torch.bernoulli() function
This function simply makes all the inputs into binary random numbers(0 or 1) from a Bernoulli Distribution. The output shape is same as the data inputted in the code.
Syntax-
torch.bernoulli(input, *, generator=None, out=None) → Tensor
Parameters-
input (Tensor) – the input tensor of probability values for the Bernoulli distribution
Key Argument-
- generator (torch.Generator, optional) – a pseudorandom number generator for sampling
- out (Tensor, optional) – the output tensor.
Example:
In this example, let’s see a basic simple random generated shape and output it in Bernoulli distribution.
Python3
import torch# this function will make a # tensor of 5X5 array with# random numbersrand_matrix = torch.rand(5, 5)print(rand_matrix)# Bernoulli distribution# this function will do the bernoulli# distribution on the given matrix and# form the new tensortorch.bernoulli(rand_matrix) |
Output:
tensor([[0.5010, 0.0622, 0.3710, 0.3325, 0.5136],
[0.0790, 0.6433, 0.8819, 0.3770, 0.8236],
[0.3458, 0.9933, 0.2282, 0.6544, 0.6823],
[0.5454, 0.5916, 0.2471, 0.6174, 0.1676],
[0.8980, 0.4162, 0.8114, 0.3744, 0.9957]])
tensor([[0., 0., 0., 0., 0.],
[0., 1., 1., 0., 0.],
[0., 1., 0., 0., 0.],
[1., 0., 0., 0., 1.],
[1., 1., 1., 0., 1.]])
5 Statistical Functions for Random Sampling in PyTorch
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()
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