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# Copyright 2017 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.
# ==============================================================================
"""Sigmoid bijector."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops.distributions import bijector
from tensorflow.python.util import deprecation
__all__ = [
"Sigmoid",
]
class Sigmoid(bijector.Bijector):
"""Bijector which computes `Y = g(X) = 1 / (1 + exp(-X))`."""
@deprecation.deprecated(
"2018-10-01",
"The TensorFlow Distributions library has moved to "
"TensorFlow Probability "
"(https://github.com/tensorflow/probability). You "
"should update all references to use `tfp.distributions` "
"instead of `tf.contrib.distributions`.",
warn_once=True)
def __init__(self, validate_args=False, name="sigmoid"):
super(Sigmoid, self).__init__(
forward_min_event_ndims=0,
validate_args=validate_args,
name=name)
def _forward(self, x):
return math_ops.sigmoid(x)
def _inverse(self, y):
return math_ops.log(y) - math_ops.log1p(-y)
def _inverse_log_det_jacobian(self, y):
return -math_ops.log(y) - math_ops.log1p(-y)
def _forward_log_det_jacobian(self, x):
return -nn_ops.softplus(-x) - nn_ops.softplus(x)
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