public void AddLayer(int size)
{
Layer newLayer = new Layer(this.Owner);
newLayer.Add(size, activation, loss, bias);
if (layers.Count == 0)
{
layers.Add(newLayer);
}
else
{
Layer previousLayer = layers.Last();
layers.Add(newLayer);
switch (connection)
{
case CONNECTIONS.ALL_IN_ALL:
//previousLayer.Synapses.Clear();
foreach (Neuron n1 in previousLayer.Neurons)
{
foreach (Neuron n2 in newLayer.Neurons)
{
Synapse s = new Synapse(this.Owner);
s.previous = n1;
s.next = n2;
s.learnFactor = learnFactor;
newLayer.Synapses.Add(s);
}
}
break;
default:
break;
}
}
}
public string GenerateSaveCode(List <string> values = null, int tab = 0)
{
string value = "";
for (int i = 1; i < tab; i++)
{
value += " ";
}
value += "ID: " + this._id.ToString() + " | Class: " + _ClassName;
if (Owner == null)
{
return("");
}
switch (_ClassName)
{
case "Synapse":
Synapse oSynapse = (Synapse)this;
value += " | Weight: " + oSynapse.weight.ToString();
value += " | Learn Factor: " + oSynapse.learnFactor.ToString();
value += " | Previous: " + oSynapse.previous.id.ToString();
value += " | Next: " + oSynapse.next.id.ToString();
if (values != null)
{
values.Add(value);
}
value += "\n";
break;
case "Neuron":
Neuron oNeuron = (Neuron)this;
value += " | Father: " + oNeuron.father.id.ToString();
value += " | Bias: " + oNeuron.bias.ToString();
value += " | Activation: " + oNeuron.activation.ToString();
value += " | Loss: " + oNeuron.loss.ToString();
if (values != null)
{
values.Add(value);
}
value += "\n";
break;
case "Layer":
Layer oLayer = (Layer)this;
value += " | Activation: " + oLayer.activation.ToString();
value += " | Loss: " + oLayer.loss.ToString();
if (values != null)
{
values.Add(value);
}
value += "\n";
foreach (Neuron item in oLayer.Neurons)
{
value += item.GenerateSaveCode(values, tab + 4);
}
foreach (Synapse item in oLayer.Synapses)
{
value += item.GenerateSaveCode(values, tab + 4);
}
break;
case "NeuralNetwork":
NeuralNetwork oNeuralNetwork = (NeuralNetwork)this;
if (values != null)
{
values.Add(value);
}
value += "\n";
foreach (Layer item in oNeuralNetwork.layers)
{
value += item.GenerateSaveCode(values, tab + 4);
}
break;
}
return(value);
}
public void LoadFromCode(List <string> code)
{
string s;
string s2;
string tag;
string[] ss;
string[] ss2;
Synapse _synapse;
Neuron _neuron;
Layer _layer;
Dictionary <int, NeuralObject> mapping = new Dictionary <int, NeuralObject>();
int _id = -1;
string _class = "";
ACTIVATION _activation = ACTIVATION.SOFTMAX;
LOSS _loss = LOSS.CROSS_ENTROPY;
int _father = -1;
double _bias = 0.00;
double _weight = 0.00;
double _learnfactor = 0.00;
int _previous = -1;
int _next = -1;
int maxID = -1;
for (int i = 0; i < code.Count; i++)
{
s = code[i].Trim();
ss = s.Split('|');
for (int j = 0; j < ss.Length; j++)
{
s = ss[j].Trim();
ss2 = s.Split(':');
s = ss2[0].Trim();
s2 = ss2[1].Trim();
switch (s)
{
default:
throw new System.Exception("unknow parameter");
case "ID":
if (!int.TryParse(s2, out _id))
{
throw new System.Exception("id not integer");
}
break;
case "Class":
_class = s2;
break;
case "Activation":
if (!Enum.TryParse <ACTIVATION>(s2, out _activation))
{
throw new System.Exception("invalid activation");
}
break;
case "Loss":
if (!Enum.TryParse <LOSS>(s2, out _loss))
{
throw new System.Exception("invalid loss");
}
break;
case "Bias":
if (!double.TryParse(s2, out _bias))
{
throw new System.Exception("bias is not double");
}
break;
case "Father":
if (!int.TryParse(s2, out _father))
{
throw new System.Exception("father not integer");
}
break;
case "Weight":
if (!double.TryParse(s2, out _weight))
{
throw new System.Exception("weight is not double");
}
break;
case "Learn Factor":
if (!double.TryParse(s2, out _learnfactor))
{
throw new System.Exception("learn factor is not double");
}
break;
case "Previous":
if (!int.TryParse(s2, out _previous))
{
throw new System.Exception("previous not integer");
}
break;
case "Next":
if (!int.TryParse(s2, out _next))
{
throw new System.Exception("next not integer");
}
break;
}
}
maxID = maxID < _id ? _id : maxID;
_lastid = _id;
switch (_class)
{
case "NeuralNetwork":
break;
case "Layer":
_layer = new Layer(this);
_layer.activation = _activation;
_layer.loss = _loss;
layers.Add(_layer);
mapping.Add(_id, _layer);
break;
case "Neuron":
_neuron = new Neuron(this);
_neuron.father = (Layer)mapping[_father];
_neuron.bias = _bias;
_neuron.activation = _activation;
_neuron.loss = _loss;
layers.Last().Neurons.Add(_neuron);
mapping.Add(_id, _neuron);
break;
case "Synapse":
_synapse = new Synapse(this);
_synapse.weight = _weight;
_synapse.learnFactor = _learnfactor;
_synapse.previous = (Neuron)mapping[_previous];
_synapse.next = (Neuron)mapping[_next];
layers.Last().Synapses.Add(_synapse);
mapping.Add(_id, _synapse);
break;
default:
break;
}
}
_lastid = maxID + 1;
}
