public OutputLayer(Activation activation, TrainingInfo trainInfo, int size)
{
var neurons = new OutputNeuron[size];
for (int i = 0; i < size; i++)
neurons[i] = new OutputNeuron(activation, trainInfo);
this.neurons = neurons;
}
public HiddenLayer(Activation activation, TrainingInfo trainInfo, int size)
{
var neurons = new Neuron[size + 1];
for (int i = 0; i < size; i++)
neurons[i] = new HiddenNeuron(activation, trainInfo);
neurons[size] = new BiasNeuron();
this.neurons = neurons;
}
public Network(Activation activation, TrainingInfo trainInfo, int inputSize, int[] hiddenSizes, int outputSize)
{
this.InputSize = inputSize;
this.HiddenSizes = hiddenSizes;
this.OutputSize = outputSize;
this.activation = activation;
this.inputLayer = new InputLayer(inputSize);
this.hiddenLayers = hiddenSizes
.Select(size => new HiddenLayer(activation, trainInfo, size))
.ToArray();
this.outputLayer = new OutputLayer(activation, trainInfo, outputSize);
ConnectLayers();
}
public Neuron(Activation activation, TrainingInfo trainInfo)
{
this.activation = activation;
this.trainInfo = trainInfo;
}
public HiddenNeuron(Activation activation, TrainingInfo trainInfo)
: base(activation, trainInfo) { }
public OutputNeuron(Activation activation, TrainingInfo trainInfo)
: base(activation, trainInfo) { }
public HiddenNeuron(Activation activation, TrainingInfo trainInfo)
: base(activation, trainInfo)
{
}
public OutputNeuron(Activation activation, TrainingInfo trainInfo)
: base(activation, trainInfo)
{
}
public Neuron(Activation activation, TrainingInfo trainInfo)
{
this.activation = activation;
this.trainInfo = trainInfo;
}