baihuawei
5 years ago
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2cbb280bea
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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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"""Categorical 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.common import dtype as mstype
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from .distribution import Distribution
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from ._utils.utils import logits_to_probs, probs_to_logits, check_tensor_type, cast_to_tensor
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class Categorical(Distribution):
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"""
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Creates a categorical distribution parameterized by either probs or logits (but not both).
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Args:
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probs (Tensor, list, numpy.ndarray, Parameter, float): event probabilities.
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logits (Tensor, list, numpy.ndarray, Parameter, float): event log-odds.
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seed (int): seed to use in sampling. Default: 0.
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dtype (mindspore.dtype): type of the distribution. Default: mstype.int32.
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name (str): name of the distribution. Default: Categorical.
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Note:
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probs must be non-negative, finite and have a non-zero sum, and it will be normalized to sum to 1.
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Examples:
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>>> # To initialize a Categorical distribution of prob is [0.5, 0.5]
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>>> import mindspore.nn.probability.distribution as msd
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>>> b = msd.Categorical(probs = [0.5, 0.5], dtype=mstype.int32)
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>>>
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>>> # To use Categorical in a network
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>>> class net(Cell):
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>>> def __init__(self, probs):
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>>> super(net, self).__init__():
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>>> self.ca = msd.Categorical(probs=probs, dtype=mstype.int32)
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>>> # All the following calls in construct are valid
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>>> def construct(self, value):
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>>>
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>>> # Similar calls can be made to logits
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>>> ans = self.ca.probs
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>>> # value should be Tensor
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>>> ans = self.ca.log_prob(value)
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>>>
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>>> # Usage of enumerate_support
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>>> ans = self.ca.enumerate_support()
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>>>
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>>> # Usage of entropy
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>>> ans = self.ca.entropy()
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>>>
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>>> # Sample
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>>> ans = self.ca.sample()
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>>> ans = self.ca.sample((2,3))
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>>> ans = self.ca.sample((2,))
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"""
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def __init__(self,
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probs=None,
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logits=None,
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seed=0,
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dtype=mstype.int32,
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name="Categorical"):
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param = dict(locals())
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super(Categorical, self).__init__(seed, dtype, name, param)
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if (probs is None) == (logits is None):
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raise ValueError("Either 'prob' or 'logits' must be specified, but not both.")
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self.reduce_sum = P.ReduceSum(keep_dims=True)
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self.log = P.Log()
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self.exp = P.Exp()
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self.shape = P.Shape()
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self.reshape = P.Reshape()
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self.div = P.RealDiv()
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self.size = P.Size()
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self.mutinomial = P.Multinomial(seed=seed)
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self.cast = P.Cast()
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self.expandim = P.ExpandDims()
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self.gather = P.GatherNd()
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self.concat = P.Concat(-1)
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if probs is not None:
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self._probs = cast_to_tensor(probs, mstype.float32)
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input_sum = self.reduce_sum(self._probs, -1)
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self._probs = self.div(self._probs, input_sum)
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self._logits = probs_to_logits(self._probs)
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self._param = self._probs
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else:
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self._logits = cast_to_tensor(logits, mstype.float32)
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input_sum = self.reduce_sum(self.exp(self._logits), -1)
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self._logits = self._logits - self.log(input_sum)
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self._probs = logits_to_probs(self._logits)
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self._param = self._logits
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self._num_events = self.shape(self._param)[-1]
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self._param2d = self.reshape(self._param, (-1, self._num_events))
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self._batch_shape = self.shape(self._param2d)[0]
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@property
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def logits(self):
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"""
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Returns the logits.
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"""
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return self._logits
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@property
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def probs(self):
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"""
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Returns the probability.
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"""
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return self._probs
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def _sample(self, sample_shape=(1,)):
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"""
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Sampling.
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Args:
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sample_shape (tuple): shape of the sample. Default: (1,).
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Returns:
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Tensor, shape is shape(probs)[:-1] + sample_shape
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"""
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if not isinstance(sample_shape, tuple):
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raise ValueError("sample shape must be a tuple")
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num_sample = 1
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for i in sample_shape:
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num_sample *= i
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probs_2d = self.reshape(self._probs, (-1, self._num_events))
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samples = self.mutinomial(probs_2d, num_sample)
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extend_shape = sample_shape
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if len(self.shape(self._probs)) > 1:
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extend_shape = self.shape(self._probs)[:-1] + sample_shape
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return self.cast(self.reshape(samples, extend_shape), self.dtype)
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def _broad_cast_shape(self, a, b):
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"""
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Broadcast Tensor shape.
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Args:
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a (Tensor): A Tensor need to Broadcast.
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b (Tensor): Another Tensor need to Broadcast.
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Returns:
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Tuple, Broadcast shape.
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"""
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shape_a = self.shape(a)
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shape_b = self.shape(b)
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size_a = len(shape_a)
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size_b = len(shape_b)
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if size_a > size_b:
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size = size_a
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shape_out = list(shape_a)
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shape_short = list(shape_b)
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diff_size = size_a - size_b
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else:
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size = size_b
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shape_out = list(shape_b)
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shape_short = list(shape_a)
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diff_size = size_b - size_a
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for i in range(diff_size, size):
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if shape_out[i] == shape_short[i - diff_size]:
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continue
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if shape_out[i] == 1 or shape_short[i - diff_size] == 1:
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shape_out[i] = shape_out[i] * shape_short[i - diff_size]
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else:
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raise ValueError(f"Shape {shape_a} and {shape_b} is not broadcastable.")
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return tuple(shape_out)
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def _log_prob(self, value):
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r"""
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Evaluate log probability.
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Args:
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value (Tensor): value to be evaluated. The dtype could be mstype.float32, bool, mstype.int32.
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"""
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if value is not None:
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check_tensor_type("value", value, [mstype.float32, bool, mstype.int32])
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value = self.expandim(self.cast(value, mstype.float32), -1)
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broad_shape = self._broad_cast_shape(value, self._logits)
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broad = P.BroadcastTo(broad_shape)
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value = broad(value)[..., :1]
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index = cast_to_tensor(np.arange(broad_shape[-1]).astype(np.float32))
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index = self.expandim(index, -1)
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index = broad(index)[..., :1]
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value = self.concat((index, value))
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value = self.cast(value, mstype.int32)
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return self.gather(self._logits, value)
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return None
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def _entropy(self):
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r"""
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Evaluate entropy.
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.. math::
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H(X) = -\sum(logits * probs)
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"""
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p_log_p = self._logits * self._probs
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return self.reduce_sum(-p_log_p, -1)
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def enumerate_support(self, expand=True):
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r"""
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Enumerate categories.
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"""
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num_events = self._num_events
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values = cast_to_tensor(np.arange(num_events).astype(np.int32), mstype.int32)
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values = self.reshape(values, (num_events, 1))
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if expand:
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values = P.BroadcastTo((num_events, self._batch_shape))(values)
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values = self.cast(values, mstype.int32)
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return values
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@ -0,0 +1,36 @@
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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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import mindspore.context as context
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import mindspore.nn.probability.distribution as msd
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context.set_context(device_target='GPU')
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def test_categorical1():
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cat1 = msd.Categorical(probs=[[0.9, 0.2], [0.9, 0.2]])
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cat1out1 = cat1.sample((1,))
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cat1out2 = cat1.sample((3, 2))
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cat1out3 = cat1.sample((6,))
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assert cat1out1.asnumpy().shape == (2, 1)
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assert cat1out2.asnumpy().shape == (2, 3, 2)
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assert cat1out3.asnumpy().shape == (2, 6)
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cat1 = msd.Categorical(probs=[0.9, 0.2])
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cat1out1 = cat1.sample((1,))
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cat1out2 = cat1.sample((3, 2))
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cat1out3 = cat1.sample((6,))
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assert cat1out1.asnumpy().shape == (1,)
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assert cat1out2.asnumpy().shape == (3, 2)
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assert cat1out3.asnumpy().shape == (6,)
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