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# Copyright 2016 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.
# ==============================================================================
"""Utilities for manipulating the loss collections.
@@add_loss
@@get_losses
@@get_regularization_loss
@@get_regularization_losses
@@get_total_loss
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES):
"""Adds a externally defined loss to the collection of losses.
Args:
loss: A loss `Tensor`.
loss_collection: Optional collection to add the loss to.
"""
if loss_collection:
ops.add_to_collection(loss_collection, loss)
def get_losses(scope=None, loss_collection=ops.GraphKeys.LOSSES):
"""Gets the list of losses from the loss_collection.
Args:
scope: An optional scope for filtering the losses to return.
loss_collection: Optional losses collection.
Returns:
a list of loss tensors.
"""
return ops.get_collection(loss_collection, scope)
def get_regularization_losses(scope=None):
"""Gets the list of regularization losses.
Args:
scope: An optional scope for filtering the losses to return.
Returns:
A list of regularization losses as Tensors.
"""
return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope)
def get_regularization_loss(scope=None, name="total_regularization_loss"):
"""Gets the total regularization loss.
Args:
scope: An optional scope for filtering the losses to return.
name: The name of the returned tensor.
Returns:
A scalar regularization loss.
"""
losses = get_regularization_losses(scope)
if losses:
return math_ops.add_n(losses, name=name)
else:
return constant_op.constant(0.0)
def get_total_loss(add_regularization_losses=True, name="total_loss"):
"""Returns a tensor whose value represents the total loss.
In particular, this adds any losses you have added with `tf.add_loss()` to
any regularization losses that have been added by regularization parameters
on layers constructors e.g. `tf.layers`. Be very sure to use this if you
are constructing a loss_op manually. Otherwise regularization arguments
on `tf.layers` methods will not function.
Args:
add_regularization_losses: A boolean indicating whether or not to use the
regularization losses in the sum.
name: The name of the returned tensor.
Returns:
A `Tensor` whose value represents the total loss.
Raises:
ValueError: if `losses` is not iterable.
"""
losses = get_losses()
if add_regularization_losses:
losses += get_regularization_losses()
return math_ops.add_n(losses, name=name)
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