Hướng dẫn dùng torch.randint python

torch.randint[low=0, high, size, \*, generator=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False] → Tensor¶

Returns a tensor filled with random integers generated uniformly between low [inclusive] and high [exclusive].

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

Note

With the global dtype default [torch.float32], this function returns a tensor with dtype torch.int64.

Parameters

low [int, optional] – Lowest integer to be drawn from the distribution. Default: 0.
high [int] – One above the highest integer to be drawn from the distribution.
size [tuple] – a tuple defining the shape of the output tensor.

Keyword Arguments

generator [torch.Generator, optional] – a pseudorandom number generator for sampling
out [Tensor, optional] – the output tensor.
dtype [torch.dtype, optional] – if None, this function returns a tensor with dtype torch.int64.
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_tensor_type[]]. 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.

Example:

>>> torch.randint[3, 5, [3,]]
tensor[[4, 3, 4]]


>>> torch.randint[10, [2, 2]]
tensor[[[0, 2],
        [5, 5]]]


>>> torch.randint[3, 10, [2, 2]]
tensor[[[4, 5],
        [6, 7]]]