From 601597bd3fba4d04de9836857e050c1ade099031 Mon Sep 17 00:00:00 2001 From: huanghui Date: Thu, 16 Dec 2021 20:24:57 +0800 Subject: [PATCH] fix tensor api doc --- .../api_python/mindspore/mindspore.Tensor.rst | 14 +++++----- mindspore/python/mindspore/common/tensor.py | 27 +++++++++---------- 2 files changed, 19 insertions(+), 22 deletions(-) diff --git a/docs/api/api_python/mindspore/mindspore.Tensor.rst b/docs/api/api_python/mindspore/mindspore.Tensor.rst index c6c672666c3..dd3c3f7a9fd 100644 --- a/docs/api/api_python/mindspore/mindspore.Tensor.rst +++ b/docs/api/api_python/mindspore/mindspore.Tensor.rst @@ -3,18 +3,18 @@ mindspore.Tensor .. py:class:: mindspore.Tensor(input_data=None, dtype=None, shape=None, init=None) - 用来存储数据。 继承自C++中的 `Tensor` 对象。有些函数是用C++实现的，有些函数是用Python实现的。 + 张量，即存储多维数组（n-dimensional array）的数据结构。 **参数：** - - **input_data** (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]) - 张量的输入数据。 - - **dtype** (:class:`mindspore.dtype`) - 输入数据应是在 `mindspore.dtype` 中定义的None、bool或numeric类型。该参数用于定义输出张量的数据类型。如果值为None，则输出张量的数据类型与 `input_data` 的相同。默认值：None。 - - **shape** (Union[tuple, list, int]) - 用来表示张量的形状，可以是整数列表、整数元组或单一整数。如果 `input_data` 已经被设置，则不需要再设置 `shape` 。默认值：None。 - - **init** (Initializer) - 用来表示初始化数据的信息。init用于在并行模式下的延迟初始化。一般情况下，不建议在其他条件下使用init接口来初始化参数。如果使用init接口来初始化参数，需要调用 `Tensor.init_data` 接口把 `Tensor` 转换为实际数据。 + - **input_data** (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]) - 被存储的数据，可以是其它Tensor，也可以是Python基本数据（如int，float，bool等），或是一个NumPy对象。默认值：None。 + - **dtype** (:class:`mindspore.dtype`) - 用于定义该Tensor的数据类型，必须是 *mindSpore.dtype* 中定义的类型。如果该参数为None，则数据类型与`input_data`一致，默认值：None。 + - **shape** (Union[tuple, list, int]) - 用于定义该Tensor的形状。如果指定了`input_data`，则无需设置该参数。默认值：None。 + - **init** (Initializer) - 用于在并行模式中延迟Tensor的数据的初始化，如果指定该参数，则`dtype`和`shape`也必须被指定。不推荐在非自动并行之外的场景下使用该接口。只有当调用`Tensor.init_data`时，才会使用指定的`init`来初始化Tensor数据。默认值：None。 **返回：** - Tensor。如果未设置 `dtype` 和 `shape` ，返回与 `input_data` 具有相同数据类型和形状的张量。如果设置了 `dtype` 或 `shape` ，则输出的张量的数据类型或形状与设置的相同。 + Tensor。 **样例：** @@ -42,7 +42,7 @@ mindspore.Tensor .. py:method:: T :property: - 返回转置后的张量。 + 返回转置后的Tensor。 .. py:method:: abs() diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index 8cfd17712d3..0bd2934e02f 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -33,28 +33,25 @@ np_types = (np.int8, np.int16, np.int32, np.int64, class Tensor(Tensor_): """ - Tensor is used for data storage. - - Tensor inherits tensor object in C++. - Some functions are implemented in C++ and some functions are implemented in Python. + Tensor is a data structure that stores an n-dimensional array. Args: - input_data (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]): Input data of the tensor. - Default: None. - dtype (:class:`mindspore.dtype`): Input data should be None, bool or numeric type defined in `mindspore.dtype`. - The argument is used to define the data type of the output tensor. If it is None, the data type of the - output tensor will be the same as the `input_data`. Default: None. - shape (Union[tuple, list, int]): A list of integers, a tuple of integers or an integer as the shape of - output. If `input_data` is available, `shape` doesn't need to be set. Default: None. + input_data (Union[Tensor, float, int, bool, tuple, list, numpy.ndarray]): The data to be stroed. It can be + another Tensor, Python number or NumPy ndarray. Default: None. + dtype (:class:`mindspore.dtype`): Used to indicate the data type of the output Tensor. The argument should + be defined in `mindspore.dtype`. If it is None, the data type of the output Tensor will be the same + as the `input_data`. Default: None. + shape (Union[tuple, list, int]): Used to indicate the shape of the output Tensor. The argument should be + a list of integers, a tuple of integers or an integer. If `input_data` is available, + `shape` doesn't need to be set. Default: None. init (Initializer): The information of init data. 'init' is used for delayed initialization in parallel mode. Usually, it is not recommended to use - 'init' interface to initialize parameters in other conditions. If 'init' interface is used to initialize - parameters, the `Tensor.init_data` API needs to be called to convert `Tensor` to the actual data. + 'init' interface to initialize Tensor in the other conditions. If 'init' interface is used to initialize + Tensor, the `Tensor.init_data` API needs to be called to convert `Tensor` to the actual data. Default: None. Outputs: - Tensor. If `dtype` and `shape` are not set, return a tensor with the same dtype and shape as `input_data`. - If `dtype` or `shape` is set, the dtype or shape of the output Tensor is consistent with the setting. + Tensor. Examples: >>> import numpy as np