def MSE

bendtorch@1.3.0.0 · main.bend:3269

import Base
import bend-ml-tensor-array@0.1.18.0/main.bend as TA

3269# mean squared error over every number (PyTorch's F.mse_loss); its gradient is 2 (y - t) / (n*c); the squares summed
3270# as a tree
3271def MSE(+n: Nat, +c: Nat, y: TA.Mat<n, c>, t: TA.Mat<n, c>) -> LossOut<TA.Mat<n, c>>:
3272  match y t:
3273    case TA.Mat{ya} TA.Mat{ta}:
3274      mse_fin(n, c, U32.from_nat(Nat.mul(n, c)), F32.from_nat(Nat.mul(n, c)), mse_l(Nat.sub(Nat.mul(n, c), 1n), 0, (2.0 / F32.from_nat(Nat.mul(n, c)) : F32), zeros_a(Nat.mul(n, c)), Array.get(F32, ya, 0), Array.get(F32, ta, 0)))