torch.randn

torch.randn(*size, *, generator=None, 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).

$$\text{out}_{i} \sim \mathcal{N}(0, 1)$$

For complex dtypes, the tensor is i.i.d. sampled from a complex normal distribution with zero mean and unit variance as

$$\text{out}_{i} \sim \mathcal{CN}(0, 1)$$

This is equivalent to separately sampling the real $(\operatorname{Re})$ and imaginary $(\operatorname{Im})$ part of $\text{out}_i$ as

$$\operatorname{Re}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2}),\quad \operatorname{Im}(\text{out}_{i}) \sim \mathcal{N}(0, \frac{1}{2})$$

The shape of the tensor is defined by the variable argument size.

Parameters

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

• generator (torch.Generator, optional) – a pseudorandom number generator for sampling
• out (Tensor, optional) – the output tensor.
• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, uses a global default (see torch.set_default_dtype()).
• layout (torch.layout, optional) – the desired layout of returned Tensor. Default: torch.strided.
• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_device()). device will 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: False.
• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

>>> 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]])