mindspore.Tensor.uniform_

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Tensor.uniform_(from_=0, to=1, *, generator=None)[source]

Update the self tensor in place by generating random numbers sampled from uniform distribution in the half-open interval \([from\_, to)\).

\[P(x)= \frac{1}{to - from\_}\]

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 uniform distribution, it can be a scalar value or a tensor of any dimension with a single element. Default: 0.

  • to (Union[number.Number, Tensor], optional) – The upper bound of the uniform distribution, it can be a scalar value or a tensor of any dimension with a single element. Default: 1.

Keyword Arguments

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

Returns

Return self Tensor.

Raises

  • TypeError – If from_ or to is neither a number nor a Tensor.

  • TypeError – If dtype of from or to is not one of: bool, int8, int16, int32, int64, uint8, float32, float64.

  • ValueError – If from_ or to is Tensor but contains multiple elements.

  • RuntimeError – If from_ is larger than to.

Supported Platforms:

Ascend

Examples

>>> import mindspore
>>> x = mindspore.ops.ones((4, 2))
>>> generator = mindspore.Generator()
>>> generator.manual_seed(100)
>>> output = x.uniform_(1., 2., generator=generator)
>>> print(output.shape)
(4, 2)