DEEP LEARNING: AI-Anesth – Science To Medicine

今日の一言：”Building Deep Network：IntelliJ IDEAにMavenを導入し、DL4Jを作動！”

Multilayer Perceptron Networkの構築
MLPClassSaturnのデータはどうなっているのか？
訓練データ：XY座標値　500個

評価データ：XY座標値　100個

要するに土星Saturnの中心核と輪を構成している２群をクラスター分類するAIアルゴリズムの構築にニューラルネットワークを使う。

登場するクラス群
ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー
Class 　 MultiLayerConfiguration　　 org.deeplearning4j.nn.conf
Class MultiLayerNetwork org.deeplearning4j.nn.multilayer
Class Evaluation org.deeplearning4j.eval
Class RecordReaderDataSetIterator org.deeplearning4j.datasets.datavec
Interface　RecordReader　　 org.datavec.api.records.reader
Interface DataSetIterator org.nd4j.linalg.dataset.api.iterator
Interface INDArray org.nd4j.linalg.api.ndarray
ーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーーー

package org.deeplearning4j.examples.feedforward.classification;

import org.datavec.api.records.reader.RecordReader;
import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
import org.datavec.api.split.FileSplit;
import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
import org.nd4j.linalg.activations.Activation;
import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
import org.deeplearning4j.eval.Evaluation;
import org.deeplearning4j.nn.api.OptimizationAlgorithm;
import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.conf.Updater;
import org.deeplearning4j.nn.conf.layers.DenseLayer;
import org.deeplearning4j.nn.conf.layers.OutputLayer;
import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
import org.deeplearning4j.nn.weights.WeightInit;
import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.io.ClassPathResource;
import org.nd4j.linalg.learning.config.Nesterovs;
import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.io.File;

/**
 * "Saturn" Data Classification Example
 *
 * Based on the data from Jason Baldridge:
 * 	https://github.com/jasonbaldridge/try-tf/tree/master/simdata
 *
 * @author Josh Patterson
 * @author Alex Black (added plots)
 *
 */
public class MLPClassifierSaturn {

    public static void main(String[] args) throws Exception {
        Nd4j.ENFORCE_NUMERICAL_STABILITY = true;
        int batchSize = 50;　//ミニバッチ法：一回にアプライさせるデータ数
        int seed = 123;  //乱数発生の初期値？　乱数発生の初期化
                         //生成される乱数は擬似乱数であるため、固定したseedを指定してやれば毎回同じ乱数が出る。
                         //デバッグ時など毎回同じ乱数を出して欲しいときに使う。

        double learningRate = 0.005; //学習率：誤差逆伝搬法における係数。
        
        int nEpochs = 30;　//エポック数とは、「一つの訓練データを何回繰り返して学習させるか」の数のことです。
　                        //効率的に学習をすすめるにはLearning Rateをepoch数に応じて減少させる。

        int numInputs = 2;　 //インプット層のニューロン数2　
        int numOutputs = 2;　//アウトプット層のニューロン数2　
        int numHiddenNodes = 20; //隠れ層のニューロン数20

        final String filenameTrain  = new ClassPathResource("/classification/saturn_data_train.csv").getFile().getPath();  //訓練データファイル指定　
        final String filenameTest  = new ClassPathResource("/classification/saturn_data_eval.csv").getFile().getPath(); //評価データファイル指定

        //Load the training data:　//訓練データのローディング
        RecordReader rr = new CSVRecordReader();  //Class CSVRecordReader: CSVRecordReader()
        rr.initialize(new FileSplit(new File(filenameTrain)));
        DataSetIterator trainIter = new RecordReaderDataSetIterator(rr,batchSize,0,2);
        
        //RecordReaderDataSetIterator iterator = new RecordReaderDataSetIterator(
 		// recordReader,
  		// batchSize,
  		// labelIndex,　　ラベルの列位置
  		// numClasses　　　データ列数
		// );


        //Load the test/evaluation data:　　//評価データのローディング
        RecordReader rrTest = new CSVRecordReader();
        rrTest.initialize(new FileSplit(new File(filenameTest)));
        DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest,batchSize,0,2);

        //log.info("Build model....");   //ネットワークアーキテクチャー　MultiLayer perceptron
        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                .seed(seed) //乱数設定?
                .updater(new Nesterovs(learningRate, 0.9))　//ネステロフの加速勾配法、モーメンタムファクター0.9でセット
                .list()
                .layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) //第１隠れ層
                        .weightInit(WeightInit.XAVIER)　//ザビエルの初期値
                        .activation(Activation.RELU)　 //活性化関数としてReLU関数
                        .build())
                .layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)　//出力層
                        .weightInit(WeightInit.XAVIER)　　//ザビエルの初期値
                        .activation(Activation.SOFTMAX)  //活性化関数としてSoftmax関数
                        .nIn(numHiddenNodes).nOut(numOutputs).build())
                .pretrain(false).backprop(true).build();


        MultiLayerNetwork model = new MultiLayerNetwork(conf);　　//モデル構築
        model.init();
        model.setListeners(new ScoreIterationListener(10));    //Print score every 10 parameter updates

        for ( int n = 0; n < nEpochs; n++) {  //モデルへのデータアプライをエポック数繰り返す
            model.fit( trainIter );
        }

        System.out.println("Evaluate model....");　　//モデルの評価
        Evaluation eval = new Evaluation(numOutputs);
        while(testIter.hasNext()){
            DataSet t = testIter.next();
            INDArray features = t.getFeatures();
            INDArray lables = t.getLabels();
            INDArray predicted = model.output(features,false);

            eval.eval(lables, predicted);

        }


        System.out.println(eval.stats());  // eval.stats() 結果のコンソール表示
        //------------------------------------------------------------------------------------
        //Training is complete. Code that follows is for plotting the data & predictions only


        double xMin = -15;
        double xMax = 15;
        double yMin = -15;
        double yMax = 15;

        //Let's evaluate the predictions at every point in the x/y input space, and plot this in the background　グラフ作成
        int nPointsPerAxis = 100;
        double[][] evalPoints = new double[nPointsPerAxis*nPointsPerAxis][2];
        int count = 0;
        for( int i=0; i<nPointsPerAxis; i++ ){
            for( int j=0; j<nPointsPerAxis; j++ ){
                double x = i * (xMax-xMin)/(nPointsPerAxis-1) + xMin;
                double y = j * (yMax-yMin)/(nPointsPerAxis-1) + yMin;

                evalPoints[count][0] = x;
                evalPoints[count][1] = y;

                count++;
            }
        }

        INDArray allXYPoints = Nd4j.create(evalPoints);
        INDArray predictionsAtXYPoints = model.output(allXYPoints);

        //Get all of the training data in a single array, and plot it:
        rr.initialize(new FileSplit(new File(filenameTrain)));
        rr.reset();
        int nTrainPoints = 500;
        trainIter = new RecordReaderDataSetIterator(rr,nTrainPoints,0,2);
        DataSet ds = trainIter.next();
        PlotUtil.plotTrainingData(ds.getFeatures(), ds.getLabels(), allXYPoints, predictionsAtXYPoints, nPointsPerAxis);


        //Get test data, run the test data through the network to generate predictions, and plot those predictions:
        rrTest.initialize(new FileSplit(new File(filenameTest)));
        rrTest.reset();
        int nTestPoints = 100;
        testIter = new RecordReaderDataSetIterator(rrTest,nTestPoints,0,2);
        ds = testIter.next();
        INDArray testPredicted = model.output(ds.getFeatures());
        PlotUtil.plotTestData(ds.getFeatures(), ds.getLabels(), testPredicted, allXYPoints, predictionsAtXYPoints, nPointsPerAxis);

        System.out.println("****************Example finished********************");
    }

}

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package org.deeplearning4j.examples.feedforward.classification;

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

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

import org.datavec.api.split.FileSplit;

import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;

import org.nd4j.linalg.activations.Activation;

import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

import org.deeplearning4j.eval.Evaluation;

import org.deeplearning4j.nn.api.OptimizationAlgorithm;

import org.deeplearning4j.nn.conf.MultiLayerConfiguration;

import org.deeplearning4j.nn.conf.NeuralNetConfiguration;

import org.deeplearning4j.nn.conf.Updater;

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

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

import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;

import org.deeplearning4j.nn.weights.WeightInit;

import org.deeplearning4j.optimize.listeners.ScoreIterationListener;

import org.nd4j.linalg.api.ndarray.INDArray;

import org.nd4j.linalg.dataset.DataSet;

import org.nd4j.linalg.factory.Nd4j;

import org.nd4j.linalg.io.ClassPathResource;

import org.nd4j.linalg.learning.config.Nesterovs;

import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;

import java.io.File;

/**

* "Saturn" Data Classification Example

* Based on the data from Jason Baldridge:

* https://github.com/jasonbaldridge/try-tf/tree/master/simdata

* @author Josh Patterson

* @author Alex Black (added plots)

public class MLPClassifierSaturn {

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

Nd4j.ENFORCE_NUMERICAL_STABILITY = true;

int batchSize = 50;　//ミニバッチ法：一回にアプライさせるデータ数

int seed = 123; //乱数発生の初期値？　乱数発生の初期化

//生成される乱数は擬似乱数であるため、固定したseedを指定してやれば毎回同じ乱数が出る。

//デバッグ時など毎回同じ乱数を出して欲しいときに使う。

double learningRate = 0.005; //学習率：誤差逆伝搬法における係数。

int nEpochs = 30;　//エポック数とは、「一つの訓練データを何回繰り返して学習させるか」の数のことです。

　 //効率的に学習をすすめるにはLearning Rateをepoch数に応じて減少させる。

int numInputs = 2;　 //インプット層のニューロン数2　

int numOutputs = 2;　//アウトプット層のニューロン数2　

int numHiddenNodes = 20; //隠れ層のニューロン数20

final String filenameTrain = new ClassPathResource("/classification/saturn_data_train.csv").getFile().getPath(); //訓練データファイル指定　

final String filenameTest = new ClassPathResource("/classification/saturn_data_eval.csv").getFile().getPath(); //評価データファイル指定

//Load the training data:　//訓練データのローディング

RecordReader rr = new CSVRecordReader(); //Class CSVRecordReader: CSVRecordReader()

rr.initialize(new FileSplit(new File(filenameTrain)));

DataSetIterator trainIter = new RecordReaderDataSetIterator(rr,batchSize,0,2);

//RecordReaderDataSetIterator iterator = new RecordReaderDataSetIterator(

// recordReader,

// batchSize,

// labelIndex,　　ラベルの列位置

// numClasses　　　データ列数

// );

//Load the test/evaluation data:　　//評価データのローディング

RecordReader rrTest = new CSVRecordReader();

rrTest.initialize(new FileSplit(new File(filenameTest)));

DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest,batchSize,0,2);

//log.info("Build model...."); //ネットワークアーキテクチャー　MultiLayer perceptron

MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()

.seed(seed) //乱数設定?

.updater(new Nesterovs(learningRate, 0.9))　//ネステロフの加速勾配法、モーメンタムファクター0.9でセット

.list()

.layer(0, new DenseLayer.Builder().nIn(numInputs).nOut(numHiddenNodes) //第１隠れ層

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

.activation(Activation.RELU)　 //活性化関数としてReLU関数

.build())

.layer(1, new OutputLayer.Builder(LossFunction.NEGATIVELOGLIKELIHOOD)　//出力層

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

.activation(Activation.SOFTMAX) //活性化関数としてSoftmax関数

.nIn(numHiddenNodes).nOut(numOutputs).build())

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

MultiLayerNetwork model = new MultiLayerNetwork(conf);　　//モデル構築

model.init();

model.setListeners(new ScoreIterationListener(10)); //Print score every 10 parameter updates

for ( int n = 0; n < nEpochs; n++) { //モデルへのデータアプライをエポック数繰り返す

model.fit( trainIter );

}

System.out.println("Evaluate model....");　　//モデルの評価

Evaluation eval = new Evaluation(numOutputs);

while(testIter.hasNext()){

DataSet t = testIter.next();

INDArray features = t.getFeatures();

INDArray lables = t.getLabels();

INDArray predicted = model.output(features,false);

eval.eval(lables, predicted);

}

System.out.println(eval.stats()); // eval.stats() 結果のコンソール表示

//------------------------------------------------------------------------------------

//Training is complete. Code that follows is for plotting the data & predictions only

double xMin = -15;

double xMax = 15;

double yMin = -15;

double yMax = 15;

//Let's evaluate the predictions at every point in the x/y input space, and plot this in the background　グラフ作成

int nPointsPerAxis = 100;

double[][] evalPoints = new double[nPointsPerAxis*nPointsPerAxis][2];

int count = 0;

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

for( int j=0; j<nPointsPerAxis; j++ ){

double x = i * (xMax-xMin)/(nPointsPerAxis-1) + xMin;

double y = j * (yMax-yMin)/(nPointsPerAxis-1) + yMin;

evalPoints[count][0] = x;

evalPoints[count][1] = y;

count++;

}

INDArray allXYPoints = Nd4j.create(evalPoints);

INDArray predictionsAtXYPoints = model.output(allXYPoints);

//Get all of the training data in a single array, and plot it:

rr.initialize(new FileSplit(new File(filenameTrain)));

rr.reset();

int nTrainPoints = 500;

trainIter = new RecordReaderDataSetIterator(rr,nTrainPoints,0,2);

DataSet ds = trainIter.next();

PlotUtil.plotTrainingData(ds.getFeatures(), ds.getLabels(), allXYPoints, predictionsAtXYPoints, nPointsPerAxis);

//Get test data, run the test data through the network to generate predictions, and plot those predictions:

rrTest.initialize(new FileSplit(new File(filenameTest)));

rrTest.reset();

int nTestPoints = 100;

testIter = new RecordReaderDataSetIterator(rrTest,nTestPoints,0,2);

ds = testIter.next();

INDArray testPredicted = model.output(ds.getFeatures());

PlotUtil.plotTestData(ds.getFeatures(), ds.getLabels(), testPredicted, allXYPoints, predictionsAtXYPoints, nPointsPerAxis);

System.out.println("****************Example finished********************");

}

コンソール出力

o.n.l.f.Nd4jBackend - Loaded [CpuBackend] backend
o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 2
o.n.n.Nd4jBlas - Number of threads used for BLAS: 2
o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CPU]; OS: [Mac OS X]
o.n.l.a.o.e.DefaultOpExecutioner - Cores: [4]; Memory: [3.6GB];
o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [MKL]
o.d.n.m.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE]
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.9500187683105468
o.d.o.l.ScoreIterationListener - Score at iteration 1 is 1.2153868865966797
o.d.o.l.ScoreIterationListener - Score at iteration 2 is 1.1008527374267578
o.d.o.l.ScoreIterationListener - Score at iteration 3 is 1.1935407257080077
o.d.o.l.ScoreIterationListener - Score at iteration 4 is 0.6177032470703125
o.d.o.l.ScoreIterationListener - Score at iteration 5 is 0.5722542190551758
..........
o.d.o.l.ScoreIterationListener - Score at iteration 295 is 0.05237224578857422
o.d.o.l.ScoreIterationListener - Score at iteration 296 is 0.057432966232299806
o.d.o.l.ScoreIterationListener - Score at iteration 297 is 0.05932496547698975
o.d.o.l.ScoreIterationListener - Score at iteration 298 is 0.05965335845947266
o.d.o.l.ScoreIterationListener - Score at iteration 299 is 0.050114612579345706
Evaluate model....


========================Evaluation Metrics========================
 # of classes:    2
 Accuracy:        1.0000
 Precision:       1.0000
 Recall:          1.0000
 F1 Score:        1.0000
Precision, recall & F1: reported for positive class (class 1 - "1") only


=========================Confusion Matrix=========================
  0  1
-------
 48  0 | 0 = 0
  0 52 | 1 = 1

Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
==================================================================
****************Example finished********************

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

o.n.n.NativeOpsHolder - Number of threads used for NativeOps: 2

o.n.n.Nd4jBlas - Number of threads used for BLAS: 2

o.n.l.a.o.e.DefaultOpExecutioner - Backend used: [CPU]; OS: [Mac OS X]

o.n.l.a.o.e.DefaultOpExecutioner - Cores: [4]; Memory: [3.6GB];

o.n.l.a.o.e.DefaultOpExecutioner - Blas vendor: [MKL]

o.d.n.m.MultiLayerNetwork - Starting MultiLayerNetwork with WorkspaceModes set to [training: ENABLED; inference: ENABLED], cacheMode set to [NONE]

o.d.o.l.ScoreIterationListener - Score at iteration 0 is 1.9500187683105468

o.d.o.l.ScoreIterationListener - Score at iteration 1 is 1.2153868865966797

o.d.o.l.ScoreIterationListener - Score at iteration 2 is 1.1008527374267578

o.d.o.l.ScoreIterationListener - Score at iteration 3 is 1.1935407257080077

o.d.o.l.ScoreIterationListener - Score at iteration 4 is 0.6177032470703125

o.d.o.l.ScoreIterationListener - Score at iteration 5 is 0.5722542190551758

..........

o.d.o.l.ScoreIterationListener - Score at iteration 295 is 0.05237224578857422

o.d.o.l.ScoreIterationListener - Score at iteration 296 is 0.057432966232299806

o.d.o.l.ScoreIterationListener - Score at iteration 297 is 0.05932496547698975

o.d.o.l.ScoreIterationListener - Score at iteration 298 is 0.05965335845947266

o.d.o.l.ScoreIterationListener - Score at iteration 299 is 0.050114612579345706

Evaluate model....

========================Evaluation Metrics========================

# of classes: 2

Accuracy: 1.0000

Precision: 1.0000

Recall: 1.0000

F1 Score: 1.0000

Precision, recall & F1: reported for positive class (class 1 - "1") only

=========================Confusion Matrix=========================

0 1

-------

48 0 | 0 = 0

0 52 | 1 = 1

Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times

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

****************Example finished********************

完璧な精度でもって、100個の評価データのうち、48個が0（中心核）、52個が1(輪）に分類された。

System.out.println(model.summary());
System.out.println(model.params());

1 2	System.out.println(model.summary()); System.out.println(model.params());

とすれば、

==========================================================================================================================================================================================================================================================
LayerName (LayerType)                   nIn,nOut  TotalParams ParamsShape                             
==========================================================================================================================================================================================================================================================
layer0 (DenseLayer)                     2,20        60        W:{2,20}, b:{1,20}                      
layer1 (OutputLayer)                    20,2        42        W:{20,2}, b:{1,2}                       
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
            Total Parameters:  102
        Trainable Parameters:  102
           Frozen Parameters:  0
==========================================================================================================================================================================================================================================================

[[    0.5051,   -0.1064,   -0.5878,    0.2147,   -0.2506,    0.1694,    0.1828,    0.3966,   -0.1159,   -1.0667,   -0.0039,    0.0414,   -0.2705,    0.0965,   -0.0151,   -0.0183,   -0.2440,    0.3099,    0.3838,   -0.1108,    0.5253,    0.6209,   -0.1041,   -0.1726,   -0.3511,   -0.1890,    0.4551,    0.1900,   -0.3496,    0.0644,    0.0187,    0.0306,    0.5229,   -0.0608,   -0.4564,    0.1865,    0.0799,    0.1576,   -0.0933,    0.4160,    0.1516,   -0.4136,    0.2882,    0.1872,   -0.2897,   -0.0136,   -0.1874,    0.5774,   -0.1431,   -0.2045,   -0.3704,    0.2499,    0.0608,   -0.1484,    0.2614,    0.9763,   -0.0547,   -0.1443,    0.1029,   -0.0322,    0.0677,   -0.7437,    0.5813,   -0.1314,   -0.3347,   -0.2371,   -0.3985,    0.4483,   -0.4175,   -0.1150,   -0.2141,    0.2760,    0.1480,   -0.1473,    0.1253,    0.6902,   -0.1761,   -0.5307,    0.2276,   -0.0136,   -0.1594,    0.4310,    0.0827,   -0.4691,    0.5227,   -0.1974,    0.1377,   -0.5734,    0.0795,    0.4972,    0.6973,   -0.2429,    0.0846,    0.2800,   -0.3221,   -0.9952,    0.0485,   -0.0923,    0.0424,    0.1793,    1.0446,   -1.0446]]
Evaluate model....

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

LayerName (LayerType) nIn,nOut TotalParams ParamsShape

layer0 (DenseLayer) 2,20 60 W:{2,20}, b:{1,20}

layer1 (OutputLayer) 20,2 42 W:{20,2}, b:{1,2}

----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Total Parameters: 102

Trainable Parameters: 102

Frozen Parameters: 0

[[ 0.5051, -0.1064, -0.5878, 0.2147, -0.2506, 0.1694, 0.1828, 0.3966, -0.1159, -1.0667, -0.0039, 0.0414, -0.2705, 0.0965, -0.0151, -0.0183, -0.2440, 0.3099, 0.3838, -0.1108, 0.5253, 0.6209, -0.1041, -0.1726, -0.3511, -0.1890, 0.4551, 0.1900, -0.3496, 0.0644, 0.0187, 0.0306, 0.5229, -0.0608, -0.4564, 0.1865, 0.0799, 0.1576, -0.0933, 0.4160, 0.1516, -0.4136, 0.2882, 0.1872, -0.2897, -0.0136, -0.1874, 0.5774, -0.1431, -0.2045, -0.3704, 0.2499, 0.0608, -0.1484, 0.2614, 0.9763, -0.0547, -0.1443, 0.1029, -0.0322, 0.0677, -0.7437, 0.5813, -0.1314, -0.3347, -0.2371, -0.3985, 0.4483, -0.4175, -0.1150, -0.2141, 0.2760, 0.1480, -0.1473, 0.1253, 0.6902, -0.1761, -0.5307, 0.2276, -0.0136, -0.1594, 0.4310, 0.0827, -0.4691, 0.5227, -0.1974, 0.1377, -0.5734, 0.0795, 0.4972, 0.6973, -0.2429, 0.0846, 0.2800, -0.3221, -0.9952, 0.0485, -0.0923, 0.0424, 0.1793, 1.0446, -1.0446]]

Evaluate model....

System.out.println(model.paramTable());

1	System.out.println(model.paramTable());

とすれば、パラメータの全容が以下のように理解できる。第一層は20+20+20で60個、第二層は20+20+2で42個、全部で102個。

{0_W=[[    
0.5051,   -0.5878,   -0.2506,    0.1828,   -0.1159,   -0.0039,   -0.2705,   -0.0151,   -0.2440,    0.3838,    0.5253,   -0.1041,   -0.3511,    0.4551,   -0.3496,    0.0187,    0.5229,   -0.4564,    0.0799,   -0.0933], 
 [  
-0.1064,    0.2147,    0.1694,    0.3966,   -1.0667,    0.0414,    0.0965,   -0.0183,    0.3099,   -0.1108,    0.6209,   -0.1726,   -0.1890,    0.1900,    0.0644,    0.0306,   -0.0608,    0.1865,    0.1576,    0.4160]], 
0_b=[[    
0.1516,   -0.4136,    0.2882,    0.1872,   -0.2897,   -0.0136,   -0.1874,    0.5774,   -0.1431,   -0.2045,   -0.3704,    0.2499,    0.0608,   -0.1484,    0.2614,    0.9763,   -0.0547,   -0.1443,    0.1029,   -0.0322]], 
1_W=[
 [    0.0677,   -0.1594], 
 [   -0.7437,    0.4310], 
 [    0.5813,    0.0827], 
 [   -0.1314,   -0.4691], 
 [   -0.3347,    0.5227], 
 [   -0.2371,   -0.1974], 
 [   -0.3985,    0.1377], 
 [    0.4483,   -0.5734], 
 [   -0.4175,    0.0795], 
 [   -0.1150,    0.4972], 
 [   -0.2141,    0.6973], 
 [    0.2760,   -0.2429], 
 [    0.1480,    0.0846], 
 [   -0.1473,    0.2800], 
 [    0.1253,   -0.3221], 
 [    0.6902,   -0.9952], 
 [   -0.1761,    0.0485], 
 [   -0.5307,   -0.0923], 
 [    0.2276,    0.0424], 
 [   -0.0136,    0.1793]], 
1_b=[[    
1.0446,   -1.0446]]}

{0_W=[[

0.5051, -0.5878, -0.2506, 0.1828, -0.1159, -0.0039, -0.2705, -0.0151, -0.2440, 0.3838, 0.5253, -0.1041, -0.3511, 0.4551, -0.3496, 0.0187, 0.5229, -0.4564, 0.0799, -0.0933],

[

-0.1064, 0.2147, 0.1694, 0.3966, -1.0667, 0.0414, 0.0965, -0.0183, 0.3099, -0.1108, 0.6209, -0.1726, -0.1890, 0.1900, 0.0644, 0.0306, -0.0608, 0.1865, 0.1576, 0.4160]],

0_b=[[

0.1516, -0.4136, 0.2882, 0.1872, -0.2897, -0.0136, -0.1874, 0.5774, -0.1431, -0.2045, -0.3704, 0.2499, 0.0608, -0.1484, 0.2614, 0.9763, -0.0547, -0.1443, 0.1029, -0.0322]],

1_W=[

[ 0.0677, -0.1594],

[ -0.7437, 0.4310],

[ 0.5813, 0.0827],

[ -0.1314, -0.4691],

[ -0.3347, 0.5227],

[ -0.2371, -0.1974],

[ -0.3985, 0.1377],

[ 0.4483, -0.5734],

[ -0.4175, 0.0795],

[ -0.1150, 0.4972],

[ -0.2141, 0.6973],

[ 0.2760, -0.2429],

[ 0.1480, 0.0846],

[ -0.1473, 0.2800],

[ 0.1253, -0.3221],

[ 0.6902, -0.9952],

[ -0.1761, 0.0485],

[ -0.5307, -0.0923],

[ 0.2276, 0.0424],

[ -0.0136, 0.1793]],

1_b=[[

1.0446, -1.0446]]}

Science To Medicine

Just My Daily Study Note by ts.anesth.kpum