Small fixes (#223)

Signed-off-by: AlexDBlack <blacka101@gmail.com>

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/TestKryo.java

@@ -17,7 +17,6 @@
 package org.deeplearning4j.spark;
 
 import org.apache.spark.serializer.SerializerInstance;
-import org.deeplearning4j.eval.*;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -28,6 +27,9 @@ import org.deeplearning4j.nn.conf.graph.rnn.LastTimeStepVertex;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
 import org.junit.Test;
+import org.nd4j.evaluation.IEvaluation;
+import org.nd4j.evaluation.classification.*;
+import org.nd4j.evaluation.regression.RegressionEvaluation;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Adam;

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/multilayer/TestSparkDl4jMultiLayer.java

@@ -19,7 +19,6 @@ package org.deeplearning4j.spark.impl.multilayer;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.spark.api.java.JavaRDD;
 import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -30,6 +29,7 @@ import org.deeplearning4j.spark.BaseSparkTest;
 import org.deeplearning4j.spark.api.TrainingMaster;
 import org.deeplearning4j.spark.impl.paramavg.ParameterAveragingTrainingMaster;
 import org.junit.Test;
+import org.nd4j.evaluation.classification.Evaluation;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;

deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestSparkMultiLayerParameterAveraging.java

@@ -29,15 +29,13 @@ import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.util.MLUtils;
 import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
 import org.deeplearning4j.datasets.iterator.impl.MnistDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
-import org.deeplearning4j.eval.ROC;
-import org.deeplearning4j.eval.ROCMultiClass;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BatchNormalization;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.conf.layers.variational.GaussianReconstructionDistribution;
@@ -56,6 +54,9 @@ import org.junit.Ignore;
 import org.junit.Rule;
 import org.junit.Test;
 import org.junit.rules.TemporaryFolder;
+import org.nd4j.evaluation.classification.Evaluation;
+import org.nd4j.evaluation.classification.ROC;
+import org.nd4j.evaluation.classification.ROCMultiClass;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
@@ -63,6 +64,7 @@ import org.nd4j.linalg.dataset.MultiDataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.io.ClassPathResource;
+import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
 import org.nd4j.linalg.learning.config.Nesterovs;
 import org.nd4j.linalg.learning.config.RmsProp;
@@ -70,7 +72,6 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 import scala.Tuple2;
 
 import java.io.File;
-import java.nio.file.Files;
 import java.nio.file.Path;
 import java.util.*;
 
@@ -121,11 +122,6 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
                         new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 5, 1, 0));
 
         MultiLayerNetwork network2 = master.fitLabeledPoint(data);
-        Evaluation evaluation = new Evaluation();
-        evaluation.eval(d.getLabels(), network2.output(d.getFeatures()));
-        System.out.println(evaluation.stats());
-
-
     }
 
 
@@ -137,20 +133,15 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
                                                         .getAbsolutePath())
                         .toJavaRDD().map(new TestFn());
 
-        DataSet d = new IrisDataSetIterator(150, 150).next();
         MultiLayerConfiguration conf =
                         new NeuralNetConfiguration.Builder().seed(123)
-                                        .optimizationAlgo(OptimizationAlgorithm.LINE_GRADIENT_DESCENT)
+                                        .updater(new Adam(1e-6))
-                                        .miniBatch(true).maxNumLineSearchIterations(10)
+                                .weightInit(WeightInit.XAVIER)
-                                        .list().layer(0,
+                                .list()
-                                                        new DenseLayer.Builder().nIn(4).nOut(100)
+                                .layer(new BatchNormalization.Builder().nIn(4).nOut(4).build())
-                                                                .weightInit(WeightInit.XAVIER)
+                                .layer(new DenseLayer.Builder().nIn(4).nOut(32).activation(Activation.RELU).build())
-                                                                .activation(Activation.RELU)
+                                .layer(new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(LossFunctions.LossFunction.MCXENT).nIn(32).nOut(3)
-                                                                .build())
+                                                                        .activation(Activation.SOFTMAX).build())
-                                        .layer(1, new org.deeplearning4j.nn.conf.layers.OutputLayer.Builder(
-                                                        LossFunctions.LossFunction.MCXENT).nIn(100).nOut(3)
-                                                                        .activation(Activation.SOFTMAX)
-                                                                        .weightInit(WeightInit.XAVIER).build())
                                         .build();
 
 
@@ -161,10 +152,7 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         SparkDl4jMultiLayer master = new SparkDl4jMultiLayer(sc, getBasicConf(),
                         new ParameterAveragingTrainingMaster(true, numExecutors(), 1, 5, 1, 0));
 
-        MultiLayerNetwork network2 = master.fitLabeledPoint(data);
+        master.fitLabeledPoint(data);
-        Evaluation evaluation = new Evaluation();
-        evaluation.eval(d.getLabels(), network2.output(d.getFeatures()));
-        System.out.println(evaluation.stats());
     }
 
     @Test(timeout = 120000L)
@@ -465,8 +453,8 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         tempDirF.deleteOnExit();
 
         int dataSetObjSize = 1;
-        int batchSizePerExecutor = 25;
+        int batchSizePerExecutor = 16;
-        int numSplits = 10;
+        int numSplits = 5;
         int averagingFrequency = 3;
         int totalExamples = numExecutors() * batchSizePerExecutor * numSplits * averagingFrequency;
         DataSetIterator iter = new MnistDataSetIterator(dataSetObjSize, totalExamples, false);