torch.rand#

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 distributionon the interval$[0,1)$[0,1)

The shape of the tensor is defined by the variable argumentsize.

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: ifNone, uses a global default (seetorch.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: ifNone, uses the current device for the default tensor type(seetorch.set_default_device()).device will be the CPUfor CPU tensor types and the current CUDA device for CUDA tensor types.

• requires_grad (bool,optional) – If autograd should record operations on thereturned tensor. Default:False.

• pin_memory (bool,optional) – If set, returned tensor would be allocated inthe 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]])