public override float OutputsDense(Floatarray result, Floatarray x_raw)
{
CHECK_ARG(x_raw.Length() == w1.Dim(1), "x_raw.Length() == w1.Dim(1)");
Floatarray z = new Floatarray();
int sparse = PGeti("sparse");
Floatarray y = new Floatarray();
Floatarray x = new Floatarray();
x.Copy(x_raw);
mvmul0(y, w1, x);
y += b1;
for (int i = 0; i < y.Length(); i++)
{
y[i] = sigmoid(y[i]);
}
if (sparse > 0)
{
ClassifierUtil.Sparsify(y, sparse);
}
mvmul0(z, w2, y);
z += b2;
for (int i = 0; i < z.Length(); i++)
{
z[i] = sigmoid(z[i]);
}
result.Copy(z);
//int idx = NarrayUtil.ArgMax(result);
//float val = NarrayUtil.Max(result);
return(Convert.ToSingle(Math.Abs(NarrayUtil.Sum(z) - 1.0)));
}
/// <summary>
/// do a single stochastic gradient descent step
/// </summary>
public void TrainOne(Floatarray z, Floatarray target, Floatarray x, float eta)
{
CHECK_ARG(target.Length() == w2.Dim(0), "target.Length() == w2.Dim(0)");
CHECK_ARG(x.Length() == w1.Dim(1), "x.Length() == w1.Dim(1)");
int sparse = PGeti("sparse");
int nhidden = this.nHidden();
int noutput = nClasses();
Floatarray delta1 = new Floatarray(nhidden);
Floatarray delta2 = new Floatarray(noutput);
Floatarray y = new Floatarray(nhidden);
mvmul0(y, w1, x);
y += b1;
for (int i = 0; i < nhidden; i++)
{
y[i] = sigmoid(y[i]);
}
if (sparse > 0)
{
ClassifierUtil.Sparsify(y, sparse);
}
mvmul0(z, w2, y);
z += b2;
for (int i = 0; i < noutput; i++)
{
z[i] = sigmoid(z[i]);
}
for (int i = 0; i < noutput; i++)
{
delta2[i] = (z[i] - target[i]) * dsigmoidy(z[i]);
}
vmmul0(delta1, delta2, w2);
for (int i = 0; i < nhidden; i++)
{
delta1[i] = delta1[i] * dsigmoidy(y[i]);
}
outer_add(w2, delta2, y, -eta);
for (int i = 0; i < noutput; i++)
{
b2[i] -= eta * delta2[i];
}
outer_add(w1, delta1, x, -eta);
for (int i = 0; i < nhidden; i++)
{
b1[i] -= eta * delta1[i];
}
}