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