.bernoulli()

In PyTorch, the .bernoulli() function is used to generate a tensor with binary values (0s and 1s) based on the probabilities provided in an input tensor. Each element in the output tensor is drawn from a Bernoulli distribution, where the probability of drawing a 1 is given by the corresponding element in the input tensor.

Syntax

torch.bernoulli(input, *, gen=None, out=None)

• input: A tensor containing probabilities (values between 0 and 1).
• gen (Optional): Generates a pseudorandom number for sampling. Defaults to None.
• out (Optional): The output tensor to store the result.

Example

The following example demonstrates the usage of the .bernoulli() function:

import torch

# Create a tensor containing probabilities
prob = torch.tensor([0.3, 0.6, 0.9, 0.4, 0.8, 0.5])

# Generate samples from the Bernoulli distribution
res = torch.bernoulli(prob)

# Print the resultant tensor
print(res)

The above code produces the following output:

tensor([0., 1., 1., 1., 1., 1.])

Note: Since the .bernoulli() function samples independently for each element, the output may vary each time the code is run.