randn_like¶
Documentation¶
-
treetensor.torch.
randn_like
(input, *args, **kwargs)[source]¶ In
treetensor
, you can userandn_like
to create a tree of tensors with numbers obey standard normal distribution like another tree.Example:
>>> import torch >>> import treetensor.torch as ttorch >>> ttorch.randn_like(torch.ones(2, 3)) # the same as torch.randn_like(torch.ones(2, 3)) tensor([[ 1.8436, 0.2601, 0.9687], [ 1.6430, -0.1765, -1.1732]]) >>> ttorch.randn_like({ ... 'a': torch.ones(2, 3), ... 'b': {'x': torch.ones(4, )}, ... }) <Tensor 0x7ff3d6f3cb38> ├── a --> tensor([[-0.1532, 1.3965, -1.2956], │ [-0.0750, 0.6475, 1.1421]]) └── b --> <Tensor 0x7ff3d6f420b8> └── x --> tensor([ 0.1730, 1.6085, 0.6487, -1.1022])
Torch Version Related
This documentation is based on torch.randn_like in torch v1.9.0+cu102. Its arguments’ arrangements depend on the version of pytorch you installed.
If some arguments listed here are not working properly, please check your pytorch’s version with the following command and find its documentation.
1 | python -c 'import torch;print(torch.__version__)' |
The arguments and keyword arguments supported in torch v1.9.0+cu102 is listed below.
Description From Torch v1.9.0+cu102¶
-
torch.
randn_like
(input, *, dtype=None, layout=None, device=None, requires_grad=False, memory_format=torch.preserve_format) → Tensor¶ 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.torch.randn_like(input)
is equivalent totorch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)
.- Args:
input (Tensor): the size of
input
will determine size of the output tensor.- Keyword args:
- dtype (
torch.dtype
, optional): the desired data type of returned Tensor. Default: if
None
, defaults to the dtype ofinput
.- layout (
torch.layout
, optional): the desired layout of returned tensor. Default: if
None
, defaults to the layout ofinput
.- device (
torch.device
, optional): the desired device of returned tensor. Default: if
None
, defaults to the device ofinput
.- requires_grad (bool, optional): If autograd should record operations on the
returned tensor. Default:
False
.- memory_format (
torch.memory_format
, optional): the desired memory format of returned Tensor. Default:
torch.preserve_format
.
- dtype (