uniform

paddle. uniform ( shape, dtype=None, min=- 1.0, max=1.0, seed=0, name=None ) [source]

This OP returns a Tensor filled with random values sampled from a uniform distribution in the range [min, max), with shape and dtype.

Examples:

Input:
  shape = [1, 2]
Output:
  result=[[0.8505902, 0.8397286]]
Parameters

  • shape (list|tuple|Tensor) – The shape of the output Tensor. If shape is a list or tuple, the elements of it should be integers or Tensors (with the shape [1], and the data type int32 or int64). If shape is a Tensor, it should be a 1-D Tensor(with the data type int32 or int64).

  • dtype (str|np.dtype, optional) – The data type of the output Tensor. Supported data types: float32, float64. Default is None, use global default dtype (see get_default_dtype for details).

  • min (float|int, optional) – The lower bound on the range of random values to generate, min is included in the range. Default is -1.0.

  • max (float|int, optional) – The upper bound on the range of random values to generate, max is excluded in the range. Default is 1.0.

  • seed (int, optional) – Random seed used for generating samples. If seed is 0, it will use the seed of the global default generator (which can be set by paddle.seed). Note that if seed is not 0, this operator will always generate the same random numbers every time. Default is 0.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name.

Returns

A Tensor filled with random values sampled from a uniform distribution in the range [min, max), with shape and dtype.

Return type

Tensor

Raises

  • TypeError – If shape is not list, tuple, Tensor.

  • TypeError – If dtype is not float32, float64.

Examples

import paddle

# example 1:
# attr shape is a list which doesn't contain Tensor.
out1 = paddle.uniform(shape=[3, 4])
# [[ 0.84524226,  0.6921872,   0.56528175,  0.71690357], # random
#  [-0.34646994, -0.45116323, -0.09902662, -0.11397249], # random
#  [ 0.433519,    0.39483607, -0.8660099,   0.83664286]] # random

# example 2:
# attr shape is a list which contains Tensor.
dim1 = paddle.to_tensor([2], 'int64')
dim2 = paddle.to_tensor([3], 'int32')
out2 = paddle.uniform(shape=[dim1, dim2])
# [[-0.9951253,   0.30757582, 0.9899647 ], # random
#  [ 0.5864527,   0.6607096,  -0.8886161]] # random

# example 3:
# attr shape is a Tensor, the data type must be int64 or int32.
shape_tensor = paddle.to_tensor([2, 3])
out3 = paddle.uniform(shape_tensor)
# [[-0.8517412,  -0.4006908,   0.2551912 ], # random
#  [ 0.3364414,   0.36278176, -0.16085452]] # random