mirror of
https://github.com/zebrajr/tensorflow.git
synced 2025-12-07 12:20:24 +01:00
854f49bd43
Changes: - Updates to docs - Several changes for Python 3 compatibility - Added license headers Base CL: 108710566
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
# Copyright 2015 Google Inc. 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.
|
|
# ==============================================================================
|
|
|
|
"""Gradients for operators defined in data_flow_ops.py."""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from six.moves import xrange # pylint: disable=redefined-builtin
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
from tensorflow.python.ops import array_ops
|
|
from tensorflow.python.ops import constant_op
|
|
from tensorflow.python.ops import data_flow_ops
|
|
from tensorflow.python.ops import gen_data_flow_ops
|
|
from tensorflow.python.ops import math_ops
|
|
|
|
|
|
@ops.RegisterGradient("DynamicStitch")
|
|
def _DynamicStitchGrads(op, grad):
|
|
"""Gradients for DynamicStitch."""
|
|
|
|
num_values = len(op.inputs) // 2
|
|
indices_grad = [None] * num_values
|
|
|
|
def AsInt32(x):
|
|
return (x if op.inputs[0].dtype == dtypes.int32 else
|
|
math_ops.cast(x, dtypes.int32))
|
|
inputs = [AsInt32(op.inputs[i]) for i in xrange(num_values)]
|
|
if isinstance(grad, ops.IndexedSlices):
|
|
output_shape = array_ops.shape(op.outputs[0])
|
|
output_rows = output_shape[0]
|
|
grad = math_ops.unsorted_segment_sum(grad.values, grad.indices, output_rows)
|
|
values_grad = [array_ops.gather(grad, inp) for inp in inputs]
|
|
return indices_grad + values_grad
|
|
|
|
|
|
ops.NoGradient("Queue")
|
|
ops.NoGradient("QueueEnqueue")
|
|
ops.NoGradient("QueueEnqueueMany")
|
|
ops.NoGradient("QueueDequeue")
|
|
ops.NoGradient("QueueDequeueMany")
|
|
ops.NoGradient("QueueClose")
|
|
ops.NoGradient("QueueSize")
|
|
|
|
ops.NoGradient("Stack")
|
|
ops.NoGradient("StackPush")
|
|
ops.NoGradient("StackPop")
|
|
ops.NoGradient("StackClose")
|