public override void LoadModel(string filename)
{
Logger.WriteLine(Logger.Level.info, "Loading bi-directional model: {0}", filename);
forwardRNN.LoadModel(filename + ".forward");
backwardRNN.LoadModel(filename + ".backward");
Hidden2OutputWeight = forwardRNN.Hidden2OutputWeight;
CRFTagTransWeights = forwardRNN.CRFTagTransWeights;
using (StreamReader sr = new StreamReader(filename))
{
BinaryReader br = new BinaryReader(sr.BaseStream);
ModelType = (MODELTYPE)br.ReadInt32();
ModelDirection = (MODELDIRECTION)br.ReadInt32();
int iflag = br.ReadInt32();
if (iflag == 1)
{
IsCRFTraining = true;
}
else
{
IsCRFTraining = false;
}
//Load basic parameters
L0 = br.ReadInt32();
L1 = br.ReadInt32();
L2 = br.ReadInt32();
DenseFeatureSize = br.ReadInt32();
}
}
public RNNDecoder(Config config)
{
Config = config;
if (Config.ModelDirection == MODELDIRECTION.BiDirectional)
{
Logger.WriteLine("Model Structure: Bi-directional RNN");
rnn = new BiRNN <Sequence>();
}
else
{
Logger.WriteLine("Model Structure: Simple RNN");
rnn = new ForwardRNN <Sequence>();
}
rnn.LoadModel(config.ModelFilePath);
Logger.WriteLine("CRF Model: {0}", rnn.IsCRFTraining);
}
public RNNDecoder(Config config)
{
Config = config;
RNN <Sequence> rnn = RNN <Sequence> .CreateRNN(Config.NetworkType);
rnn.LoadModel(config.ModelFilePath);
rnn.MaxSeqLength = config.MaxSequenceLength;
Logger.WriteLine("CRF Model: {0}", rnn.IsCRFTraining);
Logger.WriteLine($"Max Sequence Length: {rnn.MaxSeqLength}");
Logger.WriteLine($"Processor Count: {Environment.ProcessorCount}");
qRNNs = new ConcurrentQueue <RNN <Sequence> >();
for (var i = 0; i < Environment.ProcessorCount; i++)
{
qRNNs.Enqueue(rnn.Clone());
}
}
public RNNDecoder(string strModelFileName, Featurizer featurizer)
{
MODELDIRECTION modelDir = MODELDIRECTION.FORWARD;
RNNHelper.CheckModelFileType(strModelFileName, out modelDir);
if (modelDir == MODELDIRECTION.BI_DIRECTIONAL)
{
Logger.WriteLine("Model Structure: Bi-directional RNN");
m_Rnn = new BiRNN();
}
else
{
Logger.WriteLine("Model Structure: Simple RNN");
m_Rnn = new ForwardRNN();
}
m_Rnn.LoadModel(strModelFileName);
Logger.WriteLine("CRF Model: {0}", m_Rnn.IsCRFTraining);
m_Featurizer = featurizer;
}
public RNNDecoder(string strModelFileName)
{
MODELDIRECTION modelDir = MODELDIRECTION.FORWARD;
MODELTYPE modelType;
RNNHelper.CheckModelFileType(strModelFileName, out modelDir, out modelType);
if (modelDir == MODELDIRECTION.BI_DIRECTIONAL)
{
Logger.WriteLine("Model Structure: Bi-directional RNN");
rnn = new BiRNN <Sequence>();
}
else
{
Logger.WriteLine("Model Structure: Simple RNN");
rnn = new ForwardRNN <Sequence>();
}
ModelType = modelType;
rnn.LoadModel(strModelFileName);
Logger.WriteLine("CRF Model: {0}", rnn.IsCRFTraining);
}
public RNNDecoder(string strModelFileName, Featurizer featurizer)
{
MODELTYPE modelType = MODELTYPE.SIMPLE;
MODELDIRECTION modelDir = MODELDIRECTION.FORWARD;
RNN.CheckModelFileType(strModelFileName, out modelType, out modelDir);
if (modelDir == MODELDIRECTION.BI_DIRECTIONAL)
{
Logger.WriteLine("Model Structure: Bi-directional RNN");
if (modelType == MODELTYPE.SIMPLE)
{
m_Rnn = new BiRNN(new SimpleRNN(), new SimpleRNN());
}
else
{
m_Rnn = new BiRNN(new LSTMRNN(), new LSTMRNN());
}
}
else
{
if (modelType == MODELTYPE.SIMPLE)
{
Logger.WriteLine("Model Structure: Simple RNN");
m_Rnn = new SimpleRNN();
}
else
{
Logger.WriteLine("Model Structure: LSTM-RNN");
m_Rnn = new LSTMRNN();
}
}
m_Rnn.LoadModel(strModelFileName);
Logger.WriteLine("CRF Model: {0}", m_Rnn.IsCRFTraining);
m_Featurizer = featurizer;
}
public void Train()
{
//Create neural net work
Logger.WriteLine("Create a new network according settings in configuration file.");
Logger.WriteLine($"Processor Count = {Environment.ProcessorCount}");
RNN <T> rnn = RNN <T> .CreateRNN(networkType);
if (ModelSettings.IncrementalTrain)
{
Logger.WriteLine($"Loading previous trained model from {modelFilePath}.");
rnn.LoadModel(modelFilePath, true);
}
else
{
Logger.WriteLine("Create a new network.");
rnn.CreateNetwork(hiddenLayersConfig, outputLayerConfig, TrainingSet, featurizer);
//Create tag-bigram transition probability matrix only for sequence RNN mode
if (IsCRFTraining)
{
Logger.WriteLine("Initialize bigram transition for CRF output layer.");
rnn.InitializeCRFWeights(TrainingSet.CRFLabelBigramTransition);
}
}
rnn.MaxSeqLength = maxSequenceLength;
rnn.bVQ = ModelSettings.VQ != 0 ? true : false;
rnn.IsCRFTraining = IsCRFTraining;
int N = Environment.ProcessorCount * 2;
List <RNN <T> > rnns = new List <RNN <T> >();
rnns.Add(rnn);
for (int i = 1; i < N; i++)
{
rnns.Add(rnn.Clone());
}
//Initialize RNNHelper
RNNHelper.LearningRate = ModelSettings.LearningRate;
RNNHelper.vecNormalLearningRate = new Vector <float>(RNNHelper.LearningRate);
RNNHelper.GradientCutoff = ModelSettings.GradientCutoff;
RNNHelper.vecMaxGrad = new Vector <float>(RNNHelper.GradientCutoff);
RNNHelper.vecMinGrad = new Vector <float>(-RNNHelper.GradientCutoff);
RNNHelper.IsConstAlpha = ModelSettings.IsConstAlpha;
RNNHelper.MiniBatchSize = ModelSettings.MiniBatchSize;
Logger.WriteLine("");
Logger.WriteLine("Iterative training begins ...");
var bestTrainTknErrCnt = long.MaxValue;
var bestValidTknErrCnt = long.MaxValue;
var lastAlpha = RNNHelper.LearningRate;
var iter = 0;
ParallelOptions parallelOption = new ParallelOptions();
parallelOption.MaxDegreeOfParallelism = -1;
while (true)
{
if (ModelSettings.MaxIteration > 0 && iter > ModelSettings.MaxIteration)
{
Logger.WriteLine("We have trained this model {0} iteration, exit.");
break;
}
var start = DateTime.Now;
Logger.WriteLine($"Start to training {iter} iteration. learning rate = {RNNHelper.LearningRate}");
//Clean all RNN instances' status for training
foreach (var r in rnns)
{
r.CleanStatusForTraining();
}
Process(rnns, TrainingSet, RunningMode.Training);
var duration = DateTime.Now.Subtract(start);
Logger.WriteLine($"End {iter} iteration. Time duration = {duration}");
Logger.WriteLine("");
if (tknErrCnt >= bestTrainTknErrCnt && lastAlpha != RNNHelper.LearningRate)
{
//Although we reduce alpha value, we still cannot get better result.
Logger.WriteLine(
$"Current token error count({(double)tknErrCnt / (double)processedWordCnt * 100.0}%) is larger than the previous one({(double)bestTrainTknErrCnt / (double)processedWordCnt * 100.0}%) on training set. End training early.");
Logger.WriteLine("Current alpha: {0}, the previous alpha: {1}", RNNHelper.LearningRate, lastAlpha);
break;
}
lastAlpha = RNNHelper.LearningRate;
int trainTknErrCnt = tknErrCnt;
//Validate the model by validated corpus
if (ValidationSet != null)
{
Logger.WriteLine("Verify model on validated corpus.");
Process(rnns, ValidationSet, RunningMode.Validate);
Logger.WriteLine("End model verification.");
Logger.WriteLine("");
if (tknErrCnt < bestValidTknErrCnt)
{
//We got better result on validated corpus, save this model
Logger.WriteLine($"Saving better model into file {modelFilePath}, since we got a better result on validation set.");
Logger.WriteLine($"Error token percent: {(double)tknErrCnt / (double)processedWordCnt * 100.0}%, Error sequence percent: {(double)sentErrCnt / (double)processedSequence * 100.0}%");
rnn.SaveModel(modelFilePath);
bestValidTknErrCnt = tknErrCnt;
}
}
else if (trainTknErrCnt < bestTrainTknErrCnt)
{
//We don't have validate corpus, but we get a better result on training corpus
//We got better result on validated corpus, save this model
Logger.WriteLine($"Saving better model into file {modelFilePath}, although validation set doesn't exist, we have better result on training set.");
Logger.WriteLine($"Error token percent: {(double)trainTknErrCnt / (double)processedWordCnt * 100.0}%, Error sequence percent: {(double)sentErrCnt / (double)processedSequence * 100.0}%");
rnn.SaveModel(modelFilePath);
}
Logger.WriteLine("");
if (trainTknErrCnt >= bestTrainTknErrCnt)
{
//We don't have better result on training set, so reduce learning rate
RNNHelper.LearningRate = RNNHelper.LearningRate / 2.0f;
RNNHelper.vecNormalLearningRate = new Vector <float>(RNNHelper.LearningRate);
}
else
{
bestTrainTknErrCnt = trainTknErrCnt;
}
iter++;
}
}