forked from mindspore-Ecosystem/mindspore
Add Beta distribution
This commit is contained in:
peixu_ren
parent
28772d4fb7
commit
37d40bc495
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@ -19,6 +19,7 @@ Distributions are the high-level components used to construct the probabilistic
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from .distribution import Distribution
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from .transformed_distribution import TransformedDistribution
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from .bernoulli import Bernoulli
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from .beta import Beta
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from .categorical import Categorical
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from .cauchy import Cauchy
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from .exponential import Exponential
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@ -34,6 +35,7 @@ from .uniform import Uniform
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__all__ = ['Distribution',
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'TransformedDistribution',
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'Bernoulli',
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'Beta',
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'Categorical',
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'Cauchy',
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'Exponential',
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@ -0,0 +1,333 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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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"""Beta Distribution"""
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import numpy as np
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from mindspore.ops import operations as P
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from mindspore.ops import composite as C
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import mindspore.nn as nn
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from mindspore._checkparam import Validator
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from mindspore.common import dtype as mstype
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from .distribution import Distribution
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from ._utils.utils import check_greater_zero, check_distribution_name
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from ._utils.custom_ops import log_generic
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class Beta(Distribution):
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"""
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Beta distribution.
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Args:
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concentration1 (int, float, list, numpy.ndarray, Tensor, Parameter): The concentration1,
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also know as alpha of the Beta distribution.
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concentration0 (int, float, list, numpy.ndarray, Tensor, Parameter): The concentration0, also know as
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beta of the Beta distribution.
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seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
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dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
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name (str): The name of the distribution. Default: 'Beta'.
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Note:
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`concentration1` and `concentration0` must be greater than zero.
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`dist_spec_args` are `concentration1` and `concentration0`.
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`dtype` must be a float type because Beta distributions are continuous.
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Examples:
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>>> # To initialize a Beta distribution of the concentration1 3.0 and the concentration0 4.0.
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>>> import mindspore.nn.probability.distribution as msd
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>>> b = msd.Beta(3.0, 4.0, dtype=mstype.float32)
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>>>
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>>> # The following creates two independent Beta distributions.
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>>> b = msd.Beta([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
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>>>
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>>> # A Beta distribution can be initilized without arguments.
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>>> # In this case, `concentration1` and `concentration0` must be passed in through arguments.
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>>> b = msd.Beta(dtype=mstype.float32)
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>>>
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>>> # To use a Beta distribution in a network.
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>>> class net(Cell):
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... def __init__(self):
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... super(net, self).__init__():
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... self.b1 = msd.Beta(1.0, 1.0, dtype=mstype.float32)
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... self.b2 = msd.Beta(dtype=mstype.float32)
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...
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... # The following calls are valid in construct.
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... def construct(self, value, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
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...
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... # Private interfaces of probability functions corresponding to public interfaces, including
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... # `prob` and `log_prob`, have the same arguments as follows.
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... # Args:
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... # value (Tensor): the value to be evaluated.
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... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
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... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
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...
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... # Examples of `prob`.
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... # Similar calls can be made to other probability functions
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... # by replacing 'prob' by the name of the function
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... ans = self.b1.prob(value)
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... # Evaluate with respect to the distribution b.
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... ans = self.b1.prob(value, concentration1_b, concentration0_b)
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... # `concentration1` and `concentration0` must be passed in during function calls
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... ans = self.b2.prob(value, concentration1_a, concentration0_a)
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...
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...
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... # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
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... # Args:
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... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
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... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
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...
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... # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
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... ans = self.b1.concentration1() # return 1.0
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... ans = self.b1.concentration1(concentration1_b, concentration0_b) # return concentration1_b
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... # `concentration1` and `concentration0` must be passed in during function calls.
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... ans = self.b2.concentration1(concentration1_a, concentration0_a)
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...
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...
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... # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
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... # Args:
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... # dist (str): the type of the distributions. Only "Beta" is supported.
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... # concentration1_b (Tensor): the concentration1 of distribution b.
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... # concentration0_b (Tensor): the concentration0 of distribution b.
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... # concentration1_a (Tensor): the concentration1 of distribution a.
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... # Default: self._concentration1.
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... # concentration0_a (Tensor): the concentration0 of distribution a.
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... # Default: self._concentration0.
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...
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... # Examples of `kl_loss`. `cross_entropy` is similar.
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... ans = self.b1.kl_loss('Beta', concentration1_b, concentration0_b)
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... ans = self.b1.kl_loss('Beta', concentration1_b, concentration0_b,
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... concentration1_a, concentration0_a)
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... # Additional `concentration1` and `concentration0` must be passed in.
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... ans = self.b2.kl_loss('Beta', concentration1_b, concentration0_b,
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... concentration1_a, concentration0_a)
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...
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...
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... # Examples of `sample`.
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... # Args:
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... # shape (tuple): the shape of the sample. Default: ()
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... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
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... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
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... ans = self.b1.sample()
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... ans = self.b1.sample((2,3))
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... ans = self.b1.sample((2,3), concentration1_b, concentration0_b)
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... ans = self.b2.sample((2,3), concentration1_a, concentration0_a)
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"""
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def __init__(self,
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concentration1=None,
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concentration0=None,
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seed=None,
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dtype=mstype.float32,
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name="Beta"):
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"""
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Constructor of Beta.
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"""
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param = dict(locals())
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param['param_dict'] = {'concentration1': concentration1, 'concentration0': concentration0}
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valid_dtype = mstype.float_type
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Validator.check_type_name("dtype", dtype, valid_dtype, type(self).__name__)
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super(Beta, self).__init__(seed, dtype, name, param)
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self._concentration1 = self._add_parameter(concentration1, 'concentration1')
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self._concentration0 = self._add_parameter(concentration0, 'concentration0')
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if self._concentration1 is not None:
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check_greater_zero(self._concentration1, "concentration1")
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if self._concentration0 is not None:
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check_greater_zero(self._concentration0, "concentration0")
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# ops needed for the class
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self.log = log_generic
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self.log1p = P.Log1p()
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self.neg = P.Neg()
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self.pow = P.Pow()
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self.squeeze = P.Squeeze(0)
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self.cast = P.Cast()
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self.fill = P.Fill()
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self.shape = P.Shape()
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self.select = P.Select()
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self.logicaland = P.LogicalAnd()
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self.greater = P.Greater()
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self.digamma = nn.DiGamma()
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self.lbeta = nn.LBeta()
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def extend_repr(self):
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if self.is_scalar_batch:
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s = f'concentration1 = {self._concentration1}, concentration0 = {self._concentration0}'
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else:
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s = f'batch_shape = {self._broadcast_shape}'
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return s
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@property
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def concentration1(self):
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"""
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Return the concentration1, also know as the alpha of the Beta distribution.
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"""
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return self._concentration1
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@property
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def concentration0(self):
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"""
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Return the concentration0, also know as the beta of the Beta distribution.
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"""
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return self._concentration0
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def _get_dist_type(self):
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return "Beta"
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def _get_dist_args(self, concentration1=None, concentration0=None):
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if concentration1 is not None:
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self.checktensor(concentration1, 'concentration1')
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else:
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concentration1 = self._concentration1
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if concentration0 is not None:
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self.checktensor(concentration0, 'concentration0')
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else:
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concentration0 = self._concentration0
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return concentration1, concentration0
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def _mean(self, concentration1=None, concentration0=None):
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"""
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The mean of the distribution.
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"""
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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return concentration1 / (concentration1 + concentration0)
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def _var(self, concentration1=None, concentration0=None):
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"""
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The variance of the distribution.
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"""
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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total_concentration = concentration1 + concentration0
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return concentration1 * concentration0 / (self.pow(total_concentration, 2) * (total_concentration + 1.))
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def _mode(self, concentration1=None, concentration0=None):
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"""
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The mode of the distribution.
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"""
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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comp1 = self.greater(concentration1, 1.)
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comp2 = self.greater(concentration0, 1.)
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cond = self.logicaland(comp1, comp2)
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nan = self.fill(self.dtype, self.broadcast_shape, np.nan)
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mode = (concentration1 - 1.) / (concentration1 + concentration0 - 2.)
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return self.select(cond, mode, nan)
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def _entropy(self, concentration1=None, concentration0=None):
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r"""
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Evaluate entropy.
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.. math::
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H(X) = \log(\Beta(\alpha, \beta)) - (\alpha - 1) * \digamma(\alpha)
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- (\beta - 1) * \digamma(\beta) + (\alpha + \beta - 2) * \digamma(\alpha + \beta)
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"""
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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total_concentration = concentration1 + concentration0
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return self.lbeta(concentration1, concentration0) \
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- (concentration1 - 1.) * self.digamma(concentration1) \
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- (concentration0 - 1.) * self.digamma(concentration0) \
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+ (total_concentration - 2.) * self.digamma(total_concentration)
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def _cross_entropy(self, dist, concentration1_b, concentration0_b, concentration1=None, concentration0=None):
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r"""
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Evaluate cross entropy between Beta distributions.
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Args:
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dist (str): Type of the distributions. Should be "Beta" in this case.
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concentration1_b (Tensor): concentration1 of distribution b.
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concentration0_b (Tensor): concentration0 of distribution b.
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concentration1_a (Tensor): concentration1 of distribution a. Default: self._concentration1.
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concentration0_a (Tensor): concentration0 of distribution a. Default: self._concentration0.
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"""
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check_distribution_name(dist, 'Beta')
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return self._entropy(concentration1, concentration0) \
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+ self._kl_loss(dist, concentration1_b, concentration0_b, concentration1, concentration0)
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def _log_prob(self, value, concentration1=None, concentration0=None):
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r"""
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Evaluate log probability.
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Args:
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value (Tensor): The value to be evaluated.
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concentration1 (Tensor): The concentration1 of the distribution. Default: self._concentration1.
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concentration0 (Tensor): The concentration0 the distribution. Default: self._concentration0.
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.. math::
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L(x) = (\alpha - 1) * \log(x) + (\beta - 1) * \log(1 - x) - \log(\Beta(\alpha, \beta))
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"""
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value = self._check_value(value, 'value')
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value = self.cast(value, self.dtype)
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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log_unnormalized_prob = (concentration1 - 1.) * self.log(value) \
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+ (concentration0 - 1.) * self.log1p(self.neg(value))
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return log_unnormalized_prob - self.lbeta(concentration1, concentration0)
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def _kl_loss(self, dist, concentration1_b, concentration0_b, concentration1=None, concentration0=None):
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r"""
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Evaluate Beta-Beta KL divergence, i.e. KL(a||b).
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Args:
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dist (str): The type of the distributions. Should be "Beta" in this case.
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concentration1_b (Tensor): The concentration1 of distribution b.
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concentration0_b (Tensor): The concentration0 distribution b.
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concentration1_a (Tensor): The concentration1 of distribution a. Default: self._concentration1.
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concentration0_a (Tensor): The concentration0 distribution a. Default: self._concentration0.
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.. math::
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KL(a||b) = \log(\Beta(\alpha_{b}, \beta_{b})) - \log(\Beta(\alpha_{a}, \beta_{a}))
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- \digamma(\alpha_{a}) * (\alpha_{b} - \alpha_{a})
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- \digamma(\beta_{a}) * (\beta_{b} - \beta_{a})
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+ \digamma(\alpha_{a} + \beta_{a}) * (\alpha_{b} + \beta_{b} - \alpha_{a} - \beta_{a})
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"""
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check_distribution_name(dist, 'Beta')
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concentration1_b = self._check_value(concentration1_b, 'concentration1_b')
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concentration0_b = self._check_value(concentration0_b, 'concentration0_b')
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concentration1_b = self.cast(concentration1_b, self.parameter_type)
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concentration0_b = self.cast(concentration0_b, self.parameter_type)
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concentration1_a, concentration0_a = self._check_param_type(concentration1, concentration0)
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total_concentration_a = concentration1_a + concentration0_a
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total_concentration_b = concentration1_b + concentration0_b
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log_normalization_a = self.lbeta(concentration1_a, concentration0_a)
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log_normalization_b = self.lbeta(concentration1_b, concentration0_b)
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return (log_normalization_b - log_normalization_a) \
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- (self.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
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- (self.digamma(concentration0_a) * (concentration0_b - concentration0_a)) \
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+ (self.digamma(total_concentration_a) * (total_concentration_b - total_concentration_a))
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def _sample(self, shape=(), concentration1=None, concentration0=None):
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"""
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Sampling.
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Args:
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shape (tuple): The shape of the sample. Default: ().
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concentration1 (Tensor): The concentration1 of the samples. Default: self._concentration1.
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concentration0 (Tensor): The concentration0 of the samples. Default: self._concentration0.
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Returns:
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Tensor, with the shape being shape + batch_shape.
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"""
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shape = self.checktuple(shape, 'shape')
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concentration1, concentration0 = self._check_param_type(concentration1, concentration0)
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batch_shape = self.shape(concentration1 + concentration0)
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origin_shape = shape + batch_shape
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if origin_shape == ():
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sample_shape = (1,)
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else:
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sample_shape = origin_shape
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ones = self.fill(self.dtype, sample_shape, 1.0)
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sample_gamma1 = C.gamma(sample_shape, alpha=concentration1, beta=ones, seed=self.seed)
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sample_gamma2 = C.gamma(sample_shape, alpha=concentration0, beta=ones, seed=self.seed)
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sample_beta = sample_gamma1 / (sample_gamma1 + sample_gamma2)
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value = self.cast(sample_beta, self.dtype)
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if origin_shape == ():
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value = self.squeeze(value)
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return value
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@ -81,12 +81,12 @@ class Gamma(Distribution):
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... ans = self.g2.prob(value, concentration_a, rate_a)
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...
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...
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... # Functions `concentration`, `rate`, `mean`, `sd`, `var`, and `entropy` have the same arguments.
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... # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
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... # Args:
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... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
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... # rate (Tensor): the rate of the distribution. Default: self._rate.
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...
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... # Example of `concentration`, `rate`, `mean`. `sd`, `var`, and `entropy` are similar.
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... # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
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... ans = self.g1.concentration() # return 1.0
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... ans = self.g1.concentration(concentration_b, rate_b) # return concentration_b
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... # `concentration` and `rate` must be passed in during function calls.
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@ -76,11 +76,11 @@ class Poisson(Distribution):
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... ans = self.p2.prob(value, rate_a)
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...
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...
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... # Functions `mean`, `sd`, and 'var' have the same arguments as follows.
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... # Functions `mean`, `mode`, `sd`, and 'var' have the same arguments as follows.
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... # Args:
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... # rate (Tensor): the rate of the distribution. Default: self.rate.
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...
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... # Examples of `mean`. `sd`, `var`, and `entropy` are similar.
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... # Examples of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
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... ans = self.p1.mean() # return 2
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... ans = self.p1.mean(rate_b) # return 1 / rate_b
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... # `rate` must be passed in during function calls.
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@ -0,0 +1,245 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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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
|
||||
# limitations under the License.
|
||||
# ============================================================================
|
||||
"""test cases for Beta distribution"""
|
||||
import numpy as np
|
||||
from scipy import stats
|
||||
from scipy import special
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
import mindspore.nn.probability.distribution as msd
|
||||
from mindspore import Tensor
|
||||
from mindspore import dtype
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
|
||||
class Prob(nn.Cell):
|
||||
"""
|
||||
Test class: probability of Beta distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Prob, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.b.prob(x_)
|
||||
|
||||
def test_pdf():
|
||||
"""
|
||||
Test pdf.
|
||||
"""
|
||||
beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
|
||||
expect_pdf = beta_benchmark.pdf([0.25, 0.75]).astype(np.float32)
|
||||
pdf = Prob()
|
||||
output = pdf(Tensor([0.25, 0.75], dtype=dtype.float32))
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
|
||||
|
||||
class LogProb(nn.Cell):
|
||||
"""
|
||||
Test class: log probability of Beta distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(LogProb, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_):
|
||||
return self.b.log_prob(x_)
|
||||
|
||||
def test_log_likelihood():
|
||||
"""
|
||||
Test log_pdf.
|
||||
"""
|
||||
beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
|
||||
expect_logpdf = beta_benchmark.logpdf([0.25, 0.75]).astype(np.float32)
|
||||
logprob = LogProb()
|
||||
output = logprob(Tensor([0.25, 0.75], dtype=dtype.float32))
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
|
||||
|
||||
class KL(nn.Cell):
|
||||
"""
|
||||
Test class: kl_loss of Beta distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(KL, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
return self.b.kl_loss('Beta', x_, y_)
|
||||
|
||||
def test_kl_loss():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
concentration1_a = np.array([3.0]).astype(np.float32)
|
||||
concentration0_a = np.array([4.0]).astype(np.float32)
|
||||
|
||||
concentration1_b = np.array([1.0]).astype(np.float32)
|
||||
concentration0_b = np.array([1.0]).astype(np.float32)
|
||||
|
||||
total_concentration_a = concentration1_a + concentration0_a
|
||||
total_concentration_b = concentration1_b + concentration0_b
|
||||
log_normalization_a = np.log(special.beta(concentration1_a, concentration0_a))
|
||||
log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
|
||||
expect_kl_loss = (log_normalization_b - log_normalization_a) \
|
||||
- (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
|
||||
- (special.digamma(concentration0_a) * (concentration0_b - concentration0_a)) \
|
||||
+ (special.digamma(total_concentration_a) * (total_concentration_b - total_concentration_a))
|
||||
|
||||
kl_loss = KL()
|
||||
concentration1 = Tensor(concentration1_b, dtype=dtype.float32)
|
||||
concentration0 = Tensor(concentration0_b, dtype=dtype.float32)
|
||||
output = kl_loss(concentration1, concentration0)
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_kl_loss) < tol).all()
|
||||
|
||||
class Basics(nn.Cell):
|
||||
"""
|
||||
Test class: mean/sd/mode of Beta distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Basics, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([3.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
return self.b.mean(), self.b.sd(), self.b.mode()
|
||||
|
||||
def test_basics():
|
||||
"""
|
||||
Test mean/standard deviation/mode.
|
||||
"""
|
||||
basics = Basics()
|
||||
mean, sd, mode = basics()
|
||||
beta_benchmark = stats.beta(np.array([3.0]), np.array([3.0]))
|
||||
expect_mean = beta_benchmark.mean().astype(np.float32)
|
||||
expect_sd = beta_benchmark.std().astype(np.float32)
|
||||
expect_mode = [0.5]
|
||||
tol = 1e-6
|
||||
assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
|
||||
assert (np.abs(mode.asnumpy() - expect_mode) < tol).all()
|
||||
assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
|
||||
|
||||
class Sampling(nn.Cell):
|
||||
"""
|
||||
Test class: sample of Beta distribution.
|
||||
"""
|
||||
def __init__(self, shape, seed=0):
|
||||
super(Sampling, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([1.0]), seed=seed, dtype=dtype.float32)
|
||||
self.shape = shape
|
||||
|
||||
def construct(self, concentration1=None, concentration0=None):
|
||||
return self.b.sample(self.shape, concentration1, concentration0)
|
||||
|
||||
def test_sample():
|
||||
"""
|
||||
Test sample.
|
||||
"""
|
||||
shape = (2, 3)
|
||||
seed = 10
|
||||
concentration1 = Tensor([2.0], dtype=dtype.float32)
|
||||
concentration0 = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
|
||||
sample = Sampling(shape, seed=seed)
|
||||
output = sample(concentration1, concentration0)
|
||||
assert output.shape == (2, 3, 3)
|
||||
|
||||
class EntropyH(nn.Cell):
|
||||
"""
|
||||
Test class: entropy of Beta distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(EntropyH, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
return self.b.entropy()
|
||||
|
||||
def test_entropy():
|
||||
"""
|
||||
Test entropy.
|
||||
"""
|
||||
beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
|
||||
expect_entropy = beta_benchmark.entropy().astype(np.float32)
|
||||
entropy = EntropyH()
|
||||
output = entropy()
|
||||
tol = 1e-6
|
||||
assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
|
||||
|
||||
class CrossEntropy(nn.Cell):
|
||||
"""
|
||||
Test class: cross entropy between Beta distributions.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(CrossEntropy, self).__init__()
|
||||
self.b = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
entropy = self.b.entropy()
|
||||
kl_loss = self.b.kl_loss('Beta', x_, y_)
|
||||
h_sum_kl = entropy + kl_loss
|
||||
cross_entropy = self.b.cross_entropy('Beta', x_, y_)
|
||||
return h_sum_kl - cross_entropy
|
||||
|
||||
def test_cross_entropy():
|
||||
"""
|
||||
Test cross_entropy.
|
||||
"""
|
||||
cross_entropy = CrossEntropy()
|
||||
concentration1 = Tensor([3.0], dtype=dtype.float32)
|
||||
concentration0 = Tensor([2.0], dtype=dtype.float32)
|
||||
diff = cross_entropy(concentration1, concentration0)
|
||||
tol = 1e-6
|
||||
assert (np.abs(diff.asnumpy() - np.zeros(diff.shape)) < tol).all()
|
||||
|
||||
class Net(nn.Cell):
|
||||
"""
|
||||
Test class: expand single distribution instance to multiple graphs
|
||||
by specifying the attributes.
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.beta = msd.Beta(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
kl = self.beta.kl_loss('Beta', x_, y_)
|
||||
prob = self.beta.prob(kl)
|
||||
return prob
|
||||
|
||||
def test_multiple_graphs():
|
||||
"""
|
||||
Test multiple graphs case.
|
||||
"""
|
||||
prob = Net()
|
||||
concentration1_a = np.array([3.0]).astype(np.float32)
|
||||
concentration0_a = np.array([1.0]).astype(np.float32)
|
||||
concentration1_b = np.array([2.0]).astype(np.float32)
|
||||
concentration0_b = np.array([1.0]).astype(np.float32)
|
||||
ans = prob(Tensor(concentration1_b), Tensor(concentration0_b))
|
||||
|
||||
total_concentration_a = concentration1_a + concentration0_a
|
||||
total_concentration_b = concentration1_b + concentration0_b
|
||||
log_normalization_a = np.log(special.beta(concentration1_a, concentration0_a))
|
||||
log_normalization_b = np.log(special.beta(concentration1_b, concentration0_b))
|
||||
expect_kl_loss = (log_normalization_b - log_normalization_a) \
|
||||
- (special.digamma(concentration1_a) * (concentration1_b - concentration1_a)) \
|
||||
- (special.digamma(concentration0_a) * (concentration0_b - concentration0_a)) \
|
||||
+ (special.digamma(total_concentration_a) * (total_concentration_b - total_concentration_a))
|
||||
|
||||
beta_benchmark = stats.beta(np.array([3.0]), np.array([1.0]))
|
||||
expect_prob = beta_benchmark.pdf(expect_kl_loss).astype(np.float32)
|
||||
|
||||
tol = 1e-6
|
||||
assert (np.abs(ans.asnumpy() - expect_prob) < tol).all()
|
||||
|
|
@ -298,11 +298,11 @@ class Net(nn.Cell):
|
|||
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.Gamma = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
self.get_flags = msd.Gamma(np.array([3.0]), np.array([1.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self, x_, y_):
|
||||
kl = self.Gamma.kl_loss('Gamma', x_, y_)
|
||||
prob = self.Gamma.prob(kl)
|
||||
kl = self.g.kl_loss('Gamma', x_, y_)
|
||||
prob = self.g.prob(kl)
|
||||
return prob
|
||||
|
||||
def test_multiple_graphs():
|
||||
|
|
|
|||
|
|
@ -0,0 +1,212 @@
|
|||
# Copyright 2020 Huawei Technologies Co., Ltd
|
||||
#
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""
|
||||
Test nn.probability.distribution.Gamma.
|
||||
"""
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
import mindspore.nn as nn
|
||||
import mindspore.nn.probability.distribution as msd
|
||||
from mindspore import dtype
|
||||
from mindspore import Tensor
|
||||
|
||||
def test_gamma_shape_errpr():
|
||||
"""
|
||||
Invalid shapes.
|
||||
"""
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
|
||||
|
||||
def test_type():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., dtype=dtype.int32)
|
||||
|
||||
def test_name():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., name=1.0)
|
||||
|
||||
def test_seed():
|
||||
with pytest.raises(TypeError):
|
||||
msd.Gamma(0., 1., seed='seed')
|
||||
|
||||
def test_concentration1():
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(0., 1.)
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(-1., 1.)
|
||||
|
||||
def test_concentration0():
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(1., 0.)
|
||||
with pytest.raises(ValueError):
|
||||
msd.Gamma(1., -1.)
|
||||
|
||||
def test_arguments():
|
||||
"""
|
||||
args passing during initialization.
|
||||
"""
|
||||
g = msd.Gamma()
|
||||
assert isinstance(g, msd.Distribution)
|
||||
g = msd.Gamma([3.0], [4.0], dtype=dtype.float32)
|
||||
assert isinstance(g, msd.Distribution)
|
||||
|
||||
|
||||
class GammaProb(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: initialize with concentration1/concentration0.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaProb, self).__init__()
|
||||
self.gamma = msd.Gamma([3.0, 4.0], [1.0, 1.0], dtype=dtype.float32)
|
||||
|
||||
def construct(self, value):
|
||||
prob = self.gamma.prob(value)
|
||||
log_prob = self.gamma.log_prob(value)
|
||||
return prob + log_prob
|
||||
|
||||
def test_gamma_prob():
|
||||
"""
|
||||
Test probability functions: passing value through construct.
|
||||
"""
|
||||
net = GammaProb()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
ans = net(value)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
|
||||
class GammaProb1(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: initialize without concentration1/concentration0.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaProb1, self).__init__()
|
||||
self.gamma = msd.Gamma()
|
||||
|
||||
def construct(self, value, concentration1, concentration0):
|
||||
prob = self.gamma.prob(value, concentration1, concentration0)
|
||||
log_prob = self.gamma.log_prob(value, concentration1, concentration0)
|
||||
return prob + log_prob
|
||||
|
||||
def test_gamma_prob1():
|
||||
"""
|
||||
Test probability functions: passing concentration1/concentration0, value through construct.
|
||||
"""
|
||||
net = GammaProb1()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
concentration1 = Tensor([2.0, 3.0], dtype=dtype.float32)
|
||||
concentration0 = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, concentration1, concentration0)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaKl(nn.Cell):
|
||||
"""
|
||||
Test class: kl_loss of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaKl, self).__init__()
|
||||
self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
self.g2 = msd.Gamma(dtype=dtype.float32)
|
||||
|
||||
def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
|
||||
kl1 = self.g1.kl_loss('Gamma', concentration1_b, concentration0_b)
|
||||
kl2 = self.g2.kl_loss('Gamma', concentration1_b, concentration0_b, concentration1_a, concentration0_a)
|
||||
return kl1 + kl2
|
||||
|
||||
def test_kl():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
net = GammaKl()
|
||||
concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration0_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration1_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration0_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
|
||||
ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaCrossEntropy(nn.Cell):
|
||||
"""
|
||||
Test class: cross_entropy of Gamma distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaCrossEntropy, self).__init__()
|
||||
self.g1 = msd.Gamma(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
self.g2 = msd.Gamma(dtype=dtype.float32)
|
||||
|
||||
def construct(self, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
|
||||
h1 = self.g1.cross_entropy('Gamma', concentration1_b, concentration0_b)
|
||||
h2 = self.g2.cross_entropy('Gamma', concentration1_b, concentration0_b, concentration1_a, concentration0_a)
|
||||
return h1 + h2
|
||||
|
||||
def test_cross_entropy():
|
||||
"""
|
||||
Test cross entropy between Gamma distributions.
|
||||
"""
|
||||
net = GammaCrossEntropy()
|
||||
concentration1_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration0_b = Tensor(np.array([1.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration1_a = Tensor(np.array([2.0]).astype(np.float32), dtype=dtype.float32)
|
||||
concentration0_a = Tensor(np.array([3.0]).astype(np.float32), dtype=dtype.float32)
|
||||
ans = net(concentration1_b, concentration0_b, concentration1_a, concentration0_a)
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaBasics(nn.Cell):
|
||||
"""
|
||||
Test class: basic mean/sd function.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaBasics, self).__init__()
|
||||
self.g = msd.Gamma(np.array([3.0, 4.0]), np.array([4.0, 6.0]), dtype=dtype.float32)
|
||||
|
||||
def construct(self):
|
||||
mean = self.g.mean()
|
||||
sd = self.g.sd()
|
||||
mode = self.g.mode()
|
||||
return mean + sd + mode
|
||||
|
||||
def test_bascis():
|
||||
"""
|
||||
Test mean/sd/mode/entropy functionality of Gamma.
|
||||
"""
|
||||
net = GammaBasics()
|
||||
ans = net()
|
||||
assert isinstance(ans, Tensor)
|
||||
|
||||
class GammaConstruct(nn.Cell):
|
||||
"""
|
||||
Gamma distribution: going through construct.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(GammaConstruct, self).__init__()
|
||||
self.gamma = msd.Gamma([3.0], [4.0])
|
||||
self.gamma1 = msd.Gamma()
|
||||
|
||||
def construct(self, value, concentration1, concentration0):
|
||||
prob = self.gamma('prob', value)
|
||||
prob1 = self.gamma('prob', value, concentration1, concentration0)
|
||||
prob2 = self.gamma1('prob', value, concentration1, concentration0)
|
||||
return prob + prob1 + prob2
|
||||
|
||||
def test_gamma_construct():
|
||||
"""
|
||||
Test probability function going through construct.
|
||||
"""
|
||||
net = GammaConstruct()
|
||||
value = Tensor([0.5, 1.0], dtype=dtype.float32)
|
||||
concentration1 = Tensor([0.0], dtype=dtype.float32)
|
||||
concentration0 = Tensor([1.0], dtype=dtype.float32)
|
||||
ans = net(value, concentration1, concentration0)
|
||||
assert isinstance(ans, Tensor)
|
||||
Loading…
Reference in New Issue