torch.rand — PyTorch 2.7 documentation (original) (raw)

torch.rand(*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 uniform distribution on the interval [0,1)[0, 1)

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.rand(4) tensor([ 0.5204, 0.2503, 0.3525, 0.5673]) torch.rand(2, 3) tensor([[ 0.8237, 0.5781, 0.6879], [ 0.3816, 0.7249, 0.0998]])