tf.contrib.gan.losses.wargs.minimax_discriminator_loss

tf.contrib.gan.losses.wargs.minimax_discriminator_loss(
    discriminator_real_outputs,
    discriminator_gen_outputs,
    label_smoothing=0.25,
    real_weights=1.0,
    generated_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.

Original minimax discriminator loss for GANs, with label smoothing.

Note that the authors don't recommend using this loss. A more practically useful loss is modified_discriminator_loss.

L = - real_weights * log(sigmoid(D(x))) - generated_weights * log(1 - sigmoid(D(G(z))))

See Generative Adversarial Nets (https://arxiv.org/abs/1406.2661) for more details.

Args:

• discriminator_real_outputs: Discriminator output on real data.
• 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.
• real_weights: Optional Tensor whose rank is either 0, or the same rank as real_data, and must be broadcastable to real_data (i.e., all dimensions must be either 1, or the same as the corresponding dimension).
• generated_weights: Same as real_weights, but for generated_data.
• 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.