PyTorch torch.var Function
PyTorch torch Reference Manual
torch.varis a function in PyTorch used to return the variance of a tensor. Variance is the average of the squared differences between each data point and the mean, measuring the dispersion of the data.
Function Definition
torch.var(input, dim, unbiased, keepdim=False)
Usage Example
Example
import torch
x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
# Return the variance of all elements
print("Variance:", torch.var(x))
# Variance along dim=0
y = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print("dim=0 variance:", torch.var(y, dim=0))
print("dim=1 variance:", torch.var(y, dim=1))
x = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
# Return the variance of all elements
print("Variance:", torch.var(x))
# Variance along dim=0
y = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
print("dim=0 variance:", torch.var(y, dim=0))
print("dim=1 variance:", torch.var(y, dim=1))
The output result is:
方差: tensor(2.5000) dim=0 方差: tensor([2.2500, 2.2500, 2.2500]) dim=1 方差: tensor([1., 1.])
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