# torch.var_mean(): Computing the Variance and Mean of Tensors – Pytorch Tutorial

By | October 13, 2022

In pytorch, in order to compute the variance and mean of a tensor, we can use torch.var_mean() function. In this tutorial, we will use some examples to show you how to do.

## Syntax

torch.var_mean(input, dim, unbiased, keepdim=False, *, out=None)

It will return a tuple (variance, mean)

Here we should notice unbiased parameter.

• If unbiased = True, Besselâ€™s correction will be used to calculate the variance.
• If unbiased = False, Besselâ€™s correction will be not used.

When we are computing the variance and mean of a batch, we should set unbiased = False, we can find this in batch norm.

We also should notice, this function can compute variance,not standard deviation of this tensor.

## How to use torch.var_mean()?

Here is an example:

import torch

x_len = torch.randn([30,20])

var, mean = torch.var_mean(x_len, unbiased=False)
print(var, mean)

Run this code, we will see:

tensor(0.9043) tensor(0.0207)

If dim = -1, we can get:

import torch

x_len = torch.randn([30,20])

var, mean = torch.var_mean(x_len, dim= -1, unbiased=False)
print(var, mean)

The result is:

tensor([0.7645, 1.1956, 0.8395, 0.6493, 1.7741, 1.1854, 1.0927, 0.9121, 0.8873,
0.6731, 0.7896, 1.2818, 0.8340, 0.4995, 0.5665, 1.5005, 0.9981, 1.2174,
0.9213, 0.8254, 0.7897, 1.2548, 0.6810, 1.5065, 0.7350, 0.8097, 0.9055,
1.1010, 0.9379, 0.7091]) tensor([-0.1640, -0.3001,  0.1981, -0.2262,  0.0627,  0.1597, -0.1240, -0.0013,
-0.0126,  0.2951, -0.1276,  0.2819,  0.5180, -0.1067,  0.1062, -0.0230,
-0.0640, -0.0270, -0.0241, -0.4303, -0.1213, -0.0258,  0.1430,  0.1553,
-0.2193, -0.1737,  0.2104, -0.1345,  0.0473,  0.8273])

Moreover, if we want to compute standard deviation, we can do as follows:

std = torch.sqrt(var+1e-8)