Tensor

Overview

Tensor is a basic data structure in the MindSpore network computing. For details about data types in tensors, see dtype.

Tensors of different dimensions represent different data. For example, a 0-dimensional tensor represents a scalar, a 1-dimensional tensor represents a vector, a 2-dimensional tensor represents a matrix, and a 3-dimensional tensor may represent the three channels of RGB images.

All examples in this document can be run in the PyNative mode.

Tensor Structure

During tensor creation, the Tensor, float, int, bool, tuple, list, and NumPy.array types can be transferred, while tuple and list can only store float, int, and bool data.

dtype can be specified when Tensor is initialized. When the dtype is not specified, if the initial value is int, float or bool, then a 0-dimensional Tensor with data types mindspore.int32, mindspore.float32 or mindspore.bool_ will be generated respectively. If the initial values are tuple and list, the generated 1-dimensional Tensor data type corresponds to the type stored in tuple and list. If it contains multiple different types of data, follow the below priority: bool < int < float, to select the mindspore data type corresponding to the highest relative priority type. If the initial value is Tensor, the consistent data type Tensor is generated. If the initial value is NumPy.array, the corresponding data type Tensor is generated.

A code example is as follows:

import numpy as np
from mindspore import Tensor
from mindspore import dtype as mstype

x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)
y = Tensor(1.0, mstype.int32)
z = Tensor(2, mstype.int32)
m = Tensor(True, mstype.bool_)
n = Tensor((1, 2, 3), mstype.int16)
p = Tensor([4.0, 5.0, 6.0], mstype.float64)
q = Tensor(p, mstype.float64)

print(x, "\n\n", y, "\n\n", z, "\n\n", m, "\n\n", n, "\n\n", p, "\n\n", q)

The following information is displayed:

[[1 2]
 [3 4]]

1

2

True

[1 2 3]

[4. 5. 6.]

[4. 5. 6.]

Tensor Attributes and Methods

Attributes

Tensor attributes include shape and data type (dtype).

shape: a tuple
dtype: a data type of MindSpore

A code example is as follows:

import numpy as np
from mindspore import Tensor
from mindspore import dtype as mstype

x = Tensor(np.array([[1, 2], [3, 4]]), mstype.int32)
x_shape = x.shape
x_dtype = x.dtype

print(x_shape, x_dtype)

The following information is displayed:

(2, 2) Int32

Methods

Tensor methods include all, any, and asnumpy. Currently, the all and any methods support only Ascend, and the data type of Tensor is required to be mindspore.bool_. .

all(axis, keep_dims): performs the and operation on a specified dimension to reduce the dimension. axis indicates the reduced dimension, and keep_dims indicates whether to retain the reduced dimension.
any(axis, keep_dims): performs the or operation on a specified dimension to reduce the dimension. The parameter meaning is the same as that of all.
asnumpy(): converts Tensor to an array of NumPy.

A code example is as follows:

import numpy as np
from mindspore import Tensor
from mindspore import dtype as mstype

x = Tensor(np.array([[True, True], [False, False]]), mstype.bool_)
x_all = x.all()
x_any = x.any()
x_array = x.asnumpy()

print(x_all, "\n\n", x_any, "\n\n", x_array)

The following information is displayed:

False

True

[[ True  True]
 [False False]]