torch.randperm() function

This function returns a random permutation of integers.

Syntax:

torch.randperm(n, *, generator=None, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False, pin_memory=False) → Tensor

Returns a random permutation of integers from 0 to n – 1.

Parameters:

n (int) – the upper bound (exclusive)

Key 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: 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.
• pin_memory (bool, optional) – If set, returned tensor would be allocated in the pinned memory. Works only for CPU tensors. Default: False.

Example:

In this example, we are generating random numbers from 0-5 just by passing the 6 as the parameter to the torch.randperm() function in python.

Output:

tensor([4, 1, 0, 2, 3, 5])

PyTorch is an open source machine learning library used for deep learning with more flexibility and feasibility. This is an extension of NumPy.

For Statistical Functions for Random Sampling, let’s see what they are along with their easy implementations. To run all these the first step is to import Pytorch by import torch. There are 5 functions:

• torch.bernoulli()
• torch.normal()
• torch.poisson()
• torch.randn()
• torch.randperm()