40 lines
1.6 KiB
Python
40 lines
1.6 KiB
Python
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# Copyright 2017 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 calculating gradients."""
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import enum
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from tensorflow.python.util.tf_export import tf_export
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@tf_export("UnconnectedGradients")
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class UnconnectedGradients(enum.Enum):
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"""Controls how gradient computation behaves when y does not depend on x.
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The gradient of y with respect to x can be zero in two different ways: there
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could be no differentiable path in the graph connecting x to y (and so we can
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statically prove that the gradient is zero) or it could be that runtime values
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of tensors in a particular execution lead to a gradient of zero (say, if a
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relu unit happens to not be activated). To allow you to distinguish between
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these two cases you can choose what value gets returned for the gradient when
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there is no path in the graph from x to y:
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* `NONE`: Indicates that [None] will be returned if there is no path from x
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to y
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* `ZERO`: Indicates that a zero tensor will be returned in the shape of x.
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"""
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NONE = "none"
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ZERO = "zero"
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