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fix issues

This commit is contained in:
luojianing 2022-08-18 09:54:14 +08:00
parent 918f4361d5
commit c4a7cbead3
12 changed files with 114 additions and 42 deletions
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4
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.amax.rst
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@ -12,6 +12,10 @@ mindspore.Tensor.amax
返回：
与输入的张量具有相同的数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。
异常：
- **TypeError** - `axis` 不是以下数据类型之一：int、Tuple或List。
- **TypeError** - `keep_dims` 不是bool类型。

4
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.amin.rst
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@ -12,6 +12,10 @@ mindspore.Tensor.amin
返回：
与输入的张量具有相同的数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。
异常：
- **TypeError** - `axis` 不是以下数据类型之一：int、Tuple或List。
- **TypeError** - `keep_dims` 不是bool类型。

4
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.mean.rst
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@ -12,6 +12,10 @@ mindspore.Tensor.mean
返回：
与输入的张量具有相同的数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。
异常：
- **TypeError** - `axis` 不是以下数据类型之一：int、Tuple或List。
- **TypeError** - `keep_dims` 不是bool类型。

4
docs/api/api_python/mindspore/Tensor/mindspore.Tensor.prod.rst
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@ -12,6 +12,10 @@ mindspore.Tensor.prod
返回：
与输入的张量具有相同的数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。
- 如果 `axis` 为tuple(int)或list(int)，取值为(1, 2)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_0, x_3, ..., x_R)` 。
异常：
- **TypeError** - `axis` 不是以下数据类型之一：int、Tuple或List。
- **TypeError** - `keep_dims` 不是bool类型。

20
docs/api/api_python/ops/mindspore.ops.RandomCategorical.rst
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@ -5,22 +5,18 @@ mindspore.ops.RandomCategorical
从分类分布中抽取样本。
**参数：**
参数：
- **dtype** (mindspore.dtype) - 输出的类型。它的值必须是 mindspore.int16、mindspore.int32 和 mindspore.int64 之一。默认值：mindspore.int64。
- **dtype** (mindspore.dtype): 输出的类型。它的值必须是 mindspore.int16、mindspore.int32 和 mindspore.int64 之一。默认值：mindspore.int64。
**输入：**
- **logits** (Tensor): 输入Tensor。Shape为 :math:`(batch_size, num_classes)` 的二维Tensor。
- **num_sample** (int): 要抽取的样本数。只允许使用常量值。
- **seed** (int): 随机种子。只允许使用常量值。默认值：0。
**输出：**
输入：
- **logits** (Tensor) - 输入Tensor。Shape为 :math:`(batch_size, num_classes)` 的二维Tensor。
- **num_sample** (int) - 要抽取的样本数。只允许使用常量值。
- **seed** (int) - 随机种子。只允许使用常量值。默认值：0。
输出：
- **output** (Tensor) - Shape为[batch_size, num_samples]的输出Tensor。
**异常：**
异常：
- **TypeError** - 如果 `dtype` 不是以下之一：mindspore.int16、mindspore.int32、mindspore.int64。
- **TypeError** - 如果 `logits` 不是Tensor。
- **TypeError** - 如果 `num_sample` 或者 `seed` 不是 int。

2
docs/api/api_python/ops/mindspore.ops.ReduceMax.rst
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@ -15,7 +15,7 @@ mindspore.ops.ReduceMax
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
输出：
Tensor，shape与输入 `x` 相同。
与输入 `x` 具有相同数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最大值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。

2
docs/api/api_python/ops/mindspore.ops.ReduceMean.rst
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@ -15,7 +15,7 @@ mindspore.ops.ReduceMean
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
输出：
Tensor，shape与输入 `x` 相同。
与输入 `x` 具有相同数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的平均值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。

2
docs/api/api_python/ops/mindspore.ops.ReduceMin.rst
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@ -15,7 +15,7 @@
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
输出：
Tensor，shape与输入 `x` 相同。
与输入 `x` 具有相同数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的最小值。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。

2
docs/api/api_python/ops/mindspore.ops.ReduceProd.rst
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@ -15,7 +15,7 @@
- **axis** (Union[int, tuple(int), list(int)]) - 要减少的维度。默认值: ()，缩小所有维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。
输出：
Tensor，shape与输入 `x` 相同。
与输入 `x` 具有相同数据类型的Tensor。
- 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个0维Tensor，表示输入Tensor中所有元素的乘积。
- 如果 `axis` 为int，取值为1，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_0, x_2, ..., x_R)` 。

36
mindspore/python/mindspore/common/tensor.py
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@ -1590,7 +1590,9 @@ class Tensor(Tensor_):
def mean(self, axis=(), keep_dims=False):
"""
Reduce a dimension of a tensor by averaging all elements in the dimension.
Reduces a dimension of a tensor by averaging all elements in the dimension, by default. And also can
reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the
same by controlling `keep_dims`.
Args:
axis (Union[None, int, tuple(int), list(int)]): Dimensions of reduction.
@ -1601,6 +1603,13 @@ class Tensor(Tensor_):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `keep_dims` is not a bool.
@ -1642,6 +1651,13 @@ class Tensor(Tensor_):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `keep_dims` is not a bool.
@ -1676,6 +1692,13 @@ class Tensor(Tensor_):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `keep_dims` is not a bool.
@ -1697,7 +1720,9 @@ class Tensor(Tensor_):
def prod(self, axis=(), keep_dims=False):
"""
Reduce a dimension of a tensor by product all elements in the dimension.
Reduces a dimension of a tensor by product all elements in the dimension, by default. And also can
reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the
same by controlling `keep_dims`.
Args:
axis (Union[None, int, tuple(int), list(int)]): Dimensions of reduction.
@ -1708,6 +1733,13 @@ class Tensor(Tensor_):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `axis` is not one of the following: int, tuple or list.
TypeError: If `keep_dims` is not a bool.

28
mindspore/python/mindspore/ops/function/math_func.py
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@ -4274,6 +4274,13 @@ def amin(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple or list.
@ -4343,6 +4350,13 @@ def amax(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple or list.
@ -4412,6 +4426,13 @@ def mean(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple or list.
@ -4483,6 +4504,13 @@ def prod(x, axis=(), keep_dims=False):
Returns:
Tensor, has the same data type as input tensor.
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `x` is not a Tensor.
TypeError: If `axis` is not one of the following: int, tuple or list.

48
mindspore/python/mindspore/ops/operations/math_ops.py
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@ -618,17 +618,17 @@ class ReduceMean(_Reduce):
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed. Must be in the range [-rank(`x`), rank(`x`)).
Only constant value is allowed. Must be in the range [-r, r).
Outputs:
Tensor, has the same dtype as the `x`.
- If axis is (), and keep_dims is False,
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the mean of all elements in the input tensor.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(x_1, x_3, ..., x_R)`.
- If axis is tuple(int) or list(int), set as (2, 3), and keep_dims is False,
the shape of output is :math:`(x_1, x_4, ..., x_R)`.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `keep_dims` is not a bool.
@ -903,17 +903,17 @@ class ReduceMax(_Reduce):
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed. Must be in the range [-rank(x), rank(x)).
Only constant value is allowed. Must be in the range [-r, r).
Outputs:
Tensor, has the same dtype as the `x`.
- If axis is (), and keep_dims is False,
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the maximum of all elements in the input tensor.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(x_1, x_3, ..., x_R)`.
- If axis is tuple(int) or list(int), set as (2, 3), and keep_dims is False,
the shape of output is :math:`(x_1, x_4, ..., x_R)`.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int) or list(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `keep_dims` is not a bool.
@ -990,17 +990,17 @@ class ReduceMin(_Reduce):
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed. Must be in the range [-rank( `x`), rank( `x` )).
Only constant value is allowed. Must be in the range [-r, r).
Outputs:
Tensor, has the same dtype as the `x`.
- If axis is (), and keep_dims is False,
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the minimum of all elements in the input tensor.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(x_1, x_3, ..., x_R)`.
- If axis is tuple(int), set as (2, 3), and keep_dims is False,
the shape of output is :math:`(x_1, x_4, ..., x_R)`.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `keep_dims` is not a bool.
@ -1113,17 +1113,17 @@ class ReduceProd(_Reduce):
- **x** (Tensor[Number]) - The input tensor. The dtype of the tensor to be reduced is number.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **axis** (Union[int, tuple(int), list(int)]) - The dimensions to reduce. Default: (), reduce all dimensions.
Only constant value is allowed. Must be in the range [-rank( `x` ), rank( `x` )).
Only constant value is allowed. Must be in the range [-r, r).
Outputs:
Tensor, has the same dtype as the `x`.
- If axis is (), and keep_dims is False,
- If `axis` is (), and `keep_dims` is False,
the output is a 0-D tensor representing the product of all elements in the input tensor.
- If axis is int, set as 2, and keep_dims is False,
the shape of output is :math:`(x_1, x_3, ..., x_R)`.
- If axis is tuple(int), set as (2, 3), and keep_dims is False,
the shape of output is :math:`(x_1, x_4, ..., x_R)`.
- If `axis` is int, set as 1, and `keep_dims` is False,
the shape of output is :math:`(x_0, x_2, ..., x_R)`.
- If `axis` is tuple(int), set as (1, 2), and `keep_dims` is False,
the shape of output is :math:`(x_0, x_3, ..., x_R)`.
Raises:
TypeError: If `keep_dims` is not a bool.