78 lines
3.0 KiB
Python
78 lines
3.0 KiB
Python
# 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_tfe
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from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
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from tensorflow.python.util import compat
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VSpace = collections.namedtuple("VSpace", [
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"aggregate_fn", "num_elements_fn", "zeros_fn", "ones_fn",
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"zeros_like_fn", "ones_like_fn", "graph_shape_fn"
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])
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def imperative_grad(tape,
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target,
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sources,
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output_gradients=None,
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sources_raw=None,
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unconnected_gradients=UnconnectedGradients.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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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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sources_raw: if not None, a list of the source python objects from which the
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sources were generated. Should have the same length as sources. Only needs
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to be populated if unconnected_gradients is 'zero'.
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unconnected_gradients: determines the value returned if the target and
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sources are unconnected. When 'none' the value returned is None wheras when
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'zero' a zero tensor in the same shape as the sources is returned.
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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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ValueError: if the arguments are invalid.
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RuntimeError: if something goes wrong.
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"""
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try:
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unconnected_gradients = UnconnectedGradients(unconnected_gradients)
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except ValueError:
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raise ValueError(
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"Unknown value for unconnected_gradients: %r" % unconnected_gradients)
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return pywrap_tfe.TFE_Py_TapeGradient(
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tape._tape, # pylint: disable=protected-access
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target,
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sources,
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output_gradients,
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sources_raw,
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compat.as_str(unconnected_gradients.value))
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