How to Create a Tensor Whose Elements are Sampled from a Poisson Distribution in PyTorch

How to Draw Binary Random Numbers (0 or 1) from a Bernoulli Distribution in PyTorch?

Last Updated : 10 Oct, 2022

In this article, we discuss how to draw Binary Random Numbers (0 or 1) from a Bernoulli Distribution in PyTorch.

torch.bernoulli() method

torch.bernoulli() method is used to draw binary random numbers (0 or 1) from a Bernoulli distribution. This method accepts a tensor as a parameter, and this input tensor is the probability of drawing 1. The values of the input tensor should be in the range of 0 to 1. This method returns a tensor that only has values 0 or 1 and the size of this tensor is the same as the input tensor. Let’s have a look at the syntax of the given method:

Syntax: torch.bernoulli(input)

Parameters:

input (Tensor): the input tensor containing the probabilities of drawing 1.

Returns: it will returns a tensor that only has values 0 or 1 and the size of this tensor is the same as the input tensor.

Example 1

In this example, we draw Binary Random Numbers (0 or 1) from a Bernoulli Distribution using a 1-D tensor.

Python3

# Import required library
import torch
 
# create a tensor containing the 
# probability of drawing 1.
tens = torch.tensor([0.1498, 0.9845, 0.4578, 
                     0.3495, 0.2442])
print(" Input tensor: ", tens)
 
# Draw random numbers (0,1)
random_num = torch.bernoulli(tens)
 
# display result
print(" Output tensor ", random_num)

Output:

Example 2

In this example, we estimate the gradient of a function for a 2-D tensor.

Python3

# Import required library
import torch
 
# create a tensor containing the
# probability of drawing 1.
tens = torch.tensor([[0.2432, 0.7579, 0.6325],
                     [0.3464, 0.2442, 0.3847],
                     [0.4528, 0.9876, 0.8499], ])
print("\n Input tensor: \n", tens)
 
# Draw random numbers (0,1)
random_num = torch.bernoulli(tens)
 
# display result
print("\n Output tensor \n", random_num)

Output:

How to Create a Tensor Whose Elements are Sampled from a Poisson Distribution in PyTorch

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