wip until feedforward/classification.

Signed-off-by: Robert Altena <[email protected]>

Author: RobAltena

dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/MLPClassifierLinear.java

@@ -1,4 +1,4 @@
-/*******************************************************************************
+/* *****************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
@@ -49,6 +49,7 @@
  * @author Alex Black (added plots)
  *
  */
+@SuppressWarnings("DuplicatedCode")
 public class MLPClassifierLinear {
 
     public static String dataLocalPath;

dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/MLPClassifierMoon.java

@@ -1,4 +1,4 @@
-/*******************************************************************************
+/* *****************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
@@ -49,6 +49,7 @@
  * @author Alex Black (added plots)
  *
  */
+@SuppressWarnings("DuplicatedCode")
 public class MLPClassifierMoon {
 
     public static String dataLocalPath;
@@ -75,9 +76,6 @@ public static void main(String[] args) throws Exception {
         rrTest.initialize(new FileSplit(new File(dataLocalPath,"moon_data_eval.csv")));
         DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest,batchSize,0,2);
 
-        DataSet ds1 = trainIter.next();
-        DataSet ds2 = testIter.next();
-
         //log.info("Build model....");
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
                 .seed(seed)

dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/MLPClassifierSaturn.java

@@ -1,4 +1,4 @@
-/*******************************************************************************
+/* *****************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
@@ -20,7 +20,6 @@
 import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
 import org.datavec.api.split.FileSplit;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
 import org.deeplearning4j.examples.download.DownloaderUtility;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -29,6 +28,7 @@
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.optimize.listeners.ScoreIterationListener;
+import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
@@ -49,6 +49,7 @@
  * @author Alex Black (added plots)
  *
  */
+@SuppressWarnings("DuplicatedCode")
 public class MLPClassifierSaturn {
 
     public static String dataLocalPath;

dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/PlotUtil.java

@@ -1,4 +1,4 @@
-/*******************************************************************************
+/* *****************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
@@ -42,7 +42,7 @@
 /**Simple plotting methods for the MLPClassifier examples
  * @author Alex Black
  */
-public class PlotUtil {
+class PlotUtil {
 
     /**Plot the training data. Assume 2d input, classification output
      * @param features Training data features
@@ -51,7 +51,7 @@ public class PlotUtil {
      * @param backgroundOut results of network evaluation at points in x,y points in space
      * @param nDivisions Number of points (per axis, for the backgroundIn/backgroundOut arrays)
      */
-    public static void plotTrainingData(INDArray features, INDArray labels, INDArray backgroundIn, INDArray backgroundOut, int nDivisions){
+    static void plotTrainingData(INDArray features, INDArray labels, INDArray backgroundIn, INDArray backgroundOut, int nDivisions){
         double[] mins = backgroundIn.min(0).data().asDouble();
         double[] maxs = backgroundIn.max(0).data().asDouble();
 
@@ -75,7 +75,7 @@ public static void plotTrainingData(INDArray features, INDArray labels, INDArray
      * @param backgroundOut results of network evaluation at points in x,y points in space
      * @param nDivisions Number of points (per axis, for the backgroundIn/backgroundOut arrays)
      */
-    public static void plotTestData(INDArray features, INDArray labels, INDArray predicted, INDArray backgroundIn, INDArray backgroundOut, int nDivisions){
+    static void plotTestData(INDArray features, INDArray labels, INDArray predicted, INDArray backgroundIn, INDArray backgroundOut, int nDivisions){
 
         double[] mins = backgroundIn.min(0).data().asDouble();
         double[] maxs = backgroundIn.max(0).data().asDouble();
@@ -121,7 +121,7 @@ private static XYDataset createDataSetTrain(INDArray features, INDArray labels )
         int nClasses = 2; // Binary classification using one output call end sigmoid.
 
             XYSeries[] series = new XYSeries[nClasses];
-        for( int i=0; i<series.length; i++) series[i] = new XYSeries("Class " + String.valueOf(i));
+        for( int i=0; i<series.length; i++) series[i] = new XYSeries("Class " + i);
         INDArray argMax = Nd4j.getExecutioner().exec(new IMax(labels, 1));
         for( int i=0; i<nRows; i++ ){
             int classIdx = (int)argMax.getDouble(i);

dl4j-examples/src/main/java/org/deeplearning4j/examples/feedforward/classification/detectgender/PredictGenderTrain.java

@@ -1,4 +1,4 @@
-/*******************************************************************************
+/* *****************************************************************************
  * Copyright (c) 2015-2019 Skymind, Inc.
  *
  * This program and the accompanying materials are made available under the
@@ -16,14 +16,13 @@
 
 package org.deeplearning4j.examples.feedforward.classification.detectgender;
 
-/**
+/*
  * Created by KIT Solutions (www.kitsol.com) on 9/28/2016.
  */
 
 import org.datavec.api.split.FileSplit;
 import org.deeplearning4j.api.storage.StatsStorage;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
 import org.deeplearning4j.examples.download.DownloaderUtility;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -34,6 +33,7 @@
 import org.deeplearning4j.ui.api.UIServer;
 import org.deeplearning4j.ui.stats.StatsListener;
 import org.deeplearning4j.ui.storage.FileStatsStorage;
+import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
@@ -44,13 +44,14 @@
 import java.io.File;
 import java.util.ArrayList;
 
+@SuppressWarnings("DuplicatedCode")
 public class PredictGenderTrain
 {
     public String filePath;
     public static String dataLocalPath;
 
 
-    public static void main(String args[]) throws Exception {
+    public static void main(String[] args) throws Exception {
 
         dataLocalPath = DownloaderUtility.PREDICTGENDERDATA.Download();
         PredictGenderTrain dg = new PredictGenderTrain();
@@ -68,9 +69,9 @@ public void train()
         double learningRate = 0.005;// was .01 but often got errors: "o.d.optimize.solvers.BaseOptimizer - Hit termination condition on iteration 0"
         int batchSize = 100;
         int nEpochs = 10;
-        int numInputs = 0;
-        int numOutputs = 0;
-        int numHiddenNodes = 0;
+        int numInputs;
+        int numOutputs;
+        int numHiddenNodes;
 
         try(GenderRecordReader rr = new GenderRecordReader(new ArrayList<String>() {{add("M");add("F");}}))
         {