-
Notifications
You must be signed in to change notification settings - Fork 5
/
NNClassifier.java
171 lines (149 loc) · 7.58 KB
/
NNClassifier.java
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
package org.genericsystem.reinforcer;
import java.io.File;
import java.io.IOException;
import java.util.List;
import java.util.Random;
import org.datavec.api.conf.Configuration;
import org.datavec.api.io.filters.BalancedPathFilter;
import org.datavec.api.io.labels.ParentPathLabelGenerator;
import org.datavec.api.records.reader.impl.FileRecordReader;
import org.datavec.api.split.FileSplit;
import org.datavec.api.split.InputSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.deeplearning4j.earlystopping.EarlyStoppingConfiguration;
import org.deeplearning4j.earlystopping.EarlyStoppingResult;
import org.deeplearning4j.earlystopping.listener.EarlyStoppingListener;
import org.deeplearning4j.earlystopping.scorecalc.DataSetLossCalculator;
import org.deeplearning4j.earlystopping.termination.MaxEpochsTerminationCondition;
import org.deeplearning4j.earlystopping.termination.ScoreImprovementEpochTerminationCondition;
import org.deeplearning4j.earlystopping.trainer.EarlyStoppingTrainer;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.models.embeddings.loader.WordVectorSerializer;
import org.deeplearning4j.models.embeddings.wordvectors.WordVectors;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.deeplearning4j.text.tokenization.tokenizer.preprocessor.CommonPreprocessor;
import org.deeplearning4j.text.tokenization.tokenizerfactory.DefaultTokenizerFactory;
import org.deeplearning4j.text.tokenization.tokenizerfactory.TokenizerFactory;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
public class NNClassifier {
protected static final Logger log = LoggerFactory.getLogger(NNClassifier.class);
private static final int seed = 123;
private static final String[] allowedExtensions = new String[] { "txt" };
public static final Random randNumGen = new Random(seed);
private static final File frModel = new File("frWiki_no_phrase_no_postag_500_cbow_cut10.bin");
public static void main(String[] args) throws Exception {
double learningRate = 0.001;
double momentum = 0.9;
int batchSize = 1;
int nEpochs = 100;
int iterations = 1;
File parentDir = new File(System.getProperty("user.dir"), "pieces/text");
FileSplit filesInDir = new FileSplit(parentDir, allowedExtensions, randNumGen);
ParentPathLabelGenerator labelMaker = new ParentPathLabelGenerator();
BalancedPathFilter pathFilter = new BalancedPathFilter(randNumGen, allowedExtensions, labelMaker, 0, 0, 20, 0);
InputSplit[] filesInDirSplit = filesInDir.sample(pathFilter, 70, 15, 15);
InputSplit trainData = filesInDirSplit[0];
InputSplit validData = filesInDirSplit[1];
InputSplit testData = filesInDirSplit[2];
WordVectors dictionary = WordVectorSerializer.readWord2VecModel(frModel, true);
TokenizerFactory tokenizer = new DefaultTokenizerFactory();
tokenizer.setTokenPreProcessor(new CommonPreprocessor());
FileRecordReader recordReader = new VecRecordReader(dictionary, tokenizer);
Configuration readerConf = new Configuration();
readerConf.setBoolean(FileRecordReader.APPEND_LABEL, true);
recordReader.initialize(readerConf, trainData);
List<String> labels = recordReader.getLabels();
int outputNum = labels.size();
MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
.seed(seed)
.weightInit(WeightInit.XAVIER)
.iterations(iterations)
.activation(Activation.TANH)
.learningRate(learningRate)
.updater(new Nesterovs(momentum))
.gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
.regularization(true)
.l2(1e-4)
.list()
.layer(0, new DenseLayer.Builder().nIn(500).nOut(1024).build())
.layer(1, new DenseLayer.Builder().nIn(1024).nOut(1024).build())
.layer(2, new DenseLayer.Builder().nIn(1024).nOut(1024).build())
.layer(3, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD).nIn(1024).nOut(outputNum).activation(Activation.SOFTMAX).build())
.pretrain(false)
.backprop(true)
.build();
MultiLayerNetwork model = new MultiLayerNetwork(conf);
model.init();
model.setListeners(new ScoreIterationListener(10));
DataNormalization normalizer = new NormalizerStandardize();
DataSetIterator dataIter = getDataSetIterator(recordReader, readerConf, null, trainData, batchSize, outputNum);
normalizer.fit(dataIter);
dataIter.setPreProcessor(normalizer);
DataSetIterator validIter = getDataSetIterator(recordReader, readerConf, normalizer, validData, batchSize, outputNum);
EarlyStoppingConfiguration<MultiLayerNetwork> esConf = new EarlyStoppingConfiguration.Builder<MultiLayerNetwork>()
.epochTerminationConditions(new MaxEpochsTerminationCondition(nEpochs))
.evaluateEveryNEpochs(1)
.epochTerminationConditions(new ScoreImprovementEpochTerminationCondition(20))
.scoreCalculator(new DataSetLossCalculator(validIter, false))
.build();
EarlyStoppingTrainer trainer = new EarlyStoppingTrainer(esConf, model, dataIter);
trainer.setListener(new EarlyStoppingListener<MultiLayerNetwork>() {
@Override
public void onStart(EarlyStoppingConfiguration<MultiLayerNetwork> esConfig, MultiLayerNetwork net) {
}
@Override
public void onEpoch(int epochNum, double score, EarlyStoppingConfiguration<MultiLayerNetwork> esConfig, MultiLayerNetwork net) {
log.info("Epoch {}, score {}.", epochNum, score);
}
@Override
public void onCompletion(EarlyStoppingResult<MultiLayerNetwork> esResult) {
}
});
// Early stopping training not working
// log.info("Early stopping training");
// EarlyStoppingResult<MultiLayerNetwork> result = trainer.fit();
// log.info("Evaluate model....");
// dataIter = getDataSetIterator(recordReader, readerConf, normalizer, testData, batchSize, outputNum);
// Evaluation eval = result.getBestModel().evaluate(dataIter);
// log.info(eval.stats(true));
log.info("Training without early stopping");
for (int i = 0; i < nEpochs; i++) {
model.fit(dataIter);
log.info("Completed epoch {}", i);
dataIter.reset();
}
log.info("Evaluate model....");
dataIter = getDataSetIterator(recordReader, readerConf, normalizer, testData, batchSize, outputNum);
Evaluation eval = model.evaluate(dataIter);
log.info(eval.stats(true));
// File modelFile = new File("TrainedModel-" + System.currentTimeMillis() + ".zip");
// ModelSerializer.writeModel(model, modelFile, true);
// log.info("Model saved to " + modelFile);
}
private static DataSetIterator getDataSetIterator(FileRecordReader recordReader, Configuration conf, DataNormalization normalizer, InputSplit data, int batchSize, int outputNum) {
try {
recordReader.initialize(conf, data);
} catch (InterruptedException | IOException e) {
log.warn("Impossible to initialize recordReader", e);
}
DataSetIterator dataIter = new RecordReaderDataSetIterator(recordReader, batchSize, 500, outputNum);
if (normalizer != null) {
dataIter.setPreProcessor(normalizer);
}
return dataIter;
}
}