public Vector Run(Vector input)
{
CheckInput(input, InputNeurons);
InputNeurons = input;
OutputNeurons = Activation.Func(Weights * InputNeurons + Bias);
return(OutputNeurons);
}
public (Vector output, Vector error) Learn(Vector input, Vector ideal)
{
CheckIdealVector(ideal, OutputNeurons);
var actual = Run(input);
var error = new Vector(actual.Length);
for (var i = 0; i < ideal.Length; i++)
{
ideal[i] = Activation.Func(ideal[i]);
error[i] = GetError(actual[i], ideal[i]);
}
LearnWithBackPropagationError(actual, ideal);
SetNextEpoch();
return(actual, error);
}
/// <summary>
/// Calculates the output vector of the neural network
/// </summary>
public Vector Run(Vector input)
{
InitializeNeuronsWithInput(input);
for (var i = 0; i < Neurons.Length - 1; i++)
{
for (int j = 0; j < Neurons[i + 1].Length; j++)
{
for (int k = 0; k < Neurons[i].Length; k++)
{
Neurons[i + 1][j] += Weights[i][j][k] * Neurons[i][k];
}
Neurons[i + 1][j] = Activation.Func(Neurons[i + 1][j]);
}
}
return(Neurons.Last());
}