/// <summary>
/// Initializes a new instance of the <see cref="ActivationLayer"/> class.
/// </summary>
///
/// <param name="neuronsCount">Layer's neurons count.</param>
/// <param name="inputsCount">Layer's inputs count.</param>
/// <param name="function">Activation function of neurons of the layer.</param>
///
/// <remarks>The new layer is randomized (see <see cref="ActivationNeuron.Randomize"/>
/// method) after it is created.</remarks>
///
public ActivationLayer( int neuronsCount, int inputsCount, IActivationFunction function )
: base( neuronsCount, inputsCount )
{
// create each neuron
for ( int i = 0; i < neurons.Length; i++ )
neurons[i] = new ActivationNeuron( inputsCount, function );
}
/// <summary>
/// Randomizes (initializes) the weights of
/// the network using Nguyen-Widrow method's.
/// </summary>
///
public void Randomize(int layerIndex)
{
Neuron.RandRange = randRange;
for (int j = 0; j < network.Layers[layerIndex].Neurons.Length; j++)
{
ActivationNeuron neuron = network.Layers[layerIndex].Neurons[j] as ActivationNeuron;
neuron.Randomize();
double norm = 0.0;
// Calculate the Euclidean Norm for the weights
for (int k = 0; k < neuron.Weights.Length; k++)
{
norm += neuron.Weights[k] * neuron.Weights[k];
}
norm += neuron.Threshold * neuron.Threshold;
norm = System.Math.Sqrt(norm);
// Rescale the weights using beta and the norm
for (int k = 0; k < neuron.InputsCount; k++)
{
neuron.Weights[k] = beta * neuron.Weights[k] / norm;
}
neuron.Threshold = beta * neuron.Threshold / norm;
}
}
/// <summary>
/// Initializes a new instance of the <see cref="ActivationLayer"/> class.
/// </summary>
///
/// <param name="neuronsCount">Layer's neurons count.</param>
/// <param name="inputsCount">Layer's inputs count.</param>
/// <param name="function">Activation function of neurons of the layer.</param>
///
/// <remarks>The new layer is randomized (see <see cref="ActivationNeuron.Randomize"/>
/// method) after it is created.</remarks>
///
public ActivationLayer(int neuronsCount, int inputsCount, IActivationFunction function)
: base(neuronsCount, inputsCount)
{
// create each neuron
for (int i = 0; i < neurons.Length; i++)
{
neurons[i] = new ActivationNeuron(inputsCount, function);
}
}
