From 0838a692740c501a21d6d8f1def87e172db9338e Mon Sep 17 00:00:00 2001 From: Zhongkai Fu Date: Wed, 23 Dec 2015 22:52:13 -0800 Subject: [PATCH] #1. Improve logger #2. Using error token ratio to verify validated set performance --- ConvertCorpus/ConvertCorpus.csproj | 3 + ConvertCorpus/Program.cs | 3 +- RNNSharp/BiRNN.cs | 3 +- RNNSharp/Featurizer.cs | 17 +++-- RNNSharp/LSTMRNN.cs | 5 +- RNNSharp/ModelSetting.cs | 25 +++--- RNNSharp/RNN.cs | 36 ++++----- RNNSharp/RNNDecoder.cs | 9 ++- RNNSharp/RNNEncoder.cs | 33 ++++---- RNNSharp/Sentence.cs | 3 +- RNNSharp/Sequence.cs | 3 +- RNNSharp/SimpleRNN.cs | 5 +- RNNSharp/TemplateFeaturizer.cs | 2 +- RNNSharpConsole/Program.cs | 101 +++++++++++++------------ RNNSharpConsole/RNNSharpConsole.csproj | 4 + TFeatureBin/Program.cs | 8 +- dll/AdvUtils.dll | Bin 64000 -> 62464 bytes 17 files changed, 139 insertions(+), 121 deletions(-) diff --git a/ConvertCorpus/ConvertCorpus.csproj b/ConvertCorpus/ConvertCorpus.csproj index c33a002..a87f297 100644 --- a/ConvertCorpus/ConvertCorpus.csproj +++ b/ConvertCorpus/ConvertCorpus.csproj @@ -32,6 +32,9 @@ 4 + + ..\dll\AdvUtils.dll + diff --git a/ConvertCorpus/Program.cs b/ConvertCorpus/Program.cs index edfd5ea..b5bdfde 100644 --- a/ConvertCorpus/Program.cs +++ b/ConvertCorpus/Program.cs @@ -4,6 +4,7 @@ using System.Text; using System.Threading.Tasks; using System.IO; +using AdvUtils; namespace ConvertCorpus { @@ -18,7 +19,7 @@ static int ArgPos(string str, string[] args) { if (a == args.Length - 1) { - Console.WriteLine("Argument missing for {0}", str); + Logger.WriteLine(Logger.Level.info, "Argument missing for {0}", str); return -1; } return a; diff --git a/RNNSharp/BiRNN.cs b/RNNSharp/BiRNN.cs index 06c271e..b73f708 100644 --- a/RNNSharp/BiRNN.cs +++ b/RNNSharp/BiRNN.cs @@ -4,6 +4,7 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using AdvUtils; namespace RNNSharp { @@ -432,7 +433,7 @@ public override void saveNetBin(string filename) public override void loadNetBin(string filename) { - Console.WriteLine("Loading bi-directional model: {0}", filename); + Logger.WriteLine(Logger.Level.info, "Loading bi-directional model: {0}", filename); forwardRNN.loadNetBin(filename + ".forward"); backwardRNN.loadNetBin(filename + ".backward"); diff --git a/RNNSharp/Featurizer.cs b/RNNSharp/Featurizer.cs index a9ac2ce..73337cf 100644 --- a/RNNSharp/Featurizer.cs +++ b/RNNSharp/Featurizer.cs @@ -5,6 +5,7 @@ using System.Threading.Tasks; using System.IO; using Txt2Vec; +using AdvUtils; namespace RNNSharp { @@ -68,25 +69,25 @@ public void LoadFeatureConfigFromFile(string strFileName) string strValue = kv[1].Trim().ToLower(); if (strKey == WORDEMBEDDING_FILENAME) { - Console.WriteLine("Loading word embedding feature set..."); + Logger.WriteLine(Logger.Level.info, "Loading word embedding feature set..."); m_WordEmbedding = new WordEMWrapFeaturizer(strValue); continue; } else if (strKey == TFEATURE_FILENAME) { - Console.WriteLine("Loading template feature set..."); + Logger.WriteLine(Logger.Level.info, "Loading template feature set..."); m_TFeaturizer = new TemplateFeaturizer(strValue); continue; } else if (strKey == WORDEMBEDDING_COLUMN) { m_WordEmbeddingCloumn = int.Parse(strValue); - Console.WriteLine("Word embedding feature column: {0}", m_WordEmbeddingCloumn); + Logger.WriteLine(Logger.Level.info, "Word embedding feature column: {0}", m_WordEmbeddingCloumn); continue; } else if (strKey == TFEATURE_WEIGHT_TYPE) { - Console.WriteLine("TFeature weighting type: {0}", strValue); + Logger.WriteLine(Logger.Level.info, "TFeature weighting type: {0}", strValue); if (strValue == "binary") { m_TFeatureWeightType = TFEATURE_WEIGHT_TYPE_ENUM.BINARY; @@ -166,16 +167,16 @@ public void ShowFeatureSize() var fc = m_FeatureConfiguration; if (m_TFeaturizer != null) - Console.WriteLine("Template feature size: {0}", m_TFeaturizer.GetFeatureSize()); + Logger.WriteLine(Logger.Level.info, "Template feature size: {0}", m_TFeaturizer.GetFeatureSize()); if (fc.ContainsKey(TFEATURE_CONTEXT) == true) - Console.WriteLine("Template feature context size: {0}", m_TFeaturizer.GetFeatureSize() * fc[TFEATURE_CONTEXT].Count); + Logger.WriteLine(Logger.Level.info, "Template feature context size: {0}", m_TFeaturizer.GetFeatureSize() * fc[TFEATURE_CONTEXT].Count); if (fc.ContainsKey(RT_FEATURE_CONTEXT) == true) - Console.WriteLine("Run time feature size: {0}", m_TagSet.GetSize() * fc[RT_FEATURE_CONTEXT].Count); + Logger.WriteLine(Logger.Level.info, "Run time feature size: {0}", m_TagSet.GetSize() * fc[RT_FEATURE_CONTEXT].Count); if (fc.ContainsKey(WORDEMBEDDING_CONTEXT) == true) - Console.WriteLine("Word embedding feature size: {0}", m_WordEmbedding.GetDimension() * fc[WORDEMBEDDING_CONTEXT].Count); + Logger.WriteLine(Logger.Level.info, "Word embedding feature size: {0}", m_WordEmbedding.GetDimension() * fc[WORDEMBEDDING_CONTEXT].Count); } void ExtractSparseFeature(int currentState, int numStates, List features, State pState) diff --git a/RNNSharp/LSTMRNN.cs b/RNNSharp/LSTMRNN.cs index 3e10631..a423221 100644 --- a/RNNSharp/LSTMRNN.cs +++ b/RNNSharp/LSTMRNN.cs @@ -4,6 +4,7 @@ using System.Text; using System.Threading.Tasks; using System.IO; +using AdvUtils; namespace RNNSharp { @@ -135,7 +136,7 @@ private void saveLSTMWeight(LSTMWeight[][] weight, BinaryWriter fo) public override void loadNetBin(string filename) { - Console.WriteLine("Loading LSTM-RNN model: {0}", filename); + Logger.WriteLine(Logger.Level.info, "Loading LSTM-RNN model: {0}", filename); StreamReader sr = new StreamReader(filename); BinaryReader br = new BinaryReader(sr.BaseStream); @@ -367,7 +368,7 @@ public override void initMem() } } - Console.WriteLine("[TRACE] Initializing weights, random value is {0}", rand.NextDouble());// yy debug + Logger.WriteLine(Logger.Level.info, "[TRACE] Initializing weights, random value is {0}", rand.NextDouble());// yy debug initWeights(); } diff --git a/RNNSharp/ModelSetting.cs b/RNNSharp/ModelSetting.cs index 906f0ad..fe897c6 100644 --- a/RNNSharp/ModelSetting.cs +++ b/RNNSharp/ModelSetting.cs @@ -3,6 +3,7 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using AdvUtils; namespace RNNSharp { @@ -66,34 +67,34 @@ public long GetSaveStep() public void DumpSetting() { - Console.WriteLine("Model File: {0}", m_strModelFile); + Logger.WriteLine(Logger.Level.info, "Model File: {0}", m_strModelFile); if (m_ModelType == 0) { - Console.WriteLine("Model Structure: Simple RNN"); - Console.WriteLine("BPTT: {0}", m_Bptt); + Logger.WriteLine(Logger.Level.info, "Model Structure: Simple RNN"); + Logger.WriteLine(Logger.Level.info, "BPTT: {0}", m_Bptt); } else if (m_ModelType == 1) { - Console.WriteLine("Model Structure: LSTM-RNN"); + Logger.WriteLine(Logger.Level.info, "Model Structure: LSTM-RNN"); } if (m_iDir == 0) { - Console.WriteLine("RNN Direction: Forward"); + Logger.WriteLine(Logger.Level.info, "RNN Direction: Forward"); } else { - Console.WriteLine("RNN Direction: Bi-directional"); + Logger.WriteLine(Logger.Level.info, "RNN Direction: Bi-directional"); } - Console.WriteLine("Learning rate: {0}", m_LearningRate); - Console.WriteLine("Dropout: {0}", m_Dropout); - Console.WriteLine("Max Iteration: {0}", m_MaxIteration); - Console.WriteLine("Hidden layer size： {0}", m_NumHidden); - Console.WriteLine("RNN-CRF: {0}", m_bCRFTraining); + Logger.WriteLine(Logger.Level.info, "Learning rate: {0}", m_LearningRate); + Logger.WriteLine(Logger.Level.info, "Dropout: {0}", m_Dropout); + Logger.WriteLine(Logger.Level.info, "Max Iteration: {0}", m_MaxIteration); + Logger.WriteLine(Logger.Level.info, "Hidden layer size： {0}", m_NumHidden); + Logger.WriteLine(Logger.Level.info, "RNN-CRF: {0}", m_bCRFTraining); if (m_SaveStep > 0) { - Console.WriteLine("Save temporary model after every {0} sentences", m_SaveStep); + Logger.WriteLine(Logger.Level.info, "Save temporary model after every {0} sentences", m_SaveStep); } } diff --git a/RNNSharp/RNN.cs b/RNNSharp/RNN.cs index 8bb7074..78013b8 100644 --- a/RNNSharp/RNN.cs +++ b/RNNSharp/RNN.cs @@ -5,6 +5,7 @@ using System.Threading.Tasks; using System.Threading; using System.IO; +using AdvUtils; namespace RNNSharp { @@ -483,7 +484,7 @@ public virtual double TrainNet(DataSet trainingSet, int iter) { DateTime start = DateTime.Now; int[] predicted; - Console.WriteLine("[TRACE] Iter " + iter + " begins with learning rate alpha = " + alpha + " ..."); + Logger.WriteLine(Logger.Level.info, "[TRACE] Iter " + iter + " begins with learning rate alpha = " + alpha + " ..."); //Initialize varibles counter = 0; @@ -498,7 +499,7 @@ public virtual double TrainNet(DataSet trainingSet, int iter) int numSequence = trainingSet.GetSize(); int tknErrCnt = 0; int sentErrCnt = 0; - Console.WriteLine("[TRACE] Progress = 0/" + numSequence / 1000.0 + "K\r"); + Logger.WriteLine(Logger.Level.info, "[TRACE] Progress = 0/" + numSequence / 1000.0 + "K\r"); for (int curSequence = 0; curSequence < numSequence; curSequence++) { Sequence pSequence = trainingSet.Get(curSequence); @@ -532,18 +533,17 @@ public virtual double TrainNet(DataSet trainingSet, int iter) if ((curSequence + 1) % 1000 == 0) { - Console.WriteLine("[TRACE] Progress = {0} ", (curSequence + 1) / 1000 + "K/" + numSequence / 1000.0 + "K"); - Console.WriteLine(" train cross-entropy = {0} ", -logp / Math.Log10(2.0) / counter); - Console.WriteLine(" Error token ratio = {0}%", (double)tknErrCnt / (double)counter * 100); - Console.WriteLine(" Error sentence ratio = {0}%", (double)sentErrCnt / (double)curSequence * 100); + Logger.WriteLine(Logger.Level.info, "[TRACE] Progress = {0} ", (curSequence + 1) / 1000 + "K/" + numSequence / 1000.0 + "K"); + Logger.WriteLine(Logger.Level.info, " train cross-entropy = {0} ", -logp / Math.Log10(2.0) / counter); + Logger.WriteLine(Logger.Level.info, " Error token ratio = {0}%", (double)tknErrCnt / (double)counter * 100); + Logger.WriteLine(Logger.Level.info, " Error sentence ratio = {0}%", (double)sentErrCnt / (double)curSequence * 100); } if (m_SaveStep > 0 && (curSequence + 1) % m_SaveStep == 0) { //After processed every m_SaveStep sentences, save current model into a temporary file - Console.Write("Saving temporary model into file..."); + Logger.WriteLine(Logger.Level.info, "Saving temporary model into file..."); saveNetBin(m_strModelFile + ".tmp"); - Console.WriteLine("Done."); } } @@ -552,9 +552,9 @@ public virtual double TrainNet(DataSet trainingSet, int iter) double entropy = -logp / Math.Log10(2.0) / counter; double ppl = exp_10(-logp / counter); - Console.WriteLine("[TRACE] Iter " + iter + " completed"); - Console.WriteLine("[TRACE] Sentences = " + numSequence + ", time escape = " + duration + "s, speed = " + numSequence / duration.TotalSeconds); - Console.WriteLine("[TRACE] In training: log probability = " + logp + ", cross-entropy = " + entropy + ", perplexity = " + ppl); + Logger.WriteLine(Logger.Level.info, "[TRACE] Iter " + iter + " completed"); + Logger.WriteLine(Logger.Level.info, "[TRACE] Sentences = " + numSequence + ", time escape = " + duration + "s, speed = " + numSequence / duration.TotalSeconds); + Logger.WriteLine(Logger.Level.info, "[TRACE] In training: log probability = " + logp + ", cross-entropy = " + entropy + ", perplexity = " + ppl); return ppl; } @@ -831,7 +831,7 @@ public void CalculateOutputLayerError(State state, int timeat) public virtual bool ValidateNet(DataSet validationSet) { - Console.WriteLine("[TRACE] Start validation ..."); + Logger.WriteLine(Logger.Level.info, "[TRACE] Start validation ..."); int wordcn = 0; int[] predicted; int tknErrCnt = 0; @@ -875,17 +875,19 @@ public virtual bool ValidateNet(DataSet validationSet) double entropy = -logp / Math.Log10(2.0) / counter; double ppl = exp_10(-logp / counter); + double tknErrRatio = (double)tknErrCnt / (double)wordcn * 100; + double sentErrRatio = (double)sentErrCnt / (double)numSequence * 100; - Console.WriteLine("[TRACE] In validation: error token ratio = {0}% error sentence ratio = {1}%", (double)tknErrCnt / (double)wordcn * 100, (double)sentErrCnt / (double)numSequence * 100); - Console.WriteLine("[TRACE] In training: log probability = " + logp + ", cross-entropy = " + entropy + ", perplexity = " + ppl); - Console.WriteLine(); + Logger.WriteLine(Logger.Level.info, "[TRACE] In validation: error token ratio = {0}% error sentence ratio = {1}%", tknErrRatio, sentErrRatio); + Logger.WriteLine(Logger.Level.info, "[TRACE] In training: log probability = " + logp + ", cross-entropy = " + entropy + ", perplexity = " + ppl); + Logger.WriteLine(Logger.Level.info, ""); bool bUpdate = false; - if (ppl < minTknErrRatio) + if (tknErrRatio < minTknErrRatio) { //We have better result on validated set, save this model bUpdate = true; - minTknErrRatio = ppl; + minTknErrRatio = tknErrRatio; } return bUpdate; diff --git a/RNNSharp/RNNDecoder.cs b/RNNSharp/RNNDecoder.cs index ac8cde8..96ae4a7 100644 --- a/RNNSharp/RNNDecoder.cs +++ b/RNNSharp/RNNDecoder.cs @@ -4,6 +4,7 @@ using System.Text; using System.Threading.Tasks; using System.IO; +using AdvUtils; namespace RNNSharp { @@ -21,7 +22,7 @@ public RNNDecoder(string strModelFileName, Featurizer featurizer) if (modelDir == MODELDIRECTION.BI_DIRECTIONAL) { - Console.WriteLine("Model Structure: Bi-directional RNN"); + Logger.WriteLine(Logger.Level.info, "Model Structure: Bi-directional RNN"); if (modelType == MODELTYPE.SIMPLE) { m_Rnn = new BiRNN(new SimpleRNN(), new SimpleRNN()); @@ -35,18 +36,18 @@ public RNNDecoder(string strModelFileName, Featurizer featurizer) { if (modelType == MODELTYPE.SIMPLE) { - Console.WriteLine("Model Structure: Simple RNN"); + Logger.WriteLine(Logger.Level.info, "Model Structure: Simple RNN"); m_Rnn = new SimpleRNN(); } else { - Console.WriteLine("Model Structure: LSTM-RNN"); + Logger.WriteLine(Logger.Level.info, "Model Structure: LSTM-RNN"); m_Rnn = new LSTMRNN(); } } m_Rnn.loadNetBin(strModelFileName); - Console.WriteLine("CRF Model: {0}", m_Rnn.IsCRFModel()); + Logger.WriteLine(Logger.Level.info, "CRF Model: {0}", m_Rnn.IsCRFModel()); m_Featurizer = featurizer; } diff --git a/RNNSharp/RNNEncoder.cs b/RNNSharp/RNNEncoder.cs index 9dad154..cfd6111 100644 --- a/RNNSharp/RNNEncoder.cs +++ b/RNNSharp/RNNEncoder.cs @@ -4,6 +4,7 @@ using System.Text; using System.Threading.Tasks; using System.IO; +using AdvUtils; namespace RNNSharp { @@ -101,9 +102,9 @@ public void Train() rnn.setTagBigramTransition(m_LabelBigramTransition); } - Console.WriteLine(); + Logger.WriteLine(Logger.Level.info, ""); - Console.WriteLine("[TRACE] Iterative training begins ..."); + Logger.WriteLine(Logger.Level.info, "[TRACE] Iterative training begins ..."); double lastPPL = double.MaxValue; double lastAlpha = rnn.Alpha; int iter = 0; @@ -111,7 +112,7 @@ public void Train() { if (rnn.MaxIter > 0 && iter > rnn.MaxIter) { - Console.WriteLine("We have trained this model {0} iteration, exit."); + Logger.WriteLine(Logger.Level.info, "We have trained this model {0} iteration, exit."); break; } @@ -119,34 +120,32 @@ public void Train() double ppl = rnn.TrainNet(m_TrainingSet, iter); //Validate the model by validated corpus + bool betterValidateNet = false; if (rnn.ValidateNet(m_ValidationSet) == true) { //If current model is better than before, save it into file - Console.Write("Saving better model into file {0}...", m_modelSetting.GetModelFile()); + Logger.WriteLine(Logger.Level.info, "Saving better model into file {0}...", m_modelSetting.GetModelFile()); rnn.saveNetBin(m_modelSetting.GetModelFile()); - Console.WriteLine("Done."); - } - //else - //{ - // Console.Write("Loading previous best model from file {0}...", m_modelSetting.GetModelFile()); - // rnn.loadNetBin(m_modelSetting.GetModelFile()); - // Console.WriteLine("Done."); - // lastAlpha = rnn.Alpha; - // rnn.Alpha = rnn.Alpha / 2.0; - //} + betterValidateNet = true; + } + else + { + Logger.WriteLine(Logger.Level.info, "Loading previous best model from file {0}...", m_modelSetting.GetModelFile()); + rnn.loadNetBin(m_modelSetting.GetModelFile()); + } if (ppl >= lastPPL && lastAlpha != rnn.Alpha) { //Although we reduce alpha value, we still cannot get better result. - Console.WriteLine("Current perplexity({0}) is larger than the previous one({1}). End training early.", ppl, lastPPL); - Console.WriteLine("Current alpha: {0}, the previous alpha: {1}", rnn.Alpha, lastAlpha); + Logger.WriteLine(Logger.Level.info, "Current perplexity({0}) is larger than the previous one({1}). End training early.", ppl, lastPPL); + Logger.WriteLine(Logger.Level.info, "Current alpha: {0}, the previous alpha: {1}", rnn.Alpha, lastAlpha); break; } lastAlpha = rnn.Alpha; - if (ppl >= lastPPL) + if (betterValidateNet == false) { rnn.Alpha = rnn.Alpha / 2.0; } diff --git a/RNNSharp/Sentence.cs b/RNNSharp/Sentence.cs index 3f6a62d..103361d 100644 --- a/RNNSharp/Sentence.cs +++ b/RNNSharp/Sentence.cs @@ -3,6 +3,7 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using AdvUtils; namespace RNNSharp { @@ -31,7 +32,7 @@ public void DumpFeatures() sb.Append('\t'); } - Console.WriteLine(sb.ToString().Trim()); + Logger.WriteLine(Logger.Level.info, sb.ToString().Trim()); } } diff --git a/RNNSharp/Sequence.cs b/RNNSharp/Sequence.cs index 590e3a6..f80e2ce 100644 --- a/RNNSharp/Sequence.cs +++ b/RNNSharp/Sequence.cs @@ -3,6 +3,7 @@ using System.Linq; using System.Text; using System.Threading.Tasks; +using AdvUtils; namespace RNNSharp { @@ -41,7 +42,7 @@ public bool SetLabel(Sentence sent, TagSet tagSet) int tagId = tagSet.GetIndex(strTagName); if (tagId < 0) { - Console.WriteLine("Error: tag {0} is unknown.", strTagName); + Logger.WriteLine(Logger.Level.info, "Error: tag {0} is unknown.", strTagName); return false; } diff --git a/RNNSharp/SimpleRNN.cs b/RNNSharp/SimpleRNN.cs index a4ef162..81257af 100644 --- a/RNNSharp/SimpleRNN.cs +++ b/RNNSharp/SimpleRNN.cs @@ -4,6 +4,7 @@ using System.Text; using System.Threading.Tasks; using System.IO; +using AdvUtils; namespace RNNSharp { @@ -325,7 +326,7 @@ public override void initMem() mat_hiddenBpttWeight = new Matrix(L1, L1); - Console.WriteLine("[TRACE] Initializing weights, random value is {0}", rand.NextDouble());// yy debug + Logger.WriteLine(Logger.Level.info, "[TRACE] Initializing weights, random value is {0}", rand.NextDouble());// yy debug initWeights(); //Initialize BPTT @@ -398,7 +399,7 @@ public override void netFlush() //cleans all activations and error vectors public override void loadNetBin(string filename) { - Console.WriteLine("Loading SimpleRNN model: {0}", filename); + Logger.WriteLine(Logger.Level.info, "Loading SimpleRNN model: {0}", filename); StreamReader sr = new StreamReader(filename); BinaryReader br = new BinaryReader(sr.BaseStream); diff --git a/RNNSharp/TemplateFeaturizer.cs b/RNNSharp/TemplateFeaturizer.cs index a7e9f0c..4dfa81e 100644 --- a/RNNSharp/TemplateFeaturizer.cs +++ b/RNNSharp/TemplateFeaturizer.cs @@ -162,7 +162,7 @@ public void BuildIndexedFeatureIntoFile(string strFileName, List feature { if (strFeature.StartsWith("U") == false) { - Console.WriteLine("Invalidated feature: {0}", strFeature); + Logger.WriteLine(Logger.Level.info, "Invalidated feature: {0}", strFeature); continue; } diff --git a/RNNSharpConsole/Program.cs b/RNNSharpConsole/Program.cs index 62d41b4..36cbd48 100644 --- a/RNNSharpConsole/Program.cs +++ b/RNNSharpConsole/Program.cs @@ -5,6 +5,7 @@ using System.Threading.Tasks; using System.IO; using RNNSharp; +using AdvUtils; namespace RNNSharpConsole { @@ -37,57 +38,57 @@ static void Usage() { UsageTitle(); Console.WriteLine("RNNSharpConsole.exe "); - Console.WriteLine(" -mode : support train/test"); + Console.WriteLine(" -mode : train/test"); } static void UsageTrain() { UsageTitle(); Console.WriteLine("RNNSharpConsole.exe -mode train "); - Console.WriteLine("Parameters for training RNN"); + Console.WriteLine("Parameters for RNN training"); Console.WriteLine(" -trainfile "); Console.WriteLine("\tTraining corpus file"); - + Console.WriteLine(" -validfile "); Console.WriteLine("\tValidated corpus file for training"); - + Console.WriteLine(" -modelfile "); Console.WriteLine("\tEncoded model file"); - + Console.WriteLine(" -modeltype "); - Console.WriteLine("\tModel structure: 0 - simple RNN, 1 - LSTM-RNN, default is 0"); + Console.WriteLine("\tModel type: 0 - BPTT-RNN, 1 - LSTM-RNN, default is 0"); Console.WriteLine(" -dir "); - Console.WriteLine("\tRNN directional: 0 - Forward RNN, 1 - Bi-directional RNN, default is 0"); - + Console.WriteLine("\tRecurrent direction: 0 - Forward RNN, 1 - Bi-directional RNN, default is 0"); + Console.WriteLine(" -ftrfile "); Console.WriteLine("\tFeature configuration file"); - + Console.WriteLine(" -tagfile "); Console.WriteLine("\tSupported output tagid-name list file"); - + Console.WriteLine(" -alpha "); - Console.WriteLine("\tLearning rate, default is 0.1"); - + Console.WriteLine("\tInitializing learning rate, default is 0.1"); + Console.WriteLine(" -dropout "); - Console.WriteLine("\tDropout parameter [0, 1.0), default is 0"); - + Console.WriteLine("\tDropout ratio [0, 1.0), default is 0"); + Console.WriteLine(" -layersize "); - Console.WriteLine("\tHidden layer size for training, default is 200"); - + Console.WriteLine("\tHidden layer size, default is 200"); + Console.WriteLine(" -bptt "); - Console.WriteLine("\tStep for back-propagation through time for simple RNN. default is 4"); - - Console.WriteLine(" -crf <0/1>"); - Console.WriteLine("\tTraining model by RNN(0) or RNN-CRF(1), default is 0"); - + Console.WriteLine("\tStep for back-propagation through time for BPTT-RNN. default is 4"); + + Console.WriteLine(" -crf "); + Console.WriteLine("\tEnable CRF model at output, 0 is disable, 1 is enable. default is 0"); + Console.WriteLine(" -maxiter "); - Console.WriteLine("\tMaximum iteration for training, default is 20"); - + Console.WriteLine("\tMaximum iteration for training, 0 is unlimited. default is 20"); + Console.WriteLine(" -savestep "); - Console.WriteLine("\tSave temporary model after every sentence, default is 0"); - + Console.WriteLine("\tSave temporary model after every sentences, default is 0"); + Console.WriteLine(); Console.WriteLine("Example: RNNSharpConsole.exe -mode train -trainfile train.txt -validfile valid.txt -modelfile model.bin -ftrfile features.txt -tagfile tags.txt -modeltype 0 -layersize 200 -alpha 0.1 -crf 1 -maxiter 20 -savestep 200K -dir 0"); @@ -97,25 +98,25 @@ static void UsageTest() { UsageTitle(); Console.WriteLine("RNNSharpConsole.exe -mode test "); - Console.WriteLine("Parameters for predicting tags from given corpus"); + Console.WriteLine("Parameters to predict tags from given corpus by using trained model"); Console.WriteLine(" -testfile "); Console.WriteLine("\tTraining corpus file"); - + Console.WriteLine(" -modelfile "); Console.WriteLine("\tEncoded model file"); - + Console.WriteLine(" -tagfile "); Console.WriteLine("\tSupported output tagid-name list file"); - + Console.WriteLine(" -ftrfile "); Console.WriteLine("\tFeature configuration file"); - + Console.WriteLine(" -outfile "); Console.WriteLine("\tResult output file"); Console.WriteLine(" -nbest "); Console.WriteLine("\tGet N-best result. Default is 1"); - + Console.WriteLine(); Console.WriteLine("Example: RNNSharpConsole.exe -mode test -testfile test.txt -modelfile model.bin -tagfile tags.txt -ftrfile features.txt -outfile result.txt -nbest 3"); } @@ -174,7 +175,7 @@ static int ArgPos(string str, string[] args) { if (a == args.Length - 1) { - Console.WriteLine("Argument missing for {0}", str); + Logger.WriteLine(Logger.Level.info, "Argument missing for {0}", str); return -1; } return a; @@ -206,7 +207,7 @@ static void CheckCorpus(string strFileName) } } - Console.WriteLine("Record set size in {0}: {1}", strFileName, RecordCount); + Logger.WriteLine(Logger.Level.info, "Record set size in {0}: {1}", strFileName, RecordCount); sr.Close(); } @@ -235,7 +236,7 @@ static void LoadDataset(string strFileName, Featurizer featurizer, DataSet dataS //Set label for the sequence if (seq.SetLabel(sent, featurizer.GetTagSet()) == false) { - Console.WriteLine("Error: Invalidated record."); + Logger.WriteLine(Logger.Level.info, "Error: Invalidated record."); sent.DumpFeatures(); continue; } @@ -247,12 +248,10 @@ static void LoadDataset(string strFileName, Featurizer featurizer, DataSet dataS RecordCount++; if (RecordCount % 10000 == 0) { - Console.Write("{0}...", RecordCount); + Logger.WriteLine(Logger.Level.info, "{0}...", RecordCount); } } - Console.WriteLine(); - sr.Close(); } @@ -280,7 +279,7 @@ static void Main(string[] args) } else { - Console.WriteLine("Specify running mode is required."); + Logger.WriteLine(Logger.Level.err, "Running mode is required."); Usage(); } } @@ -310,7 +309,7 @@ private static void Test() { if (String.IsNullOrEmpty(strTagFile) == true) { - Console.WriteLine("FAILED: The tag mapping file {0} isn't specified.", strTagFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The tag mapping file {0} isn't specified.", strTagFile); UsageTest(); return; } @@ -320,21 +319,21 @@ private static void Test() if (String.IsNullOrEmpty(strModelFile) == true) { - Console.WriteLine("FAILED: The model file {0} isn't specified.", strModelFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The model file {0} isn't specified.", strModelFile); UsageTest(); return; } if (String.IsNullOrEmpty(strFeatureConfigFile) == true) { - Console.WriteLine("FAILED: The feature configuration file {0} isn't specified.", strFeatureConfigFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The feature configuration file {0} isn't specified.", strFeatureConfigFile); UsageTest(); return; } if (strOutputFile.Length == 0) { - Console.WriteLine("FAILED: The output file name should not be empty."); + Logger.WriteLine(Logger.Level.err, "FAILED: The output file name should not be empty."); UsageTest(); return; } @@ -351,7 +350,7 @@ private static void Test() if (File.Exists(strTestFile) == false) { - Console.WriteLine("FAILED: The test corpus {0} isn't existed.", strTestFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The test corpus {0} isn't existed.", strTestFile); UsageTest(); return; } @@ -392,7 +391,7 @@ private static void Test() int[][] output = decoder.ProcessNBest(sent, nBest); if (output == null) { - Console.WriteLine("FAILED: decode failed. Dump current sentence..."); + Logger.WriteLine(Logger.Level.err, "FAILED: decode failed. Dump current sentence..."); sent.DumpFeatures(); return; } @@ -420,9 +419,11 @@ private static void Test() private static void Train() { + Logger.LogFile = "RNNSharpConsole.log"; + if (File.Exists(strTagFile) == false) { - Console.WriteLine("FAILED: The tag mapping file {0} isn't existed.", strTagFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The tag mapping file {0} isn't existed.", strTagFile); UsageTrain(); return; } @@ -448,7 +449,7 @@ private static void Train() if (File.Exists(strFeatureConfigFile) == false) { - Console.WriteLine("FAILED: The feature configuration file {0} isn't existed.", strFeatureConfigFile); + Logger.WriteLine(Logger.Level.err, "FAILED: The feature configuration file {0} isn't existed.", strFeatureConfigFile); UsageTrain(); return; } @@ -458,13 +459,13 @@ private static void Train() if (featurizer.IsRunTimeFeatureUsed() == true && iDir == 1) { - Console.WriteLine("FAILED: Run time feature is not available for bi-directional RNN model."); + Logger.WriteLine(Logger.Level.err, "FAILED: Run time feature is not available for bi-directional RNN model."); UsageTrain(); return; } if (String.IsNullOrEmpty(strTrainFile) == true) { - Console.WriteLine("FAILED: The training corpus isn't specified."); + Logger.WriteLine(Logger.Level.err, "FAILED: The training corpus isn't specified."); UsageTrain(); return; } @@ -476,7 +477,7 @@ private static void Train() DataSet dataSetValidation = null; if (String.IsNullOrEmpty(strValidFile) == true) { - Console.WriteLine("FAILED: The validation corpus isn't specified."); + Logger.WriteLine(Logger.Level.err, "FAILED: The validation corpus isn't specified."); return; } //LoadFeatureConfig validated corpus and extract feature set @@ -490,7 +491,7 @@ private static void Train() if (iCRF == 1) { - Console.WriteLine("Initialize output tag bigram transition probability..."); + Logger.WriteLine(Logger.Level.info, "Initialize output tag bigram transition probability..."); //Build tag bigram transition matrix dataSetTrain.BuildLabelBigramTransition(); encoder.SetLabelBigramTransition(dataSetTrain.GetLabelBigramTransition()); diff --git a/RNNSharpConsole/RNNSharpConsole.csproj b/RNNSharpConsole/RNNSharpConsole.csproj index 3e23a86..b939270 100644 --- a/RNNSharpConsole/RNNSharpConsole.csproj +++ b/RNNSharpConsole/RNNSharpConsole.csproj @@ -32,6 +32,10 @@ 4 + + False + ..\dll\AdvUtils.dll + diff --git a/TFeatureBin/Program.cs b/TFeatureBin/Program.cs index b42d828..1e8e625 100644 --- a/TFeatureBin/Program.cs +++ b/TFeatureBin/Program.cs @@ -27,7 +27,7 @@ static int ArgPos(string str, string[] args) { if (a == args.Length - 1) { - Console.WriteLine("Argument missing for {0}", str); + Logger.WriteLine(Logger.Level.info, "Argument missing for {0}", str); return -1; } return a; @@ -161,11 +161,11 @@ static void BuildMode() static IDictionary ExtractFeatureSetFromFile() { //Load templates from given file - Console.WriteLine("Loading feature template from {0}...", strTemplateFile); + Logger.WriteLine(Logger.Level.info, "Loading feature template from {0}...", strTemplateFile); templateFeaturizer = new TemplateFeaturizer(); templateFeaturizer.LoadTemplateFromFile(strTemplateFile); - Console.WriteLine("Generate feature set..."); + Logger.WriteLine(Logger.Level.info, "Generate feature set..."); BigDictionary feature2freq = new BigDictionary(); List record = new List(); StreamReader srCorpus = new StreamReader(strInputFile, Encoding.UTF8); @@ -218,7 +218,7 @@ static IDictionary ExtractFeatureSetFromFile() srCorpus.Close(); //Only save the feature whose frequency is not less than minfreq - Console.WriteLine("Filter out features whose frequency is less than {0}", minfreq); + Logger.WriteLine(Logger.Level.info, "Filter out features whose frequency is less than {0}", minfreq); SortedDictionary features = new SortedDictionary(StringComparer.Ordinal); foreach (KeyValuePair pair in feature2freq) { diff --git a/dll/AdvUtils.dll b/dll/AdvUtils.dll index 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