standard_normal¶

paddle. standard_normal ( shape, dtype=None, name=None ) [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, 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).
name (str, optional) – Name for the operation (optional, default is None). For more information, please refer to Name.

Returns

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

Return type

Tensor

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]])
>>> 

          