- torch.randn(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) Tensor ¶
Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
The shape of the tensor is defined by the variable argument
size (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
- Keyword Arguments:
torch.Generator, optional) – a pseudorandom number generator for sampling
out (Tensor, optional) – the output tensor.
torch.layout, optional) – the desired layout of returned Tensor. Default:
torch.device, optional) – the desired device of returned tensor. Default: if
None, uses the current device for the default tensor type (see
devicewill be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default:
pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default:
>>> torch.randn(4) tensor([-2.1436, 0.9966, 2.3426, -0.6366]) >>> torch.randn(2, 3) tensor([[ 1.5954, 2.8929, -1.0923], [ 1.1719, -0.4709, -0.1996]])