# Copyright 2015 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.
|
# ==============================================================================
|
|
"""The gradient of the tutorial zero_out op."""
|
|
from __future__ import absolute_import
|
from __future__ import division
|
from __future__ import print_function
|
|
from tensorflow.python.framework import ops
|
from tensorflow.python.ops import array_ops
|
from tensorflow.python.ops import sparse_ops
|
|
|
@ops.RegisterGradient("ZeroOut")
|
def _zero_out_grad(op, grad):
|
"""The gradients for `zero_out`.
|
|
Args:
|
op: The `zero_out` `Operation` that we are differentiating, which we can use
|
to find the inputs and outputs of the original op.
|
grad: Gradient with respect to the output of the `zero_out` op.
|
|
Returns:
|
Gradients with respect to the input of `zero_out`.
|
"""
|
to_zero = op.inputs[0]
|
shape = array_ops.shape(to_zero)
|
index = array_ops.zeros_like(shape)
|
first_grad = array_ops.reshape(grad, [-1])[0]
|
to_zero_grad = sparse_ops.sparse_to_dense([index], shape, first_grad, 0)
|
return [to_zero_grad] # List of one Tensor, since we have one input
|
