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.TagFile = strTagFile;
RNNConfig.Tags = tagSet;
RNNConfig.ModelFile = strModelFile;
RNNConfig.HiddenLayerSizeList = hiddenLayerSizeList;
RNNConfig.IsCRFTraining = (iCRF == 1) ? true : false;
RNNConfig.ModelDirection = iDir;
RNNConfig.VQ = iVQ;
RNNConfig.ModelType = ParseLayerType(hiddenLayerType);
RNNConfig.OutputLayerType = ParseLayerType(outputLayerType);
RNNConfig.MaxIteration = maxIter;
RNNConfig.SaveStep = savestep;
RNNConfig.LearningRate = alpha;
RNNConfig.Dropout = dropout;
RNNConfig.Bptt = bptt;
RNNConfig.GradientCutoff = gradientCutoff;
RNNConfig.NCESampleSize = nceSampleSize;
//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;
}
//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);
RNNConfig.TrainDataSet = encoder.TrainingSet;
if (String.IsNullOrEmpty(strValidFile) == false)
{
//LoadFeatureConfig validated corpus and extract feature set
Logger.WriteLine("Loading validated corpus from {0}", strValidFile);
encoder.ValidationSet = new DataSet(tagSet.GetSize());
LoadDataset(strValidFile, featurizer, encoder.ValidationSet);
}
else
{
Logger.WriteLine("Validated corpus isn't specified.");
encoder.ValidationSet = null;
}
if (iCRF == 1)
{
Logger.WriteLine("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()
{
Logger.LogFile = "RNNSharpConsole.log";
if (File.Exists(tagFilePath) == false)
{
Logger.WriteLine(Logger.Level.err, $"FAILED: The tag mapping file {tagFilePath} doesn't exist.");
UsageTrain();
return;
}
//Load tag id and its name from file
var tagSet = new TagSet(tagFilePath);
//Create configuration instance and set parameters
var RNNConfig = new ModelSetting
{
VQ = iVQ,
MaxIteration = maxIter,
SaveStep = savestep,
LearningRate = alpha,
GradientCutoff = gradientCutoff,
IsConstAlpha = constAlpha
};
//Dump RNN setting on console
RNNConfig.DumpSetting();
if (File.Exists(configFilePath) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The feature configuration file {0} doesn't exist.",
configFilePath);
UsageTrain();
return;
}
//Create feature extractors and load word embedding data from file
var config = new Config(configFilePath, tagSet);
config.ShowFeatureSize();
if (File.Exists(trainFilePath) == false)
{
Logger.WriteLine(Logger.Level.err, "FAILED: The training corpus doesn't exist.");
UsageTrain();
return;
}
if (config.ModelType == MODELTYPE.SeqLabel)
{
//Create RNN encoder and save necessary parameters
var encoder = new RNNEncoder <Sequence>(RNNConfig, config)
{
TrainingSet = new DataSet <Sequence>(tagSet.GetSize())
};
//LoadFeatureConfig training corpus and extract feature set
LoadDataset(trainFilePath, config, encoder.TrainingSet);
if (string.IsNullOrEmpty(validFilePath) == false)
{
//LoadFeatureConfig validated corpus and extract feature set
Logger.WriteLine("Loading validated corpus from {0}", validFilePath);
encoder.ValidationSet = new DataSet <Sequence>(tagSet.GetSize());
LoadDataset(validFilePath, config, encoder.ValidationSet);
}
else
{
Logger.WriteLine("Validated corpus isn't specified.");
encoder.ValidationSet = null;
}
if (encoder.IsCRFTraining)
{
Logger.WriteLine("Initialize output tag bigram transition probability...");
//Build tag bigram transition matrix
encoder.TrainingSet.BuildLabelBigramTransition();
}
//Start to train the model
encoder.Train();
}
else
{
//Create RNN encoder and save necessary parameters
var encoder = new RNNEncoder <SequencePair>(RNNConfig, config)
{
TrainingSet = new DataSet <SequencePair>(tagSet.GetSize())
};
//LoadFeatureConfig training corpus and extract feature set
LoadSeq2SeqDataSet(trainFilePath, config, encoder.TrainingSet);
if (string.IsNullOrEmpty(validFilePath) == false)
{
//LoadFeatureConfig validated corpus and extract feature set
Logger.WriteLine("Loading validated corpus from {0}", validFilePath);
encoder.ValidationSet = new DataSet <SequencePair>(tagSet.GetSize());
LoadSeq2SeqDataSet(validFilePath, config, encoder.ValidationSet);
}
else
{
Logger.WriteLine("Validated corpus isn't specified.");
encoder.ValidationSet = null;
}
if (encoder.IsCRFTraining)
{
Logger.WriteLine("Initialize output tag bigram transition probability...");
//Build tag bigram transition matrix
encoder.TrainingSet.BuildLabelBigramTransition();
}
//Start to train the model
encoder.Train();
}
}