Playing with some new code 2 - clean build/test

Signed-off-by: brian <brian@brutex.de>

brutex-extended-tests/src/test/java/net/brutex/gan/App.java

@@ -47,6 +47,7 @@ import org.datavec.image.transform.ShowImageTransform;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
@@ -54,9 +55,11 @@ import org.deeplearning4j.nn.conf.layers.DropoutLayer;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
+import org.deeplearning4j.nn.conf.weightnoise.WeightNoise;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.nn.weights.WeightInitXavier;
+import org.deeplearning4j.optimize.listeners.PerformanceListener;
 import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -181,6 +184,7 @@ public class App {
         .gradientNormalization( GradientNormalization.RenormalizeL2PerLayer)
         .gradientNormalizationThreshold( 100 )
         //.weightInitFn( new WeightInitXavier() ) //this is internal
+            .weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
         .weightInit( WeightInit.XAVIER)
         //.activationFn( new ActivationIdentity()) //this is internal
         .activation( Activation.IDENTITY )
@@ -232,10 +236,10 @@ public class App {
 
     copyParams(gen, dis, gan);
 
-    //gen.setListeners(new PerformanceListener(10, true));
-    //dis.setListeners(new PerformanceListener(10, true));
-    //gan.setListeners(new PerformanceListener(10, true));
-    gan.setListeners(new ScoreToChartListener("gan"));
+    gen.addTrainingListeners(new PerformanceListener(10, true));
+    dis.addTrainingListeners(new PerformanceListener(10, true));
+    gan.addTrainingListeners(new PerformanceListener(10, true));
+    gan.addTrainingListeners(new ScoreToChartListener("gan"));
     //dis.setListeners(new ScoreToChartListener("dis"));
 
     gan.fit(Nd4j.rand(batchSize, CHANNELS, X_DIM, Y_DIM), Nd4j.zeros(batchSize, 1));
@@ -322,23 +326,25 @@ public class App {
     int genLayerCount = gen.getLayers().length;
     for (int i = 0; i < gan.getLayers().length; i++) {
       if (i < genLayerCount) {
-        gen.getLayer(i).setParams(gan.getLayer(i).params());
+        if(gan.getLayer(i).getParams() != null)
+         gen.getLayer(i).setParams(gan.getLayer(i).getParams());
       } else {
-        dis.getLayer(i - genLayerCount).setParams(gan.getLayer(i).params());
+        if(gan.getLayer(i).getParams() != null)
+        dis.getLayer(i - genLayerCount).setParams(gan.getLayer(i).getParams());
       }
     }
   }
 
   private static void updateGen(MultiLayerNetwork gen, MultiLayerNetwork gan) {
     for (int i = 0; i < gen.getLayers().length; i++) {
-      gen.getLayer(i).setParams(gan.getLayer(i).params());
+      gen.getLayer(i).setParams(gan.getLayer(i).getParams());
     }
   }
 
   private static void updateGan(MultiLayerNetwork gen, MultiLayerNetwork dis, MultiLayerNetwork gan) {
     int genLayerCount = gen.getLayers().length;
     for (int i = genLayerCount; i < gan.getLayers().length; i++) {
-      gan.getLayer(i).setParams(dis.getLayer(i - genLayerCount).params());
+      gan.getLayer(i).setParams(dis.getLayer(i - genLayerCount).getParams());
     }
   }
 

brutex-extended-tests/src/test/java/net/brutex/gan/GAN.java

@@ -115,15 +115,15 @@ public class GAN {
 
 
   public void setGeneratorListeners(BaseTrainingListener[] listeners) {
-    generator.setListeners(listeners);
+    generator.addTrainingListeners(listeners);
   }
 
   public void setDiscriminatorListeners(BaseTrainingListener[] listeners) {
-    discriminator.setListeners(listeners);
+    discriminator.addTrainingListeners(listeners);
   }
 
   public void setGanListeners(BaseTrainingListener[] listeners) {
-    gan.setListeners(listeners);
+    gan.addTrainingListeners(listeners);
   }
 
   public void fit(DataSetIterator realData, int numEpochs) {
@@ -239,9 +239,9 @@ public class GAN {
     int genLayerCount = generator.getLayers().length;
     for (int i = 0; i < gan.getLayers().length; i++) {
       if (i < genLayerCount) {
-        generator.getLayer(i).setParams(gan.getLayer(i).params());
+        generator.getLayer(i).setParams(gan.getLayer(i).getParams());
       } else {
-        discriminator.getLayer(i - genLayerCount).setParams(gan.getLayer(i).params());
+        discriminator.getLayer(i - genLayerCount).setParams(gan.getLayer(i).getParams());
       }
     }
   }
@@ -252,7 +252,7 @@ public class GAN {
    */
   private void updateGeneratorFromGan() {
     for (int i = 0; i < generator.getLayers().length; i++) {
-      generator.getLayer(i).setParams(gan.getLayer(i).params());
+      generator.getLayer(i).setParams(gan.getLayer(i).getParams());
     }
   }
 
@@ -263,7 +263,7 @@ public class GAN {
   private void updateGanWithDiscriminator() {
     int genLayerCount = generator.getLayers().length;
     for (int i = genLayerCount; i < gan.getLayers().length; i++) {
-      gan.getLayer(i).setParams(discriminator.getLayer(i - genLayerCount).params());
+      gan.getLayer(i).setParams(discriminator.getLayer(i - genLayerCount).getParams());
     }
   }
 

brutex-extended-tests/src/test/java/net/brutex/gan/MnistDCGANExample.java

@@ -155,8 +155,8 @@ public class MnistDCGANExample {
         .updater(new RmsProp.Builder().learningRate(0.0008).rmsDecay(1e-8).build())
         .build();
 
-    gan.getGenerator().setListeners(new PerformanceListener(1, true));
-    gan.getDiscriminator().setListeners(new PerformanceListener(1, true));
+    gan.getGenerator().addTrainingListeners(new PerformanceListener(1, true));
+    gan.getDiscriminator().addTrainingListeners(new PerformanceListener(1, true));
 
     Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
 

brutex-extended-tests/src/test/java/net/brutex/spark/TestServer2.java

@@ -205,7 +205,7 @@ public class TestServer2 {
         //PostgresStatsStorage psqlStore = new PostgresStatsStorage();
         int listenerFrequency = 2;
         //net.setListeners(new StatsListener(psqlStore, listenerFrequency), new StatsListener(statsStorage, listenerFrequency), new ScoreIterationListener(200));
-        net.setListeners(new StatsListener(statsStorage, listenerFrequency), new ScoreIterationListener(200));
+        net.addTrainingListeners(new StatsListener(statsStorage, listenerFrequency), new ScoreIterationListener(200));
 
 
         //Attach the StatsStorage instance to the UI: this allows the contents of the StatsStorage to be visualized

brutex-extended-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestBaselineGenerator.java

@@ -290,7 +290,7 @@ public class IntegrationTestBaselineGenerator {
                     for (int i : layersToTrain) {
                         mln.pretrainLayer(i, dsi);
                     }
-                    paramsPostTraining = mln.params();
+                    paramsPostTraining = mln.getModelParams();
                 } else if (modelType == ModelType.CG) {
                     String[] layersToTrain = tc.getUnsupervisedTrainLayersCG();
                     Preconditions.checkState(layersToTrain != null, "ILayer names must not be null");
@@ -298,7 +298,7 @@ public class IntegrationTestBaselineGenerator {
                     for (String i : layersToTrain) {
                         cg.pretrainLayer(i, iter);
                     }
-                    paramsPostTraining = cg.params();
+                    paramsPostTraining = cg.getModelParams();
                 } else {
                     throw new UnsupportedOperationException("SameDiff not supported for unsupervised training tests");
                 }
@@ -314,7 +314,7 @@ public class IntegrationTestBaselineGenerator {
 
                 CollectScoresListener l = new CollectScoresListener(1);
                 if (modelType != ModelType.SAMEDIFF)
-                    m.setListeners(l);
+                    m.addTrainingListeners(l);
 
                 History h = null;
                 if (modelType == ModelType.MLN) {
@@ -349,7 +349,7 @@ public class IntegrationTestBaselineGenerator {
                         }
                     } else {
                         File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME);
-                        IntegrationTestRunner.write(m.params(), p);
+                        IntegrationTestRunner.write(m.getModelParams(), p);
                     }
                 }
             }

brutex-extended-tests/src/test/java/org/deeplearning4j/integration/IntegrationTestRunner.java

@@ -191,7 +191,7 @@ public class IntegrationTestRunner {
 
                 MultiLayerNetwork loaded = MultiLayerNetwork.load(savedModel, true);
                 assertEquals(loaded.getNetConfiguration(), mln.getNetConfiguration(), "Configs not equal");
-                assertEquals( loaded.params(), mln.params(), "Params not equal");
+                assertEquals( loaded.getModelParams(), mln.getModelParams(), "Params not equal");
                 assertEquals( loaded.getParamTable(), mln.getParamTable(), "Param table not equal");
             } else if(config instanceof ComputationGraphConfiguration ){
                 ComputationGraphConfiguration cgc = (ComputationGraphConfiguration) config;
@@ -201,7 +201,7 @@ public class IntegrationTestRunner {
 
                 ComputationGraph loaded = ComputationGraph.load(savedModel, true);
                 assertEquals(loaded.getComputationGraphConfiguration(), cg.getComputationGraphConfiguration(), "Configs not equal" );
-                assertEquals( loaded.params(), cg.params(), "Params not equal");
+                assertEquals( loaded.getModelParams(), cg.getModelParams(), "Params not equal");
                 assertEquals(loaded.getParamTable(), cg.getParamTable(), "Param table not equal");
             } else if(config instanceof SameDiff){
                 sd = (SameDiff)config;
@@ -389,7 +389,7 @@ public class IntegrationTestRunner {
                 for( int i : layersToTrain){
                     mln.pretrainLayer(i, dsi);
                 }
-                paramsPostTraining = mln.params();
+                paramsPostTraining = mln.getModelParams();
                 layers = mln.getLayers();
             } else if(modelType == ModelType.CG) {
                 String[] layersToTrain = tc.getUnsupervisedTrainLayersCG();
@@ -398,7 +398,7 @@ public class IntegrationTestRunner {
                 for( String i : layersToTrain){
                     cg.pretrainLayer(i, iter);
                 }
-                paramsPostTraining = cg.params();
+                paramsPostTraining = cg.getModelParams();
                 layers = cg.getLayers();
             } else {
                 throw new UnsupportedOperationException("Unsupported layerwise pretraining not supported for SameDiff models");
@@ -439,7 +439,7 @@ public class IntegrationTestRunner {
             CountingMultiDataSetIterator countingIter = new CountingMultiDataSetIterator(trainData, isTbptt, tbpttLength);
             CollectScoresListener l = new CollectScoresListener(1);
             if(modelType != ModelType.SAMEDIFF) {
-                m.setListeners(l);
+                m.addTrainingListeners(l);
             }
 
             int iterBefore;
@@ -519,10 +519,10 @@ public class IntegrationTestRunner {
                 if(modelType != ModelType.SAMEDIFF) {
                     File p = new File(testBaseDir, IntegrationTestRunner.PARAMS_POST_TRAIN_FILENAME);
                     INDArray paramsExp = read(p);
-                    INDArray z = exceedsRelError(m.params(), paramsExp, tc.getMaxRelativeErrorParamsPostTraining(), tc.getMinAbsErrorParamsPostTraining());
+                    INDArray z = exceedsRelError(m.getModelParams(), paramsExp, tc.getMaxRelativeErrorParamsPostTraining(), tc.getMinAbsErrorParamsPostTraining());
                     int count = z.sumNumber().intValue();
                     if (count > 0) {
-                        logFailedParams(20, "Parameter", layers, z, paramsExp, m.params());
+                        logFailedParams(20, "Parameter", layers, z, paramsExp, m.getModelParams());
                     }
                     assertEquals( 0, count, "Number of params exceeded max relative error");
                 } else {
@@ -607,12 +607,12 @@ public class IntegrationTestRunner {
                 ModelSerializer.writeModel(m, f, true);
                 MultiLayerNetwork restored = MultiLayerNetwork.load(f, true);
                 assertEquals(mln.getNetConfiguration(), restored.getNetConfiguration());
-                assertEquals(mln.params(), restored.params());
+                assertEquals(mln.getModelParams(), restored.getModelParams());
             } else if(modelType == ModelType.CG){
                 ModelSerializer.writeModel(m, f, true);
                 ComputationGraph restored = ComputationGraph.load(f, true);
                 assertEquals(cg.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
-                assertEquals(cg.params(), restored.params());
+                assertEquals(cg.getModelParams(), restored.getModelParams());
             } else {
                 sd.save(f, true);
                 SameDiff restored = SameDiff.load(f, true);

brutex-extended-tests/src/test/java/org/deeplearning4j/integration/TestUtils.java

@@ -49,7 +49,7 @@ public class TestUtils {
             restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
             assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
-            assertEquals(net.params(), restored.params());
+            assertEquals(net.getModelParams(), restored.getModelParams());
         } catch (IOException e){
             //Should never happen
             throw new RuntimeException(e);
@@ -74,7 +74,7 @@ public class TestUtils {
             restored = ModelSerializer.restoreComputationGraph(bais, true);
 
             assertEquals(net.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
-            assertEquals(net.params(), restored.params());
+            assertEquals(net.getModelParams(), restored.getModelParams());
         } catch (IOException e){
             //Should never happen
             throw new RuntimeException(e);

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationIdentity.java

@@ -26,7 +26,7 @@ import org.nd4j.common.primitives.Pair;
 import org.nd4j.linalg.activations.BaseActivationFunction;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
-/**
+/** The ActivationIdentity activation function, just returns the input as is.
  * f(x) = x
  */
 @EqualsAndHashCode(callSuper = false)

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/workspace/BaseWorkspaceMgr.java

@@ -195,7 +195,7 @@ public abstract class BaseWorkspaceMgr<T extends Enum<T>> implements WorkspaceMg
     }
 
     @Override
-    public INDArray validateArrayLocation(@NonNull T arrayType, @NonNull INDArray array, boolean migrateIfInvalid, boolean exceptionIfDetached) {
+    public INDArray validateArrayLocation(T arrayType, INDArray array, boolean migrateIfInvalid, boolean exceptionIfDetached) {
         validateConfig(arrayType);
 
         if(scopeOutOfWs.contains(arrayType)){

cavis-dnn/cavis-dnn-core/build.gradle

@@ -19,6 +19,7 @@ dependencies {
     testImplementation projects.cavisNative.cavisNativeCommon
     testImplementation projects.cavisNd4j.cavisNd4jCommonTests
     testImplementation projects.cavisDnn.cavisDnnCommonTests
+    testImplementation projects.cavisDnn.cavisDnnNn
 
     implementation "org.apache.commons:commons-lang3"
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/LayerHelperValidationUtil.java

@@ -116,7 +116,7 @@ public class LayerHelperValidationUtil {
 
         MultiLayerNetwork net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
         net2With.init();
-        net2With.params().assign(netOrig.params());
+        net2With.getModelParams().assign(netOrig.getModelParams());
         log.info("Removing all except for specified helpers from network copy 2: " + t.getAllowHelpersForClasses());
         removeHelpers(net2With.getLayers(), t.getAllowHelpersForClasses());
 
@@ -124,7 +124,7 @@ public class LayerHelperValidationUtil {
             Preconditions.checkNotNull(t.getFeatures(), "Features are not set (null)");
 
             for (boolean train : new boolean[]{false, true}) {
-                assertEquals(net1NoHelper.params(), net2With.params());
+                assertEquals(net1NoHelper.getModelParams(), net2With.getModelParams());
                 String s = "Feed forward test - " + t.getTestName() + " - " + (train ? "Train: " : "Test: ");
                 List<INDArray> ff1;
                 try {
@@ -180,7 +180,7 @@ public class LayerHelperValidationUtil {
                 double maxRE = relError.maxNumber().doubleValue();
                 log.info(s + "Output, max relative error: " + maxRE);
 
-                assertEquals(net1NoHelper.params(), net2With.params());  //Check that forward pass does not modify params
+                assertEquals(net1NoHelper.getModelParams(), net2With.getModelParams());  //Check that forward pass does not modify params
                 assertTrue(maxRE < t.getMaxRelError(), s + "Max RE: " + maxRE);
             }
         }
@@ -255,24 +255,24 @@ public class LayerHelperValidationUtil {
 
             net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
             net2With.init();
-            net2With.params().assign(netOrig.params());
+            net2With.getModelParams().assign(netOrig.getModelParams());
             log.info("Removing all except for specified layer helpers from network copy 2: " + t.getAllowHelpersForClasses());
             removeHelpers(net2With.getLayers(), t.getAllowHelpersForClasses());
 
             CollectScoresListener listener = new CollectScoresListener(1);
-            net2With.setListeners(listener);
+            net2With.addTrainingListeners(listener);
             net2With.fit(t.getData());
 
             for( int i=0; i<2; i++ ) {
 
                 net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
                 net2With.init();
-                net2With.params().assign(netOrig.params());
+                net2With.getModelParams().assign(netOrig.getModelParams());
                 log.info("Removing all except for specified layer helpers from network copy 2: " + t.getAllowHelpersForClasses());
                 removeHelpers(net2With.getLayers(), t.getAllowHelpersForClasses());
 
                 CollectScoresListener listener2 = new CollectScoresListener(1);
-                net2With.setListeners(listener2);
+                net2With.addTrainingListeners(listener2);
                 net2With.fit(t.getData());
 
                 DoubleArrayList listOrig = listener.getListScore();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/TestUtils.java

@@ -25,7 +25,7 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.RNNFormat;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.samediff.AbstractSameDiffLayer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.layers.convolution.ConvolutionLayer;
@@ -67,7 +67,7 @@ public class TestUtils {
             restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
             assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
-            assertEquals(net.params(), restored.params());
+            assertEquals(net.getModelParams(), restored.getModelParams());
         } catch (IOException e){
             //Should never happen
             throw new RuntimeException(e);
@@ -91,7 +91,7 @@ public class TestUtils {
             restored = ModelSerializer.restoreComputationGraph(bais, true);
 
             assertEquals(net.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
-            assertEquals(net.params(), restored.params());
+            assertEquals(net.getModelParams(), restored.getModelParams());
         } catch (IOException e){
             //Should never happen
             throw new RuntimeException(e);
@@ -205,8 +205,8 @@ public class TestUtils {
         return null;
     }
 
-    public static L2Regularization getL2Reg(BaseLayer baseLayer){
-        return getL2Reg(baseLayer.getRegularization());
+    public static L2Regularization getL2Reg(BaseLayerConfiguration baseLayerConfiguration){
+        return getL2Reg(baseLayerConfiguration.getRegularization());
     }
 
     public static L2Regularization getL2Reg(List<Regularization> l){
@@ -218,7 +218,7 @@ public class TestUtils {
         return null;
     }
 
-    public static WeightDecay getWeightDecayReg(BaseLayer bl){
+    public static WeightDecay getWeightDecayReg(BaseLayerConfiguration bl){
         return getWeightDecayReg(bl.getRegularization());
     }
 
@@ -231,7 +231,7 @@ public class TestUtils {
         return null;
     }
 
-    public static double getL1(BaseLayer layer) {
+    public static double getL1(BaseLayerConfiguration layer) {
         List<Regularization> l = layer.getRegularization();
         return getL1(l);
     }
@@ -246,7 +246,7 @@ public class TestUtils {
         return l1Reg.getL1().valueAt(0,0);
     }
 
-    public static double getL2(BaseLayer layer) {
+    public static double getL2(BaseLayerConfiguration layer) {
         List<Regularization> l = layer.getRegularization();
         return getL2(l);
     }
@@ -269,7 +269,7 @@ public class TestUtils {
         return getL2(layer.getRegularization());
     }
 
-    public static double getWeightDecay(BaseLayer layer) {
+    public static double getWeightDecay(BaseLayerConfiguration layer) {
         return getWeightDecayReg(layer.getRegularization()).getCoeff().valueAt(0,0);
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/datasets/iterator/DataSetIteratorTest.java

@@ -32,7 +32,6 @@ import org.deeplearning4j.eval.Evaluation;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
@@ -183,7 +182,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
         MultiLayerNetwork model = new MultiLayerNetwork(builder.build());
         model.init();
 
-        model.setListeners(new ScoreIterationListener(listenerFreq));
+        model.addTrainingListeners(new ScoreIterationListener(listenerFreq));
 
         model.fit(lfw.next());
 
@@ -247,7 +246,7 @@ public class DataSetIteratorTest extends BaseDL4JTest {
         //model.setListeners(Arrays.asList((TrainingListener) new ScoreIterationListener(listenerFreq)));
 
         CollectScoresIterationListener listener = new CollectScoresIterationListener(listenerFreq);
-        model.setListeners(listener);
+        model.addTrainingListeners(listener);
 
         model.fit(cifar);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStopping.java

@@ -226,7 +226,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
@@ -255,7 +255,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         MultipleEpochsIterator mIter = new MultipleEpochsIterator(10, irisIter);
@@ -304,7 +304,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
@@ -343,7 +343,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                     .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
 
@@ -386,7 +386,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
 
@@ -430,7 +430,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
         int nSamples = 100;
         //Generate the training data
         INDArray x = Nd4j.linspace(-10, 10, nSamples).reshape(nSamples, 1);
@@ -473,7 +473,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         MultipleEpochsIterator mIter = new MultipleEpochsIterator(10, irisIter);
@@ -496,9 +496,9 @@ public class TestEarlyStopping extends BaseDL4JTest {
 
         assertEquals(net.getnLayers(), mln.getnLayers());
         assertEquals(net.getNetConfiguration().getOptimizationAlgo(), mln.getNetConfiguration().getOptimizationAlgo());
-        BaseLayer bl = (BaseLayer) net.getLayerConfiguration();
-        assertEquals(bl.getActivationFn().toString(), ((BaseLayer) mln.getLayerConfiguration()).getActivationFn().toString());
-        assertEquals(bl.getIUpdater(), ((BaseLayer) mln.getLayerConfiguration()).getIUpdater());
+        BaseLayerConfiguration bl = (BaseLayerConfiguration) net.getLayerConfiguration();
+        assertEquals(bl.getActivationFn().toString(), ((BaseLayerConfiguration) mln.getLayerConfiguration()).getActivationFn().toString());
+        assertEquals(bl.getIUpdater(), ((BaseLayerConfiguration) mln.getLayerConfiguration()).getIUpdater());
     }
 
     @Test
@@ -511,7 +511,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();
@@ -792,7 +792,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
 
         TestListener tl = new TestListener();
-        net.setListeners(tl);
+        net.addTrainingListeners(tl);
 
         DataSetIterator irisIter = new IrisDataSetIterator(50, 150);
         EarlyStoppingModelSaver<MultiLayerNetwork> saver = new InMemoryModelSaver<>();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/earlystopping/TestEarlyStoppingCompGraph.java

@@ -84,7 +84,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
@@ -128,7 +128,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
@@ -165,7 +165,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
 
@@ -207,7 +207,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
 
@@ -241,7 +241,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build(), "in")
                         .setOutputs("0").build();
         ComputationGraph net = new ComputationGraph(conf);
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator irisIter = new IrisDataSetIterator(150, 150);
         EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();
@@ -538,7 +538,7 @@ public class TestEarlyStoppingCompGraph extends BaseDL4JTest {
         ComputationGraph net = new ComputationGraph(conf);
 
         TestEarlyStopping.TestListener tl = new TestEarlyStopping.TestListener();
-        net.setListeners(tl);
+        net.addTrainingListeners(tl);
 
         DataSetIterator irisIter = new IrisDataSetIterator(50, 150);
         EarlyStoppingModelSaver<ComputationGraph> saver = new InMemoryModelSaver<>();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/eval/EvalTest.java

@@ -84,7 +84,7 @@ public class EvalTest extends BaseDL4JTest {
         // Instantiate model
         MultiLayerNetwork model = new MultiLayerNetwork(conf);
         model.init();
-        model.addListeners(new ScoreIterationListener(1));
+        model.addTrainingListeners(new ScoreIterationListener(1));
 
         // Train-test split
         DataSetIterator iter = new IrisDataSetIterator(150, 150);
@@ -324,7 +324,7 @@ public class EvalTest extends BaseDL4JTest {
             MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
             net2.init();
 
-            net2.setParams(net1.params());
+            net2.setParams(net1.getModelParams());
 
             for(boolean useMask : new boolean[]{false, true}) {
 
@@ -405,7 +405,7 @@ public class EvalTest extends BaseDL4JTest {
             ComputationGraph net2 = new ComputationGraph(conf2);
             net2.init();
 
-            net2.setParams(net1.params());
+            net2.setParams(net1.getModelParams());
 
             for (boolean useMask : new boolean[]{false, true}) {
 
@@ -492,7 +492,7 @@ public class EvalTest extends BaseDL4JTest {
         DataSetIterator iter = new IrisDataSetIterator(30, 150);
         DataSetIterator iterTest = new IrisDataSetIterator(30, 150);
 
-        net.setListeners(new EvaluativeListener(iterTest, 3));
+        net.addTrainingListeners(new EvaluativeListener(iterTest, 3));
 
         for( int i=0; i<3; i++ ){
             net.fit(iter);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/BNGradientCheckTest.java

@@ -26,7 +26,6 @@ import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.distribution.UniformDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
@@ -219,11 +218,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                                 mln.setInput(ds.getFeatures());
                                 mln.setLabels(ds.getLabels());
                                 mln.computeGradientAndScore();
-                                double scoreBefore = mln.score();
+                                double scoreBefore = mln.getScore();
                                 for (int k = 0; k < 20; k++)
                                     mln.fit(ds);
                                 mln.computeGradientAndScore();
-                                double scoreAfter = mln.score();
+                                double scoreAfter = mln.getScore();
                                 //Can't test in 'characteristic mode of operation' if not learning
                                 String msg = name
                                         + " - score did not (sufficiently) decrease during learning - activationFn="
@@ -323,11 +322,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                                 mln.setInput(ds.getFeatures());
                                 mln.setLabels(ds.getLabels());
                                 mln.computeGradientAndScore();
-                                double scoreBefore = mln.score();
+                                double scoreBefore = mln.getScore();
                                 for (int k = 0; k < 10; k++)
                                     mln.fit(ds);
                                 mln.computeGradientAndScore();
-                                double scoreAfter = mln.score();
+                                double scoreAfter = mln.getScore();
                                 //Can't test in 'characteristic mode of operation' if not learning
                                 String msg = name
                                         + " - score did not (sufficiently) decrease during learning - activationFn="
@@ -554,11 +553,11 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                             net.setInput(0, ds.getFeatures());
                             net.setLabels(ds.getLabels());
                             net.computeGradientAndScore();
-                            double scoreBefore = net.score();
+                            double scoreBefore = net.getScore();
                             for (int k = 0; k < 20; k++)
                                 net.fit(ds);
                             net.computeGradientAndScore();
-                            double scoreAfter = net.score();
+                            double scoreAfter = net.getScore();
                             //Can't test in 'characteristic mode of operation' if not learning
                             String msg = name
                                     + " - score did not (sufficiently) decrease during learning - activationFn="

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNNGradientCheckTest.java

@@ -27,7 +27,6 @@ import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.CNN2DFormat;
 import org.deeplearning4j.nn.conf.ConvolutionMode;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
@@ -120,11 +119,11 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                         mln.setInput(ds.getFeatures());
                         mln.setLabels(ds.getLabels());
                         mln.computeGradientAndScore();
-                        double scoreBefore = mln.score();
+                        double scoreBefore = mln.getScore();
                         for (int j = 0; j < 10; j++)
                             mln.fit(ds);
                         mln.computeGradientAndScore();
-                        double scoreAfter = mln.score();
+                        double scoreAfter = mln.getScore();
                         //Can't test in 'characteristic mode of operation' if not learning
                         String msg = name + " - score did not (sufficiently) decrease during learning - activationFn="
                                 + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation
@@ -212,11 +211,11 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                 mln.setInput(ds.getFeatures());
                 mln.setLabels(ds.getLabels());
                 mln.computeGradientAndScore();
-                double scoreBefore = mln.score();
+                double scoreBefore = mln.getScore();
                 for (int j = 0; j < 10; j++)
                     mln.fit(ds);
                 mln.computeGradientAndScore();
-                double scoreAfter = mln.score();
+                double scoreAfter = mln.getScore();
                 //Can't test in 'characteristic mode of operation' if not learning
                 String msg = testName
                         + "- score did not (sufficiently) decrease during learning - activationFn="

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTests.java

@@ -105,11 +105,11 @@ public class GradientCheckTests extends BaseDL4JTest {
             mln.setInput(ds.getFeatures());
             mln.setLabels(ds.getLabels());
             mln.computeGradientAndScore();
-            double scoreBefore = mln.score();
+            double scoreBefore = mln.getScore();
             for (int j = 0; j < 10; j++)
                 mln.fit(ds);
             mln.computeGradientAndScore();
-            double scoreAfter = mln.score();
+            double scoreAfter = mln.getScore();
             //Can't test in 'characteristic mode of operation' if not learning
             String msg = "testMinibatchApplication() - score did not (sufficiently) decrease during learning - activationFn="
                     + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation
@@ -184,11 +184,11 @@ public class GradientCheckTests extends BaseDL4JTest {
                         mln.setInput(ds.getFeatures());
                         mln.setLabels(ds.getLabels());
                         mln.computeGradientAndScore();
-                        double scoreBefore = mln.score();
+                        double scoreBefore = mln.getScore();
                         for (int j = 0; j < 10; j++)
                             mln.fit(ds);
                         mln.computeGradientAndScore();
-                        double scoreAfter = mln.score();
+                        double scoreAfter = mln.getScore();
                         //Can't test in 'characteristic mode of operation' if not learning
                         String msg = "testGradMLP2LayerIrisSimple() - score did not (sufficiently) decrease during learning - activationFn="
                                         + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation
@@ -278,11 +278,11 @@ public class GradientCheckTests extends BaseDL4JTest {
                             mln.setInput(ds.getFeatures());
                             mln.setLabels(ds.getLabels());
                             mln.computeGradientAndScore();
-                            double scoreBefore = mln.score();
+                            double scoreBefore = mln.getScore();
                             for (int j = 0; j < 10; j++)
                                 mln.fit(ds);
                             mln.computeGradientAndScore();
-                            double scoreAfter = mln.score();
+                            double scoreAfter = mln.getScore();
                             //Can't test in 'characteristic mode of operation' if not learning
                             String msg = "testGradMLP2LayerIrisSimple() - score did not (sufficiently) decrease during learning - activationFn="
                                             + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation
@@ -452,11 +452,11 @@ public class GradientCheckTests extends BaseDL4JTest {
                             mln.setInput(ds.getFeatures());
                             mln.setLabels(ds.getLabels());
                             mln.computeGradientAndScore();
-                            double scoreBefore = mln.score();
+                            double scoreBefore = mln.getScore();
                             for (int j = 0; j < 10; j++)
                                 mln.fit(ds);
                             mln.computeGradientAndScore();
-                            double scoreAfter = mln.score();
+                            double scoreAfter = mln.getScore();
                             //Can't test in 'characteristic mode of operation' if not learning
                             msg = "testGradMLP2LayerIrisSimple() - score did not (sufficiently) decrease during learning - activationFn="
                                             + afn + ", lossFn=" + lf + ", outputActivation=" + outputActivation
@@ -523,13 +523,13 @@ public class GradientCheckTests extends BaseDL4JTest {
             netGraph.setInputs(features);
             netGraph.setLabels(labels);
             netGraph.computeGradientAndScore();
-            double scoreBefore = netGraph.score();
+            double scoreBefore = netGraph.getScore();
 
             String msg;
             for (int epoch = 0; epoch < 5; epoch++)
                 netGraph.fit(new INDArray[]{features}, new INDArray[]{labels});
             netGraph.computeGradientAndScore();
-            double scoreAfter = netGraph.score();
+            double scoreAfter = netGraph.getScore();
             //Can't test in 'characteristic mode of operation' if not learning
             msg = "elementWiseMultiplicationLayerTest() - score did not (sufficiently) decrease during learning - activationFn="
                     + "Id" + ", lossFn=" + "Cos-sim" + ", outputActivation=" + "Id"
@@ -757,11 +757,11 @@ public class GradientCheckTests extends BaseDL4JTest {
                             mln.setInput(ds.getFeatures());
                             mln.setLabels(ds.getLabels());
                             mln.computeGradientAndScore();
-                            double scoreBefore = mln.score();
+                            double scoreBefore = mln.getScore();
                             for (int j = 0; j < 10; j++)
                                 mln.fit(ds);
                             mln.computeGradientAndScore();
-                            double scoreAfter = mln.score();
+                            double scoreAfter = mln.getScore();
                             //Can't test in 'characteristic mode of operation' if not learning
                             String msg = "testGradMLP2LayerIrisSimple() - score did not (sufficiently) decrease during learning - activationFn="
                                     + afn + ", lossFn=" + lf + ", layerNorm=" + layerNorm + ", outputActivation=" + outputActivation

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/LossFunctionGradientCheck.java

@@ -666,7 +666,7 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
                     net.init();
 
                     //Check params to avoid test flakiness on small or large params
-                    INDArray params = net.params();
+                    INDArray params = net.getModelParams();
                     for( int x=0; x<params.length(); x++ ){
                         while(Math.abs(params.getDouble(x)) < 0.01 || Math.abs(params.getDouble(x)) > 1.5){
                             double d = Nd4j.getRandom().nextDouble();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/MultiLayerNeuralNetConfigurationTest.java

@@ -37,10 +37,9 @@ import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.exception.DL4JInvalidConfigException;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.GlobalPoolingLayer;
@@ -254,8 +253,8 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
     MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
     model2.init();
 
-    float[] p1 = model1.params().data().asFloat();
-    float[] p2 = model2.params().data().asFloat();
+    float[] p1 = model1.getModelParams().data().asFloat();
+    float[] p2 = model2.getModelParams().data().asFloat();
     System.out.println(Arrays.toString(p1));
     System.out.println(Arrays.toString(p2));
 
@@ -266,20 +265,20 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
   public void testTrainingListener() {
     MultiLayerNetwork model1 = new MultiLayerNetwork(getConf());
     model1.init();
-    model1.addListeners(new ScoreIterationListener(1));
+    model1.addTrainingListeners(new ScoreIterationListener(1));
 
     MultiLayerNetwork model2 = new MultiLayerNetwork(getConf());
-    model2.addListeners(new ScoreIterationListener(1));
+    model2.addTrainingListeners(new ScoreIterationListener(1));
     model2.init();
 
     Layer[] l1 = model1.getLayers();
       for (int i = 0; i < l1.length; i++) {
-          assertTrue(l1[i].getListeners() != null && l1[i].getListeners().size() == 1);
+          assertTrue(l1[i].getTrainingListeners() != null && l1[i].getTrainingListeners().size() == 1);
       }
 
     Layer[] l2 = model2.getLayers();
       for (int i = 0; i < l2.length; i++) {
-          assertTrue(l2[i].getListeners() != null && l2[i].getListeners().size() == 1);
+          assertTrue(l2[i].getTrainingListeners() != null && l2[i].getTrainingListeners().size() == 1);
       }
   }
 
@@ -384,10 +383,10 @@ public class MultiLayerNeuralNetConfigurationTest extends BaseDL4JTest {
             .weightInit(WeightInit.XAVIER).activation(Activation.SOFTMAX).build())
         .inputType(InputType.convolutional(28, 28, 1)).build();
 
-    org.deeplearning4j.nn.conf.layers.BaseLayer l0 = (BaseLayer) conf.getConf(0).getLayer();
-    org.deeplearning4j.nn.conf.layers.BaseLayer l1 = (BaseLayer) conf.getConf(1).getLayer();
-    org.deeplearning4j.nn.conf.layers.BaseLayer l2 = (BaseLayer) conf.getConf(2).getLayer();
-    org.deeplearning4j.nn.conf.layers.BaseLayer l3 = (BaseLayer) conf.getConf(3).getLayer();
+    BaseLayerConfiguration l0 = (BaseLayerConfiguration) conf.getConf(0).getLayer();
+    BaseLayerConfiguration l1 = (BaseLayerConfiguration) conf.getConf(1).getLayer();
+    BaseLayerConfiguration l2 = (BaseLayerConfiguration) conf.getConf(2).getLayer();
+    BaseLayerConfiguration l3 = (BaseLayerConfiguration) conf.getConf(3).getLayer();
 
     assertEquals(0.5, ((Adam) l0.getUpdaterByParam("b")).getLearningRate(), 1e-6);
     assertEquals(1e-2, ((Adam) l0.getUpdaterByParam("W")).getLearningRate(), 1e-6);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/NeuralNetConfigurationTest.java

@@ -25,7 +25,7 @@ import org.deeplearning4j.TestUtils;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.BatchNormalization;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
@@ -100,7 +100,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
     @Test
     public void testClone() {
         NeuralNetConfiguration conf = getConfig(1, 1, new WeightInitUniform(), true);
-        BaseLayer bl = (BaseLayer) conf.getFlattenedLayerConfigurations().get(0);
+        BaseLayerConfiguration bl = (BaseLayerConfiguration) conf.getFlattenedLayerConfigurations().get(0);
         conf.setStepFunction(new DefaultStepFunction());
 
         NeuralNetConfiguration conf2 = conf.clone();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/graph/ShiftVertexTest.java

@@ -158,7 +158,7 @@ public class ShiftVertexTest extends BaseDL4JTest {
         cg.setInput(0, input);
         cg.setLabel(0, target);
         cg.computeGradientAndScore();
-        double score_dl4j = cg.score();
+        double score_dl4j = cg.getScore();
         Map<String, INDArray> weights = cg.getParamTable();
         Gradient g = cg.gradient();
         Map<String, INDArray> gradients = g.gradientForVariable();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/layers/LayerConfigTest.java

@@ -72,8 +72,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals("relu", ((BaseLayer) conf.getConf(0).getLayer()).getActivationFn().toString());
-        assertEquals("relu", ((BaseLayer) conf.getConf(1).getLayer()).getActivationFn().toString());
+        assertEquals("relu", ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getActivationFn().toString());
+        assertEquals("relu", ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getActivationFn().toString());
 
         //With
         conf = NeuralNetConfiguration.builder().activation(Activation.RELU)
@@ -83,8 +83,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals("relu", ((BaseLayer) conf.getConf(0).getLayer()).getActivationFn().toString());
-        assertEquals("tanh", ((BaseLayer) conf.getConf(1).getLayer()).getActivationFn().toString());
+        assertEquals("relu", ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getActivationFn().toString());
+        assertEquals("tanh", ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getActivationFn().toString());
     }
 
 
@@ -99,11 +99,11 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayer) conf.getConf(0).getLayer()).getWeightInitFn());
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayer) conf.getConf(1).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInitFn());
 
-        assertEquals(1, ((BaseLayer) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
-        assertEquals(1, ((BaseLayer) conf.getConf(1).getLayer()).getBiasInit(), 0.0);
+        assertEquals(1, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
+        assertEquals(1, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getBiasInit(), 0.0);
 
         //With:
         final Distribution overriddenDistribution = new UniformDistribution(0, 1);
@@ -117,11 +117,11 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayer) conf.getConf(0).getLayer()).getWeightInitFn());
-        assertEquals(new WeightInitDistribution(overriddenDistribution), ((BaseLayer) conf.getConf(1).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(overriddenDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInitFn());
 
-        assertEquals(1, ((BaseLayer) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
-        assertEquals(0, ((BaseLayer) conf.getConf(1).getLayer()).getBiasInit(), 0.0);
+        assertEquals(1, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
+        assertEquals(0, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getBiasInit(), 0.0);
     }
 
     /*
@@ -137,8 +137,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.3, ((BaseLayer) conf.getConf(0).getLayer()).getLearningRate(), 0.0);
-        assertEquals(0.3, ((BaseLayer) conf.getConf(1).getLayer()).getLearningRate(), 0.0);
+        assertEquals(0.3, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getLearningRate(), 0.0);
+        assertEquals(0.3, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getLearningRate(), 0.0);
 
         //With:
         conf = NeuralNetConfiguration.builder().learningRate(0.3)
@@ -148,8 +148,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.3, ((BaseLayer) conf.getConf(0).getLayer()).getLearningRate(), 0.0);
-        assertEquals(0.2, ((BaseLayer) conf.getConf(1).getLayer()).getLearningRate(), 0.0);
+        assertEquals(0.3, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getLearningRate(), 0.0);
+        assertEquals(0.2, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getLearningRate(), 0.0);
 
         //L1 and L2 without layerwise override:
         conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.2)
@@ -158,10 +158,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.1, ((BaseLayer) conf.getConf(0).getLayer()).getL1(), 0.0);
-        assertEquals(0.1, ((BaseLayer) conf.getConf(1).getLayer()).getL1(), 0.0);
-        assertEquals(0.2, ((BaseLayer) conf.getConf(0).getLayer()).getL2(), 0.0);
-        assertEquals(0.2, ((BaseLayer) conf.getConf(1).getLayer()).getL2(), 0.0);
+        assertEquals(0.1, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getL1(), 0.0);
+        assertEquals(0.1, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getL1(), 0.0);
+        assertEquals(0.2, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getL2(), 0.0);
+        assertEquals(0.2, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getL2(), 0.0);
 
         //L1 and L2 with layerwise override:
         conf = NeuralNetConfiguration.builder().l1(0.1).l2(0.2)
@@ -170,10 +170,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.9, ((BaseLayer) conf.getConf(0).getLayer()).getL1(), 0.0);
-        assertEquals(0.1, ((BaseLayer) conf.getConf(1).getLayer()).getL1(), 0.0);
-        assertEquals(0.2, ((BaseLayer) conf.getConf(0).getLayer()).getL2(), 0.0);
-        assertEquals(0.8, ((BaseLayer) conf.getConf(1).getLayer()).getL2(), 0.0);
+        assertEquals(0.9, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getL1(), 0.0);
+        assertEquals(0.1, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getL1(), 0.0);
+        assertEquals(0.2, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getL2(), 0.0);
+        assertEquals(0.8, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getL2(), 0.0);
     }*/
 
 
@@ -213,8 +213,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.1, ((Nesterovs)((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
-        assertEquals(0.1, ((Nesterovs)((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
 
         Map<Integer, Double> testMomentumAfter2 = new HashMap<>();
         testMomentumAfter2.put(0, 0.2);
@@ -227,8 +227,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         net = new MultiLayerNetwork(conf);
         net.init();
-        assertEquals(0.1, ((Nesterovs)((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
-        assertEquals(0.2, ((Nesterovs)((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.2, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
     }
 
     @Test
@@ -239,10 +239,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertTrue(((BaseLayer) conf.getConf(0).getLayer()).getIUpdater() instanceof AdaDelta);
-        assertTrue(((BaseLayer) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
-        assertEquals(0.5, ((AdaDelta)((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getRho(), 0.0);
-        assertEquals(0.01, ((AdaDelta)((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertEquals(0.5, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertEquals(0.01, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
 
         conf = NeuralNetConfiguration.builder().updater(new RmsProp(1.0, 2.0, RmsProp.DEFAULT_RMSPROP_EPSILON))
                         .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).updater(new RmsProp(1.0, 1.0, RmsProp.DEFAULT_RMSPROP_EPSILON)).build())
@@ -252,10 +252,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertTrue(((BaseLayer) conf.getConf(0).getLayer()).getIUpdater() instanceof RmsProp);
-        assertTrue(((BaseLayer) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
-        assertEquals(1.0, ((RmsProp) ((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getRmsDecay(), 0.0);
-        assertEquals(0.5, ((AdaDelta) ((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater() instanceof RmsProp);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertEquals(1.0, ((RmsProp) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getRmsDecay(), 0.0);
+        assertEquals(0.5, ((AdaDelta) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
     }
 
 
@@ -270,10 +270,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.5, ((Adam) ((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getBeta1(), 0.0);
-        assertEquals(0.6, ((Adam) ((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getBeta1(), 0.0);
-        assertEquals(0.5, ((Adam) ((BaseLayer) conf.getConf(0).getLayer()).getIUpdater()).getBeta2(), 0.0);
-        assertEquals(0.7, ((Adam) ((BaseLayer) conf.getConf(1).getLayer()).getIUpdater()).getBeta2(), 0.0);
+        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getBeta1(), 0.0);
+        assertEquals(0.6, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getBeta1(), 0.0);
+        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getBeta2(), 0.0);
+        assertEquals(0.7, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getBeta2(), 0.0);
     }
 
     @Test
@@ -287,13 +287,11 @@ public class LayerConfigTest extends BaseDL4JTest {
                         .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-
-        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue,
-                        conf.getConf(0).getLayer().getGradientNormalization());
-        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue,
-                        conf.getConf(1).getLayer().getGradientNormalization());
-        assertEquals(10, conf.getConf(0).getLayer().getGradientNormalizationThreshold(), 0.0);
-        assertEquals(10, conf.getConf(1).getLayer().getGradientNormalizationThreshold(), 0.0);
+        BaseLayerConfiguration bconf = (BaseLayerConfiguration)  conf.getConf(0).getLayer();
+        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue, bconf.getGradientNormalization());
+        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue, bconf.getGradientNormalization());
+        assertEquals(10, bconf.getGradientNormalizationThreshold(), 0.0);
+        assertEquals(10, bconf.getGradientNormalizationThreshold(), 0.0);
 
         //With:
         conf = NeuralNetConfiguration.builder()
@@ -308,11 +306,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue,
-                        conf.getConf(0).getLayer().getGradientNormalization());
-        assertEquals(GradientNormalization.None, conf.getConf(1).getLayer().getGradientNormalization());
-        assertEquals(10, conf.getConf(0).getLayer().getGradientNormalizationThreshold(), 0.0);
-        assertEquals(2.5, conf.getConf(1).getLayer().getGradientNormalizationThreshold(), 0.0);
+        assertEquals(GradientNormalization.ClipElementWiseAbsoluteValue, bconf.getGradientNormalization());
+        assertEquals(GradientNormalization.None, bconf.getGradientNormalization());
+        assertEquals(10, bconf.getGradientNormalizationThreshold(), 0.0);
+        assertEquals(2.5, bconf.getGradientNormalizationThreshold(), 0.0);
     }
 
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/layers/LayerConfigValidationTest.java

@@ -162,12 +162,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        BaseLayer layerConf = (BaseLayer) net.getLayer(0).getLayerConfiguration();
+        BaseLayerConfiguration layerConf = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
         assertEquals(expectedMomentum, ((Nesterovs) layerConf.getIUpdater()).getMomentum(), 1e-3);
         assertNull(TestUtils.getL1Reg(layerConf.getRegularization()));
         assertEquals(0.5, TestUtils.getL2(layerConf), 1e-3);
 
-        BaseLayer layerConf1 = (BaseLayer) net.getLayer(1).getLayerConfiguration();
+        BaseLayerConfiguration layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
         assertEquals(0.4, ((Nesterovs) layerConf1.getIUpdater()).getMomentum(), 1e-3);
 
         // Adam Updater
@@ -178,11 +178,11 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        layerConf = (BaseLayer) net.getLayer(0).getLayerConfiguration();
+        layerConf = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
         assertEquals(0.3, TestUtils.getL1(layerConf), 1e-3);
         assertEquals(0.5, TestUtils.getL2(layerConf), 1e-3);
 
-        layerConf1 = (BaseLayer) net.getLayer(1).getLayerConfiguration();
+        layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
         assertEquals(expectedAdamMeanDecay, ((Adam) layerConf1.getIUpdater()).getBeta1(), 1e-3);
         assertEquals(expectedAdamVarDecay, ((Adam) layerConf1.getIUpdater()).getBeta2(), 1e-3);
         assertEquals(new WeightInitDistribution(expectedDist), layerConf1.getWeightInitFn());
@@ -196,12 +196,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        layerConf = (BaseLayer) net.getLayer(0).getLayerConfiguration();
+        layerConf = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
         assertEquals(expectedRmsDecay, ((RmsProp) layerConf.getIUpdater()).getRmsDecay(), 1e-3);
         assertNull(TestUtils.getL1Reg(layerConf.getRegularization()));
         assertNull(TestUtils.getL2Reg(layerConf.getRegularization()));
 
-        layerConf1 = (BaseLayer) net.getLayer(1).getLayerConfiguration();
+        layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
         assertEquals(0.4, ((RmsProp) layerConf1.getIUpdater()).getRmsDecay(), 1e-3);
 
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/conf/weightnoise/TestWeightNoise.java

@@ -29,7 +29,7 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
@@ -75,9 +75,9 @@ public class TestWeightNoise extends BaseDL4JTest {
             MultiLayerNetwork net = new MultiLayerNetwork(conf);
             net.init();
 
-            assertEquals(wn, ((BaseLayer) net.getLayer(0).getLayerConfiguration()).getWeightNoise());
-            assertEquals(new DropConnect(0.25), ((BaseLayer) net.getLayer(1).getLayerConfiguration()).getWeightNoise());
-            assertEquals(wn, ((BaseLayer) net.getLayer(2).getLayerConfiguration()).getWeightNoise());
+            assertEquals(wn, ((BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration()).getWeightNoise());
+            assertEquals(new DropConnect(0.25), ((BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration()).getWeightNoise());
+            assertEquals(wn, ((BaseLayerConfiguration) net.getLayer(2).getLayerConfiguration()).getWeightNoise());
 
             TestUtils.testModelSerialization(net);
 
@@ -95,9 +95,9 @@ public class TestWeightNoise extends BaseDL4JTest {
             ComputationGraph graph = new ComputationGraph(conf2);
             graph.init();
 
-            assertEquals(wn, ((BaseLayer) graph.getLayer(0).getLayerConfiguration()).getWeightNoise());
-            assertEquals(new DropConnect(0.25), ((BaseLayer) graph.getLayer(1).getLayerConfiguration()).getWeightNoise());
-            assertEquals(wn, ((BaseLayer) graph.getLayer(2).getLayerConfiguration()).getWeightNoise());
+            assertEquals(wn, ((BaseLayerConfiguration) graph.getLayer(0).getLayerConfiguration()).getWeightNoise());
+            assertEquals(new DropConnect(0.25), ((BaseLayerConfiguration) graph.getLayer(1).getLayerConfiguration()).getWeightNoise());
+            assertEquals(wn, ((BaseLayerConfiguration) graph.getLayer(2).getLayerConfiguration()).getWeightNoise());
 
             TestUtils.testModelSerialization(graph);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/dtypes/DTypeTests.java

@@ -124,7 +124,7 @@ import org.deeplearning4j.nn.conf.layers.recurrent.TimeDistributed;
 import org.deeplearning4j.nn.conf.layers.util.MaskLayer;
 import org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer;
 import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
-import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer;
+import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayerConfiguration;
 import org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer;
 import org.deeplearning4j.nn.conf.preprocessor.CnnToFeedForwardPreProcessor;
 import org.deeplearning4j.nn.conf.preprocessor.CnnToRnnPreProcessor;
@@ -260,8 +260,8 @@ public class DTypeTests extends BaseDL4JTest {
         for (NeuralNetConfiguration nnc : conf.getNetConfigurations()) {
             LayerConfiguration l = nnc.getFlattenedLayerConfigurations().get(0);
             seenLayers.add(l.getClass());
-            if (l instanceof BaseWrapperLayer) {
-                BaseWrapperLayer bwl = (BaseWrapperLayer) l;
+            if (l instanceof BaseWrapperLayerConfiguration) {
+                BaseWrapperLayerConfiguration bwl = (BaseWrapperLayerConfiguration) l;
                 seenLayers.add(bwl.getUnderlying().getClass());
             } else if (l instanceof Bidirectional) {
                 seenLayers.add(((Bidirectional) l).getFwd().getClass());
@@ -321,17 +321,17 @@ public class DTypeTests extends BaseDL4JTest {
             net.setInput(inD);
             net.setLabels(lD);
             net.computeGradientAndScore();
-            double scoreDouble = net.score();
+            double scoreDouble = net.getScore();
             INDArray grads = net.getFlattenedGradients();
             INDArray u = net.getUpdater().getStateViewArray();
-            assertEquals(DataType.DOUBLE, net.params().dataType());
+            assertEquals(DataType.DOUBLE, net.getModelParams().dataType());
             assertEquals(DataType.DOUBLE, grads.dataType());
             assertEquals(DataType.DOUBLE, u.dataType());
 
 
             MultiLayerNetwork netFloat = net.convertDataType(DataType.FLOAT);
             netFloat.initGradientsView();
-            assertEquals(DataType.FLOAT, netFloat.params().dataType());
+            assertEquals(DataType.FLOAT, netFloat.getModelParams().dataType());
             assertEquals(DataType.FLOAT, netFloat.getFlattenedGradients().dataType());
             assertEquals(DataType.FLOAT, netFloat.getUpdater(true).getStateViewArray().dataType());
             INDArray inF = inD.castTo(DataType.FLOAT);
@@ -340,7 +340,7 @@ public class DTypeTests extends BaseDL4JTest {
             netFloat.setInput(inF);
             netFloat.setLabels(lF);
             netFloat.computeGradientAndScore();
-            double scoreFloat = netFloat.score();
+            double scoreFloat = netFloat.getScore();
             INDArray gradsFloat = netFloat.getFlattenedGradients();
             INDArray uFloat = netFloat.getUpdater().getStateViewArray();
 
@@ -352,7 +352,7 @@ public class DTypeTests extends BaseDL4JTest {
 
             MultiLayerNetwork netFP16 = net.convertDataType(DataType.HALF);
             netFP16.initGradientsView();
-            assertEquals(DataType.HALF, netFP16.params().dataType());
+            assertEquals(DataType.HALF, netFP16.getModelParams().dataType());
             assertEquals(DataType.HALF, netFP16.getFlattenedGradients().dataType());
             assertEquals(DataType.HALF, netFP16.getUpdater(true).getStateViewArray().dataType());
 
@@ -362,7 +362,7 @@ public class DTypeTests extends BaseDL4JTest {
             netFP16.setInput(inH);
             netFP16.setLabels(lH);
             netFP16.computeGradientAndScore();
-            double scoreHalf = netFP16.score();
+            double scoreHalf = netFP16.getScore();
             INDArray gradsHalf = netFP16.getFlattenedGradients();
             INDArray uHalf = netFP16.getUpdater().getStateViewArray();
 
@@ -406,17 +406,17 @@ public class DTypeTests extends BaseDL4JTest {
             net.setInput(0, inD);
             net.setLabels(lD);
             net.computeGradientAndScore();
-            double scoreDouble = net.score();
+            double scoreDouble = net.getScore();
             INDArray grads = net.getFlattenedGradients();
             INDArray u = net.getUpdater().getStateViewArray();
-            assertEquals(DataType.DOUBLE, net.params().dataType());
+            assertEquals(DataType.DOUBLE, net.getModelParams().dataType());
             assertEquals(DataType.DOUBLE, grads.dataType());
             assertEquals(DataType.DOUBLE, u.dataType());
 
 
             ComputationGraph netFloat = net.convertDataType(DataType.FLOAT);
             netFloat.initGradientsView();
-            assertEquals(DataType.FLOAT, netFloat.params().dataType());
+            assertEquals(DataType.FLOAT, netFloat.getModelParams().dataType());
             assertEquals(DataType.FLOAT, netFloat.getFlattenedGradients().dataType());
             assertEquals(DataType.FLOAT, netFloat.getUpdater(true).getStateViewArray().dataType());
             INDArray inF = inD.castTo(DataType.FLOAT);
@@ -425,7 +425,7 @@ public class DTypeTests extends BaseDL4JTest {
             netFloat.setInput(0, inF);
             netFloat.setLabels(lF);
             netFloat.computeGradientAndScore();
-            double scoreFloat = netFloat.score();
+            double scoreFloat = netFloat.getScore();
             INDArray gradsFloat = netFloat.getFlattenedGradients();
             INDArray uFloat = netFloat.getUpdater().getStateViewArray();
 
@@ -437,7 +437,7 @@ public class DTypeTests extends BaseDL4JTest {
 
             ComputationGraph netFP16 = net.convertDataType(DataType.HALF);
             netFP16.initGradientsView();
-            assertEquals(DataType.HALF, netFP16.params().dataType());
+            assertEquals(DataType.HALF, netFP16.getModelParams().dataType());
             assertEquals(DataType.HALF, netFP16.getFlattenedGradients().dataType());
             assertEquals(DataType.HALF, netFP16.getUpdater(true).getStateViewArray().dataType());
 
@@ -447,7 +447,7 @@ public class DTypeTests extends BaseDL4JTest {
             netFP16.setInput(0, inH);
             netFP16.setLabels(lH);
             netFP16.computeGradientAndScore();
-            double scoreHalf = netFP16.score();
+            double scoreHalf = netFP16.getScore();
             INDArray gradsHalf = netFP16.getFlattenedGradients();
             INDArray uHalf = netFP16.getUpdater().getStateViewArray();
 
@@ -536,7 +536,7 @@ public class DTypeTests extends BaseDL4JTest {
                     net.init();
 
                     net.initGradientsView();
-                    assertEquals(networkDtype, net.params().dataType(), msg);
+                    assertEquals(networkDtype, net.getModelParams().dataType(), msg);
                     assertEquals(networkDtype, net.getFlattenedGradients().dataType(), msg);
                     assertEquals(networkDtype, net.getUpdater(true).getStateViewArray().dataType(), msg);
 
@@ -641,7 +641,7 @@ public class DTypeTests extends BaseDL4JTest {
                     net.init();
 
                     net.initGradientsView();
-                    assertEquals(networkDtype, net.params().dataType(), msg);
+                    assertEquals(networkDtype, net.getModelParams().dataType(), msg);
                     assertEquals(networkDtype, net.getFlattenedGradients().dataType(), msg);
                     assertEquals(networkDtype, net.getUpdater(true).getStateViewArray().dataType(), msg);
 
@@ -754,7 +754,7 @@ public class DTypeTests extends BaseDL4JTest {
                     net.init();
 
                     net.initGradientsView();
-                    assertEquals(networkDtype, net.params().dataType(), msg);
+                    assertEquals(networkDtype, net.getModelParams().dataType(), msg);
                     assertEquals(networkDtype, net.getFlattenedGradients().dataType(), msg);
                     assertEquals(networkDtype, net.getUpdater(true).getStateViewArray().dataType(), msg);
 
@@ -827,7 +827,7 @@ public class DTypeTests extends BaseDL4JTest {
                 net.init();
 
                 net.initGradientsView();
-                assertEquals(networkDtype, net.params().dataType(), msg);
+                assertEquals(networkDtype, net.getModelParams().dataType(), msg);
                 assertEquals(networkDtype, net.getFlattenedGradients().dataType(), msg);
                 assertEquals(networkDtype, net.getUpdater(true).getStateViewArray().dataType(), msg);
 
@@ -916,7 +916,7 @@ public class DTypeTests extends BaseDL4JTest {
                     net.init();
 
                     net.initGradientsView();
-                    assertEquals(networkDtype, net.params().dataType(), msg);
+                    assertEquals(networkDtype, net.getModelParams().dataType(), msg);
                     assertEquals(networkDtype, net.getFlattenedGradients().dataType(), msg);
                     assertEquals(networkDtype, net.getUpdater(true).getStateViewArray().dataType(), msg);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/ComputationGraphTestRNN.java

@@ -520,9 +520,9 @@ public class ComputationGraphTestRNN extends BaseDL4JTest {
         INDArray inputLong = Nd4j.rand(miniBatchSize, nIn, timeSeriesLength);
         INDArray labelsLong = Nd4j.rand(miniBatchSize, nOut, timeSeriesLength);
 
-        INDArray initialParams = graph.params().dup();
+        INDArray initialParams = graph.getModelParams().dup();
         graph.fit(new INDArray[] {inputLong}, new INDArray[] {labelsLong});
-        INDArray afterParams = graph.params();
+        INDArray afterParams = graph.getModelParams();
 
         assertNotEquals(initialParams, afterParams);
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestCompGraphCNN.java

@@ -117,7 +117,7 @@ public class TestCompGraphCNN extends BaseDL4JTest {
         boolean orderOK = Arrays.equals(expOrder1, order) || Arrays.equals(expOrder2, order);
         assertTrue(orderOK);
 
-        INDArray params = graph.params();
+        INDArray params = graph.getModelParams();
         assertNotNull(params);
 
         // confirm param shape is what is expected
@@ -129,7 +129,7 @@ public class TestCompGraphCNN extends BaseDL4JTest {
 
         // params are set
         graph.setParams(arr);
-        params = graph.params();
+        params = graph.getModelParams();
         assertEquals(arr, params);
 
         //Number of inputs and outputs:

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestCompGraphUnsupervised.java

@@ -108,7 +108,7 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
                 }
             }
 
-            int count = Nd4j.getExecutioner().exec(new MatchCondition(cg.params(), Conditions.isNan())).getInt(0);
+            int count = Nd4j.getExecutioner().exec(new MatchCondition(cg.getModelParams(), Conditions.isNan())).getInt(0);
             assertEquals(0, count);
 
 
@@ -125,7 +125,7 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
                 }
             }
 
-            count = Nd4j.getExecutioner().exec(new MatchCondition(cg.params(), Conditions.isNan())).getInt(0);
+            count = Nd4j.getExecutioner().exec(new MatchCondition(cg.getModelParams(), Conditions.isNan())).getInt(0);
             assertEquals(0, count);
         }
     }
@@ -176,7 +176,7 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
             Nd4j.getRandom().setSeed(12345);
             cg.pretrainLayer("0", ds);
 
-            assertEquals(net.params(), cg.params());
+            assertEquals(net.getModelParams(), cg.getModelParams());
         }
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestComputationGraphNetwork.java

@@ -159,7 +159,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         DataSet ds = iris.next();
 
         graph.setInput(0, ds.getFeatures());
-        net.setParams(graph.params());
+        net.setParams(graph.getModelParams());
         Map<String, INDArray> activations = graph.feedForward(false);
 
         List<INDArray> feedForward = net.feedForward(ds.getFeatures());
@@ -184,7 +184,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         int[] expOrder = new int[]{0, 1, 2};
         assertArrayEquals(expOrder, order); //Only one valid order: 0 (input) -> 1 (firstlayer) -> 2 (outputlayer)
 
-        INDArray params = graph.params();
+        INDArray params = graph.getModelParams();
         assertNotNull(params);
 
         int nParams = getNumParams();
@@ -194,7 +194,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         assertEquals(nParams, arr.length());
 
         graph.setParams(arr);
-        params = graph.params();
+        params = graph.getModelParams();
         assertEquals(arr, params);
 
         //Number of inputs and outputs:
@@ -315,8 +315,8 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         graph.fit(iris);
 
         //Check that parameters are equal for both models after fitting:
-        INDArray paramsMLN = net.params();
-        INDArray paramsGraph = graph.params();
+        INDArray paramsMLN = net.getModelParams();
+        INDArray paramsGraph = graph.getModelParams();
 
         assertNotEquals(params, paramsGraph);
         assertEquals(paramsMLN, paramsGraph);
@@ -636,7 +636,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         ComputationGraph net = new ComputationGraph(conf);
         net.init();
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         DataSetIterator iter = new IrisDataSetIterator(10, 150);
         net.pretrain(iter);
@@ -675,7 +675,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         ComputationGraph netNoReg = new ComputationGraph(confNoReg);
         netNoReg.init();
-        netNoReg.setParams(net.params().dup());
+        netNoReg.setParams(net.getModelParams().dup());
 
         //Score single example, and compare to scoreExamples:
         INDArray input = Nd4j.rand(3, nIn);
@@ -878,13 +878,13 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         net.setParam("first_b", Nd4j.ones(1, 5));
         net.setParam("output_W", Nd4j.ones(5, 3));
         net.setParam("output_b", Nd4j.ones(1, 3));
-        INDArray actualParams = net.params();
+        INDArray actualParams = net.getModelParams();
 
         // Confirm params
         assertEquals(Nd4j.ones(1, 43), actualParams);
 
         net.update(expectedGradient);
-        actualParams = net.params();
+        actualParams = net.getModelParams();
         assertEquals(Nd4j.ones(1, 43).addi(1), actualParams);
     }
 
@@ -1638,7 +1638,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         conf3.setTopologicalOrderStr(null);
         ComputationGraph cg3 = new ComputationGraph(conf3);
         cg3.init();
-        cg3.setParams(cg2.params());
+        cg3.setParams(cg2.getModelParams());
 
         int[] order3 = cg3.topologicalSortOrder();
         List<String> strOrder3 = cg.getComputationGraphConfiguration().getTopologicalOrderStr();
@@ -1712,7 +1712,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         exp.add(ComputationGraph.class);
 
         MultiLayerTest.CheckModelsListener listener = new MultiLayerTest.CheckModelsListener();
-        net.setListeners(listener);
+        net.addTrainingListeners(listener);
 
         INDArray f = Nd4j.create(1,10);
         INDArray l = Nd4j.create(1,10);
@@ -1874,7 +1874,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         ComputationGraph cg = new ComputationGraph(conf);
         cg.init();
-        cg.params().assign(Nd4j.linspace(1, 220, 220).reshape(1, -11));
+        cg.getModelParams().assign(Nd4j.linspace(1, 220, 220).reshape(1, -11));
 
         INDArray p0w = cg.getParam("layer_zero_W");
         assertEquals(Nd4j.linspace(1, 100, 100).reshape('f', 10, 10), p0w);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestSetGetParameters.java

@@ -56,7 +56,7 @@ public class TestSetGetParameters extends BaseDL4JTest {
 
         ComputationGraph net = new ComputationGraph(conf);
         net.init();
-        INDArray params = net.params();
+        INDArray params = net.getModelParams();
 
 
         ComputationGraph net2 = new ComputationGraph(conf);
@@ -65,11 +65,11 @@ public class TestSetGetParameters extends BaseDL4JTest {
         ComputationGraph net3 = new ComputationGraph(conf);
         net3.init(params, false);
 
-        assertEquals(params, net2.params());
-        assertEquals(params, net3.params());
+        assertEquals(params, net2.getModelParams());
+        assertEquals(params, net3.getModelParams());
 
-        assertNotSame(params, net2.params()); //Different objects due to clone
-        assertSame(params, net3.params()); //Same object due to clone
+        assertNotSame(params, net2.getModelParams()); //Different objects due to clone
+        assertSame(params, net3.getModelParams()); //Same object due to clone
 
 
         Map<String, INDArray> paramsMap = net.getParamTable();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/graph/TestVariableLengthTSCG.java

@@ -103,14 +103,14 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
             net.setInput(0, in1);
             net.setLabel(0, labels1);
             net.computeGradientAndScore();
-            double score1 = net.score();
+            double score1 = net.getScore();
             Gradient g1 = net.gradient();
 
             net.setInput(0, in2);
             net.setLabel(0, labels2);
             net.setLayerMaskArrays(null, new INDArray[] {labelMask});
             net.computeGradientAndScore();
-            double score2 = net.score();
+            double score2 = net.getScore();
             Gradient g2 = net.gradient();
 
             //Scores and gradients should be identical for two cases (given mask array)
@@ -134,7 +134,7 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
                 }
                 net.setLabel(0, labels2);
                 net.computeGradientAndScore();
-                double score2a = net.score();
+                double score2a = net.getScore();
                 Gradient g2a = net.gradient();
                 assertEquals(score2, score2a, 1e-6);
                 for (String s : g2map.keySet()) {
@@ -200,7 +200,7 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
             net.setInput(0, in1);
             net.setLabel(0, labels1);
             net.computeGradientAndScore();
-            double score1 = net.score();
+            double score1 = net.getScore();
             Gradient g1 = net.gradient();
             Map<String, INDArray> map = g1.gradientForVariable();
             for (String s : map.keySet()) {
@@ -211,7 +211,7 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
             net.setLabel(0, labels2);
             net.setLayerMaskArrays(new INDArray[] {inputMask}, null);
             net.computeGradientAndScore();
-            double score2 = net.score();
+            double score2 = net.getScore();
             Gradient g2 = net.gradient();
             Map<String, INDArray> activations2 = net.feedForward();
 
@@ -236,7 +236,7 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
                 net.setInput(0, in2);
                 net.setLayerMaskArrays(new INDArray[]{inputMask}, null);
                 net.computeGradientAndScore();
-                double score2a = net.score();
+                double score2a = net.getScore();
                 Gradient g2a = net.gradient();
                 assertEquals(score2, score2a, 1e-12);
                 for (String s : g2.gradientForVariable().keySet()) {
@@ -330,7 +330,7 @@ public class TestVariableLengthTSCG extends BaseDL4JTest {
                         net.setLabel(0, labels);
 
                         net.computeGradientAndScore();
-                        double score = net.score();
+                        double score = net.getScore();
 
                         assertEquals(expScore, score, 0.1, msg);
                     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/BaseLayerConfigurationTest.java

@@ -40,7 +40,7 @@ import java.util.Map;
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertNotEquals;
 
-public class BaseLayerTest extends BaseDL4JTest {
+public class BaseLayerConfigurationTest extends BaseDL4JTest {
 
     protected INDArray weight = Nd4j.create(new double[] {0.10, -0.20, -0.15, 0.05}, new int[] {2, 2});
     protected INDArray bias = Nd4j.create(new double[] {0.5, 0.5}, new int[] {1, 2});

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/CacheModeTest.java

@@ -56,10 +56,10 @@ public class CacheModeTest extends BaseDL4JTest {
         INDArray out2 = net2.output(in);
         assertEquals(out1, out2);
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
         net1.fit(in, labels);
         net2.fit(in, labels);
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
     }
 
     private static NeuralNetConfiguration getConf(CacheMode cacheMode){
@@ -99,10 +99,10 @@ public class CacheModeTest extends BaseDL4JTest {
             INDArray out2 = net2.output(in);
             assertEquals(out1, out2);
 
-            assertEquals(net1.params(), net2.params());
+            assertEquals(net1.getModelParams(), net2.getModelParams());
             net1.fit(in, labels);
             net2.fit(in, labels);
-            assertEquals(net1.params(), net2.params());
+            assertEquals(net1.getModelParams(), net2.getModelParams());
         }
     }
 
@@ -145,10 +145,10 @@ public class CacheModeTest extends BaseDL4JTest {
         INDArray out2 = net2.outputSingle(in);
         assertEquals(out1, out2);
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
         net1.fit(new DataSet(in, labels));
         net2.fit(new DataSet(in, labels));
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
     }
 
     private static ComputationGraphConfiguration getConfCG(CacheMode cacheMode){

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/CenterLossOutputLayerTest.java

@@ -121,7 +121,7 @@ public class CenterLossOutputLayerTest extends BaseDL4JTest {
             graph.setInput(0, input);
             graph.setLabel(0, labels);
             graph.computeGradientAndScore();
-            results[i] = graph.score();
+            results[i] = graph.getScore();
         }
 
         assertNotEquals(results[0], results[1]);
@@ -137,7 +137,7 @@ public class CenterLossOutputLayerTest extends BaseDL4JTest {
 
         ComputationGraph net = getCNNMnistConfig();
         net.init();
-        net.setListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         for (int i = 0; i < 50; i++) {
             net.fit(mnistTrain.next());

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/DropoutLayerTest.java

@@ -265,7 +265,7 @@ public class DropoutLayerTest extends BaseDL4JTest {
         MultiLayerNetwork netSeparate = new MultiLayerNetwork(confSeparate);
         netSeparate.init();
 
-        assertEquals(netIntegrated.params(), netSeparate.params());
+        assertEquals(netIntegrated.getModelParams(), netSeparate.getModelParams());
 
         Nd4j.getRandom().setSeed(12345);
         netIntegrated.fit(next);
@@ -273,7 +273,7 @@ public class DropoutLayerTest extends BaseDL4JTest {
         Nd4j.getRandom().setSeed(12345);
         netSeparate.fit(next);
 
-        assertEquals(netIntegrated.params(), netSeparate.params());
+        assertEquals(netIntegrated.getModelParams(), netSeparate.getModelParams());
 
         // check parameters
         assertEquals(netIntegrated.getLayer(0).getParam("W"), netSeparate.getLayer(0).getParam("W"));

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/FrozenLayerTest.java

@@ -80,7 +80,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
                         .setFeatureExtractor(1).build();
 
         INDArray paramsLastTwoLayers =
-                        Nd4j.hstack(modelToFineTune.getLayer(2).params(), modelToFineTune.getLayer(3).params());
+                        Nd4j.hstack(modelToFineTune.getLayer(2).getParams(), modelToFineTune.getLayer(3).getParams());
         MultiLayerNetwork notFrozen = new MultiLayerNetwork(
             (NeuralNetConfiguration) overallConf.clone()
                             .layer(0, new Builder().nIn(2).nOut(3).build())
@@ -102,9 +102,9 @@ public class FrozenLayerTest extends BaseDL4JTest {
             modelNow.fit(randomData);
         }
 
-        INDArray expected = Nd4j.hstack(modelToFineTune.getLayer(0).params(), modelToFineTune.getLayer(1).params(),
-                        notFrozen.params());
-        INDArray act = modelNow.params();
+        INDArray expected = Nd4j.hstack(modelToFineTune.getLayer(0).getParams(), modelToFineTune.getLayer(1).getParams(),
+                        notFrozen.getModelParams());
+        INDArray act = modelNow.getModelParams();
         assertEquals(expected, act);
     }
 
@@ -136,7 +136,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         assertEquals(modelNow.getNetConfiguration().toJson(), clonedModel.getNetConfiguration().toJson());
 
         //Check params
-        assertEquals(modelNow.params(), clonedModel.params());
+        assertEquals(modelNow.getModelParams(), clonedModel.getModelParams());
 
         MultiLayerNetwork notFrozen = new MultiLayerNetwork(
             (NeuralNetConfiguration) overallConf.layer(0, new Builder().nIn(2).nOut(3).build())
@@ -145,7 +145,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
                                                             .activation(Activation.SOFTMAX).nIn(3).nOut(3)
                                                             .build())
                             .build(),
-                        Nd4j.hstack(modelToFineTune.getLayer(2).params(), modelToFineTune.getLayer(3).params()));
+                        Nd4j.hstack(modelToFineTune.getLayer(2).getParams(), modelToFineTune.getLayer(3).getParams()));
 
         int i = 0;
         while (i < 5) {
@@ -155,10 +155,10 @@ public class FrozenLayerTest extends BaseDL4JTest {
             i++;
         }
 
-        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).params(),
-                        modelToFineTune.getLayer(1).params(), notFrozen.params());
-        assertEquals(expectedParams, modelNow.params());
-        assertEquals(expectedParams, clonedModel.params());
+        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).getParams(),
+                        modelToFineTune.getLayer(1).getParams(), notFrozen.getModelParams());
+        assertEquals(expectedParams, modelNow.getModelParams());
+        assertEquals(expectedParams, clonedModel.getModelParams());
 
     }
 
@@ -199,8 +199,8 @@ public class FrozenLayerTest extends BaseDL4JTest {
                         .setOutputs("layer1").build());
 
         notFrozen.init();
-        notFrozen.setParams(Nd4j.hstack(modelToFineTune.getLayer("layer2").params(),
-                        modelToFineTune.getLayer("layer3").params()));
+        notFrozen.setParams(Nd4j.hstack(modelToFineTune.getLayer("layer2").getParams(),
+                        modelToFineTune.getLayer("layer3").getParams()));
 
         int i = 0;
         while (i < 5) {
@@ -209,8 +209,8 @@ public class FrozenLayerTest extends BaseDL4JTest {
             i++;
         }
 
-        assertEquals(Nd4j.hstack(modelToFineTune.getLayer("layer0").params(),
-                        modelToFineTune.getLayer("layer1").params(), notFrozen.params()), modelNow.params());
+        assertEquals(Nd4j.hstack(modelToFineTune.getLayer("layer0").getParams(),
+                        modelToFineTune.getLayer("layer1").getParams(), notFrozen.getModelParams()), modelNow.getModelParams());
     }
 
     @Test
@@ -244,7 +244,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         assertEquals(clonedModel.getComputationGraphConfiguration().toJson(), modelNow.getComputationGraphConfiguration().toJson());
 
         //Check params
-        assertEquals(modelNow.params(), clonedModel.params());
+        assertEquals(modelNow.getModelParams(), clonedModel.getModelParams());
 
         ComputationGraph notFrozen = new ComputationGraph(overallConf.graphBuilder().addInputs("layer0In")
                         .addLayer("layer0", new DenseLayer.Builder().nIn(2).nOut(3).build(), "layer0In")
@@ -256,8 +256,8 @@ public class FrozenLayerTest extends BaseDL4JTest {
                                         "layer0")
                         .setOutputs("layer1").build());
         notFrozen.init();
-        notFrozen.setParams(Nd4j.hstack(modelToFineTune.getLayer("layer2").params(),
-                        modelToFineTune.getLayer("layer3").params()));
+        notFrozen.setParams(Nd4j.hstack(modelToFineTune.getLayer("layer2").getParams(),
+                        modelToFineTune.getLayer("layer3").getParams()));
 
 
         int i = 0;
@@ -268,10 +268,10 @@ public class FrozenLayerTest extends BaseDL4JTest {
             i++;
         }
 
-        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer("layer0").params(),
-                        modelToFineTune.getLayer("layer1").params(), notFrozen.params());
-        assertEquals(expectedParams, modelNow.params());
-        assertEquals(expectedParams, clonedModel.params());
+        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer("layer0").getParams(),
+                        modelToFineTune.getLayer("layer1").getParams(), notFrozen.getModelParams());
+        assertEquals(expectedParams, modelNow.getModelParams());
+        assertEquals(expectedParams, clonedModel.getModelParams());
     }
 
 
@@ -305,7 +305,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
         net2.init();
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
 
 
         String json = conf2.toJson();
@@ -362,7 +362,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         ComputationGraph net2 = new ComputationGraph(conf2);
         net2.init();
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
 
 
         String json = conf2.toJson();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/FrozenLayerWithBackpropTest.java

@@ -75,7 +75,7 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
         net2.init();
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
 
 
         String json = conf2.toJson();
@@ -130,7 +130,7 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
         ComputationGraph net2 = new ComputationGraph(conf2);
         net2.init();
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
 
 
         String json = conf2.toJson();
@@ -170,19 +170,19 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
 
         MultiLayerNetwork network = new MultiLayerNetwork(conf1);
         network.init();
-        INDArray unfrozenLayerParams = network.getLayer(0).params().dup();
-        INDArray frozenLayerParams1 = network.getLayer(1).params().dup();
-        INDArray frozenLayerParams2 = network.getLayer(2).params().dup();
-        INDArray frozenOutputLayerParams = network.getLayer(3).params().dup();
+        INDArray unfrozenLayerParams = network.getLayer(0).getParams().dup();
+        INDArray frozenLayerParams1 = network.getLayer(1).getParams().dup();
+        INDArray frozenLayerParams2 = network.getLayer(2).getParams().dup();
+        INDArray frozenOutputLayerParams = network.getLayer(3).getParams().dup();
 
         for (int i = 0; i < 100; i++) {
             network.fit(randomData);
         }
 
-        assertNotEquals(unfrozenLayerParams, network.getLayer(0).params());
-        assertEquals(frozenLayerParams1, network.getLayer(1).params());
-        assertEquals(frozenLayerParams2, network.getLayer(2).params());
-        assertEquals(frozenOutputLayerParams, network.getLayer(3).params());
+        assertNotEquals(unfrozenLayerParams, network.getLayer(0).getParams());
+        assertEquals(frozenLayerParams1, network.getLayer(1).getParams());
+        assertEquals(frozenLayerParams2, network.getLayer(2).getParams());
+        assertEquals(frozenOutputLayerParams, network.getLayer(3).getParams());
 
     }
 
@@ -228,19 +228,19 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
 
         ComputationGraph computationGraph = new ComputationGraph(computationGraphConf);
         computationGraph.init();
-        INDArray unfrozenLayerParams = computationGraph.getLayer(frozenBranchUnfrozenLayer0).params().dup();
-        INDArray frozenLayerParams1 = computationGraph.getLayer(frozenBranchFrozenLayer1).params().dup();
-        INDArray frozenLayerParams2 = computationGraph.getLayer(frozenBranchFrozenLayer2).params().dup();
-        INDArray frozenOutputLayerParams = computationGraph.getLayer(frozenBranchOutput).params().dup();
+        INDArray unfrozenLayerParams = computationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams().dup();
+        INDArray frozenLayerParams1 = computationGraph.getLayer(frozenBranchFrozenLayer1).getParams().dup();
+        INDArray frozenLayerParams2 = computationGraph.getLayer(frozenBranchFrozenLayer2).getParams().dup();
+        INDArray frozenOutputLayerParams = computationGraph.getLayer(frozenBranchOutput).getParams().dup();
 
         for (int i = 0; i < 100; i++) {
             computationGraph.fit(randomData);
         }
 
-        assertNotEquals(unfrozenLayerParams, computationGraph.getLayer(frozenBranchUnfrozenLayer0).params());
-        assertEquals(frozenLayerParams1, computationGraph.getLayer(frozenBranchFrozenLayer1).params());
-        assertEquals(frozenLayerParams2, computationGraph.getLayer(frozenBranchFrozenLayer2).params());
-        assertEquals(frozenOutputLayerParams, computationGraph.getLayer(frozenBranchOutput).params());
+        assertNotEquals(unfrozenLayerParams, computationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams());
+        assertEquals(frozenLayerParams1, computationGraph.getLayer(frozenBranchFrozenLayer1).getParams());
+        assertEquals(frozenLayerParams2, computationGraph.getLayer(frozenBranchFrozenLayer2).getParams());
+        assertEquals(frozenOutputLayerParams, computationGraph.getLayer(frozenBranchOutput).getParams());
 
     }
 
@@ -275,17 +275,17 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
                 .build();
         MultiLayerNetwork frozenNetwork = new MultiLayerNetwork(confFrozen);
         frozenNetwork.init();
-        INDArray unfrozenLayerParams = frozenNetwork.getLayer(0).params().dup();
-        INDArray frozenLayerParams1 = frozenNetwork.getLayer(1).params().dup();
-        INDArray frozenLayerParams2 = frozenNetwork.getLayer(2).params().dup();
-        INDArray frozenOutputLayerParams = frozenNetwork.getLayer(3).params().dup();
+        INDArray unfrozenLayerParams = frozenNetwork.getLayer(0).getParams().dup();
+        INDArray frozenLayerParams1 = frozenNetwork.getLayer(1).getParams().dup();
+        INDArray frozenLayerParams2 = frozenNetwork.getLayer(2).getParams().dup();
+        INDArray frozenOutputLayerParams = frozenNetwork.getLayer(3).getParams().dup();
 
         MultiLayerNetwork sgdNetwork = new MultiLayerNetwork(confSgd);
         sgdNetwork.init();
-        INDArray unfrozenSgdLayerParams = sgdNetwork.getLayer(0).params().dup();
-        INDArray frozenSgdLayerParams1 = sgdNetwork.getLayer(1).params().dup();
-        INDArray frozenSgdLayerParams2 = sgdNetwork.getLayer(2).params().dup();
-        INDArray frozenSgdOutputLayerParams = sgdNetwork.getLayer(3).params().dup();
+        INDArray unfrozenSgdLayerParams = sgdNetwork.getLayer(0).getParams().dup();
+        INDArray frozenSgdLayerParams1 = sgdNetwork.getLayer(1).getParams().dup();
+        INDArray frozenSgdLayerParams2 = sgdNetwork.getLayer(2).getParams().dup();
+        INDArray frozenSgdOutputLayerParams = sgdNetwork.getLayer(3).getParams().dup();
 
         for (int i = 0; i < 100; i++) {
             frozenNetwork.fit(randomData);
@@ -294,10 +294,10 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
             sgdNetwork.fit(randomData);
         }
 
-        assertEquals(frozenNetwork.getLayer(0).params(), sgdNetwork.getLayer(0).params());
-        assertEquals(frozenNetwork.getLayer(1).params(), sgdNetwork.getLayer(1).params());
-        assertEquals(frozenNetwork.getLayer(2).params(), sgdNetwork.getLayer(2).params());
-        assertEquals(frozenNetwork.getLayer(3).params(), sgdNetwork.getLayer(3).params());
+        assertEquals(frozenNetwork.getLayer(0).getParams(), sgdNetwork.getLayer(0).getParams());
+        assertEquals(frozenNetwork.getLayer(1).getParams(), sgdNetwork.getLayer(1).getParams());
+        assertEquals(frozenNetwork.getLayer(2).getParams(), sgdNetwork.getLayer(2).getParams());
+        assertEquals(frozenNetwork.getLayer(3).getParams(), sgdNetwork.getLayer(3).getParams());
 
     }
 
@@ -360,17 +360,17 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
 
         ComputationGraph frozenComputationGraph = new ComputationGraph(computationGraphConf);
         frozenComputationGraph.init();
-        INDArray unfrozenLayerParams = frozenComputationGraph.getLayer(frozenBranchUnfrozenLayer0).params().dup();
-        INDArray frozenLayerParams1 = frozenComputationGraph.getLayer(frozenBranchFrozenLayer1).params().dup();
-        INDArray frozenLayerParams2 = frozenComputationGraph.getLayer(frozenBranchFrozenLayer2).params().dup();
-        INDArray frozenOutputLayerParams = frozenComputationGraph.getLayer(frozenBranchOutput).params().dup();
+        INDArray unfrozenLayerParams = frozenComputationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams().dup();
+        INDArray frozenLayerParams1 = frozenComputationGraph.getLayer(frozenBranchFrozenLayer1).getParams().dup();
+        INDArray frozenLayerParams2 = frozenComputationGraph.getLayer(frozenBranchFrozenLayer2).getParams().dup();
+        INDArray frozenOutputLayerParams = frozenComputationGraph.getLayer(frozenBranchOutput).getParams().dup();
 
         ComputationGraph sgdComputationGraph = new ComputationGraph(computationSgdGraphConf);
         sgdComputationGraph.init();
-        INDArray unfrozenSgdLayerParams = sgdComputationGraph.getLayer(frozenBranchUnfrozenLayer0).params().dup();
-        INDArray frozenSgdLayerParams1 = sgdComputationGraph.getLayer(frozenBranchFrozenLayer1).params().dup();
-        INDArray frozenSgdLayerParams2 = sgdComputationGraph.getLayer(frozenBranchFrozenLayer2).params().dup();
-        INDArray frozenSgdOutputLayerParams = sgdComputationGraph.getLayer(frozenBranchOutput).params().dup();
+        INDArray unfrozenSgdLayerParams = sgdComputationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams().dup();
+        INDArray frozenSgdLayerParams1 = sgdComputationGraph.getLayer(frozenBranchFrozenLayer1).getParams().dup();
+        INDArray frozenSgdLayerParams2 = sgdComputationGraph.getLayer(frozenBranchFrozenLayer2).getParams().dup();
+        INDArray frozenSgdOutputLayerParams = sgdComputationGraph.getLayer(frozenBranchOutput).getParams().dup();
 
         for (int i = 0; i < 100; i++) {
             frozenComputationGraph.fit(randomData);
@@ -379,10 +379,10 @@ public class FrozenLayerWithBackpropTest extends BaseDL4JTest {
             sgdComputationGraph.fit(randomData);
         }
 
-        assertEquals(frozenComputationGraph.getLayer(frozenBranchUnfrozenLayer0).params(), sgdComputationGraph.getLayer(frozenBranchUnfrozenLayer0).params());
-        assertEquals(frozenComputationGraph.getLayer(frozenBranchFrozenLayer1).params(), sgdComputationGraph.getLayer(frozenBranchFrozenLayer1).params());
-        assertEquals(frozenComputationGraph.getLayer(frozenBranchFrozenLayer2).params(), sgdComputationGraph.getLayer(frozenBranchFrozenLayer2).params());
-        assertEquals(frozenComputationGraph.getLayer(frozenBranchOutput).params(), sgdComputationGraph.getLayer(frozenBranchOutput).params());
+        assertEquals(frozenComputationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams(), sgdComputationGraph.getLayer(frozenBranchUnfrozenLayer0).getParams());
+        assertEquals(frozenComputationGraph.getLayer(frozenBranchFrozenLayer1).getParams(), sgdComputationGraph.getLayer(frozenBranchFrozenLayer1).getParams());
+        assertEquals(frozenComputationGraph.getLayer(frozenBranchFrozenLayer2).getParams(), sgdComputationGraph.getLayer(frozenBranchFrozenLayer2).getParams());
+        assertEquals(frozenComputationGraph.getLayer(frozenBranchOutput).getParams(), sgdComputationGraph.getLayer(frozenBranchOutput).getParams());
 
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/OutputLayerTest.java

@@ -68,9 +68,9 @@ public class OutputLayerTest extends BaseDL4JTest {
         INDArray params = Nd4j.create(1, numParams);
         OutputLayer l = (OutputLayer) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf,
                         Collections.singletonList(new ScoreIterationListener(1)), 0, params, true, params.dataType());
-        params = l.params();
+        params = l.getModelParams();
         l.setParamsTable(params);
-        assertEquals(params, l.params());
+        assertEquals(params, l.getModelParams());
     }
 
     @Test
@@ -217,8 +217,8 @@ public class OutputLayerTest extends BaseDL4JTest {
             //However: OutputLayer version has miniBatch*timeSeriesLength "examples" (after reshaping)
             //RnnOutputLayer has miniBatch examples
             //Hence: expect difference in scores by factor of timeSeriesLength
-            double score = mln.score() * timeSeriesLength;
-            double scoreRNN = mlnRnn.score();
+            double score = mln.getScore() * timeSeriesLength;
+            double scoreRNN = mlnRnn.getScore();
 
             assertFalse(Double.isNaN(score));
             assertFalse(Double.isNaN(scoreRNN));
@@ -234,7 +234,7 @@ public class OutputLayerTest extends BaseDL4JTest {
 
             RnnOutputLayer rnnol = (RnnOutputLayer) mlnRnn.getOutputLayer();
             //assertArrayEquals(rnnol.getInput().shape(),new int[]{miniBatchSize,layerSize,timeSeriesLength});
-            //Input may be set by BaseLayer methods. Thus input may end up as reshaped 2d version instead of original 3d version.
+            //Input may be set by BaseLayerConfiguration methods. Thus input may end up as reshaped 2d version instead of original 3d version.
             //Not ideal, but everything else works.
             assertArrayEquals(rnnol.getLabels().shape(), new long[] {miniBatchSize, nOut, timeSeriesLength});
 
@@ -303,7 +303,7 @@ public class OutputLayerTest extends BaseDL4JTest {
         MultiLayerNetwork mln2 = new MultiLayerNetwork(conf2);
         mln2.init();
 
-        mln2.setParams(mln.params());
+        mln2.setParams(mln.getModelParams());
 
         INDArray in = Nd4j.rand(miniBatchSize, nIn, timeSeriesLength);
 
@@ -330,7 +330,7 @@ public class OutputLayerTest extends BaseDL4JTest {
         mln2.computeGradientAndScore();
 
         assertEquals(mln.gradient().gradient(), mln2.gradient().gradient());
-        assertEquals(mln.score(), mln2.score(), 1e-6);
+        assertEquals(mln.getScore(), mln2.getScore(), 1e-6);
 
         TestUtils.testModelSerialization(mln);
     }
@@ -386,7 +386,7 @@ public class OutputLayerTest extends BaseDL4JTest {
                 mln2.init();
 
 
-                mln2.setParams(mln.params());
+                mln2.setParams(mln.getModelParams());
 
 
                 INDArray in = Nd4j.rand(3, 3, 5, 5);
@@ -407,7 +407,7 @@ public class OutputLayerTest extends BaseDL4JTest {
                 mln.computeGradientAndScore();
                 mln2.computeGradientAndScore();
 
-                assertEquals(mln.score(), mln2.score(), 1e-6);
+                assertEquals(mln.getScore(), mln2.getScore(), 1e-6);
                 assertEquals(mln.gradient().gradient(), mln2.gradient().gradient());
 
                 //Also check computeScoreForExamples
@@ -479,7 +479,7 @@ public class OutputLayerTest extends BaseDL4JTest {
                 graph2.init();
 
 
-                graph2.setParams(graph.params());
+                graph2.setParams(graph.getModelParams());
 
 
                 INDArray in = Nd4j.rand(3, 3, 5, 5);
@@ -500,7 +500,7 @@ public class OutputLayerTest extends BaseDL4JTest {
                 graph.computeGradientAndScore();
                 graph2.computeGradientAndScore();
 
-                assertEquals(graph.score(), graph2.score(), 1e-6);
+                assertEquals(graph.getScore(), graph2.getScore(), 1e-6);
                 assertEquals(graph.gradient().gradient(), graph2.gradient().gradient());
 
                 //Also check computeScoreForExamples

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/SeedTest.java

@@ -59,13 +59,13 @@ public class SeedTest extends BaseDL4JTest {
         layer.fit(data.getFeatures(), LayerWorkspaceMgr.noWorkspaces());
 
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        double score = layer.score();
-        INDArray parameters = layer.params();
+        double score = layer.getScore();
+        INDArray parameters = layer.getParams();
         layer.setParams(parameters);
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
 
-        double score2 = layer.score();
-        assertEquals(parameters, layer.params());
+        double score2 = layer.getScore();
+        assertEquals(parameters, layer.getParams());
         assertEquals(score, score2, 1e-4);
     }
 }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvDataFormatTests.java

@@ -845,9 +845,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
 
     public static void testHelper(TestCase tc) {
 
-        tc.net2.params().assign(tc.net1.params());
-        tc.net3.params().assign(tc.net1.params());
-        tc.net4.params().assign(tc.net1.params());
+        tc.net2.getModelParams().assign(tc.net1.getModelParams());
+        tc.net3.getModelParams().assign(tc.net1.getModelParams());
+        tc.net4.getModelParams().assign(tc.net1.getModelParams());
 
         //Test forward pass:
         INDArray inNCHW = tc.inNCHW;
@@ -909,9 +909,9 @@ public class ConvDataFormatTests extends BaseDL4JTest {
         tc.net3.fit(inNHWC, tc.labelsNHWC);
         tc.net4.fit(inNHWC, tc.labelsNHWC);
 
-        assertEquals(tc.net1.params(), tc.net2.params(), tc.msg);
-        assertEquals(tc.net1.params(), tc.net3.params(), tc.msg);
-        assertEquals(tc.net1.params(), tc.net4.params(), tc.msg);
+        assertEquals(tc.net1.getModelParams(), tc.net2.getModelParams(), tc.msg);
+        assertEquals(tc.net1.getModelParams(), tc.net3.getModelParams(), tc.msg);
+        assertEquals(tc.net1.getModelParams(), tc.net4.getModelParams(), tc.msg);
 
         //Test serialization
         MultiLayerNetwork net1a = TestUtils.testModelSerialization(tc.net1);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/ConvolutionLayerTest.java

@@ -30,7 +30,6 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ConvolutionMode;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.RNNFormat;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.Convolution1DLayer;
@@ -38,7 +37,6 @@ import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
-import org.deeplearning4j.nn.weights.WeightInitNormal;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -450,10 +448,10 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
 
         MultiLayerNetwork net = getCNNMLNConfig(true, false);
 
-        INDArray paramsOrig = net.params().dup();
+        INDArray paramsOrig = net.getModelParams().dup();
         net.setParams(paramsOrig);
 
-        INDArray params2 = net.params();
+        INDArray params2 = net.getModelParams();
 
         assertEquals(paramsOrig, params2);
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/custom/TestCustomLayers.java

@@ -154,7 +154,7 @@ public class TestCustomLayers extends BaseDL4JTest {
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
         net2.init();
 
-        assertEquals(net2.params(), net.params());
+        assertEquals(net2.getModelParams(), net.getModelParams());
 
         INDArray testFeatures = Nd4j.rand(1, 10);
         INDArray testLabels = Nd4j.zeros(1, 10);
@@ -207,7 +207,7 @@ public class TestCustomLayers extends BaseDL4JTest {
         ComputationGraph net2 = new ComputationGraph(conf2);
         net2.init();
 
-        assertEquals(net2.params(), net.params());
+        assertEquals(net2.getModelParams(), net.getModelParams());
 
         INDArray testFeatures = Nd4j.rand(1, 10);
         INDArray testLabels = Nd4j.zeros(1, 10);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/custom/testclasses/CustomLayer.java

@@ -56,7 +56,7 @@ public class CustomLayer extends FeedForwardLayer {
                                                        boolean initializeParams, DataType networkDataType) {
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
         CustomLayerImpl ret = new CustomLayerImpl(lconf, networkDataType);
-        ret.setListeners(trainingListeners);
+        ret.addTrainingListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/custom/testclasses/CustomOutputLayer.java

@@ -54,7 +54,7 @@ public class CustomOutputLayer extends BaseOutputLayer {
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
         CustomOutputLayerImpl ret = new CustomOutputLayerImpl(lconf, networkDataType);
-        ret.setListeners(trainingListeners);
+        ret.addTrainingListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/dense/DenseTest.java

@@ -72,7 +72,7 @@ public class DenseTest extends BaseDL4JTest {
 
         DataSet test = iter.next();
 
-        assertEquals(model.params(), model2.params());
+        assertEquals(model.getModelParams(), model2.getModelParams());
 
         Evaluation eval = new Evaluation();
         INDArray output = model.output(test.getFeatures());
@@ -99,7 +99,7 @@ public class DenseTest extends BaseDL4JTest {
 
         DataSet test = iter.next();
 
-        assertEquals(model.params(), model2.params());
+        assertEquals(model.getModelParams(), model2.getModelParams());
 
         Evaluation eval = new Evaluation();
         INDArray output = model.output(test.getFeatures());

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java

@@ -169,7 +169,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.init();
         net2.init();
 
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         int batchSize = 3;
         INDArray inEmbedding = Nd4j.create(batchSize, 1);
@@ -216,7 +216,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.init();
         net2.init();
 
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         int batchSize = 3;
         INDArray inEmbedding = Nd4j.create(batchSize, 1);
@@ -262,7 +262,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.init();
         net2.init();
 
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         int batchSize = 3;
         INDArray inEmbedding = Nd4j.create(batchSize, 1);
@@ -287,7 +287,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.computeGradientAndScore();
         net2.computeGradientAndScore();
 
-        assertEquals(net2.score(), net.score(), 1e-6);
+        assertEquals(net2.getScore(), net.getScore(), 1e-6);
 
         Map<String, INDArray> gradient = net.gradient().gradientForVariable();
         Map<String, INDArray> gradient2 = net2.gradient().gradientForVariable();
@@ -323,7 +323,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.init();
         net2.init();
 
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         int batchSize = 3;
         INDArray inEmbedding = Nd4j.create(batchSize, 1);
@@ -349,7 +349,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net2.computeGradientAndScore();
 
 //        System.out.println(net.score() + "\t" + net2.score());
-        assertEquals(net2.score(), net.score(), 1e-6);
+        assertEquals(net2.getScore(), net.getScore(), 1e-6);
 
         Map<String, INDArray> gradient = net.gradient().gradientForVariable();
         Map<String, INDArray> gradient2 = net2.gradient().gradientForVariable();
@@ -395,7 +395,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net.init();
         net2.init();
 
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         INDArray inEmbedding = Nd4j.create(batchSize, 1, timeSeriesLength);
         INDArray inOneHot = Nd4j.create(batchSize, nClassesIn, timeSeriesLength);
@@ -422,7 +422,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         net2.computeGradientAndScore();
 
 //        System.out.println(net.score() + "\t" + net2.score());
-        assertEquals(net2.score(), net.score(), 1e-5);
+        assertEquals(net2.getScore(), net.getScore(), 1e-5);
 
         Map<String, INDArray> gradient = net.gradient().gradientForVariable();
         Map<String, INDArray> gradient2 = net2.gradient().gradientForVariable();
@@ -484,7 +484,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                 MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
                 net2.init();
 
-                net2.setParams(net.params().dup());
+                net2.setParams(net.getModelParams().dup());
 
                 INDArray inEmbedding = Nd4j.zeros(nExamples, 1, timeSeriesLength);
                 INDArray inDense = Nd4j.zeros(nExamples, numInputClasses, timeSeriesLength);
@@ -523,7 +523,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                 net2.computeGradientAndScore();
 
 //                System.out.println(net.score() + "\t" + net2.score());
-                assertEquals(net2.score(), net.score(), 1e-5);
+                assertEquals(net2.getScore(), net.getScore(), 1e-5);
 
                 Map<String, INDArray> gradients = net.gradient().gradientForVariable();
                 Map<String, INDArray> gradients2 = net2.gradient().gradientForVariable();
@@ -640,7 +640,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                         MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
                         net2.init();
 
-                        net2.setParams(net.params().dup());
+                        net2.setParams(net.getModelParams().dup());
 
                         INDArray inEmbedding = Nd4j.zeros(inLabelDtype, inputRank == 2 ? new long[]{nExamples, timeSeriesLength} : new long[]{nExamples, 1, timeSeriesLength});
                         INDArray inDense = Nd4j.zeros(inLabelDtype, nExamples, numInputClasses, timeSeriesLength);
@@ -678,7 +678,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                         net.computeGradientAndScore();
                         net2.computeGradientAndScore();
 
-                        assertEquals(net2.score(), net.score(), 1e-5);
+                        assertEquals(net2.getScore(), net.getScore(), 1e-5);
 
                         Map<String, INDArray> gradients = net.gradient().gradientForVariable();
                         Map<String, INDArray> gradients2 = net2.gradient().gradientForVariable();
@@ -777,9 +777,9 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                 MultiLayerNetwork net3 = new MultiLayerNetwork(conf3);
                 net3.init();
 
-                INDArray p1 = net.params();
-                INDArray p2 = net2.params();
-                INDArray p3 = net3.params();
+                INDArray p1 = net.getModelParams();
+                INDArray p2 = net2.getModelParams();
+                INDArray p3 = net3.getModelParams();
                 boolean eq = p1.equalsWithEps(p2, 1e-4);
                 String str = (seq ? "EmbeddingSequenceLayer" : "EmbeddingLayer") + " - " + wi;
                 assertTrue(eq, str + " p1/p2 params not equal");

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/objdetect/TestYolo2OutputLayer.java

@@ -514,7 +514,7 @@ public class TestYolo2OutputLayer extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-        net.setListeners(new ScoreIterationListener(100));
+        net.addTrainingListeners(new ScoreIterationListener(100));
 
         int nEpochs = 1000;
         DataSet ds = iter.next();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/ocnn/OCNNOutputLayerTest.java

@@ -79,13 +79,13 @@ public class OCNNOutputLayerTest extends BaseDL4JTest {
         if (doLearningFirst) {
             //Run a number of iterations of learning
             network.setInput(arr);
-            network.setListeners(new ScoreIterationListener(1));
+            network.addTrainingListeners(new ScoreIterationListener(1));
             network.computeGradientAndScore();
-            double scoreBefore = network.score();
+            double scoreBefore = network.getScore();
             for (int j = 0; j < 10; j++)
                 network.fit(ds);
             network.computeGradientAndScore();
-            double scoreAfter = network.score();
+            double scoreAfter = network.getScore();
             //Can't test in 'characteristic mode of operation' if not learning
             String msg = "testLayer() - score did not (sufficiently) decrease during learning - activationFn="
                     + "relu" + ", lossFn=" + "ocnn" + ", "  + "sigmoid"
@@ -147,7 +147,7 @@ public class OCNNOutputLayerTest extends BaseDL4JTest {
         tmpFile.deleteOnExit();
 
         MultiLayerNetwork multiLayerNetwork = ModelSerializer.restoreMultiLayerNetwork(tmpFile);
-        assertEquals(network.params(),multiLayerNetwork.params());
+        assertEquals(network.getModelParams(),multiLayerNetwork.getModelParams());
         assertEquals(network.numParams(),multiLayerNetwork.numParams());
 
     }
@@ -187,7 +187,7 @@ public class OCNNOutputLayerTest extends BaseDL4JTest {
                 .build();
         MultiLayerNetwork network = new MultiLayerNetwork(configuration);
         network.init();
-        network.setListeners(new ScoreIterationListener(1));
+        network.addTrainingListeners(new ScoreIterationListener(1));
         return network;
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/BidirectionalTest.java

@@ -124,7 +124,7 @@ public class BidirectionalTest extends BaseDL4JTest {
                 assertEquals(n1, n2);
             }
 
-            net2.setParams(net1.params());  //Assuming exact same layout here...
+            net2.setParams(net1.getModelParams());  //Assuming exact same layout here...
 
             INDArray in;
             if (rnnDataFormat == NCW){
@@ -154,7 +154,7 @@ public class BidirectionalTest extends BaseDL4JTest {
             net2.computeGradientAndScore();
 
             //Ensure scores are equal:
-            assertEquals(net1.score(), net2.score(), 1e-6);
+            assertEquals(net1.getScore(), net2.getScore(), 1e-6);
 
             //Ensure gradients are equal:
             Gradient g1 = net1.gradient();
@@ -174,8 +174,8 @@ public class BidirectionalTest extends BaseDL4JTest {
             net1.fit(in, labels);
             net2.fit(in, labels);
 
-            INDArray p1 = net1.params();
-            INDArray p2 = net2.params();
+            INDArray p1 = net1.getModelParams();
+            INDArray p2 = net2.getModelParams();
             assertEquals(p1, p2);
         }
     }
@@ -232,7 +232,7 @@ public class BidirectionalTest extends BaseDL4JTest {
                 assertEquals(n1, n2);
             }
 
-            net2.setParams(net1.params());  //Assuming exact same layout here...
+            net2.setParams(net1.getModelParams());  //Assuming exact same layout here...
 
             INDArray in = Nd4j.rand(3, 10, 5);
 
@@ -253,7 +253,7 @@ public class BidirectionalTest extends BaseDL4JTest {
             net2.computeGradientAndScore();
 
             //Ensure scores are equal:
-            assertEquals(net1.score(), net2.score(), 1e-6);
+            assertEquals(net1.getScore(), net2.getScore(), 1e-6);
 
             //Ensure gradients are equal:
             Gradient g1 = net1.gradient();
@@ -273,8 +273,8 @@ public class BidirectionalTest extends BaseDL4JTest {
             net1.fit(new DataSet(in, labels));
             net2.fit(new DataSet(in, labels));
 
-            INDArray p1 = net1.params();
-            INDArray p2 = net2.params();
+            INDArray p1 = net1.getModelParams();
+            INDArray p2 = net2.getModelParams();
             assertEquals(p1, p2);
         }
     }
@@ -340,7 +340,7 @@ public class BidirectionalTest extends BaseDL4JTest {
             net1.computeGradientAndScore();
             net2.computeGradientAndScore();
 
-            assertEquals(net1.score(), net2.score(), 1e-6);
+            assertEquals(net1.getScore(), net2.getScore(), 1e-6);
             assertEquals(net1.gradient().gradient(), net2.gradient().gradient());
         }
     }
@@ -403,7 +403,7 @@ public class BidirectionalTest extends BaseDL4JTest {
             net1.computeGradientAndScore();
             net2.computeGradientAndScore();
 
-            assertEquals(net1.score(), net2.score(), 1e-6);
+            assertEquals(net1.getScore(), net2.getScore(), 1e-6);
             assertEquals(net1.gradient().gradient(), net2.gradient().gradient());
         }
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/GravesBidirectionalLSTMTest.java

@@ -277,7 +277,7 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
 
         final INDArray act1 = bidirectionalLSTM.activate(sig, false, LayerWorkspaceMgr.noWorkspaces());
 
-        params = bidirectionalLSTM.params();
+        params = bidirectionalLSTM.getModelParams();
 
         bidirectionalLSTM.setParamsTable(params);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/RnnDataFormatTests.java

@@ -285,9 +285,9 @@ public class RnnDataFormatTests extends BaseDL4JTest {
 
         public static void testHelper(TestCase tc) {
 
-            tc.net2.params().assign(tc.net1.params());
-            tc.net3.params().assign(tc.net1.params());
-            tc.net4.params().assign(tc.net1.params());
+            tc.net2.getModelParams().assign(tc.net1.getModelParams());
+            tc.net3.getModelParams().assign(tc.net1.getModelParams());
+            tc.net4.getModelParams().assign(tc.net1.getModelParams());
 
             INDArray inNCW = tc.inNCW;
             INDArray inNWC = tc.inNCW.permute(0, 2, 1).dup();
@@ -352,9 +352,9 @@ public class RnnDataFormatTests extends BaseDL4JTest {
             tc.net3.fit(inNWC, tc.labelsNWC);
             tc.net4.fit(inNWC, tc.labelsNWC);
 
-            assertEquals(tc.net1.params(), tc.net2.params(), tc.msg);
-            assertEquals(tc.net1.params(), tc.net3.params(), tc.msg);
-            assertEquals(tc.net1.params(), tc.net4.params(), tc.msg);
+            assertEquals(tc.net1.getModelParams(), tc.net2.getModelParams(), tc.msg);
+            assertEquals(tc.net1.getModelParams(), tc.net3.getModelParams(), tc.msg);
+            assertEquals(tc.net1.getModelParams(), tc.net4.getModelParams(), tc.msg);
 
             //Test serialization
             MultiLayerNetwork net1a = TestUtils.testModelSerialization(tc.net1);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestRnnLayers.java

@@ -23,7 +23,6 @@ package org.deeplearning4j.nn.layers.recurrent;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.TestUtils;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.RNNFormat;
 import org.deeplearning4j.nn.conf.dropout.TestDropout;
 import org.deeplearning4j.nn.conf.layers.GravesLSTM;
@@ -173,8 +172,8 @@ public class TestRnnLayers extends BaseDL4JTest {
             MultiLayerNetwork netD2 = new MultiLayerNetwork(confD2);
             netD2.init();
 
-            assertEquals(net.params(), netD.params(), s);
-            assertEquals(net.params(), netD2.params(), s);
+            assertEquals(net.getModelParams(), netD.getModelParams(), s);
+            assertEquals(net.getModelParams(), netD2.getModelParams(), s);
 
             INDArray f = Nd4j.rand(DataType.FLOAT, 3, 10, 10);
 
@@ -193,7 +192,7 @@ public class TestRnnLayers extends BaseDL4JTest {
             INDArray l = TestUtils.randomOneHotTimeSeries(3, 10, 10, 12345);
             net.fit(f.dup(), l);
             netD.fit(f.dup(), l);
-            assertNotEquals(net.params(), netD.params(), s);
+            assertNotEquals(net.getModelParams(), netD.getModelParams(), s);
 
             netD2.fit(f.dup(), l);
             netD2.fit(f.dup(), l);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/recurrent/TestTimeDistributed.java

@@ -115,7 +115,7 @@ public class TestTimeDistributed extends BaseDL4JTest {
                     net1.fit(ds);
                     net2.fit(ds);
 
-                    assertEquals(net1.params(), net2.params());
+                    assertEquals(net1.getModelParams(), net2.getModelParams());
 
                     MultiLayerNetwork net3 = TestUtils.testModelSerialization(net2);
                     out2 = net2.output(in);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffDense.java

@@ -124,10 +124,10 @@ public class TestSameDiffDense extends BaseDL4JTest {
                     MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
                     net2.init();
 
-                    net.params().assign(net2.params());
+                    net.getModelParams().assign(net2.getModelParams());
 
                     //Check params:
-                    assertEquals(net2.params(), net.params());
+                    assertEquals(net2.getModelParams(), net.getModelParams());
                     Map<String, INDArray> params1 = net.getParamTable();
                     Map<String, INDArray> params2 = net2.getParamTable();
                     assertEquals(params2, params1);
@@ -209,10 +209,10 @@ public class TestSameDiffDense extends BaseDL4JTest {
                     MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
                     net2.init();
 
-                    assertEquals(net2.params(), net.params());
+                    assertEquals(net2.getModelParams(), net.getModelParams());
 
                     //Check params:
-                    assertEquals(net2.params(), net.params());
+                    assertEquals(net2.getModelParams(), net.getModelParams());
                     Map<String, INDArray> params1 = net.getParamTable();
                     Map<String, INDArray> params2 = net2.getParamTable();
                     assertEquals(params2, params1);
@@ -287,10 +287,10 @@ public class TestSameDiffDense extends BaseDL4JTest {
                     MultiLayerNetwork netStandard = new MultiLayerNetwork(conf2);
                     netStandard.init();
 
-                    netSD.params().assign(netStandard.params());
+                    netSD.getModelParams().assign(netStandard.getModelParams());
 
                     //Check params:
-                    assertEquals(netStandard.params(), netSD.params());
+                    assertEquals(netStandard.getModelParams(), netSD.getModelParams());
                     assertEquals(netStandard.getParamTable(), netSD.getParamTable());
 
                     INDArray in = Nd4j.rand(minibatch, nIn);
@@ -379,10 +379,10 @@ public class TestSameDiffDense extends BaseDL4JTest {
             MultiLayerNetwork netStandard = new MultiLayerNetwork(conf2);
             netStandard.init();
 
-            netSD.params().assign(netStandard.params());
+            netSD.getModelParams().assign(netStandard.getModelParams());
 
             //Check params:
-            assertEquals(netStandard.params(), netSD.params());
+            assertEquals(netStandard.getModelParams(), netSD.getModelParams());
             assertEquals(netStandard.getParamTable(), netSD.getParamTable());
 
             DataSetIterator iter = new IrisDataSetIterator(150, 150);
@@ -398,7 +398,7 @@ public class TestSameDiffDense extends BaseDL4JTest {
                 netStandard.fit(ds);
                 String s = String.valueOf(i);
                 assertEquals( netStandard.getFlattenedGradients(), netSD.getFlattenedGradients(), s);
-                assertEquals( netStandard.params(), netSD.params(), s);
+                assertEquals( netStandard.getModelParams(), netSD.getModelParams(), s);
                 assertEquals( netStandard.getUpdater().getStateViewArray(), netSD.getUpdater().getStateViewArray(), s);
             }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffDenseVertex.java

@@ -100,10 +100,10 @@ public class TestSameDiffDenseVertex extends BaseDL4JTest {
                     ComputationGraph netStandard = new ComputationGraph(conf2);
                     netStandard.init();
 
-                    netSD.params().assign(netStandard.params());
+                    netSD.getModelParams().assign(netStandard.getModelParams());
 
                     //Check params:
-                    assertEquals(netStandard.params(), netSD.params());
+                    assertEquals(netStandard.getModelParams(), netSD.getModelParams());
                     assertEquals(netStandard.getParamTable(), netSD.getParamTable());
 
                     INDArray in = Nd4j.rand(minibatch, nIn);
@@ -160,7 +160,7 @@ public class TestSameDiffDenseVertex extends BaseDL4JTest {
                         netStandard.fit(ds);
 
                         assertEquals(netStandard.getParamTable(), netSD.getParamTable());
-                        assertEquals(netStandard.params(), netSD.params());
+                        assertEquals(netStandard.getModelParams(), netSD.getModelParams());
                         assertEquals(netStandard.getFlattenedGradients(), netSD.getFlattenedGradients());
                     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffLambda.java

@@ -98,7 +98,7 @@ public class TestSameDiffLambda extends BaseDL4JTest {
             ComputationGraph std = new ComputationGraph(confStd);
             std.init();
 
-            lambda.setParams(std.params());
+            lambda.setParams(std.getModelParams());
 
             INDArray in = Nd4j.rand(3, 5);
             INDArray labels = TestUtils.randomOneHot(3, 5);
@@ -119,7 +119,7 @@ public class TestSameDiffLambda extends BaseDL4JTest {
                 std.fit(ds);
 
                 String s = String.valueOf(i);
-                assertEquals(std.params(), lambda.params(), s);
+                assertEquals(std.getModelParams(), lambda.getModelParams(), s);
                 assertEquals(std.getFlattenedGradients(), lambda.getFlattenedGradients(), s);
             }
 
@@ -182,7 +182,7 @@ public class TestSameDiffLambda extends BaseDL4JTest {
             ComputationGraph std = new ComputationGraph(confStd);
             std.init();
 
-            lambda.setParams(std.params());
+            lambda.setParams(std.getModelParams());
 
             INDArray in1 = Nd4j.rand(3, 5);
             INDArray in2 = Nd4j.rand(3, 5);
@@ -204,7 +204,7 @@ public class TestSameDiffLambda extends BaseDL4JTest {
                 std.fit(mds);
 
                 String s = String.valueOf(i);
-                assertEquals(std.params(), lambda.params(), s);
+                assertEquals(std.getModelParams(), lambda.getModelParams(), s);
                 assertEquals(std.getFlattenedGradients(), lambda.getFlattenedGradients(), s);
             }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/samediff/TestSameDiffOutput.java

@@ -85,7 +85,7 @@ public class TestSameDiffOutput extends BaseDL4JTest {
             netSD.fit(ds);
             netStd.fit(ds);
 
-            assertEquals(netStd.params(), netSD.params());
+            assertEquals(netStd.getModelParams(), netSD.getModelParams());
             assertEquals(netStd.getFlattenedGradients(), netSD.getFlattenedGradients());
         }
 
@@ -131,7 +131,7 @@ public class TestSameDiffOutput extends BaseDL4JTest {
             MultiLayerNetwork netStd = new MultiLayerNetwork(confStd);
             netStd.init();
 
-            netSD.params().assign(netStd.params());
+            netSD.getModelParams().assign(netStd.getModelParams());
 
             assertEquals(netStd.getParamTable(), netSD.getParamTable());
 
@@ -165,7 +165,7 @@ public class TestSameDiffOutput extends BaseDL4JTest {
                 netSD.fit(ds);
                 netStd.fit(ds);
                 String s = String.valueOf(i);
-                assertEquals( netStd.params(), netSD.params(), s);
+                assertEquals( netStd.getModelParams(), netSD.getModelParams(), s);
                 assertEquals( netStd.getFlattenedGradients(), netSD.getFlattenedGradients(),s );
             }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/variational/TestVAE.java

@@ -77,7 +77,7 @@ public class TestVAE extends BaseDL4JTest {
         net.init();
 
         System.out.println("Exp num params: " + expNumParams);
-        assertEquals(expNumParams, net.getLayer(0).params().length());
+        assertEquals(expNumParams, net.getLayer(0).getParams().length());
         Map<String, INDArray> paramTable = net.getLayer(0).getParamTable();
         int count = 0;
         for (INDArray arr : paramTable.values()) {

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/misc/CloseNetworkTests.java

@@ -79,7 +79,7 @@ public class CloseNetworkTests extends BaseDL4JTest {
 
                 net.close();
 
-                assertTrue(net.params().wasClosed());
+                assertTrue(net.getModelParams().wasClosed());
                 if(train) {
                     assertTrue(net.getGradientsViewArray().wasClosed());
                     Updater u = net.getUpdater(false);
@@ -127,7 +127,7 @@ public class CloseNetworkTests extends BaseDL4JTest {
 
                 net.close();
 
-                assertTrue(net.params().wasClosed());
+                assertTrue(net.getModelParams().wasClosed());
                 if(train) {
                     assertTrue(net.getGradientsViewArray().wasClosed());
                     Updater u = net.getUpdater(false);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/misc/LargeNetTest.java

@@ -57,7 +57,7 @@ public class LargeNetTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        INDArray params = net.params();
+        INDArray params = net.getModelParams();
         long paramsLength = params.length();
         long expParamsLength = 10_000_000L * 300 + 300 * 10 + 10;
         assertEquals(expParamsLength, paramsLength);
@@ -91,7 +91,7 @@ public class LargeNetTest extends BaseDL4JTest {
         ComputationGraph net = new ComputationGraph(conf);
         net.init();
 
-        INDArray params = net.params();
+        INDArray params = net.getModelParams();
         long paramsLength = params.length();
         long expParamsLength = 10_000_000L * 300 + 300 * 10 + 10;
         assertEquals(expParamsLength, paramsLength);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/misc/TestLrChanges.java

@@ -76,7 +76,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.init();
         net2.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf2.setIterationCount(conf.getIterationCount());
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         assertEquals(0.1, net.getLearningRate(0).doubleValue(), 0.0);
         net.setLearningRate(0, 0.5);  //Set LR for layer 0 to 0.5
@@ -96,7 +96,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net2.fit(in, l);
         }
 
-        assertEquals(net.params(), net2.params());
+        assertEquals(net.getModelParams(), net2.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net2.getUpdater().getStateViewArray());
 
         INDArray in1 = Nd4j.rand(10, 10);
@@ -110,7 +110,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.setLabels(l1);
         net2.computeGradientAndScore();
 
-        assertEquals(net.score(), net2.score(), 1e-8);
+        assertEquals(net.getScore(), net2.getScore(), 1e-8);
 
 
         //Now: Set *all* LRs to say 0.3...
@@ -126,7 +126,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net3.init();
         net3.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf3.setIterationCount(conf.getIterationCount());
-        net3.setParams(net.params().dup());
+        net3.setParams(net.getModelParams().dup());
 
         net.setLearningRate(0.3);
 
@@ -139,7 +139,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net3.fit(in, l);
         }
 
-        assertEquals(net.params(), net3.params());
+        assertEquals(net.getModelParams(), net3.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net3.getUpdater().getStateViewArray());
     }
 
@@ -206,7 +206,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.init();
         net2.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf2.setIterationCount(conf.getIterationCount());
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         net.setLearningRate(new ExponentialSchedule(ScheduleType.ITERATION, 0.5, 0.8 ));  //Set LR for layer 0 to 0.5
 
@@ -224,7 +224,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net2.fit(in, l);
         }
 
-        assertEquals(net.params(), net2.params());
+        assertEquals(net.getModelParams(), net2.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net2.getUpdater().getStateViewArray());
     }
 
@@ -270,7 +270,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.init();
         net2.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf2.setIterationCount(conf.getIterationCount());
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         assertEquals(0.1, net.getLearningRate("0").doubleValue(), 0.0);
         net.setLearningRate("0", 0.5);  //Set LR for layer 0 to 0.5
@@ -290,7 +290,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net2.fit(new DataSet(in, l));
         }
 
-        assertEquals(net.params(), net2.params());
+        assertEquals(net.getModelParams(), net2.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net2.getUpdater().getStateViewArray());
 
         INDArray in1 = Nd4j.rand(10, 10);
@@ -304,7 +304,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.setLabels(l1);
         net2.computeGradientAndScore();
 
-        assertEquals(net.score(), net2.score(), 1e-8);
+        assertEquals(net.getScore(), net2.getScore(), 1e-8);
 
 
         //Now: Set *all* LRs to say 0.3...
@@ -320,7 +320,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net3.init();
         net3.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf3.setIterationCount(conf.getIterationCount());
-        net3.setParams(net.params().dup());
+        net3.setParams(net.getModelParams().dup());
 
         net.setLearningRate(0.3);
 
@@ -333,7 +333,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net3.fit(new DataSet(in, l));
         }
 
-        assertEquals(net.params(), net3.params());
+        assertEquals(net.getModelParams(), net3.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net3.getUpdater().getStateViewArray());
     }
 
@@ -375,7 +375,7 @@ public class TestLrChanges extends BaseDL4JTest {
         net2.init();
         net2.getUpdater().getStateViewArray().assign(net.getUpdater().getStateViewArray());
         conf2.setIterationCount(conf.getIterationCount());
-        net2.setParams(net.params().dup());
+        net2.setParams(net.getModelParams().dup());
 
         net.setLearningRate(new ExponentialSchedule(ScheduleType.ITERATION, 0.5, 0.8 ));  //Set LR for layer 0 to 0.5
 
@@ -393,7 +393,7 @@ public class TestLrChanges extends BaseDL4JTest {
             net2.fit(new DataSet(in, l));
         }
 
-        assertEquals(net.params(), net2.params());
+        assertEquals(net.getModelParams(), net2.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), net2.getUpdater().getStateViewArray());
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/misc/TestNetConversion.java

@@ -77,14 +77,14 @@ public class TestNetConversion extends BaseDL4JTest {
             n.computeGradientAndScore();
             cg.computeGradientAndScore();
 
-            assertEquals(n.score(), cg.score(), 1e-6);
+            assertEquals(n.getScore(), cg.getScore(), 1e-6);
 
             assertEquals(n.gradient().gradient(), cg.gradient().gradient());
 
             n.fit(in, labels);
             cg.fit(new INDArray[]{in}, new INDArray[]{labels});
 
-            assertEquals(n.params(), cg.params());
+            assertEquals(n.getModelParams(), cg.getModelParams());
         }
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/misc/WorkspaceTests.java

@@ -476,7 +476,7 @@ public class WorkspaceTests extends BaseDL4JTest {
 
             final ComputationGraph computationGraph = new ComputationGraph(config);
             computationGraph.init();
-            computationGraph.setListeners(new ScoreIterationListener(3));
+            computationGraph.addTrainingListeners(new ScoreIterationListener(3));
 
             WSTestDataSetIterator iterator = new WSTestDataSetIterator();
             computationGraph.fit(iterator);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/BackPropMLPTest.java

@@ -54,7 +54,7 @@ public class BackPropMLPTest extends BaseDL4JTest {
     public void testMLPTrivial() {
         //Simplest possible case: 1 hidden layer, 1 hidden neuron, batch size of 1.
         MultiLayerNetwork network = new MultiLayerNetwork(getIrisMLPSimpleConfig(new int[] {1}, Activation.SIGMOID));
-        network.setListeners(new ScoreIterationListener(1));
+        network.addTrainingListeners(new ScoreIterationListener(1));
         network.init();
 
         DataSetIterator iter = new IrisDataSetIterator(1, 10);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTest.java

@@ -64,7 +64,7 @@ import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
 import org.deeplearning4j.nn.conf.layers.AutoEncoder;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.BatchNormalization;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
@@ -184,13 +184,13 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork network3 = new MultiLayerNetwork(conf);
     network3.init();
 
-    INDArray params = network3.params();
+    INDArray params = network3.getModelParams();
     INDArray weights = network3.getLayer(0).getParam(DefaultParamInitializer.WEIGHT_KEY).dup();
     INDArray bias = network3.getLayer(0).getParam(DefaultParamInitializer.BIAS_KEY).dup();
     network3.setParameters(params);
     assertEquals(weights, network3.getLayer(0).getParam(DefaultParamInitializer.WEIGHT_KEY));
     assertEquals(bias, network3.getLayer(0).getParam(DefaultParamInitializer.BIAS_KEY));
-    INDArray params4 = network3.params();
+    INDArray params4 = network3.getModelParams();
     assertEquals(params, params4);
   }
 
@@ -211,7 +211,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     MultiLayerNetwork network = new MultiLayerNetwork(conf);
     network.init();
-    network.setListeners(new ScoreIterationListener(1));
+    network.addTrainingListeners(new ScoreIterationListener(1));
 
     DataSetIterator iter = new IrisDataSetIterator(150, 150);
 
@@ -242,7 +242,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     MultiLayerNetwork network = new MultiLayerNetwork(conf);
     network.init();
-    network.setListeners(new ScoreIterationListener(1));
+    network.addTrainingListeners(new ScoreIterationListener(1));
 
     DataSetIterator iter = new IrisDataSetIterator(150, 150);
 
@@ -330,7 +330,7 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork model = new MultiLayerNetwork(conf);
     model.init();
 
-    model.addListeners(new ScoreIterationListener(listenerFreq));
+    model.addTrainingListeners(new ScoreIterationListener(listenerFreq));
 
     log.info("Train model....");
     int cnt = 0;
@@ -503,7 +503,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     assertEquals(layerNameList.get(0), net.getLayer(0).getLayerConfiguration().getLayerName());
     assertEquals(layerNameList, net.getLayerNames());
-    BaseLayer b = (BaseLayer) net.getLayer(layerNameList.get(2)).getLayerConfiguration();
+    BaseLayerConfiguration b = (BaseLayerConfiguration) net.getLayer(layerNameList.get(2)).getLayerConfiguration();
     assertEquals("softmax", b.getActivationFn().toString());
   }
 
@@ -535,7 +535,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     MultiLayerNetwork netNoReg = new MultiLayerNetwork(confNoReg);
     netNoReg.init();
-    netNoReg.setParameters(net.params().dup());
+    netNoReg.setParameters(net.getModelParams().dup());
 
     //Score single example, and compare to scoreExamples:
     INDArray input = Nd4j.rand(3, nIn);
@@ -703,7 +703,7 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork net = new MultiLayerNetwork(conf);
     net.init();
     net.fit(iter.next());
-    // TODO validate actual layer gradientView - issue getting var out of BaseLayer w/o adding MLN getter that gets confused with local gradient vars
+    // TODO validate actual layer gradientView - issue getting var out of BaseLayerConfiguration w/o adding MLN getter that gets confused with local gradient vars
     Gradient actualGradient = net.gradient;
     assertNotEquals(expectedGradient.getGradientFor("0_W"), actualGradient.getGradientFor("0_W"));
 
@@ -716,13 +716,13 @@ public class MultiLayerTest extends BaseDL4JTest {
     net.setParam("0_b", Nd4j.ones(1, 5));
     net.setParam("1_W", Nd4j.ones(5, 3));
     net.setParam("1_b", Nd4j.ones(1, 3));
-    INDArray actualParams = net.params();
+    INDArray actualParams = net.getModelParams();
 
     // Confirm params
     assertEquals(expectedGradient.gradient(), actualParams);
 
     net.update(expectedGradient);
-    actualParams = net.params();
+    actualParams = net.getModelParams();
     assertEquals(Nd4j.ones(1, 43).addi(1), actualParams);
   }
 
@@ -762,7 +762,7 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork aePre = getAeModel(true, nIn, nOut);
     int actualNP = (int) aePre.numParams();
     assertEquals(2 * (nIn * nOut + nOut) + nIn, actualNP);
-    INDArray params = aePre.params();
+    INDArray params = aePre.getModelParams();
     assertEquals(params.length(), actualNP); // check num params
     Map<String, INDArray> paramTable = aePre.getParamTable();
     assertTrue(paramTable.containsKey("0_vb")); // check vb exists for pretrain layer
@@ -774,7 +774,7 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork aeNoPre = getAeModel(false, nIn, nOut);
     actualNP = (int) aeNoPre.numParams();
     assertEquals(2 * (nIn * nOut + nOut) + nIn, actualNP);
-    params = aeNoPre.params();
+    params = aeNoPre.getModelParams();
     assertEquals(params.length(), actualNP);
     paramTable = aePre.getParamTable();
     assertTrue(paramTable.containsKey("0_vb"));
@@ -865,14 +865,14 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
     net2.init();
 
-    BaseLayer bl0 = (BaseLayer) net2.getLayer(0).getLayerConfiguration();
+    BaseLayerConfiguration bl0 = (BaseLayerConfiguration) net2.getLayer(0).getLayerConfiguration();
     assertEquals(0.1, TestUtils.getL1(bl0.getRegularizationBias()), 1e-6);
     assertEquals(0.2, TestUtils.getL2(bl0.getRegularizationBias()), 1e-6);
 
     INDArray features = Nd4j.rand(10, 10);
     INDArray labels = Nd4j.rand(10, 10);
 
-    net2.setParams(net1.params().dup());
+    net2.setParams(net1.getModelParams().dup());
 
     net1.setInput(features);
     net1.setLabels(labels);
@@ -888,15 +888,15 @@ public class MultiLayerTest extends BaseDL4JTest {
     r = net2.calcRegularizationScore(true);
     assertEquals(0.0, r, 0.0);
 
-    double s1 = net1.score();
-    double s2 = net2.score();
+    double s1 = net1.getScore();
+    double s2 = net2.getScore();
     assertEquals(s1, s2, 1e-6); //Biases initialized to 0 -> should initially have same score
 
     for (int i = 0; i < 10; i++) {
       net1.fit(features, labels);
     }
 
-    net2.setParams(net1.params().dup());
+    net2.setParams(net1.getModelParams().dup());
     net1.computeGradientAndScore();
     net2.computeGradientAndScore();
 
@@ -906,8 +906,8 @@ public class MultiLayerTest extends BaseDL4JTest {
     r = net2.calcRegularizationScore(true);
     assertTrue(r > 0.0);
 
-    s1 = net1.score();
-    s2 = net2.score();
+    s1 = net1.getScore();
+    s2 = net2.getScore();
 
     assertNotEquals(s1, s2, 1e-6); //Scores should differ due to bias l1/l2
 
@@ -1022,11 +1022,11 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork net2 = new MultiLayerNetwork(conf2);
     net2.init();
 
-    assertNotEquals(net1.params(), net2.params());
+    assertNotEquals(net1.getModelParams(), net2.getModelParams());
     assertNotEquals(net1.getParamTable(), net2.getParamTable());
 
     net1.setParamTable(net2.getParamTable());
-    assertEquals(net1.params(), net2.params());
+    assertEquals(net1.getModelParams(), net2.getModelParams());
     assertEquals(net1.getParamTable(), net2.getParamTable());
   }
 
@@ -1412,7 +1412,7 @@ public class MultiLayerTest extends BaseDL4JTest {
     exp.add(MultiLayerNetwork.class);
 
     CheckModelsListener listener = new CheckModelsListener();
-    net.setListeners(listener);
+    net.addTrainingListeners(listener);
 
     INDArray f = Nd4j.create(1, 10);
     INDArray l = Nd4j.create(1, 10);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/MultiLayerTestRNN.java

@@ -753,9 +753,9 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
 
         DataSet ds = new DataSet(features, labels, maskArrayInput, maskArrayOutput);
 
-        INDArray initialParams = mln.params().dup();
+        INDArray initialParams = mln.getModelParams().dup();
         mln.fit(ds);
-        INDArray afterParams = mln.params();
+        INDArray afterParams = mln.getModelParams();
         assertNotEquals(initialParams, afterParams);
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/TestMasking.java

@@ -172,7 +172,7 @@ public class TestMasking extends BaseDL4JTest {
                 net.setLabels(labels);
 
                 net.computeGradientAndScore();
-                double score1 = net.score();
+                double score1 = net.getScore();
                 INDArray grad1 = net.gradient().gradient();
 
                 //Now: change the label values for the masked steps. The
@@ -187,7 +187,7 @@ public class TestMasking extends BaseDL4JTest {
 
                 assertNotEquals(labels, newLabels);
 
-                double score2 = net.score();
+                double score2 = net.getScore();
                 INDArray grad2 = net.gradient().gradient();
 
                 assertEquals(score1, score2, 1e-6);
@@ -214,7 +214,7 @@ public class TestMasking extends BaseDL4JTest {
                 graph.setLabels(labels);
                 graph.computeGradientAndScore();
 
-                double gScore1 = graph.score();
+                double gScore1 = graph.getScore();
                 INDArray gGrad1 = graph.gradient().gradient();
 
                 graph.setLayerMaskArrays(null, new INDArray[] {labelMask});
@@ -222,7 +222,7 @@ public class TestMasking extends BaseDL4JTest {
                 graph.setLabels(newLabels);
                 graph.computeGradientAndScore();
 
-                double gScore2 = graph.score();
+                double gScore2 = graph.getScore();
                 INDArray gGrad2 = graph.gradient().gradient();
 
                 assertEquals(gScore1, gScore2, 1e-6);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/TestSetGetParameters.java

@@ -53,12 +53,12 @@ public class TestSetGetParameters extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        INDArray initParams = net.params().dup();
+        INDArray initParams = net.getModelParams().dup();
         Map<String, INDArray> initParams2 = net.getParamTable();
 
-        net.setParams(net.params());
+        net.setParams(net.getModelParams());
 
-        INDArray initParamsAfter = net.params();
+        INDArray initParamsAfter = net.getModelParams();
         Map<String, INDArray> initParams2After = net.getParamTable();
 
         for (String s : initParams2.keySet()) {
@@ -71,7 +71,7 @@ public class TestSetGetParameters extends BaseDL4JTest {
         INDArray randomParams = Nd4j.rand(initParams.dataType(), initParams.shape());
         net.setParams(randomParams.dup());
 
-        assertEquals(net.params(), randomParams);
+        assertEquals(net.getModelParams(), randomParams);
     }
 
     @Test
@@ -90,12 +90,12 @@ public class TestSetGetParameters extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        INDArray initParams = net.params().dup();
+        INDArray initParams = net.getModelParams().dup();
         Map<String, INDArray> initParams2 = net.getParamTable();
 
-        net.setParams(net.params());
+        net.setParams(net.getModelParams());
 
-        INDArray initParamsAfter = net.params();
+        INDArray initParamsAfter = net.getModelParams();
         Map<String, INDArray> initParams2After = net.getParamTable();
 
         for (String s : initParams2.keySet()) {
@@ -108,7 +108,7 @@ public class TestSetGetParameters extends BaseDL4JTest {
         INDArray randomParams = Nd4j.rand(initParams.dataType(), initParams.shape());
         net.setParams(randomParams.dup());
 
-        assertEquals(net.params(), randomParams);
+        assertEquals(net.getModelParams(), randomParams);
     }
 
     @Test
@@ -128,7 +128,7 @@ public class TestSetGetParameters extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-        INDArray params = net.params();
+        INDArray params = net.getModelParams();
 
 
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf);
@@ -137,11 +137,11 @@ public class TestSetGetParameters extends BaseDL4JTest {
         MultiLayerNetwork net3 = new MultiLayerNetwork(conf);
         net3.init(params, false);
 
-        assertEquals(params, net2.params());
-        assertEquals(params, net3.params());
+        assertEquals(params, net2.getModelParams());
+        assertEquals(params, net3.getModelParams());
 
-        assertNotSame(params, net2.params()); //Different objects due to clone
-        assertSame(params, net3.params()); //Same object due to clone
+        assertNotSame(params, net2.getModelParams()); //Different objects due to clone
+        assertSame(params, net3.getModelParams()); //Same object due to clone
 
 
         Map<String, INDArray> paramsMap = net.getParamTable();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/multilayer/TestVariableLengthTS.java

@@ -103,14 +103,14 @@ public class TestVariableLengthTS extends BaseDL4JTest {
             net.setInput(in1);
             net.setLabels(labels1);
             net.computeGradientAndScore();
-            double score1 = net.score();
+            double score1 = net.getScore();
             Gradient g1 = net.gradient();
 
             net.setInput(in2);
             net.setLabels(labels2);
             net.setLayerMaskArrays(null, labelMask);
             net.computeGradientAndScore();
-            double score2 = net.score();
+            double score2 = net.getScore();
             Gradient g2 = net.gradient();
 
             //Scores and gradients should be identical for two cases (given mask array)
@@ -134,7 +134,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
                 }
                 net.setLabels(labels2);
                 net.computeGradientAndScore();
-                double score2a = net.score();
+                double score2a = net.getScore();
                 Gradient g2a = net.gradient();
                 assertEquals(score2, score2a, 1e-6);
                 for (String s : g2map.keySet()) {
@@ -196,7 +196,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
             net.setInput(in1);
             net.setLabels(labels1);
             net.computeGradientAndScore();
-            double score1 = net.score();
+            double score1 = net.getScore();
             Gradient g1 = net.gradient();
             Map<String, INDArray> map1 = g1.gradientForVariable();
             for (String s : map1.keySet()) {
@@ -207,7 +207,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
             net.setLabels(labels2);
             net.setLayerMaskArrays(inputMask, null);
             net.computeGradientAndScore();
-            double score2 = net.score();
+            double score2 = net.getScore();
             Gradient g2 = net.gradient();
 
             net.setInput(in2);
@@ -240,7 +240,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
                 net.setInput(in2);
                 net.setLayerMaskArrays(inputMask, null);
                 net.computeGradientAndScore();
-                double score2a = net.score();
+                double score2a = net.getScore();
                 Gradient g2a = net.gradient();
                 assertEquals(score2, score2a, 1e-12);
                 for (String s : g2.gradientForVariable().keySet()) {
@@ -327,7 +327,7 @@ public class TestVariableLengthTS extends BaseDL4JTest {
                         mln.setLabels(labels);
 
                         mln.computeGradientAndScore();
-                        double score = mln.score();
+                        double score = mln.getScore();
 
                         assertEquals(expScore, score, 0.1, msg);
                     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/rl/TestMultiModelGradientApplication.java

@@ -77,7 +77,7 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
                 MultiLayerNetwork net2GradUpd = new MultiLayerNetwork(conf.clone());
                 net2GradUpd.init();
 
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
 
                 INDArray f = Nd4j.rand(minibatch, nIn);
                 INDArray l = Nd4j.create(minibatch, nOut);
@@ -109,17 +109,17 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
 
                 //Also: if we apply the gradient using a subi op, we should get the same final params as if we did a fit op
                 // on the original network
-                net2GradUpd.params().subi(g.gradient());
+                net2GradUpd.getModelParams().subi(g.gradient());
 
                 net1GradCalc.fit(f, l);
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
 
 
                 //=============================
                 if (!(u instanceof Sgd)) {
                     net2GradUpd.getUpdater().getStateViewArray().assign(net1GradCalc.getUpdater().getStateViewArray());
                 }
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
                 assertEquals(net1GradCalc.getUpdater().getStateViewArray(),
                                 net2GradUpd.getUpdater().getStateViewArray());
 
@@ -130,7 +130,7 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
                 for (int i = 0; i < 100; i++) {
                     net1GradCalc.fit(f, l);
                     net2GradUpd.fit(f, l);
-                    assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                    assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
                 }
             }
         }
@@ -169,7 +169,7 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
                 ComputationGraph net2GradUpd = new ComputationGraph(conf.clone());
                 net2GradUpd.init();
 
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
 
                 INDArray f = Nd4j.rand(minibatch, nIn);
                 INDArray l = Nd4j.create(minibatch, nOut);
@@ -201,16 +201,16 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
 
                 //Also: if we apply the gradient using a subi op, we should get the same final params as if we did a fit op
                 // on the original network
-                net2GradUpd.params().subi(g.gradient());
+                net2GradUpd.getModelParams().subi(g.gradient());
 
                 net1GradCalc.fit(new INDArray[] {f}, new INDArray[] {l});
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
 
                 //=============================
                 if (!(u instanceof Sgd)) {
                     net2GradUpd.getUpdater().getStateViewArray().assign(net1GradCalc.getUpdater().getStateViewArray());
                 }
-                assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
                 assertEquals(net1GradCalc.getUpdater().getStateViewArray(),
                                 net2GradUpd.getUpdater().getStateViewArray());
 
@@ -222,7 +222,7 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
                 for (int i = 0; i < 100; i++) {
                     net1GradCalc.fit(new INDArray[] {f}, new INDArray[] {l});
                     net2GradUpd.fit(new INDArray[] {f}, new INDArray[] {l});
-                    assertEquals(net1GradCalc.params(), net2GradUpd.params());
+                    assertEquals(net1GradCalc.getModelParams(), net2GradUpd.getModelParams());
                 }
             }
         }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningCompGraphTest.java

@@ -25,7 +25,6 @@ import org.deeplearning4j.TestUtils;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.constraint.UnitNormConstraint;
 import org.deeplearning4j.nn.conf.distribution.ConstantDistribution;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
@@ -94,7 +93,7 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
 
         ComputationGraph modelToFineTune = new ComputationGraph(expectedConf);
         modelToFineTune.init();
-        modelToFineTune.setParams(expectedModel.params());
+        modelToFineTune.setParams(expectedModel.getModelParams());
         //model after applying changes with transfer learning
         ComputationGraph modelNow =
                         new TransferLearning.GraphBuilder(modelToFineTune)
@@ -108,8 +107,8 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
         //Check params after fit
         modelNow.fit(randomData);
         expectedModel.fit(randomData);
-        assertEquals(modelNow.score(), expectedModel.score(), 1e-8);
-        assertEquals(modelNow.params(), expectedModel.params());
+        assertEquals(modelNow.getScore(), expectedModel.getScore(), 1e-8);
+        assertEquals(modelNow.getModelParams(), expectedModel.getModelParams());
     }
 
     @Test
@@ -139,9 +138,9 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
                         //.setOutputs("layer3")
                         .build();
 
-        BaseLayer bl0 = ((BaseLayer) modelNow.getLayer("layer0").getLayerConfiguration());
-        BaseLayer bl1 = ((BaseLayer) modelNow.getLayer("layer1").getLayerConfiguration());
-        BaseLayer bl3 = ((BaseLayer) modelNow.getLayer("layer3").getLayerConfiguration());
+        BaseLayerConfiguration bl0 = ((BaseLayerConfiguration) modelNow.getLayer("layer0").getLayerConfiguration());
+        BaseLayerConfiguration bl1 = ((BaseLayerConfiguration) modelNow.getLayer("layer1").getLayerConfiguration());
+        BaseLayerConfiguration bl3 = ((BaseLayerConfiguration) modelNow.getLayer("layer3").getLayerConfiguration());
         assertEquals(bl0.getWeightInitFn(), new WeightInitDistribution(new NormalDistribution(1, 1e-1)));
         assertEquals(bl1.getWeightInitFn(), new WeightInitXavier());
         assertEquals(bl1.getWeightInitFn(), new WeightInitXavier());
@@ -161,22 +160,22 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
         modelExpectedArch.init();
 
         //modelNow should have the same architecture as modelExpectedArch
-        assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer0").params().shape(),
-                        modelNow.getLayer("layer0").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer1").params().shape(),
-                        modelNow.getLayer("layer1").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer2").params().shape(),
-                        modelNow.getLayer("layer2").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer3").params().shape(),
-                        modelNow.getLayer("layer3").params().shape());
+        assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer0").getParams().shape(),
+                        modelNow.getLayer("layer0").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer1").getParams().shape(),
+                        modelNow.getLayer("layer1").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer2").getParams().shape(),
+                        modelNow.getLayer("layer2").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer3").getParams().shape(),
+                        modelNow.getLayer("layer3").getParams().shape());
 
-        modelNow.setParams(modelExpectedArch.params());
+        modelNow.setParams(modelExpectedArch.getModelParams());
         //fit should give the same results
         modelExpectedArch.fit(randomData);
         modelNow.fit(randomData);
-        assertEquals(modelExpectedArch.score(), modelNow.score(), 1e-8);
-        assertEquals(modelExpectedArch.params(), modelNow.params());
+        assertEquals(modelExpectedArch.getScore(), modelNow.getScore(), 1e-8);
+        assertEquals(modelExpectedArch.getModelParams(), modelNow.getModelParams());
     }
 
     @Test
@@ -227,22 +226,22 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
         modelExpectedArch.init();
 
         //modelNow should have the same architecture as modelExpectedArch
-        assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer0").params().shape(),
-                        modelNow.getLayer("layer0").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer1").params().shape(),
-                        modelNow.getLayer("layer1").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer2").params().shape(),
-                        modelNow.getLayer("layer2").params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer("layer3").params().shape(),
-                        modelNow.getLayer("layer3").params().shape());
+        assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer0").getParams().shape(),
+                        modelNow.getLayer("layer0").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer1").getParams().shape(),
+                        modelNow.getLayer("layer1").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer2").getParams().shape(),
+                        modelNow.getLayer("layer2").getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer("layer3").getParams().shape(),
+                        modelNow.getLayer("layer3").getParams().shape());
 
-        modelNow.setParams(modelExpectedArch.params());
+        modelNow.setParams(modelExpectedArch.getModelParams());
         //fit should give the same results
         modelExpectedArch.fit(randomData);
         modelNow.fit(randomData);
-        assertEquals(modelExpectedArch.score(), modelNow.score(), 1e-8);
-        assertEquals(modelExpectedArch.params(), modelNow.params());
+        assertEquals(modelExpectedArch.getScore(), modelNow.getScore(), 1e-8);
+        assertEquals(modelExpectedArch.getModelParams(), modelNow.getModelParams());
     }
 
     @Test
@@ -385,14 +384,14 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
 
         assertEquals(modelExpectedArch.getComputationGraphConfiguration().toJson(), modelNow.getComputationGraphConfiguration().toJson());
 
-        modelNow.setParams(modelExpectedArch.params());
+        modelNow.setParams(modelExpectedArch.getModelParams());
         int i = 0;
         while (i < 5) {
             modelExpectedArch.fit(randomData);
             modelNow.fit(randomData);
             i++;
         }
-        assertEquals(modelExpectedArch.params(), modelNow.params());
+        assertEquals(modelExpectedArch.getModelParams(), modelNow.getModelParams());
 
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningComplex.java

@@ -26,10 +26,9 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration.NeuralNetConfigurationBuilder;
 import org.deeplearning4j.nn.conf.graph.MergeVertex;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.graph.ComputationGraph;
@@ -99,7 +98,7 @@ public class TransferLearningComplex extends BaseDL4JTest {
             }
 
             //Also check config:
-            BaseLayer bl = ((BaseLayer) l.getLayerConfiguration());
+            BaseLayerConfiguration bl = ((BaseLayerConfiguration) l.getLayerConfiguration());
             assertEquals(new Adam(2e-2), bl.getIUpdater());
             assertEquals(Activation.LEAKYRELU.getActivationFunction(), bl.getActivationFn());
         }
@@ -154,8 +153,8 @@ public class TransferLearningComplex extends BaseDL4JTest {
                                         .setOutputs("outRight").build();
         ComputationGraph modelOther = new ComputationGraph(otherConf);
         modelOther.init();
-        modelOther.getLayer("denseRight0").setParams(modelToTune.getLayer("denseRight0").params());
-        modelOther.getLayer("outRight").setParams(modelToTune.getLayer("outRight").params());
+        modelOther.getLayer("denseRight0").setParams(modelToTune.getLayer("denseRight0").getParams());
+        modelOther.getLayer("outRight").setParams(modelToTune.getLayer("outRight").getParams());
 
         modelToTune.getVertex("denseCentre0").setLayerAsFrozen();
         ComputationGraph modelNow =
@@ -179,11 +178,11 @@ public class TransferLearningComplex extends BaseDL4JTest {
             assertEquals(otherRandData.getFeatures(0),
                             modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0"));
 
-            assertEquals(modelOther.getLayer("denseRight0").params(), modelNow.getLayer("denseRight0").params());
-            assertEquals(modelOther.getLayer("denseRight0").params(), modelToTune.getLayer("denseRight0").params());
+            assertEquals(modelOther.getLayer("denseRight0").getParams(), modelNow.getLayer("denseRight0").getParams());
+            assertEquals(modelOther.getLayer("denseRight0").getParams(), modelToTune.getLayer("denseRight0").getParams());
 
-            assertEquals(modelOther.getLayer("outRight").params(), modelNow.getLayer("outRight").params());
-            assertEquals(modelOther.getLayer("outRight").params(), modelToTune.getLayer("outRight").params());
+            assertEquals(modelOther.getLayer("outRight").getParams(), modelNow.getLayer("outRight").getParams());
+            assertEquals(modelOther.getLayer("outRight").getParams(), modelToTune.getLayer("outRight").getParams());
             n++;
         }
 
@@ -237,11 +236,11 @@ public class TransferLearningComplex extends BaseDL4JTest {
             assertEquals(otherRandData.getFeatures(0),
                             modelToTune.feedForward(randData.getFeatures(), false).get("denseCentre0"));
 
-            assertEquals(modelToTune.getLayer("denseRight0").params(), modelNow.getLayer("denseRight0").params());
+            assertEquals(modelToTune.getLayer("denseRight0").getParams(), modelNow.getLayer("denseRight0").getParams());
 
-            assertEquals(modelToTune.getLayer("outRight").params(), modelNow.getLayer("outRight").params());
+            assertEquals(modelToTune.getLayer("outRight").getParams(), modelNow.getLayer("outRight").getParams());
 
-            assertEquals(modelToTune.getLayer("outCentre").params(), modelNow.getLayer("outCentre").params());
+            assertEquals(modelToTune.getLayer("outCentre").getParams(), modelNow.getLayer("outCentre").getParams());
             n++;
         }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningHelperTest.java

@@ -178,25 +178,25 @@ public class TransferLearningHelperTest extends BaseDL4JTest {
         TransferLearningHelper helper = new TransferLearningHelper(modelToTune, "denseCentre2");
         MultiDataSet featurizedDataSet = helper.featurize(origData);
 
-        assertEquals(modelIdentical.getLayer("denseRight0").params(), modelToTune.getLayer("denseRight0").params());
+        assertEquals(modelIdentical.getLayer("denseRight0").getParams(), modelToTune.getLayer("denseRight0").getParams());
         modelIdentical.fit(origData);
         helper.fitFeaturized(featurizedDataSet);
 
-        assertEquals(modelIdentical.getLayer("denseCentre0").params(), modelToTune.getLayer("denseCentre0").params());
-        assertEquals(modelIdentical.getLayer("denseCentre1").params(), modelToTune.getLayer("denseCentre1").params());
-        assertEquals(modelIdentical.getLayer("denseCentre2").params(), modelToTune.getLayer("denseCentre2").params());
-        assertEquals(modelIdentical.getLayer("denseCentre3").params(), modelToTune.getLayer("denseCentre3").params());
-        assertEquals(modelIdentical.getLayer("outCentre").params(), modelToTune.getLayer("outCentre").params());
+        assertEquals(modelIdentical.getLayer("denseCentre0").getParams(), modelToTune.getLayer("denseCentre0").getParams());
+        assertEquals(modelIdentical.getLayer("denseCentre1").getParams(), modelToTune.getLayer("denseCentre1").getParams());
+        assertEquals(modelIdentical.getLayer("denseCentre2").getParams(), modelToTune.getLayer("denseCentre2").getParams());
+        assertEquals(modelIdentical.getLayer("denseCentre3").getParams(), modelToTune.getLayer("denseCentre3").getParams());
+        assertEquals(modelIdentical.getLayer("outCentre").getParams(), modelToTune.getLayer("outCentre").getParams());
         assertEquals(modelIdentical.getLayer("denseRight").getNetConfiguration().toJson(),
                         modelToTune.getLayer("denseRight").getNetConfiguration().toJson());
-        assertEquals(modelIdentical.getLayer("denseRight").params(), modelToTune.getLayer("denseRight").params());
+        assertEquals(modelIdentical.getLayer("denseRight").getParams(), modelToTune.getLayer("denseRight").getParams());
         assertEquals(modelIdentical.getLayer("denseRight0").getNetConfiguration().toJson(),
                         modelToTune.getLayer("denseRight0").getNetConfiguration().toJson());
         //assertEquals(modelIdentical.getLayer("denseRight0").params(),modelToTune.getLayer("denseRight0").params());
-        assertEquals(modelIdentical.getLayer("denseRight1").params(), modelToTune.getLayer("denseRight1").params());
-        assertEquals(modelIdentical.getLayer("outRight").params(), modelToTune.getLayer("outRight").params());
-        assertEquals(modelIdentical.getLayer("denseLeft0").params(), modelToTune.getLayer("denseLeft0").params());
-        assertEquals(modelIdentical.getLayer("outLeft").params(), modelToTune.getLayer("outLeft").params());
+        assertEquals(modelIdentical.getLayer("denseRight1").getParams(), modelToTune.getLayer("denseRight1").getParams());
+        assertEquals(modelIdentical.getLayer("outRight").getParams(), modelToTune.getLayer("outRight").getParams());
+        assertEquals(modelIdentical.getLayer("denseLeft0").getParams(), modelToTune.getLayer("denseLeft0").getParams());
+        assertEquals(modelIdentical.getLayer("outLeft").getParams(), modelToTune.getLayer("outLeft").getParams());
 
 //        log.info(modelIdentical.summary());
 //        log.info(helper.unfrozenGraph().summary());
@@ -230,7 +230,7 @@ public class TransferLearningHelperTest extends BaseDL4JTest {
         TransferLearningHelper helper = new TransferLearningHelper(modelToFineTune, 1);
 
         INDArray paramsLastTwoLayers =
-                        Nd4j.hstack(modelToFineTune.getLayer(2).params(), modelToFineTune.getLayer(3).params());
+                        Nd4j.hstack(modelToFineTune.getLayer(2).getParams(), modelToFineTune.getLayer(3).getParams());
         MultiLayerNetwork notFrozen = new MultiLayerNetwork(
             (NeuralNetConfiguration) overallConf.clone().list()
                             .layer(0, new Builder().nIn(2).nOut(3).build())
@@ -248,9 +248,9 @@ public class TransferLearningHelperTest extends BaseDL4JTest {
             modelNow.fit(randomData);
         }
 
-        INDArray expected = Nd4j.hstack(modelToFineTune.getLayer(0).params(), modelToFineTune.getLayer(1).params(),
-                        notFrozen.params());
-        INDArray act = modelNow.params();
+        INDArray expected = Nd4j.hstack(modelToFineTune.getLayer(0).getParams(), modelToFineTune.getLayer(1).getParams(),
+                        notFrozen.getModelParams());
+        INDArray act = modelNow.getModelParams();
         assertEquals(expected, act);
     }
 }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/transferlearning/TransferLearningMLNTest.java

@@ -91,7 +91,7 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
                         .build();
 
         for (org.deeplearning4j.nn.api.Layer l : modelNow.getLayers()) {
-            BaseLayer bl = ((BaseLayer) l.getLayerConfiguration());
+            BaseLayerConfiguration bl = ((BaseLayerConfiguration) l.getLayerConfiguration());
             assertEquals(new RmsProp(0.5), bl.getIUpdater());
         }
 
@@ -107,9 +107,9 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
                                                         .build())
                         .build());
         expectedModel.init();
-        expectedModel.setParams(modelToFineTune.params().dup());
+        expectedModel.setParams(modelToFineTune.getModelParams().dup());
 
-        assertEquals(expectedModel.params(), modelNow.params());
+        assertEquals(expectedModel.getModelParams(), modelNow.getModelParams());
 
         //Check json
         NeuralNetConfiguration expectedConf = expectedModel.getNetConfiguration();
@@ -119,9 +119,9 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         modelNow.fit(randomData);
         expectedModel.fit(randomData);
 
-        assertEquals(modelNow.score(), expectedModel.score(), 1e-6);
-        INDArray pExp = expectedModel.params();
-        INDArray pNow = modelNow.params();
+        assertEquals(modelNow.getScore(), expectedModel.getScore(), 1e-6);
+        INDArray pExp = expectedModel.getModelParams();
+        INDArray pNow = modelNow.getModelParams();
         assertEquals(pExp, pNow);
     }
 
@@ -160,9 +160,9 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         //Will fail - expected because of dist and weight init changes
         //assertEquals(modelExpectedArch.getConfiguration().toJson(), modelNow.getConfiguration().toJson());
 
-        BaseLayer bl0 = ((BaseLayer) modelNow.getNetConfiguration().getConf(0).getLayer());
-        BaseLayer bl1 = ((BaseLayer) modelNow.getNetConfiguration().getConf(1).getLayer());
-        BaseLayer bl3 = ((BaseLayer) modelNow.getNetConfiguration().getConf(3).getLayer());
+        BaseLayerConfiguration bl0 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(0).getLayer());
+        BaseLayerConfiguration bl1 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(1).getLayer());
+        BaseLayerConfiguration bl3 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(3).getLayer());
         assertEquals(bl0.getWeightInitFn().getClass(), WeightInitXavier.class);
         try {
             assertEquals(JsonMappers.getMapper().writeValueAsString(bl1.getWeightInitFn()),
@@ -173,18 +173,18 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         assertEquals(bl3.getWeightInitFn(), new WeightInitXavier());
 
         //modelNow should have the same architecture as modelExpectedArch
-        assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(2).params().shape(), modelNow.getLayer(2).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(3).params().shape(), modelNow.getLayer(3).params().shape());
+        assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(0).getParams().shape(), modelNow.getLayer(0).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(1).getParams().shape(), modelNow.getLayer(1).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(2).getParams().shape(), modelNow.getLayer(2).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(3).getParams().shape(), modelNow.getLayer(3).getParams().shape());
 
-        modelNow.setParams(modelExpectedArch.params());
+        modelNow.setParams(modelExpectedArch.getModelParams());
         //fit should give the same results
         modelExpectedArch.fit(randomData);
         modelNow.fit(randomData);
-        assertEquals(modelExpectedArch.score(), modelNow.score(), 0.000001);
-        assertEquals(modelExpectedArch.params(), modelNow.params());
+        assertEquals(modelExpectedArch.getScore(), modelNow.getScore(), 0.000001);
+        assertEquals(modelExpectedArch.getModelParams(), modelNow.getModelParams());
     }
 
 
@@ -227,20 +227,20 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         modelExpectedArch.init();
 
         //modelNow should have the same architecture as modelExpectedArch
-        assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(2).params().shape(), modelNow.getLayer(2).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(3).params().shape(), modelNow.getLayer(3).params().shape());
+        assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(0).getParams().shape(), modelNow.getLayer(0).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(1).getParams().shape(), modelNow.getLayer(1).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(2).getParams().shape(), modelNow.getLayer(2).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(3).getParams().shape(), modelNow.getLayer(3).getParams().shape());
 
-        modelNow.setParams(modelExpectedArch.params());
+        modelNow.setParams(modelExpectedArch.getModelParams());
         //fit should give the same results
         modelExpectedArch.fit(randomData);
         modelNow.fit(randomData);
-        double scoreExpected = modelExpectedArch.score();
-        double scoreActual = modelNow.score();
+        double scoreExpected = modelExpectedArch.getScore();
+        double scoreActual = modelNow.getScore();
         assertEquals(scoreExpected, scoreActual, 1e-4);
-        assertEquals(modelExpectedArch.params(), modelNow.params());
+        assertEquals(modelExpectedArch.getModelParams(), modelNow.getModelParams());
     }
 
     @Test
@@ -370,14 +370,14 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         assertEquals(modelExpectedArch.getNetConfiguration().getConf(5).toJson(),
                         modelNow.getNetConfiguration().getConf(5).toJson());
 
-        assertArrayEquals(modelExpectedArch.params().shape(), modelNow.params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
+        assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(0).getParams().shape(), modelNow.getLayer(0).getParams().shape());
         //subsampling has no params
         //assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(2).params().shape(), modelNow.getLayer(2).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(3).params().shape(), modelNow.getLayer(3).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(4).params().shape(), modelNow.getLayer(4).params().shape());
-        assertArrayEquals(modelExpectedArch.getLayer(5).params().shape(), modelNow.getLayer(5).params().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(2).getParams().shape(), modelNow.getLayer(2).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(3).getParams().shape(), modelNow.getLayer(3).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(4).getParams().shape(), modelNow.getLayer(4).getParams().shape());
+        assertArrayEquals(modelExpectedArch.getLayer(5).getParams().shape(), modelNow.getLayer(5).getParams().shape());
 
     }
 
@@ -449,23 +449,23 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
                         .inputType(InputType.convolutionalFlat(12, 12, 20)).build());
         notFrozen.init();
 
-        assertArrayEquals(modelToFineTune.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
+        assertArrayEquals(modelToFineTune.getLayer(0).getParams().shape(), modelNow.getLayer(0).getParams().shape());
         //subsampling has no params
         //assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
-        assertArrayEquals(notFrozen.getLayer(0).params().shape(), modelNow.getLayer(2).params().shape());
-        modelNow.getLayer(2).setParams(notFrozen.getLayer(0).params());
+        assertArrayEquals(notFrozen.getLayer(0).getParams().shape(), modelNow.getLayer(2).getParams().shape());
+        modelNow.getLayer(2).setParams(notFrozen.getLayer(0).getParams());
         //subsampling has no params
         //assertArrayEquals(notFrozen.getLayer(1).params().shape(), modelNow.getLayer(3).params().shape());
-        assertArrayEquals(notFrozen.getLayer(2).params().shape(), modelNow.getLayer(4).params().shape());
-        modelNow.getLayer(4).setParams(notFrozen.getLayer(2).params());
-        assertArrayEquals(notFrozen.getLayer(3).params().shape(), modelNow.getLayer(5).params().shape());
-        modelNow.getLayer(5).setParams(notFrozen.getLayer(3).params());
-        assertArrayEquals(notFrozen.getLayer(4).params().shape(), modelNow.getLayer(6).params().shape());
-        modelNow.getLayer(6).setParams(notFrozen.getLayer(4).params());
-        assertArrayEquals(notFrozen.getLayer(5).params().shape(), modelNow.getLayer(7).params().shape());
-        modelNow.getLayer(7).setParams(notFrozen.getLayer(5).params());
-        assertArrayEquals(notFrozen.getLayer(6).params().shape(), modelNow.getLayer(8).params().shape());
-        modelNow.getLayer(8).setParams(notFrozen.getLayer(6).params());
+        assertArrayEquals(notFrozen.getLayer(2).getParams().shape(), modelNow.getLayer(4).getParams().shape());
+        modelNow.getLayer(4).setParams(notFrozen.getLayer(2).getParams());
+        assertArrayEquals(notFrozen.getLayer(3).getParams().shape(), modelNow.getLayer(5).getParams().shape());
+        modelNow.getLayer(5).setParams(notFrozen.getLayer(3).getParams());
+        assertArrayEquals(notFrozen.getLayer(4).getParams().shape(), modelNow.getLayer(6).getParams().shape());
+        modelNow.getLayer(6).setParams(notFrozen.getLayer(4).getParams());
+        assertArrayEquals(notFrozen.getLayer(5).getParams().shape(), modelNow.getLayer(7).getParams().shape());
+        modelNow.getLayer(7).setParams(notFrozen.getLayer(5).getParams());
+        assertArrayEquals(notFrozen.getLayer(6).getParams().shape(), modelNow.getLayer(8).getParams().shape());
+        modelNow.getLayer(8).setParams(notFrozen.getLayer(6).getParams());
 
         int i = 0;
         while (i < 3) {
@@ -474,8 +474,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
             i++;
         }
 
-        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).params(), notFrozen.params());
-        assertEquals(expectedParams, modelNow.params());
+        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).getParams(), notFrozen.getModelParams());
+        assertEquals(expectedParams, modelNow.getModelParams());
     }
 
 
@@ -503,13 +503,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
 
 
         //Check original net isn't modified:
-        BaseLayer l0 = (BaseLayer) net.getLayer(0).getLayerConfiguration();
+        BaseLayerConfiguration l0 = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
         assertEquals(new Adam(1e-4), l0.getIUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
         assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
 
-        BaseLayer l1 = (BaseLayer) net.getLayer(1).getLayerConfiguration();
+        BaseLayerConfiguration l1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
         assertEquals(new Adam(1e-4), l1.getIUpdater());
         assertEquals(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
         assertEquals(new WeightInitRelu(), l1.getWeightInitFn());
@@ -518,13 +518,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         assertEquals(BackpropType.Standard, conf.getBackpropType());
 
         //Check new net has only the appropriate things modified (i.e., LR)
-        l0 = (BaseLayer) net2.getLayer(0).getLayerConfiguration();
+        l0 = (BaseLayerConfiguration) net2.getLayer(0).getLayerConfiguration();
         assertEquals(new Adam(2e-2), l0.getIUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
         assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
 
-        l1 = (BaseLayer) net2.getLayer(1).getLayerConfiguration();
+        l1 = (BaseLayerConfiguration) net2.getLayer(1).getLayerConfiguration();
         assertEquals(new Adam(2e-2), l1.getIUpdater());
         assertEquals(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
         assertEquals(new WeightInitRelu(), l1.getWeightInitFn());
@@ -586,17 +586,17 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
                         .build());
         notFrozen.init();
 
-        assertArrayEquals(modelToFineTune.getLayer(0).params().shape(), modelNow.getLayer(0).params().shape());
+        assertArrayEquals(modelToFineTune.getLayer(0).getParams().shape(), modelNow.getLayer(0).getParams().shape());
         //subsampling has no params
         //assertArrayEquals(modelExpectedArch.getLayer(1).params().shape(), modelNow.getLayer(1).params().shape());
-        assertArrayEquals(notFrozen.getLayer(0).params().shape(), modelNow.getLayer(2).params().shape());
-        modelNow.getLayer(2).setParams(notFrozen.getLayer(0).params());
-        assertArrayEquals(notFrozen.getLayer(1).params().shape(), modelNow.getLayer(3).params().shape());
-        modelNow.getLayer(3).setParams(notFrozen.getLayer(1).params());
-        assertArrayEquals(notFrozen.getLayer(2).params().shape(), modelNow.getLayer(4).params().shape());
-        modelNow.getLayer(4).setParams(notFrozen.getLayer(2).params());
-        assertArrayEquals(notFrozen.getLayer(3).params().shape(), modelNow.getLayer(5).params().shape());
-        modelNow.getLayer(5).setParams(notFrozen.getLayer(3).params());
+        assertArrayEquals(notFrozen.getLayer(0).getParams().shape(), modelNow.getLayer(2).getParams().shape());
+        modelNow.getLayer(2).setParams(notFrozen.getLayer(0).getParams());
+        assertArrayEquals(notFrozen.getLayer(1).getParams().shape(), modelNow.getLayer(3).getParams().shape());
+        modelNow.getLayer(3).setParams(notFrozen.getLayer(1).getParams());
+        assertArrayEquals(notFrozen.getLayer(2).getParams().shape(), modelNow.getLayer(4).getParams().shape());
+        modelNow.getLayer(4).setParams(notFrozen.getLayer(2).getParams());
+        assertArrayEquals(notFrozen.getLayer(3).getParams().shape(), modelNow.getLayer(5).getParams().shape());
+        modelNow.getLayer(5).setParams(notFrozen.getLayer(3).getParams());
 
         int i = 0;
         while (i < 3) {
@@ -605,8 +605,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
             i++;
         }
 
-        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).params(), notFrozen.params());
-        assertEquals(expectedParams, modelNow.params());
+        INDArray expectedParams = Nd4j.hstack(modelToFineTune.getLayer(0).getParams(), notFrozen.getModelParams());
+        assertEquals(expectedParams, modelNow.getModelParams());
     }
 
     @Test

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/updater/TestUpdaters.java

@@ -99,7 +99,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -144,7 +144,7 @@ public class TestUpdaters extends BaseDL4JTest {
                 msdx.put(key, msdxTmp);
                 count++;
             }
-            assertEquals(rho, ((AdaDelta)layer.layerConf().getIUpdater()).getRho(), 1e-4);
+            assertEquals(rho, ((AdaDelta)layer.getTypedLayerConfiguration().getIUpdater()).getRho(), 1e-4);
         }
 
         assertEquals(4, count);
@@ -165,7 +165,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -185,7 +185,7 @@ public class TestUpdaters extends BaseDL4JTest {
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
             count++;
         }
-        assertEquals(lr, ((AdaGrad)layer.layerConf().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((AdaGrad)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -209,7 +209,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -245,8 +245,8 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(beta1, ((Adam)layer.layerConf().getIUpdater()).getBeta1(), 1e-4);
-        assertEquals(beta2, ((Adam)layer.layerConf().getIUpdater()).getBeta2(), 1e-4);
+        assertEquals(beta1, ((Adam)layer.getTypedLayerConfiguration().getIUpdater()).getBeta1(), 1e-4);
+        assertEquals(beta2, ((Adam)layer.getTypedLayerConfiguration().getIUpdater()).getBeta2(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -273,7 +273,7 @@ public class TestUpdaters extends BaseDL4JTest {
         layer.setBackpropGradientsViewArray(gradients);
 
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -362,7 +362,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -398,8 +398,8 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(beta1, ((AdaMax)layer.layerConf().getIUpdater()).getBeta1(), 1e-4);
-        assertEquals(beta2, ((AdaMax)layer.layerConf().getIUpdater()).getBeta2(), 1e-4);
+        assertEquals(beta1, ((AdaMax)layer.getTypedLayerConfiguration().getIUpdater()).getBeta1(), 1e-4);
+        assertEquals(beta2, ((AdaMax)layer.getTypedLayerConfiguration().getIUpdater()).getBeta2(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -418,7 +418,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -443,7 +443,7 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(mu, ((Nesterovs)layer.layerConf().getIUpdater()).getMomentum(), 1e-4);
+        assertEquals(mu, ((Nesterovs)layer.getTypedLayerConfiguration().getIUpdater()).getMomentum(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -465,7 +465,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.layerConf().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -495,7 +495,7 @@ public class TestUpdaters extends BaseDL4JTest {
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
             lastG.put(key, lastGTmp);
         }
-        assertEquals(rmsDecay, ((RmsProp)layer.layerConf().getIUpdater()).getRmsDecay(), 1e-4);
+        assertEquals(rmsDecay, ((RmsProp)layer.getTypedLayerConfiguration().getIUpdater()).getRmsDecay(), 1e-4);
     }
 
     @Test
@@ -527,7 +527,7 @@ public class TestUpdaters extends BaseDL4JTest {
             gradExpected = val.mul(lr);
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
         }
-        assertEquals(lr, ((Sgd)layer.layerConf().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
     }
 
 
@@ -769,7 +769,7 @@ public class TestUpdaters extends BaseDL4JTest {
             gradExpected = val.mul(lr);
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
         }
-        assertEquals(lr, ((Sgd)layer.layerConf().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
 
 
         //Test with pretrain == false
@@ -797,7 +797,7 @@ public class TestUpdaters extends BaseDL4JTest {
         layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         updater = UpdaterCreator.getUpdater(layer);
-        assertEquals(lr, ((Sgd)layer.layerConf().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
     }
 
     @Test
@@ -858,11 +858,11 @@ public class TestUpdaters extends BaseDL4JTest {
             //Check first updater block:
             UpdaterBlock ub0 = blocks.get(0);
             assertEquals(3, ub0.getLayersAndVariablesInBlock().size());
-            assertEquals("l0", ub0.getLayersAndVariablesInBlock().get(0).getLayer().getConfig().getLayerName());
+            assertEquals("l0", ub0.getLayersAndVariablesInBlock().get(0).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.WEIGHT_KEY, ub0.getLayersAndVariablesInBlock().get(0).getParamName());
-            assertEquals("l0", ub0.getLayersAndVariablesInBlock().get(1).getLayer().getConfig().getLayerName());
+            assertEquals("l0", ub0.getLayersAndVariablesInBlock().get(1).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.BIAS_KEY, ub0.getLayersAndVariablesInBlock().get(1).getParamName());
-            assertEquals("l1", ub0.getLayersAndVariablesInBlock().get(2).getLayer().getConfig().getLayerName());
+            assertEquals("l1", ub0.getLayersAndVariablesInBlock().get(2).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.WEIGHT_KEY, ub0.getLayersAndVariablesInBlock().get(2).getParamName());
 
             int nParams0 = 10 * 10 + 10 + 10 * 10;
@@ -875,7 +875,7 @@ public class TestUpdaters extends BaseDL4JTest {
             //Check second updater block:
             UpdaterBlock ub1 = blocks.get(1);
             assertEquals(1, ub1.getLayersAndVariablesInBlock().size());
-            assertEquals("l1", ub1.getLayersAndVariablesInBlock().get(0).getLayer().getConfig().getLayerName());
+            assertEquals("l1", ub1.getLayersAndVariablesInBlock().get(0).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.BIAS_KEY, ub1.getLayersAndVariablesInBlock().get(0).getParamName());
 
             int nParams1 = 10;
@@ -888,9 +888,9 @@ public class TestUpdaters extends BaseDL4JTest {
             //Check third updater block:
             UpdaterBlock ub2 = blocks.get(2);
             assertEquals(2, ub2.getLayersAndVariablesInBlock().size());
-            assertEquals("l2", ub2.getLayersAndVariablesInBlock().get(0).getLayer().getConfig().getLayerName());
+            assertEquals("l2", ub2.getLayersAndVariablesInBlock().get(0).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.WEIGHT_KEY, ub2.getLayersAndVariablesInBlock().get(0).getParamName());
-            assertEquals("l2", ub2.getLayersAndVariablesInBlock().get(1).getLayer().getConfig().getLayerName());
+            assertEquals("l2", ub2.getLayersAndVariablesInBlock().get(1).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.BIAS_KEY, ub2.getLayersAndVariablesInBlock().get(1).getParamName());
 
             int nParams2 = 10 * 10 + 10;
@@ -903,9 +903,9 @@ public class TestUpdaters extends BaseDL4JTest {
             //Check fourth updater block:
             UpdaterBlock ub3 = blocks.get(3);
             assertEquals(2, ub3.getLayersAndVariablesInBlock().size());
-            assertEquals("l3", ub3.getLayersAndVariablesInBlock().get(0).getLayer().getConfig().getLayerName());
+            assertEquals("l3", ub3.getLayersAndVariablesInBlock().get(0).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.WEIGHT_KEY, ub3.getLayersAndVariablesInBlock().get(0).getParamName());
-            assertEquals("l3", ub3.getLayersAndVariablesInBlock().get(1).getLayer().getConfig().getLayerName());
+            assertEquals("l3", ub3.getLayersAndVariablesInBlock().get(1).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.BIAS_KEY, ub3.getLayersAndVariablesInBlock().get(1).getParamName());
 
             int nParams3 = 10 * 10 + 10;
@@ -918,9 +918,9 @@ public class TestUpdaters extends BaseDL4JTest {
             //Check fifth updater black
             UpdaterBlock ub4 = blocks.get(4);
             assertEquals(2, ub4.getLayersAndVariablesInBlock().size());
-            assertEquals("l4", ub4.getLayersAndVariablesInBlock().get(0).getLayer().getConfig().getLayerName());
+            assertEquals("l4", ub4.getLayersAndVariablesInBlock().get(0).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.WEIGHT_KEY, ub4.getLayersAndVariablesInBlock().get(0).getParamName());
-            assertEquals("l4", ub4.getLayersAndVariablesInBlock().get(1).getLayer().getConfig().getLayerName());
+            assertEquals("l4", ub4.getLayersAndVariablesInBlock().get(1).getLayer().getTrainingConfig().getLayerName());
             assertEquals(DefaultParamInitializer.BIAS_KEY, ub4.getLayersAndVariablesInBlock().get(1).getParamName());
 
             int nParams4 = 10 * 10 + 10;

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/updater/custom/TestCustomUpdater.java

@@ -22,7 +22,7 @@ package org.deeplearning4j.nn.updater.custom;
 
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
@@ -61,18 +61,18 @@ public class TestCustomUpdater extends BaseDL4JTest {
                         .build();
 
         //First: Check updater config
-        assertTrue(((BaseLayer) conf1.getConf(0).getLayer()).getIUpdater() instanceof CustomIUpdater);
-        assertTrue(((BaseLayer) conf1.getConf(1).getLayer()).getIUpdater() instanceof CustomIUpdater);
-        assertTrue(((BaseLayer) conf2.getConf(0).getLayer()).getIUpdater() instanceof Sgd);
-        assertTrue(((BaseLayer) conf2.getConf(1).getLayer()).getIUpdater() instanceof Sgd);
+        assertTrue(((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getIUpdater() instanceof CustomIUpdater);
+        assertTrue(((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getIUpdater() instanceof CustomIUpdater);
+        assertTrue(((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getIUpdater() instanceof Sgd);
+        assertTrue(((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getIUpdater() instanceof Sgd);
 
-        CustomIUpdater u0_0 = (CustomIUpdater) ((BaseLayer) conf1.getConf(0).getLayer()).getIUpdater();
-        CustomIUpdater u0_1 = (CustomIUpdater) ((BaseLayer) conf1.getConf(1).getLayer()).getIUpdater();
+        CustomIUpdater u0_0 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getIUpdater();
+        CustomIUpdater u0_1 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getIUpdater();
         assertEquals(lr, u0_0.getLearningRate(), 1e-6);
         assertEquals(lr, u0_1.getLearningRate(), 1e-6);
 
-        Sgd u1_0 = (Sgd) ((BaseLayer) conf2.getConf(0).getLayer()).getIUpdater();
-        Sgd u1_1 = (Sgd) ((BaseLayer) conf2.getConf(1).getLayer()).getIUpdater();
+        Sgd u1_0 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getIUpdater();
+        Sgd u1_1 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getIUpdater();
         assertEquals(lr, u1_0.getLearningRate(), 1e-6);
         assertEquals(lr, u1_1.getLearningRate(), 1e-6);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/optimize/solver/BackTrackLineSearchTest.java

@@ -81,7 +81,7 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
 
         BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, layer.getOptimizer());
-        double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
+        double step = lineSearch.optimize(layer.getModelParams(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
 
         assertEquals(1.0, step, 1e-3);
     }
@@ -97,11 +97,11 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         layer.setInput(irisData.getFeatures(), LayerWorkspaceMgr.noWorkspaces());
         layer.setLabels(irisData.getLabels());
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        score1 = layer.score();
+        score1 = layer.getScore();
 
         BackTrackLineSearch lineSearch =
                         new BackTrackLineSearch(layer, new NegativeDefaultStepFunction(), layer.getOptimizer());
-        double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
+        double step = lineSearch.optimize(layer.getModelParams(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
 
         assertEquals(1.0, step, 1e-3);
     }
@@ -118,18 +118,18 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         layer.setInput(irisData.getFeatures(), LayerWorkspaceMgr.noWorkspaces());
         layer.setLabels(irisData.getLabels());
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        score1 = layer.score();
+        score1 = layer.getScore();
         INDArray origGradient = layer.gradient().gradient().dup();
 
         NegativeDefaultStepFunction sf = new NegativeDefaultStepFunction();
         BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, sf, layer.getOptimizer());
-        double step = lineSearch.optimize(layer.params(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
-        INDArray currParams = layer.params();
+        double step = lineSearch.optimize(layer.getModelParams(), layer.gradient().gradient(), layer.gradient().gradient(), LayerWorkspaceMgr.noWorkspacesImmutable());
+        INDArray currParams = layer.getModelParams();
         sf.step(currParams, origGradient, step);
         layer.setParamsTable(currParams);
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
 
-        score2 = layer.score();
+        score2 = layer.getScore();
 
         assertTrue(score1 > score2, "score1=" + score1 + ", score2=" + score2);
 
@@ -146,19 +146,19 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         layer.setInput(irisData.getFeatures(), LayerWorkspaceMgr.noWorkspaces());
         layer.setLabels(irisData.getLabels());
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        score1 = layer.score();
+        score1 = layer.getScore();
         INDArray origGradient = layer.gradient().gradient().dup();
 
         DefaultStepFunction sf = new DefaultStepFunction();
         BackTrackLineSearch lineSearch = new BackTrackLineSearch(layer, sf, layer.getOptimizer());
-        double step = lineSearch.optimize(layer.params().dup(), layer.gradient().gradient().dup(),
+        double step = lineSearch.optimize(layer.getModelParams().dup(), layer.gradient().gradient().dup(),
                         layer.gradient().gradient().dup(), LayerWorkspaceMgr.noWorkspacesImmutable());
 
-        INDArray currParams = layer.params();
+        INDArray currParams = layer.getModelParams();
         sf.step(currParams, origGradient, step);
         layer.setParamsTable(currParams);
         layer.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        score2 = layer.score();
+        score2 = layer.getScore();
 
         assertTrue(score1 < score2, "score1 = " + score1 + ", score2 = " + score2);
     }
@@ -190,12 +190,12 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.SIGMOID, optimizer));
         network.init();
         TrainingListener listener = new ScoreIterationListener(10);
-        network.setListeners(Collections.singletonList(listener));
+        network.addTrainingListeners(Collections.singletonList(listener));
         double oldScore = network.score(data);
         for( int i=0; i<100; i++ ) {
             network.fit(data.getFeatures(), data.getLabels());
         }
-        double score = network.score();
+        double score = network.getScore();
         assertTrue(score < oldScore);
     }
 
@@ -208,13 +208,13 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, optimizer));
         network.init();
         TrainingListener listener = new ScoreIterationListener(10);
-        network.setListeners(Collections.singletonList(listener));
+        network.addTrainingListeners(Collections.singletonList(listener));
         double firstScore = network.score(data);
 
         for( int i=0; i<5; i++ ) {
             network.fit(data.getFeatures(), data.getLabels());
         }
-        double score = network.score();
+        double score = network.getScore();
         assertTrue(score < firstScore);
 
     }
@@ -227,13 +227,13 @@ public class BackTrackLineSearchTest extends BaseDL4JTest {
         MultiLayerNetwork network = new MultiLayerNetwork(getIrisMultiLayerConfig(Activation.RELU, optimizer));
         network.init();
         TrainingListener listener = new ScoreIterationListener(10);
-        network.setListeners(Collections.singletonList(listener));
+        network.addTrainingListeners(Collections.singletonList(listener));
         double oldScore = network.score(data);
 
         for( int i=0; i<5; i++ ) {
             network.fit(data.getFeatures(), data.getLabels());
         }
-        double score = network.score();
+        double score = network.getScore();
         assertTrue(score < oldScore);
 
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/optimize/solver/TestOptimizers.java

@@ -28,6 +28,7 @@ import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
 import org.deeplearning4j.nn.api.*;
 import org.deeplearning4j.nn.conf.CacheMode;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.BaseLayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
@@ -211,38 +212,38 @@ public class TestOptimizers extends BaseDL4JTest {
             System.out.println("---------\n Alg= " + oa + ", nIter= " + numLineSearchIter + ", nDimensions= "
                             + nDimensions);
 
-        NeuralNetConfiguration conf = NeuralNetConfiguration.builder().maxNumLineSearchIterations(numLineSearchIter)
+        LayerConfiguration conf = NeuralNetConfiguration.builder().maxNumLineSearchIterations(numLineSearchIter)
                         .updater(new Sgd(1e-2))
-                        .layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
-        conf.addNetWideVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
+                        .layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build().getFlattenedLayerConfigurations().get(0);
+        conf.addVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
 
         Random rng = new DefaultRandom(12345L);
         org.nd4j.linalg.api.rng.distribution.Distribution dist =
                         new org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution(rng, -10, 10);
         IModel m = new SphereFunctionModel(nDimensions, dist, conf);
         m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        double scoreBefore = m.score();
+        double scoreBefore = m.getScore();
         assertTrue(!Double.isNaN(scoreBefore) && !Double.isInfinite(scoreBefore));
         if (PRINT_OPT_RESULTS) {
             System.out.println("Before:");
             System.out.println(scoreBefore);
-            System.out.println(m.params());
+            System.out.println(m.getModelParams());
         }
 
-        ConvexOptimizer opt = getOptimizer(oa, conf, m);
+        ConvexOptimizer opt = getOptimizer(oa, conf.getNetConfiguration(), m);
 
         opt.setupSearchState(m.gradientAndScore());
         for( int i=0; i<100; i++ ) {
             opt.optimize(LayerWorkspaceMgr.noWorkspaces());
         }
         m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-        double scoreAfter = m.score();
+        double scoreAfter = m.getScore();
 
         assertTrue(!Double.isNaN(scoreAfter) && !Double.isInfinite(scoreAfter));
         if (PRINT_OPT_RESULTS) {
             System.out.println("After:");
             System.out.println(scoreAfter);
-            System.out.println(m.params());
+            System.out.println(m.getModelParams());
         }
 
         //Expected behaviour after optimization:
@@ -279,17 +280,17 @@ public class TestOptimizers extends BaseDL4JTest {
                             .layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
             conf.addNetWideVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
 
-            IModel m = new SphereFunctionModel(100, dist, conf);
+            IModel m = new SphereFunctionModel(100, dist, conf.getFlattenedLayerConfigurations().get(0));
             if (i == 0) {
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[0] = m.score(); //Before optimization
+                scores[0] = m.getScore(); //Before optimization
             } else {
                 ConvexOptimizer opt = getOptimizer(oa, conf, m);
                 for( int j=0; j<100; j++ ) {
                     opt.optimize(LayerWorkspaceMgr.noWorkspaces());
                 }
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[i] = m.score();
+                scores[i] = m.getScore();
                 assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
             }
         }
@@ -316,7 +317,7 @@ public class TestOptimizers extends BaseDL4JTest {
         private static final long serialVersionUID = -6963606137417355405L;
 
         private SphereFunctionModel(int nParams, org.nd4j.linalg.api.rng.distribution.Distribution distribution,
-                        NeuralNetConfiguration conf) {
+                        LayerConfiguration conf) {
             super(distribution.sample(new int[] {1, nParams}), conf);
         }
 
@@ -437,7 +438,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public void setListeners(TrainingListener... listeners) {
+        public void addTrainingListeners(TrainingListener... listeners) {
 
         }
 
@@ -499,17 +500,17 @@ public class TestOptimizers extends BaseDL4JTest {
                             .layer(new DenseLayer.Builder().nIn(1).nOut(1).build()).build();
             conf.addNetWideVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
 
-            IModel m = new RastriginFunctionModel(10, conf);
+            IModel m = new RastriginFunctionModel(10, conf.getFlattenedLayerConfigurations().get(0));
             int nParams = (int)m.numParams();
             if (i == 0) {
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[0] = m.score(); //Before optimization
+                scores[0] = m.getScore(); //Before optimization
             } else {
                 ConvexOptimizer opt = getOptimizer(oa, conf, m);
                 opt.getUpdater().setStateViewArray((Layer) m, Nd4j.create(new int[] {1, nParams}, 'c'), true);
                 opt.optimize(LayerWorkspaceMgr.noWorkspaces());
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[i] = m.score();
+                scores[i] = m.getScore();
                 assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]));
             }
         }
@@ -540,7 +541,7 @@ public class TestOptimizers extends BaseDL4JTest {
     private static class RastriginFunctionModel extends SimpleOptimizableModel {
         private static final long serialVersionUID = -1772954508787487941L;
 
-        private RastriginFunctionModel(int nDimensions, NeuralNetConfiguration conf) {
+        private RastriginFunctionModel(int nDimensions, LayerConfiguration conf) {
             super(initParams(nDimensions), conf);
         }
 
@@ -710,7 +711,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public void setListeners(TrainingListener... listeners) {
+        public void addTrainingListeners(TrainingListener... listeners) {
 
         }
 
@@ -768,15 +769,15 @@ public class TestOptimizers extends BaseDL4JTest {
                             .build();
             conf.addNetWideVariable("W"); //Normally done by ParamInitializers, but obviously that isn't done here
 
-            IModel m = new RosenbrockFunctionModel(100, conf);
+            IModel m = new RosenbrockFunctionModel(100, conf.getFlattenedLayerConfigurations().get(0));
             if (i == 0) {
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[0] = m.score(); //Before optimization
+                scores[0] = m.getScore(); //Before optimization
             } else {
                 ConvexOptimizer opt = getOptimizer(oa, conf, m);
                 opt.optimize(LayerWorkspaceMgr.noWorkspaces());
                 m.computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-                scores[i] = m.score();
+                scores[i] = m.getScore();
                 assertTrue(!Double.isNaN(scores[i]) && !Double.isInfinite(scores[i]), "NaN or infinite score: " + scores[i]);
             }
         }
@@ -810,7 +811,7 @@ public class TestOptimizers extends BaseDL4JTest {
     private static class RosenbrockFunctionModel extends SimpleOptimizableModel {
         private static final long serialVersionUID = -5129494342531033706L;
 
-        private RosenbrockFunctionModel(int nDimensions, NeuralNetConfiguration conf) {
+        private RosenbrockFunctionModel(int nDimensions, LayerConfiguration conf) {
             super(initParams(nDimensions), conf);
         }
 
@@ -995,7 +996,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public void setListeners(TrainingListener... listeners) {
+        public void addTrainingListeners(TrainingListener... listeners) {
 
         }
 
@@ -1029,13 +1030,31 @@ public class TestOptimizers extends BaseDL4JTest {
         private static final long serialVersionUID = 4409380971404019303L;
         protected INDArray parameters;
         protected INDArray gradientView;
-        protected final NeuralNetConfiguration conf;
+        protected final LayerConfiguration conf;
         protected Gradient gradient;
         protected double score;
 
+        /**
+         * @return 1d parameter vector
+         */
+        @Override
+        public INDArray getParams() {
+            throw new RuntimeException("Not implemented");
+        }
+
+        /**
+         * Get a reference to the network this layer is part of.
+         *
+         * @return
+         */
+        @Override
+        public IModel getNet() {
+           throw new RuntimeException("Not implemented");
+        }
+
         /**@param parameterInit Initial parameters. Also determines dimensionality of problem. Should be row vector.
          */
-        private SimpleOptimizableModel(INDArray parameterInit, NeuralNetConfiguration conf) {
+        private SimpleOptimizableModel(INDArray parameterInit, LayerConfiguration conf) {
             this.parameters = parameterInit.dup();
             this.gradientView = Nd4j.create(parameterInit.shape());
             this.conf = conf;
@@ -1048,17 +1067,12 @@ public class TestOptimizers extends BaseDL4JTest {
          */
         @Override
         public LayerConfiguration getLayerConfiguration() {
-            return this.conf.getFirstLayer();
+            return this.conf;
         }
 
         @Override
-        public void addListeners(TrainingListener... listener) {
-            // no-op
-        }
-
-        @Override
-        public TrainingConfig getConfig() {
-            return conf.getFirstLayer();
+        public ITraininableLayerConfiguration getTrainingConfig() {
+            return (BaseLayerConfiguration) conf;
         }
 
         /**
@@ -1092,7 +1106,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public void setListeners(TrainingListener... listeners) {
+        public void addTrainingListeners(TrainingListener... listeners) {
 
         }
 
@@ -1112,7 +1126,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public double score() {
+        public double getScore() {
             return score;
         }
 
@@ -1132,7 +1146,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public INDArray params() {
+        public INDArray getModelParams() {
             return parameters;
         }
 
@@ -1154,7 +1168,7 @@ public class TestOptimizers extends BaseDL4JTest {
         @Override
         public Pair<Gradient, Double> gradientAndScore() {
             computeGradientAndScore(LayerWorkspaceMgr.noWorkspaces());
-            return new Pair<>(gradient(), score());
+            return new Pair<>(gradient(), getScore());
         }
 
         @Override
@@ -1164,7 +1178,7 @@ public class TestOptimizers extends BaseDL4JTest {
 
         @Override
         public NeuralNetConfiguration getNetConfiguration() {
-            return conf;
+            return conf.getNetConfiguration();
         }
 
         @Override
@@ -1225,12 +1239,12 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public Collection<TrainingListener> getListeners() {
+        public Collection<TrainingListener> getTrainingListeners() {
             return null;
         }
 
         @Override
-        public void setListeners(Collection<TrainingListener> listeners) {
+        public void addTrainingListeners(Collection<TrainingListener> listeners) {
             throw new UnsupportedOperationException();
         }
 
@@ -1310,4 +1324,6 @@ public class TestOptimizers extends BaseDL4JTest {
         public void close(){
         }
     }
+
+
 }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/optimizer/listener/TestCheckpointListener.java

@@ -76,7 +76,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .keepAll()
                 .saveEveryNEpochs(2)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         for(int i=0; i<10; i++ ){
             net.fit(iter);
@@ -125,7 +125,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .keepLast(3)
                 .saveEveryNIterations(5)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         for(int i=0; i<20; i++ ){   //40 iterations total
             net.fit(iter);
@@ -167,7 +167,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
 
         MultiLayerNetwork netStatic2 = CheckpointListener.loadLastCheckpointMLN(f);
         assertEquals(35, netStatic2.getIterationCount());
-        assertEquals(netStatic.params(), netStatic2.params());
+        assertEquals(netStatic.getModelParams(), netStatic2.getModelParams());
     }
 
     @Test
@@ -182,7 +182,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .keepLast(3)
                 .saveEvery(4900, TimeUnit.MILLISECONDS)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         for(int i=0; i<3; i++ ){   //10 iterations total
             net.fit(iter);
@@ -226,7 +226,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .keepLastAndEvery(3, 3)
                 .saveEveryNEpochs(2)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         for(int i=0; i<20; i++ ){   //40 iterations total
             net.fit(iter);
@@ -272,7 +272,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .keepAll()
                 .saveEveryNEpochs(1)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         for(int i=0; i<3; i++ ){
             net.fit(iter);
@@ -294,7 +294,7 @@ public class TestCheckpointListener extends BaseDL4JTest {
                 .saveEveryNEpochs(1)
                 .deleteExisting(true)
                 .build();
-        net.setListeners(l);
+        net.addTrainingListeners(l);
 
         net.fit(iter);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/optimizer/listener/TestFailureListener.java

@@ -58,7 +58,7 @@ public class TestFailureListener extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        net.setListeners(new FailureTestingListener(
+        net.addTrainingListeners(new FailureTestingListener(
 //                FailureTestingListener.FailureMode.OOM,
                 FailureTestingListener.FailureMode.SYSTEM_EXIT_1,
                 new FailureTestingListener.IterationEpochTrigger(false, 10)));
@@ -84,7 +84,7 @@ public class TestFailureListener extends BaseDL4JTest {
         assertNotNull(username);
         assertFalse(username.isEmpty());
 
-        net.setListeners(new FailureTestingListener(
+        net.addTrainingListeners(new FailureTestingListener(
                 FailureTestingListener.FailureMode.SYSTEM_EXIT_1,
                 new FailureTestingListener.Or(
                         new FailureTestingListener.IterationEpochTrigger(false, 10000),
@@ -112,7 +112,7 @@ public class TestFailureListener extends BaseDL4JTest {
         assertNotNull(hostname);
         assertFalse(hostname.isEmpty());
 
-        net.setListeners(new FailureTestingListener(
+        net.addTrainingListeners(new FailureTestingListener(
                 FailureTestingListener.FailureMode.ILLEGAL_STATE,
                 new FailureTestingListener.And(
                         new FailureTestingListener.HostNameTrigger(hostname),

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/optimizer/listener/TestListeners.java

@@ -77,17 +77,17 @@ public class TestListeners extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        net.setListeners(new ScoreIterationListener(), new TestRoutingListener());
+        net.addTrainingListeners(new ScoreIterationListener(), new TestRoutingListener());
 
         for (Layer l : net.getLayers()) {
-            Collection<TrainingListener> layerListeners = l.getListeners();
+            Collection<TrainingListener> layerListeners = l.getTrainingListeners();
             assertEquals(2, layerListeners.size(), l.getClass().toString());
             TrainingListener[] lArr = layerListeners.toArray(new TrainingListener[2]);
             assertTrue(lArr[0] instanceof ScoreIterationListener);
             assertTrue(lArr[1] instanceof TestRoutingListener);
         }
 
-        Collection<TrainingListener> netListeners = net.getListeners();
+        Collection<TrainingListener> netListeners = net.getTrainingListeners();
         assertEquals(2, netListeners.size());
         TrainingListener[] lArr = netListeners.toArray(new TrainingListener[2]);
         assertTrue(lArr[0] instanceof ScoreIterationListener);
@@ -101,17 +101,17 @@ public class TestListeners extends BaseDL4JTest {
         ComputationGraph cg = new ComputationGraph(gConf);
         cg.init();
 
-        cg.setListeners(new ScoreIterationListener(), new TestRoutingListener());
+        cg.addTrainingListeners(new ScoreIterationListener(), new TestRoutingListener());
 
         for (Layer l : cg.getLayers()) {
-            Collection<TrainingListener> layerListeners = l.getListeners();
+            Collection<TrainingListener> layerListeners = l.getTrainingListeners();
             assertEquals(2, layerListeners.size());
             lArr = layerListeners.toArray(new TrainingListener[2]);
             assertTrue(lArr[0] instanceof ScoreIterationListener);
             assertTrue(lArr[1] instanceof TestRoutingListener);
         }
 
-        netListeners = cg.getListeners();
+        netListeners = cg.getTrainingListeners();
         assertEquals(2, netListeners.size());
         lArr = netListeners.toArray(new TrainingListener[2]);
         assertTrue(lArr[0] instanceof ScoreIterationListener);
@@ -180,7 +180,7 @@ public class TestListeners extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-        net.setListeners(listeners);
+        net.addTrainingListeners(listeners);
 
         net.fit(iter);
 
@@ -199,7 +199,7 @@ public class TestListeners extends BaseDL4JTest {
             listeners2.add(il2);
         }
 
-        net.setListeners(listeners2);
+        net.addTrainingListeners(listeners2);
         net.fit(iter);
     }
 
@@ -216,7 +216,7 @@ public class TestListeners extends BaseDL4JTest {
         net.init();
 
         TestListener tl = new TestListener();
-        net.setListeners(tl);
+        net.addTrainingListeners(tl);
 
         DataSetIterator irisIter = new IrisDataSetIterator(50, 150);
 
@@ -260,7 +260,7 @@ public class TestListeners extends BaseDL4JTest {
         tl = new TestListener();
 
         ComputationGraph cg = net.toComputationGraph();
-        cg.setListeners(tl);
+        cg.addTrainingListeners(tl);
 
         cg.fit(irisIter, 2);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/parallelism/RandomTests.java

@@ -94,7 +94,7 @@ public class RandomTests extends BaseDL4JTest {
 
         // at the end of day, model params has to
         for (int i = 0; i < models.size(); i++) {
-            assertEquals(models.get(0).params(), models.get(i).params());
+            assertEquals(models.get(0).getModelParams(), models.get(i).getModelParams());
         }
     }
 
@@ -119,7 +119,7 @@ public class RandomTests extends BaseDL4JTest {
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf);
         net2.init();
 
-        assertEquals(net1.params(), net2.params());
+        assertEquals(net1.getModelParams(), net2.getModelParams());
 
         NeuralNetConfiguration fromJson = NeuralNetConfiguration.fromJson(json);
 
@@ -127,6 +127,6 @@ public class RandomTests extends BaseDL4JTest {
         MultiLayerNetwork net3 = new MultiLayerNetwork(fromJson);
         net3.init();
 
-        assertEquals(net1.params(), net3.params());
+        assertEquals(net1.getModelParams(), net3.getModelParams());
     }
 }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/perf/listener/TestSystemInfoPrintListener.java

@@ -63,7 +63,7 @@ public class TestSystemInfoPrintListener extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-        net.setListeners(systemInfoFilePrintListener);
+        net.addTrainingListeners(systemInfoFilePrintListener);
 
         DataSetIterator iter = new IrisDataSetIterator(10, 150);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest050.java

@@ -87,7 +87,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new Nesterovs().stateSize(net.numParams());
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -126,7 +126,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(new WeightDecay(0.2, false), TestUtils.getWeightDecayReg(l1));
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -170,7 +170,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest060.java

@@ -89,7 +89,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new Nesterovs().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -132,7 +132,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(1.5, l1.getGradientNormalizationThreshold(), 1e-5);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -178,7 +178,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest071.java

@@ -90,7 +90,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
 
         long numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new Nesterovs().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -133,7 +133,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(1.5, l1.getGradientNormalizationThreshold(), 1e-5);
 
         long numParams = net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -179,7 +179,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
 
         long numParams = net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest080.java

@@ -94,7 +94,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(0.15, n.getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new Nesterovs().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -143,7 +143,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(1.5, l1.getGradientNormalizationThreshold(), 1e-5);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }
@@ -194,7 +194,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
 
         int numParams = (int)net.numParams();
-        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.params());
+        assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
         int updaterSize = (int) new RmsProp().stateSize(numParams);
         assertEquals(Nd4j.linspace(1, updaterSize, updaterSize, Nd4j.dataType()).reshape(1,numParams), net.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b3.java

@@ -97,7 +97,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
 
             assertEquals(dt, in.dataType());
             assertEquals(dt, outExp.dataType());
-            assertEquals(dt, net.params().dataType());
+            assertEquals(dt, net.getModelParams().dataType());
             assertEquals(dt, net.getFlattenedGradients().dataType());
             assertEquals(dt, net.getUpdater().getStateViewArray().dataType());
 
@@ -109,7 +109,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
             List<INDArray> activations = net.feedForward(in);
 
             assertEquals(dt, net.getNetConfiguration().getDataType());
-            assertEquals(dt, net.params().dataType());
+            assertEquals(dt, net.getModelParams().dataType());
             assertEquals( outExp, outAct, dtype);
         }
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b4.java

@@ -116,7 +116,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
 
             assertEquals(dtype, in.dataType());
             assertEquals(dtype, outExp.dataType());
-            assertEquals(dtype, net.params().dataType());
+            assertEquals(dtype, net.getModelParams().dataType());
             assertEquals(dtype, net.getFlattenedGradients().dataType());
             assertEquals(dtype, net.getUpdater().getStateViewArray().dataType());
 
@@ -126,7 +126,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
             assertEquals(dtype, outAct.dataType());
 
             assertEquals(dtype, net.getNetConfiguration().getDataType());
-            assertEquals(dtype, net.params().dataType());
+            assertEquals(dtype, net.getModelParams().dataType());
             boolean eq = outExp.equalsWithEps(outAct, 0.01);
             assertTrue(eq, "Test for dtype: " + dtypeName + "\n" + outExp + " vs " + outAct);
         }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100b6.java

@@ -98,7 +98,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
 
             assertEquals(dtype, in.dataType());
             assertEquals(dtype, outExp.dataType());
-            assertEquals(dtype, net.params().dataType());
+            assertEquals(dtype, net.getModelParams().dataType());
             assertEquals(dtype, net.getFlattenedGradients().dataType());
             assertEquals(dtype, net.getUpdater().getStateViewArray().dataType());
 
@@ -108,7 +108,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
             assertEquals(dtype, outAct.dataType());
 
             assertEquals(dtype, net.getNetConfiguration().getDataType());
-            assertEquals(dtype, net.params().dataType());
+            assertEquals(dtype, net.getModelParams().dataType());
             boolean eq = outExp.equalsWithEps(outAct, 0.01);
             assertTrue( eq, "Test for dtype: " + dtypeName + " - " + outExp + " vs " + outAct);
         }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/customlayer100a/CustomLayer.java

@@ -76,7 +76,7 @@ public class CustomLayer extends FeedForwardLayer {
         //For the most part, it's the same for each type of layer
 
         CustomLayerImpl myCustomLayer = new CustomLayerImpl(lconf, networkDataType);
-        myCustomLayer.setListeners(iterationListeners);             //Set the iteration listeners, if any
+        myCustomLayer.addTrainingListeners(iterationListeners);             //Set the iteration listeners, if any
         myCustomLayer.setIndex(layerIndex);                         //Integer index of the layer
 
         //Parameter view array: In Deeplearning4j, the network parameters for the entire network (all layers) are

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/customlayer100a/CustomLayerImpl.java

@@ -20,7 +20,6 @@
 
 package org.deeplearning4j.regressiontest.customlayer100a;
 
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.gradient.DefaultGradient;
 import org.deeplearning4j.nn.gradient.Gradient;
@@ -56,7 +55,7 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
         INDArray firstHalf = output.get(NDArrayIndex.all(), NDArrayIndex.interval(0, columns / 2));
         INDArray secondHalf = output.get(NDArrayIndex.all(), NDArrayIndex.interval(columns / 2, columns));
 
-        IActivation activation1 = layerConf().getActivationFn();
+        IActivation activation1 = getTypedLayerConfiguration().getActivationFn();
         IActivation activation2 = ((CustomLayer) getLayerConfiguration()).getSecondActivationFunction();
 
         //IActivation function instances modify the activation functions in-place
@@ -75,7 +74,7 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
     @Override
     public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
         /*
-        The baockprop gradient method here is very similar to the BaseLayer backprop gradient implementation
+        The baockprop gradient method here is very similar to the BaseLayerConfiguration backprop gradient implementation
         The only major difference is the two activation functions we have added in this example.
 
         Note that epsilon is dL/da - i.e., the derivative of the loss function with respect to the activations.
@@ -105,14 +104,14 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
         INDArray epsilonFirstHalf = epsilon.get(NDArrayIndex.all(), NDArrayIndex.interval(0, columns / 2));
         INDArray epsilonSecondHalf = epsilon.get(NDArrayIndex.all(), NDArrayIndex.interval(columns / 2, columns));
 
-        IActivation activation1 = layerConf().getActivationFn();
+        IActivation activation1 = getTypedLayerConfiguration().getActivationFn();
         IActivation activation2 = ((CustomLayer) getLayerConfiguration()).getSecondActivationFunction();
 
         //IActivation backprop method modifies the 'firstHalf' and 'secondHalf' arrays in-place, to contain dL/dz
         activation1.backprop(firstHalf, epsilonFirstHalf);
         activation2.backprop(secondHalf, epsilonSecondHalf);
 
-        //The remaining code for this method: just copy & pasted from BaseLayer.backpropGradient
+        //The remaining code for this method: just copy & pasted from BaseLayerConfiguration.backpropGradient
 //        INDArray delta = epsilon.muli(activationDerivative);
         if (maskArray != null) {
             activationDerivative.muliColumnVector(maskArray);
@@ -128,7 +127,7 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
         ret.gradientForVariable().put(DefaultParamInitializer.WEIGHT_KEY, weightGrad);
         ret.gradientForVariable().put(DefaultParamInitializer.BIAS_KEY, biasGrad);
 
-        INDArray epsilonNext = paramsTable.get(DefaultParamInitializer.WEIGHT_KEY).mmul(activationDerivative.transpose()).transpose();
+        INDArray epsilonNext = getParamTable().get(DefaultParamInitializer.WEIGHT_KEY).mmul(activationDerivative.transpose()).transpose();
 
         return new Pair<>(ret, epsilonNext);
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/samediff/CompareTrainingImplementations.java

@@ -190,7 +190,7 @@ public class CompareTrainingImplementations extends BaseDL4JTest {
 
 
                 //Check score
-                double scoreDl4j = net.score();
+                double scoreDl4j = net.getScore();
                 double scoreSd = map.get(lossMse.name()).getDouble(0) + sd.calcRegularizationScore();
                 assertEquals(scoreDl4j, scoreSd, 1e-6, testName);
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/util/CrashReportingUtilTest.java

@@ -104,7 +104,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
-        net.addListeners(new ScoreIterationListener(1));
+        net.addTrainingListeners(new ScoreIterationListener(1));
 
         //Test net that hasn't been trained yet
         Exception e = new Exception();
@@ -161,7 +161,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
         CrashReportingUtil.crashDumpOutputDirectory(dir);
 
         ComputationGraph cg = net.toComputationGraph();
-        cg.setListeners(new ScoreIterationListener(1));
+        cg.addTrainingListeners(new ScoreIterationListener(1));
 
         //Test net that hasn't been trained yet
         CrashReportingUtil.writeMemoryCrashDump(cg, e);

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/util/ModelGuesserTest.java

@@ -156,7 +156,7 @@ public class ModelGuesserTest extends BaseDL4JTest {
 
         MultiLayerNetwork network = (MultiLayerNetwork) ModelGuesser.loadModelGuess(tempFile.getAbsolutePath());
         assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
-        assertEquals(net.params(), network.params());
+        assertEquals(net.getModelParams(), network.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
 
     }
@@ -173,7 +173,7 @@ public class ModelGuesserTest extends BaseDL4JTest {
             MultiLayerNetwork network = (MultiLayerNetwork) ModelGuesser.loadModelGuess(inputStream);
             Assertions.assertNotNull(network);
             assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
-            assertEquals(net.params(), network.params());
+            assertEquals(net.getModelParams(), network.getModelParams());
             assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
         }
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/util/ModelSerializerTest.java

@@ -81,7 +81,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
         MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(tempFile);
 
         assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
-        assertEquals(net.params(), network.params());
+        assertEquals(net.getModelParams(), network.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
 
@@ -125,7 +125,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
         MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(fis);
 
         assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
-        assertEquals(net.params(), network.params());
+        assertEquals(net.getModelParams(), network.getModelParams());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
 
@@ -151,7 +151,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
         ComputationGraph network = ModelSerializer.restoreComputationGraph(tempFile);
 
         assertEquals(network.getComputationGraphConfiguration().toJson(), cg.getComputationGraphConfiguration().toJson());
-        assertEquals(cg.params(), network.params());
+        assertEquals(cg.getModelParams(), network.getModelParams());
         assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
 
@@ -177,7 +177,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
         ComputationGraph network = ModelSerializer.restoreComputationGraph(fis);
 
         assertEquals(network.getComputationGraphConfiguration().toJson(), cg.getComputationGraphConfiguration().toJson());
-        assertEquals(cg.params(), network.params());
+        assertEquals(cg.getModelParams(), network.getModelParams());
         assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
 
@@ -346,7 +346,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         //Also test reading  both model and normalizer from stream (correctly)
         Pair<MultiLayerNetwork,Normalizer> pair = ModelSerializer.restoreMultiLayerNetworkAndNormalizer(new FileInputStream(tempFile), true);
-        assertEquals(net.params(), pair.getFirst().params());
+        assertEquals(net.getModelParams(), pair.getFirst().getModelParams());
         assertNotNull(pair.getSecond());
     }
 
@@ -395,7 +395,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         //Also test reading  both model and normalizer from stream (correctly)
         Pair<ComputationGraph,Normalizer> pair = ModelSerializer.restoreComputationGraphAndNormalizer(new FileInputStream(tempFile), true);
-        assertEquals(net.params(), pair.getFirst().params());
+        assertEquals(net.getModelParams(), pair.getFirst().getModelParams());
         assertNotNull(pair.getSecond());
     }
 
@@ -496,6 +496,6 @@ public class ModelSerializerTest extends BaseDL4JTest {
         assertTrue(entries.contains("otherData.bin"));
 
         ComputationGraph restoredNet = ModelSerializer.restoreComputationGraph(tempFile);
-        assertEquals(net.params(), restoredNet.params());
+        assertEquals(net.getModelParams(), restoredNet.getModelParams());
     }
 }

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/TFOpLayer.java

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.modelimport.keras.layers;
 
 import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.RNNFormat;
@@ -80,10 +79,6 @@ public class TFOpLayer extends LayerConfiguration {
     public  void setNIn(InputType inputType, boolean override){}
 
 
-    @Override
-    public GradientNormalization getGradientNormalization(){return null;}
-
-
     @Override
     public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
                                                                 Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView,
@@ -91,14 +86,11 @@ public class TFOpLayer extends LayerConfiguration {
 
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
         TFOpLayerImpl tfOpLayerImpl = new TFOpLayerImpl(nodeDef, constants, lconf, networkDataType);
-        tfOpLayerImpl.setListeners(trainingListeners);
+        tfOpLayerImpl.addTrainingListeners(trainingListeners);
         tfOpLayerImpl.setIndex(layerIndex);
         return tfOpLayerImpl;
     }
 
-    @Override
-    public double getGradientNormalizationThreshold(){return 0.;}
-
     @Override
     public List<Regularization> getRegularizationByParam(String paramName){return null;}
 

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLSTM.java

@@ -31,7 +31,7 @@ import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.conf.layers.recurrent.LastTimeStep;
 import org.deeplearning4j.nn.conf.layers.util.MaskZeroLayer;
-import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayer;
+import org.deeplearning4j.nn.conf.layers.wrapper.BaseWrapperLayerConfiguration;
 import org.deeplearning4j.nn.modelimport.keras.KerasLayer;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.InvalidKerasConfigurationException;
 import org.deeplearning4j.nn.modelimport.keras.exceptions.UnsupportedKerasConfigurationException;
@@ -448,8 +448,8 @@ public class KerasLSTM extends KerasLayer {
 
 
         FeedForwardLayer ffl;
-        if(this.layer instanceof BaseWrapperLayer){
-            BaseWrapperLayer bwl = (BaseWrapperLayer)this.layer;
+        if(this.layer instanceof BaseWrapperLayerConfiguration){
+            BaseWrapperLayerConfiguration bwl = (BaseWrapperLayerConfiguration)this.layer;
             ffl = (FeedForwardLayer)bwl.getUnderlying();
         } else {
             ffl = (FeedForwardLayer) this.layer;