Bernoulli distribution is commonly used in statistics. It is a discrete probability distribution that conducts the outcome (single or failure) of a specific experiment. Its outcomes are usually labeled as “1” and “0”, where 1 symbolizes “success” and 0 defines “failure”. PyTorch provides the “torch.bernoulli()” method which enables users to generate binary random numbers using the Bernoulli distribution/

This article will exemplify the method to generate binary random numbers using the Bernoulli distribution in PyTorch.

**How to Generate Binary Random Numbers from a Bernoulli Distribution in PyTorch?**

To generate binary random numbers from a Bernoulli Distribution, follow the below-listed steps:

- Import PyTorch library
- Create/define the desired input tensor
- Generate binary random numbers using the “torch.bernoulli()” method
- Print the generated binary random numbers

Check out the examples below for a better understanding:

**Example 1: Generate Binary Random Numbers From Bernoulli Distribution Using 1D Tensor**

In the first section, we will define a 1D tensor and generate binary random numbers from the Bernoulli distribution using it.

**Step 1: Import PyTorch Library**

First, type the following line to import the “**torch**” library for generating binary numbers from the Bernoulli distribution:

`import torch`

**Step 2: Define 1D Tensor **

Then, define a desired 1D tensor and display its elements. Here, we are defining the following “**T1**” tensor from a list using the “**torch.tensor()**” function:

```
T1 = torch.tensor([0.1498, 0.9845, 0.4578, 0.3495, 0.2442])
print(T1)
```

This has created a 1D tensor as seen below:

**Step 3: Generate Binary Random Numbers **

Now, utilize the “**torch.bernoulli()**” method and specify the input tensor to generate binary random numbers from the Bernoulli distribution:

`random_num = torch.bernoulli(T1)`

**Step 4: Print Generated Random Numbers**

Finally, display the generated binary random numbers:

`print(random_num)`

The below output displays the generated binary random numbers from the Bernoulli distribution:

**Example 2: Generate Binary Random Numbers From Bernoulli Distribution Using 2D Tensor**

In the second section, we will define a 2D tensor and generate binary random numbers from the Bernoulli distribution using it.

**Step 1: Import PyTorch Library**

First, install the “**torch**” library using the provided line:

`import torch`

**Step 2: Define 2D Tensor**

Next, define the desired 2D tensor and print its elements. Here, we are making a 2D tensor from random numbers via the “**torch.rand()**” function:

```
T2 = torch.rand(2,3)
print(T2)
```

This has created a 2D tensor:

**Step 3: Generate Binary Random Numbers **

Now, generate binary random numbers from the Bernoulli distribution using the “**torch.bernoulli()**” method and specify the input tensor:

`random_num = torch.bernoulli(T2)`

**Step 4: Print Generated Random Numbers**

Finally, display the generated binary random numbers:

`print(random_num)`

In the below output, the generated binary random numbers from the Bernoulli distribution can be seen:

We have efficiently explained the method of generating binary random numbers from the Bernoulli distribution in PyTorch.

**Note**: Click on the provided link to access our Google Colab Notebook.

**Conclusion**

To generate binary random numbers from the Bernoulli distribution in PyTorch, install the “**torch**” library. Then, define the desired tensor and view its elements. Next, use the “**torch.bernoulli()**” method and specify the input tensor to generate/create binary random numbers using the Bernoulli distribution. Lastly, print the generated binary random numbers. This article has exemplified the method to generate binary random numbers using the Bernoulli distribution in PyTorch.