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, Bessel’s correction will be used to calculate the variance.
- If unbiased = False, Bessel’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)