public Layer(LayerCreationInfo layerCreationInfo, bool IsNetworkUsingBias, LayerType type, double minWeight, double maxWeight)
{
LayerNo = layerCreationInfo.LayerNo;
PreviousLayerNeuronsCount = layerCreationInfo.PreviousLayerNeuronsCount;
LayerActivationFunction = layerCreationInfo.LayerActivationFunction;
this.Type = type;
this.MinWeight = minWeight;
this.MaxWeight = maxWeight;
Neurons = new List <Neuron>();
for (int i = 0; i < layerCreationInfo.HowManyNeuronsPerLayer; i++)
{
Neurons.Add(new Neuron(
LayerNo == 0 ? 1 : PreviousLayerNeuronsCount,
layerCreationInfo.LayerNo,
false,
layerCreationInfo.LayerActivationFunction,
MinWeight,
MaxWeight));
}
if (IsNetworkUsingBias && type != LayerType.OUTPUT)
{
Neurons.Last().IsBias = true;
}
}
public static NeuralNetwork LoadNetworkFromFile(String filename)
{
NeuralNetwork neuralNetwork = null;
try
{
StreamReader sr = new StreamReader(filename);
String str = sr.ReadLine(); // headers
String[] strTab;
str = sr.ReadLine();
strTab = str.Split(';');
bool isNetworkUsingBias = bool.Parse(strTab[1]);
str = sr.ReadLine();
strTab = str.Split(';');
double minWeight = double.Parse(strTab[1]);
str = sr.ReadLine();
strTab = str.Split(';');
double maxWeight = double.Parse(strTab[1]);
str = sr.ReadLine();
strTab = str.Split(';');
LearningMethod methodOfLearning = strTab[1] == "0" ? LearningMethod.LINEAR : LearningMethod.NOT_LINEAR;
str = sr.ReadLine();
strTab = str.Split(';');
String name = strTab[1];
String[] layersNeuronsStr = sr.ReadLine().Split(';'); // neurons in layers
String[] layersActivationFunctionStr = sr.ReadLine().Split(';'); // neurons in layers
List <LayerCreationInfo> layerCreationInfos = new List <LayerCreationInfo>();
for (int i = 1; i < layersNeuronsStr.Length; i++)
{
int layerIdx = i - 1;
LayerCreationInfo lci = new LayerCreationInfo();
lci.HowManyNeuronsPerLayer = int.Parse(layersNeuronsStr[i]);
lci.LayerNo = layerIdx;
lci.PreviousLayerNeuronsCount = layerIdx == 0 ? 0 : layerCreationInfos[layerIdx - 1].HowManyNeuronsPerLayer;
int LayerActivationFunctionInt = int.Parse(layersActivationFunctionStr[i]);
lci.LayerActivationFunction = GetActivationFunctionById(LayerActivationFunctionInt);
layerCreationInfos.Add(lci);
}
Topology topology = new Topology(layerCreationInfos, isNetworkUsingBias, minWeight, maxWeight);
neuralNetwork = new NeuralNetwork(topology, minWeight, maxWeight, methodOfLearning, name);
// provide saved neurons weights:
for (int layerNo = 0; layerNo < neuralNetwork.Topology.Layers.Count; layerNo++)
{
Layer layer = neuralNetwork.Topology.Layers[layerNo];
for (int neuronNo = 0; neuronNo < layer.Neurons.Count; neuronNo++)
{
String[] inputsString = sr.ReadLine().Split(';');
for (int inputNo = 0; inputNo < layer.Neurons[neuronNo].Inputs.Count; inputNo++)
{
layer.Neurons[neuronNo].Inputs[inputNo].Weight = double.Parse(inputsString[inputNo + 1]);
}
}
}
neuralNetwork.PropagateValuesForward(); // maybe not needed now, but for order it's good to get network in proper state.
return(neuralNetwork);
}
catch (Exception ex)
{
Console.WriteLine("Error while loading network: " + ex.Message);
return(null);
}
}