standard_normal

paddle. standard_normal ( shape: ShapeLike, dtype: DTypeLike | None = None, name: str | None = None, *, out: paddle.Tensor | None = None, device: PlaceLike | None = None, requires_grad: bool = False ) → Tensor [source]

Returns a Tensor filled with random values sampled from a standard normal distribution with mean 0 and standard deviation 1, with shape and dtype.

Parameters

  • shape (tuple|list|Tensor) – Shape of the Tensor to be created. The data type is int32 or int64 . If shape is a list or tuple, each element of it should be integer or 0-D Tensor with shape []. If shape is an Tensor, it should be an 1-D Tensor which represents a list.

  • dtype (str|np.dtype|paddle.dtype|None, optional) – The data type of the output Tensor. Supported data types: float16, bfloat16, float32, float64, complex64, complex128. Default is None, use global default dtype (see get_default_dtype for details).

  • name (str|None, optional) – Name for the operation (optional, default is None). For more information, please refer to api_guide_Name.

  • out (Tensor, optional) – The output tensor.

  • device (PlaceLike|None, optional) – The desired device of returned tensor. if None, uses the current device for the default tensor type (see paddle.device.set_device()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types. Default: None.

  • requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Returns

Tensor, A Tensor filled with random values sampled from a standard

normal distribution with mean 0 and standard deviation 1, with shape and dtype.

Examples

>>> import paddle

>>> 
>>> # example 1: attr shape is a list which doesn't contain Tensor.
>>> out1 = paddle.standard_normal(shape=[2, 3])
>>> print(out1)
>>> 
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[-0.33719197, -0.25688133, -0.42868865],
 [-0.27804616, -0.25058213, -0.28209466]])
>>> 

>>> # example 2: attr shape is a list which contains Tensor.
>>> dim1 = paddle.to_tensor(2, 'int64')
>>> dim2 = paddle.to_tensor(3, 'int32')
>>> out2 = paddle.standard_normal(shape=[dim1, dim2, 2])
>>> print(out2)
>>> 
Tensor(shape=[2, 3, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[ 0.81888396, -0.64831746],
  [ 1.28911388, -1.88154876],
  [-0.03271919, -0.32410008]],
 [[-0.20224631,  0.46683890],
  [ 1.91947734,  0.71657443],
  [ 0.33410960, -0.64256823]]])
>>> 

>>> # example 3: attr shape is a Tensor, the data type must be int64 or int32.
>>> shape_tensor = paddle.to_tensor([2, 3])
>>> out3 = paddle.standard_normal(shape_tensor)
>>> print(out3)
>>> 
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.01182475, -0.44895259, -1.79227340],
 [ 1.52022707, -0.83830303,  0.05261501]])
>>> 

>>> # example 4: attr dtype is complex64.
>>> paddle.seed(200)
>>> shape_tensor = paddle.to_tensor([2, 3])
>>> out4 = paddle.standard_normal(shape_tensor, dtype='complex64')
>>> print(out4)
Tensor(shape=[2, 3], dtype=complex64, place=Place(cpu), stop_gradient=True,
[[ (0.13755313+0.09320746j),  (0.79550129-0.41801897j),
   (-0.67300206-0.09163689j)],
 [ (0.17453042-0.90028328j),  (0.16270922-1.30863023j),
   (0.94287461+0.06869461j)]])