public layer_description(LAYERTYPE Layer_type, int Start_neurons_num,
int Adding_neurons_num = 0, int Dropout_perc = 0, float Neurons_adding_timewait = 1f)
{
layer_type = Layer_type;
start_neurons_num = Start_neurons_num;
adding_neurons_num = Adding_neurons_num;
dropout_perc = Dropout_perc;
neurons_adding_timewait = Neurons_adding_timewait;
}
public static void CheckModelFileType(string filename, out MODELDIRECTION modelDir, out MODELTYPE modelType)
{
using (StreamReader sr = new StreamReader(filename))
{
BinaryReader br = new BinaryReader(sr.BaseStream);
LAYERTYPE layerType = (LAYERTYPE)br.ReadInt32();
modelDir = (MODELDIRECTION)br.ReadInt32();
modelType = (MODELTYPE)br.ReadInt32();
Logger.WriteLine("Model information:");
Logger.WriteLine("File name : '{0}'", filename);
Logger.WriteLine("Direction: '{0}'", modelDir);
Logger.WriteLine("Model type: '{0}'", modelType);
Logger.WriteLine("Layer type: '{0}'", layerType);
}
}
public override void LoadModel(string filename)
{
Logger.WriteLine("Loading SimpleRNN model: {0}", filename);
StreamReader sr = new StreamReader(filename);
BinaryReader br = new BinaryReader(sr.BaseStream);
LAYERTYPE layerType = (LAYERTYPE)br.ReadInt32();
ModelDirection = (MODELDIRECTION)br.ReadInt32();
ModelType = (MODELTYPE)br.ReadInt32();
IsCRFTraining = br.ReadBoolean();
//Create cells of each layer
int layerSize = br.ReadInt32();
HiddenLayerList = new List <SimpleLayer>();
for (int i = 0; i < layerSize; i++)
{
SimpleLayer layer = null;
if (layerType == LAYERTYPE.BPTT)
{
layer = new BPTTLayer();
}
else
{
layer = new LSTMLayer();
}
layer.Load(br);
HiddenLayerList.Add(layer);
}
Logger.WriteLine("Create output layer");
OutputLayer = new SimpleLayer();
OutputLayer.Load(br);
if (IsCRFTraining == true)
{
Logger.WriteLine("Loading CRF tag trans weights...");
CRFTagTransWeights = RNNHelper.LoadMatrix(br);
}
sr.Close();
}
private GameObject InstantiateNeuron(LAYERTYPE l_type)
{
switch (l_type)
{
case (LAYERTYPE.MPL):
return(Instantiate(neuron_l0));
case (LAYERTYPE.CONVOLUTIONAL):
return(Instantiate(kernel_l0));
case (LAYERTYPE.POOLING):
return(Instantiate(pooling_kernel_l0));
case (LAYERTYPE.INPUT):
return(Instantiate(input_kernel_l0));
default:
return(Instantiate(neuron_l0));
}
}
private void Activate(int neuron_id, int layer_id)
{
//show neuron to bhe active
Appear(neuron_id, layer_id);
LAYERTYPE l_type = layer_types[layer_id];
LineRenderer curlr;
//show input links to be active
if (layer_id < layer_types.Count - 1)
{
LAYERTYPE next_l_type = layer_types[layer_id + 1];
if (l_type == LAYERTYPE.CONVOLUTIONAL && next_l_type == LAYERTYPE.POOLING)
{
curlr = layers[layer_id][neuron_id].transform.GetChild(neuron_id).GetComponent <LineRenderer>();
StartCoroutine(Appear(curlr));
}
}
if (layer_id == 1 && layer_types[0] == LAYERTYPE.INPUT)
{
show_input_pattern_convolutional(neuron_id);
}
}
void AddLayer(LAYERTYPE layer_type)
{
int layer_id = layers.Count;
GameObject new_l = new GameObject();
new_l.name = "Layer" + layer_id;
//neurons of layer container
layers.Add(new List <GameObject>());
new_l.transform.parent = MPL.transform;
new_l.transform.position = new Vector3(layer_id * distance_between_layers,
new_l.transform.position.y, new_l.transform.position.z);
//add first neuron to layer
GameObject new_neuron = InstantiateNeuron(layer_type);
new_neuron.name = "n" + layer_id + "0";
new_neuron.transform.parent = new_l.transform;
new_neuron.transform.position = new Vector3(new_l.transform.position.x, new_l.transform.position.y, new_l.transform.position.z);
new_neuron.GetComponent <Renderer>().material.color = GetNeuronColor(layer_id);
layers[layer_id].Add(new_neuron);
StartCoroutine(ChangeColor(new_neuron));
if (layer_id > 0)
{
if (layer_types[layer_id] == LAYERTYPE.POOLING && layer_types[layer_id - 1] == LAYERTYPE.CONVOLUTIONAL)
{
StopCoroutine(ChangeColor(new_neuron));
}
foreach (var n in layers[layer_id - 1])
{
Addlink(n, new_neuron);
}
}
}
public override void LoadModel(string filename)
{
Logger.WriteLine(Logger.Level.info, "Loading bi-directional model: {0}", filename);
using (StreamReader sr = new StreamReader(filename))
{
BinaryReader br = new BinaryReader(sr.BaseStream);
LAYERTYPE layerType = (LAYERTYPE)br.ReadInt32();
ModelDirection = (MODELDIRECTION)br.ReadInt32();
ModelType = (MODELTYPE)br.ReadInt32();
IsCRFTraining = br.ReadBoolean();
int layerSize = br.ReadInt32();
//Load forward layers from file
forwardHiddenLayers = new List <SimpleLayer>();
for (int i = 0; i < layerSize; i++)
{
SimpleLayer layer = null;
if (layerType == LAYERTYPE.BPTT)
{
Logger.WriteLine("Create BPTT hidden layer");
layer = new BPTTLayer();
}
else
{
Logger.WriteLine("Create LSTM hidden layer");
layer = new LSTMLayer();
}
layer.Load(br);
forwardHiddenLayers.Add(layer);
}
//Load backward layers from file
backwardHiddenLayers = new List <SimpleLayer>();
for (int i = 0; i < layerSize; i++)
{
SimpleLayer layer = null;
if (layerType == LAYERTYPE.BPTT)
{
Logger.WriteLine("Create BPTT hidden layer");
layer = new BPTTLayer();
}
else
{
Logger.WriteLine("Create LSTM hidden layer");
layer = new LSTMLayer();
}
layer.Load(br);
backwardHiddenLayers.Add(layer);
}
Logger.WriteLine("Create output layer");
OutputLayer = new SimpleLayer();
OutputLayer.Load(br);
if (IsCRFTraining == true)
{
Logger.WriteLine("Loading CRF tag trans weights...");
CRFTagTransWeights = RNNHelper.LoadMatrix(br);
}
}
}