mindspore.ops.UniformInt

class mindspore.ops.UniformInt(seed=0, seed2=0)[source]

Produces random integer values i, uniformly distributed on the closed interval [minval, maxval), that is, distributed according to the discrete probability function:

\[\text{P}(i|a,b) = \frac{1}{b-a+1},\]

where the \(a\) indicates the min distribution parameter, the \(b\) indicates the max distribution parameter.

Note

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

  • Random seed: a set of regular random numbers can be obtained through some complex mathematical algorithms, and the random seed determines the initial value of this random number. If the random seed is the same in two separate calls, the random number generated will not change.

  • Using the Philox algorithm to scramble seed and seed2 to obtain random seed so that the user doesn’t need to worry about which seed is more important.

Parameters

  • seed (int, optional) – The operator-level random seed, used to generate random numbers, must be non-negative. Default: 0 .

  • seed2 (int, optional) – The global random seed, which combines with the operator-level random seed to determine the final generated random number, must be non-negative. Default: 0 .

Inputs:

  • shape (Union[tuple, Tensor]) - The shape of random tensor to be generated. Only constant value is allowed.

  • minval (Tensor) - The distribution parameter, \(a\). It defines the minimum possibly generated value, with int32 data type. Only one number is supported.

  • maxval (Tensor) - The distribution parameter, \(b\). It defines the maximum possibly generated value, with int32 data type. Only one number is supported.

Outputs:

Tensor. The shape is the same as the input ‘shape’, and the data type is int32.

Raises

  • TypeError – If neither seed nor seed2 is an int.

  • TypeError – If shape is neither a tuple nor a Tensor.

  • TypeError – If neither minval nor maxval is a Tensor.

  • ValueError – If shape is not a constant value.

Supported Platforms:

Ascend GPU CPU

Examples

>>> from mindspore import Tensor, ops
>>> from mindspore import dtype as mstype
>>> shape = (2, 4)
>>> minval = Tensor(1, mstype.int32)
>>> maxval = Tensor(5, mstype.int32)
>>> uniform_int = ops.UniformInt(seed=10)
>>> output = uniform_int(shape, minval, maxval)
>>> result = output.shape
>>> print(result)
(2, 4)