public BackPropNetwork(NetworkProperties props, BackPropProperties backProps, Random rnd)
{
this.rnd = rnd; // for Shuffle()
this.backProps = backProps;
this.Network = new NeuralNetwork(props, rnd);
// back-prop related arrays below
this.hGrads = new double[props.NumHidden];
this.oGrads = new double[props.NumOutput];
this.ihPrevWeightsDelta = NetworkData.MakeMatrix(props.NumInput, props.NumHidden);
this.hPrevBiasesDelta = new double[props.NumHidden];
this.hoPrevWeightsDelta = NetworkData.MakeMatrix(props.NumHidden, props.NumOutput);
this.oPrevBiasesDelta = new double[props.NumOutput];
}
public BackPropNetwork(NetworkProperties props, BackPropProperties backProps, Random rnd)
{
this.rnd = rnd; // for Shuffle()
this.backProps = backProps;
this.Network = new NeuralNetwork(props, rnd);
// back-prop related arrays below
this.hGrads = new double[props.NumHidden];
this.oGrads = new double[props.NumOutput];
this.ihPrevWeightsDelta = NetworkData.MakeMatrix(props.NumInput, props.NumHidden);
this.hPrevBiasesDelta = new double[props.NumHidden];
this.hoPrevWeightsDelta = NetworkData.MakeMatrix(props.NumHidden, props.NumOutput);
this.oPrevBiasesDelta = new double[props.NumOutput];
}
static INeuralNetwork BuildBackPropNetwork()
{
var props = new NetworkProperties {
InitWeightMin = -0.1,
InitWeightMax = 0.1,
NumHidden = Globals.gNumHidden, // orig 2
NumInput = Globals.gNumInput, // orig 4
NumOutput = Globals.gNumOutput // orig 3
};
var backProps = new BackPropProperties
{
learnRate = 0.05,
maxEprochs = Globals.gMaxIterations, // orig 2000,
momentum = 0.00,
weightDecay = 0.000,
mseStopCondition = 0.020
};
return new BackPropNetwork(props, backProps, new Random(0));
}
static INeuralNetwork BuildBackPropNetwork()
{
var props = new NetworkProperties {
InitWeightMin = -0.1,
InitWeightMax = 0.1,
NumHidden = 2,
NumInput = 4,
NumOutput = 3
};
var backProps = new BackPropProperties
{
learnRate = 0.05,
maxEprochs = 2000,
momentum = 0.00,
weightDecay = 0.000,
mseStopCondition = 0.020
};
return new BackPropNetwork(props, backProps, new Random(0));
}
