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# torch.randn¶

torch.randn(*size, *, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, pin_memory=False)

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

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]])


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