private void Train()
{
//Layer 0
List <NeuralNetwork> hiddenLayer1 = neuralNetwork.FindAll(x => x.LayerNumber == 0);
double[] weightedSum1 = new double[20];
for (int j = 0; j < hiddenLayer1.Count; j++)
{
weightedSum1[j] = perceptron.Execute(analyserEntry.Layer1Weights[j], publicAddressDouble, hiddenLayer1[j].Bias);
}
for (int k = 0; k < weightedSum1.Length; k++)
{
weightedSum1[k] = activationFunctions.LeakyReLU(weightedSum1[k]);
}
//Layer 1
List <NeuralNetwork> hiddenLayer2 = neuralNetwork.FindAll(x => x.LayerNumber == 1);
double[] weightedSum2 = new double[64];
for (int j = 0; j < hiddenLayer2.Count; j++)
{
weightedSum2[j] = perceptron.Execute(analyserEntry.Layer2Weights[j], weightedSum1, hiddenLayer2[j].Bias);
}
for (int k = 0; k < weightedSum2.Length; k++)
{
weightedSum2[k] = activationFunctions.LeakyReLU(weightedSum2[k]);
}
//Layer 2
List <NeuralNetwork> hiddenLayer3 = neuralNetwork.FindAll(x => x.LayerNumber == 2);
double[] weightedSum3 = new double[128];
for (int j = 0; j < hiddenLayer3.Count; j++)
{
weightedSum3[j] = perceptron.Execute(analyserEntry.Layer3Weights[j], weightedSum2, hiddenLayer3[j].Bias);
}
for (int k = 0; k < weightedSum3.Length; k++)
{
weightedSum3[k] = activationFunctions.LeakyReLU(weightedSum3[k]);
}
//Output Layer
List <NeuralNetwork> outputLayer = neuralNetwork.FindAll(x => x.LayerNumber == 3);
double[] weightedSum4 = new double[256];
for (int j = 0; j < outputLayer.Count; j++)
{
weightedSum4[j] = perceptron.Execute(analyserEntry.Layer4Weights[j], weightedSum3, outputLayer[j].Bias);
}
for (int k = 0; k < weightedSum4.Length; k++)
{
weightedSum4[k] = activationFunctions.BinaryStep(weightedSum4[k]);
}
Assess(weightedSum4);
}
public float activation(bool derivate = false)
{
if (type == PerceptronType.Type.input || type == PerceptronType.Type.bias)
{
return(state);
}
if (activation_type == ActivationType.Type.sigmoid)
{
return(ActivationFunctions.Sigmoid(state, derivate));
}
else if (activation_type == ActivationType.Type.relu)
{
return(ActivationFunctions.RelU(state, derivate));
}
else if (activation_type == ActivationType.Type.tanh)
{
return(ActivationFunctions.TanH(state, derivate));
}
else if (activation_type == ActivationType.Type.identity)
{
return(ActivationFunctions.Identity(state, derivate));
}
else if (activation_type == ActivationType.Type.lrelu)
{
return(ActivationFunctions.LeakyReLU(state, derivate));
}
else
{
return(ActivationFunctions.Sigmoid(state, derivate));
}
}
