| public Input1d ( Floatarray v, int i ) : void | ||
| v | Floatarray | |
| i | int | |
| return | void | |
public override void TrainDense(IDataset ds)
{
int nclasses = ds.nClasses();
float miters = PGetf("miters");
int niters = (int)(ds.nSamples() * miters);
niters = Math.Max(1000, Math.Min(10000000,niters));
double err = 0.0;
Floatarray x = new Floatarray();
Floatarray z = new Floatarray();
Floatarray target = new Floatarray(nclasses);
int count = 0;
for (int i = 0; i < niters; i++)
{
int row = i % ds.nSamples();
ds.Output(target, row);
ds.Input1d(x, row);
TrainOne(z, target, x, PGetf("eta"));
err += NarrayUtil.Dist2Squared(z, target);
count++;
}
err /= count;
Global.Debugf("info", " {4} n {0} niters={1} eta={2:0.#####} errors={3:0.########}",
ds.nSamples(), niters, PGetf("eta"), err, FullName);
}
public void InitData(IDataset ds, int nhidden, Intarray newc2i = null, Intarray newi2c = null)
{
CHECK_ARG(nhidden > 1 && nhidden < 1000000, "nhidden > 1 && nhidden < 1000000");
int ninput = ds.nFeatures();
int noutput = ds.nClasses();
w1.Resize(nhidden, ninput);
b1.Resize(nhidden);
w2.Resize(noutput, nhidden);
b2.Resize(noutput);
Intarray indexes = new Intarray();
NarrayUtil.RPermutation(indexes, ds.nSamples());
Floatarray v = new Floatarray();
for (int i = 0; i < w1.Dim(0); i++)
{
int row = indexes[i];
ds.Input1d(v, row);
float normv = (float)NarrayUtil.Norm2(v);
v /= normv * normv;
NarrayRowUtil.RowPut(w1, i, v);
}
ClassifierUtil.fill_random(b1, -1e-6f, 1e-6f);
ClassifierUtil.fill_random(w2, -1.0f / nhidden, 1.0f / nhidden);
ClassifierUtil.fill_random(b2, -1e-6f, 1e-6f);
if (newc2i != null)
c2i.Copy(newc2i);
if (newi2c != null)
i2c.Copy(newi2c);
}
