxiv.org/abs/1610.09585 for more details.
Args:
generator_fn: A python lambda that takes `generator_inputs` as inputs and
returns the outputs of the GAN generator.
discriminator_fn: A python lambda that takes `real_data`/`generated data`
and `generator_inputs`. Outputs a tuple consisting of two Tensors:
(1) real/fake logits in the range [-inf, inf]
(2) classification logits in the range [-inf, inf]
real_data: A Tensor representing the real data.
generator_inputs: A Tensor or list of Tensors to the generator. In the
vanilla GAN case, this might be a single noise Tensor. In the conditional
GAN case, this might be the generator's conditioning.
one_hot_labels: A Tensor holding one-hot-labels for the batch. Needed by
acgan_loss.
generator_scope: Optional generator variable scope. Useful if you want to
reuse a subgraph that has already been created.
discriminator_scope: Optional discriminator variable scope. Useful if you
want to reuse a subgraph that has already been created.
check_shapes: If `True`, check that generator produces Tensors that are the
same shape as real data. Otherwise, skip this check.
Returns:
A ACGANModel namedtuple.
Raises:
ValueError: If the generator outputs a Tensor that isn't the same shape as
`real_data`.
TypeError: If the discriminator does not output a tuple consisting of
(discrimination logits, classification logits).
"""
# Create models
with variable_scope.variable_scope(generator_scope) as gen_scope:
generator_inputs = _convert_tensor_or_l_or_d(generator_inputs)
generated_data = generator_fn(generator_inputs)
with variable_scope.variable_scope(discriminator_scope) as dis_scope:
(discriminator_gen_outputs, discriminator_gen_classification_logits
) = _validate_acgan_discriminator_outputs(
discriminator_fn(generated_data, generator_inputs))
with variable_scope.variable_scope(dis_scope, reuse=True):
real_data = ops.convert_to_tensor(real_data)
(discriminator_real_outputs, discriminator_real_classification_logits
) = _validate_acgan_discriminator_outputs(
discriminator_fn(real_data, generator_inputs))
if check_shapes:
if not generated_data.shape.is_compatible_with(real_data.shape):
raise ValueError(
'Generator output shape (%s) must be the same shape as real data '
'(%s).' % (generated_data.shape, real_data.shape))
# Get model-specific variables.
generator_variables = variables_lib.get_trainable_variables(gen_scope)
discriminator_variables = variables_lib.get_trainable_variables(dis_scope)
return namedtuples.ACGANModel(generator_inputs, generated_data,
generator_variables, gen_scope, generator_fn,
real_data, discriminator_real_outputs,
discriminator_gen_outputs,
discriminator_variables, dis_scope,
discriminator_fn, one_hot_labels,
discriminator_real_classification_logits,
discriminator_gen_classification_logits)
