private void NumericGradCheckInternal(Model model)
{
const int passCount = 10;
for (int i = 0; i < passCount; i++)
{
model.FirstInputValue = Utils.GetRandomNumber(_rnd, 0.05, 10);
Matrix target = new Matrix(2 * model.FirstInputValue);
model.ForwardPass(target);
model.LossLayer.ComputeLossGradients(target);
model[1].ComputeGradient();
model[0].ComputeGradient();
double fa = model[0].Weights.Extra[0, 0]; // Analytical gradient - dy/dw
double initWeight = model[0].Weights.Primal[0, 0];
model[0].Weights.Primal[0, 0] = initWeight + GradUtils.H;
model.ForwardPass(target);
double f1Val = model.FirstLossValue;
model[0].Weights.Primal[0, 0] = initWeight - GradUtils.H;
model.ForwardPass(target);
double f2Val = model.FirstLossValue;
Assert.IsTrue(GradUtils.ChechAnalGrad(fa, f1Val, f2Val), "Failed on " + i + " try");
model[1].ApplyGradient();
model[0].ApplyGradient();
}
}
private static void CheckSquareInternal(double h, double t)
{
SquareError errorOp = new SquareError();
double fa1 = errorOp.ComputeLossGradient(h, t);
double f1Val = errorOp.ForwardPass(h + GradUtils.H, t);
double f2Val = errorOp.ForwardPass(h - GradUtils.H, t);
bool res = GradUtils.ChechAnalGrad(fa1, f1Val, f2Val);
Assert.IsTrue(res);
}
