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optimize code docs about 6 issue items

This commit is contained in:
tacyi139 2022-01-04 11:47:32 +08:00
parent 5f32eb5ff4
commit dded9df85f
1 changed files with 23 additions and 22 deletions
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45
mindspore/python/mindspore/ops/operations/math_ops.py
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@ -1692,20 +1692,20 @@ class Neg(PrimitiveWithInfer):
class InplaceAdd(PrimitiveWithInfer):
"""
Adds v into specified rows of x. Computes y = x; y[i,] += v.
Adds `v` into specified rows of `x`. Computes `y` = `x`; y[i,] += `v`.
Args:
indices (Union[int, tuple]): Indices into the left-most dimension of x, and determines which rows of x
to add with v. It is an integer or a tuple, whose value is in [0, the first dimension size of x).
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of `x`
to add with `v`. It is an integer or a tuple, whose value is in [0, the first dimension size of `x`).
Inputs:
- **x** (Tensor) - The first input is a tensor whose data type is float16, float32 or int32.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **input_v** (Tensor) - The second input is a tensor that has the same dimension sizes as x except
- **input_v** (Tensor) - The second input is a tensor that has the same dimension sizes as `x` except
the first dimension, which must be the same as indices' size. It has the same data type with `x`.
Outputs:
Tensor, has the same shape and dtype as x.
Tensor, has the same shape and dtype as `x`.
Raises:
TypeError: If `indices` is neither int nor tuple.
@ -1761,20 +1761,20 @@ class InplaceAdd(PrimitiveWithInfer):
class InplaceSub(PrimitiveWithInfer):
"""
Subtracts v into specified rows of x. Computes y = x; y[i, :] -= v.
Subtracts `v` into specified rows of `x`. Computes `y` = `x`; y[i,] -= `v.
Args:
indices (Union[int, tuple]): Indices into the left-most dimension of x, and determines which rows of x
to subtract with v. It is an int or tuple, whose value is in [0, the first dimension size of x).
indices (Union[int, tuple]): Indices into the left-most dimension of `x`, and determines which rows of `x`
to subtract with `v`. It is an int or tuple, whose value is in [0, the first dimension size of `x`).
Inputs:
- **x** (Tensor) - The first input is a tensor whose data type is float16, float32 or int32.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **input_v** (Tensor) - The second input is a tensor who has the same dimension sizes as x except
- **input_v** (Tensor) - The second input is a tensor who has the same dimension sizes as `x` except
the first dimension, which must be the same as indices' size. It has the same data type with `x`.
Outputs:
Tensor, has the same shape and dtype as x.
Tensor, has the same shape and dtype as `x`.
Raises:
TypeError: If `indices` is neither int nor tuple.
@ -3475,11 +3475,11 @@ class ApproximateEqual(_LogicBinaryOp):
& \text{ if } \left | x_{i} - y_{i} \right | \ge \text{tolerance},\ \ False
\end{cases}
where :math:`\text{tolerance}` indicates Acceptable maximum tolerance.
where `tolerance` indicates Acceptable maximum tolerance.
Inputs of `x` and `y` comply with the implicit type conversion rules to make the data types consistent.
If they have different data types, the lower priority data type will be converted to
the relatively highest priority data type.
If they have different data types, the lower precision data type will be converted to
the relatively highest precision data type.
Args:
tolerance (float): The maximum deviation that two elements can be considered equal. Default: 1e-05.
@ -3487,15 +3487,15 @@ class ApproximateEqual(_LogicBinaryOp):
Inputs:
- **x** (Tensor) - A tensor. Must be one of the following types: float32, float16.
:math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
- **y** (Tensor) - A tensor of the same type and shape as 'x'.
- **y** (Tensor) - A tensor of the same type and shape as `x`.
Outputs:
Tensor, the shape is the same as the shape of 'x', and the data type is bool.
Tensor, the shape is the same as the shape of `x`, and the data type is bool.
Raises:
TypeError: If `tolerance` is not a float.
RuntimeError: If the data type of `x`, `y` conversion of Parameter is required
when data type conversion of Parameter is not supported.
RuntimeError: If the data type of `x`, `y` conversion of Parameter is given
but data type conversion of Parameter is not supported.
Supported Platforms:
``Ascend``
@ -3964,8 +3964,8 @@ class IsNan(Primitive):
.. math::
out_i = \begin{cases}
& \text{ if } x_{i} = \text{Nan},\ \ True \\
& \text{ if } x_{i} \ne \text{Nan},\ \ False
& \ True,\ \text{ if } x_{i} = \text{Nan} \\
& \ False,\ \text{ if } x_{i} \ne \text{Nan}
\end{cases}
where :math:`Nan` means not a number.
@ -5094,11 +5094,11 @@ class BesselI1e(Primitive):
class Inv(Primitive):
r"""
Computes Inv(Reciprocal) of input tensor element-wise.
Computes Reciprocal of input tensor element-wise.
.. math::
out_i = out_i = \frac{1}{x_{i} }
out_i = \frac{1}{x_{i} }
Inputs:
- **x** (Tensor) - The shape of tensor is
@ -5358,7 +5358,8 @@ class IndexAdd(Primitive):
... def __init__(self):
... super(Net, self).__init__()
... self.index_add = ops.IndexAdd(axis=1)
... self.x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32))
... self.x = Parameter(Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]), mindspore.float32),
... name="name_x")
... self.indices = Tensor(np.array([0, 2]), mindspore.int32)
...
... def construct(self, y):