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# Copyright 2016 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.
# ==============================================================================
"""The Bernoulli distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.contrib.distributions.python.ops import distribution
from tensorflow.contrib.distributions.python.ops import distribution_util
from tensorflow.contrib.distributions.python.ops import kullback_leibler
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn
from tensorflow.python.ops import random_ops
class Bernoulli(distribution.Distribution):
"""Bernoulli distribution.
The Bernoulli distribution with `probs` parameter, i.e., the probability of a
`1` outcome (vs a `0` outcome).
"""
def __init__(self,
logits=None,
probs=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="Bernoulli"):
"""Construct Bernoulli distributions.
Args:
logits: An N-D `Tensor` representing the log-odds of a `1` event. Each
entry in the `Tensor` parametrizes an independent Bernoulli distribution
where the probability of an event is sigmoid(logits). Only one of
`logits` or `probs` should be passed in.
probs: An N-D `Tensor` representing the probability of a `1`
event. Each entry in the `Tensor` parameterizes an independent
Bernoulli distribution. Only one of `logits` or `probs` should be passed
in.
dtype: The type of the event samples. Default: `int32`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`,
statistics (e.g., mean, mode, variance) use the value "`NaN`" to
indicate the result is undefined. When `False`, an exception is raised
if one or more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
Raises:
ValueError: If p and logits are passed, or if neither are passed.
"""
parameters = locals()
with ops.name_scope(name):
self._logits, self._probs = distribution_util.get_logits_and_probs(
logits=logits,
probs=probs,
validate_args=validate_args,
name=name)
super(Bernoulli, self).__init__(
dtype=dtype,
reparameterization_type=distribution.NOT_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
graph_parents=[self._logits, self._probs],
name=name)
@staticmethod
def _param_shapes(sample_shape):
return {"logits": ops.convert_to_tensor(sample_shape, dtype=dtypes.int32)}
@property
def logits(self):
"""Log-odds of a `1` outcome (vs `0`)."""
return self._logits
@property
def probs(self):
"""Probability of a `1` outcome (vs `0`)."""
return self._probs
def _batch_shape_tensor(self):
return array_ops.shape(self._logits)
def _batch_shape(self):
return self._logits.get_shape()
def _event_shape_tensor(self):
return array_ops.constant([], dtype=dtypes.int32)
def _event_shape(self):
return tensor_shape.scalar()
def _sample_n(self, n, seed=None):
new_shape = array_ops.concat([[n], self.batch_shape_tensor()], 0)
uniform = random_ops.random_uniform(
new_shape, seed=seed, dtype=self.probs.dtype)
sample = math_ops.less(uniform, self.probs)
return math_ops.cast(sample, self.dtype)
def _log_prob(self, event):
# TODO(jaana): The current sigmoid_cross_entropy_with_logits has
# inconsistent behavior for logits = inf/-inf.
event = math_ops.cast(event, self.logits.dtype)
logits = self.logits
# sigmoid_cross_entropy_with_logits doesn't broadcast shape,
# so we do this here.
def _broadcast(logits, event):
return (array_ops.ones_like(event) * logits,
array_ops.ones_like(logits) * event)
# First check static shape.
if (event.get_shape().is_fully_defined() and
logits.get_shape().is_fully_defined()):
if event.get_shape() != logits.get_shape():
logits, event = _broadcast(logits, event)
else:
logits, event = control_flow_ops.cond(
distribution_util.same_dynamic_shape(logits, event),
lambda: (logits, event),
lambda: _broadcast(logits, event))
return -nn.sigmoid_cross_entropy_with_logits(labels=event, logits=logits)
def _prob(self, event):
return math_ops.exp(self._log_prob(event))
def _entropy(self):
return (-self.logits * (math_ops.sigmoid(self.logits) - 1) +
nn.softplus(-self.logits))
def _mean(self):
return array_ops.identity(self.probs)
def _variance(self):
return self._mean() * (1. - self.probs)
def _mode(self):
"""Returns `1` if `prob > 0.5` and `0` otherwise."""
return math_ops.cast(self.probs > 0.5, self.dtype)
class BernoulliWithSigmoidProbs(Bernoulli):
"""Bernoulli with `probs = nn.sigmoid(logits)`."""
def __init__(self,
logits=None,
dtype=dtypes.int32,
validate_args=False,
allow_nan_stats=True,
name="BernoulliWithSigmoidProbs"):
parameters = locals()
with ops.name_scope(name):
super(BernoulliWithSigmoidProbs, self).__init__(
probs=nn.sigmoid(logits, name="sigmoid_probs"),
dtype=dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
@kullback_leibler.RegisterKL(Bernoulli, Bernoulli)
def _kl_bernoulli_bernoulli(a, b, name=None):
"""Calculate the batched KL divergence KL(a || b) with a and b Bernoulli.
Args:
a: instance of a Bernoulli distribution object.
b: instance of a Bernoulli distribution object.
name: (optional) Name to use for created operations.
default is "kl_bernoulli_bernoulli".
Returns:
Batchwise KL(a || b)
"""
with ops.name_scope(name, "kl_bernoulli_bernoulli",
values=[a.logits, b.logits]):
delta_probs0 = nn.softplus(-b.logits) - nn.softplus(-a.logits)
delta_probs1 = nn.softplus(b.logits) - nn.softplus(a.logits)
return (math_ops.sigmoid(a.logits) * delta_probs0
+ math_ops.sigmoid(-a.logits) * delta_probs1)
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