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update nn.prob doc 

fix typo

update doc
This commit is contained in:
Zichun Ye 2022-01-08 17:07:59 +08:00
parent f24026f613
commit d51483f235
26 changed files with 90 additions and 114 deletions
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2
docs/api/api_python/nn_probability/mindspore.nn.probability.bijector.GumbelCDF.rst
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@ -39,7 +39,7 @@ mindspore.nn.probability.bijector.GumbelCDF
>>>
>>> # 初始化GumbelCDF Bijector，loc设置为1.0和scale设置为2.0。
>>> gumbel_cdf = msb.GumbelCDF(1.0, 2.0)
>>> # 在网络中使用ScalarAffinebijector。
>>> # 在网络中使用GumbelCDF bijector。
>>> x = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> y = Tensor([0.1, 0.2, 0.3], dtype=mindspore.float32)
>>> ans1 = gumbel_cdf.forward(x)

4
docs/api/api_python/nn_probability/mindspore.nn.probability.bijector.PowerTransform.rst
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@ -3,7 +3,7 @@ mindspore.nn.probability.bijector.PowerTransform
.. py:class:: mindspore.nn.probability.bijector.PowerTransform(power=0., name='PowerTransform')
乘方Bijector（Power Bijector）。
乘方Bijector（PowerTransform Bijector）。
此Bijector对应的映射函数为：
.. math::
@ -11,7 +11,7 @@ mindspore.nn.probability.bijector.PowerTransform
其中幂c >= 0。
Power Bijector将输入从 `[-1/c, inf]` 映射到 `[0, inf]` 。
PowerTransform Bijector将输入从 `[-1/c, inf]` 映射到 `[0, inf]` 。
当 `c=0` 时，此Bijector等于 :class:`mindspore.nn.probability.bijector.Exp` Bijector。

2
docs/api/api_python/nn_probability/mindspore.nn.probability.distribution.Gumbel.rst
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@ -15,7 +15,7 @@ mindspore.nn.probability.distribution.Gumbel
- **loc** (int, float, list, numpy.ndarray, Tensor) - Gumbel分布的位置。
- **scale** (int, float, list, numpy.ndarray, Tensor) - Gumbel分布的尺度。
- **seed** (int) - 采样时使用的种子。如果为None，则使用全局种子。默认值：None。
- **seed** (int) - 采样时使用的种子。如果为None，则使用全局种子。默认值：0。
- **dtype** (mindspore.dtype) - 分布类型。默认值：mindspore.float32。
- **name** (str) - 分布的名称。默认值：'Gumbel'。

2
docs/api/api_python/nn_probability/mindspore.nn.probability.distribution.LogNormal.rst
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@ -16,7 +16,7 @@ mindspore.nn.probability.distribution.LogNormal
- **loc** (int, float, list, numpy.ndarray, Tensor) - 基础正态分布的平均值。默认值：None。
- **scale** (int, float, list, numpy.ndarray, Tensor) - 基础正态分布的标准差。默认值：None。
- **seed** (int) - 采样时使用的种子。如果为None，则使用全局种子。默认值：None。
- **seed** (int) - 采样时使用的种子。如果为None，则使用全局种子。默认值：0。
- **dtype** (mindspore.dtype) - 分布类型。默认值：mindspore.float32。
- **name** (str) - 分布的名称。默认值：'LogNormal'。

4
mindspore/python/mindspore/nn/probability/bijector/bijector.py
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@ -316,8 +316,8 @@ class Bijector(Cell):
Args:
name (str): The name of the function.
*args (list): A list of positional arguments that the function needs.
**kwargs (dict): A dictionary of keyword arguments that the function needs.
*args (list): the list of positional arguments forwarded to subclasses.
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
"""
if name == 'forward':
return self.forward(*args, **kwargs)

8
mindspore/python/mindspore/nn/probability/bijector/exp.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Power Bijector"""
"""Exp Bijector"""
from .power_transform import PowerTransform
@ -28,15 +28,15 @@ class Exp(PowerTransform):
name (str): The name of the Bijector. Default: 'Exp'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible apis of the Exp bijector are defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
It should be notice that the inputs to the APIs of the Exp bijector should be always a tensor.
For more details of all APIs, including the inputs and outputs of the APIs of the Exp bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:

8
mindspore/python/mindspore/nn/probability/bijector/gumbel_cdf.py
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@ -33,16 +33,16 @@ class GumbelCDF(Bijector):
name (str): The name of the Bijector. Default: 'GumbelCDF'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Gumbel_cdf bijector are defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor,
It should be notice that the inputs of APIs of the Gumbel_cdf bijector should be always a tensor,
with a shape that can be broadcasted to that of `loc` and `scale`.
For more details of all APIs, including the inputs and outputs,
For more details of all APIs, including the inputs and outputs of APIs of the Gumbel_cdf bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:
@ -67,7 +67,7 @@ class GumbelCDF(Bijector):
>>>
>>> # To initialize a GumbelCDF bijector of loc 1.0, and scale 2.0.
>>> gumbel_cdf = msb.GumbelCDF(1.0, 2.0)
>>> # To use a ScalarAffine bijector in a network.
>>> # To use a GumbelCDF bijector in a network.
>>> x = Tensor([1, 2, 3], dtype=mindspore.float32)
>>> y = Tensor([0.1, 0.2, 0.3], dtype=mindspore.float32)
>>> ans1 = gumbel_cdf.forward(x)

12
mindspore/python/mindspore/nn/probability/bijector/invert.py
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@ -26,18 +26,6 @@ class Invert(Bijector):
name (str): The name of the Bijector. Default: "". When name is set to "", it is actually
'Invert' + bijector.name.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

10
mindspore/python/mindspore/nn/probability/bijector/power_transform.py
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@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""Power Bijector"""
"""PowerTransform Bijector"""
from mindspore.ops import operations as P
from ..distribution._utils.utils import check_greater_equal_zero
from ..distribution._utils.custom_ops import exp_generic, log_generic
@ -21,7 +21,7 @@ from .bijector import Bijector
class PowerTransform(Bijector):
r"""
Power Bijector.
PowerTransform Bijector.
This Bijector performs the operation:
.. math::
@ -38,16 +38,16 @@ class PowerTransform(Bijector):
name (str): The name of the bijector. Default: 'PowerTransform'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the PowerTransform bijector are defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor,
It should be notice that the inputs to APIs of the PowerTransform bijector should be always a tensor,
with a shape that can be broadcasted to that of `power`.
For more details of all APIs, including the inputs and outputs,
For more details of all APIs, including the inputs and outputs of the PowerTransform bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:

6
mindspore/python/mindspore/nn/probability/bijector/scalar_affine.py
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@ -34,16 +34,16 @@ class ScalarAffine(Bijector):
name (str): The name of the bijector. Default: 'ScalarAffine'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Scalar affine bijector is defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor,
It should be notice that the inputs to APIs of the Scalar affine bijector should be always a tensor,
with a shape that can be broadcasted to that of `shift` and `scale`.
For more details of all APIs, including the inputs and outputs,
For more details of all APIs, including the inputs and outputs of APIs of the scalar affine bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:

6
mindspore/python/mindspore/nn/probability/bijector/softplus.py
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@ -35,16 +35,16 @@ class Softplus(Bijector):
name (str): The name of the Bijector. Default: 'Softplus'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Softplus bijector is defined in the base class, including:
- **forward**
- **inverse**
- **forward_log_jacobian**
- **backward_log_jacobian**
It should be notice that the input should be always a tensor,
It should be notice that the inputs of APIs of APIs of the Softplus bijector should be always a tensor,
with a shape that can be broadcasted to that of `sharpness`.
For more details of all APIs, including the inputs and outputs,
For more details of all APIs, including the inputs and outputs of APIs of the Softplus bijector,
please refer to :class:`mindspore.nn.probability.bijector.Bijector`, and examples below.
Supported Platforms:

8
mindspore/python/mindspore/nn/probability/distribution/bernoulli.py
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@ -35,16 +35,15 @@ class Bernoulli(Distribution):
name (str): The name of the distribution. Default: 'Bernoulli'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of Bernoulli distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of the APIs of the Bernoulli distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``
@ -55,7 +54,6 @@ class Bernoulli(Distribution):
Raises:
ValueError: When p <= 0 or p >=1.
TypeError: When the input `dtype` is not a subclass of float.
Examples:
>>> import mindspore

7
mindspore/python/mindspore/nn/probability/distribution/beta.py
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@ -44,16 +44,15 @@ class Beta(Distribution):
name (str): The name of the distribution. Default: 'Beta'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Beta distribution are defined in the base class, including:
- `prob` and `log_prob`
- `mean`, `sd`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of APIs of the Beta distribution
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend``

7
mindspore/python/mindspore/nn/probability/distribution/categorical.py
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@ -40,16 +40,15 @@ class Categorical(Distribution):
name (str): The name of the distribution. Default: Categorical.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Categorical distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of the APIs of the Categorical distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

7
mindspore/python/mindspore/nn/probability/distribution/cauchy.py
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@ -42,16 +42,15 @@ class Cauchy(Distribution):
name (str): The name of the distribution. Default: 'Cauchy'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Cauchy distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mode` and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of the APIs of the Cauchy distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend``

34
mindspore/python/mindspore/nn/probability/distribution/distribution.py
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@ -78,7 +78,7 @@ class Distribution(Cell):
# if not a transformed distribution, set the following attribute
if 'distribution' not in self.parameters.keys():
self.parameter_type = set_param_type(
self.parameters['param_dict'], dtype)
self.parameters.get('param_dict', {}), dtype)
self._batch_shape = self._calc_batch_shape()
self._is_scalar_batch = self._check_is_scalar_batch()
self._broadcast_shape = self._batch_shape
@ -397,7 +397,7 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
"""
return self._call_log_prob(value, *args, **kwargs)
@ -421,7 +421,7 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
"""
return self._call_prob(value, *args, **kwargs)
@ -445,7 +445,7 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
Output:
@ -490,7 +490,7 @@ class Distribution(Cell):
**kwargs (dict: the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
Output:
@ -517,7 +517,7 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
Output:
@ -553,7 +553,7 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
A distribution can be optionally passed to the function by passing its dist_spec_args through
A distribution can be optionally passed to the function by passing its `dist_spec_args` through
`args` or `kwargs`.
Output:
@ -583,8 +583,8 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
dist_spec_args of distribution b must be passed to the function through `args` or `kwargs`.
Passing in dist_spec_args of distribution a is optional.
`dist_spec_args` of distribution b must be passed to the function through `args` or `kwargs`.
Passing in `dist_spec_args` of distribution a is optional.
Output:
Tensor, the kl loss function of the distribution.
@ -604,7 +604,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the mean of the distribution.
@ -624,7 +624,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the mode of the distribution.
@ -641,7 +641,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the standard deviation of the distribution.
@ -658,7 +658,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the variance of the distribution.
@ -696,7 +696,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the entropy of the distribution.
@ -713,8 +713,8 @@ class Distribution(Cell):
**kwargs (dict): the dictionary of keyword arguments forwarded to subclasses.
Note:
dist_spec_args of distribution b must be passed to the function through `args` or `kwargs`.
Passing in dist_spec_args of distribution a is optional.
`dist_spec_args` of distribution b must be passed to the function through `args` or `kwargs`.
Passing in `dist_spec_args` of distribution a is optional.
Output:
Tensor, the cross_entropy of two distributions.
@ -744,7 +744,7 @@ class Distribution(Cell):
Note:
A distribution can be optionally passed to the function by passing its *dist_spec_args* through
*args* or *kwargs*.
`args` or `kwargs`.
Output:
Tensor, the sample generated from the distribution.

7
mindspore/python/mindspore/nn/probability/distribution/exponential.py
View File

@ -41,16 +41,15 @@ class Exponential(Distribution):
name (str): The name of the distribution. Default: 'Exponential'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Exp distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Exp distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

7
mindspore/python/mindspore/nn/probability/distribution/gamma.py
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@ -46,16 +46,15 @@ class Gamma(Distribution):
name (str): The name of the distribution. Default: 'Gamma'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Gamma distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Gamma distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend``

9
mindspore/python/mindspore/nn/probability/distribution/geometric.py
View File

@ -32,22 +32,21 @@ class Geometric(Distribution):
trials when the first success is achieved.
Args:
probs (int, float, list, numpy.ndarray, Tensor): The probability of success. Default: None.
probs (float, list, numpy.ndarray, Tensor): The probability of success. Default: None.
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'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Geometric distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Geometric distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

9
mindspore/python/mindspore/nn/probability/distribution/gumbel.py
View File

@ -39,18 +39,21 @@ class Gumbel(TransformedDistribution):
Args:
loc (int, float, list, numpy.ndarray, Tensor): The location of Gumbel distribution. Default: None.
scale (int, float, list, numpy.ndarray, Tensor): The scale of Gumbel distribution. Default: None.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: None.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): the name of the distribution. Default: 'Gumbel'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Gumbel distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
For more details of all APIs, including the inputs and outputs of all APIs of the Gumbel distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``
@ -58,13 +61,13 @@ class Gumbel(TransformedDistribution):
`scale` must be greater than zero.
`dist_spec_args` are `loc` and `scale`.
`dtype` must be a float type because Gumbel distributions are continuous.
`kl_loss` and `cross_entropy` are not supported on GPU backend.
Raises:
ValueError: When scale <= 0.
TypeError: When the input `dtype` is not a subclass of float.
Examples:
>>> import numpy as np
>>> import mindspore
>>> import mindspore.nn as nn
>>> import mindspore.nn.probability.distribution as msd

9
mindspore/python/mindspore/nn/probability/distribution/log_normal.py
View File

@ -40,22 +40,21 @@ class LogNormal(msd.TransformedDistribution):
loc (int, float, list, numpy.ndarray, Tensor): The mean of the underlying Normal distribution. Default: None.
scale (int, float, list, numpy.ndarray, Tensor): The standard deviation of the underlying
Normal distribution. Default: None.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: None.
seed (int): the seed used in sampling. The global seed is used if it is None. Default: 0.
dtype (mindspore.dtype): type of the distribution. Default: mstype.float32.
name (str): the name of the distribution. Default: 'LogNormal'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Log-Normal distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of APIs of the Log-Normal distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

7
mindspore/python/mindspore/nn/probability/distribution/logistic.py
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@ -42,16 +42,15 @@ class Logistic(Distribution):
name (str): The name of the distribution. Default: 'Logistic'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Logistic distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Logistic distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

7
mindspore/python/mindspore/nn/probability/distribution/normal.py
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@ -44,16 +44,15 @@ class Normal(Distribution):
name (str): The name of the distribution. Default: 'Normal'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Normal distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Normal distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

8
mindspore/python/mindspore/nn/probability/distribution/poisson.py
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@ -38,16 +38,15 @@ class Poisson(Distribution):
name (str): The name of the distribution. Default: 'Poisson'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Poisson distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `mode`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Poisson distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend``
@ -58,7 +57,6 @@ class Poisson(Distribution):
Raises:
ValueError: When rate <= 0.
TypeError: When the input `dtype` is not a subclass of float.
Examples:
>>> import mindspore

7
mindspore/python/mindspore/nn/probability/distribution/transformed_distribution.py
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@ -41,15 +41,14 @@ class TransformedDistribution(Distribution):
name (str): The name of the transformed distribution. Default: 'transformed_distribution'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the transformed distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the transformed distribution,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``

7
mindspore/python/mindspore/nn/probability/distribution/uniform.py
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@ -42,16 +42,15 @@ class Uniform(Distribution):
name (str): The name of the distribution. Default: 'Uniform'.
Inputs and Outputs of APIs:
The accessible api is defined in the base class, including:
The accessible APIs of the Uniform distribution are defined in the base class, including:
- `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`
- `mean`, `sd`, `var`, and `entropy`
- `kl_loss` and `cross_entropy`
- `sample`
It should be notice that the input should be always a tensor.
For more details of all APIs, including the inputs and outputs,
please refer to :class:`mindspore.nn.probability.bijector.Distribution`, and examples below.
For more details of all APIs, including the inputs and outputs of all APIs of the Uniform distribution ,
please refer to :class:`mindspore.nn.probability.distribution.Distribution`, and examples below.
Supported Platforms:
``Ascend`` ``GPU``