func (d *MLP) MeanSquareErr(input, target [][]float64) float64 {
sum := 0.0
for i := 0; i < len(target); i++ {
sum += nnet.SquareErrBetweenTwoVector(d.Forward(input[i]), target[i])
}
return 0.5 * sum / float64(len(target))
}
// ReconstructionError returns reconstruction error.
// Use mean of Gaussian when computing reconstruction error.
func (rbm *GBRBM) ReconstructionError(data [][]float64, numSteps int) float64 {
err := 0.0
for _, v := range data {
reconstructed := rbm.Reconstruct(v, numSteps, true)
err += nnet.SquareErrBetweenTwoVector(v, reconstructed)
}
return 0.5 * err / float64(len(data))
}