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fix math format

This commit is contained in:
luojianing 2022-09-05 17:10:22 +08:00
parent eec34072c0
commit 18cd9d7577
9 changed files with 18 additions and 18 deletions
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2
docs/api/api_python/ops/mindspore.ops.RandomCategorical.rst
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@ -9,7 +9,7 @@ mindspore.ops.RandomCategorical
- **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。
- **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。

2
docs/api/api_python/ops/mindspore.ops.func_matrix_diag_part.rst
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@ -15,7 +15,7 @@ mindspore.ops.matrix_diag_part
返回：
Tensor，与 `x` 的类型相同。
设 `x` 有r维 `(I， J， ...， M， N)` 。设 `max_diag_len` 为所有对角线长度中的最大值，则 :math:`max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`。 设 `num_diags` 为输出的维度数，则有 :math:`num_diags = k[1] - k[0] + 1`。如果 :math:`num_diags == 1`，则输出Tensor的维度为r - 1，分别为 :math:`[I, J, ..., L, max_diag_len]`。 否则，输出Tensor的维度为r，分别为 :math:`[I, J, ..., L, num_diags, max_diag_len]`。
设 `x` 有r维 `(I， J， ...， M， N)` 。设 `max_diag_len` 为所有对角线长度中的最大值，则 :math:`max\_diag\_len = min(M + min(k[1], 0), N + min(-k[0], 0))`。 设 `num_diags` 为输出的维度数，则有 :math:`num\_diags = k[1] - k[0] + 1`。如果 :math:`num\_diags == 1`，则输出Tensor的维度为r - 1，分别为 :math:`[I, J, ..., L, max\_diag\_len]`。 否则，输出Tensor的维度为r，分别为 :math:`[I, J, ..., L, num\_diags, max\_diag\_len]`。
异常：
- **TypeError** - `x` 不为Tensor。

2
docs/api/api_python/ops/mindspore.ops.func_print.rst → docs/api/api_python/ops/mindspore.ops.func_print_.rst
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@ -10,7 +10,7 @@ mindspore.ops.print_
.. note::
在PyNative模式下，请使用Python print函数。在Ascend平台上的Graph模式下，bool、int和float将被转换为Tensor进行打印，str保持不变。
该方法用于代码调试。当同时print大量数据时，为了保证主进程不受影响，可能会丢失一些数据。如果需要记录完整数据，推荐使用 `Summary` 功能，具体可查看
`Summary <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html?highlight=summary#>`_。
`Summary <https://www.mindspore.cn/mindinsight/docs/zh-CN/master/summary_record.html?highlight=summary#>`_ 。
参数：
- **input_x** (Union[Tensor, bool, int, float, str]) - print_的输入。支持多个输入，用'，'分隔。

4
docs/api/api_python/ops/mindspore.ops.func_random_categorical.rst
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@ -6,13 +6,13 @@ mindspore.ops.random_categorical
从分类分布中抽取样本。
参数：
- **logits** (Tensor) - 输入Tensor。Shape为 :math:`(batch_size, num_classes)` 的二维Tensor。
- **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
- **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。
返回：
Tensor，Shape为 :math:`(batch_size, num_samples)` 的输出Tensor。
Tensor，Shape为 :math:`(batch\_size, num\_samples)` 的输出Tensor。
异常：
- **TypeError** - 如果 `dtype` 不是以下之一：mindspore.int16、mindspore.int32、mindspore.int64。

2
docs/api/api_python/ops/mindspore.ops.func_slice.rst
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@ -10,7 +10,7 @@ mindspore.ops.slice
.. note::
`begin` 的起始值为0，`size` 的起始值为1。
如果 `size[i]` 为-1，则维度i中的所有剩余元素都包含在切片中。这相当于 :math:`size[i] = input_x.shape(i) - begin[i]` 。
如果 `size[i]` 为-1，则维度i中的所有剩余元素都包含在切片中。这相当于 :math:`size[i] = input\_x.shape(i) - begin[i]` 。
参数：
- **input_x** (Tensor) - Slice的输入，任意维度的Tensor。

2
mindspore/python/mindspore/common/tensor.py
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@ -4390,7 +4390,7 @@ class Tensor(Tensor_):
mindspore.int32 and mindspore.int64. Default: mindspore.int64.
Returns:
Tensor, the output Tensor with shape :math:`(batch_size, num_samples)`.
Tensor, the output Tensor with shape :math:`(batch\_size, num\_samples)`.
Raises:
TypeError: If `dtype` is not one of the following: mindspore.int16, mindspore.int32, mindspore.int64.

12
mindspore/python/mindspore/ops/function/array_func.py
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@ -1053,7 +1053,7 @@ def select(cond, x, y):
def slice(input_x, begin, size):
"""
r"""
Slices a tensor in the specified shape.
Slice the tensor `input_x` in shape of `size` and starting at the location specified by `begin`,
@ -1064,7 +1064,7 @@ def slice(input_x, begin, size):
`begin` is zero-based and `size` is one-based.
If `size[i]` is -1, all remaining elements in dimension i are included in the slice.
This is equivalent to setting :math:`size[i] = input_x.shape(i) - begin[i]`.
This is equivalent to setting :math:`size[i] = input\_x.shape(i) - begin[i]`.
Args:
input_x (Tensor): The target tensor.
@ -2999,10 +2999,10 @@ def matrix_diag_part(x, k=0, padding_value=0, align="RIGHT_LEFT"):
Returns:
A Tensor. Has the same type as `x`.
Assume `x` has r dimensions :math:`[I, J, ..., L, M, N]`. Let `max_diag_len` be the maximum length among all
diagonals to be extracted, :math:`max_diag_len = min(M + min(k[1], 0), N + min(-k[0], 0))`
Let `num_diags` be the number of diagonals to extract, :math:`num_diags = k[1] - k[0] + 1`.
If :math:`num_diags == 1`, the output tensor is of rank r - 1 with shape :math:`[I, J, ..., L, max_diag_len]`
Otherwise, the output tensor has rank r with dimensions :math:`[I, J, ..., L, num_diags, max_diag_len]`
diagonals to be extracted, :math:`max\_diag\_len = min(M + min(k[1], 0), N + min(-k[0], 0))`
Let `num_diags` be the number of diagonals to extract, :math:`num\_diags = k[1] - k[0] + 1`.
If :math:`num\_diags == 1`, the output tensor is of rank r - 1 with shape :math:`[I, J, ..., L, max\_diag\_len]`
Otherwise, the output tensor has rank r with dimensions :math:`[I, J, ..., L, num\_diags, max\_diag\_len]`
Raises:
TypeError: If `x` is not Tensor.

4
mindspore/python/mindspore/ops/function/random_func.py
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@ -133,14 +133,14 @@ def random_categorical(logits, num_sample, seed=0, dtype=mstype.int64):
Generates random samples from a given categorical distribution tensor.
Args:
logits (Tensor): The input tensor. 2-D Tensor with shape :math:`(batch_size, num_classes)`.
logits (Tensor): The input tensor. 2-D Tensor with shape :math:`(batch\_size, num\_classes)`.
num_sample (int): Number of sample to be drawn. Only constant values is allowed.
seed (int): Random seed. Only constant values is allowed. Default: 0.
dtype (mindspore.dtype): The type of output. Its value must be one of mindspore.int16,
mindspore.int32 and mindspore.int64. Default: mindspore.int64.
Returns:
Tensor, The output Tensor with shape :math:`(batch_size, num_samples)`.
Tensor, The output Tensor with shape :math:`(batch\_size, num\_samples)`.
Raises:
TypeError: If `dtype` is not one of the following: mindspore.int16, mindspore.int32, mindspore.int64.

6
mindspore/python/mindspore/ops/operations/array_ops.py
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@ -6842,7 +6842,7 @@ class TensorScatterElements(Primitive):
class ExtractVolumePatches(Primitive):
"""
r"""
Extract patches from input and put them in the "depth" output dimension. 3D extension of extract_image_patches.
Args:
@ -6875,8 +6875,8 @@ class ExtractVolumePatches(Primitive):
ValueError: If input_x's shape has zero.
ValueError: If one of kernel_size or strides' first two numbers is not 1.
ValueError: If padding = "VALID" and input - kernel_size is less than 0 in d, h or w dimension.
ValueError: If padding = "SAME" and :math:`padding_needed = ((input_x + strides - 1) / strides - 1) *
strides + kernel_size - input` is less than 0 in d, h or w dimension.
ValueError: If padding = "SAME" and :math:`padding\_needed = ((input\_x + strides - 1) / strides - 1) *
strides + kernel\_size - input` is less than 0 in d, h or w dimension.
ValueError: If x_h is not 1 or x_w is not 1 and x_w + padding_needed - k_w - s_w is less than 0.
ValueError: If x_d * x_h * x_w is greater than 2048.