mindspore.ops.uniform

mindspore.ops.uniform(shape, minval, maxval, seed=None, dtype=mstype.float32)[source]

Generates random numbers according to the Uniform random number distribution.

Note

The number in tensor minval should be strictly less than maxval at any position after broadcasting.

Parameters

  • shape (tuple) – The shape of random tensor to be generated.

  • minval (Tensor) – The distribution parameter a. It defines the minimum possible generated value, with int32 or float32 data type. If dtype is int32, only one number is allowed.

  • maxval (Tensor) – The distribution parameter b. It defines the maximum possible generated value, with int32 or float32 data type. If dtype is int32, only one number is allowed.

  • seed (int) – Seed is used as entropy source for the random number engines to generate pseudo-random numbers, must be non-negative. Default: None, which will be treated as 0.

  • dtype (mindspore.dtype) – type of the Uniform distribution. If it is int32, it generates numbers from discrete uniform distribution; if it is float32, it generates numbers from continuous uniform distribution. It only supports these two data types. Default: mstype.float32.

Returns

Tensor. The shape should be equal to the broadcasted shape between the input shape and shapes of minval and maxval. The dtype is designated as the input dtype.

Supported Platforms:

Ascend GPU

Examples

>>> # For discrete uniform distribution, only one number is allowed for both minval and maxval:
>>> shape = (4, 2)
>>> minval = Tensor(1, mstype.int32)
>>> maxval = Tensor(2, mstype.int32)
>>> output = C.uniform(shape, minval, maxval, seed=5, dtype=mstype.int32)
>>>
>>> # For continuous uniform distribution, minval and maxval can be multi-dimentional:
>>> shape = (3, 1, 2)
>>> minval = Tensor(np.array([[3, 4], [5, 6]]), mstype.float32)
>>> maxval = Tensor([8.0, 10.0], mstype.float32)
>>> output = C.uniform(shape, minval, maxval, seed=5)
>>> result = output.shape
>>> print(result)
(3, 2, 2)