randn_like

paddle. randn_like ( x: Tensor, dtype: DTypeLike | None = None, name: str | None = None ) → Tensor [source]

Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.

Parameters :

• x (Tensor) – The input multi-dimensional tensor which specifies shape. The dtype of x can be float16, bfloat16, float32, float64, complex64, complex128.

• dtype (str|np.dtype|paddle.dtype|None, optional) – The data type of the output tensor. Supported data types: float16, bfloat16, float32, float64, complex64, complex128. If dtype is None, the data type is the same as x’s data type. Default is None.

• name (str|None, 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 :

Tensor, A Tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.

Examples

>>> import paddle

>>> # example 1:
>>> # dtype is None and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out1 = paddle.randn_like(x)
>>> print(out1)
>>> 
Tensor(shape=[1, 2], dtype=float32, place=Place(cpu), stop_gradient=True,
[[ 0.51785558, -0.10632933]])
>>> 
>>> print(out1.dtype)
paddle.float32

>>> # example 2:
>>> # dtype is None and the dtype of x is float64
>>> x = paddle.zeros((1,2)).astype("float64")
>>> out2 = paddle.randn_like(x)
>>> print(out2)
>>> 
Tensor(shape=[1, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 0.64437317, -1.26898670]])
>>> 
>>> print(out2.dtype)
paddle.float64

>>> # example 3:
>>> # dtype is float64 and the dtype of x is float32
>>> x = paddle.zeros((1,2)).astype("float32")
>>> out3 = paddle.randn_like(x, dtype="float64")
>>> print(out3)
>>> 
Tensor(shape=[1, 2], dtype=float64, place=Place(cpu), stop_gradient=True,
[[ 1.45264642, -1.33133914]])
>>> 
>>> print(out3.dtype)
paddle.float64