private static void Train()
{
Logger.LogFile = "RNNSharpConsole.log";
if (File.Exists(strTagFile) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The tag mapping file {0} isn't existed.", strTagFile);
UsageTrain();
return;
}
//Load tag id and its name from file
TagSet tagSet = new TagSet(strTagFile);
//Create configuration instance and set parameters
ModelSetting RNNConfig = new ModelSetting();
RNNConfig.ModelFile = strModelFile;
RNNConfig.NumHidden = layersize;
RNNConfig.IsCRFTraining = (iCRF == 1) ? true : false;
RNNConfig.ModelDirection = iDir;
RNNConfig.ModelType = modelType;
RNNConfig.MaxIteration = maxIter;
RNNConfig.SaveStep = savestep;
RNNConfig.LearningRate = alpha;
RNNConfig.Dropout = dropout;
RNNConfig.Bptt = bptt;
//Dump RNN setting on console
RNNConfig.DumpSetting();
if (File.Exists(strFeatureConfigFile) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The feature configuration file {0} doesn't exist.", strFeatureConfigFile);
UsageTrain();
return;
}
//Create feature extractors and load word embedding data from file
Featurizer featurizer = new Featurizer(strFeatureConfigFile, tagSet);
featurizer.ShowFeatureSize();
if (featurizer.IsRunTimeFeatureUsed() == true && iDir == 1)
{
Logger.WriteLine(Logger.Level.err, "FAILED: Run time feature is not available for bi-directional RNN model.");
UsageTrain();
return;
}
if (File.Exists(strTrainFile) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The training corpus doesn't exist.");
UsageTrain();
return;
}
if (File.Exists(strValidFile) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The validation corpus doesn't exist.");
UsageTrain();
return;
}
//Create RNN encoder and save necessary parameters
RNNEncoder encoder = new RNNEncoder(RNNConfig);
//LoadFeatureConfig training corpus and extract feature set
encoder.TrainingSet = new DataSet(tagSet.GetSize());
LoadDataset(strTrainFile, featurizer, encoder.TrainingSet);
//LoadFeatureConfig validated corpus and extract feature set
encoder.ValidationSet = new DataSet(tagSet.GetSize());
LoadDataset(strValidFile, featurizer, encoder.ValidationSet);
if (iCRF == 1)
{
Logger.WriteLine(Logger.Level.info, "Initialize output tag bigram transition probability...");
//Build tag bigram transition matrix
encoder.TrainingSet.BuildLabelBigramTransition();
}
//Start to train the model
encoder.Train();
}
private static void Train()
{
if (File.Exists(strTagFile) == false)
{
Console.WriteLine("FAILED: The tag mapping file {0} isn't existed.", strTagFile);
UsageTrain();
return;
}
//Load tag id and its name from file
TagSet tagSet = new TagSet(strTagFile);
//Create configuration instance and set parameters
ModelSetting RNNConfig = new ModelSetting();
RNNConfig.SetModelFile(strModelFile);
RNNConfig.SetNumHidden(layersize);
RNNConfig.SetCRFTraining((iCRF == 1) ? true : false);
RNNConfig.SetDir(iDir);
RNNConfig.SetModelType(modelType);
RNNConfig.SetMaxIteration(maxIter);
RNNConfig.SetSaveStep(savestep);
RNNConfig.SetLearningRate(alpha);
RNNConfig.SetRegularization(beta);
RNNConfig.SetBptt(bptt);
//Dump RNN setting on console
RNNConfig.DumpSetting();
if (File.Exists(strFeatureConfigFile) == false)
{
Console.WriteLine("FAILED: The feature configuration file {0} isn't existed.", strFeatureConfigFile);
UsageTrain();
return;
}
//Create feature extractors and load word embedding data from file
Featurizer featurizer = new Featurizer(strFeatureConfigFile, tagSet);
featurizer.ShowFeatureSize();
if (File.Exists(strTrainFile) == false)
{
Console.WriteLine("FAILED: The training corpus {0} isn't existed.", strTrainFile);
UsageTrain();
return;
}
//LoadFeatureConfig training corpus and extract feature set
DataSet dataSetTrain = new DataSet(tagSet.GetSize());
LoadDataset(strTrainFile, featurizer, dataSetTrain);
if (File.Exists(strValidFile) == false)
{
Console.WriteLine("FAILED: The validated corpus {0} isn't existed.", strValidFile);
UsageTrain();
return;
}
//LoadFeatureConfig validated corpus and extract feature set
DataSet dataSetValidation = new DataSet(tagSet.GetSize());
LoadDataset(strValidFile, featurizer, dataSetValidation);
//Create RNN encoder and save necessary parameters
RNNEncoder encoder = new RNNEncoder(RNNConfig);
encoder.SetTrainingSet(dataSetTrain);
encoder.SetValidationSet(dataSetValidation);
if (iCRF == 1)
{
Console.WriteLine("Initialize output tag bigram transition probability...");
//Build tag bigram transition matrix
dataSetTrain.BuildLabelBigramTransition();
encoder.SetLabelBigramTransition(dataSetTrain.GetLabelBigramTransition());
}
//Start to train the model
encoder.Train();
}
