private void SetPretrainedModel()
{
//Load pre-trained model. It supports embedding model and auto-encoder model
var preTrainTypeValue = config.GetValueRequired(PRETRAIN_TYPE);
Logger.WriteLine("Pretrain type: {0}", preTrainTypeValue);
if (preTrainTypeValue.Equals(RNNSharp.PRETRAIN_TYPE.AutoEncoder.ToString(),
StringComparison.InvariantCultureIgnoreCase))
{
preTrainType = RNNSharp.PRETRAIN_TYPE.AutoEncoder;
var autoEncoderConfigFilePath = GetFilePath(currentDirectory,
config.GetValueRequired(AUTOENCODER_CONFIG));
Logger.WriteLine($"Loading auto encoder model. Config file = '{autoEncoderConfigFilePath}'");
autoEncoder = InitializeAutoEncoder(autoEncoderConfigFilePath);
}
else
{
preTrainType = RNNSharp.PRETRAIN_TYPE.Embedding;
var preTrainedModelFilePath = config.GetValueOptional(PRETRAINEDMODEL_FILENAME);
if (string.IsNullOrEmpty(preTrainedModelFilePath) == false)
{
preTrainedModelFilePath = GetFilePath(currentDirectory, preTrainedModelFilePath);
if (preTrainedModel != null)
{
throw new ArgumentException(
"Static pretrained model has already been loaded. Please check if settings is duplicated in configuration file.");
}
Logger.WriteLine($"Loading pretrained embedding model: {preTrainedModelFilePath}");
preTrainedModel = new WordEMWrapFeaturizer(preTrainedModelFilePath);
}
var preTrainedRawModelFilePath = config.GetValueOptional(PRETRAINEDMODEL_RAW_FILENAME);
if (string.IsNullOrEmpty(preTrainedRawModelFilePath) == false)
{
preTrainedRawModelFilePath = GetFilePath(currentDirectory, preTrainedRawModelFilePath);
if (preTrainedModel != null)
{
throw new ArgumentException(
"Static pretrained model has already been loaded. Please check if settings is duplicated in configuration file.");
}
Logger.WriteLine($"Loading pretrained embedding model {preTrainedRawModelFilePath} in text format");
preTrainedModel = new WordEMWrapFeaturizer(preTrainedRawModelFilePath, true);
}
preTrainedModelColumn = int.Parse(config.GetValueRequired(PRETRAINEDMODEL_COLUMN));
Logger.WriteLine("Pretrained model feature column: {0}", preTrainedModelColumn);
var preTrainedModelContext = config.GetValueRequired(WORDEMBEDDING_CONTEXT);
featureContext.Add(WORDEMBEDDING_CONTEXT, new List <int>());
foreach (var contextOffset in preTrainedModelContext.Split(','))
{
featureContext[WORDEMBEDDING_CONTEXT].Add(int.Parse(contextOffset));
}
Logger.WriteLine($"Pretrained model context offset : {preTrainedModelContext}");
}
}
//The format of configuration file
public void LoadFeatureConfigFromFile(string strFileName)
{
StreamReader sr = new StreamReader(strFileName);
string strLine = null;
FeatureContext = new Dictionary <string, List <int> >();
while ((strLine = sr.ReadLine()) != null)
{
strLine = strLine.Trim();
if (strLine.Length == 0)
{
//Emtpy line, ignore it
continue;
}
if (strLine.StartsWith("#") == true)
{
//Comments line, ignore it
continue;
}
int idxSeparator = strLine.IndexOf(':');
string strKey = strLine.Substring(0, idxSeparator).Trim();
string strValue = strLine.Substring(idxSeparator + 1).Trim();
if (strKey == PRETRAINEDMODEL_FILENAME)
{
if (PretainedModel != null)
{
throw new ArgumentException("Static pretrained model has already been loaded. Please check if settings is duplicated in configuration file.");
}
Logger.WriteLine("Loading pretrained dense feature set from model {0}", strValue);
PretainedModel = new WordEMWrapFeaturizer(strValue);
}
else if (strKey == PRETRAINEDMODEL_RAW_FILENAME)
{
if (PretainedModel != null)
{
throw new ArgumentException("Static pretrained model has already been loaded. Please check if settings is duplicated in configuration file.");
}
Logger.WriteLine("Loading pretrained dense feature set from model {0} in text format", strValue);
PretainedModel = new WordEMWrapFeaturizer(strValue, true);
}
else if (strKey == TFEATURE_FILENAME)
{
Logger.WriteLine("Loading template feature set...");
TFeaturizer = new TemplateFeaturizer(strValue);
}
else if (strKey == PRETRAINEDMODEL_COLUMN)
{
PretrainedModelColumn = int.Parse(strValue);
Logger.WriteLine("Pretrained model feature column: {0}", PretrainedModelColumn);
}
else if (strKey == TFEATURE_WEIGHT_TYPE)
{
Logger.WriteLine("TFeature weighting type: {0}", strValue);
if (strValue == "binary")
{
TFeatureWeightType = TFEATURE_WEIGHT_TYPE_ENUM.BINARY;
}
else
{
TFeatureWeightType = TFEATURE_WEIGHT_TYPE_ENUM.FREQUENCY;
}
}
else if (strKey == PRETRAIN_TYPE)
{
if (strValue.Equals(RNNSharp.PRETRAIN_TYPE.AUTOENCODER.ToString(), StringComparison.InvariantCultureIgnoreCase))
{
preTrainType = RNNSharp.PRETRAIN_TYPE.AUTOENCODER;
}
else
{
preTrainType = RNNSharp.PRETRAIN_TYPE.EMBEDDING;
}
Logger.WriteLine("Pretrain type: {0}", preTrainType);
}
else if (strKey == AUTOENCODER_FEATURECONFIG)
{
autoEncoderFeatureConfigFile = strValue;
Logger.WriteLine("Auto encoder configuration file: {0}", autoEncoderFeatureConfigFile);
}
else if (strKey == AUTOENCODER_MODEL)
{
autoEncoderModelFile = strValue;
Logger.WriteLine("Auto encoder model file: {0}", autoEncoderModelFile);
}
else
{
string[] values = strValue.Split(',');
if (FeatureContext.ContainsKey(strKey) == false)
{
FeatureContext.Add(strKey, new List <int>());
}
foreach (string value in values)
{
FeatureContext[strKey].Add(int.Parse(value));
}
}
}
sr.Close();
}