static void SimpleVNNBackpropNoMomentumSpeedTest()
{
var nn = new vnn(INPUT_SIZE, HIDDEN_SIZE, OUTPUT_SIZE, (mlp) => addon.RandomizeUniform(mlp));
var tr = new trainerNoMomentum(nn);
WriteLine($"Time: {measureBackpropSpeed(nn, tr)}");
}
public static void simple_portable(vnn nn)
{
//var tr = new trainerModern(nn);
var tr = new trainerNoMomentum(nn);
var tset = data.DataSets.twoParamTest;
Stopwatch sw = Stopwatch.StartNew();
const double ACCRATE = 0.05;
for (int i = 0; i < 500; i++)
{
//tr.TrainEpoch(tset.inputs, tset.outputs, 0.1, 0.9);
for (int j = 0, to = tset.size; j < to; j++)
{
tr.TrainOne(tset.inputs[j], tset.outputs[j], 0.1);
}
}
var acc = tset.getAccuracy(nn, ACCRATE);
// if (acc < 0.9) { throw new Exception($"Not enought accuracy: {acc.ToString("N2")}"); }
WriteLine("Elapsed = " + sw.ElapsedMilliseconds + " ms.");
WriteLine($"Accuracy (+-{ACCRATE}) = {acc.ToString("N2")}");
WriteLine("Random MSE = " + tset.getRandomMSE(nn, 500));
}
public VNNWrapper(vnn _nn, string _savepath)
{
this.savepath = _savepath;
this.nn = _nn;
this.nncopy = nn.Copy();
tr = new trainerNoMomentum(this.nncopy);
slider = vutils.VSlider.RunAsync();
manip = slider.AddWatch(setLearningRate, learningRate, 0, 0.2, 0.01);
manip.Name = "Learning Rate";
}
public void learnStart(string data_file)
{
open(data_file, out stream, out len, out nn, out nnfile, out dir, out file);
tr = new trainerNoMomentum(nn);
prog = new progressing(this);
reportTh = prog.ReportAsync();
TrainingLoop();
Save();
End = true;
}