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.
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.A loss Tensor. The shape depends on reduction
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/gan/losses/wargs/minimax_discriminator_loss