mindspore.ops.rand_like

mindspore.ops.rand_like(input, seed=None, *, dtype=None)[source]

Returns a new tensor that fills numbers from the uniform distribution over an interval $[0, 1)$ based on the given shape and dtype.

Warning

The Ascend backend does not support the reproducibility of random numbers, so the seed parameter has no effect.

Parameters

input (Tensor) – Input Tensor to specify the output shape and its default dtype.
seed (int, optional) – Random seed, must be greater or equal to 0. Default: None , and 0 will be used.

Keyword Arguments

dtype (mindspore.dtype, optional) – Designated tensor dtype, it must be float type. If None, the same dtype of input will be applied. Default: None .

Returns

Tensor, with the designated shape and dtype, filled with random numbers from the uniform distribution on the interval $[0, 1)$ .

Raises

TypeError – If seed is not a non-negative integer.
ValueError – If dtype is not a mstype.float_type type.

Supported Platforms:: Ascend GPU CPU

Examples

>>> import mindspore as ms
>>> from mindspore import Tensor, ops
>>> a = Tensor([[2, 3, 4], [1, 2, 3]])
>>> print(ops.rand_like(a, dtype=ms.float32))
[[4.1702199e-01 9.9718481e-01 7.2032452e-01]
 [9.3255734e-01 1.1438108e-04 1.2812445e-01]]