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!9191 Fixes minor issues in distribution classes

Browse Source 
From: @shallydeng
Reviewed-by: @zichun_ye,@sunnybeike
Signed-off-by: @sunnybeike
pull/9191/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent a6845395e6 b9dae8e63b
commit 8eecda1b3f
9 changed files with 257 additions and 221 deletions
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161
mindspore/nn/probability/distribution/beta.py
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@ -43,84 +43,95 @@ class Beta(Distribution):
`dtype` must be a float type because Beta distributions are continuous.
Examples:
>>> # To initialize a Beta distribution of the concentration1 3.0 and the concentration0 4.0.
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> b = msd.Beta(3.0, 4.0, dtype=mstype.float32)
>>>
>>> # The following creates two independent Beta distributions.
>>> b = msd.Beta([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
>>>
>>> from mindspore import Tensor
>>> # To initialize a Beta distribution of the concentration1 3.0 and the concentration0 4.0.
>>> b1 = msd.Beta([3.0], [4.0], dtype=mindspore.float32)
>>> # A Beta distribution can be initilized without arguments.
>>> # In this case, `concentration1` and `concentration0` must be passed in through arguments.
>>> b = msd.Beta(dtype=mstype.float32)
>>>
>>> # To use a Beta distribution in a network.
>>> class net(Cell):
... def __init__(self):
... super(net, self).__init__():
... self.b1 = msd.Beta(1.0, 1.0, dtype=mstype.float32)
... self.b2 = msd.Beta(dtype=mstype.float32)
...
... # The following calls are valid in construct.
... def construct(self, value, concentration1_b, concentration0_b, concentration1_a, concentration0_a):
...
... # Private interfaces of probability functions corresponding to public interfaces, including
... # `prob` and `log_prob`, have the same arguments as follows.
... # Args:
... # value (Tensor): the value to be evaluated.
... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
...
... # Examples of `prob`.
... # Similar calls can be made to other probability functions
... # by replacing 'prob' by the name of the function
... ans = self.b1.prob(value)
... # Evaluate with respect to the distribution b.
... ans = self.b1.prob(value, concentration1_b, concentration0_b)
... # `concentration1` and `concentration0` must be passed in during function calls
... ans = self.b2.prob(value, concentration1_a, concentration0_a)
...
...
... # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
... # Args:
... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
...
... # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
... ans = self.b1.concentration1() # return 1.0
... ans = self.b1.concentration1(concentration1_b, concentration0_b) # return concentration1_b
... # `concentration1` and `concentration0` must be passed in during function calls.
... ans = self.b2.concentration1(concentration1_a, concentration0_a)
...
...
... # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
... # Args:
... # dist (str): the type of the distributions. Only "Beta" is supported.
... # concentration1_b (Tensor): the concentration1 of distribution b.
... # concentration0_b (Tensor): the concentration0 of distribution b.
... # concentration1_a (Tensor): the concentration1 of distribution a.
... # Default: self._concentration1.
... # concentration0_a (Tensor): the concentration0 of distribution a.
... # Default: self._concentration0.
...
... # Examples of `kl_loss`. `cross_entropy` is similar.
... ans = self.b1.kl_loss('Beta', concentration1_b, concentration0_b)
... ans = self.b1.kl_loss('Beta', concentration1_b, concentration0_b,
... concentration1_a, concentration0_a)
... # Additional `concentration1` and `concentration0` must be passed in.
... ans = self.b2.kl_loss('Beta', concentration1_b, concentration0_b,
... concentration1_a, concentration0_a)
...
...
... # Examples of `sample`.
... # Args:
... # shape (tuple): the shape of the sample. Default: ()
... # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
... # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
... ans = self.b1.sample()
... ans = self.b1.sample((2,3))
... ans = self.b1.sample((2,3), concentration1_b, concentration0_b)
... ans = self.b2.sample((2,3), concentration1_a, concentration0_a)
>>> b2 = msd.Beta(dtype=mindspore.float32)
>>> # Here are some tensors used below for testing
>>> value = Tensor([0.1, 0.5, 1.5], dtype=mindspore.float32)
>>> concentration1_a = Tensor([2.0], dtype=mindspore.float32)
>>> concentration0_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32)
>>> concentration1_b = Tensor([1.0], dtype=mindspore.float32)
>>> concentration0_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32)
>>> # Private interfaces of probability functions corresponding to public interfaces, including
>>> # `prob` and `log_prob`, have the same arguments as follows.
>>> # Args:
>>> # value (Tensor): the value to be evaluated.
>>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
>>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
>>> # Examples of `prob`.
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' by the name of the function
>>> ans = b1.prob(value)
>>> print(ans)
[0.43740022 1.8750011 nan]
>>> # Evaluate with respect to the distribution b.
>>> ans = b1.prob(value, concentration1_b, concentration0_b)
>>> print(ans)
[0.99999964 1.0606599 nan]
>>> # `concentration1` and `concentration0` must be passed in during function calls
>>> ans = b2.prob(value, concentration1_a, concentration0_a)
>>> print(ans)
[0.5400001 1.5000001 nan]
>>> # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
>>> # Args:
>>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
>>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
>>> # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
>>> ans = b1.mean()
>>> print(ans)
[0.42857143]
>>> ans = b1.mean(concentration1_b, concentration0_b)
>>> print(ans)
[0.5 0.4 0.33333334]
>>> # `concentration1` and `concentration0` must be passed in during function calls.
>>> ans = b2.mean(concentration1_a, concentration0_a)
>>> print(ans)
[0.5 0.5 0.5]
>>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
>>> # Args:
>>> # dist (str): the type of the distributions. Only "Beta" is supported.
>>> # concentration1_b (Tensor): the concentration1 of distribution b.
>>> # concentration0_b (Tensor): the concentration0 of distribution b.
>>> # concentration1_a (Tensor): the concentration1 of distribution a.
>>> # Default: self._concentration1.
>>> # concentration0_a (Tensor): the concentration0 of distribution a.
>>> # Default: self._concentration0.
>>> # Examples of `kl_loss`. `cross_entropy` is similar.
>>> ans = b1.kl_loss('Beta', concentration1_b, concentration0_b)
>>> print(ans)
[0.34434414 0.24721336 0.26786423]
>>> ans = b1.kl_loss('Beta', concentration1_b, concentration0_b,
>>> concentration1_a, concentration0_a)
>>> print(ans)
[0.12509346 0.13629508 0.26527953]
>>> # Additional `concentration1` and `concentration0` must be passed in.
>>> ans = b2.kl_loss('Beta', concentration1_b, concentration0_b,
>>> concentration1_a, concentration0_a)
>>> print(ans)
[0.12509346 0.13629508 0.26527953]
>>> # Examples of `sample`.
>>> # Args:
>>> # shape (tuple): the shape of the sample. Default: ()
>>> # concentration1 (Tensor): the concentration1 of the distribution. Default: self._concentration1.
>>> # concentration0 (Tensor): the concentration0 of the distribution. Default: self._concentration0.
>>> ans = b1.sample()
>>> print(ans.shape)
(1,)
>>> ans = b1.sample((2,3))
>>> print(ans.shape)
(2, 3, 1)
>>> ans = b1.sample((2,3), concentration1_b, concentration0_b)
>>> print(ans.shape)
(2, 3, 3)
>>> ans = b2.sample((2,3), concentration1_a, concentration0_a)
>>> print(ans.shape)
(2, 3, 3)
"""
def __init__(self,

37
mindspore/nn/probability/distribution/categorical.py
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@ -154,6 +154,7 @@ class Categorical(Distribution):
self.expand_dim = P.ExpandDims()
self.fill = P.Fill()
self.gather = P.GatherNd()
self.greater = P.Greater()
self.issubclass = P.IsSubClass()
self.less = P.Less()
self.log = log_generic
@ -277,16 +278,21 @@ class Categorical(Distribution):
probs (Tensor): Event probabilities. Default: self.probs.
"""
value = self._check_value(value, 'value')
# cast value to int to find the right integer to compute index
if self.issubclass(self.dtype, mstype.float_):
value = self.cast(value, self.index_type)
else:
value = self.cast(value, self.dtype)
# cast int to float for the broadcasting below
value = self.cast(value, mstype.float32)
probs = self._check_param_type(probs)
logits = self.log(probs)
# find the right integer to compute index
# here we simulate casting to int but still keeping float dtype
value = self.cast(value, self.dtypeop(probs))
zeros = self.fill(self.dtypeop(value), self.shape(value), 0.0)
between_zero_neone = self.logicand(self.less(value, 0,),
self.greater(value, -1.))
value = self.select(between_zero_neone,
zeros,
P.Floor()(value))
# handle the case when value is of shape () and probs is a scalar batch
drop_dim = False
if self.shape(value) == () and self.shape(probs)[:-1] == ():
@ -314,8 +320,6 @@ class Categorical(Distribution):
out_of_bound = self.squeeze_last_axis(self.logicor(\
self.less(value, 0.0), self.less(num_classes-1, value)))
# deal with the case the there is only one class.
zeros = self.fill(mstype.float32, self.shape(out_of_bound), 0.0)
out_of_bound = self.logicand(out_of_bound, self.less(zeros, num_classes-1))
value_clipped = self.clip_by_value(value, 0.0, num_classes - 1)
value_clipped = self.cast(value_clipped, self.index_type)
# create index from 0 ... NumOfLabels
@ -341,12 +345,19 @@ class Categorical(Distribution):
probs (Tensor): Event probabilities. Default: self.probs.
"""
value = self._check_value(value, 'value')
if self.issubclass(self.dtype, mstype.float_):
value = self.cast(value, self.index_type)
else:
value = self.cast(value, self.dtype)
probs = self._check_param_type(probs)
# find the right integer to compute index
# here we simulate casting to int but still keeping float dtype
value = self.cast(value, self.dtypeop(probs))
zeros = self.fill(self.dtypeop(value), self.shape(value), 0.0)
between_zero_neone = self.logicand(self.less(value, 0,),
self.greater(value, -1.))
value = self.select(between_zero_neone,
zeros,
P.Floor()(value))
# handle the case when value is of shape () and probs is a scalar batch
drop_dim = False
if self.shape(value) == () and self.shape(probs)[:-1] == ():

4
mindspore/nn/probability/distribution/exponential.py
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@ -40,12 +40,10 @@ class Exponential(Distribution):
Examples:
>>> import mindspore
>>> import mindspore.context as context
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
>>> # To initialize a Bernoulli distribution of the probability 0.5.
>>> # To initialize a Exponential distribution of the probability 0.5.
>>> e1 = msd.Exponential(0.5, dtype=mindspore.float32)
>>> # An Exponential distribution can be initialized without arguments.
>>> # In this case, `rate` must be passed in through `args` during function calls.

146
mindspore/nn/probability/distribution/gamma.py
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@ -43,80 +43,86 @@ class Gamma(Distribution):
`dtype` must be a float type because Gamma distributions are continuous.
Examples:
>>> # To initialize a Gamma distribution of the concentration 3.0 and the rate 4.0.
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> g = msd.Gamma(3.0, 4.0, dtype=mstype.float32)
>>>
>>> # The following creates two independent Gamma distributions.
>>> g = msd.Gamma([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
>>>
>>> from mindspore import Tensor
>>> # To initialize a Gamma distribution of the concentration 3.0 and the rate 4.0.
>>> g1 = msd.Gamma([3.0], [4.0], dtype=mindspore.float32)
>>> # A Gamma distribution can be initilized without arguments.
>>> # In this case, `concentration` and `rate` must be passed in through arguments.
>>> g = msd.Gamma(dtype=mstype.float32)
>>> g2 = msd.Gamma(dtype=mindspore.float32)
>>> # Here are some tensors used below for testing
>>> value = Tensor([1.0, 2.0, 3.0], dtype=mindspore.float32)
>>> concentration_a = Tensor([2.0], dtype=mindspore.float32)
>>> rate_a = Tensor([2.0, 2.0, 2.0], dtype=mindspore.float32)
>>> concentration_b = Tensor([1.0], dtype=mindspore.float32)
>>> rate_b = Tensor([1.0, 1.5, 2.0], dtype=mindspore.float32)
>>>
>>> # To use a Gamma distribution in a network.
>>> class net(Cell):
... def __init__(self):
... super(net, self).__init__():
... self.g1 = msd.Gamma(1.0, 1.0, dtype=mstype.float32)
... self.g2 = msd.Gamma(dtype=mstype.float32)
...
... # The following calls are valid in construct.
... def construct(self, value, concentration_b, rate_b, concentration_a, rate_a):
...
... # Private interfaces of probability functions corresponding to public interfaces, including
... # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
... # Args:
... # value (Tensor): the value to be evaluated.
... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
... # rate (Tensor): the rate of the distribution. Default: self._rate.
...
... # Examples of `prob`.
... # Similar calls can be made to other probability functions
... # by replacing 'prob' by the name of the function
... ans = self.g1.prob(value)
... # Evaluate with respect to the distribution b.
... ans = self.g1.prob(value, concentration_b, rate_b)
... # `concentration` and `rate` must be passed in during function calls
... ans = self.g2.prob(value, concentration_a, rate_a)
...
...
... # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
... # Args:
... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
... # rate (Tensor): the rate of the distribution. Default: self._rate.
...
... # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
... ans = self.g1.concentration() # return 1.0
... ans = self.g1.concentration(concentration_b, rate_b) # return concentration_b
... # `concentration` and `rate` must be passed in during function calls.
... ans = self.g2.concentration(concentration_a, rate_a)
...
...
... # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
... # Args:
... # dist (str): the type of the distributions. Only "Gamma" is supported.
... # concentration_b (Tensor): the concentration of distribution b.
... # rate_b (Tensor): the rate of distribution b.
... # concentration_a (Tensor): the concentration of distribution a. Default: self._concentration.
... # rate_a (Tensor): the rate of distribution a. Default: self._rate.
...
... # Examples of `kl_loss`. `cross_entropy` is similar.
... ans = self.g1.kl_loss('Gamma', concentration_b, rate_b)
... ans = self.g1.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
... # Additional `concentration` and `rate` must be passed in.
... ans = self.g2.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
...
...
... # Examples of `sample`.
... # Args:
... # shape (tuple): the shape of the sample. Default: ()
... # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
... # rate (Tensor): the rate of the distribution. Default: self._rate.
... ans = self.g1.sample()
... ans = self.g1.sample((2,3))
... ans = self.g1.sample((2,3), concentration_b, rate_b)
... ans = self.g2.sample((2,3), concentration_a, rate_a)
>>> # Private interfaces of probability functions corresponding to public interfaces, including
>>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
>>> # Args:
>>> # value (Tensor): the value to be evaluated.
>>> # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
>>> # rate (Tensor): the rate of the distribution. Default: self._rate.
>>> # Examples of `prob`.
>>> # Similar calls can be made to other probability functions
>>> # by replacing 'prob' by the name of the function
>>> # ans = g1.prob(value)
>>> # # Evaluate with respect to the distribution b.
>>> # ans = g1.prob(value, concentration_b, rate_b)
>>> # # `concentration` and `rate` must be passed in during function calls
>>> # ans = g2.prob(value, concentration_a, rate_a)
>>> # Functions `mean`, `sd`, `mode`, `var`, and `entropy` have the same arguments.
>>> # Args:
>>> # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
>>> # rate (Tensor): the rate of the distribution. Default: self._rate.
>>> # Example of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
>>> ans = g1.mean()
>>> print(ans)
[0.75]
>>> ans = g1.mean(concentration_b, rate_b)
>>> print(ans)
[1. 0.6666667 0.5 ]
>>> # `concentration` and `rate` must be passed in during function calls.
>>> ans = g2.mean(concentration_a, rate_a)
>>> print(ans)
[1. 1. 1.]
>>> # Interfaces of 'kl_loss' and 'cross_entropy' are the same:
>>> # Args:
>>> # dist (str): the type of the distributions. Only "Gamma" is supported.
>>> # concentration_b (Tensor): the concentration of distribution b.
>>> # rate_b (Tensor): the rate of distribution b.
>>> # concentration_a (Tensor): the concentration of distribution a. Default: self._concentration.
>>> # rate_a (Tensor): the rate of distribution a. Default: self._rate.
>>> # Examples of `kl_loss`. `cross_entropy` is similar.
>>> ans = g1.kl_loss('Gamma', concentration_b, rate_b)
>>> print(ans)
[0.28871584 0.2582507 0.34556866]
>>> ans = g1.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
>>> print(ans)
[0.11593175 0.21046662 0.42278457]
>>> # Additional `concentration` and `rate` must be passed in.
>>> ans = g2.kl_loss('Gamma', concentration_b, rate_b, concentration_a, rate_a)
>>> print(ans)
[0.11593175 0.21046662 0.42278457]
>>> # Examples of `sample`.
>>> # Args:
>>> # shape (tuple): the shape of the sample. Default: ()
>>> # concentration (Tensor): the concentration of the distribution. Default: self._concentration.
>>> # rate (Tensor): the rate of the distribution. Default: self._rate.
>>> ans = g1.sample()
>>> print(ans.shape)
(1,)
>>> ans = g1.sample((2,3))
>>> print(ans.shape)
(2, 3, 1)
>>> ans = g1.sample((2,3), concentration_b, rate_b)
>>> print(ans.shape)
(2, 3, 3)
>>> ans = g2.sample((2,3), concentration_a, rate_a)
>>> print(ans.shape)
(2, 3, 3)
"""
def __init__(self,

4
mindspore/nn/probability/distribution/geometric.py
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@ -44,9 +44,9 @@ class Geometric(Distribution):
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> # To initialize a Bernoulli distribution of the probability 0.5.
>>> # To initialize a Geometric distribution of the probability 0.5.
>>> g1 = msd.Geometric(0.5, dtype=mindspore.int32)
>>> # A Bernoulli distribution can be initialized without arguments.
>>> # A Geometric distribution can be initialized without arguments.
>>> # In this case, `probs` must be passed in through arguments during function calls.
>>> g2 = msd.Geometric(dtype=mindspore.int32)
>>>

6
mindspore/nn/probability/distribution/gumbel.py
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@ -47,7 +47,7 @@ class Gumbel(TransformedDistribution):
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> context.set_context(mode=1, device_target="GPU")
>>> context.set_context(mode=1)
>>> # To initialize a Gumbel distribution of `loc` 3.0 and `scale` 4.0.
>>> gumbel = msd.Gumbel(3.0, 4.0, dtype=mindspore.float32)
>>> # Private interfaces of probability functions corresponding to public interfaces, including
@ -236,8 +236,8 @@ class Gumbel(TransformedDistribution):
scale_b = self._check_value(scale_b, 'scale_b')
loc_b = self.cast(loc_b, self.parameter_type)
scale_b = self.cast(scale_b, self.parameter_type)
return self.log(scale_b) - self.log(self.scale) +\
np.euler_gamma * (self.scale / scale_b - 1.) +\
return self.log(scale_b / self.scale) +\
np.euler_gamma * (self.scale / scale_b - 1.) + (self.loc - loc_b) / scale_b +\
self.expm1((loc_b - self.loc) / scale_b + self.lgamma(self.scale / scale_b + 1.))
def _sample(self, shape=()):

6
mindspore/nn/probability/distribution/logistic.py
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@ -134,6 +134,7 @@ class Logistic(Distribution):
# ops needed for the class
self.cast = P.Cast()
self.const = P.ScalarToArray()
self.consttensor = P.ScalarToTensor()
self.dtypeop = P.DType()
self.exp = exp_generic
self.expm1 = P.Expm1()
@ -154,6 +155,7 @@ class Logistic(Distribution):
self.threshold = np.log(np.finfo(np.float32).eps) + 1.
self.tiny = np.finfo(np.float).tiny
self.sd_const = np.pi/np.sqrt(3)
def _softplus(self, x):
too_small = self.less(x, self.threshold)
@ -219,8 +221,8 @@ class Logistic(Distribution):
"""
The standard deviation of the distribution.
"""
loc, scale = self._check_param_type(loc, scale)
return scale * self.const(np.pi) / self.sqrt(self.const(3.0))
_, scale = self._check_param_type(loc, scale)
return scale * self.consttensor(self.sd_const, self.dtypeop(scale))
def _entropy(self, loc=None, scale=None):
r"""

112
mindspore/nn/probability/distribution/poisson.py
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@ -39,62 +39,70 @@ class Poisson(Distribution):
`dist_spec_args` is `rate`.
Examples:
>>> # To initialize an Poisson distribution of the rate 0.5.
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> p = msd.Poisson(0.5, dtype=mstype.float32)
>>>
>>> # The following creates two independent Poisson distributions.
>>> p = msd.Poisson([0.5, 0.5], dtype=mstype.float32)
>>>
>>> from mindspore import Tensor
>>> # To initialize an Poisson distribution of the rate 0.5.
>>> p1 = msd.Poisson(0.5, dtype=mindspore.float32)
>>> # An Poisson distribution can be initilized without arguments.
>>> # In this case, `rate` must be passed in through `args` during function calls.
>>> p = msd.Poisson(dtype=mstype.float32)
>>> p2 = msd.Poisson(dtype=mindspore.float32)
>>>
>>> # Here are some tensors used below for testing
>>> value = Tensor([1, 2, 3], dtype=mindspore.int32)
>>> rate_a = Tensor([0.6], dtype=mindspore.float32)
>>> rate_b = Tensor([0.2, 0.5, 0.4], dtype=mindspore.float32)
>>>
>>> # To use an Poisson distribution in a network.
>>> class net(Cell):
... def __init__(self):
... super(net, self).__init__():
... self.p1 = msd.Poisson(0.5, dtype=mstype.float32)
... self.p2 = msd.Poisson(dtype=mstype.float32)
...
... # All the following calls in construct are valid.
... def construct(self, value, rate_b, rate_a):
...
... # Private interfaces of probability functions corresponding to public interfaces, including
... # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, are the same as follows.
... # Args:
... # value (Tensor): the value to be evaluated.
... # rate (Tensor): the rate of the distribution. Default: self.rate.
...
... # Examples of `prob`.
... # Similar calls can be made to other probability functions
... # by replacing `prob` by the name of the function.
... ans = self.p1.prob(value)
... # Evaluate with respect to distribution b.
... ans = self.p1.prob(value, rate_b)
... # `rate` must be passed in during function calls.
... ans = self.p2.prob(value, rate_a)
...
...
... # Functions `mean`, `mode`, `sd`, and 'var' have the same arguments as follows.
... # Args:
... # rate (Tensor): the rate of the distribution. Default: self.rate.
...
... # Examples of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
... ans = self.p1.mean() # return 2
... ans = self.p1.mean(rate_b) # return 1 / rate_b
... # `rate` must be passed in during function calls.
... ans = self.p2.mean(rate_a)
...
...
... # Examples of `sample`.
... # Args:
... # shape (tuple): the shape of the sample. Default: ()
... # probs1 (Tensor): the rate of the distribution. Default: self.rate.
... ans = self.p1.sample()
... ans = self.p1.sample((2,3))
... ans = self.p1.sample((2,3), rate_b)
... ans = self.p2.sample((2,3), rate_a)
>>> # Private interfaces of probability functions corresponding to public interfaces, including
>>> # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, are the same as follows.
>>> # Args:
>>> # value (Tensor): the value to be evaluated.
>>> # rate (Tensor): the rate of the distribution. Default: self.rate.
>>> # Examples of `prob`.
>>> # Similar calls can be made to other probability functions
>>> # by replacing `prob` by the name of the function.
>>> ans = p1.prob(value)
>>> print(ans)
[0.3032652 0.0758163 0.01263604]
>>> # Evaluate with respect to distribution b.
>>> ans = p1.prob(value, rate_b)
>>> print(ans)
[0.16374607 0.0758163 0.00715008]
>>> # `rate` must be passed in during function calls.
>>> ans = p2.prob(value, rate_a)
>>> print(ans)
[0.32928684 0.09878606 0.01975721]
>>> # Functions `mean`, `mode`, `sd`, and 'var' have the same arguments as follows.
>>> # Args:
>>> # rate (Tensor): the rate of the distribution. Default: self.rate.
>>> # Examples of `mean`, `sd`, `mode`, `var`, and `entropy` are similar.
>>> ans = p1.mean() # return 2
>>> print(ans)
0.5
>>> ans = p1.mean(rate_b) # return 1 / rate_b
>>> print(ans)
[0.2 0.5 0.4]
>>> # `rate` must be passed in during function calls.
>>> ans = p2.mean(rate_a)
>>> print(ans)
[0.6]
>>> # Examples of `sample`.
>>> # Args:
>>> # shape (tuple): the shape of the sample. Default: ()
>>> # probs1 (Tensor): the rate of the distribution. Default: self.rate.
>>> ans = p1.sample()
>>> print(ans.shape)
()
>>> ans = p1.sample((2,3))
>>> print(ans.shape)
(2, 3)
>>> ans = p1.sample((2,3), rate_b)
>>> print(ans.shape)
(2, 3, 3)
>>> ans = p2.sample((2,3), rate_a)
>>> print(ans.shape)
(2, 3, 1)
"""
def __init__(self,

2
mindspore/nn/probability/distribution/uniform.py
Unescape View File

@ -44,7 +44,7 @@ class Uniform(Distribution):
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd
>>> from mindspore import Tensor
>>> context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
>>> context.set_context(mode=context.GRAPH_MODE)
>>> # To initialize a Uniform distribution of the lower bound 0.0 and the higher bound 1.0.
>>> u1 = msd.Uniform(0.0, 1.0, dtype=mindspore.float32)
>>> # A Uniform distribution can be initialized without arguments.

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