// передача сигналов от слоя layer к этому
public void SetInputs(NeuralLayer layer)
{
double[] outputs = layer.GetOutputs();
for (int i = 0; i < inputsSize; i++)
{
inputs[i] = outputs[i];
}
}
// получение массива ошибок относительно следующего слоя
public double[] GetErrors(NeuralLayer nextLayer, double[] nextErrors)
{
double[] errors = new double[outputsSize];
for (int i = 0; i < outputsSize; i++)
{
errors[i] = 0;
for (int j = 0; j < nextErrors.Length; j++)
{
errors[i] += nextErrors[j] * nextLayer.GetWeight(i, j);
}
}
return(errors);
}
public NeuralNetwork(int inputs, int outputs, int[] hiddenNeuronsSize, ActivationFunctionType hiddenActivation = ActivationFunctionType.sigmoid,
ActivationFunctionType outputActivation = ActivationFunctionType.noChange)
{
this.inputs = inputs;
this.outputs = outputs;
this.hiddenNeuronsSize = hiddenNeuronsSize;
inputLayer = new NeuralLayer(inputs, inputs, NeuralLayerType.input, ActivationFunctionType.noChange);
hiddenLayers = new NeuralLayer[hiddenNeuronsSize.Length];
hiddenLayers[0] = new NeuralLayer(inputs, hiddenNeuronsSize[0], NeuralLayerType.hidden, hiddenActivation);
for (int i = 1; i < hiddenNeuronsSize.Length; i++)
{
hiddenLayers[i] = new NeuralLayer(hiddenNeuronsSize[i - 1], hiddenNeuronsSize[i], NeuralLayerType.hidden, hiddenActivation);
}
outputLayer = new NeuralLayer(hiddenNeuronsSize[hiddenNeuronsSize.Length - 1], outputs, NeuralLayerType.output, outputActivation);
}
