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# Copyright 2017 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Losses for Generator and Discriminator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
def discriminator_loss(predictions, labels, missing_tokens):
"""Discriminator loss based on predictions and labels.
Args:
predictions: Discriminator linear predictions Tensor of shape [batch_size,
sequence_length]
labels: Labels for predictions, Tensor of shape [batch_size,
sequence_length]
missing_tokens: Indicator for the missing tokens. Evaluate the loss only
on the tokens that were missing.
Returns:
loss: Scalar tf.float32 loss.
"""
loss = tf.losses.sigmoid_cross_entropy(labels,
predictions,
weights=missing_tokens)
loss = tf.Print(
loss, [loss, labels, missing_tokens],
message='loss, labels, missing_tokens',
summarize=25,
first_n=25)
return loss
def cross_entropy_loss_matrix(gen_labels, gen_logits):
"""Computes the cross entropy loss for G.
Args:
gen_labels: Labels for the correct token.
gen_logits: Generator logits.
Returns:
loss_matrix: Loss matrix of shape [batch_size, sequence_length].
"""
cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=gen_labels, logits=gen_logits)
return cross_entropy_loss
def GAN_loss_matrix(dis_predictions):
"""Computes the cross entropy loss for G.
Args:
dis_predictions: Discriminator predictions.
Returns:
loss_matrix: Loss matrix of shape [batch_size, sequence_length].
"""
eps = tf.constant(1e-7, tf.float32)
gan_loss_matrix = -tf.log(dis_predictions + eps)
return gan_loss_matrix
def generator_GAN_loss(predictions):
"""Generator GAN loss based on Discriminator predictions."""
return -tf.log(tf.reduce_mean(predictions))
def generator_blended_forward_loss(gen_logits, gen_labels, dis_predictions,
is_real_input):
"""Computes the masked-loss for G. This will be a blend of cross-entropy
loss where the true label is known and GAN loss where the true label has been
masked.
Args:
gen_logits: Generator logits.
gen_labels: Labels for the correct token.
dis_predictions: Discriminator predictions.
is_real_input: Tensor indicating whether the label is present.
Returns:
loss: Scalar tf.float32 total loss.
"""
cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=gen_labels, logits=gen_logits)
gan_loss = -tf.log(dis_predictions)
loss_matrix = tf.where(is_real_input, cross_entropy_loss, gan_loss)
return tf.reduce_mean(loss_matrix)
def wasserstein_generator_loss(gen_logits, gen_labels, dis_values,
is_real_input):
"""Computes the masked-loss for G. This will be a blend of cross-entropy
loss where the true label is known and GAN loss where the true label is
missing.
Args:
gen_logits: Generator logits.
gen_labels: Labels for the correct token.
dis_values: Discriminator values Tensor of shape [batch_size,
sequence_length].
is_real_input: Tensor indicating whether the label is present.
Returns:
loss: Scalar tf.float32 total loss.
"""
cross_entropy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=gen_labels, logits=gen_logits)
# Maximize the dis_values (minimize the negative)
gan_loss = -dis_values
loss_matrix = tf.where(is_real_input, cross_entropy_loss, gan_loss)
loss = tf.reduce_mean(loss_matrix)
return loss
def wasserstein_discriminator_loss(real_values, fake_values):
"""Wasserstein discriminator loss.
Args:
real_values: Value given by the Wasserstein Discriminator to real data.
fake_values: Value given by the Wasserstein Discriminator to fake data.
Returns:
loss: Scalar tf.float32 loss.
"""
real_avg = tf.reduce_mean(real_values)
fake_avg = tf.reduce_mean(fake_values)
wasserstein_loss = real_avg - fake_avg
return wasserstein_loss
def wasserstein_discriminator_loss_intrabatch(values, is_real_input):
"""Wasserstein discriminator loss. This is an odd variant where the value
difference is between the real tokens and the fake tokens within a single
batch.
Args:
values: Value given by the Wasserstein Discriminator of shape [batch_size,
sequence_length] to an imputed batch (real and fake).
is_real_input: tf.bool Tensor of shape [batch_size, sequence_length]. If
true, it indicates that the label is known.
Returns:
wasserstein_loss: Scalar tf.float32 loss.
"""
zero_tensor = tf.constant(0., dtype=tf.float32, shape=[])
present = tf.cast(is_real_input, tf.float32)
missing = tf.cast(1 - present, tf.float32)
# Counts for real and fake tokens.
real_count = tf.reduce_sum(present)
fake_count = tf.reduce_sum(missing)
# Averages for real and fake token values.
real = tf.mul(values, present)
fake = tf.mul(values, missing)
real_avg = tf.reduce_sum(real) / real_count
fake_avg = tf.reduce_sum(fake) / fake_count
# If there are no real or fake entries in the batch, we assign an average
# value of zero.
real_avg = tf.where(tf.equal(real_count, 0), zero_tensor, real_avg)
fake_avg = tf.where(tf.equal(fake_count, 0), zero_tensor, fake_avg)
wasserstein_loss = real_avg - fake_avg
return wasserstein_loss
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