64 lines
2.3 KiB
Python
64 lines
2.3 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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"""Code for backpropagation using the tape utilities."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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from tensorflow.python import pywrap_tensorflow
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VSpace = collections.namedtuple(
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"VSpace",
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["aggregate_fn", "num_elements_fn", "tensor_id", "zeros", "ones"])
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def imperative_grad(
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vspace,
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tape,
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target,
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sources,
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output_gradients=None):
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"""Computes gradients from the imperatively defined tape on top of the stack.
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Works by filtering the tape, computing how many downstream usages are of each
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tensor and entry, and repeatedly applying backward functions until we have
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gradients for all sources.
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Args:
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vspace: the vector space in which to differentiate.
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tape: the gradient tape which stores the trace.
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target: either a Tensor or list of Tensors to be differentiated.
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sources: list of Tensors for which we want gradients
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output_gradients: if not None, a list of gradient provided for each Target,
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or None if we are to use the target's computed downstream gradient.
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Returns:
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the gradient wrt each of the sources.
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Raises:
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RuntimeError: if something goes wrong.
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ValueError: if there is no sequence of differentiable operations connecting
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a source and any target Tensor. This can happen either if the target is
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not computed based on the source, if the tracing was set up incorrectly,
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or if only non-differentiable functions of the source were used in the
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computation of target.
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"""
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return pywrap_tensorflow.TFE_Py_TapeGradient(
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tape._tape, vspace, target, sources, output_gradients) # pylint: disable=protected-access
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