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

• Last Updated : 05 Jun, 2022

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

## torch.bernoulli() method

troch.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: My Personal Notes arrow_drop_up
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