diff --git a/docs/api/api_python/ops/mindspore.ops.func_clamp.rst b/docs/api/api_python/ops/mindspore.ops.func_clamp.rst index bfaca6cc188..688d5129938 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_clamp.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_clamp.rst @@ -3,9 +3,9 @@ mindspore.ops.clamp .. py:function:: mindspore.ops.clamp(x, min=None, max=None) - 将输入Tensor值裁剪到指定的最小值和最大值之间。 + 将输入Tensor的值裁剪到指定的最小值和最大值之间。 - 限制 :math:`x` 的范围，其 :math:`x` 的最小值为 `min` ，最大值为 `max` 。 + 限制 :math:`x` 的范围，其最小值为 `min` ，最大值为 `max` 。 .. math:: out_i= \left\{ diff --git a/docs/api/api_python/ops/mindspore.ops.func_full_like.rst b/docs/api/api_python/ops/mindspore.ops.func_full_like.rst index b0d9171a88b..baf2cd95e1e 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_full_like.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_full_like.rst @@ -3,7 +3,7 @@ mindspore.ops.full_like .. py:function:: mindspore.ops.full_like(x, fill_value, *, dtype=None) - 返回一个与输入相同大小的Tensor，填充 `fill_value`。'ops.full_like(x, fill_value)'相当于'ops.full(x.shape, fill_value, dtype=x.dtype)'。 + 返回一个与输入相同大小的Tensor，填充 `fill_value` 。 `ops.full_like(x, fill_value)` 相当于 `ops.full(x.shape, fill_value, dtype=x.dtype)` 。 参数： - **x** (Tensor) - `x` 的shape决定输出Tensor的shape。 diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index e82acf1f4b1..a81809ea912 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -717,8 +717,8 @@ def full(size, fill_value, *, dtype=None): # pylint: disable=redefined-outer-nam def full_like(x, fill_value, *, dtype=None): """ - Returns a Tensor with the same size as `x` filled with `fill_value`. 'ops.full_like(x, fill_value)' is - equivalent to 'ops.full(x.shape, fill_value, dtype=x.dtype)'. + Returns a Tensor with the same size as `x` filled with `fill_value`. `ops.full_like(x, fill_value)` is + equivalent to `ops.full(x.shape, fill_value, dtype=x.dtype)` . Args: x (Tensor): The shape of `x` will determine shape of the output Tensor. @@ -5862,7 +5862,7 @@ def diagonal(input, offset=0, dim1=0, dim2=1): Returns specified diagonals of `input`. If `input` is 2-D, returns the diagonal of `input` with the given offset. - If `a` has more than two + If `input` has more than two dimensions, then the axes specified by `dim1` and `dim2` are used to determine the 2-D sub-array whose diagonal is returned. The shape of the resulting array can be determined by removing `dim1` and `dim2` and appending an index diff --git a/mindspore/python/mindspore/ops/function/clip_func.py b/mindspore/python/mindspore/ops/function/clip_func.py index dd81fb24a51..9d489a8cca7 100644 --- a/mindspore/python/mindspore/ops/function/clip_func.py +++ b/mindspore/python/mindspore/ops/function/clip_func.py @@ -153,7 +153,7 @@ def clip_by_value(x, clip_value_min=None, clip_value_max=None): def clamp(x, min=None, max=None): r""" - Clamps tensor values to a specified min and max. + Clamps tensor values between the specified minimum value and maximum value. Limits the value of :math:`x` to a range, whose lower limit is `min` and upper limit is `max` . @@ -181,7 +181,7 @@ def clamp(x, min=None, max=None): max (Union(Tensor, float, int)): The maximum value. Default: None. Returns: - (Union(Tensor, tuple[Tensor], list[Tensor])), a clipped Tensor or a tuple or a list of clipped Tensor. + Union(Tensor, tuple[Tensor], list[Tensor]), a clipped Tensor or a tuple or a list of clipped Tensor. The data type and shape are the same as x. Raises: diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py index 7940e7e7ae4..f53769016a7 100644 --- a/mindspore/python/mindspore/ops/function/math_func.py +++ b/mindspore/python/mindspore/ops/function/math_func.py @@ -1293,7 +1293,7 @@ def logdet(x): Returns: Tensor, the log determinant of `x`. If the matrix determinant is smaller than 0, nan will be returned. If the - matrix determinant is 0, -inf will be returned. + matrix determinant is 0, -inf will be returned. Raises: TypeError: If dtype of `x` is not float32, float64, Complex64 or Complex128. @@ -4503,11 +4503,11 @@ def heaviside(x, values): Args: x (Tensor): The input tensor. With real number data type. - values (Tensor): The values to use where x is zero. Values can be broadcast with x. - 'x' should have the same dtype with 'values'. + values (Tensor): The values to use where `x` is zero. Values can be broadcast with `x` . + `x` should have the same dtype with 'values'. Returns: - Tensor, has the same type as 'x' and 'values'. + Tensor, has the same type as `x` and `values`. Raises: TypeError: If `x` or `values` is not Tensor. diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index a7b9ca9f879..4a657e699fe 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -686,8 +686,8 @@ def max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=No max_unpool1d takes the output of maxpool1d as input including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero. Typically the input - is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out}` - or :math:`(C, H_{out}`. The operation is as follows. + is of shape :math:`(N, C, H_{in})` or :math:`(C, H_{in})`, and the output is of shape :math:`(N, C, H_{out})` + or :math:`(C, H_{out})`. The operation is as follows. .. math:: \begin{array}{ll} \\ @@ -4607,7 +4607,7 @@ def batch_norm(input_x, running_mean, running_var, weight, bias, training=False, .. warning:: - If this operation is used for inferring and output "reserve_space_1" and "reserve_space_2" are usable, - then "reserve_space_1" and "reserve_space_2" have the same value as "mean" and "variance" respectively. + then "reserve_space_1" and "reserve_space_2" have the same value as "mean" and "variance" respectively. - For Ascend 310, the result accuracy fails to reach 1‰ due to the square root instruction. Note: @@ -5480,7 +5480,7 @@ def lp_pool2d(x, norm_type, kernel_size, stride=None, ceil_mode=False): stride (Union[int, tuple[int]]): The distance of kernel moving, an int number that represents the height and width of movement are both strides, or a tuple of two int numbers that represent height and width of movement respectively, if the value is None, - the default value `kernel_size` is used; + the default value `kernel_size` is used. ceil_mode (bool): Whether to use ceil or floor to calculate output shape. Default: False. Returns: