DEEP LEARNING: RNN LSTM : AI-Anesth 7

さて、いよいよRNN LSTM解析による時系列連続データの解析へ：
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まずは、UCI Classification Exampleのデータをチェックする。
https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data.html
以下のように60サンプル一行で600行。つまり、たとえば1秒1サンプル等で1分間のバイタルデータと考えればよい。

28.7812 34.4632 31.3381 31.2834 28.9207 33.7596 25.3969 27.7849 35.2479 27.1159 32.8717 29.2171 36.0253 32.337  34.5249 32.8717 34.1173 26.5235 27.6623 26.3693 25.7744 29.27   30.7326 29.5054 33.0292 25.04   28.9167 24.3437 26.1203 34.9424 25.0293 26.6311 35.6541 28.4353 29.1495 28.1584 26.1927 33.3182 30.9772 27.0443 35.5344 26.2353 28.9964 32.0036 31.0558 34.2553 28.0721 28.9402 35.4973 29.747  31.4333 24.5556 33.7431 25.0466 34.9318 34.9879 32.4721 33.3759 25.4652 25.8717
24.8923 25.741  27.5532 32.8217 27.8789 31.5926 31.4861 35.5469 27.9516 31.6595 27.5415 31.1887 27.4867 31.391  27.811  24.488  27.5918 35.6273 35.4102 31.4167 30.7447 24.1311 35.1422 30.4719 31.9874 33.6615 25.5511 30.4686 33.6472 25.0701 34.0765 32.5981 28.3038 26.1471 26.9414 31.5203 33.1089 24.1491 28.5157 25.7906 

35.9519 26.5301 24.8578 25.9562 32.8357 28.5322 26.3458 30.6213 28.9861 29.4047 32.5577 31.0205 26.6418 28.4331 33.6564 26.4244 28.4661 34.2484 32.1005 26.691
31.3987 30.6316 26.3983 24.2905 27.8613 28.5491 24.9717 32.4358 25.2239 27.3068 31.8387 27.2587 28.2572 26.5819 24.0455 35.0625 31.5717 32.5614 31.0308 34.1202 26.9337 31.4781 35.0173 32.3851 24.3323 30.2001 31.2452 26.6814 31.5137 28.8778 27.3086 24.246  26.9631 25.2919 31.6114 24.7131 27.4809 24.2075 26.8059 35.1253 32.6293 31.0561 26.3583 28.0861 31.4391 27.3057 29.6082 35.9725 34.1444 27.1717 33.6318 26.5966 25.5387 32.5434 25.5772 29.9897 31.351  33.9002 29.5446 29.343
............

28.7812 34.4632 31.3381 31.2834 28.9207 33.7596 25.3969 27.7849 35.2479 27.1159 32.8717 29.2171 36.0253 32.337 34.5249 32.8717 34.1173 26.5235 27.6623 26.3693 25.7744 29.27 30.7326 29.5054 33.0292 25.04 28.9167 24.3437 26.1203 34.9424 25.0293 26.6311 35.6541 28.4353 29.1495 28.1584 26.1927 33.3182 30.9772 27.0443 35.5344 26.2353 28.9964 32.0036 31.0558 34.2553 28.0721 28.9402 35.4973 29.747 31.4333 24.5556 33.7431 25.0466 34.9318 34.9879 32.4721 33.3759 25.4652 25.8717

24.8923 25.741 27.5532 32.8217 27.8789 31.5926 31.4861 35.5469 27.9516 31.6595 27.5415 31.1887 27.4867 31.391 27.811 24.488 27.5918 35.6273 35.4102 31.4167 30.7447 24.1311 35.1422 30.4719 31.9874 33.6615 25.5511 30.4686 33.6472 25.0701 34.0765 32.5981 28.3038 26.1471 26.9414 31.5203 33.1089 24.1491 28.5157 25.7906

35.9519 26.5301 24.8578 25.9562 32.8357 28.5322 26.3458 30.6213 28.9861 29.4047 32.5577 31.0205 26.6418 28.4331 33.6564 26.4244 28.4661 34.2484 32.1005 26.691

31.3987 30.6316 26.3983 24.2905 27.8613 28.5491 24.9717 32.4358 25.2239 27.3068 31.8387 27.2587 28.2572 26.5819 24.0455 35.0625 31.5717 32.5614 31.0308 34.1202 26.9337 31.4781 35.0173 32.3851 24.3323 30.2001 31.2452 26.6814 31.5137 28.8778 27.3086 24.246 26.9631 25.2919 31.6114 24.7131 27.4809 24.2075 26.8059 35.1253 32.6293 31.0561 26.3583 28.0861 31.4391 27.3057 29.6082 35.9725 34.1444 27.1717 33.6318 26.5966 25.5387 32.5434 25.5772 29.9897 31.351 33.9002 29.5446 29.343

............

データの分類は以下のようになっている。（図のラベルEは、Fの間違いだろう）
1-100 Normal　（C)
101-200 Cyclic　（B)
201-300 Increasing trend　（E)
301-400 Decreasing trend　（A)
401-500 Upward shift　　　（D)
501-600 Downward shift　　（F)

エクセルでグラフにしてみると、

450サンプルで訓練して、残り150サンプルで評価テストを行う。
DL4JのUCISequenceClassificationExample.javaに解説を加えると、

package org.deeplearning4j.examples.recurrent.seqClassification;

import org.apache.commons.io.FileUtils;
import org.apache.commons.io.IOUtils;
import org.datavec.api.berkeley.Pair;
import org.datavec.api.records.reader.SequenceRecordReader;
import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;
import org.datavec.api.split.NumberedFileInputSplit;
import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.GradientNormalization;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.GravesLSTM;
import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
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.lossfunctions.LossFunctions;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.File;
import java.net.URL;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Random;

/**
 * Sequence Classification Example Using a LSTM Recurrent Neural Network
* @author Alex Black
**/
public class UCISequenceClassificationExample {
    private static final Logger log = LoggerFactory.getLogger(UCISequenceClassificationExample.class);

    //'baseDir': Base directory for the data. Change this if you want to save the data somewhere else
    private static File baseDir = new File("src/main/resources/uci/");
    private static File baseTrainDir = new File(baseDir, "train");
    private static File featuresDirTrain = new File(baseTrainDir, "features");
    private static File labelsDirTrain = new File(baseTrainDir, "labels");
    private static File baseTestDir = new File(baseDir, "test");
    private static File featuresDirTest = new File(baseTestDir, "features");
    private static File labelsDirTest = new File(baseTestDir, "labels");

    public static void main(String[] args) throws Exception {
        downloadUCIData(); //UCIデータのダウンロード

        // ----- Load the training data -----
        //Note that we have 450 training files for features: train/features/0.csv through train/features/449.csv
        SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();　//訓練データ用（450データ）
        trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));
        SequenceRecordReader trainLabels = new CSVSequenceRecordReader();　//訓練データラベル用（450データ）
        trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));

        int miniBatchSize = 10;  //ミニバッチサイズ
        int numLabelClasses = 6; //クラス分けは６種類
        DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses,
            false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

        //Normalize the training data　　//訓練データの正規化
        DataNormalization normalizer = new NormalizerStandardize();
        normalizer.fit(trainData);              //Collect training data statistics
        trainData.reset();

        //Use previously collected statistics to normalize on-the-fly. Each DataSet returned by 'trainData' iterator will be normalized
        trainData.setPreProcessor(normalizer);


        // ----- Load the test data -----
        //Same process as for the training data.
        SequenceRecordReader testFeatures = new CSVSequenceRecordReader();　//テストデータ用（150データ）
        testFeatures.initialize(new NumberedFileInputSplit(featuresDirTest.getAbsolutePath() + "/%d.csv", 0, 149));
        SequenceRecordReader testLabels = new CSVSequenceRecordReader();　//テストデータラベル用（150データ）
        testLabels.initialize(new NumberedFileInputSplit(labelsDirTest.getAbsolutePath() + "/%d.csv", 0, 149));

        DataSetIterator testData = new SequenceRecordReaderDataSetIterator(testFeatures, testLabels, miniBatchSize, numLabelClasses,
            false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

        testData.setPreProcessor(normalizer);   //Note that we are using the exact same normalization process as the training data


        // ----- Configure the network -----
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() //MLCコンフ
                .seed(123)    //Random number generator seed for improved repeatability. Optional.
                .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) //SGD法確率的勾配法
                .weightInit(WeightInit.XAVIER) //初期値　ザビエル法
                .updater(Updater.NESTEROVS).momentum(0.9) //ネステロフ法
                .learningRate(0.005) //学習率
                .gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue)  //Not always required, but helps with this data set
                .gradientNormalizationThreshold(0.5)
                .list()
                .layer(0, new GravesLSTM.Builder().activation(Activation.TANH).nIn(1).nOut(10).build())　//入力層　第ゼロ層
                .layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
                        .activation(Activation.SOFTMAX).nIn(10).nOut(numLabelClasses).build())　//出力層　第1層
                .pretrain(false).backprop(true).build();

        MultiLayerNetwork net = new MultiLayerNetwork(conf);  //MultiLayerNetworkクラスのインスタンス生成
        net.init();

        net.setListeners(new ScoreIterationListener(20));   //Print the score (loss function value) every 20 iterations


        // ----- Train the network, evaluating the test set performance at each epoch -----
        int nEpochs = 40;  //エポック数20
        String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";
        for (int i = 0; i < nEpochs; i++) {
            net.fit(trainData); //訓練データをMultiLayerNetworkクラスのインスタンスへ適用

            //Evaluate on the test set:
            Evaluation evaluation = net.evaluate(testData);　//評価データをMultiLayerNetworkクラスのインスタンスへ適用
            log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1()));

            testData.reset();
            trainData.reset();
        }

        log.info("----- Example Complete -----");
    }

    //This method downloads the data, and converts the "one time series per line" format into a suitable
    //CSV sequence format that DataVec (CsvSequenceRecordReader) and DL4J can read.
    private static void downloadUCIData() throws Exception {　　//UCIのサイトからデータをダウンロードする関数
        if (baseDir.exists()) return;    //BaseDir = "src/main/resources/uci/"があるならダウンロードしない;

        String url = "https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data";
        String data = IOUtils.toString(new URL(url));  //上記url からdataへダウンロード

        String[] lines = data.split("\n");　//ダウンロードdataを改行コードでスプリット

        //Create directories
        baseDir.mkdir();
        baseTrainDir.mkdir();
        featuresDirTrain.mkdir();
        labelsDirTrain.mkdir();
        baseTestDir.mkdir();
        featuresDirTest.mkdir();
        labelsDirTest.mkdir();

        int lineCount = 0;
        List<Pair<String, Integer>> contentAndLabels = new ArrayList<>();　 
// List<データ型名> オブジェクト名 = new ArrayList<データ型名>();

        for (String line : lines) {
            String transposed = line.replaceAll(" +", "\n"); //空白を改行へ変換

            //Labels: first 100 examples (lines) are label 0, second 100 examples are label 1, and so on
            contentAndLabels.add(new Pair<>(transposed, lineCount++ / 100));
        }

        //Randomize and do a train/test split:
        Collections.shuffle(contentAndLabels, new Random(12345));

        int nTrain = 450;   //75% train, 25% test
        int trainCount = 0;
        int testCount = 0;
        for (Pair<String, Integer> p : contentAndLabels) {
            //Write output in a format we can read, in the appropriate locations
            File outPathFeatures;　//データ値ファイル
            File outPathLabels; //データラベルファイル
            if (trainCount < nTrain) {　　//カウンタが450未満であれば、以下のファイル作成
                outPathFeatures = new File(featuresDirTrain, trainCount + ".csv");
                outPathLabels = new File(labelsDirTrain, trainCount + ".csv");
                trainCount++;
            } else {  //それ以外はテスト用データ＆データラベルファイル作成
                outPathFeatures = new File(featuresDirTest, testCount + ".csv");
                outPathLabels = new File(labelsDirTest, testCount + ".csv");
                testCount++;
            }

            FileUtils.writeStringToFile(outPathFeatures, p.getFirst());　//データ値をデータファイルへ記載
            FileUtils.writeStringToFile(outPathLabels, p.getSecond().toString());　//データラベル値をデータラベルファイルへ記載
        }
    }
}

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package org.deeplearning4j.examples.recurrent.seqClassification;

import org.apache.commons.io.FileUtils;

import org.apache.commons.io.IOUtils;

import org.datavec.api.berkeley.Pair;

import org.datavec.api.records.reader.SequenceRecordReader;

import org.datavec.api.records.reader.impl.csv.CSVSequenceRecordReader;

import org.datavec.api.split.NumberedFileInputSplit;

import org.deeplearning4j.datasets.datavec.SequenceRecordReaderDataSetIterator;

import org.deeplearning4j.eval.Evaluation;

import org.deeplearning4j.nn.api.OptimizationAlgorithm;

import org.deeplearning4j.nn.conf.GradientNormalization;

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;

import org.deeplearning4j.nn.conf.NeuralNetConfiguration;

import org.deeplearning4j.nn.conf.Updater;

import org.deeplearning4j.nn.conf.layers.GravesLSTM;

import org.deeplearning4j.nn.conf.layers.RnnOutputLayer;

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

import org.deeplearning4j.nn.weights.WeightInit;

import org.deeplearning4j.optimize.listeners.ScoreIterationListener;

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.lossfunctions.LossFunctions;

import org.slf4j.Logger;

import org.slf4j.LoggerFactory;

import java.io.File;

import java.net.URL;

import java.util.ArrayList;

import java.util.Collections;

import java.util.List;

import java.util.Random;

/**

* Sequence Classification Example Using a LSTM Recurrent Neural Network

* @author Alex Black

**/

public class UCISequenceClassificationExample {

private static final Logger log = LoggerFactory.getLogger(UCISequenceClassificationExample.class);

//'baseDir': Base directory for the data. Change this if you want to save the data somewhere else

private static File baseDir = new File("src/main/resources/uci/");

private static File baseTrainDir = new File(baseDir, "train");

private static File featuresDirTrain = new File(baseTrainDir, "features");

private static File labelsDirTrain = new File(baseTrainDir, "labels");

private static File baseTestDir = new File(baseDir, "test");

private static File featuresDirTest = new File(baseTestDir, "features");

private static File labelsDirTest = new File(baseTestDir, "labels");

public static void main(String[] args) throws Exception {

downloadUCIData(); //UCIデータのダウンロード

// ----- Load the training data -----

//Note that we have 450 training files for features: train/features/0.csv through train/features/449.csv

SequenceRecordReader trainFeatures = new CSVSequenceRecordReader();　//訓練データ用（450データ）

trainFeatures.initialize(new NumberedFileInputSplit(featuresDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));

SequenceRecordReader trainLabels = new CSVSequenceRecordReader();　//訓練データラベル用（450データ）

trainLabels.initialize(new NumberedFileInputSplit(labelsDirTrain.getAbsolutePath() + "/%d.csv", 0, 449));

int miniBatchSize = 10; //ミニバッチサイズ

int numLabelClasses = 6; //クラス分けは６種類

DataSetIterator trainData = new SequenceRecordReaderDataSetIterator(trainFeatures, trainLabels, miniBatchSize, numLabelClasses,

false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

//Normalize the training data　　//訓練データの正規化

DataNormalization normalizer = new NormalizerStandardize();

normalizer.fit(trainData); //Collect training data statistics

trainData.reset();

//Use previously collected statistics to normalize on-the-fly. Each DataSet returned by 'trainData' iterator will be normalized

trainData.setPreProcessor(normalizer);

// ----- Load the test data -----

//Same process as for the training data.

SequenceRecordReader testFeatures = new CSVSequenceRecordReader();　//テストデータ用（150データ）

testFeatures.initialize(new NumberedFileInputSplit(featuresDirTest.getAbsolutePath() + "/%d.csv", 0, 149));

SequenceRecordReader testLabels = new CSVSequenceRecordReader();　//テストデータラベル用（150データ）

testLabels.initialize(new NumberedFileInputSplit(labelsDirTest.getAbsolutePath() + "/%d.csv", 0, 149));

DataSetIterator testData = new SequenceRecordReaderDataSetIterator(testFeatures, testLabels, miniBatchSize, numLabelClasses,

false, SequenceRecordReaderDataSetIterator.AlignmentMode.ALIGN_END);

testData.setPreProcessor(normalizer); //Note that we are using the exact same normalization process as the training data

// ----- Configure the network -----

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder() //MLCコンフ

.seed(123) //Random number generator seed for improved repeatability. Optional.

.optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT).iterations(1) //SGD法確率的勾配法

.weightInit(WeightInit.XAVIER) //初期値　ザビエル法

.updater(Updater.NESTEROVS).momentum(0.9) //ネステロフ法

.learningRate(0.005) //学習率

.gradientNormalization(GradientNormalization.ClipElementWiseAbsoluteValue) //Not always required, but helps with this data set

.gradientNormalizationThreshold(0.5)

.list()

.layer(0, new GravesLSTM.Builder().activation(Activation.TANH).nIn(1).nOut(10).build())　//入力層　第ゼロ層

.layer(1, new RnnOutputLayer.Builder(LossFunctions.LossFunction.MCXENT)

.activation(Activation.SOFTMAX).nIn(10).nOut(numLabelClasses).build())　//出力層　第1層

.pretrain(false).backprop(true).build();

MultiLayerNetwork net = new MultiLayerNetwork(conf); //MultiLayerNetworkクラスのインスタンス生成

net.init();

net.setListeners(new ScoreIterationListener(20)); //Print the score (loss function value) every 20 iterations

// ----- Train the network, evaluating the test set performance at each epoch -----

int nEpochs = 40; //エポック数20

String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";

for (int i = 0; i < nEpochs; i++) {

net.fit(trainData); //訓練データをMultiLayerNetworkクラスのインスタンスへ適用

//Evaluate on the test set:

Evaluation evaluation = net.evaluate(testData);　//評価データをMultiLayerNetworkクラスのインスタンスへ適用

log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1()));

testData.reset();

trainData.reset();

}

log.info("----- Example Complete -----");

}

//This method downloads the data, and converts the "one time series per line" format into a suitable

//CSV sequence format that DataVec (CsvSequenceRecordReader) and DL4J can read.

private static void downloadUCIData() throws Exception {　　//UCIのサイトからデータをダウンロードする関数

if (baseDir.exists()) return; //BaseDir = "src/main/resources/uci/"があるならダウンロードしない;

String url = "https://archive.ics.uci.edu/ml/machine-learning-databases/synthetic_control-mld/synthetic_control.data";

String data = IOUtils.toString(new URL(url)); //上記url からdataへダウンロード

String[] lines = data.split("\n");　//ダウンロードdataを改行コードでスプリット

//Create directories

baseDir.mkdir();

baseTrainDir.mkdir();

featuresDirTrain.mkdir();

labelsDirTrain.mkdir();

baseTestDir.mkdir();

featuresDirTest.mkdir();

labelsDirTest.mkdir();

int lineCount = 0;

List<Pair<String, Integer>> contentAndLabels = new ArrayList<>();　

// List<データ型名> オブジェクト名 = new ArrayList<データ型名>();

for (String line : lines) {

String transposed = line.replaceAll(" +", "\n"); //空白を改行へ変換

//Labels: first 100 examples (lines) are label 0, second 100 examples are label 1, and so on

contentAndLabels.add(new Pair<>(transposed, lineCount++ / 100));

}

//Randomize and do a train/test split:

Collections.shuffle(contentAndLabels, new Random(12345));

int nTrain = 450; //75% train, 25% test

int trainCount = 0;

int testCount = 0;

for (Pair<String, Integer> p : contentAndLabels) {

//Write output in a format we can read, in the appropriate locations

File outPathFeatures;　//データ値ファイル

File outPathLabels; //データラベルファイル

if (trainCount < nTrain) {　　//カウンタが450未満であれば、以下のファイル作成

outPathFeatures = new File(featuresDirTrain, trainCount + ".csv");

outPathLabels = new File(labelsDirTrain, trainCount + ".csv");

trainCount++;

} else { //それ以外はテスト用データ＆データラベルファイル作成

outPathFeatures = new File(featuresDirTest, testCount + ".csv");

outPathLabels = new File(labelsDirTest, testCount + ".csv");

testCount++;

}

FileUtils.writeStringToFile(outPathFeatures, p.getFirst());　//データ値をデータファイルへ記載

FileUtils.writeStringToFile(outPathLabels, p.getSecond().toString());　//データラベル値をデータラベルファイルへ記載

}

このプログラムの動作結果は、

o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend
o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 4
o.d.n.c.MultiLayerConfiguration - Warning: new network default sets pretrain to false.
o.d.n.c.MultiLayerConfiguration - Warning: new network default sets backprop to true.
o.n.n.Nd4jBlas - Number of threads used for BLAS: 4
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.7749994277954102
o.d.o.l.ScoreIterationListener - Score at iteration 20 is 1.6404422760009765
o.d.o.l.ScoreIterationListener - Score at iteration 40 is 1.4809866905212403
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 0: Accuracy = 0.52, F1 = 0.55
o.d.o.l.ScoreIterationListener - Score at iteration 60 is 1.4945117950439453
o.d.o.l.ScoreIterationListener - Score at iteration 80 is 1.2671661376953125
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 1: Accuracy = 0.51, F1 = 0.60
o.d.o.l.ScoreIterationListener - Score at iteration 100 is 1.2718023300170898
o.d.o.l.ScoreIterationListener - Score at iteration 120 is 1.157396125793457
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 2: Accuracy = 0.52, F1 = 0.61
o.d.o.l.ScoreIterationListener - Score at iteration 140 is 1.0480785369873047
o.d.o.l.ScoreIterationListener - Score at iteration 160 is 1.0282784461975099
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 3: Accuracy = 0.65, F1 = 0.68
o.d.o.l.ScoreIterationListener - Score at iteration 180 is 0.9446487426757812
o.d.o.l.ScoreIterationListener - Score at iteration 200 is 0.7568672180175782
o.d.o.l.ScoreIterationListener - Score at iteration 220 is 0.7893207550048829
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 4: Accuracy = 0.75, F1 = 0.74
o.d.o.l.ScoreIterationListener - Score at iteration 240 is 0.8301044464111328
o.d.o.l.ScoreIterationListener - Score at iteration 260 is 0.6512429237365722
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 5: Accuracy = 0.73, F1 = 0.74
o.d.o.l.ScoreIterationListener - Score at iteration 280 is 0.8295143127441407
o.d.o.l.ScoreIterationListener - Score at iteration 300 is 0.7406437873840332
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 6: Accuracy = 0.73, F1 = 0.74
o.d.o.l.ScoreIterationListener - Score at iteration 320 is 0.6647858619689941
o.d.o.l.ScoreIterationListener - Score at iteration 340 is 0.5469734191894531
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 7: Accuracy = 0.74, F1 = 0.77
o.d.o.l.ScoreIterationListener - Score at iteration 360 is 0.7112821578979492
o.d.o.l.ScoreIterationListener - Score at iteration 380 is 0.5319311141967773
o.d.o.l.ScoreIterationListener - Score at iteration 400 is 0.5527594566345215
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 8: Accuracy = 0.77, F1 = 0.78
o.d.o.l.ScoreIterationListener - Score at iteration 420 is 0.6434410095214844
o.d.o.l.ScoreIterationListener - Score at iteration 440 is 0.3948651313781738
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 9: Accuracy = 0.81, F1 = 0.80
o.d.o.l.ScoreIterationListener - Score at iteration 460 is 0.5814063072204589
o.d.o.l.ScoreIterationListener - Score at iteration 480 is 0.5292294979095459
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 10: Accuracy = 0.83, F1 = 0.82
o.d.o.l.ScoreIterationListener - Score at iteration 500 is 0.4800821304321289
o.d.o.l.ScoreIterationListener - Score at iteration 520 is 0.5798452377319336
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 11: Accuracy = 0.85, F1 = 0.84
o.d.o.l.ScoreIterationListener - Score at iteration 540 is 0.48386478424072266
o.d.o.l.ScoreIterationListener - Score at iteration 560 is 0.38257732391357424
o.d.o.l.ScoreIterationListener - Score at iteration 580 is 0.6095143318176269
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 12: Accuracy = 0.87, F1 = 0.86
o.d.o.l.ScoreIterationListener - Score at iteration 600 is 0.45578536987304685
o.d.o.l.ScoreIterationListener - Score at iteration 620 is 0.3001575946807861
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 13: Accuracy = 0.87, F1 = 0.87
o.d.o.l.ScoreIterationListener - Score at iteration 640 is 0.3721646785736084
o.d.o.l.ScoreIterationListener - Score at iteration 660 is 0.3161902904510498
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 14: Accuracy = 0.87, F1 = 0.86
o.d.o.l.ScoreIterationListener - Score at iteration 680 is 0.3897043943405151
o.d.o.l.ScoreIterationListener - Score at iteration 700 is 0.2337266206741333
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 15: Accuracy = 0.89, F1 = 0.89
o.d.o.l.ScoreIterationListener - Score at iteration 720 is 0.3756225109100342
o.d.o.l.ScoreIterationListener - Score at iteration 740 is 0.35205769538879395
o.d.o.l.ScoreIterationListener - Score at iteration 760 is 0.5966990947723388
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 16: Accuracy = 0.90, F1 = 0.89
o.d.o.l.ScoreIterationListener - Score at iteration 780 is 0.3741513729095459
o.d.o.l.ScoreIterationListener - Score at iteration 800 is 0.19865083694458008
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 17: Accuracy = 0.93, F1 = 0.92
o.d.o.l.ScoreIterationListener - Score at iteration 820 is 0.24331939220428467
o.d.o.l.ScoreIterationListener - Score at iteration 840 is 0.20865292549133302
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 18: Accuracy = 0.94, F1 = 0.93
o.d.o.l.ScoreIterationListener - Score at iteration 860 is 0.27184548377990725
o.d.o.l.ScoreIterationListener - Score at iteration 880 is 0.15169112682342528
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 19: Accuracy = 0.95, F1 = 0.94
o.d.o.l.ScoreIterationListener - Score at iteration 900 is 0.22165002822875976
o.d.o.l.ScoreIterationListener - Score at iteration 920 is 0.23092069625854492
o.d.o.l.ScoreIterationListener - Score at iteration 940 is 0.30478367805480955
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 20: Accuracy = 0.94, F1 = 0.93
o.d.o.l.ScoreIterationListener - Score at iteration 960 is 0.34931130409240724
o.d.o.l.ScoreIterationListener - Score at iteration 980 is 0.14310016632080078
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 21: Accuracy = 0.92, F1 = 0.92
o.d.o.l.ScoreIterationListener - Score at iteration 1000 is 0.15780065059661866
o.d.o.l.ScoreIterationListener - Score at iteration 1020 is 0.12818331718444825
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 22: Accuracy = 0.95, F1 = 0.94
o.d.o.l.ScoreIterationListener - Score at iteration 1040 is 0.17120853662490845
o.d.o.l.ScoreIterationListener - Score at iteration 1060 is 0.11253215074539184
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 23: Accuracy = 0.95, F1 = 0.95
o.d.o.l.ScoreIterationListener - Score at iteration 1080 is 0.15977177619934083
o.d.o.l.ScoreIterationListener - Score at iteration 1100 is 0.2035053253173828
o.d.o.l.ScoreIterationListener - Score at iteration 1120 is 0.17972662448883056
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 24: Accuracy = 0.96, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1140 is 0.32100744247436525
o.d.o.l.ScoreIterationListener - Score at iteration 1160 is 0.09598696231842041
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 25: Accuracy = 0.95, F1 = 0.95
o.d.o.l.ScoreIterationListener - Score at iteration 1180 is 0.10765376091003417
o.d.o.l.ScoreIterationListener - Score at iteration 1200 is 0.09369413256645202
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 26: Accuracy = 0.97, F1 = 0.97
o.d.o.l.ScoreIterationListener - Score at iteration 1220 is 0.11920651197433471
o.d.o.l.ScoreIterationListener - Score at iteration 1240 is 0.07327424287796021
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 27: Accuracy = 0.97, F1 = 0.97
o.d.o.l.ScoreIterationListener - Score at iteration 1260 is 0.10964394807815551
o.d.o.l.ScoreIterationListener - Score at iteration 1280 is 0.0825955867767334
o.d.o.l.ScoreIterationListener - Score at iteration 1300 is 0.07877322435379028
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 28: Accuracy = 0.99, F1 = 0.99
o.d.o.l.ScoreIterationListener - Score at iteration 1320 is 0.2995795726776123
o.d.o.l.ScoreIterationListener - Score at iteration 1340 is 0.06287476420402527
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 29: Accuracy = 0.97, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1360 is 0.07786862254142761
o.d.o.l.ScoreIterationListener - Score at iteration 1380 is 0.06103749275207519
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 30: Accuracy = 0.97, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1400 is 0.08928256034851074
o.d.o.l.ScoreIterationListener - Score at iteration 1420 is 0.050720810890197754
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 31: Accuracy = 0.97, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1440 is 0.08523998856544494
o.d.o.l.ScoreIterationListener - Score at iteration 1460 is 0.053772914409637454
o.d.o.l.ScoreIterationListener - Score at iteration 1480 is 0.06376101970672607
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 32: Accuracy = 0.97, F1 = 0.97
o.d.o.l.ScoreIterationListener - Score at iteration 1500 is 0.24013910293579102
o.d.o.l.ScoreIterationListener - Score at iteration 1520 is 0.05150462388992309
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 33: Accuracy = 0.97, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1540 is 0.07334737777709961
o.d.o.l.ScoreIterationListener - Score at iteration 1560 is 0.05247232913970947
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 34: Accuracy = 0.97, F1 = 0.96
o.d.o.l.ScoreIterationListener - Score at iteration 1580 is 0.27067511081695556
o.d.o.l.ScoreIterationListener - Score at iteration 1600 is 0.03868082761764526
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 35: Accuracy = 0.97, F1 = 0.97
o.d.o.l.ScoreIterationListener - Score at iteration 1620 is 0.06288898587226868
o.d.o.l.ScoreIterationListener - Score at iteration 1640 is 0.046552729606628415
o.d.o.l.ScoreIterationListener - Score at iteration 1660 is 0.05294256806373596
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 36: Accuracy = 0.96, F1 = 0.95
o.d.o.l.ScoreIterationListener - Score at iteration 1680 is 0.07542706727981567
o.d.o.l.ScoreIterationListener - Score at iteration 1700 is 0.03922495543956757
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 37: Accuracy = 0.95, F1 = 0.95
o.d.o.l.ScoreIterationListener - Score at iteration 1720 is 0.046846285462379456
o.d.o.l.ScoreIterationListener - Score at iteration 1740 is 0.03945627212524414
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 38: Accuracy = 0.95, F1 = 0.94
o.d.o.l.ScoreIterationListener - Score at iteration 1760 is 0.04861399531364441
o.d.o.l.ScoreIterationListener - Score at iteration 1780 is 0.4658164978027344
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 39: Accuracy = 0.95, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - ----- Example Complete -----

プロセスは終了コード 0 で完了しました

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o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend

o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 4

o.d.n.c.MultiLayerConfiguration - Warning: new network default sets pretrain to false.

o.d.n.c.MultiLayerConfiguration - Warning: new network default sets backprop to true.

o.n.n.Nd4jBlas - Number of threads used for BLAS: 4

o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.7749994277954102

o.d.o.l.ScoreIterationListener - Score at iteration 20 is 1.6404422760009765

o.d.o.l.ScoreIterationListener - Score at iteration 40 is 1.4809866905212403

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 0: Accuracy = 0.52, F1 = 0.55

o.d.o.l.ScoreIterationListener - Score at iteration 60 is 1.4945117950439453

o.d.o.l.ScoreIterationListener - Score at iteration 80 is 1.2671661376953125

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 1: Accuracy = 0.51, F1 = 0.60

o.d.o.l.ScoreIterationListener - Score at iteration 100 is 1.2718023300170898

o.d.o.l.ScoreIterationListener - Score at iteration 120 is 1.157396125793457

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 2: Accuracy = 0.52, F1 = 0.61

o.d.o.l.ScoreIterationListener - Score at iteration 140 is 1.0480785369873047

o.d.o.l.ScoreIterationListener - Score at iteration 160 is 1.0282784461975099

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 3: Accuracy = 0.65, F1 = 0.68

o.d.o.l.ScoreIterationListener - Score at iteration 180 is 0.9446487426757812

o.d.o.l.ScoreIterationListener - Score at iteration 200 is 0.7568672180175782

o.d.o.l.ScoreIterationListener - Score at iteration 220 is 0.7893207550048829

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 4: Accuracy = 0.75, F1 = 0.74

o.d.o.l.ScoreIterationListener - Score at iteration 240 is 0.8301044464111328

o.d.o.l.ScoreIterationListener - Score at iteration 260 is 0.6512429237365722

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 5: Accuracy = 0.73, F1 = 0.74

o.d.o.l.ScoreIterationListener - Score at iteration 280 is 0.8295143127441407

o.d.o.l.ScoreIterationListener - Score at iteration 300 is 0.7406437873840332

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 6: Accuracy = 0.73, F1 = 0.74

o.d.o.l.ScoreIterationListener - Score at iteration 320 is 0.6647858619689941

o.d.o.l.ScoreIterationListener - Score at iteration 340 is 0.5469734191894531

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 7: Accuracy = 0.74, F1 = 0.77

o.d.o.l.ScoreIterationListener - Score at iteration 360 is 0.7112821578979492

o.d.o.l.ScoreIterationListener - Score at iteration 380 is 0.5319311141967773

o.d.o.l.ScoreIterationListener - Score at iteration 400 is 0.5527594566345215

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 8: Accuracy = 0.77, F1 = 0.78

o.d.o.l.ScoreIterationListener - Score at iteration 420 is 0.6434410095214844

o.d.o.l.ScoreIterationListener - Score at iteration 440 is 0.3948651313781738

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 9: Accuracy = 0.81, F1 = 0.80

o.d.o.l.ScoreIterationListener - Score at iteration 460 is 0.5814063072204589

o.d.o.l.ScoreIterationListener - Score at iteration 480 is 0.5292294979095459

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 10: Accuracy = 0.83, F1 = 0.82

o.d.o.l.ScoreIterationListener - Score at iteration 500 is 0.4800821304321289

o.d.o.l.ScoreIterationListener - Score at iteration 520 is 0.5798452377319336

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 11: Accuracy = 0.85, F1 = 0.84

o.d.o.l.ScoreIterationListener - Score at iteration 540 is 0.48386478424072266

o.d.o.l.ScoreIterationListener - Score at iteration 560 is 0.38257732391357424

o.d.o.l.ScoreIterationListener - Score at iteration 580 is 0.6095143318176269

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 12: Accuracy = 0.87, F1 = 0.86

o.d.o.l.ScoreIterationListener - Score at iteration 600 is 0.45578536987304685

o.d.o.l.ScoreIterationListener - Score at iteration 620 is 0.3001575946807861

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 13: Accuracy = 0.87, F1 = 0.87

o.d.o.l.ScoreIterationListener - Score at iteration 640 is 0.3721646785736084

o.d.o.l.ScoreIterationListener - Score at iteration 660 is 0.3161902904510498

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 14: Accuracy = 0.87, F1 = 0.86

o.d.o.l.ScoreIterationListener - Score at iteration 680 is 0.3897043943405151

o.d.o.l.ScoreIterationListener - Score at iteration 700 is 0.2337266206741333

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 15: Accuracy = 0.89, F1 = 0.89

o.d.o.l.ScoreIterationListener - Score at iteration 720 is 0.3756225109100342

o.d.o.l.ScoreIterationListener - Score at iteration 740 is 0.35205769538879395

o.d.o.l.ScoreIterationListener - Score at iteration 760 is 0.5966990947723388

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 16: Accuracy = 0.90, F1 = 0.89

o.d.o.l.ScoreIterationListener - Score at iteration 780 is 0.3741513729095459

o.d.o.l.ScoreIterationListener - Score at iteration 800 is 0.19865083694458008

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 17: Accuracy = 0.93, F1 = 0.92

o.d.o.l.ScoreIterationListener - Score at iteration 820 is 0.24331939220428467

o.d.o.l.ScoreIterationListener - Score at iteration 840 is 0.20865292549133302

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 18: Accuracy = 0.94, F1 = 0.93

o.d.o.l.ScoreIterationListener - Score at iteration 860 is 0.27184548377990725

o.d.o.l.ScoreIterationListener - Score at iteration 880 is 0.15169112682342528

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 19: Accuracy = 0.95, F1 = 0.94

o.d.o.l.ScoreIterationListener - Score at iteration 900 is 0.22165002822875976

o.d.o.l.ScoreIterationListener - Score at iteration 920 is 0.23092069625854492

o.d.o.l.ScoreIterationListener - Score at iteration 940 is 0.30478367805480955

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 20: Accuracy = 0.94, F1 = 0.93

o.d.o.l.ScoreIterationListener - Score at iteration 960 is 0.34931130409240724

o.d.o.l.ScoreIterationListener - Score at iteration 980 is 0.14310016632080078

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 21: Accuracy = 0.92, F1 = 0.92

o.d.o.l.ScoreIterationListener - Score at iteration 1000 is 0.15780065059661866

o.d.o.l.ScoreIterationListener - Score at iteration 1020 is 0.12818331718444825

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 22: Accuracy = 0.95, F1 = 0.94

o.d.o.l.ScoreIterationListener - Score at iteration 1040 is 0.17120853662490845

o.d.o.l.ScoreIterationListener - Score at iteration 1060 is 0.11253215074539184

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 23: Accuracy = 0.95, F1 = 0.95

o.d.o.l.ScoreIterationListener - Score at iteration 1080 is 0.15977177619934083

o.d.o.l.ScoreIterationListener - Score at iteration 1100 is 0.2035053253173828

o.d.o.l.ScoreIterationListener - Score at iteration 1120 is 0.17972662448883056

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 24: Accuracy = 0.96, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1140 is 0.32100744247436525

o.d.o.l.ScoreIterationListener - Score at iteration 1160 is 0.09598696231842041

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 25: Accuracy = 0.95, F1 = 0.95

o.d.o.l.ScoreIterationListener - Score at iteration 1180 is 0.10765376091003417

o.d.o.l.ScoreIterationListener - Score at iteration 1200 is 0.09369413256645202

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 26: Accuracy = 0.97, F1 = 0.97

o.d.o.l.ScoreIterationListener - Score at iteration 1220 is 0.11920651197433471

o.d.o.l.ScoreIterationListener - Score at iteration 1240 is 0.07327424287796021

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 27: Accuracy = 0.97, F1 = 0.97

o.d.o.l.ScoreIterationListener - Score at iteration 1260 is 0.10964394807815551

o.d.o.l.ScoreIterationListener - Score at iteration 1280 is 0.0825955867767334

o.d.o.l.ScoreIterationListener - Score at iteration 1300 is 0.07877322435379028

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 28: Accuracy = 0.99, F1 = 0.99

o.d.o.l.ScoreIterationListener - Score at iteration 1320 is 0.2995795726776123

o.d.o.l.ScoreIterationListener - Score at iteration 1340 is 0.06287476420402527

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 29: Accuracy = 0.97, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1360 is 0.07786862254142761

o.d.o.l.ScoreIterationListener - Score at iteration 1380 is 0.06103749275207519

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 30: Accuracy = 0.97, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1400 is 0.08928256034851074

o.d.o.l.ScoreIterationListener - Score at iteration 1420 is 0.050720810890197754

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 31: Accuracy = 0.97, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1440 is 0.08523998856544494

o.d.o.l.ScoreIterationListener - Score at iteration 1460 is 0.053772914409637454

o.d.o.l.ScoreIterationListener - Score at iteration 1480 is 0.06376101970672607

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 32: Accuracy = 0.97, F1 = 0.97

o.d.o.l.ScoreIterationListener - Score at iteration 1500 is 0.24013910293579102

o.d.o.l.ScoreIterationListener - Score at iteration 1520 is 0.05150462388992309

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 33: Accuracy = 0.97, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1540 is 0.07334737777709961

o.d.o.l.ScoreIterationListener - Score at iteration 1560 is 0.05247232913970947

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 34: Accuracy = 0.97, F1 = 0.96

o.d.o.l.ScoreIterationListener - Score at iteration 1580 is 0.27067511081695556

o.d.o.l.ScoreIterationListener - Score at iteration 1600 is 0.03868082761764526

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 35: Accuracy = 0.97, F1 = 0.97

o.d.o.l.ScoreIterationListener - Score at iteration 1620 is 0.06288898587226868

o.d.o.l.ScoreIterationListener - Score at iteration 1640 is 0.046552729606628415

o.d.o.l.ScoreIterationListener - Score at iteration 1660 is 0.05294256806373596

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 36: Accuracy = 0.96, F1 = 0.95

o.d.o.l.ScoreIterationListener - Score at iteration 1680 is 0.07542706727981567

o.d.o.l.ScoreIterationListener - Score at iteration 1700 is 0.03922495543956757

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 37: Accuracy = 0.95, F1 = 0.95

o.d.o.l.ScoreIterationListener - Score at iteration 1720 is 0.046846285462379456

o.d.o.l.ScoreIterationListener - Score at iteration 1740 is 0.03945627212524414

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 38: Accuracy = 0.95, F1 = 0.94

o.d.o.l.ScoreIterationListener - Score at iteration 1760 is 0.04861399531364441

o.d.o.l.ScoreIterationListener - Score at iteration 1780 is 0.4658164978027344

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 39: Accuracy = 0.95, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample - ----- Example Complete -----

プロセスは終了コード 0 で完了しました

以下のコードをトレーニング、評価コードに加えることで、

          log.info(evaluation.stats());
          System.out.println(evaluation.confusionToString());

1 2	log.info(evaluation.stats()); System.out.println(evaluation.confusionToString());

評価結果を随時、トラッキングできる。

// ----- Train the network, evaluating the test set performance at each epoch -----
        int nEpochs = 40;
        String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";
        for (int i = 0; i < nEpochs; i++) {
            net.fit(trainData);

            //Evaluate on the test set:
            Evaluation evaluation = net.evaluate(testData);
            log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1()));

            log.info(evaluation.stats());
            System.out.println(evaluation.confusionToString());

            testData.reset();
            trainData.reset();
        }

        log.info("----- Example Complete -----")

// ----- Train the network, evaluating the test set performance at each epoch -----

int nEpochs = 40;

String str = "Test set evaluation at epoch %d: Accuracy = %.2f, F1 = %.2f";

for (int i = 0; i < nEpochs; i++) {

net.fit(trainData);

//Evaluate on the test set:

Evaluation evaluation = net.evaluate(testData);

log.info(String.format(str, i, evaluation.accuracy(), evaluation.f1()));

log.info(evaluation.stats());

System.out.println(evaluation.confusionToString());

testData.reset();

trainData.reset();

}

log.info("----- Example Complete -----")

トレーニングが進む経過と、正解率の向上を観察できる出力結果は、以下の通り：

o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend
o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 4
o.d.n.c.MultiLayerConfiguration - Warning: new network default sets pretrain to false.
o.d.n.c.MultiLayerConfiguration - Warning: new network default sets backprop to true.
o.n.n.Nd4jBlas - Number of threads used for BLAS: 4
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.7749994277954102
o.d.o.l.ScoreIterationListener - Score at iteration 20 is 1.6404422760009765
o.d.o.l.ScoreIterationListener - Score at iteration 40 is 1.4809866905212403
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 0: Accuracy = 0.52, F1 = 0.55
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 0: 14 times
Examples labeled as 1 classified by model as 1: 7 times
Examples labeled as 1 classified by model as 2: 3 times
Examples labeled as 1 classified by model as 4: 3 times
Examples labeled as 1 classified by model as 5: 2 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 5: 21 times
Examples labeled as 4 classified by model as 2: 29 times
Examples labeled as 4 classified by model as 4: 1 times
Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================
 Accuracy:        0.52
 Precision:       0.5593
 Recall:          0.5458
 F1 Score:        0.5524
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |      14      7      3      0      3      2
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0      0      0     21
4  4          |       0      0     29      0      1      0
5  5          |       0      0      0      0      0     22

o.d.o.l.ScoreIterationListener - Score at iteration 60 is 1.4945117950439453
o.d.o.l.ScoreIterationListener - Score at iteration 80 is 1.2671661376953125
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 1: Accuracy = 0.51, F1 = 0.60
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 0: 23 times
Examples labeled as 1 classified by model as 1: 5 times
Examples labeled as 1 classified by model as 2: 1 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 5: 21 times
Examples labeled as 4 classified by model as 2: 29 times
Examples labeled as 4 classified by model as 4: 1 times
Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================
 Accuracy:        0.5067
 Precision:       0.6931
 Recall:          0.5343
 F1 Score:        0.6034
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |      23      5      1      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0      0      0     21
4  4          |       0      0     29      0      1      0
5  5          |       0      0      0      0      0     22

o.d.o.l.ScoreIterationListener - Score at iteration 100 is 1.2718023300170898
o.d.o.l.ScoreIterationListener - Score at iteration 120 is 1.157396125793457
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 2: Accuracy = 0.52, F1 = 0.61
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 0: 21 times
Examples labeled as 1 classified by model as 1: 7 times
Examples labeled as 1 classified by model as 2: 1 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 5: 21 times
Examples labeled as 4 classified by model as 2: 29 times
Examples labeled as 4 classified by model as 4: 1 times
Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================
 Accuracy:        0.52
 Precision:       0.6976
 Recall:          0.5458
 F1 Score:        0.6124
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |      21      7      1      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0      0      0     21
4  4          |       0      0     29      0      1      0
5  5          |       0      0      0      0      0     22

o.d.o.l.ScoreIterationListener - Score at iteration 140 is 1.0480785369873047
o.d.o.l.ScoreIterationListener - Score at iteration 160 is 1.0282784461975099
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 3: Accuracy = 0.65, F1 = 0.68
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 0: 3 times
Examples labeled as 1 classified by model as 1: 25 times
Examples labeled as 1 classified by model as 4: 1 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 5: 21 times
Examples labeled as 4 classified by model as 2: 28 times
Examples labeled as 4 classified by model as 4: 2 times
Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================
 Accuracy:        0.6467
 Precision:       0.703
 Recall:          0.6548
 F1 Score:        0.678
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       3     25      0      0      1      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0      0      0     21
4  4          |       0      0     28      0      2      0
5  5          |       0      0      0      0      0     22

o.d.o.l.ScoreIterationListener - Score at iteration 180 is 0.9446487426757812
o.d.o.l.ScoreIterationListener - Score at iteration 200 is 0.7568672180175782
o.d.o.l.ScoreIterationListener - Score at iteration 220 is 0.7893207550048829
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 4: Accuracy = 0.75, F1 = 0.74
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 28 times
Examples labeled as 1 classified by model as 5: 1 times
Examples labeled as 2 classified by model as 2: 13 times
Examples labeled as 2 classified by model as 4: 9 times
Examples labeled as 3 classified by model as 5: 21 times
Examples labeled as 4 classified by model as 2: 6 times
Examples labeled as 4 classified by model as 4: 24 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 5: 21 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================
 Accuracy:        0.7467
 Precision:       0.7726
 Recall:          0.7185
 F1 Score:        0.7445
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     28      0      0      0      1
2  2          |       0      0     13      0      9      0
3  3          |       0      0      0      0      0     21
4  4          |       0      0      6      0     24      0
5  5          |       1      0      0      0      0     21

o.d.o.l.ScoreIterationListener - Score at iteration 240 is 0.8301044464111328
o.d.o.l.ScoreIterationListener - Score at iteration 260 is 0.6512429237365722
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 5: Accuracy = 0.73, F1 = 0.74
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 14 times
Examples labeled as 2 classified by model as 4: 8 times
Examples labeled as 3 classified by model as 3: 9 times
Examples labeled as 3 classified by model as 5: 12 times
Examples labeled as 4 classified by model as 2: 18 times
Examples labeled as 4 classified by model as 4: 12 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.7333
 Precision:       0.7542
 Recall:          0.729
 F1 Score:        0.7414
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     14      0      8      0
3  3          |       0      0      0      9      0     12
4  4          |       0      0     18      0     12      0
5  5          |       1      0      0      1      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 280 is 0.8295143127441407
o.d.o.l.ScoreIterationListener - Score at iteration 300 is 0.7406437873840332
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 6: Accuracy = 0.73, F1 = 0.74
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 20 times
Examples labeled as 2 classified by model as 4: 2 times
Examples labeled as 3 classified by model as 3: 11 times
Examples labeled as 3 classified by model as 5: 10 times
Examples labeled as 4 classified by model as 2: 23 times
Examples labeled as 4 classified by model as 4: 7 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 3: 5 times
Examples labeled as 5 classified by model as 5: 16 times


==========================Scores========================================
 Accuracy:        0.7267
 Precision:       0.7515
 Recall:          0.7323
 F1 Score:        0.7417
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     20      0      2      0
3  3          |       0      0      0     11      0     10
4  4          |       0      0     23      0      7      0
5  5          |       1      0      0      5      0     16

o.d.o.l.ScoreIterationListener - Score at iteration 320 is 0.6647858619689941
o.d.o.l.ScoreIterationListener - Score at iteration 340 is 0.5469734191894531
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 7: Accuracy = 0.74, F1 = 0.77
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 11 times
Examples labeled as 3 classified by model as 5: 10 times
Examples labeled as 4 classified by model as 2: 23 times
Examples labeled as 4 classified by model as 4: 7 times
Examples labeled as 5 classified by model as 3: 6 times
Examples labeled as 5 classified by model as 5: 16 times


==========================Scores========================================
 Accuracy:        0.74
 Precision:       0.7919
 Recall:          0.7474
 F1 Score:        0.769
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     11      0     10
4  4          |       0      0     23      0      7      0
5  5          |       0      0      0      6      0     16

o.d.o.l.ScoreIterationListener - Score at iteration 360 is 0.7112821578979492
o.d.o.l.ScoreIterationListener - Score at iteration 380 is 0.5319311141967773
o.d.o.l.ScoreIterationListener - Score at iteration 400 is 0.5527594566345215
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 8: Accuracy = 0.77, F1 = 0.78
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 10 times
Examples labeled as 3 classified by model as 5: 11 times
Examples labeled as 4 classified by model as 2: 17 times
Examples labeled as 4 classified by model as 4: 13 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 3: 6 times
Examples labeled as 5 classified by model as 5: 15 times


==========================Scores========================================
 Accuracy:        0.7667
 Precision:       0.7882
 Recall:          0.7652
 F1 Score:        0.7765
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     10      0     11
4  4          |       0      0     17      0     13      0
5  5          |       1      0      0      6      0     15

o.d.o.l.ScoreIterationListener - Score at iteration 420 is 0.6434410095214844
o.d.o.l.ScoreIterationListener - Score at iteration 440 is 0.3948651313781738
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 9: Accuracy = 0.81, F1 = 0.80
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 10 times
Examples labeled as 3 classified by model as 5: 11 times
Examples labeled as 4 classified by model as 2: 8 times
Examples labeled as 4 classified by model as 4: 22 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 1: 2 times
Examples labeled as 5 classified by model as 3: 6 times
Examples labeled as 5 classified by model as 5: 13 times


==========================Scores========================================
 Accuracy:        0.8133
 Precision:       0.7997
 Recall:          0.8001
 F1 Score:        0.7999
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     10      0     11
4  4          |       0      0      8      0     22      0
5  5          |       1      2      0      6      0     13

o.d.o.l.ScoreIterationListener - Score at iteration 460 is 0.5814063072204589
o.d.o.l.ScoreIterationListener - Score at iteration 480 is 0.5292294979095459
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 10: Accuracy = 0.83, F1 = 0.82
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 10 times
Examples labeled as 3 classified by model as 5: 11 times
Examples labeled as 4 classified by model as 2: 8 times
Examples labeled as 4 classified by model as 4: 22 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 3: 6 times
Examples labeled as 5 classified by model as 5: 15 times


==========================Scores========================================
 Accuracy:        0.8267
 Precision:       0.8164
 Recall:          0.8152
 F1 Score:        0.8158
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     10      0     11
4  4          |       0      0      8      0     22      0
5  5          |       1      0      0      6      0     15

o.d.o.l.ScoreIterationListener - Score at iteration 500 is 0.4800821304321289
o.d.o.l.ScoreIterationListener - Score at iteration 520 is 0.5798452377319336
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 11: Accuracy = 0.85, F1 = 0.84
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 14 times
Examples labeled as 3 classified by model as 5: 7 times
Examples labeled as 4 classified by model as 2: 4 times
Examples labeled as 4 classified by model as 4: 26 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 1: 2 times
Examples labeled as 5 classified by model as 3: 8 times
Examples labeled as 5 classified by model as 5: 11 times


==========================Scores========================================
 Accuracy:        0.8533
 Precision:       0.832
 Recall:          0.8389
 F1 Score:        0.8354
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     14      0      7
4  4          |       0      0      4      0     26      0
5  5          |       1      2      0      8      0     11

o.d.o.l.ScoreIterationListener - Score at iteration 540 is 0.48386478424072266
o.d.o.l.ScoreIterationListener - Score at iteration 560 is 0.38257732391357424
o.d.o.l.ScoreIterationListener - Score at iteration 580 is 0.6095143318176269
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 12: Accuracy = 0.87, F1 = 0.86
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 13 times
Examples labeled as 3 classified by model as 5: 8 times
Examples labeled as 4 classified by model as 2: 4 times
Examples labeled as 4 classified by model as 4: 26 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 1: 1 times
Examples labeled as 5 classified by model as 3: 5 times
Examples labeled as 5 classified by model as 5: 15 times


==========================Scores========================================
 Accuracy:        0.8733
 Precision:       0.8584
 Recall:          0.8613
 F1 Score:        0.8598
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     13      0      8
4  4          |       0      0      4      0     26      0
5  5          |       1      1      0      5      0     15

o.d.o.l.ScoreIterationListener - Score at iteration 600 is 0.45578536987304685
o.d.o.l.ScoreIterationListener - Score at iteration 620 is 0.3001575946807861
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 13: Accuracy = 0.87, F1 = 0.87
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 24 times
Examples labeled as 0 classified by model as 1: 2 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 14 times
Examples labeled as 3 classified by model as 5: 7 times
Examples labeled as 4 classified by model as 2: 4 times
Examples labeled as 4 classified by model as 4: 26 times
Examples labeled as 5 classified by model as 1: 2 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 16 times


==========================Scores========================================
 Accuracy:        0.8733
 Precision:       0.8664
 Recall:          0.8639
 F1 Score:        0.8652
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      24      2      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     14      0      7
4  4          |       0      0      4      0     26      0
5  5          |       0      2      0      4      0     16

o.d.o.l.ScoreIterationListener - Score at iteration 640 is 0.3721646785736084
o.d.o.l.ScoreIterationListener - Score at iteration 660 is 0.3161902904510498
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 14: Accuracy = 0.87, F1 = 0.86
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 15 times
Examples labeled as 3 classified by model as 5: 6 times
Examples labeled as 4 classified by model as 2: 5 times
Examples labeled as 4 classified by model as 4: 25 times
Examples labeled as 5 classified by model as 1: 1 times
Examples labeled as 5 classified by model as 3: 7 times
Examples labeled as 5 classified by model as 5: 14 times


==========================Scores========================================
 Accuracy:        0.8733
 Precision:       0.8605
 Recall:          0.864
 F1 Score:        0.8623
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     15      0      6
4  4          |       0      0      5      0     25      0
5  5          |       0      1      0      7      0     14

o.d.o.l.ScoreIterationListener - Score at iteration 680 is 0.3897043943405151
o.d.o.l.ScoreIterationListener - Score at iteration 700 is 0.2337266206741333
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 15: Accuracy = 0.89, F1 = 0.89
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 16 times
Examples labeled as 3 classified by model as 5: 5 times
Examples labeled as 4 classified by model as 2: 5 times
Examples labeled as 4 classified by model as 4: 25 times
Examples labeled as 5 classified by model as 3: 6 times
Examples labeled as 5 classified by model as 5: 16 times


==========================Scores========================================
 Accuracy:        0.8933
 Precision:       0.884
 Recall:          0.8871
 F1 Score:        0.8855
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     16      0      5
4  4          |       0      0      5      0     25      0
5  5          |       0      0      0      6      0     16

o.d.o.l.ScoreIterationListener - Score at iteration 720 is 0.3756225109100342
o.d.o.l.ScoreIterationListener - Score at iteration 740 is 0.35205769538879395
o.d.o.l.ScoreIterationListener - Score at iteration 760 is 0.5966990947723388
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 16: Accuracy = 0.90, F1 = 0.89
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 16 times
Examples labeled as 3 classified by model as 5: 5 times
Examples labeled as 4 classified by model as 2: 4 times
Examples labeled as 4 classified by model as 4: 26 times
Examples labeled as 5 classified by model as 3: 5 times
Examples labeled as 5 classified by model as 5: 17 times


==========================Scores========================================
 Accuracy:        0.9
 Precision:       0.8896
 Recall:          0.8926
 F1 Score:        0.8911
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     16      0      5
4  4          |       0      0      4      0     26      0
5  5          |       0      0      0      5      0     17

o.d.o.l.ScoreIterationListener - Score at iteration 780 is 0.3741513729095459
o.d.o.l.ScoreIterationListener - Score at iteration 800 is 0.19865083694458008
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 17: Accuracy = 0.93, F1 = 0.92
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 17 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 5 times
Examples labeled as 5 classified by model as 5: 17 times


==========================Scores========================================
 Accuracy:        0.9333
 Precision:       0.925
 Recall:          0.9228
 F1 Score:        0.9239
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     17      0      4
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      5      0     17

o.d.o.l.ScoreIterationListener - Score at iteration 820 is 0.24331939220428467
o.d.o.l.ScoreIterationListener - Score at iteration 840 is 0.20865292549133302
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 18: Accuracy = 0.94, F1 = 0.93
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 17 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 5 times
Examples labeled as 5 classified by model as 5: 17 times


==========================Scores========================================
 Accuracy:        0.94
 Precision:       0.9304
 Recall:          0.9304
 F1 Score:        0.9304
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     17      0      4
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      5      0     17

o.d.o.l.ScoreIterationListener - Score at iteration 860 is 0.27184548377990725
o.d.o.l.ScoreIterationListener - Score at iteration 880 is 0.15169112682342528
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 19: Accuracy = 0.95, F1 = 0.94
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 28 times
Examples labeled as 1 classified by model as 5: 1 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.9467
 Precision:       0.9394
 Recall:          0.9401
 F1 Score:        0.9398
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     28      0      0      0      1
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 900 is 0.22165002822875976
o.d.o.l.ScoreIterationListener - Score at iteration 920 is 0.23092069625854492
o.d.o.l.ScoreIterationListener - Score at iteration 940 is 0.30478367805480955
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 20: Accuracy = 0.94, F1 = 0.93
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 25 times
Examples labeled as 0 classified by model as 5: 1 times
Examples labeled as 1 classified by model as 0: 1 times
Examples labeled as 1 classified by model as 1: 28 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 19 times
Examples labeled as 3 classified by model as 5: 2 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.94
 Precision:       0.9354
 Recall:          0.9341
 F1 Score:        0.9348
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      25      0      0      0      0      1
1  1          |       1     28      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     19      0      2
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 960 is 0.34931130409240724
o.d.o.l.ScoreIterationListener - Score at iteration 980 is 0.14310016632080078
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 21: Accuracy = 0.92, F1 = 0.92
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 23 times
Examples labeled as 0 classified by model as 1: 1 times
Examples labeled as 0 classified by model as 5: 2 times
Examples labeled as 1 classified by model as 0: 1 times
Examples labeled as 1 classified by model as 1: 28 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 20 times
Examples labeled as 3 classified by model as 5: 1 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 1: 1 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 17 times


==========================Scores========================================
 Accuracy:        0.92
 Precision:       0.916
 Recall:          0.9161
 F1 Score:        0.9161
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      23      1      0      0      0      2
1  1          |       1     28      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     20      0      1
4  4          |       0      0      1      0     29      0
5  5          |       0      1      0      4      0     17

o.d.o.l.ScoreIterationListener - Score at iteration 1000 is 0.15780065059661866
o.d.o.l.ScoreIterationListener - Score at iteration 1020 is 0.12818331718444825
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 22: Accuracy = 0.95, F1 = 0.94
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 20 times
Examples labeled as 3 classified by model as 5: 1 times
Examples labeled as 4 classified by model as 2: 2 times
Examples labeled as 4 classified by model as 4: 28 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.9467
 Precision:       0.9432
 Recall:          0.9431
 F1 Score:        0.9431
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     20      0      1
4  4          |       0      0      2      0     28      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 1040 is 0.17120853662490845
o.d.o.l.ScoreIterationListener - Score at iteration 1060 is 0.11253215074539184
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 23: Accuracy = 0.95, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 3 times
Examples labeled as 5 classified by model as 5: 19 times


==========================Scores========================================
 Accuracy:        0.9533
 Precision:       0.9481
 Recall:          0.9459
 F1 Score:        0.947
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      3      0     19

o.d.o.l.ScoreIterationListener - Score at iteration 1080 is 0.15977177619934083
o.d.o.l.ScoreIterationListener - Score at iteration 1100 is 0.2035053253173828
o.d.o.l.ScoreIterationListener - Score at iteration 1120 is 0.17972662448883056
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 24: Accuracy = 0.96, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 20 times
Examples labeled as 3 classified by model as 5: 1 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.96
 Precision:       0.9581
 Recall:          0.9542
 F1 Score:        0.9561
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     20      0      1
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 1140 is 0.32100744247436525
o.d.o.l.ScoreIterationListener - Score at iteration 1160 is 0.09598696231842041
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 25: Accuracy = 0.95, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 20 times
Examples labeled as 3 classified by model as 5: 1 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.9533
 Precision:       0.9503
 Recall:          0.9486
 F1 Score:        0.9495
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     20      0      1
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 1180 is 0.10765376091003417
o.d.o.l.ScoreIterationListener - Score at iteration 1200 is 0.09369413256645202
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 26: Accuracy = 0.97, F1 = 0.97
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 21 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 4 times
Examples labeled as 5 classified by model as 5: 18 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.9661
 Recall:          0.9641
 F1 Score:        0.9651
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     21      0      0
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      4      0     18

o.d.o.l.ScoreIterationListener - Score at iteration 1220 is 0.11920651197433471
o.d.o.l.ScoreIterationListener - Score at iteration 1240 is 0.07327424287796021
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 27: Accuracy = 0.97, F1 = 0.97
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 21 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 3 times
Examples labeled as 5 classified by model as 5: 19 times


==========================Scores========================================
 Accuracy:        0.9733
 Precision:       0.9719
 Recall:          0.9717
 F1 Score:        0.9718
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     21      0      0
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      3      0     19

o.d.o.l.ScoreIterationListener - Score at iteration 1260 is 0.10964394807815551
o.d.o.l.ScoreIterationListener - Score at iteration 1280 is 0.0825955867767334
o.d.o.l.ScoreIterationListener - Score at iteration 1300 is 0.07877322435379028
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 28: Accuracy = 0.99, F1 = 0.99
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 21 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 21 times


==========================Scores========================================
 Accuracy:        0.9867
 Precision:       0.9852
 Recall:          0.9869
 F1 Score:        0.986
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     21      0      0
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      1      0     21

o.d.o.l.ScoreIterationListener - Score at iteration 1320 is 0.2995795726776123
o.d.o.l.ScoreIterationListener - Score at iteration 1340 is 0.06287476420402527
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 29: Accuracy = 0.97, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 19 times
Examples labeled as 3 classified by model as 5: 2 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 21 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.964
 Recall:          0.9634
 F1 Score:        0.9637
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     19      0      2
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      1      0     21

o.d.o.l.ScoreIterationListener - Score at iteration 1360 is 0.07786862254142761
o.d.o.l.ScoreIterationListener - Score at iteration 1380 is 0.06103749275207519
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 30: Accuracy = 0.97, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 19 times
Examples labeled as 3 classified by model as 5: 2 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 2 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.9617
 Recall:          0.9634
 F1 Score:        0.9626
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     19      0      2
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      2      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1400 is 0.08928256034851074
o.d.o.l.ScoreIterationListener - Score at iteration 1420 is 0.050720810890197754
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 31: Accuracy = 0.97, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 21 times
Examples labeled as 2 classified by model as 4: 1 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 21 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.965
 Recall:          0.961
 F1 Score:        0.963
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     21      0      1      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      1      0     21

o.d.o.l.ScoreIterationListener - Score at iteration 1440 is 0.08523998856544494
o.d.o.l.ScoreIterationListener - Score at iteration 1460 is 0.053772914409637454
o.d.o.l.ScoreIterationListener - Score at iteration 1480 is 0.06376101970672607
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 32: Accuracy = 0.97, F1 = 0.97
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 17 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 5: 22 times


==========================Scores========================================
 Accuracy:        0.9733
 Precision:       0.9744
 Recall:          0.9683
 F1 Score:        0.9713
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     17      0      4
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      0      0     22

o.d.o.l.ScoreIterationListener - Score at iteration 1500 is 0.24013910293579102
o.d.o.l.ScoreIterationListener - Score at iteration 1520 is 0.05150462388992309
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 33: Accuracy = 0.97, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.9633
 Recall:          0.961
 F1 Score:        0.9622
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       1      0      0      1      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1540 is 0.07334737777709961
o.d.o.l.ScoreIterationListener - Score at iteration 1560 is 0.05247232913970947
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 34: Accuracy = 0.97, F1 = 0.96
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 3: 2 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9667
 Precision:       0.9616
 Recall:          0.961
 F1 Score:        0.9613
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       0      0      0      2      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1580 is 0.27067511081695556
o.d.o.l.ScoreIterationListener - Score at iteration 1600 is 0.03868082761764526
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 35: Accuracy = 0.97, F1 = 0.97
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 4: 30 times
Examples labeled as 5 classified by model as 0: 1 times
Examples labeled as 5 classified by model as 5: 21 times


==========================Scores========================================
 Accuracy:        0.9733
 Precision:       0.973
 Recall:          0.9686
 F1 Score:        0.9708
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      0      0     30      0
5  5          |       1      0      0      0      0     21

o.d.o.l.ScoreIterationListener - Score at iteration 1620 is 0.06288898587226868
o.d.o.l.ScoreIterationListener - Score at iteration 1640 is 0.046552729606628415
o.d.o.l.ScoreIterationListener - Score at iteration 1660 is 0.05294256806373596
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 36: Accuracy = 0.96, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 18 times
Examples labeled as 3 classified by model as 5: 3 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 2 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.96
 Precision:       0.9543
 Recall:          0.9555
 F1 Score:        0.9549
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     18      0      3
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      2      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1680 is 0.07542706727981567
o.d.o.l.ScoreIterationListener - Score at iteration 1700 is 0.03922495543956757
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 37: Accuracy = 0.95, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 17 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 1: 1 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9533
 Precision:       0.9502
 Recall:          0.9475
 F1 Score:        0.9489
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     17      0      4
4  4          |       0      0      1      0     29      0
5  5          |       0      1      0      1      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1720 is 0.046846285462379456
o.d.o.l.ScoreIterationListener - Score at iteration 1740 is 0.03945627212524414
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 38: Accuracy = 0.95, F1 = 0.94
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 16 times
Examples labeled as 3 classified by model as 5: 5 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 1: 1 times
Examples labeled as 5 classified by model as 3: 1 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9467
 Precision:       0.9441
 Recall:          0.9396
 F1 Score:        0.9418
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     16      0      5
4  4          |       0      0      1      0     29      0
5  5          |       0      1      0      1      0     20

o.d.o.l.ScoreIterationListener - Score at iteration 1760 is 0.04861399531364441
o.d.o.l.ScoreIterationListener - Score at iteration 1780 is 0.4658164978027344
o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 39: Accuracy = 0.95, F1 = 0.95
o.d.e.r.s.UCISequenceClassificationExample - 
Examples labeled as 0 classified by model as 0: 26 times
Examples labeled as 1 classified by model as 1: 29 times
Examples labeled as 2 classified by model as 2: 22 times
Examples labeled as 3 classified by model as 3: 17 times
Examples labeled as 3 classified by model as 5: 4 times
Examples labeled as 4 classified by model as 2: 1 times
Examples labeled as 4 classified by model as 4: 29 times
Examples labeled as 5 classified by model as 3: 2 times
Examples labeled as 5 classified by model as 5: 20 times


==========================Scores========================================
 Accuracy:        0.9533
 Precision:       0.9474
 Recall:          0.9475
 F1 Score:        0.9475
========================================================================
   Predicted:         0      1      2      3      4      5
   Actual:
0  0          |      26      0      0      0      0      0
1  1          |       0     29      0      0      0      0
2  2          |       0      0     22      0      0      0
3  3          |       0      0      0     17      0      4
4  4          |       0      0      1      0     29      0
5  5          |       0      0      0      2      0     20

o.d.e.r.s.UCISequenceClassificationExample - ----- Example Complete -----

プロセスは終了コード 0 で完了しました

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o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend

o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 4

o.d.n.c.MultiLayerConfiguration - Warning: new network default sets pretrain to false.

o.d.n.c.MultiLayerConfiguration - Warning: new network default sets backprop to true.

o.n.n.Nd4jBlas - Number of threads used for BLAS: 4

o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.7749994277954102

o.d.o.l.ScoreIterationListener - Score at iteration 20 is 1.6404422760009765

o.d.o.l.ScoreIterationListener - Score at iteration 40 is 1.4809866905212403

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 0: Accuracy = 0.52, F1 = 0.55

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 0: 14 times

Examples labeled as 1 classified by model as 1: 7 times

Examples labeled as 1 classified by model as 2: 3 times

Examples labeled as 1 classified by model as 4: 3 times

Examples labeled as 1 classified by model as 5: 2 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 5: 21 times

Examples labeled as 4 classified by model as 2: 29 times

Examples labeled as 4 classified by model as 4: 1 times

Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================

Accuracy: 0.52

Precision: 0.5593

Recall: 0.5458

F1 Score: 0.5524

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 14 7 3 0 3 2

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 0 0 21

4 4 | 0 0 29 0 1 0

5 5 | 0 0 0 0 0 22

o.d.o.l.ScoreIterationListener - Score at iteration 60 is 1.4945117950439453

o.d.o.l.ScoreIterationListener - Score at iteration 80 is 1.2671661376953125

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 1: Accuracy = 0.51, F1 = 0.60

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 0: 23 times

Examples labeled as 1 classified by model as 1: 5 times

Examples labeled as 1 classified by model as 2: 1 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 5: 21 times

Examples labeled as 4 classified by model as 2: 29 times

Examples labeled as 4 classified by model as 4: 1 times

Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================

Accuracy: 0.5067

Precision: 0.6931

Recall: 0.5343

F1 Score: 0.6034

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 23 5 1 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 0 0 21

4 4 | 0 0 29 0 1 0

5 5 | 0 0 0 0 0 22

o.d.o.l.ScoreIterationListener - Score at iteration 100 is 1.2718023300170898

o.d.o.l.ScoreIterationListener - Score at iteration 120 is 1.157396125793457

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 2: Accuracy = 0.52, F1 = 0.61

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 0: 21 times

Examples labeled as 1 classified by model as 1: 7 times

Examples labeled as 1 classified by model as 2: 1 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 5: 21 times

Examples labeled as 4 classified by model as 2: 29 times

Examples labeled as 4 classified by model as 4: 1 times

Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================

Accuracy: 0.52

Precision: 0.6976

Recall: 0.5458

F1 Score: 0.6124

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 21 7 1 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 0 0 21

4 4 | 0 0 29 0 1 0

5 5 | 0 0 0 0 0 22

o.d.o.l.ScoreIterationListener - Score at iteration 140 is 1.0480785369873047

o.d.o.l.ScoreIterationListener - Score at iteration 160 is 1.0282784461975099

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 3: Accuracy = 0.65, F1 = 0.68

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 0: 3 times

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Examples labeled as 4 classified by model as 2: 28 times

Examples labeled as 4 classified by model as 4: 2 times

Examples labeled as 5 classified by model as 5: 22 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================

Accuracy: 0.6467

Precision: 0.703

Recall: 0.6548

F1 Score: 0.678

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 3 25 0 0 1 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 0 0 21

4 4 | 0 0 28 0 2 0

5 5 | 0 0 0 0 0 22

o.d.o.l.ScoreIterationListener - Score at iteration 180 is 0.9446487426757812

o.d.o.l.ScoreIterationListener - Score at iteration 200 is 0.7568672180175782

o.d.o.l.ScoreIterationListener - Score at iteration 220 is 0.7893207550048829

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 4: Accuracy = 0.75, F1 = 0.74

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 28 times

Examples labeled as 1 classified by model as 5: 1 times

Examples labeled as 2 classified by model as 2: 13 times

Examples labeled as 2 classified by model as 4: 9 times

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Examples labeled as 4 classified by model as 2: 6 times

Examples labeled as 4 classified by model as 4: 24 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 5: 21 times

Warning: class 3 was never predicted by the model. This class was excluded from the average precision

==========================Scores========================================

Accuracy: 0.7467

Precision: 0.7726

Recall: 0.7185

F1 Score: 0.7445

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 28 0 0 0 1

2 2 | 0 0 13 0 9 0

3 3 | 0 0 0 0 0 21

4 4 | 0 0 6 0 24 0

5 5 | 1 0 0 0 0 21

o.d.o.l.ScoreIterationListener - Score at iteration 240 is 0.8301044464111328

o.d.o.l.ScoreIterationListener - Score at iteration 260 is 0.6512429237365722

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 5: Accuracy = 0.73, F1 = 0.74

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 14 times

Examples labeled as 2 classified by model as 4: 8 times

Examples labeled as 3 classified by model as 3: 9 times

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Examples labeled as 4 classified by model as 2: 18 times

Examples labeled as 4 classified by model as 4: 12 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.7333

Precision: 0.7542

Recall: 0.729

F1 Score: 0.7414

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 14 0 8 0

3 3 | 0 0 0 9 0 12

4 4 | 0 0 18 0 12 0

5 5 | 1 0 0 1 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 280 is 0.8295143127441407

o.d.o.l.ScoreIterationListener - Score at iteration 300 is 0.7406437873840332

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 6: Accuracy = 0.73, F1 = 0.74

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 20 times

Examples labeled as 2 classified by model as 4: 2 times

Examples labeled as 3 classified by model as 3: 11 times

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Examples labeled as 4 classified by model as 2: 23 times

Examples labeled as 4 classified by model as 4: 7 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 3: 5 times

Examples labeled as 5 classified by model as 5: 16 times

==========================Scores========================================

Accuracy: 0.7267

Precision: 0.7515

Recall: 0.7323

F1 Score: 0.7417

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 20 0 2 0

3 3 | 0 0 0 11 0 10

4 4 | 0 0 23 0 7 0

5 5 | 1 0 0 5 0 16

o.d.o.l.ScoreIterationListener - Score at iteration 320 is 0.6647858619689941

o.d.o.l.ScoreIterationListener - Score at iteration 340 is 0.5469734191894531

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 7: Accuracy = 0.74, F1 = 0.77

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 11 times

Examples labeled as 3 classified by model as 5: 10 times

Examples labeled as 4 classified by model as 2: 23 times

Examples labeled as 4 classified by model as 4: 7 times

Examples labeled as 5 classified by model as 3: 6 times

Examples labeled as 5 classified by model as 5: 16 times

==========================Scores========================================

Accuracy: 0.74

Precision: 0.7919

Recall: 0.7474

F1 Score: 0.769

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 11 0 10

4 4 | 0 0 23 0 7 0

5 5 | 0 0 0 6 0 16

o.d.o.l.ScoreIterationListener - Score at iteration 360 is 0.7112821578979492

o.d.o.l.ScoreIterationListener - Score at iteration 380 is 0.5319311141967773

o.d.o.l.ScoreIterationListener - Score at iteration 400 is 0.5527594566345215

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 8: Accuracy = 0.77, F1 = 0.78

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 10 times

Examples labeled as 3 classified by model as 5: 11 times

Examples labeled as 4 classified by model as 2: 17 times

Examples labeled as 4 classified by model as 4: 13 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 3: 6 times

Examples labeled as 5 classified by model as 5: 15 times

==========================Scores========================================

Accuracy: 0.7667

Precision: 0.7882

Recall: 0.7652

F1 Score: 0.7765

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 10 0 11

4 4 | 0 0 17 0 13 0

5 5 | 1 0 0 6 0 15

o.d.o.l.ScoreIterationListener - Score at iteration 420 is 0.6434410095214844

o.d.o.l.ScoreIterationListener - Score at iteration 440 is 0.3948651313781738

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 9: Accuracy = 0.81, F1 = 0.80

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

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Examples labeled as 3 classified by model as 5: 11 times

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Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 1: 2 times

Examples labeled as 5 classified by model as 3: 6 times

Examples labeled as 5 classified by model as 5: 13 times

==========================Scores========================================

Accuracy: 0.8133

Precision: 0.7997

Recall: 0.8001

F1 Score: 0.7999

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 10 0 11

4 4 | 0 0 8 0 22 0

5 5 | 1 2 0 6 0 13

o.d.o.l.ScoreIterationListener - Score at iteration 460 is 0.5814063072204589

o.d.o.l.ScoreIterationListener - Score at iteration 480 is 0.5292294979095459

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 10: Accuracy = 0.83, F1 = 0.82

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 10 times

Examples labeled as 3 classified by model as 5: 11 times

Examples labeled as 4 classified by model as 2: 8 times

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Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 3: 6 times

Examples labeled as 5 classified by model as 5: 15 times

==========================Scores========================================

Accuracy: 0.8267

Precision: 0.8164

Recall: 0.8152

F1 Score: 0.8158

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 10 0 11

4 4 | 0 0 8 0 22 0

5 5 | 1 0 0 6 0 15

o.d.o.l.ScoreIterationListener - Score at iteration 500 is 0.4800821304321289

o.d.o.l.ScoreIterationListener - Score at iteration 520 is 0.5798452377319336

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 11: Accuracy = 0.85, F1 = 0.84

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 14 times

Examples labeled as 3 classified by model as 5: 7 times

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Examples labeled as 4 classified by model as 4: 26 times

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Examples labeled as 5 classified by model as 5: 11 times

==========================Scores========================================

Accuracy: 0.8533

Precision: 0.832

Recall: 0.8389

F1 Score: 0.8354

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 14 0 7

4 4 | 0 0 4 0 26 0

5 5 | 1 2 0 8 0 11

o.d.o.l.ScoreIterationListener - Score at iteration 540 is 0.48386478424072266

o.d.o.l.ScoreIterationListener - Score at iteration 560 is 0.38257732391357424

o.d.o.l.ScoreIterationListener - Score at iteration 580 is 0.6095143318176269

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 12: Accuracy = 0.87, F1 = 0.86

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 13 times

Examples labeled as 3 classified by model as 5: 8 times

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Examples labeled as 5 classified by model as 5: 15 times

==========================Scores========================================

Accuracy: 0.8733

Precision: 0.8584

Recall: 0.8613

F1 Score: 0.8598

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 13 0 8

4 4 | 0 0 4 0 26 0

5 5 | 1 1 0 5 0 15

o.d.o.l.ScoreIterationListener - Score at iteration 600 is 0.45578536987304685

o.d.o.l.ScoreIterationListener - Score at iteration 620 is 0.3001575946807861

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 13: Accuracy = 0.87, F1 = 0.87

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 24 times

Examples labeled as 0 classified by model as 1: 2 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 14 times

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Examples labeled as 5 classified by model as 1: 2 times

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Examples labeled as 5 classified by model as 5: 16 times

==========================Scores========================================

Accuracy: 0.8733

Precision: 0.8664

Recall: 0.8639

F1 Score: 0.8652

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 24 2 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 14 0 7

4 4 | 0 0 4 0 26 0

5 5 | 0 2 0 4 0 16

o.d.o.l.ScoreIterationListener - Score at iteration 640 is 0.3721646785736084

o.d.o.l.ScoreIterationListener - Score at iteration 660 is 0.3161902904510498

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 14: Accuracy = 0.87, F1 = 0.86

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 15 times

Examples labeled as 3 classified by model as 5: 6 times

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Examples labeled as 4 classified by model as 4: 25 times

Examples labeled as 5 classified by model as 1: 1 times

Examples labeled as 5 classified by model as 3: 7 times

Examples labeled as 5 classified by model as 5: 14 times

==========================Scores========================================

Accuracy: 0.8733

Precision: 0.8605

Recall: 0.864

F1 Score: 0.8623

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 15 0 6

4 4 | 0 0 5 0 25 0

5 5 | 0 1 0 7 0 14

o.d.o.l.ScoreIterationListener - Score at iteration 680 is 0.3897043943405151

o.d.o.l.ScoreIterationListener - Score at iteration 700 is 0.2337266206741333

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 15: Accuracy = 0.89, F1 = 0.89

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 16 times

Examples labeled as 3 classified by model as 5: 5 times

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Examples labeled as 5 classified by model as 3: 6 times

Examples labeled as 5 classified by model as 5: 16 times

==========================Scores========================================

Accuracy: 0.8933

Precision: 0.884

Recall: 0.8871

F1 Score: 0.8855

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 16 0 5

4 4 | 0 0 5 0 25 0

5 5 | 0 0 0 6 0 16

o.d.o.l.ScoreIterationListener - Score at iteration 720 is 0.3756225109100342

o.d.o.l.ScoreIterationListener - Score at iteration 740 is 0.35205769538879395

o.d.o.l.ScoreIterationListener - Score at iteration 760 is 0.5966990947723388

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 16: Accuracy = 0.90, F1 = 0.89

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 16 times

Examples labeled as 3 classified by model as 5: 5 times

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Examples labeled as 5 classified by model as 3: 5 times

Examples labeled as 5 classified by model as 5: 17 times

==========================Scores========================================

Accuracy: 0.9

Precision: 0.8896

Recall: 0.8926

F1 Score: 0.8911

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 16 0 5

4 4 | 0 0 4 0 26 0

5 5 | 0 0 0 5 0 17

o.d.o.l.ScoreIterationListener - Score at iteration 780 is 0.3741513729095459

o.d.o.l.ScoreIterationListener - Score at iteration 800 is 0.19865083694458008

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 17: Accuracy = 0.93, F1 = 0.92

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

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Examples labeled as 3 classified by model as 3: 17 times

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Examples labeled as 5 classified by model as 3: 5 times

Examples labeled as 5 classified by model as 5: 17 times

==========================Scores========================================

Accuracy: 0.9333

Precision: 0.925

Recall: 0.9228

F1 Score: 0.9239

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 17 0 4

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 5 0 17

o.d.o.l.ScoreIterationListener - Score at iteration 820 is 0.24331939220428467

o.d.o.l.ScoreIterationListener - Score at iteration 840 is 0.20865292549133302

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 18: Accuracy = 0.94, F1 = 0.93

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 17 times

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Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 3: 5 times

Examples labeled as 5 classified by model as 5: 17 times

==========================Scores========================================

Accuracy: 0.94

Precision: 0.9304

Recall: 0.9304

F1 Score: 0.9304

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 17 0 4

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 5 0 17

o.d.o.l.ScoreIterationListener - Score at iteration 860 is 0.27184548377990725

o.d.o.l.ScoreIterationListener - Score at iteration 880 is 0.15169112682342528

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 19: Accuracy = 0.95, F1 = 0.94

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 28 times

Examples labeled as 1 classified by model as 5: 1 times

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Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.9467

Precision: 0.9394

Recall: 0.9401

F1 Score: 0.9398

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 28 0 0 0 1

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 900 is 0.22165002822875976

o.d.o.l.ScoreIterationListener - Score at iteration 920 is 0.23092069625854492

o.d.o.l.ScoreIterationListener - Score at iteration 940 is 0.30478367805480955

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 20: Accuracy = 0.94, F1 = 0.93

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 25 times

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Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.94

Precision: 0.9354

Recall: 0.9341

F1 Score: 0.9348

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 25 0 0 0 0 1

1 1 | 1 28 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 19 0 2

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 960 is 0.34931130409240724

o.d.o.l.ScoreIterationListener - Score at iteration 980 is 0.14310016632080078

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 21: Accuracy = 0.92, F1 = 0.92

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 23 times

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Examples labeled as 5 classified by model as 5: 17 times

==========================Scores========================================

Accuracy: 0.92

Precision: 0.916

Recall: 0.9161

F1 Score: 0.9161

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 23 1 0 0 0 2

1 1 | 1 28 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 20 0 1

4 4 | 0 0 1 0 29 0

5 5 | 0 1 0 4 0 17

o.d.o.l.ScoreIterationListener - Score at iteration 1000 is 0.15780065059661866

o.d.o.l.ScoreIterationListener - Score at iteration 1020 is 0.12818331718444825

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 22: Accuracy = 0.95, F1 = 0.94

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

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Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.9467

Precision: 0.9432

Recall: 0.9431

F1 Score: 0.9431

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 20 0 1

4 4 | 0 0 2 0 28 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 1040 is 0.17120853662490845

o.d.o.l.ScoreIterationListener - Score at iteration 1060 is 0.11253215074539184

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 23: Accuracy = 0.95, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 3: 3 times

Examples labeled as 5 classified by model as 5: 19 times

==========================Scores========================================

Accuracy: 0.9533

Precision: 0.9481

Recall: 0.9459

F1 Score: 0.947

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 3 0 19

o.d.o.l.ScoreIterationListener - Score at iteration 1080 is 0.15977177619934083

o.d.o.l.ScoreIterationListener - Score at iteration 1100 is 0.2035053253173828

o.d.o.l.ScoreIterationListener - Score at iteration 1120 is 0.17972662448883056

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 24: Accuracy = 0.96, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 20 times

Examples labeled as 3 classified by model as 5: 1 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 3: 4 times

Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.96

Precision: 0.9581

Recall: 0.9542

F1 Score: 0.9561

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 20 0 1

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 1140 is 0.32100744247436525

o.d.o.l.ScoreIterationListener - Score at iteration 1160 is 0.09598696231842041

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 25: Accuracy = 0.95, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 20 times

Examples labeled as 3 classified by model as 5: 1 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 4 times

Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.9533

Precision: 0.9503

Recall: 0.9486

F1 Score: 0.9495

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 20 0 1

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 1180 is 0.10765376091003417

o.d.o.l.ScoreIterationListener - Score at iteration 1200 is 0.09369413256645202

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 26: Accuracy = 0.97, F1 = 0.97

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 21 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 4 times

Examples labeled as 5 classified by model as 5: 18 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.9661

Recall: 0.9641

F1 Score: 0.9651

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 21 0 0

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 4 0 18

o.d.o.l.ScoreIterationListener - Score at iteration 1220 is 0.11920651197433471

o.d.o.l.ScoreIterationListener - Score at iteration 1240 is 0.07327424287796021

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 27: Accuracy = 0.97, F1 = 0.97

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 21 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 3 times

Examples labeled as 5 classified by model as 5: 19 times

==========================Scores========================================

Accuracy: 0.9733

Precision: 0.9719

Recall: 0.9717

F1 Score: 0.9718

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 21 0 0

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 3 0 19

o.d.o.l.ScoreIterationListener - Score at iteration 1260 is 0.10964394807815551

o.d.o.l.ScoreIterationListener - Score at iteration 1280 is 0.0825955867767334

o.d.o.l.ScoreIterationListener - Score at iteration 1300 is 0.07877322435379028

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 28: Accuracy = 0.99, F1 = 0.99

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 21 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 21 times

==========================Scores========================================

Accuracy: 0.9867

Precision: 0.9852

Recall: 0.9869

F1 Score: 0.986

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 21 0 0

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 1 0 21

o.d.o.l.ScoreIterationListener - Score at iteration 1320 is 0.2995795726776123

o.d.o.l.ScoreIterationListener - Score at iteration 1340 is 0.06287476420402527

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 29: Accuracy = 0.97, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 19 times

Examples labeled as 3 classified by model as 5: 2 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 21 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.964

Recall: 0.9634

F1 Score: 0.9637

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 19 0 2

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 1 0 21

o.d.o.l.ScoreIterationListener - Score at iteration 1360 is 0.07786862254142761

o.d.o.l.ScoreIterationListener - Score at iteration 1380 is 0.06103749275207519

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 30: Accuracy = 0.97, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 19 times

Examples labeled as 3 classified by model as 5: 2 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 2 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.9617

Recall: 0.9634

F1 Score: 0.9626

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 19 0 2

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 2 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1400 is 0.08928256034851074

o.d.o.l.ScoreIterationListener - Score at iteration 1420 is 0.050720810890197754

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 31: Accuracy = 0.97, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 21 times

Examples labeled as 2 classified by model as 4: 1 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 21 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.965

Recall: 0.961

F1 Score: 0.963

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 21 0 1 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 1 0 21

o.d.o.l.ScoreIterationListener - Score at iteration 1440 is 0.08523998856544494

o.d.o.l.ScoreIterationListener - Score at iteration 1460 is 0.053772914409637454

o.d.o.l.ScoreIterationListener - Score at iteration 1480 is 0.06376101970672607

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 32: Accuracy = 0.97, F1 = 0.97

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 17 times

Examples labeled as 3 classified by model as 5: 4 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 5: 22 times

==========================Scores========================================

Accuracy: 0.9733

Precision: 0.9744

Recall: 0.9683

F1 Score: 0.9713

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 17 0 4

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 0 0 22

o.d.o.l.ScoreIterationListener - Score at iteration 1500 is 0.24013910293579102

o.d.o.l.ScoreIterationListener - Score at iteration 1520 is 0.05150462388992309

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 33: Accuracy = 0.97, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.9633

Recall: 0.961

F1 Score: 0.9622

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 1 0 0 1 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1540 is 0.07334737777709961

o.d.o.l.ScoreIterationListener - Score at iteration 1560 is 0.05247232913970947

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 34: Accuracy = 0.97, F1 = 0.96

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 3: 2 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9667

Precision: 0.9616

Recall: 0.961

F1 Score: 0.9613

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 0 0 0 2 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1580 is 0.27067511081695556

o.d.o.l.ScoreIterationListener - Score at iteration 1600 is 0.03868082761764526

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 35: Accuracy = 0.97, F1 = 0.97

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 4: 30 times

Examples labeled as 5 classified by model as 0: 1 times

Examples labeled as 5 classified by model as 5: 21 times

==========================Scores========================================

Accuracy: 0.9733

Precision: 0.973

Recall: 0.9686

F1 Score: 0.9708

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 0 0 30 0

5 5 | 1 0 0 0 0 21

o.d.o.l.ScoreIterationListener - Score at iteration 1620 is 0.06288898587226868

o.d.o.l.ScoreIterationListener - Score at iteration 1640 is 0.046552729606628415

o.d.o.l.ScoreIterationListener - Score at iteration 1660 is 0.05294256806373596

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 36: Accuracy = 0.96, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 18 times

Examples labeled as 3 classified by model as 5: 3 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 2 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.96

Precision: 0.9543

Recall: 0.9555

F1 Score: 0.9549

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 18 0 3

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 2 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1680 is 0.07542706727981567

o.d.o.l.ScoreIterationListener - Score at iteration 1700 is 0.03922495543956757

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 37: Accuracy = 0.95, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 17 times

Examples labeled as 3 classified by model as 5: 4 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 1: 1 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9533

Precision: 0.9502

Recall: 0.9475

F1 Score: 0.9489

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 17 0 4

4 4 | 0 0 1 0 29 0

5 5 | 0 1 0 1 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1720 is 0.046846285462379456

o.d.o.l.ScoreIterationListener - Score at iteration 1740 is 0.03945627212524414

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 38: Accuracy = 0.95, F1 = 0.94

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 16 times

Examples labeled as 3 classified by model as 5: 5 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 1: 1 times

Examples labeled as 5 classified by model as 3: 1 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9467

Precision: 0.9441

Recall: 0.9396

F1 Score: 0.9418

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 16 0 5

4 4 | 0 0 1 0 29 0

5 5 | 0 1 0 1 0 20

o.d.o.l.ScoreIterationListener - Score at iteration 1760 is 0.04861399531364441

o.d.o.l.ScoreIterationListener - Score at iteration 1780 is 0.4658164978027344

o.d.e.r.s.UCISequenceClassificationExample - Test set evaluation at epoch 39: Accuracy = 0.95, F1 = 0.95

o.d.e.r.s.UCISequenceClassificationExample -

Examples labeled as 0 classified by model as 0: 26 times

Examples labeled as 1 classified by model as 1: 29 times

Examples labeled as 2 classified by model as 2: 22 times

Examples labeled as 3 classified by model as 3: 17 times

Examples labeled as 3 classified by model as 5: 4 times

Examples labeled as 4 classified by model as 2: 1 times

Examples labeled as 4 classified by model as 4: 29 times

Examples labeled as 5 classified by model as 3: 2 times

Examples labeled as 5 classified by model as 5: 20 times

==========================Scores========================================

Accuracy: 0.9533

Precision: 0.9474

Recall: 0.9475

F1 Score: 0.9475

========================================================================

Predicted: 0 1 2 3 4 5

Actual:

0 0 | 26 0 0 0 0 0

1 1 | 0 29 0 0 0 0

2 2 | 0 0 22 0 0 0

3 3 | 0 0 0 17 0 4

4 4 | 0 0 1 0 29 0

5 5 | 0 0 0 2 0 20

o.d.e.r.s.UCISequenceClassificationExample - ----- Example Complete -----

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