public static GRNNLayer readGRNN(string path)
{
FileStream fs = new FileStream(path, FileMode.Open);
BinaryFormatter bf = new BinaryFormatter();
GRNNLayer ps = bf.Deserialize(fs) as GRNNLayer;
fs.Close();
return(ps);
}
public static void Run()
{
System.DateTime currentTime = System.DateTime.Now;
readword();
Global.inputDim = 100;
Global.hiddenDim = 100;
readfourword();
Global.randn = new Normal();
DataSet X = new DataSet();
if (Global.isRead == 1)
{
string path = "model//deepNetwork";
Global.upLSTMLayer = LSTMLayer.readLSTM(path + "//lstmmodel.txt");
Global.upLSTMLayerr = LSTMLayer.readLSTM(path + "//lstmmodelr.txt");
Global.GRNNLayer1 = GRNNLayer.readGRNN(path + "//grnnmodel1.txt");
Global.GRNNLayer2 = GRNNLayer.readGRNN(path + "//grnnmodel2.txt");
Global.GRNNLayer3 = GRNNLayer.readGRNN(path + "//grnnmodel3.txt");
Global.GRNNLayer4 = GRNNLayer.readGRNN(path + "//grnnmodel4.txt");
Global.feedForwardLayer = FeedForwardLayer.readFF(path + "//feedforwardmodel.txt");
Global.wordEmbedding = LSTMLayer.getSerializeWordembedding(path + "//embedding.txt", Global.wordEmbedding);
}
else
{
Global.GRNNLayer1 = new GRNNLayer();
Global.GRNNLayer2 = new GRNNLayer();
Global.GRNNLayer3 = new GRNNLayer();
Global.GRNNLayer4 = new GRNNLayer();
Global.upLSTMLayer = new LSTMLayer();
Global.upLSTMLayerr = new LSTMLayer();
Global.feedForwardLayer = new FeedForwardLayer();
}
Trainer.train(X);
Global.swLog.Close();
System.DateTime currentTime_1 = System.DateTime.Now;
Console.WriteLine(currentTime_1 - currentTime);
Console.Read();
}
static void Main(string[] args)
{
Console.WriteLine("Choose running mode: 1. training, 2. testing");
string mode = Console.ReadLine();
if (mode == "1")
{
Global.mode = "train";
}
else if (mode == "2")
{
Global.mode = "test";
}
//Console.WriteLine("Choose reading mode: 1. read saved model, 2. read model trained on MSR dataset.");
//string read = Console.ReadLine();
//string modes = Console.ReadLine();
//if (modes == "1")
//{
// Global.isRead = true;
//}
//else if (modes == "2")
//{
// Global.isRead = false;
//}
Console.WriteLine("Choose the bigram feature mode: 1. read bigram features, 2. create bigram features");
string bigramfeature = Console.ReadLine();
if (bigramfeature == "1")
{
Global.isReadBigramfeature = true;
}
else if (bigramfeature == "2")
{
Global.isReadBigramfeature = false;
}
readword();
readbigramword();
readidoimword();
Global.randn = new Normal();
Global._UpLSTMLayer = LSTMLayer.readLSTM("model\\lstmmodel.txt");
Global._UpLSTMLayerr = LSTMLayer.readLSTM("model\\lstmmodelr.txt");
// Global._LSTMLayerr = LSTMLayer.readLSTM("model\\lstmmodelr.txt");
Global._GRNNLayer1 = GRNNLayer.readGRNN("model\\grnnmodel1.txt");
Global._GRNNLayer2 = GRNNLayer.readGRNN("model\\grnnmodel2.txt");
Global._GRNNLayer3 = GRNNLayer.readGRNN("model\\grnnmodel3.txt");
Global._GRNNLayer4 = GRNNLayer.readGRNN("model\\grnnmodel4.txt");
Global._feedForwardLayer = FeedForwardLayer.readFF("model\\feedforwardmodel.txt");
LSTMLayer.getSerializeWordembedding();
LSTMLayer.getSerializeBigramWordembedding();
DataSet X = new DataSet();
Trainer.train(X);
Global.swLog.Close();
}