| # 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. |
| # ============================================================================== |
| |
| """Gradients for operators defined in data_flow_ops.py.""" |
| |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import indexed_slices |
| from tensorflow.python.framework import ops |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import data_flow_ops |
| from tensorflow.python.ops import math_ops |
| |
| |
| @ops.RegisterGradient("DynamicPartition") |
| def _DynamicPartitionGrads(op, *grads): |
| """Gradients for DynamicPartition.""" |
| data = op.inputs[0] |
| indices = op.inputs[1] |
| num_partitions = op.get_attr("num_partitions") |
| |
| prefix_shape = array_ops.shape(indices) |
| original_indices = array_ops.reshape( |
| math_ops.range(math_ops.reduce_prod(prefix_shape)), prefix_shape) |
| partitioned_indices = data_flow_ops.dynamic_partition( |
| original_indices, indices, num_partitions) |
| reconstructed = data_flow_ops.parallel_dynamic_stitch(partitioned_indices, |
| grads) |
| reconstructed = array_ops.reshape(reconstructed, array_ops.shape(data)) |
| return [reconstructed, None] |
| |
| |
| @ops.RegisterGradient("DynamicStitch") |
| @ops.RegisterGradient("ParallelDynamicStitch") |
| def _DynamicStitchGrads(op, grad): |
| """Gradients for DynamicStitch and ParallelDynamicStitch.""" |
| |
| 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 range(num_values)] |
| if isinstance(grad, indexed_slices.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.NotDifferentiable("Queue") |
| ops.NotDifferentiable("QueueEnqueue") |
| ops.NotDifferentiable("QueueEnqueueMany") |
| ops.NotDifferentiable("QueueDequeue") |
| ops.NotDifferentiable("QueueDequeueMany") |
| ops.NotDifferentiable("QueueDequeueUpTo") |
| ops.NotDifferentiable("QueueClose") |
| ops.NotDifferentiable("QueueSize") |
| |
| ops.NotDifferentiable("Stack") |
| ops.NotDifferentiable("StackPush") |
| ops.NotDifferentiable("StackPop") |
| ops.NotDifferentiable("StackClose") |
| |
| ops.NotDifferentiable("GetSessionHandle") |
| ops.NotDifferentiable("GetSessionHandleV2") |
| ops.NotDifferentiable("GetSessionTensor") |
| ops.NotDifferentiable("DeleteSessionTensor") |