113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Utilities for manipulating the loss collections."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from tensorflow.python.eager import context
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("losses.add_loss")
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def add_loss(loss, loss_collection=ops.GraphKeys.LOSSES):
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"""Adds a externally defined loss to the collection of losses.
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Args:
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loss: A loss `Tensor`.
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loss_collection: Optional collection to add the loss to.
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"""
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# Since we have no way of figuring out when a training iteration starts or
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# ends, holding on to a loss when executing eagerly is indistingishable from
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# leaking memory. We instead leave the collection empty.
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if loss_collection and not context.executing_eagerly():
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ops.add_to_collection(loss_collection, loss)
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@tf_export("losses.get_losses")
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def get_losses(scope=None, loss_collection=ops.GraphKeys.LOSSES):
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"""Gets the list of losses from the loss_collection.
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Args:
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scope: An optional scope name for filtering the losses to return.
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loss_collection: Optional losses collection.
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Returns:
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a list of loss tensors.
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"""
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return ops.get_collection(loss_collection, scope)
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@tf_export("losses.get_regularization_losses")
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def get_regularization_losses(scope=None):
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"""Gets the list of regularization losses.
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Args:
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scope: An optional scope name for filtering the losses to return.
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Returns:
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A list of regularization losses as Tensors.
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"""
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return ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES, scope)
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@tf_export("losses.get_regularization_loss")
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def get_regularization_loss(scope=None, name="total_regularization_loss"):
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"""Gets the total regularization loss.
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Args:
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scope: An optional scope name for filtering the losses to return.
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name: The name of the returned tensor.
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Returns:
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A scalar regularization loss.
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"""
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losses = get_regularization_losses(scope)
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if losses:
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return math_ops.add_n(losses, name=name)
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else:
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return constant_op.constant(0.0)
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@tf_export("losses.get_total_loss")
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def get_total_loss(add_regularization_losses=True, name="total_loss"):
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"""Returns a tensor whose value represents the total loss.
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In particular, this adds any losses you have added with `tf.add_loss()` to
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any regularization losses that have been added by regularization parameters
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on layers constructors e.g. `tf.layers`. Be very sure to use this if you
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are constructing a loss_op manually. Otherwise regularization arguments
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on `tf.layers` methods will not function.
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Args:
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add_regularization_losses: A boolean indicating whether or not to use the
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regularization losses in the sum.
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name: The name of the returned tensor.
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Returns:
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A `Tensor` whose value represents the total loss.
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Raises:
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ValueError: if `losses` is not iterable.
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"""
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losses = get_losses()
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if add_regularization_losses:
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losses += get_regularization_losses()
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return math_ops.add_n(losses, name=name)
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