# tf.contrib.gan.losses.wargs.modified_generator_loss

tf.contrib.gan.losses.wargs.modified_generator_loss(
discriminator_gen_outputs,
label_smoothing=0.0,
weights=1.0,
scope=None,
loss_collection=tf.GraphKeys.LOSSES,
reduction=losses.Reduction.SUM_BY_NONZERO_WEIGHTS,
)


Modified generator loss for GANs.

L = -log(sigmoid(D(G(z))))

This is the trick used in the original paper to avoid vanishing gradients early in training. See Generative Adversarial Nets (https://arxiv.org/abs/1406.2661) for more details.

#### Args:

• discriminator_gen_outputs: Discriminator output on generated data. Expected to be in the range of (-inf, inf).
• label_smoothing: The amount of smoothing for positive labels. This technique is taken from Improved Techniques for Training GANs (https://arxiv.org/abs/1606.03498). 0.0 means no smoothing.
• weights: Optional Tensor whose rank is either 0, or the same rank as discriminator_gen_outputs, and must be broadcastable to labels (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.