criterion = MSECriterion()

Creates a criterion that measures the mean squared error between n elements in the input x and output y: 

loss(x,y) = forward(x,y) = 1/n \sum |x_i-y_i|^2 .

If x and y are =d=-dimensional Tensors with a total of n elements, the sum operation still operates over all the elements, and divides by n. The two tensors must have the same number of elements (but their sizes might be different...)

The division by n can be avoided if one sets the internal variable sizeAverage to false: 

criterion = nn.MSECriterion()
criterion.sizeAverage = false