96e32564c0
Tweaks naming to be a little more consistent (jvp(primals=) instead of jvp(target=) to match the constructor argument name). Also adds a Hessian-vector product example and some doctesting to the docstring. RELNOTES: Added tf.autodiff.ForwardAccumulator for forward-mode autodiff PiperOrigin-RevId: 273846345
31 lines
1.4 KiB
Python
31 lines
1.4 KiB
Python
# Copyright 2015 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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"""Implements the graph generation for computation of gradients."""
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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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# pylint: disable=unused-import
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from tensorflow.python.eager import function
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from tensorflow.python.eager.backprop import GradientTape
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from tensorflow.python.eager.forwardprop import ForwardAccumulator
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from tensorflow.python.ops.custom_gradient import custom_gradient
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from tensorflow.python.ops.gradients_util import AggregationMethod
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from tensorflow.python.ops.gradients_impl import gradients
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from tensorflow.python.ops.gradients_impl import hessians
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from tensorflow.python.ops.unconnected_gradients import UnconnectedGradients
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# pylint: enable=unused-import
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