tf.contrib.gan.losses.wargs.wasserstein_gradient_penalty

tf.contrib.gan.losses.wargs.wasserstein_gradient_penalty(
    real_data,
    generated_data,
    generator_inputs,
    discriminator_fn,
    discriminator_scope,
    epsilon=1e-10,
    target=1.0,
    one_sided=False,
    weights=1.0,
    scope=None,
    loss_collection=tf.GraphKeys.LOSSES,
    reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
    add_summaries=False
)

Defined in tensorflow/contrib/gan/python/losses/python/losses_impl.py.

The gradient penalty for the Wasserstein discriminator loss.

See Improved Training of Wasserstein GANs (https://arxiv.org/abs/1704.00028) for more details.

Args:

• real_data: Real data.
• generated_data: Output of the generator.
• generator_inputs: Exact argument to pass to the generator, which is used as optional conditioning to the discriminator.
• discriminator_fn: A discriminator function that conforms to TFGAN API.
• discriminator_scope: If not None, reuse discriminators from this scope.
• epsilon: A small positive number added for numerical stability when computing the gradient norm.
• target: Optional Python number or Tensor indicating the target value of gradient norm. Defaults to 1.0.
• one_sided: If True, penalty proposed in https://arxiv.org/abs/1709.08894 is used. Defaults to False.
• weights: Optional Tensor whose rank is either 0, or the same rank as real_data and generated_data, and must be broadcastable to them (i.e., all dimensions must be either 1, or the same as the corresponding dimension).
• scope: The scope for the operations performed in computing the loss.
• loss_collection: collection to which this loss will be added.
• reduction: A tf.losses.Reduction to apply to loss.
• add_summaries: Whether or not to add summaries for the loss.

Returns:

A loss Tensor. The shape depends on reduction.

Raises:

• ValueError: If the rank of data Tensors is unknown.