public void CreateSynapsisNetwork(InputLayer il)
{
int k = 0;
for (int i = 0; i < Neurons.Length; i++)
{
for (int j = 0; j < il.Neurons.Length; j++)
{
Synapse temp = new Synapse(WeightRecords[k], il.Neurons[j], Neurons[i]);
il.Neurons[j].AddSynapsis(temp);
Neurons[i].AddSynapsis(temp, false);
k++;
}
}
}
public void Init_CreateSynapsisNetwork(InputLayer il)
{
Random rn = new Random();
for (int i = 0; i < Neurons.Length; i++)
{
for (int j = 0; j < il.Neurons.Length; j++)
{
float weightTemp = ((float)rn.Next(-10, 10)) / 10.0f;
Synapse temp = new Synapse(weightTemp, il.Neurons[j], Neurons[i]);
il.Neurons[j].AddSynapsis(temp);
Neurons[i].AddSynapsis(temp, false);
WeightRecords.Add(weightTemp);
}
}
DataStream.Instance.WriteWBOnFile(WeightRecords, SynapsesFile);
}
public Network(List <int> layers, bool init)
{
ILayer = new InputLayer(layers[0]);
OLayer = new OutputLayer(layers[layers.Count - 1], "Weights/s_outputL.json", "Biases/b_outputL.json", init);
HLayers = new List <HiddenLayer>();
for (int i = 1; i < layers.Count - 1; i++)
{
HLayers.Add(new HiddenLayer(layers[i], "Weights/s_layer" + i + ".json", "Biases/b_layer" + i + ".json", init));
}
NumberOfLayers = HLayers.Count + 2;
Costs = new List <float>();
OLayer.OutputAsDigits();
if (init)
{
Init_CreateSynapseNetworks();
}
else
{
CreateSynapseNetworks();
}
}
