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.

  • If neither seed nor seed2 is assigned a non-zero value, a randomly generated seed is used instead.

Parameters

  • seed (int) – Random seed, must be non-negative. Default: 0.

  • seed2 (int) – Random seed2, must be non-negative. A second seed to avoid seed collision. 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

>>> 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)