73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
# Copyright 2016 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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"""Identity bijector."""
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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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from tensorflow.python.framework import constant_op
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from tensorflow.python.ops.distributions import bijector
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from tensorflow.python.util import deprecation
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__all__ = [
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"Identity",
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]
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class Identity(bijector.Bijector):
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"""Compute Y = g(X) = X.
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Example Use:
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```python
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# Create the Y=g(X)=X transform which is intended for Tensors with 1 batch
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# ndim and 1 event ndim (i.e., vector of vectors).
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identity = Identity()
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x = [[1., 2],
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[3, 4]]
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x == identity.forward(x) == identity.inverse(x)
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```
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"""
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@deprecation.deprecated(
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"2019-01-01",
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"The TensorFlow Distributions library has moved to "
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"TensorFlow Probability "
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"(https://github.com/tensorflow/probability). You "
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"should update all references to use `tfp.distributions` "
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"instead of `tf.distributions`.",
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warn_once=True)
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def __init__(self, validate_args=False, name="identity"):
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super(Identity, self).__init__(
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forward_min_event_ndims=0,
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is_constant_jacobian=True,
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validate_args=validate_args,
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name=name)
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def _forward(self, x):
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return x
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def _inverse(self, y):
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return y
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def _inverse_log_det_jacobian(self, y):
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return constant_op.constant(0., dtype=y.dtype)
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def _forward_log_det_jacobian(self, x):
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return constant_op.constant(0., dtype=x.dtype)
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