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torch.randn — PyTorch 2.12 documentation

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Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution). For complex dtypes, the tensor is i.i.d. sampled from a complex normal distribution with zero mean and unit variance as This is equivalent to separately sampling the real ( Re ⁡ ) (Re) and imaginary ( Im ⁡ ) (Im) part of out 𝑖 out i ​ as The shape of the tensor is defined by the variable argument size. 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. 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 (torc

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). out i ∼ N ( 0 , 1 ) \text{out}_{i} \sim \mathcal{N}(0, 1) out i ​ ∼ 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 out i ∼ C N ( 0 , 1 ) \text{out}_{i} \sim \mathcal{CN}(0, 1) out i ​ ∼ C N (

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