randn

paddle. randn ( shape, dtype=None, name=None ) [源代码]

返回符合标准正态分布（均值为 0，标准差为 1 的正态随机分布）的随机 Tensor，形状为 shape，数据类型为 dtype。

参数

  • shape (list|tuple|Tensor) - 生成的随机 Tensor 的形状。如果 shape 是 list、tuple，则其中的元素可以是 int，或者是形状为[]且数据类型为 int32、int64 的 0-D Tensor。如果 shape 是 Tensor，则是数据类型为 int32、int64 的 1-D Tensor。

  • dtype (str|np.dtype，可选) - 输出 Tensor 的数据类型，支持 float32、float64、complex64、complex128。当该参数值为 None 时，输出 Tensor 的数据类型为 float32。默认值为 None。

  • name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。

返回

Tensor：符合标准正态分布的随机 Tensor，形状为 shape，数据类型为 dtype。

示例代码

>>> import paddle

>>> # example 1: attr shape is a list which doesn't contain Tensor.
>>> out1 = paddle.randn(shape=[2, 3])
>>> print(out1)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.29270014, -0.02925120, -1.07807338],
 [ 1.19966674, -0.46673676, -0.18050613]])

>>> # example 2: attr shape is a list which contains Tensor.
>>> dim1 = paddle.to_tensor(2, 'int64')
>>> dim2 = paddle.to_tensor(3, 'int32')
>>> out2 = paddle.randn(shape=[dim1, dim2, 2])
>>> print(out2)
Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[-0.26019713,  0.54994684],
  [ 0.46403214, -1.41178775],
  [-0.15682915, -0.26639181]],
 [[ 0.01364388, -2.81676364],
  [ 0.86996621,  0.07524570],
  [ 0.21443737,  0.90938759]]])

>>> # example 3: attr shape is a Tensor, the data type must be int64 or int32.
>>> shape_tensor = paddle.to_tensor([2, 3])
>>> out3 = paddle.randn(shape_tensor)
>>> print(out3)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.57575506, -1.60349274, -0.27124876],
 [ 1.08381045,  0.81270242, -0.26763600]])

>>> # example 4: attr dtype is complex64.
>>> paddle.seed(200)
>>> shape_tensor = paddle.to_tensor([2, 3])
>>> out4 = paddle.randn(shape_tensor, dtype='complex64')
>>> print(out4)
Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True,
[[ (0.1375531256198883+0.0932074561715126j) ,
   (0.7955012917518616-0.41801896691322327j),
  (-0.6730020642280579-0.09163688868284225j)],
 [ (0.17453041672706604-0.9002832770347595j),
   (0.16270922124385834-1.3086302280426025j),
   (0.9428746104240417+0.06869460642337799j)]])

使用本API的教程文档