Function neuronika::nn::loss::mse_loss

pub fn mse_loss<T, U, V>(
    input: VarDiff<T, U>, 
    target: Var<V>, 
    reduction: Reduction
) -> VarDiff<MSELoss<T, V>, MSELossBackward<U, T, V>> where
    T: Data,
    U: Gradient<Dim = T::Dim> + Overwrite,
    V: Data<Dim = T::Dim>, 

Computes the mean squared error (squared L2 norm) between each element in the input x and target y.

       1   n
Lᴏss = ―   ∑ (xᵢ- ʏᵢ)²
       n  i=1