laywerrobot/lib/python3.6/site-packages/tensorflow/python/ops/distributions/gamma.py
2020-08-27 21:55:39 +02:00

322 lines
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Python

# 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 Gamma distribution class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
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 check_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
from tensorflow.python.ops.distributions import distribution
from tensorflow.python.ops.distributions import kullback_leibler
from tensorflow.python.ops.distributions import util as distribution_util
from tensorflow.python.util.tf_export import tf_export
__all__ = [
"Gamma",
"GammaWithSoftplusConcentrationRate",
]
@tf_export("distributions.Gamma")
class Gamma(distribution.Distribution):
"""Gamma distribution.
The Gamma distribution is defined over positive real numbers using
parameters `concentration` (aka "alpha") and `rate` (aka "beta").
#### Mathematical Details
The probability density function (pdf) is,
```none
pdf(x; alpha, beta, x > 0) = x**(alpha - 1) exp(-x beta) / Z
Z = Gamma(alpha) beta**(-alpha)
```
where:
* `concentration = alpha`, `alpha > 0`,
* `rate = beta`, `beta > 0`,
* `Z` is the normalizing constant, and,
* `Gamma` is the [gamma function](
https://en.wikipedia.org/wiki/Gamma_function).
The cumulative density function (cdf) is,
```none
cdf(x; alpha, beta, x > 0) = GammaInc(alpha, beta x) / Gamma(alpha)
```
where `GammaInc` is the [lower incomplete Gamma function](
https://en.wikipedia.org/wiki/Incomplete_gamma_function).
The parameters can be intuited via their relationship to mean and stddev,
```none
concentration = alpha = (mean / stddev)**2
rate = beta = mean / stddev**2 = concentration / mean
```
Distribution parameters are automatically broadcast in all functions; see
examples for details.
Warning: The samples of this distribution are always non-negative. However,
the samples that are smaller than `np.finfo(dtype).tiny` are rounded
to this value, so it appears more often than it should.
This should only be noticeable when the `concentration` is very small, or the
`rate` is very large. See note in `tf.random_gamma` docstring.
Samples of this distribution are reparameterized (pathwise differentiable).
The derivatives are computed using the approach described in the paper
[Michael Figurnov, Shakir Mohamed, Andriy Mnih.
Implicit Reparameterization Gradients, 2018](https://arxiv.org/abs/1805.08498)
#### Examples
```python
dist = tf.distributions.Gamma(concentration=3.0, rate=2.0)
dist2 = tf.distributions.Gamma(concentration=[3.0, 4.0], rate=[2.0, 3.0])
```
Compute the gradients of samples w.r.t. the parameters:
```python
concentration = tf.constant(3.0)
rate = tf.constant(2.0)
dist = tf.distributions.Gamma(concentration, rate)
samples = dist.sample(5) # Shape [5]
loss = tf.reduce_mean(tf.square(samples)) # Arbitrary loss function
# Unbiased stochastic gradients of the loss function
grads = tf.gradients(loss, [concentration, rate])
```
"""
def __init__(self,
concentration,
rate,
validate_args=False,
allow_nan_stats=True,
name="Gamma"):
"""Construct Gamma with `concentration` and `rate` parameters.
The parameters `concentration` and `rate` must be shaped in a way that
supports broadcasting (e.g. `concentration + rate` is a valid operation).
Args:
concentration: Floating point tensor, the concentration params of the
distribution(s). Must contain only positive values.
rate: Floating point tensor, the inverse scale params of the
distribution(s). Must contain only positive values.
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:
TypeError: if `concentration` and `rate` are different dtypes.
"""
parameters = dict(locals())
with ops.name_scope(name, values=[concentration, rate]) as name:
with ops.control_dependencies([
check_ops.assert_positive(concentration),
check_ops.assert_positive(rate),
] if validate_args else []):
self._concentration = array_ops.identity(
concentration, name="concentration")
self._rate = array_ops.identity(rate, name="rate")
check_ops.assert_same_float_dtype(
[self._concentration, self._rate])
super(Gamma, self).__init__(
dtype=self._concentration.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=distribution.FULLY_REPARAMETERIZED,
parameters=parameters,
graph_parents=[self._concentration,
self._rate],
name=name)
@staticmethod
def _param_shapes(sample_shape):
return dict(
zip(("concentration", "rate"), ([ops.convert_to_tensor(
sample_shape, dtype=dtypes.int32)] * 2)))
@property
def concentration(self):
"""Concentration parameter."""
return self._concentration
@property
def rate(self):
"""Rate parameter."""
return self._rate
def _batch_shape_tensor(self):
return array_ops.broadcast_dynamic_shape(
array_ops.shape(self.concentration),
array_ops.shape(self.rate))
def _batch_shape(self):
return array_ops.broadcast_static_shape(
self.concentration.get_shape(),
self.rate.get_shape())
def _event_shape_tensor(self):
return constant_op.constant([], dtype=dtypes.int32)
def _event_shape(self):
return tensor_shape.scalar()
@distribution_util.AppendDocstring(
"""Note: See `tf.random_gamma` docstring for sampling details and
caveats.""")
def _sample_n(self, n, seed=None):
return random_ops.random_gamma(
shape=[n],
alpha=self.concentration,
beta=self.rate,
dtype=self.dtype,
seed=seed)
def _log_prob(self, x):
return self._log_unnormalized_prob(x) - self._log_normalization()
def _cdf(self, x):
x = self._maybe_assert_valid_sample(x)
# Note that igamma returns the regularized incomplete gamma function,
# which is what we want for the CDF.
return math_ops.igamma(self.concentration, self.rate * x)
def _log_unnormalized_prob(self, x):
x = self._maybe_assert_valid_sample(x)
return (self.concentration - 1.) * math_ops.log(x) - self.rate * x
def _log_normalization(self):
return (math_ops.lgamma(self.concentration)
- self.concentration * math_ops.log(self.rate))
def _entropy(self):
return (self.concentration
- math_ops.log(self.rate)
+ math_ops.lgamma(self.concentration)
+ ((1. - self.concentration) *
math_ops.digamma(self.concentration)))
def _mean(self):
return self.concentration / self.rate
def _variance(self):
return self.concentration / math_ops.square(self.rate)
def _stddev(self):
return math_ops.sqrt(self.concentration) / self.rate
@distribution_util.AppendDocstring(
"""The mode of a gamma distribution is `(shape - 1) / rate` when
`shape > 1`, and `NaN` otherwise. If `self.allow_nan_stats` is `False`,
an exception will be raised rather than returning `NaN`.""")
def _mode(self):
mode = (self.concentration - 1.) / self.rate
if self.allow_nan_stats:
nan = array_ops.fill(
self.batch_shape_tensor(),
np.array(np.nan, dtype=self.dtype.as_numpy_dtype()),
name="nan")
return array_ops.where(self.concentration > 1., mode, nan)
else:
return control_flow_ops.with_dependencies([
check_ops.assert_less(
array_ops.ones([], self.dtype),
self.concentration,
message="mode not defined when any concentration <= 1"),
], mode)
def _maybe_assert_valid_sample(self, x):
check_ops.assert_same_float_dtype(tensors=[x], dtype=self.dtype)
if not self.validate_args:
return x
return control_flow_ops.with_dependencies([
check_ops.assert_positive(x),
], x)
class GammaWithSoftplusConcentrationRate(Gamma):
"""`Gamma` with softplus of `concentration` and `rate`."""
def __init__(self,
concentration,
rate,
validate_args=False,
allow_nan_stats=True,
name="GammaWithSoftplusConcentrationRate"):
parameters = dict(locals())
with ops.name_scope(name, values=[concentration, rate]) as name:
super(GammaWithSoftplusConcentrationRate, self).__init__(
concentration=nn.softplus(concentration,
name="softplus_concentration"),
rate=nn.softplus(rate, name="softplus_rate"),
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
@kullback_leibler.RegisterKL(Gamma, Gamma)
def _kl_gamma_gamma(g0, g1, name=None):
"""Calculate the batched KL divergence KL(g0 || g1) with g0 and g1 Gamma.
Args:
g0: instance of a Gamma distribution object.
g1: instance of a Gamma distribution object.
name: (optional) Name to use for created operations.
Default is "kl_gamma_gamma".
Returns:
kl_gamma_gamma: `Tensor`. The batchwise KL(g0 || g1).
"""
with ops.name_scope(name, "kl_gamma_gamma", values=[
g0.concentration, g0.rate, g1.concentration, g1.rate]):
# Result from:
# http://www.fil.ion.ucl.ac.uk/~wpenny/publications/densities.ps
# For derivation see:
# http://stats.stackexchange.com/questions/11646/kullback-leibler-divergence-between-two-gamma-distributions pylint: disable=line-too-long
return (((g0.concentration - g1.concentration)
* math_ops.digamma(g0.concentration))
+ math_ops.lgamma(g1.concentration)
- math_ops.lgamma(g0.concentration)
+ g1.concentration * math_ops.log(g0.rate)
- g1.concentration * math_ops.log(g1.rate)
+ g0.concentration * (g1.rate / g0.rate - 1.))