# 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.
# ============================================================================
"""Categorical Distribution"""
from mindspore.ops import operations as P
import mindspore.nn as nn
from mindspore.common import dtype as mstype
from .distribution import Distribution
from ._utils.utils import logits_to_probs, probs_to_logits, check_type, cast_to_tensor, \
raise_probs_logits_error
[docs]class Categorical(Distribution):
"""
Create a categorical distribution parameterized by either probabilities or logits (but not both).
Args:
probs (Tensor, list, numpy.ndarray, Parameter): Event probabilities.
logits (Tensor, list, numpy.ndarray, Parameter, float): Event log-odds.
seed (int): The global seed is used in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the distribution. Default: mstype.int32.
name (str): The name of the distribution. Default: Categorical.
Note:
`probs` must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1.
Examples:
>>> # To initialize a Categorical distribution of prob is [0.5, 0.5]
>>> import mindspore.nn.probability.distribution as msd
>>> b = msd.Categorical(probs = [0.5, 0.5], dtype=mstype.int32)
>>>
>>> # To use Categorical in a network
>>> class net(Cell):
>>> def __init__(self, probs):
>>> super(net, self).__init__():
>>> self.ca = msd.Categorical(probs=probs, dtype=mstype.int32)
>>> # All the following calls in construct are valid
>>> def construct(self, value):
>>>
>>> # Similar calls can be made to logits
>>> ans = self.ca.probs
>>> # value must be Tensor(mstype.float32, bool, mstype.int32)
>>> ans = self.ca.log_prob(value)
>>>
>>> # Usage of enumerate_support
>>> ans = self.ca.enumerate_support()
>>>
>>> # Usage of entropy
>>> ans = self.ca.entropy()
>>>
>>> # Sample
>>> ans = self.ca.sample()
>>> ans = self.ca.sample((2,3))
>>> ans = self.ca.sample((2,))
"""
def __init__(self,
probs=None,
logits=None,
seed=None,
dtype=mstype.int32,
name="Categorical"):
param = dict(locals())
valid_dtype = mstype.int_type
check_type(dtype, valid_dtype, "Categorical")
super(Categorical, self).__init__(seed, dtype, name, param)
if (probs is None) == (logits is None):
raise_probs_logits_error()
self.reduce_sum = P.ReduceSum(keep_dims=True)
self.reduce_sum1 = P.ReduceSum(keep_dims=False)
self.log = P.Log()
self.exp = P.Exp()
self.shape = P.Shape()
self.reshape = P.Reshape()
self.div = P.RealDiv()
self.size = P.Size()
self.mutinomial = P.Multinomial(seed=self.seed)
self.cast = P.Cast()
self.expandim = P.ExpandDims()
self.gather = P.GatherNd()
self.concat = P.Concat(-1)
self.transpose = P.Transpose()
if probs is not None:
self._probs = cast_to_tensor(probs, mstype.float32)
input_sum = self.reduce_sum(self._probs, -1)
self._probs = self.div(self._probs, input_sum)
self._logits = probs_to_logits(self._probs)
self._param = self._probs
else:
self._logits = cast_to_tensor(logits, mstype.float32)
input_sum = self.reduce_sum(self.exp(self._logits), -1)
self._logits = self._logits - self.log(input_sum)
self._probs = logits_to_probs(self._logits)
self._param = self._logits
self._num_events = self.shape(self._param)[-1]
self._param2d = self.reshape(self._param, (-1, self._num_events))
self._batch_shape = self.shape(self._param)[:-1]
self._batch_shape_n = (1,) * len(self._batch_shape)
@property
def logits(self):
"""
Return the logits.
"""
return self._logits
@property
def probs(self):
"""
Return the probability.
"""
return self._probs
def _sample(self, sample_shape=()):
"""
Sampling.
Args:
sample_shape (tuple): The shape of the sample. Default: ().
Returns:
Tensor, shape is shape(probs)[:-1] + sample_shape
"""
self.checktuple(sample_shape, 'shape')
num_sample = 1
for i in sample_shape:
num_sample *= i
probs_2d = self.reshape(self._probs, (-1, self._num_events))
samples = self.mutinomial(probs_2d, num_sample)
samples = self.transpose(samples, (1, 0))
extend_shape = sample_shape
if len(self.shape(self._probs)) > 1:
extend_shape = sample_shape + self.shape(self._probs)[:-1]
return self.cast(self.reshape(samples, extend_shape), self.dtype)
def _log_prob(self, value):
r"""
Evaluate log probability.
Args:
value (Tensor): The value to be evaluated.
"""
value = self._check_value(value, 'value')
value = self.expandim(self.cast(value, mstype.float32), -1)
broad_shape = self.shape(value + self._logits)
broad = P.BroadcastTo(broad_shape)
logits_pmf = self.reshape(broad(self._logits), (-1, broad_shape[-1]))
value = self.reshape(broad(value)[..., :1], (-1, 1))
index = nn.Range(0., self.shape(value)[0], 1)()
index = self.reshape(index, (-1, 1))
value = self.concat((index, value))
value = self.cast(value, mstype.int32)
return self.reshape(self.gather(logits_pmf, value), broad_shape[:-1])
def _entropy(self):
r"""
Evaluate entropy.
.. math::
H(X) = -\sum(logits * probs)
"""
p_log_p = self._logits * self._probs
return self.reduce_sum1(-p_log_p, -1)
[docs] def enumerate_support(self, expand=True):
r"""
Enumerate categories.
Args:
expand (Bool): Whether to expand.
"""
num_events = self._num_events
values = nn.Range(0., num_events, 1)()
values = self.reshape(values, (num_events,) + self._batch_shape_n)
if expand:
values = P.BroadcastTo((num_events,) + self._batch_shape)(values)
values = self.cast(values, mstype.int32)
return values