public NeuroItem(int num, Action <NeuroSignal> inputDataRequest, ActivationFooType activation)
{
ActivationMethod = activation;
dataSum = new NeuroSignal(num);
dataOut = new NeuroSignal(num);
NeedInput = inputDataRequest;
}
public NeuroLayer(int id, int countIn, int countOut, LayerRole role)
{
sync = new object();
Role = role;
LayerID = id;
nucleus = new Dictionary <int, NeuroItem>();
input = new Dictionary <int, NeuroSignal>();
output = new Dictionary <int, NeuroSignal>();
weights =
Role == LayerRole.Input ? Matrix.Matrix.One(countIn, countOut) :
Role == LayerRole.Output ? Matrix.Matrix.Half(countIn, countOut) :
Matrix.Matrix.Half(countIn, countOut);
ActivationFooType activation =
Role == LayerRole.Input ? ActivationFooType.Identity : ActivationFooType.Sigmoid;
#region -> TMP
if (id == 2)
{
weights[0, 0] = 0.9; weights[0, 1] = 0.3; weights[0, 2] = 0.4;
weights[1, 0] = 0.2; weights[1, 1] = 0.8; weights[1, 2] = 0.2;
weights[2, 0] = 0.1; weights[2, 1] = 0.5; weights[2, 2] = 0.6;
}
if (id == 3)
{
weights[0, 0] = 0.3; weights[0, 1] = 0.7; weights[0, 2] = 0.5;
weights[1, 0] = 0.6; weights[1, 1] = 0.5; weights[1, 2] = 0.2;
weights[2, 0] = 0.8; weights[2, 1] = 0.1; weights[2, 2] = 0.9;
}
#endregion
for (int num = 0; num < countIn; num++)
{
input.Add(num, new NeuroSignal(num));
}
for (int num = 0; num < countOut; num++)
{
NeuroItem ni =
new NeuroItem(num, TransmitInput, activation);
nucleus.Add(num, ni);
output.Add(num, new NeuroSignal(num));
}
}
