/// <summary>
/// https://en.wikipedia.org/wiki/Artificial_neuron
/// input1*weight1
/// input1*weight2
/// bias(+1*weight)
/// </summary>
/// <param name="n_inputs"></param>
/// <param name="activation"></param>
public NeuronNode(int n_inputs, string activation)
{
this.n_inputs = n_inputs;
this.multiplyNodes = new List <MultiplyNode>(); //for inputs and weights
this.sumNode = new SumNode(); //for sum of inputs*weights
this.previous_deltas = new List <double>(); // values of las iteration
this.gradients = new List <List <double> >(); //local gradients
MultiplyNode mulNode;
//init inputs
//collect inputs and corresponding weights
for (int i = 0; i < this.n_inputs; i++)
{
mulNode = new MultiplyNode();
double b = grnd.NextGaussian(0.3, 0.5);
mulNode.x = new List <double>()
{
1.0, b
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
}
//init bias node and weight
mulNode = new MultiplyNode();
double m = grnd.NextGaussian(0.0, 0.01);
mulNode.x = new List <double>()
{
1.0, m
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
if (activation.Equals("sigmoid"))
{
this.activation_node = new SigmoidNode();
}
if (activation.Equals("relu"))
{
this.activation_node2 = new RelUNode();
}
else
{
throw new NotImplementedException("Activation function is not supported");
}
this.activation = activation;
}
private static GaussianRandom grnd = new GaussianRandom(); //gausova raspodela za random vrednosti
/// <summary>
/// https://en.wikipedia.org/wiki/Artificial_neuron
/// ulaz1*tezina1
/// ulaz1*tezina2
/// bias(+1*tezina)
/// sve se sabere
/// provuce se kroz aktivacionu funkciju
/// </summary>
/// <param name="n_inputs"></param>
/// <param name="activation"> String naziv aktivacione funkcije </param>
public NeuronNode(int n_inputs, string activation)
{
this.n_inputs = n_inputs;
this.multiplyNodes = new List <MultiplyNode>(); //for inputs and weights
this.sumNode = new SumNode(); //for sum of inputs*weights
this.previous_deltas = new List <double>(); //vrednost delti u prosloj iteraciji
this.gradients = new List <List <double> >(); //lokalni gradijenti
MultiplyNode mulNode;
//init inputs
//collect inputs and corresponding weights
for (int i = 0; i < this.n_inputs; i++)
{
mulNode = new MultiplyNode();
double b = grnd.NextGaussian(0.3, 0.5);
mulNode.x = new List <double>()
{
1.0, b
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
}
//init bias node and weight
mulNode = new MultiplyNode();
double m = grnd.NextGaussian(0.0, 0.01);
mulNode.x = new List <double>()
{
1.0, m
};
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
// TODO: Ovde dodajem ako dodajem neku drugu aktivacionu funkciju
/*
* if else(activation.Equals("TangesHiperbolic").....
*/
if (activation.Equals("sigmoid"))
{
this.activation_node = new SigmoidNode();
}
else
{
throw new NotImplementedException("Activation function is not supported");
}
}
/// <summary>
/// https://en.wikipedia.org/wiki/Artificial_neuron
/// ulaz1*tezina1
/// ulaz1*tezina2
/// bias(+1*tezina)
/// sve se sabere
/// provuce se kroz aktivacionu funkciju
/// </summary>
/// <param name="n_inputs"></param>
/// <param name="activation"></param>
public NeuronNode(int n_inputs, string activation)
{
this.n_inputs = n_inputs;
this.multiplyNodes = new List <MultiplyNode>(); //for inputs and weights
this.sumNode = new SumNode(); //for sum of inputs*weights
this.previous_deltas = new List <double>(); //vrednost delti u prosloj iteraciji
this.gradients = new List <List <double> >(); //lokalni gradijenti
MultiplyNode mulNode;
//init inputs
//collect inputs and corresponding weights
for (int i = 0; i < this.n_inputs; i++)
{
mulNode = new MultiplyNode();
mulNode.x = new List <double>()
{
1.0, (rnd.NextDouble() - 0.5) / 50.0
}; //izemdju -0.1 i 0.1
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
}
//init bias node and weight
mulNode = new MultiplyNode();
mulNode.x = new List <double>()
{
1.0, (rnd.NextDouble() - 0.5 / 50.0)
}; // -0.01 do 0.01
this.multiplyNodes.Add(mulNode);
previous_deltas.Add(0.0);
if (activation.Equals("sigmoid"))
{
this.activation_node = new SigmoidNode();
}
else
{
throw new NotImplementedException("Activation function is not supported");
}
}