def z_generator(self,
shape,
distribution_fn=tf.random.uniform,
minval=-1.0,
maxval=1.0,
stddev=1.0,
name=None):
"""Random noise distributions as TF op.
Args:
shape: A 1-D integer Tensor or Python array.
distribution_fn: Function that create a Tensor. If the function has any
of the arguments 'minval', 'maxval' or 'stddev' these are passed to it.
minval: The lower bound on the range of random values to generate.
maxval: The upper bound on the range of random values to generate.
stddev: The standard deviation of a normal distribution.
name: A name for the operation.
Returns:
Tensor with the given shape and dtype tf.float32.
"""
return utils.call_with_accepted_args(distribution_fn,
shape=shape,
minval=minval,
maxval=maxval,
stddev=stddev,
name=name)
def batch_norm(self, inputs, **kwargs):
if self._batch_norm_fn is None:
return inputs
args = kwargs.copy()
args["inputs"] = inputs
if "use_sn" not in args:
args["use_sn"] = self._spectral_norm
return utils.call_with_accepted_args(self._batch_norm_fn, **args)
def get_penalty_loss(fn=no_penalty, **kwargs): """Returns the penalty loss.""" return utils.call_with_accepted_args(fn, **kwargs)
def get_losses(fn=non_saturating, **kwargs):
"""Returns the losses for the discriminator and generator."""
return utils.call_with_accepted_args(fn, **kwargs)