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. The format is \((N,*)\) where \(*\) means, any number of additional dimensions and the length of \((N,*)\) should be less than 8 in broadcast operation.
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: mindspore.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.
- Raises
- 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, mindspore.int32) >>> maxval = Tensor(2, mindspore.int32) >>> output = ops.uniform(shape, minval, maxval, seed=5, dtype=mindspore.int32) >>> >>> # For continuous uniform distribution, minval and maxval can be multi-dimentional: >>> shape = (3, 1, 2) >>> minval = Tensor(np.array([[3, 4], [5, 6]]), mindspore.float32) >>> maxval = Tensor([8.0, 10.0], mindspore.float32) >>> output = ops.uniform(shape, minval, maxval, seed=5) >>> result = output.shape >>> print(result) (3, 2, 2)