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mindspore.Tensor.random_

Tensor.random_(from_=0, to=None, *, generator=None)

Fill the tensor with numbers sampled from a discrete uniform distribution over an interval $[from\mathrm{\_},to-1]$.

Warning

This is an experimental API that is subject to change or deletion.

Parameters

• from_ (Union[number.Number, Tensor], optional) – the lower bound of the generated random number. It can be a scalar value or a Tensor of any dimension with only a single element. Default: 0.

• to (Union[number.Number, Tensor], optional) – the upper bound of the generated random number. By default it's the upper limit of the input data type. It can be a scalar value or a Tensor of any dimension with only a single element. Default: None.

Keyword Arguments

generator (mindspore.Generator, optional) – a pseudorandom number generator. Default: None, uses the default pseudorandom number generator.

Returns

The input tensor.

Raises

• TypeError – If from_ or to is not integer.

• RuntimeError – If from_ >= to.

Supported Platforms:

Ascend

Examples

>>> from mindspore import Tensor
>>> a = Tensor([[2, 3, 4], [1, 2, 3]])
>>> from_ = 0
>>> to = 5
>>> print(a.random_(from_, to).shape)
(2, 3)