public float[] Training(float []x)
{
BackPropNeuralNet bnn = new BackPropNeuralNet(numInput, numHidden, numOutput);
float[] yValues = new float[numOutput];
float[] tValues = new float[numOutput];
tValues.SetValue(0, 0);
int maxEpochs = 10000;
float errorThresh = 0.00001f;
Console.WriteLine("\nSetting max epochs = " + maxEpochs + " and error threshold = " + errorThresh.ToString("F6"));
int epoch = 0;
double error = double.MaxValue;
Console.WriteLine("\nBeginning training using back-propagation\n");
while (epoch < maxEpochs) // train
{
if (epoch % 20 == 0) Console.WriteLine("epoch = " + epoch);
yValues = bnn.ComputeOutputs(x);
error = Helpers.Error(tValues, yValues);
if (error < errorThresh)
{
Console.WriteLine("Found weights and bias values that meet the error criterion at epoch " + epoch);
break;
}
bnn.UpdateWeights(tValues, learnRate, learnRate);
++epoch;
} // train loop
float[] finalWeights = bnn.GetWeights();
return finalWeights;
}
public float[] Test(float[] weighs,float []x)
{
BackPropNeuralNet b = new BackPropNeuralNet(numInput,numHidden, numOutput);
b.SetWeights(weighs);
return b.ComputeOutputs(x);
}