diff --git a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/IrisClassifier.java b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/IrisClassifier.java
index bf341209e1..b9c794650e 100644
--- a/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/IrisClassifier.java
+++ b/deeplearning4j/src/main/java/com/baeldung/deeplearning4j/IrisClassifier.java
@@ -3,15 +3,16 @@ package com.baeldung.deeplearning4j;
 import org.datavec.api.records.reader.RecordReader;
 import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
 import org.datavec.api.split.FileSplit;
-import org.datavec.api.util.ClassPathResource;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
+import org.deeplearning4j.nn.conf.BackpropType;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
+import org.deeplearning4j.util.NetworkUtils;
+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;
@@ -19,6 +20,7 @@ import org.nd4j.linalg.dataset.SplitTestAndTrain;
 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.io.ClassPathResource;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 import java.io.IOException;
@@ -49,11 +51,11 @@ public class IrisClassifier {
         DataSet testData = testAndTrain.getTest();
 
         MultiLayerConfiguration configuration = new NeuralNetConfiguration.Builder()
-                .iterations(1000)
+                .maxNumLineSearchIterations(1000)
                 .activation(Activation.TANH)
                 .weightInit(WeightInit.XAVIER)
-                .learningRate(0.1)
-                .regularization(true).l2(0.0001)
+                //.regularization(true)
+                .l2(0.0001)
                 .list()
                 .layer(0, new DenseLayer.Builder().nIn(FEATURES_COUNT).nOut(3)
                         .build())
@@ -62,14 +64,15 @@ public class IrisClassifier {
                 .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.NEGATIVELOGLIKELIHOOD)
                         .activation(Activation.SOFTMAX)
                         .nIn(3).nOut(CLASSES_COUNT).build())
-                .backprop(true).pretrain(false)
+                .backpropType(BackpropType.Standard)//.pretrain(false)
                 .build();
 
         MultiLayerNetwork model = new MultiLayerNetwork(configuration);
         model.init();
+        NetworkUtils.setLearningRate(model, 0.1);
         model.fit(trainingData);
 
-        INDArray output = model.output(testData.getFeatureMatrix());
+        INDArray output = model.output(testData.getFeatures());
 
         Evaluation eval = new Evaluation(CLASSES_COUNT);
         eval.eval(testData.getLabels(), output);