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fixed some doc issues in distribution classes.

This commit is contained in:
Xun Deng 2020-12-07 16:03:08 -05:00
parent 6b5626634c
commit 051baa726c
14 changed files with 29 additions and 29 deletions
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6
mindspore/nn/probability/bijector/invert.py
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@ -23,7 +23,7 @@ class Invert(Bijector):
Args:
bijector (Bijector): Base Bijector.
name (str): The name of the Bijector. Default: Invert.
name (str): The name of the Bijector. Default: 'Invert' + bijector.name.
Supported Platforms:
``Ascend`` ``GPU``
@ -55,10 +55,10 @@ class Invert(Bijector):
def __init__(self,
bijector,
name='Invert'):
name=""):
param = dict(locals())
validator.check_value_type('bijector', bijector, [Bijector], "Invert")
name = (name + bijector.name) if name == 'Invert' else name
name = name or ('Invert' + bijector.name)
super(Invert, self).__init__(is_constant_jacobian=bijector.is_constant_jacobian,
is_injective=bijector.is_injective,
name=name,

2
mindspore/nn/probability/distribution/bernoulli.py
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@ -27,7 +27,7 @@ class Bernoulli(Distribution):
Bernoulli Distribution.
Args:
probs (float, list, numpy.ndarray, Tensor, Parameter): The probability of that the outcome is 1.
probs (float, list, numpy.ndarray, Tensor): The probability of that the outcome is 1.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.int32.
name (str): The name of the distribution. Default: 'Bernoulli'.

8
mindspore/nn/probability/distribution/beta.py
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@ -29,9 +29,9 @@ class Beta(Distribution):
Beta distribution.
Args:
concentration1 (list, numpy.ndarray, Tensor, Parameter): The concentration1,
concentration1 (list, numpy.ndarray, Tensor): The concentration1,
also know as alpha of the Beta distribution.
concentration0 (list, numpy.ndarray, Tensor, Parameter): The concentration0, also know as
concentration0 (list, numpy.ndarray, Tensor): The concentration0, also know as
beta of the Beta distribution.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
@ -154,9 +154,9 @@ class Beta(Distribution):
# As some operators can't accept scalar input, check the type here
if isinstance(concentration0, float):
raise TypeError("Parameter concentration0 can't be scalar")
raise TypeError("Input concentration0 can't be scalar")
if isinstance(concentration1, float):
raise TypeError("Parameter concentration1 can't be scalar")
raise TypeError("Input concentration1 can't be scalar")
super(Beta, self).__init__(seed, dtype, name, param)

2
mindspore/nn/probability/distribution/categorical.py
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@ -31,7 +31,7 @@ class Categorical(Distribution):
Create a categorical distribution parameterized by event probabilities.
Args:
probs (Tensor, list, numpy.ndarray, Parameter): Event probabilities.
probs (Tensor, list, numpy.ndarray): Event probabilities.
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 event samples. Default: mstype.int32.
name (str): The name of the distribution. Default: Categorical.

4
mindspore/nn/probability/distribution/cauchy.py
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@ -28,8 +28,8 @@ class Cauchy(Distribution):
Cauchy distribution.
Args:
loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of the Cauchy distribution.
scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of the Cauchy distribution.
loc (int, float, list, numpy.ndarray, Tensor): The location of the Cauchy distribution.
scale (int, float, list, numpy.ndarray, Tensor): The scale of the Cauchy distribution.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Cauchy'.

2
mindspore/nn/probability/distribution/exponential.py
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@ -28,7 +28,7 @@ class Exponential(Distribution):
Example class: Exponential Distribution.
Args:
rate (float, list, numpy.ndarray, Tensor, Parameter): The inverse scale.
rate (float, list, numpy.ndarray, Tensor): The inverse scale.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Exponential'.

8
mindspore/nn/probability/distribution/gamma.py
View File

@ -29,9 +29,9 @@ class Gamma(Distribution):
Gamma distribution.
Args:
concentration (list, numpy.ndarray, Tensor, Parameter): The concentration,
concentration (list, numpy.ndarray, Tensor): The concentration,
also know as alpha of the Gamma distribution.
rate (list, numpy.ndarray, Tensor, Parameter): The rate, also know as
rate (list, numpy.ndarray, Tensor): The rate, also know as
beta of the Gamma distribution.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
@ -150,9 +150,9 @@ class Gamma(Distribution):
# As some operators can't accept scalar input, check the type here
if isinstance(concentration, (int, float)):
raise TypeError("Parameter concentration can't be scalar")
raise TypeError("Input concentration can't be scalar")
if isinstance(rate, (int, float)):
raise TypeError("Parameter rate can't be scalar")
raise TypeError("Input rate can't be scalar")
super(Gamma, self).__init__(seed, dtype, name, param)

2
mindspore/nn/probability/distribution/geometric.py
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@ -30,7 +30,7 @@ class Geometric(Distribution):
when the first success is achieved.
Args:
probs (float, list, numpy.ndarray, Tensor, Parameter): The probability of success.
probs (float, list, numpy.ndarray, Tensor): The probability of success.
seed (int): The seed used in sampling. Global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.int32.
name (str): The name of the distribution. Default: 'Geometric'.

4
mindspore/nn/probability/distribution/gumbel.py
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@ -29,8 +29,8 @@ class Gumbel(TransformedDistribution):
Gumbel distribution.
Args:
loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of Gumbel distribution.
scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of Gumbel distribution.
loc (int, float, list, numpy.ndarray, Tensor): The location of Gumbel distribution.
scale (int, float, list, numpy.ndarray, Tensor): The scale of Gumbel distribution.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): the name of the distribution. Default: 'Gumbel'.

4
mindspore/nn/probability/distribution/log_normal.py
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@ -28,8 +28,8 @@ class LogNormal(msd.TransformedDistribution):
logarithm is normally distributed. It is constructed as the exponential transformation of a Normal distribution.
Args:
loc (int, float, list, numpy.ndarray, Tensor, Parameter): The mean of the underlying Normal distribution.
scale (int, float, list, numpy.ndarray, Tensor, Parameter): The standard deviation of the underlying
loc (int, float, list, numpy.ndarray, Tensor): The mean of the underlying Normal distribution.
scale (int, float, list, numpy.ndarray, Tensor): The standard deviation of the underlying
Normal distribution.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.

4
mindspore/nn/probability/distribution/logistic.py
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@ -28,8 +28,8 @@ class Logistic(Distribution):
Logistic distribution.
Args:
loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of the Logistic distribution.
scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of the Logistic distribution.
loc (int, float, list, numpy.ndarray, Tensor): The location of the Logistic distribution.
scale (int, float, list, numpy.ndarray, Tensor): The scale of the Logistic distribution.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Logistic'.

4
mindspore/nn/probability/distribution/normal.py
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@ -28,8 +28,8 @@ class Normal(Distribution):
Normal distribution.
Args:
mean (int, float, list, numpy.ndarray, Tensor, Parameter): The mean of the Normal distribution.
sd (int, float, list, numpy.ndarray, Tensor, Parameter): The standard deviation of the Normal distribution.
mean (int, float, list, numpy.ndarray, Tensor): The mean of the Normal distribution.
sd (int, float, list, numpy.ndarray, Tensor): The standard deviation of the Normal distribution.
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Normal'.

4
mindspore/nn/probability/distribution/poisson.py
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@ -29,7 +29,7 @@ class Poisson(Distribution):
Poisson Distribution.
Args:
rate (list, numpy.ndarray, Tensor, Parameter): The rate of the Poisson distribution..
rate (list, numpy.ndarray, Tensor): The rate of the Poisson distribution..
seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Poisson'.
@ -123,7 +123,7 @@ class Poisson(Distribution):
# As some operators can't accept scalar input, check the type here
if isinstance(rate, (int, float)):
raise TypeError("Parameter rate can't be scalar")
raise TypeError("Input rate can't be scalar")
super(Poisson, self).__init__(seed, dtype, name, param)

4
mindspore/nn/probability/distribution/uniform.py
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@ -28,8 +28,8 @@ class Uniform(Distribution):
Example class: Uniform Distribution.
Args:
low (int, float, list, numpy.ndarray, Tensor, Parameter): The lower bound of the distribution.
high (int, float, list, numpy.ndarray, Tensor, Parameter): The upper bound of the distribution.
low (int, float, list, numpy.ndarray, Tensor): The lower bound of the distribution.
high (int, float, list, numpy.ndarray, Tensor): The upper bound of the distribution.
seed (int): The seed uses in sampling. The global seed is used if it is None. Default: None.
dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
name (str): The name of the distribution. Default: 'Uniform'.