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.

Parameters

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

  • seed2 (int) – Random seed2, must be non-negative. Default: 0.

Inputs:

  • shape (tuple) - 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.

Raises

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

  • TypeError – If shape is not a tuple.

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

  • ValueError – If shape is not a constant value.

Outputs:

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

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)