Playing with some new code 2 - clean build

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

.old/deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark/src/test/java/org/deeplearning4j/spark/impl/misc/TestFrozenLayers.java

@@ -74,7 +74,7 @@ public class TestFrozenLayers extends BaseSparkTest {
         MultiLayerNetwork withFrozen = new TransferLearning.Builder(origModel).fineTuneConfiguration(finetune)
                         .setFeatureExtractor(1).build();
 
-        Map<String, INDArray> m = withFrozen.paramTable();
+        Map<String, INDArray> m = withFrozen.getParamTable();
         Map<String, INDArray> pCopy = new HashMap<>();
         for (Map.Entry<String, INDArray> entry : m.entrySet()) {
             pCopy.put(entry.getKey(), entry.getValue().dup());
@@ -110,7 +110,7 @@ public class TestFrozenLayers extends BaseSparkTest {
 
         MultiLayerNetwork fitted = sNet.getNetwork();
 
-        Map<String, INDArray> fittedParams = fitted.paramTable();
+        Map<String, INDArray> fittedParams = fitted.getParamTable();
 
         for (Map.Entry<String, INDArray> entry : fittedParams.entrySet()) {
             INDArray orig = pCopy.get(entry.getKey());
@@ -151,7 +151,7 @@ public class TestFrozenLayers extends BaseSparkTest {
         ComputationGraph withFrozen = new TransferLearning.GraphBuilder(origModel).fineTuneConfiguration(finetune)
                         .setFeatureExtractor("1").build();
 
-        Map<String, INDArray> m = withFrozen.paramTable();
+        Map<String, INDArray> m = withFrozen.getParamTable();
         Map<String, INDArray> pCopy = new HashMap<>();
         for (Map.Entry<String, INDArray> entry : m.entrySet()) {
             pCopy.put(entry.getKey(), entry.getValue().dup());
@@ -187,7 +187,7 @@ public class TestFrozenLayers extends BaseSparkTest {
 
         ComputationGraph fitted = sNet.getNetwork();
 
-        Map<String, INDArray> fittedParams = fitted.paramTable();
+        Map<String, INDArray> fittedParams = fitted.getParamTable();
 
         for (Map.Entry<String, INDArray> entry : fittedParams.entrySet()) {
             INDArray orig = pCopy.get(entry.getKey());

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

@@ -200,8 +200,8 @@ public class GAN {
 
     Layer[] disLayers = ganDiscriminator.getLayers();
     Layer[] layers = ArrayUtils.addAll(genLayers, disLayers);
-    NeuralNetConfiguration genConf = generator.getConfiguration();
+    NeuralNetConfiguration genConf = generator.getNetConfiguration();
-    NeuralNetConfiguration disConf = ganDiscriminator.getConfiguration();
+    NeuralNetConfiguration disConf = ganDiscriminator.getNetConfiguration();
     LayerConfiguration[] confLayers = new LayerConfiguration[layers.length];
 
     Map<Integer, InputPreProcessor> preProcessors = new HashMap<>();

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

@@ -190,7 +190,7 @@ public class IntegrationTestRunner {
                 m = mln;
 
                 MultiLayerNetwork loaded = MultiLayerNetwork.load(savedModel, true);
-                assertEquals(loaded.getConfiguration(), mln.getConfiguration(), "Configs not equal");
+                assertEquals(loaded.getNetConfiguration(), mln.getNetConfiguration(), "Configs not equal");
                 assertEquals( loaded.params(), mln.params(), "Params not equal");
                 assertEquals( loaded.getParamTable(), mln.getParamTable(), "Param table not equal");
             } else if(config instanceof ComputationGraphConfiguration ){
@@ -202,7 +202,7 @@ 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.paramTable(), cg.paramTable(), "Param table not equal");
+                assertEquals(loaded.getParamTable(), cg.getParamTable(), "Param table not equal");
             } else if(config instanceof SameDiff){
                 sd = (SameDiff)config;
                 SameDiff loaded = SameDiff.load(savedModel, true);
@@ -426,8 +426,8 @@ public class IntegrationTestRunner {
             boolean isTbptt;
             int tbpttLength;
             if(modelType == ModelType.MLN){
-                isTbptt = mln.getConfiguration().getBackpropType() == BackpropType.TruncatedBPTT;
+                isTbptt = mln.getNetConfiguration().getBackpropType() == BackpropType.TruncatedBPTT;
-                tbpttLength = mln.getConfiguration().getTbpttFwdLength();
+                tbpttLength = mln.getNetConfiguration().getTbpttFwdLength();
             } else if(modelType == ModelType.CG) {
                 isTbptt = cg.getComputationGraphConfiguration().getBackpropType() == BackpropType.TruncatedBPTT;
                 tbpttLength = cg.getComputationGraphConfiguration().getTbpttFwdLength();
@@ -606,7 +606,7 @@ public class IntegrationTestRunner {
             if (modelType == ModelType.MLN) {
                 ModelSerializer.writeModel(m, f, true);
                 MultiLayerNetwork restored = MultiLayerNetwork.load(f, true);
-                assertEquals(mln.getConfiguration(), restored.getConfiguration());
+                assertEquals(mln.getNetConfiguration(), restored.getNetConfiguration());
                 assertEquals(mln.params(), restored.params());
             } else if(modelType == ModelType.CG){
                 ModelSerializer.writeModel(m, f, true);
@@ -742,7 +742,7 @@ public class IntegrationTestRunner {
         //Collect preprocessor coverage information:
         Collection<InputPreProcessor> preProcessors;
         if (isMLN) {
-            preProcessors = mln.getConfiguration().getInputPreProcessors().values();
+            preProcessors = mln.getNetConfiguration().getInputPreProcessors().values();
         } else {
             preProcessors = new ArrayList<>();
             for (org.deeplearning4j.nn.conf.graph.GraphVertex gv : cg.getComputationGraphConfiguration().getVertices().values()) {
@@ -834,7 +834,7 @@ public class IntegrationTestRunner {
                 } else {
                     paramPrefix = l.getLayerConfiguration().getLayerName() + "_";
                 }
-                Map<String,INDArray> paramTable = l.paramTable();
+                Map<String,INDArray> paramTable = l.getParamTable();
                 for(Map.Entry<String,INDArray> e : paramTable.entrySet()){
                     out.put(paramPrefix + e.getKey(), e.getValue().dup());
                 }
@@ -1088,7 +1088,7 @@ public class IntegrationTestRunner {
                     if(pSoFar + n < i){
                         pSoFar += n;
                     } else {
-                        for(Map.Entry<String,INDArray> e : l.paramTable().entrySet()){
+                        for(Map.Entry<String,INDArray> e : l.getParamTable().entrySet()){
                             pSoFar += e.getValue().length();
                             if(pSoFar >= i){
                                 pName = e.getKey();

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

@@ -48,7 +48,7 @@ public class TestUtils {
             ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
             restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
-            assertEquals(net.getConfiguration(), restored.getConfiguration());
+            assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
             assertEquals(net.params(), restored.params());
         } catch (IOException e){
             //Should never happen
@@ -56,7 +56,7 @@ public class TestUtils {
         }
 
         //Also check the NeuralNetConfiguration is serializable (required by Spark etc)
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         serializeDeserializeJava(conf);
 
         return restored;

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

@@ -109,12 +109,12 @@ public class LayerHelperValidationUtil {
         }
 
 
-        MultiLayerNetwork net1NoHelper = new MultiLayerNetwork(netOrig.getConfiguration().clone());
+        MultiLayerNetwork net1NoHelper = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
         net1NoHelper.init();
         log.info("Removing all layer helpers from network copy 1");
         removeHelpers(net1NoHelper.getLayers(), null);
 
-        MultiLayerNetwork net2With = new MultiLayerNetwork(netOrig.getConfiguration().clone());
+        MultiLayerNetwork net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
         net2With.init();
         net2With.params().assign(netOrig.params());
         log.info("Removing all except for specified helpers from network copy 2: " + t.getAllowHelpersForClasses());
@@ -253,7 +253,7 @@ public class LayerHelperValidationUtil {
             Preconditions.checkNotNull(t.getData(), "DataSetIterator is not set (null)");
             log.info("Testing run-to-run consistency of training with layer helper");
 
-            net2With = new MultiLayerNetwork(netOrig.getConfiguration().clone());
+            net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
             net2With.init();
             net2With.params().assign(netOrig.params());
             log.info("Removing all except for specified layer helpers from network copy 2: " + t.getAllowHelpersForClasses());
@@ -265,7 +265,7 @@ public class LayerHelperValidationUtil {
 
             for( int i=0; i<2; i++ ) {
 
-                net2With = new MultiLayerNetwork(netOrig.getConfiguration().clone());
+                net2With = new MultiLayerNetwork(netOrig.getNetConfiguration().clone());
                 net2With.init();
                 net2With.params().assign(netOrig.params());
                 log.info("Removing all except for specified layer helpers from network copy 2: " + t.getAllowHelpersForClasses());

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

@@ -66,7 +66,7 @@ public class TestUtils {
             ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
             restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
-            assertEquals(net.getConfiguration(), restored.getConfiguration());
+            assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
             assertEquals(net.params(), restored.params());
         } catch (IOException e){
             //Should never happen
@@ -74,7 +74,7 @@ public class TestUtils {
         }
 
         //Also check the NeuralNetConfiguration is serializable (required by Spark etc)
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         serializeDeserializeJava(conf);
 
         return restored;

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

@@ -622,7 +622,7 @@ public class EvalTest extends BaseDL4JTest {
 
 
         //Disable validation, and check same thing:
-        net.getConfiguration().setValidateOutputLayerConfig(false);
+        net.getNetConfiguration().setValidateOutputLayerConfig(false);
         net.evaluate(iter);
         net.evaluateROCMultiClass(iter, 0);
 

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

@@ -511,7 +511,7 @@ public class GradientCheckTests extends BaseDL4JTest {
             ComputationGraph netGraph = new ComputationGraph(conf);
             netGraph.init();
 
-            log.info("params before learning: " + netGraph.getLayer(1).paramTable());
+            log.info("params before learning: " + netGraph.getLayer(1).getParamTable());
 
             //Run a number of iterations of learning manually make some pseudo data
             //the ides is simple: since we do a element wise multiplication layer (just a scaling), we want the cos sim
@@ -538,7 +538,7 @@ public class GradientCheckTests extends BaseDL4JTest {
             assertTrue( scoreAfter < 0.8 * scoreBefore, msg);
 
 //        expectation in case linear regression(with only element wise multiplication layer): large weight for the fourth weight
-            log.info("params after learning: " + netGraph.getLayer(1).paramTable());
+            log.info("params after learning: " + netGraph.getLayer(1).getParamTable());
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(netGraph).inputs(new INDArray[]{features})
                     .labels(new INDArray[]{labels}));

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

@@ -100,14 +100,14 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
     @Test
     public void testClone() {
         NeuralNetConfiguration conf = getConfig(1, 1, new WeightInitUniform(), true);
-        BaseLayer bl = (BaseLayer) conf.getFirstLayer();
+        BaseLayer bl = (BaseLayer) conf.getFlattenedLayerConfigurations().get(0);
         conf.setStepFunction(new DefaultStepFunction());
 
         NeuralNetConfiguration conf2 = conf.clone();
 
         assertEquals(conf, conf2);
         assertNotSame(conf, conf2);
-        assertNotSame(conf.getFirstLayer(), conf2.getFirstLayer());
+        assertNotSame(conf.getFlattenedLayerConfigurations().get(0), conf2.getFlattenedLayerConfigurations().get(0));
         assertNotSame(conf.getStepFunction(), conf2.getStepFunction());
     }
 
@@ -119,9 +119,9 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
                         .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).layer(layer).build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer model = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer model = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         INDArray modelWeights = model.getParam(DefaultParamInitializer.WEIGHT_KEY);
 
 
@@ -130,9 +130,9 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
         NeuralNetConfiguration conf2 = NeuralNetConfiguration.builder().seed(123)
                         .optimizationAlgo(OptimizationAlgorithm.CONJUGATE_GRADIENT).layer(layer2).build();
 
-        long numParams2 = conf2.getFirstLayer().initializer().numParams(conf);
+        long numParams2 = conf2.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params2 = Nd4j.create(1, numParams);
-        Layer model2 = conf2.getFirstLayer().instantiate(conf2, null, 0, params2, true, params.dataType());
+        Layer model2 = conf2.getFlattenedLayerConfigurations().get(0).instantiate(conf2, null, 0, params2, true, params.dataType());
         INDArray modelWeights2 = model2.getParam(DefaultParamInitializer.WEIGHT_KEY);
 
         assertEquals(modelWeights, modelWeights2);
@@ -208,9 +208,9 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
 
     private static Layer getLayer(int nIn, int nOut, IWeightInit weightInit, boolean preTrain) {
         NeuralNetConfiguration conf = getConfig(nIn, nOut, weightInit, preTrain);
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        return conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
     }
 
 
@@ -235,7 +235,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        ConvexOptimizer opt = new StochasticGradientDescent(net.getConfiguration(),
+        ConvexOptimizer opt = new StochasticGradientDescent(net.getNetConfiguration(),
                         new NegativeDefaultStepFunction(), null, net);
         assertEquals(lr, ((Sgd)net.getLayer(0).getLayerConfiguration().getUpdaterByParam("W")).getLearningRate(), 1e-4);
         assertEquals(biasLr, ((Sgd)net.getLayer(0).getLayerConfiguration().getUpdaterByParam("b")).getLearningRate(), 1e-4);
@@ -295,7 +295,7 @@ public class NeuralNetConfigurationTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        ConvexOptimizer opt = new StochasticGradientDescent(net.getConfiguration(),
+        ConvexOptimizer opt = new StochasticGradientDescent(net.getNetConfiguration(),
                         new NegativeDefaultStepFunction(), null, net);
         assertEquals(l1, TestUtils.getL1(net.getLayer(0).getLayerConfiguration().getRegularizationByParam("W")), 1e-4);
         List<Regularization> r = net.getLayer(0).getLayerConfiguration().getRegularizationByParam("b");

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

@@ -456,7 +456,7 @@ public class TestConstraints extends BaseDL4JTest {
             INDArray label = Nd4j.rand(1, 1);
             g.fit(new INDArray[]{in1, in2}, new INDArray[]{label});
 
-            for(Map.Entry<String,INDArray> e : g.paramTable().entrySet()){
+            for(Map.Entry<String,INDArray> e : g.getParamTable().entrySet()){
                 if(!e.getKey().contains("W")){
                     continue;
                 }

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

@@ -82,9 +82,9 @@ public class TestDropout extends BaseDL4JTest {
                 .setOutputs("2")
                 .build();
 
-        assertEquals(new Dropout(0.6), ((LayerVertex)conf2.getVertices().get("0")).getNetConfiguration().getFirstLayer().getIDropout());
+        assertEquals(new Dropout(0.6), ((LayerVertex)conf2.getVertices().get("0")).getLayerConfiguration().getIDropout());
-        assertEquals(new Dropout(0.7), ((LayerVertex)conf2.getVertices().get("1")).getNetConfiguration().getFirstLayer().getIDropout());
+        assertEquals(new Dropout(0.7), ((LayerVertex)conf2.getVertices().get("1")).getLayerConfiguration().getIDropout());
-        assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).getNetConfiguration().getFirstLayer().getIDropout());
+        assertEquals(new AlphaDropout(0.5), ((LayerVertex)conf2.getVertices().get("2")).getLayerConfiguration().getIDropout());
     }
 
     @Test

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

@@ -232,7 +232,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
         cg.computeGradientAndScore();
 
         // Let's figure out what our params are now.
-        Map<String, INDArray> params = cg.paramTable();
+        Map<String, INDArray> params = cg.getParamTable();
         INDArray dense1_W = nullsafe(params.get("dense1_W"));
         INDArray dense1_b = nullsafe(params.get("dense1_b"));
         INDArray dense2_W = nullsafe(params.get("dense2_W"));
@@ -408,7 +408,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
         cg.computeGradientAndScore();
 
         // Let's figure out what our params are now.
-        Map<String, INDArray> params = cg.paramTable();
+        Map<String, INDArray> params = cg.getParamTable();
         INDArray dense1_W = nullsafe(params.get("dense1_W"));
         INDArray dense1_b = nullsafe(params.get("dense1_b"));
         INDArray dense2_W = nullsafe(params.get("dense2_W"));
@@ -578,7 +578,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
         cg.computeGradientAndScore();
 
         // Let's figure out what our params are now.
-        Map<String, INDArray> params = cg.paramTable();
+        Map<String, INDArray> params = cg.getParamTable();
         INDArray dense1_W = nullsafe(params.get("dense1_W"));
         INDArray dense1_b = nullsafe(params.get("dense1_b"));
         INDArray dense2_W = nullsafe(params.get("dense2_W"));

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

@@ -159,7 +159,7 @@ public class ShiftVertexTest extends BaseDL4JTest {
         cg.setLabel(0, target);
         cg.computeGradientAndScore();
         double score_dl4j = cg.score();
-        Map<String, INDArray> weights = cg.paramTable();
+        Map<String, INDArray> weights = cg.getParamTable();
         Gradient g = cg.gradient();
         Map<String, INDArray> gradients = g.gradientForVariable();
         Map<String, INDArray> manual_gradients = new TreeMap<String, INDArray>();

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

@@ -212,21 +212,21 @@ public class LayerBuilderTest extends BaseDL4JTest {
         try (ByteArrayInputStream bis = new ByteArrayInputStream(data); ObjectInput in = new ObjectInputStream(bis)) {
             confActual = (NeuralNetConfiguration) in.readObject();
         }
-        assertEquals(confExpected.getFirstLayer(), confActual.getFirstLayer(), "unequal Java serialization");
+        assertEquals(confExpected.getFlattenedLayerConfigurations().get(0), confActual.getFlattenedLayerConfigurations().get(0), "unequal Java serialization");
 
         // check JSON
         String json = confExpected.toJson();
         confActual = NeuralNetConfiguration.fromJson(json);
-        assertEquals(confExpected.getFirstLayer(), confActual.getFirstLayer(), "unequal JSON serialization");
+        assertEquals(confExpected.getFlattenedLayerConfigurations().get(0), confActual.getFlattenedLayerConfigurations().get(0), "unequal JSON serialization");
 
         // check YAML
         String yaml = confExpected.toYaml();
         confActual = NeuralNetConfiguration.fromYaml(yaml);
-        assertEquals(confExpected.getFirstLayer(), confActual.getFirstLayer(), "unequal YAML serialization");
+        assertEquals(confExpected.getFlattenedLayerConfigurations().get(0), confActual.getFlattenedLayerConfigurations().get(0), "unequal YAML serialization");
 
         // check the layer's use of callSuper on equals method
-        confActual.getFirstLayer().setIDropout(new Dropout(new java.util.Random().nextDouble()));
+        confActual.getFlattenedLayerConfigurations().get(0).setIDropout(new Dropout(new java.util.Random().nextDouble()));
-        assertNotEquals( confExpected.getFirstLayer(), confActual.getFirstLayer(), "broken equals method (missing callSuper?)");
+        assertNotEquals( confExpected, confActual, "broken equals method (missing callSuper?)");
     }
 
 }

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

@@ -62,9 +62,9 @@ public class TestPreProcessors extends BaseDL4JTest {
                                             .nOut(layerSize).build())
                             .build();
 
-            long numParams = nnc.getFirstLayer().initializer().numParams(nnc);
+            long numParams = nnc.getFlattenedLayerConfigurations().get(0).initializer().numParams(nnc);
             INDArray params = Nd4j.create(1, numParams);
-            DenseLayer layer = (DenseLayer) nnc.getFirstLayer().instantiate(nnc, null, 0, params, true, params.dataType());
+            DenseLayer layer = (DenseLayer) nnc.getFlattenedLayerConfigurations().get(0).instantiate(nnc, null, 0, params, true, params.dataType());
             layer.setInputMiniBatchSize(miniBatchSize);
 
             INDArray activations3dc = Nd4j.create(new int[] {miniBatchSize, layerSize, timeSeriesLength}, 'c');
@@ -147,9 +147,9 @@ public class TestPreProcessors extends BaseDL4JTest {
                                             .nOut(layerSize).build())
                             .build();
 
-            val numParams = nnc.getFirstLayer().initializer().numParams(nnc);
+            val numParams = nnc.getFlattenedLayerConfigurations().get(0).initializer().numParams(nnc);
             INDArray params = Nd4j.create(1, numParams);
-            DenseLayer layer = (DenseLayer) nnc.getFirstLayer().instantiate(nnc, null, 0, params, true, params.dataType());
+            DenseLayer layer = (DenseLayer) nnc.getFlattenedLayerConfigurations().get(0).instantiate(nnc, null, 0, params, true, params.dataType());
             layer.setInputMiniBatchSize(miniBatchSize);
 
             INDArray rand = Nd4j.rand(miniBatchSize * timeSeriesLength, layerSize);
@@ -232,10 +232,10 @@ public class TestPreProcessors extends BaseDL4JTest {
                                                                                             .nOut(nChannels).build())
                                                             .build();
 
-                            val numParams = nnc.getFirstLayer().initializer().numParams(nnc);
+                            val numParams = nnc.getFlattenedLayerConfigurations().get(0).initializer().numParams(nnc);
                             INDArray params = Nd4j.create(1, numParams);
                             ConvolutionLayer layer =
-                                            (ConvolutionLayer) nnc.getFirstLayer().instantiate(nnc, null, 0, params, true, params.dataType());
+                                            (ConvolutionLayer) nnc.getFlattenedLayerConfigurations().get(0).instantiate(nnc, null, 0, params, true, params.dataType());
                             layer.setInputMiniBatchSize(miniBatchSize);
 
                             INDArray activationsCnn = Nd4j.rand(miniBatchSize * timeSeriesLength, nChannels,
@@ -314,10 +314,10 @@ public class TestPreProcessors extends BaseDL4JTest {
                                                                                             .nOut(nChannels).build())
                                                             .build();
 
-                            val numParams = nnc.getFirstLayer().initializer().numParams(nnc);
+                            val numParams = nnc.getFlattenedLayerConfigurations().get(0).initializer().numParams(nnc);
                             INDArray params = Nd4j.create(1, numParams);
                             ConvolutionLayer layer =
-                                            (ConvolutionLayer) nnc.getFirstLayer().instantiate(nnc, null, 0, params, true, params.dataType());
+                                            (ConvolutionLayer) nnc.getFlattenedLayerConfigurations().get(0).instantiate(nnc, null, 0, params, true, params.dataType());
                             layer.setInputMiniBatchSize(miniBatchSize);
 
                             val shape_rnn = new long[] {miniBatchSize, nChannels * inputHeight * inputWidth,

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

@@ -256,9 +256,9 @@ public class DTypeTests extends BaseDL4JTest {
     }
 
     public static void logUsedClasses(MultiLayerNetwork net) {
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         for (NeuralNetConfiguration nnc : conf.getNetConfigurations()) {
-            LayerConfiguration l = nnc.getFirstLayer();
+            LayerConfiguration l = nnc.getFlattenedLayerConfigurations().get(0);
             seenLayers.add(l.getClass());
             if (l instanceof BaseWrapperLayer) {
                 BaseWrapperLayer bwl = (BaseWrapperLayer) l;
@@ -281,7 +281,7 @@ public class DTypeTests extends BaseDL4JTest {
         for (GraphVertex gv : conf.getVertices().values()) {
             seenVertices.add(gv.getClass());
             if (gv instanceof LayerVertex) {
-                seenLayers.add(((LayerVertex) gv).getNetConfiguration().getFirstLayer().getClass());
+                seenLayers.add(((LayerVertex) gv).getLayerConfiguration().getClass());
                 InputPreProcessor ipp = ((LayerVertex) gv).getPreProcessor();
                 if (ipp != null) {
                     seenPreprocs.add(ipp.getClass());

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

@@ -96,11 +96,11 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
             Map<String,INDArray> paramsBefore = new HashMap<>();
 
             //Pretrain first layer
-            for(Map.Entry<String,INDArray> e : cg.paramTable().entrySet()){
+            for(Map.Entry<String,INDArray> e : cg.getParamTable().entrySet()){
                 paramsBefore.put(e.getKey(), e.getValue().dup());
             }
             cg.pretrainLayer("vae1", ds);
-            for(Map.Entry<String,INDArray> e : cg.paramTable().entrySet()){
+            for(Map.Entry<String,INDArray> e : cg.getParamTable().entrySet()){
                 if(e.getKey().startsWith("vae1")){
                     assertNotEquals(paramsBefore.get(e.getKey()), e.getValue());
                 } else {
@@ -113,11 +113,11 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
 
 
             //Pretrain second layer
-            for(Map.Entry<String,INDArray> e : cg.paramTable().entrySet()){
+            for(Map.Entry<String,INDArray> e : cg.getParamTable().entrySet()){
                 paramsBefore.put(e.getKey(), e.getValue().dup());
             }
             cg.pretrainLayer("vae2", ds);
-            for(Map.Entry<String,INDArray> e : cg.paramTable().entrySet()){
+            for(Map.Entry<String,INDArray> e : cg.getParamTable().entrySet()){
                 if(e.getKey().startsWith("vae2")){
                     assertNotEquals(paramsBefore.get(e.getKey()), e.getValue());
                 } else {

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

@@ -406,9 +406,9 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
                 .addLayer("rnn", new GravesLSTM.Builder().nOut(5).build(), "in")
                 .addLayer("out", new RnnOutputLayer.Builder().nOut(5).activation(Activation.SOFTMAX).build(), "rnn").setOutputs("out").build();
 
-        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf1.getVertices().get("rnn")).getNetConfiguration().getFirstLayer())
+        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf1.getVertices().get("rnn")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                 .getNIn());
-        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf1.getVertices().get("out")).getNetConfiguration().getFirstLayer())
+        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf1.getVertices().get("out")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                 .getNIn());
 
         LayerVertex lv1 = (LayerVertex) conf1.getVertices().get("rnn");
@@ -423,9 +423,9 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
                 .addLayer("out", new RnnOutputLayer.Builder().nOut(5).activation(Activation.SOFTMAX).build(), "ff")
                 .setOutputs("out").build();
 
-        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf2.getVertices().get("ff")).getNetConfiguration().getFirstLayer())
+        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf2.getVertices().get("ff")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                 .getNIn());
-        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf2.getVertices().get("out")).getNetConfiguration().getFirstLayer())
+        assertEquals(5, ((FeedForwardLayer) ((LayerVertex) conf2.getVertices().get("out")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                 .getNIn());
 
         lv1 = (LayerVertex) conf2.getVertices().get("ff");
@@ -460,7 +460,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         LayerVertex lv4 = (LayerVertex) conf3.getVertices().get("out");
         assertNull(lv4.getPreProcessor());
         //Check nIns:
-        assertEquals(7 * 7 * 3, ((FeedForwardLayer) lv3.getNetConfiguration().getFirstLayer()).getNIn());
+        assertEquals(7 * 7 * 3, ((FeedForwardLayer) lv3.getNetConfiguration().getFlattenedLayerConfigurations().get(0)).getNIn());
 
         //CNN->Dense, RNN->Dense, Dense->RNN
         ComputationGraphConfiguration conf4 =
@@ -495,9 +495,9 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         LayerVertex lv5 = (LayerVertex) conf4.getVertices().get("out");
         assertTrue(lv5.getPreProcessor() instanceof FeedForwardToRnnPreProcessor);
         //Check nIns:
-        assertEquals(7 * 7 * 3, ((FeedForwardLayer) lv3.getNetConfiguration().getFirstLayer()).getNIn());
+        assertEquals(7 * 7 * 3, ((FeedForwardLayer) lv3.getNetConfiguration().getFlattenedLayerConfigurations().get(0)).getNIn());
-        assertEquals(5, ((FeedForwardLayer) lv4.getNetConfiguration().getFirstLayer()).getNIn());
+        assertEquals(5, ((FeedForwardLayer) lv4.getNetConfiguration().getFlattenedLayerConfigurations().get(0)).getNIn());
-        assertEquals(20, ((FeedForwardLayer) lv5.getNetConfiguration().getFirstLayer()).getNIn()); //10+10 out of the merge vertex -> 20 in to output layer vertex
+        assertEquals(20, ((FeedForwardLayer) lv5.getNetConfiguration().getFlattenedLayerConfigurations().get(0)).getNIn()); //10+10 out of the merge vertex -> 20 in to output layer vertex
 
 
         //Input to 2 CNN layers:
@@ -903,7 +903,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
                 .build();
 
         LayerVertex lv = (LayerVertex) conf.getVertices().get("layer");
-        FeedForwardLayer l = ((FeedForwardLayer) (lv).getNetConfiguration().getFirstLayer());
+        FeedForwardLayer l = ((FeedForwardLayer) (lv).getNetConfiguration().getFlattenedLayerConfigurations().get(0));
         assertEquals(3, l.getNIn());
         assertNull(lv.getPreProcessor());
 
@@ -920,7 +920,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
                 .build();
 
         lv = (LayerVertex) conf.getVertices().get("layer");
-        l = ((FeedForwardLayer) (lv).getNetConfiguration().getFirstLayer());
+        l = ((FeedForwardLayer) (lv).getNetConfiguration().getFlattenedLayerConfigurations().get(0));
         assertEquals(3, l.getNIn());
         assertNotNull(lv.getPreProcessor());
         InputPreProcessor preProcessor = lv.getPreProcessor();
@@ -945,7 +945,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         //Check subsampling layer:
         lv = (LayerVertex) conf.getVertices().get("l0");
-        SubsamplingLayer sl = ((SubsamplingLayer) (lv).getNetConfiguration().getFirstLayer());
+        SubsamplingLayer sl = ((SubsamplingLayer) (lv).getNetConfiguration().getFlattenedLayerConfigurations().get(0));
         assertNotNull(lv.getPreProcessor());
         preProcessor = lv.getPreProcessor();
         assertTrue(preProcessor instanceof FeedForwardToCnnPreProcessor);
@@ -955,7 +955,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         assertEquals(3, preproc.getNumChannels());
         //Check dense layer
         lv = (LayerVertex) conf.getVertices().get("layer");
-        l = ((FeedForwardLayer) (lv).getNetConfiguration().getFirstLayer());
+        l = ((FeedForwardLayer) (lv).getNetConfiguration().getFlattenedLayerConfigurations().get(0));
         assertEquals(3, l.getNIn());
         assertNull(lv.getPreProcessor());
 
@@ -1673,7 +1673,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
             ComputationGraph g = new ComputationGraph(conf2);
             g.init();
-            g.setParamTable(cg.paramTable());
+            g.setParamTable(cg.getParamTable());
             int[] origOrder = g.topologicalSortOrder();
 
             INDArray[] out4 = g.output(in);

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

@@ -72,9 +72,9 @@ public class TestSetGetParameters extends BaseDL4JTest {
         assertSame(params, net3.params()); //Same object due to clone
 
 
-        Map<String, INDArray> paramsMap = net.paramTable();
+        Map<String, INDArray> paramsMap = net.getParamTable();
-        Map<String, INDArray> paramsMap2 = net2.paramTable();
+        Map<String, INDArray> paramsMap2 = net2.getParamTable();
-        Map<String, INDArray> paramsMap3 = net3.paramTable();
+        Map<String, INDArray> paramsMap3 = net3.getParamTable();
         for (String s : paramsMap.keySet()) {
             assertEquals(paramsMap.get(s), paramsMap2.get(s));
             assertEquals(paramsMap.get(s), paramsMap3.get(s));

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

@@ -57,10 +57,10 @@ public class BaseLayerTest extends BaseDL4JTest {
     @Test
     public void testSetExistingParamsConvolutionSingleLayer() {
         Layer layer = configureSingleLayer();
-        assertNotEquals(paramTable, layer.paramTable());
+        assertNotEquals(paramTable, layer.getParamTable());
 
         layer.setParamTable(paramTable);
-        assertEquals(paramTable, layer.paramTable());
+        assertEquals(paramTable, layer.getParamTable());
     }
 
 
@@ -69,9 +69,9 @@ public class BaseLayerTest extends BaseDL4JTest {
         MultiLayerNetwork net = configureMultiLayer();
 
         for (Layer layer : net.getLayers()) {
-            assertNotEquals(paramTable, layer.paramTable());
+            assertNotEquals(paramTable, layer.getParamTable());
             layer.setParamTable(paramTable);
-            assertEquals(paramTable, layer.paramTable());
+            assertEquals(paramTable, layer.getParamTable());
         }
     }
 
@@ -83,9 +83,9 @@ public class BaseLayerTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
                         .layer(new ConvolutionLayer.Builder().nIn(nIn).nOut(nOut).build()).build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        return conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
     }
 
 

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

@@ -133,7 +133,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         MultiLayerNetwork clonedModel = modelNow.clone();
 
         //Check json
-        assertEquals(modelNow.getConfiguration().toJson(), clonedModel.getConfiguration().toJson());
+        assertEquals(modelNow.getNetConfiguration().toJson(), clonedModel.getNetConfiguration().toJson());
 
         //Check params
         assertEquals(modelNow.params(), clonedModel.params());

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

@@ -64,9 +64,9 @@ public class OutputLayerTest extends BaseDL4JTest {
                                         .lossFunction(LossFunctions.LossFunction.MCXENT).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        OutputLayer l = (OutputLayer) conf.getFirstLayer().instantiate(conf,
+        OutputLayer l = (OutputLayer) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf,
                         Collections.singletonList(new ScoreIterationListener(1)), 0, params, true, params.dataType());
         params = l.params();
         l.setParamsTable(params);

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

@@ -43,7 +43,7 @@ public class RepeatVectorTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().seed(123)
                 .dataType(DataType.DOUBLE)
                 .layer(new RepeatVector.Builder(REPEAT).build()).build();
-        return conf.getFirstLayer().instantiate(conf, null, 0,
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0,
                 null, false, DataType.DOUBLE);
     }
 

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

@@ -52,9 +52,9 @@ public class SeedTest extends BaseDL4JTest {
         NeuralNetConfiguration conf =
                         NeuralNetConfiguration.builder().layer(layerType).seed(123).build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(Nd4j.create(1, numParams));
         layer.fit(data.getFeatures(), LayerWorkspaceMgr.noWorkspaces());
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Convolution3DTest.java

@@ -90,9 +90,9 @@ public class Convolution3DTest extends BaseDL4JTest {
                         .dataFormat(Convolution3D.DataFormat.NCDHW).convolutionMode(mode).hasBias(false)
                         .build())
                 .build();
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.ones(1, numParams);
-        return conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
     }
 
     public INDArray getData() throws Exception {

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

@@ -258,9 +258,9 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
 
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().layer(cnn).build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
 
         assertEquals(1, layer.getParam("b").size(0));
     }
@@ -319,9 +319,9 @@ public class ConvolutionLayerTest extends BaseDL4JTest {
 
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().layer(layer).build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        return conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
     }
 
     public Layer getMNISTConfig() {

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

@@ -62,7 +62,7 @@ public class SpaceToDepthTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
                 .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).seed(123)
                 .layer(new SpaceToDepthLayer.Builder(blockSize, dataFormat).build()).build();
-        return conf.getFirstLayer().instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
     }
 
     @Test

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

@@ -172,7 +172,7 @@ public class SubsamplingLayerTest extends BaseDL4JTest {
                         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).seed(123)
                         .layer(new SubsamplingLayer.Builder(pooling, new int[] {2, 2}).build()).build();
 
-        return conf.getFirstLayer().instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
     }
 
     public INDArray getData() throws Exception {

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

@@ -287,28 +287,28 @@ public class TestConvolutionModes extends BaseDL4JTest {
                                     .activation(Activation.SOFTMAX).nOut(3).build(), "7")
                             .setOutputs("8").build();
 
-            assertEquals(cm, ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("0")).getNetConfiguration().getFirstLayer())
+            assertEquals(cm, ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("0")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Strict,
-                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("1")).getNetConfiguration().getFirstLayer())
+                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("1")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Truncate,
-                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("2")).getNetConfiguration().getFirstLayer())
+                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("2")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Same,
-                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("3")).getNetConfiguration().getFirstLayer())
+                            ((ConvolutionLayer) ((LayerVertex) conf.getVertices().get("3")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
 
-            assertEquals(cm, ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("4")).getNetConfiguration().getFirstLayer())
+            assertEquals(cm, ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("4")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Strict,
-                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("5")).getNetConfiguration().getFirstLayer())
+                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("5")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Truncate,
-                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("6")).getNetConfiguration().getFirstLayer())
+                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("6")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
             assertEquals(ConvolutionMode.Same,
-                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("7")).getNetConfiguration().getFirstLayer())
+                            ((SubsamplingLayer) ((LayerVertex) conf.getVertices().get("7")).getNetConfiguration().getFlattenedLayerConfigurations().get(0))
                                             .getConvolutionMode());
         }
     }

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Upsampling1DTest.java

@@ -107,7 +107,7 @@ public class Upsampling1DTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
                         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).seed(123)
                         .layer(new Upsampling1D.Builder(size).build()).build();
-        return conf.getFirstLayer().instantiate(conf, null, 0,
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0,
                 null, true, Nd4j.defaultFloatingPointType());
     }
 

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/nn/layers/convolution/Upsampling2DTest.java

@@ -111,7 +111,7 @@ public class Upsampling2DTest extends BaseDL4JTest {
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
                         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).seed(123)
                         .layer(new Upsampling2D.Builder(size).build()).build();
-        return conf.getFirstLayer().instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
+        return conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, null, true, Nd4j.defaultFloatingPointType());
     }
 
     public INDArray getData() throws Exception {

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

@@ -27,6 +27,7 @@ import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
 import org.deeplearning4j.optimize.api.TrainingListener;
@@ -53,13 +54,14 @@ public class CustomLayer extends FeedForwardLayer {
     public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
                                                        Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView,
                                                        boolean initializeParams, DataType networkDataType) {
-        CustomLayerImpl ret = new CustomLayerImpl(conf, networkDataType);
+        LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        CustomLayerImpl ret = new CustomLayerImpl(lconf, networkDataType);
         ret.setListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);
         ret.setParamTable(paramTable);
-        ret.setLayerConfiguration(conf);
+        ret.setLayerConfiguration(lconf);
         return ret;
     }
 

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

@@ -21,11 +21,12 @@
 package org.deeplearning4j.nn.layers.custom.testclasses;
 
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.layers.BaseLayer;
 import org.nd4j.linalg.api.buffer.DataType;
 
 public class CustomLayerImpl extends BaseLayer<CustomLayer> {
-    public CustomLayerImpl(NeuralNetConfiguration conf, DataType dataType) {
+    public CustomLayerImpl(LayerConfiguration conf, DataType dataType) {
         super(conf, dataType);
     }
 

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

@@ -29,6 +29,7 @@ import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.BaseOutputLayer;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.nd4j.linalg.api.buffer.DataType;
@@ -51,13 +52,14 @@ public class CustomOutputLayer extends BaseOutputLayer {
     @Override
     public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners,
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
-        CustomOutputLayerImpl ret = new CustomOutputLayerImpl(conf, networkDataType);
+        LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        CustomOutputLayerImpl ret = new CustomOutputLayerImpl(lconf, networkDataType);
         ret.setListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);
         ret.setParamTable(paramTable);
-        ret.setLayerConfiguration(conf);
+        ret.setLayerConfiguration(lconf);
         return ret;
     }
 

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

@@ -21,6 +21,7 @@
 package org.deeplearning4j.nn.layers.custom.testclasses;
 
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.layers.BaseOutputLayer;
 import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
@@ -28,7 +29,7 @@ import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 
 public class CustomOutputLayerImpl extends BaseOutputLayer<CustomOutputLayer> {
-    public CustomOutputLayerImpl(NeuralNetConfiguration conf, DataType dataType) {
+    public CustomOutputLayerImpl(LayerConfiguration conf, DataType dataType) {
         super(conf, dataType);
     }
 

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

@@ -53,9 +53,9 @@ public class DenseTest extends BaseDL4JTest {
 
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().layer(build).build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, Nd4j.defaultFloatingPointType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, Nd4j.defaultFloatingPointType());
 
         assertEquals(1, layer.getParam("b").size(0));
     }

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

@@ -130,12 +130,12 @@ public class BatchNormalizationTest extends BaseDL4JTest {
         BatchNormalization bN = b.build();
         NeuralNetConfiguration conf = NeuralNetConfiguration.builder().layer(bN).build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = null;
         if (numParams > 0) {
             params = Nd4j.create(1, numParams);
         }
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params == null ? Nd4j.defaultFloatingPointType() : params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params == null ? Nd4j.defaultFloatingPointType() : params.dataType());
         if (numParams > 0) {
             layer.setBackpropGradientsViewArray(Nd4j.create(1, numParams));
         }

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

@@ -123,7 +123,7 @@ public class OCNNOutputLayerTest extends BaseDL4JTest {
         DataSet filtered = next.filterBy(new int[]{0, 1});
         for (int i = 0; i < 10; i++) {
             network.setEpochCount(i);
-            network.getConfiguration().setEpochCount(i);
+            network.getNetConfiguration().setEpochCount(i);
             network.fit(filtered);
         }
 

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

@@ -68,10 +68,10 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
                                         .nOut(nHiddenUnits).dataFormat(rnnDataFormat).activation(Activation.TANH).build())
                         .build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
         final GravesBidirectionalLSTM layer =
-                        (GravesBidirectionalLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+                        (GravesBidirectionalLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
 
         //Data: has shape [miniBatchSize,nIn,timeSeriesLength];
         //Output/activations has shape [miniBatchsize,nHiddenUnits,timeSeriesLength];
@@ -135,11 +135,11 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
                                         .dist(new UniformDistribution(0, 1)).activation(Activation.TANH).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
         GravesBidirectionalLSTM lstm =
-                        (GravesBidirectionalLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+                        (GravesBidirectionalLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
-        lstm.setBackpropGradientsViewArray(Nd4j.create(1, conf.getFirstLayer().initializer().numParams(conf)));
+        lstm.setBackpropGradientsViewArray(Nd4j.create(1, conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf)));
         //Set input, do a forward pass:
         lstm.activate(inputData, false, LayerWorkspaceMgr.noWorkspaces());
         assertNotNull(lstm.input());
@@ -207,10 +207,10 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
                                         .dist(new UniformDistribution(0, 1)).activation(Activation.TANH).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
         final GravesBidirectionalLSTM lstm =
-                        (GravesBidirectionalLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+                        (GravesBidirectionalLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         final INDArray input = Nd4j.rand(miniBatchSize, nIn, timeSeriesLength);
         lstm.setInput(input, LayerWorkspaceMgr.noWorkspaces());
 
@@ -266,9 +266,9 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
                         .build();
 
 
-        long numParams = confBidirectional.getFirstLayer().initializer().numParams(confBidirectional);
+        long numParams = confBidirectional.getFlattenedLayerConfigurations().get(0).initializer().numParams(confBidirectional);
         INDArray params = Nd4j.create(1, numParams);
-        final GravesBidirectionalLSTM bidirectionalLSTM = (GravesBidirectionalLSTM) confBidirectional.getFirstLayer()
+        final GravesBidirectionalLSTM bidirectionalLSTM = (GravesBidirectionalLSTM) confBidirectional.getFlattenedLayerConfigurations().get(0)
                         .instantiate(confBidirectional, null, 0, params, true, params.dataType());
 
 
@@ -311,19 +311,19 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
                                         .weightInit(WeightInit.ZERO).activation(Activation.TANH).build())
                         .build();
 
-        long numParams = confForwards.getFirstLayer().initializer().numParams(confForwards);
+        long numParams = confForwards.getFlattenedLayerConfigurations().get(0).initializer().numParams(confForwards);
         INDArray params = Nd4j.create(1, numParams);
-        long numParamsBD = confBidirectional.getFirstLayer().initializer().numParams(confBidirectional);
+        long numParamsBD = confBidirectional.getFlattenedLayerConfigurations().get(0).initializer().numParams(confBidirectional);
         INDArray paramsBD = Nd4j.create(1, numParamsBD);
-        final GravesBidirectionalLSTM bidirectionalLSTM = (GravesBidirectionalLSTM) confBidirectional.getFirstLayer()
+        final GravesBidirectionalLSTM bidirectionalLSTM = (GravesBidirectionalLSTM) confBidirectional.getFlattenedLayerConfigurations().get(0)
                         .instantiate(confBidirectional, null, 0, paramsBD, true, params.dataType());
         final GravesLSTM forwardsLSTM =
-                        (GravesLSTM) confForwards.getFirstLayer().instantiate(confForwards, null, 0, params, true, params.dataType());
+                        (GravesLSTM) confForwards.getFlattenedLayerConfigurations().get(0).instantiate(confForwards, null, 0, params, true, params.dataType());
 
         bidirectionalLSTM.setBackpropGradientsViewArray(
-                        Nd4j.create(1, confBidirectional.getFirstLayer().initializer().numParams(confBidirectional)));
+                        Nd4j.create(1, confBidirectional.getFlattenedLayerConfigurations().get(0).initializer().numParams(confBidirectional)));
         forwardsLSTM.setBackpropGradientsViewArray(
-                        Nd4j.create(1, confForwards.getFirstLayer().initializer().numParams(confForwards)));
+                        Nd4j.create(1, confForwards.getFlattenedLayerConfigurations().get(0).initializer().numParams(confForwards)));
 
 
         final INDArray sig = (rnnDataFormat == RNNFormat.NCW)?Nd4j.rand(miniBatchSize, nIn, timeSeriesLength):
@@ -546,7 +546,7 @@ public class GravesBidirectionalLSTMTest extends BaseDL4JTest {
             net.init();
 
             assertEquals(gateAfn, ((org.deeplearning4j.nn.conf.layers.GravesBidirectionalLSTM) net.getLayer(0).getNetConfiguration()
-                            .getFirstLayer()).getGateActivationFn().toString());
+                            .getFlattenedLayerConfigurations().get(0)).getGateActivationFn().toString());
 
             INDArray in = Nd4j.rand(3, 2, 5);
             INDArray labels = Nd4j.rand(3, 2, 5);

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

@@ -63,9 +63,9 @@ public class GravesLSTMTest extends BaseDL4JTest {
                                                         .nOut(nHiddenUnits).activation(Activation.TANH).build())
                                         .build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        GravesLSTM layer = (GravesLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        GravesLSTM layer = (GravesLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
 
         //Data: has shape [miniBatchSize,nIn,timeSeriesLength];
         //Output/activations has shape [miniBatchsize,nHiddenUnits,timeSeriesLength];
@@ -109,10 +109,10 @@ public class GravesLSTMTest extends BaseDL4JTest {
                                         .dist(new UniformDistribution(0, 1)).activation(Activation.TANH).build())
                         .build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        GravesLSTM lstm = (GravesLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        GravesLSTM lstm = (GravesLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
-        lstm.setBackpropGradientsViewArray(Nd4j.create(1, conf.getFirstLayer().initializer().numParams(conf)));
+        lstm.setBackpropGradientsViewArray(Nd4j.create(1, conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf)));
         //Set input, do a forward pass:
         lstm.activate(inputData, false, LayerWorkspaceMgr.noWorkspaces());
         assertNotNull(lstm.input());
@@ -160,9 +160,9 @@ public class GravesLSTMTest extends BaseDL4JTest {
                                         .activation(Activation.TANH).build())
                         .build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        GravesLSTM lstm = (GravesLSTM) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        GravesLSTM lstm = (GravesLSTM) conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         INDArray input = Nd4j.rand(miniBatchSize, nIn, timeSeriesLength);
         lstm.setInput(input, LayerWorkspaceMgr.noWorkspaces());
 

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

@@ -73,7 +73,7 @@ public class TestSameDiffConv extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        Map<String, INDArray> pt1 = net.getLayer(0).paramTable();
+        Map<String, INDArray> pt1 = net.getLayer(0).getParamTable();
         assertNotNull(pt1);
         assertEquals(2, pt1.size());
         assertNotNull(pt1.get(ConvolutionParamInitializer.WEIGHT_KEY));

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

@@ -71,7 +71,7 @@ public class TestSameDiffDense extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        Map<String, INDArray> pt1 = net.getLayer(0).paramTable();
+        Map<String, INDArray> pt1 = net.getLayer(0).getParamTable();
         assertNotNull(pt1);
         assertEquals(2, pt1.size());
         assertNotNull(pt1.get(DefaultParamInitializer.WEIGHT_KEY));

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

@@ -104,7 +104,7 @@ public class TestSameDiffDenseVertex extends BaseDL4JTest {
 
                     //Check params:
                     assertEquals(netStandard.params(), netSD.params());
-                    assertEquals(netStandard.paramTable(), netSD.paramTable());
+                    assertEquals(netStandard.getParamTable(), netSD.getParamTable());
 
                     INDArray in = Nd4j.rand(minibatch, nIn);
                     INDArray l = TestUtils.randomOneHot(minibatch, nOut, 12345);
@@ -159,7 +159,7 @@ public class TestSameDiffDenseVertex extends BaseDL4JTest {
                         netSD.fit(ds);
                         netStandard.fit(ds);
 
-                        assertEquals(netStandard.paramTable(), netSD.paramTable());
+                        assertEquals(netStandard.getParamTable(), netSD.getParamTable());
                         assertEquals(netStandard.params(), netSD.params());
                         assertEquals(netStandard.getFlattenedGradients(), netSD.getFlattenedGradients());
                     }

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

@@ -63,7 +63,7 @@ public class TestVAE extends BaseDL4JTest {
                                                         .build())
                                         .build();
 
-        LayerConfiguration c = mlc.getFirstLayer();
+        LayerConfiguration c = mlc.getFlattenedLayerConfigurations().get(0);
         org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder vae =
             (VariationalAutoencoder) c;
 
@@ -78,7 +78,7 @@ public class TestVAE extends BaseDL4JTest {
 
         System.out.println("Exp num params: " + expNumParams);
         assertEquals(expNumParams, net.getLayer(0).params().length());
-        Map<String, INDArray> paramTable = net.getLayer(0).paramTable();
+        Map<String, INDArray> paramTable = net.getLayer(0).getParamTable();
         int count = 0;
         for (INDArray arr : paramTable.values()) {
             count += arr.length();
@@ -135,7 +135,7 @@ public class TestVAE extends BaseDL4JTest {
         net.init();
         net.initGradientsView(); //TODO this should happen automatically
 
-        Map<String, INDArray> paramTable = net.getLayer(0).paramTable();
+        Map<String, INDArray> paramTable = net.getLayer(0).getParamTable();
         Map<String, INDArray> gradTable =
                         ((org.deeplearning4j.nn.layers.variational.VariationalAutoencoder) net.getLayer(0))
                                         .getGradientViews();
@@ -175,7 +175,7 @@ public class TestVAE extends BaseDL4JTest {
         org.deeplearning4j.nn.layers.variational.VariationalAutoencoder layer =
                         (org.deeplearning4j.nn.layers.variational.VariationalAutoencoder) net.getLayer(0);
 
-        Map<String, INDArray> layerParams = layer.paramTable();
+        Map<String, INDArray> layerParams = layer.getParamTable();
         Map<String, INDArray> layerGradViews = layer.getGradientViews();
 
         layer.setInput(Nd4j.rand(3, 10), LayerWorkspaceMgr.noWorkspaces());
@@ -239,7 +239,7 @@ public class TestVAE extends BaseDL4JTest {
         net.pretrainLayer(0, input);
 
         //Get a snapshot of the pretrain params after fitting:
-        Map<String, INDArray> layerParams = layer.paramTable();
+        Map<String, INDArray> layerParams = layer.getParamTable();
         Map<String, INDArray> pretrainParamsBefore = new HashMap<>();
         for (String s : layerParams.keySet()) {
             if (layer.isPretrainParam(s)) {
@@ -255,7 +255,7 @@ public class TestVAE extends BaseDL4JTest {
             net.fit(features, labels);
         }
 
-        Map<String, INDArray> layerParamsAfter = layer.paramTable();
+        Map<String, INDArray> layerParamsAfter = layer.getParamTable();
 
         for (String s : pretrainParamsBefore.keySet()) {
             INDArray before = pretrainParamsBefore.get(s);

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

@@ -104,13 +104,13 @@ public class WorkspaceTests extends BaseDL4JTest {
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf.clone());
         net.init();
-        net.getConfiguration().setInferenceWorkspaceMode(WorkspaceMode.ENABLED);
+        net.getNetConfiguration().setInferenceWorkspaceMode(WorkspaceMode.ENABLED);
-        net.getConfiguration().setTrainingWorkspaceMode(WorkspaceMode.ENABLED);
+        net.getNetConfiguration().setTrainingWorkspaceMode(WorkspaceMode.ENABLED);
 
         MultiLayerNetwork net2 = new MultiLayerNetwork(conf.clone());
         net2.init();
-        net2.getConfiguration().setInferenceWorkspaceMode(WorkspaceMode.NONE);
+        net2.getNetConfiguration().setInferenceWorkspaceMode(WorkspaceMode.NONE);
-        net2.getConfiguration().setTrainingWorkspaceMode(WorkspaceMode.NONE);
+        net2.getNetConfiguration().setTrainingWorkspaceMode(WorkspaceMode.NONE);
 
         INDArray in = Nd4j.rand(1, 2, 5, 5);
 

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

@@ -817,15 +817,15 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     DataSetIterator iter = new IrisDataSetIterator(50, 150);
 
-    assertEquals(0, network.getConfiguration().getIterationCount());
+    assertEquals(0, network.getNetConfiguration().getIterationCount());
     network.fit(iter);
-    assertEquals(3, network.getConfiguration().getIterationCount());
+    assertEquals(3, network.getNetConfiguration().getIterationCount());
     iter.reset();
     network.fit(iter);
-    assertEquals(6, network.getConfiguration().getIterationCount());
+    assertEquals(6, network.getNetConfiguration().getIterationCount());
     iter.reset();
     network.fit(iter.next());
-    assertEquals(7, network.getConfiguration().getIterationCount());
+    assertEquals(7, network.getNetConfiguration().getIterationCount());
 
     ByteArrayOutputStream baos = new ByteArrayOutputStream();
     ModelSerializer.writeModel(network, baos, true);
@@ -833,7 +833,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     ByteArrayInputStream bais = new ByteArrayInputStream(asBytes);
     MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(bais, true);
-    assertEquals(7, net.getConfiguration().getIterationCount());
+    assertEquals(7, net.getNetConfiguration().getIterationCount());
   }
 
 
@@ -1072,20 +1072,20 @@ public class MultiLayerTest extends BaseDL4JTest {
     MultiLayerNetwork net = new MultiLayerNetwork(conf);
     net.init();
 
-    assertEquals(0, net.getConfiguration().getEpochCount());
+    assertEquals(0, net.getNetConfiguration().getEpochCount());
 
     DataSetIterator iter = new IrisDataSetIterator(150, 150);
 
     for (int i = 0; i < 4; i++) {
-      assertEquals(i, net.getConfiguration().getEpochCount());
+      assertEquals(i, net.getNetConfiguration().getEpochCount());
       net.fit(iter);
-      assertEquals(i + 1, net.getConfiguration().getEpochCount());
+      assertEquals(i + 1, net.getNetConfiguration().getEpochCount());
     }
 
-    assertEquals(4, net.getConfiguration().getEpochCount());
+    assertEquals(4, net.getNetConfiguration().getEpochCount());
 
     MultiLayerNetwork restored = TestUtils.testModelSerialization(net);
-    assertEquals(4, restored.getConfiguration().getEpochCount());
+    assertEquals(4, restored.getNetConfiguration().getEpochCount());
   }
 
   @Test

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

@@ -86,7 +86,7 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
         Layer layer = network.getLayer(0);
         assertTrue(layer instanceof GravesLSTM);
 
-        Map<String, INDArray> paramTable = layer.paramTable();
+        Map<String, INDArray> paramTable = layer.getParamTable();
         assertEquals(3, paramTable.size()); //2 sets of weights, 1 set of biases
 
         INDArray recurrentWeights = paramTable.get(GravesLSTMParamInitializer.RECURRENT_WEIGHT_KEY);
@@ -131,7 +131,7 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
             Layer layer = network.getLayer(i);
             assertTrue(layer instanceof GravesLSTM);
 
-            Map<String, INDArray> paramTable = layer.paramTable();
+            Map<String, INDArray> paramTable = layer.getParamTable();
             assertEquals(3, paramTable.size()); //2 sets of weights, 1 set of biases
 
             int layerNIn = (i == 0 ? nIn : nHiddenUnits[i - 1]);
@@ -458,9 +458,9 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
 
         mlnTBPTT.clearTbpttState = false;
 
-        assertEquals(BackpropType.TruncatedBPTT, mlnTBPTT.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, mlnTBPTT.getNetConfiguration().getBackpropType());
-        assertEquals(timeSeriesLength, mlnTBPTT.getConfiguration().getTbpttFwdLength());
+        assertEquals(timeSeriesLength, mlnTBPTT.getNetConfiguration().getTbpttFwdLength());
-        assertEquals(timeSeriesLength, mlnTBPTT.getConfiguration().getTbpttBackLength());
+        assertEquals(timeSeriesLength, mlnTBPTT.getNetConfiguration().getTbpttBackLength());
 
         INDArray inputData = Nd4j.rand(miniBatchSize, nIn, timeSeriesLength);
         INDArray labels = Nd4j.rand(miniBatchSize, nOut, timeSeriesLength);

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

@@ -124,8 +124,8 @@ public class TestMultiModelGradientApplication extends BaseDL4JTest {
                                 net2GradUpd.getUpdater().getStateViewArray());
 
                 //Remove the next 2 lines: fails - as net 1 is 1 iteration ahead
-                net1GradCalc.getConfiguration().setIterationCount(0);
+                net1GradCalc.getNetConfiguration().setIterationCount(0);
-                net2GradUpd.getConfiguration().setIterationCount(0);
+                net2GradUpd.getNetConfiguration().setIterationCount(0);
 
                 for (int i = 0; i < 100; i++) {
                     net1GradCalc.fit(f, l);

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

@@ -127,7 +127,7 @@ public class TestFrozenLayers extends BaseDL4JTest {
                 }
 
                 Map<String,INDArray> paramsBefore = new LinkedHashMap<>();
-                for(Map.Entry<String,INDArray> entry : transfer.paramTable().entrySet()){
+                for(Map.Entry<String,INDArray> entry : transfer.getParamTable().entrySet()){
                     paramsBefore.put(entry.getKey(), entry.getValue().dup());
                 }
 
@@ -137,7 +137,7 @@ public class TestFrozenLayers extends BaseDL4JTest {
                     transfer.fit(new INDArray[]{f},new INDArray[]{l});
                 }
 
-                for(Map.Entry<String,INDArray> entry : transfer.paramTable().entrySet()){
+                for(Map.Entry<String,INDArray> entry : transfer.getParamTable().entrySet()){
                     String s = msg + " - " + entry.getKey();
                     if(entry.getKey().startsWith("5_")){
                         //Non-frozen layer

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

@@ -70,9 +70,9 @@ public class TestTransferLearningModelSerializer extends BaseDL4JTest {
         assertTrue(withFrozen.getLayer(0) instanceof FrozenLayer);
         assertTrue(withFrozen.getLayer(1) instanceof FrozenLayer);
 
-        assertTrue(withFrozen.getConfiguration().getConf(0)
+        assertTrue(withFrozen.getNetConfiguration().getConf(0)
                         .getLayer() instanceof org.deeplearning4j.nn.conf.layers.misc.FrozenLayer);
-        assertTrue(withFrozen.getConfiguration().getConf(1)
+        assertTrue(withFrozen.getNetConfiguration().getConf(1)
                         .getLayer() instanceof org.deeplearning4j.nn.conf.layers.misc.FrozenLayer);
 
         MultiLayerNetwork restored = TestUtils.testModelSerialization(withFrozen);
@@ -120,8 +120,8 @@ public class TestTransferLearningModelSerializer extends BaseDL4JTest {
         assertTrue(withFrozen.getLayer(1) instanceof FrozenLayer);
 
         Map<String, GraphVertex> m = withFrozen.getComputationGraphConfiguration().getVertices();
-        LayerConfiguration l0 = ((LayerVertex) m.get("0")).getNetConfiguration().getFirstLayer();
+        LayerConfiguration l0 = ((LayerVertex) m.get("0")).getLayerConfiguration();
-        LayerConfiguration l1 = ((LayerVertex) m.get("1")).getNetConfiguration().getFirstLayer();
+        LayerConfiguration l1 = ((LayerVertex) m.get("1")).getLayerConfiguration();
         assertTrue(l0 instanceof org.deeplearning4j.nn.conf.layers.misc.FrozenLayer);
         assertTrue(l1 instanceof org.deeplearning4j.nn.conf.layers.misc.FrozenLayer);
 

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

@@ -605,13 +605,13 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
 
         cg2.output(arr);
 
-        Map<String,INDArray> m = new HashMap<>(cg.paramTable());
+        Map<String,INDArray> m = new HashMap<>(cg.getParamTable());
         m.put("newOut_W", m.remove("out_W"));
         m.put("newOut_b", m.remove("out_b"));
         cg2.setParamTable(m);
 
-        Map<String,INDArray> p1 = cg.paramTable();
+        Map<String,INDArray> p1 = cg.getParamTable();
-        Map<String,INDArray> p2 = cg2.paramTable();
+        Map<String,INDArray> p2 = cg2.getParamTable();
         for(String s : p1.keySet()){
             INDArray i1 = p1.get(s);
             INDArray i2 = p2.get(s.replaceAll("out", "newOut"));
@@ -651,13 +651,13 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
 
         cg2.output(arr);
 
-        Map<String,INDArray> m = new HashMap<>(cg.paramTable());
+        Map<String,INDArray> m = new HashMap<>(cg.getParamTable());
         m.put("newOut_W", m.remove("out_W"));
         m.put("newOut_b", m.remove("out_b"));
         cg2.setParamTable(m);
 
-        Map<String,INDArray> p1 = cg.paramTable();
+        Map<String,INDArray> p1 = cg.getParamTable();
-        Map<String,INDArray> p2 = cg2.paramTable();
+        Map<String,INDArray> p2 = cg2.getParamTable();
         for(String s : p1.keySet()){
             INDArray i1 = p1.get(s);
             INDArray i2 = p2.get(s.replaceAll("out", "newOut"));

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

@@ -112,8 +112,8 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         assertEquals(expectedModel.params(), modelNow.params());
 
         //Check json
-        NeuralNetConfiguration expectedConf = expectedModel.getConfiguration();
+        NeuralNetConfiguration expectedConf = expectedModel.getNetConfiguration();
-        assertEquals(expectedConf.toJson(), modelNow.getConfiguration().toJson());
+        assertEquals(expectedConf.toJson(), modelNow.getNetConfiguration().toJson());
 
         //Check params after fit
         modelNow.fit(randomData);
@@ -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.getConfiguration().getConf(0).getLayer());
+        BaseLayer bl0 = ((BaseLayer) modelNow.getNetConfiguration().getConf(0).getLayer());
-        BaseLayer bl1 = ((BaseLayer) modelNow.getConfiguration().getConf(1).getLayer());
+        BaseLayer bl1 = ((BaseLayer) modelNow.getNetConfiguration().getConf(1).getLayer());
-        BaseLayer bl3 = ((BaseLayer) modelNow.getConfiguration().getConf(3).getLayer());
+        BaseLayer bl3 = ((BaseLayer) modelNow.getNetConfiguration().getConf(3).getLayer());
         assertEquals(bl0.getWeightInitFn().getClass(), WeightInitXavier.class);
         try {
             assertEquals(JsonMappers.getMapper().writeValueAsString(bl1.getWeightInitFn()),
@@ -357,18 +357,18 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
                         .setInputPreProcessor(4, new FeedForwardToRnnPreProcessor()).build();
 
         //modelNow should have the same architecture as modelExpectedArch
-        assertEquals(modelExpectedArch.getConfiguration().getConf(0).toJson(),
+        assertEquals(modelExpectedArch.getNetConfiguration().getConf(0).toJson(),
-                        modelNow.getConfiguration().getConf(0).toJson());
+                        modelNow.getNetConfiguration().getConf(0).toJson());
         //some learning related info the subsampling layer will not be overwritten
         //assertTrue(modelExpectedArch.getConfiguration().getConf(1).toJson().equals(modelNow.getConfiguration().getConf(1).toJson()));
-        assertEquals(modelExpectedArch.getConfiguration().getConf(2).toJson(),
+        assertEquals(modelExpectedArch.getNetConfiguration().getConf(2).toJson(),
-                        modelNow.getConfiguration().getConf(2).toJson());
+                        modelNow.getNetConfiguration().getConf(2).toJson());
-        assertEquals(modelExpectedArch.getConfiguration().getConf(3).toJson(),
+        assertEquals(modelExpectedArch.getNetConfiguration().getConf(3).toJson(),
-                        modelNow.getConfiguration().getConf(3).toJson());
+                        modelNow.getNetConfiguration().getConf(3).toJson());
-        assertEquals(modelExpectedArch.getConfiguration().getConf(4).toJson(),
+        assertEquals(modelExpectedArch.getNetConfiguration().getConf(4).toJson(),
-                        modelNow.getConfiguration().getConf(4).toJson());
+                        modelNow.getNetConfiguration().getConf(4).toJson());
-        assertEquals(modelExpectedArch.getConfiguration().getConf(5).toJson(),
+        assertEquals(modelExpectedArch.getNetConfiguration().getConf(5).toJson(),
-                        modelNow.getConfiguration().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());
@@ -530,7 +530,7 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         assertEquals(new WeightInitRelu(), l1.getWeightInitFn());
         assertEquals(0.2, TestUtils.getL2(l1), 1e-6);
 
-        assertEquals(BackpropType.TruncatedBPTT, net2.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, net2.getNetConfiguration().getBackpropType());
     }
 
     @Test

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

@@ -52,9 +52,9 @@ public class TestGradientNormalization extends BaseDL4JTest {
                                         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         INDArray gradArray = Nd4j.rand(1, 220).muli(10).subi(5);
         layer.setBackpropGradientsViewArray(gradArray);
         INDArray weightGrad = Shape.newShapeNoCopy(gradArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, 200)),
@@ -98,9 +98,9 @@ public class TestGradientNormalization extends BaseDL4JTest {
                                         .gradientNormalization(GradientNormalization.RenormalizeL2PerParamType).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(Nd4j.create(params.shape()));
         Updater updater = UpdaterCreator.getUpdater(layer);
         INDArray weightGrad = Nd4j.rand(10, 20);
@@ -131,9 +131,9 @@ public class TestGradientNormalization extends BaseDL4JTest {
                                         .gradientNormalizationThreshold(threshold).build())
                         .build();
 
-        long numParams = conf.getFirstLayer().initializer().numParams(conf);
+        long numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         INDArray gradArray = Nd4j.rand(1, 220).muli(10).subi(5);
         layer.setBackpropGradientsViewArray(gradArray);
         INDArray weightGrad = Shape.newShapeNoCopy(gradArray.get(NDArrayIndex.point(0), NDArrayIndex.interval(0, 200)),
@@ -187,9 +187,9 @@ public class TestGradientNormalization extends BaseDL4JTest {
                                             .gradientNormalizationThreshold(threshold).build())
                             .build();
 
-            val numParams = conf.getFirstLayer().initializer().numParams(conf);
+            val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
             INDArray params = Nd4j.create(1, numParams);
-            Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+            Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
             INDArray gradArray = Nd4j.rand(1, 220).muli(t == 0 ? 0.05 : 10).subi(t == 0 ? 0 : 5);
             layer.setBackpropGradientsViewArray(gradArray);
             INDArray weightGrad =
@@ -242,9 +242,9 @@ public class TestGradientNormalization extends BaseDL4JTest {
                                         .gradientNormalizationThreshold(threshold).build())
                         .build();
 
-        val numParams = conf.getFirstLayer().initializer().numParams(conf);
+        val numParams = conf.getFlattenedLayerConfigurations().get(0).initializer().numParams(conf);
         INDArray params = Nd4j.create(1, numParams);
-        Layer layer = conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
+        Layer layer = conf.getFlattenedLayerConfigurations().get(0).instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(Nd4j.create(params.shape()));
         Updater updater = UpdaterCreator.getUpdater(layer);
         INDArray weightGrad = Nd4j.rand(10, 20).muli(0.05);

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

@@ -20,6 +20,7 @@
 
 package org.deeplearning4j.optimize.solver;
 
+import lombok.NonNull;
 import lombok.val;
 import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.BaseDL4JTest;
@@ -44,6 +45,7 @@ import org.deeplearning4j.optimize.solvers.LineGradientDescent;
 import org.deeplearning4j.optimize.solvers.StochasticGradientDescent;
 import org.deeplearning4j.optimize.stepfunctions.NegativeDefaultStepFunction;
 import org.junit.jupiter.api.Test;
+import org.nd4j.evaluation.IEvaluation;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -52,7 +54,9 @@ import org.nd4j.linalg.api.ops.impl.transforms.strict.Sin;
 import org.nd4j.linalg.api.rng.DefaultRandom;
 import org.nd4j.linalg.api.rng.Random;
 import org.nd4j.linalg.dataset.DataSet;
+import org.nd4j.linalg.dataset.api.MultiDataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 import org.nd4j.linalg.exception.ND4JArraySizeException;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.conditions.Condition;
@@ -317,6 +321,90 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
 
+        /**
+         * This method returns updater state (if applicable), null otherwise
+         *
+         * @return
+         */
+        @Override
+        public INDArray updaterState() {
+            return null;
+        }
+
+        /**
+         * This method fits model with a given DataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(org.nd4j.linalg.dataset.api.DataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(MultiDataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given DataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(DataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(MultiDataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(DataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(MultiDataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * @param netConfiguration
+         */
+        @Override
+        public void setNetConfiguration(@NonNull NeuralNetConfiguration netConfiguration) {
+
+        }
+
         @Override
         public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) {
             // Gradients: d(x^2)/dx = 2x
@@ -464,6 +552,90 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
 
+        /**
+         * This method returns updater state (if applicable), null otherwise
+         *
+         * @return
+         */
+        @Override
+        public INDArray updaterState() {
+            return null;
+        }
+
+        /**
+         * This method fits model with a given DataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(org.nd4j.linalg.dataset.api.DataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(MultiDataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given DataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(DataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(MultiDataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(DataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(MultiDataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * @param netConfiguration
+         */
+        @Override
+        public void setNetConfiguration(@NonNull NeuralNetConfiguration netConfiguration) {
+
+        }
+
         @Override
         public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) {
             //Gradient decomposes due to sum, so:
@@ -649,6 +821,90 @@ public class TestOptimizers extends BaseDL4JTest {
             return dist.sample(new int[] {1, nDimensions});
         }
 
+        /**
+         * This method returns updater state (if applicable), null otherwise
+         *
+         * @return
+         */
+        @Override
+        public INDArray updaterState() {
+            return null;
+        }
+
+        /**
+         * This method fits model with a given DataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(org.nd4j.linalg.dataset.api.DataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSet
+         *
+         * @param dataSet
+         */
+        @Override
+        public void fit(MultiDataSet dataSet) {
+
+        }
+
+        /**
+         * This method fits model with a given DataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(DataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method fits model with a given MultiDataSetIterator
+         *
+         * @param iterator
+         */
+        @Override
+        public void fit(MultiDataSetIterator iterator) {
+
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(DataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * This method executes evaluation of the model against given iterator and evaluation
+         * implementations
+         *
+         * @param iterator
+         * @param evaluations
+         */
+        @Override
+        public <T extends IEvaluation> T[] doEvaluation(MultiDataSetIterator iterator,
+            T... evaluations) {
+            return null;
+        }
+
+        /**
+         * @param netConfiguration
+         */
+        @Override
+        public void setNetConfiguration(@NonNull NeuralNetConfiguration netConfiguration) {
+
+        }
+
         @Override
         public void computeGradientAndScore(LayerWorkspaceMgr workspaceMgr) {
             val nDims = parameters.length();
@@ -912,7 +1168,7 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public void setLayerConfiguration(NeuralNetConfiguration layerConfiguration) {
+        public void setLayerConfiguration(LayerConfiguration layerConfiguration) {
             throw new UnsupportedOperationException();
         }
 
@@ -934,13 +1190,13 @@ public class TestOptimizers extends BaseDL4JTest {
         }
 
         @Override
-        public Map<String, INDArray> paramTable() {
+        public Map<String, INDArray> getParamTable() {
             return Collections.singletonMap("W", getParam("W"));
         }
 
         @Override
-        public Map<String, INDArray> paramTable(boolean backpropParamsOnly) {
+        public Map<String, INDArray> getParamTable(boolean backpropParamsOnly) {
-            return paramTable();
+            return getParamTable();
         }
 
         @Override

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

@@ -65,7 +65,7 @@ public class RegressionTest050 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -99,7 +99,7 @@ public class RegressionTest050 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -138,7 +138,7 @@ public class RegressionTest050 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         ConvolutionLayer l0 = (ConvolutionLayer) conf.getConf(0).getLayer();

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

@@ -67,7 +67,7 @@ public class RegressionTest060 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -101,7 +101,7 @@ public class RegressionTest060 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -144,7 +144,7 @@ public class RegressionTest060 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         ConvolutionLayer l0 = (ConvolutionLayer) conf.getConf(0).getLayer();
@@ -190,7 +190,7 @@ public class RegressionTest060 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         GravesLSTM l0 = (GravesLSTM) conf.getConf(0).getLayer();

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

@@ -68,7 +68,7 @@ public class RegressionTest071 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -102,7 +102,7 @@ public class RegressionTest071 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -145,7 +145,7 @@ public class RegressionTest071 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         ConvolutionLayer l0 = (ConvolutionLayer) conf.getConf(0).getLayer();
@@ -191,7 +191,7 @@ public class RegressionTest071 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         GravesLSTM l0 = (GravesLSTM) conf.getConf(0).getLayer();

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

@@ -67,7 +67,7 @@ public class RegressionTest080 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -106,7 +106,7 @@ public class RegressionTest080 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(2, conf.getNetConfigurations().size());
 
         DenseLayer l0 = (DenseLayer) conf.getConf(0).getLayer();
@@ -155,7 +155,7 @@ public class RegressionTest080 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         ConvolutionLayer l0 = (ConvolutionLayer) conf.getConf(0).getLayer();
@@ -206,7 +206,7 @@ public class RegressionTest080 extends BaseDL4JTest {
 
         MultiLayerNetwork net = ModelSerializer.restoreMultiLayerNetwork(f, true);
 
-        NeuralNetConfiguration conf = net.getConfiguration();
+        NeuralNetConfiguration conf = net.getNetConfiguration();
         assertEquals(3, conf.getNetConfigurations().size());
 
         GravesLSTM l0 = (GravesLSTM) conf.getConf(0).getLayer();

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/regressiontest/RegressionTest100a.java

@@ -107,9 +107,9 @@ public class RegressionTest100a extends BaseDL4JTest {
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new RmsProp(0.1), l0.getIUpdater());
 
-        assertEquals(BackpropType.TruncatedBPTT, net.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
-        assertEquals(50, net.getConfiguration().getTbpttBackLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
-        assertEquals(50, net.getConfiguration().getTbpttFwdLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttFwdLength());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100a/GravesLSTMCharModelingExample_Output_100a.bin");

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

@@ -108,7 +108,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
 
             List<INDArray> activations = net.feedForward(in);
 
-            assertEquals(dt, net.getConfiguration().getDataType());
+            assertEquals(dt, net.getNetConfiguration().getDataType());
             assertEquals(dt, net.params().dataType());
             assertEquals( outExp, outAct, dtype);
         }
@@ -142,9 +142,9 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
 
-        assertEquals(BackpropType.TruncatedBPTT, net.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
-        assertEquals(50, net.getConfiguration().getTbpttBackLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
-        assertEquals(50, net.getConfiguration().getTbpttFwdLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttFwdLength());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b3/GravesLSTMCharModelingExample_Output_100b3.bin");

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

@@ -125,7 +125,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
             INDArray outAct = net.output(in);
             assertEquals(dtype, outAct.dataType());
 
-            assertEquals(dtype, net.getConfiguration().getDataType());
+            assertEquals(dtype, net.getNetConfiguration().getDataType());
             assertEquals(dtype, net.params().dataType());
             boolean eq = outExp.equalsWithEps(outAct, 0.01);
             assertTrue(eq, "Test for dtype: " + dtypeName + "\n" + outExp + " vs " + outAct);
@@ -160,9 +160,9 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
         assertEquals(new Adam(0.005), l2.getIUpdater());
 
-        assertEquals(BackpropType.TruncatedBPTT, net.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
-        assertEquals(50, net.getConfiguration().getTbpttBackLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
-        assertEquals(50, net.getConfiguration().getTbpttFwdLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttFwdLength());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b4/GravesLSTMCharModelingExample_Output_100b4.bin");

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

@@ -107,7 +107,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
             INDArray outAct = net.output(in);
             assertEquals(dtype, outAct.dataType());
 
-            assertEquals(dtype, net.getConfiguration().getDataType());
+            assertEquals(dtype, net.getNetConfiguration().getDataType());
             assertEquals(dtype, net.params().dataType());
             boolean eq = outExp.equalsWithEps(outAct, 0.01);
             assertTrue( eq, "Test for dtype: " + dtypeName + " - " + outExp + " vs " + outAct);
@@ -142,9 +142,9 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
         assertEquals(new Adam(0.005), l2.getIUpdater());
 
-        assertEquals(BackpropType.TruncatedBPTT, net.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
-        assertEquals(50, net.getConfiguration().getTbpttBackLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
-        assertEquals(50, net.getConfiguration().getTbpttFwdLength());
+        assertEquals(50, net.getNetConfiguration().getTbpttFwdLength());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b6/GravesLSTMCharModelingExample_Output_100b6.bin");

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

@@ -28,6 +28,7 @@ import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
 import org.deeplearning4j.nn.conf.memory.MemoryReport;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
@@ -68,11 +69,13 @@ public class CustomLayer extends FeedForwardLayer {
     @Override
     public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> iterationListeners,
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
+
+        LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(0);
         //The instantiate method is how we go from the configuration class (i.e., this class) to the implementation class
         // (i.e., a CustomLayerImpl instance)
         //For the most part, it's the same for each type of layer
 
-        CustomLayerImpl myCustomLayer = new CustomLayerImpl(conf, networkDataType);
+        CustomLayerImpl myCustomLayer = new CustomLayerImpl(lconf, networkDataType);
         myCustomLayer.setListeners(iterationListeners);             //Set the iteration listeners, if any
         myCustomLayer.setIndex(layerIndex);                         //Integer index of the layer
 
@@ -87,7 +90,7 @@ public class CustomLayer extends FeedForwardLayer {
         // are in turn a view of the 'layerParamsView' array.
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);
         myCustomLayer.setParamTable(paramTable);
-        myCustomLayer.setLayerConfiguration(conf);
+        myCustomLayer.setLayerConfiguration(lconf);
         return myCustomLayer;
     }
 

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

@@ -21,6 +21,7 @@
 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;
 import org.deeplearning4j.nn.layers.BaseLayer;
@@ -35,7 +36,7 @@ import org.nd4j.common.primitives.Pair;
 
 public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic parameter here: the configuration class type
 
-    public CustomLayerImpl(NeuralNetConfiguration conf, DataType dataType) {
+    public CustomLayerImpl(LayerConfiguration conf, DataType dataType) {
         super(conf, dataType);
     }
 
@@ -56,7 +57,7 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
         INDArray secondHalf = output.get(NDArrayIndex.all(), NDArrayIndex.interval(columns / 2, columns));
 
         IActivation activation1 = layerConf().getActivationFn();
-        IActivation activation2 = ((CustomLayer) layerConfiguration.getFirstLayer()).getSecondActivationFunction();
+        IActivation activation2 = ((CustomLayer) getLayerConfiguration()).getSecondActivationFunction();
 
         //IActivation function instances modify the activation functions in-place
         activation1.getActivation(firstHalf, training);
@@ -105,7 +106,7 @@ public class CustomLayerImpl extends BaseLayer<CustomLayer> { //Generic paramete
         INDArray epsilonSecondHalf = epsilon.get(NDArrayIndex.all(), NDArrayIndex.interval(columns / 2, columns));
 
         IActivation activation1 = layerConf().getActivationFn();
-        IActivation activation2 = ((CustomLayer) layerConfiguration.getFirstLayer()).getSecondActivationFunction();
+        IActivation activation2 = ((CustomLayer) getLayerConfiguration()).getSecondActivationFunction();
 
         //IActivation backprop method modifies the 'firstHalf' and 'secondHalf' arrays in-place, to contain dL/dz
         activation1.backprop(firstHalf, epsilonFirstHalf);

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

@@ -155,7 +155,7 @@ public class ModelGuesserTest extends BaseDL4JTest {
         ModelSerializer.writeModel(net, tempFile, true);
 
         MultiLayerNetwork network = (MultiLayerNetwork) ModelGuesser.loadModelGuess(tempFile.getAbsolutePath());
-        assertEquals(network.getConfiguration().toJson(), net.getConfiguration().toJson());
+        assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
         assertEquals(net.params(), network.params());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
 
@@ -172,7 +172,7 @@ public class ModelGuesserTest extends BaseDL4JTest {
         try (InputStream inputStream = new FileInputStream(tempFile)) {
             MultiLayerNetwork network = (MultiLayerNetwork) ModelGuesser.loadModelGuess(inputStream);
             Assertions.assertNotNull(network);
-            assertEquals(network.getConfiguration().toJson(), net.getConfiguration().toJson());
+            assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
             assertEquals(net.params(), network.params());
             assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
         }

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

@@ -80,7 +80,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(tempFile);
 
-        assertEquals(network.getConfiguration().toJson(), net.getConfiguration().toJson());
+        assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
         assertEquals(net.params(), network.params());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
@@ -124,7 +124,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         MultiLayerNetwork network = ModelSerializer.restoreMultiLayerNetwork(fis);
 
-        assertEquals(network.getConfiguration().toJson(), net.getConfiguration().toJson());
+        assertEquals(network.getNetConfiguration().toJson(), net.getNetConfiguration().toJson());
         assertEquals(net.params(), network.params());
         assertEquals(net.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/configurations/KerasModelImportTest.java

@@ -24,7 +24,6 @@ import java.util.List;
 import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.nn.conf.CNN2DFormat;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.ConvolutionLayer;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.SubsamplingLayer;
@@ -57,7 +56,7 @@ public class KerasModelImportTest extends BaseDL4JTest {
     @Test
     public void testNCHWNWHCChangeImport() {
         MultiLayerNetwork model = loadModel("modelimport/keras/weights/conv2dnchw/simpleconv2d.hdf5");
-        List<LayerConfiguration> layerConfigs = model.getConfiguration().getFlattenedLayerConfigurations();
+        List<LayerConfiguration> layerConfigs = model.getNetConfiguration().getFlattenedLayerConfigurations();
         ConvolutionLayer convolutionLayer = (ConvolutionLayer) layerConfigs.get(0);
         assertEquals(CNN2DFormat.NCHW,convolutionLayer.getCnn2dDataFormat());
         SubsamplingLayer subsamplingLayer = (SubsamplingLayer) layerConfigs.get(1);

cavis-dnn/cavis-dnn-nlp/src/test/java/org/deeplearning4j/models/word2vec/Word2VecTestsSmall.java

@@ -208,7 +208,7 @@ public class Word2VecTestsSmall extends BaseDL4JTest {
         ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
         final MultiLayerNetwork restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
-        assertEquals(net.getConfiguration(), restored.getConfiguration());
+        assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
         assertTrue(net.params().equalsWithEps(restored.params(), 2e-3));
     }
 }

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/api/IModel.java

@@ -46,6 +46,15 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 
 public interface IModel {
 
+  /**
+   * The param table
+   *
+   * @return
+   */
+
+  Map<String, INDArray> getParamTable();
+  Map<String, INDArray> getParamTable(boolean backpropOnly);
+
   /**
    * This method returns updater state (if applicable), null otherwise
    *
@@ -273,6 +282,7 @@ public interface IModel {
    * @param listeners new listeners
    */
   void setListeners(TrainingListener... listeners);
+  void setListeners(Collection<TrainingListener> listeners);
 
   /**
    * Add TrainingListeners to the model

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/NeuralNetConfiguration.java

@@ -1126,6 +1126,17 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
     return getFlattenedLayerConfigurations().get(index);
   }
 
+  /**
+   * Deprecated, do not use. Workaround for old tests
+   * and getFlattenedLayerConfigurations().get(0);
+   * @return
+   */
+  @Deprecated
+  public LayerConfiguration getFirstLayer() {
+    log.warn("This getFirstLayer method is an ugly workaround and will be removed.");
+    return getFlattenedLayerConfigurations().get(0);
+  }
+
   public static abstract class NeuralNetConfigurationBuilder<C extends NeuralNetConfiguration,
       B extends NeuralNetConfiguration.NeuralNetConfigurationBuilder<C, B>> extends
       NeuralNetBaseBuilderConfigurationBuilder<C, B> {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerConfiguration.java

@@ -258,6 +258,9 @@ public abstract class LayerConfiguration implements TrainingConfig, Serializable
                         "Not supported: all layers with parameters should override this method");
     }
 
+    @Getter
+    private IUpdater iUpdater;
+
     @Override
     public void setDataType(DataType dataType) {
         //No-op for most layers

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java

@@ -2443,6 +2443,14 @@ public class MultiLayerNetwork extends ArtificialNeuralNetwork implements Serial
     }
   }
 
+  /**
+   * @param listeners
+   */
+  @Override
+  public void setListeners(Collection<TrainingListener> listeners) {
+    setListeners(listeners.toArray(new TrainingListener[]{}));
+  }
+
   /**
    * @deprecated Use {@link #getListeners()}
    */
@@ -4525,4 +4533,5 @@ public class MultiLayerNetwork extends ArtificialNeuralNetwork implements Serial
   public String toString() {
     return getNetConfiguration().toString();
   }
+
 }

cavis-dnn/cavis-dnn-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/EarlyStoppingParallelTrainer.java

@@ -94,13 +94,13 @@ public class EarlyStoppingParallelTrainer<T extends IModel> implements IEarlySto
             Collection<TrainingListener> listeners = ((MultiLayerNetwork) model).getListeners();
             Collection<TrainingListener> newListeners = new LinkedList<>(listeners);
             newListeners.add(trainerListener);
-            model.setListeners(newListeners);
+            model.setListeners(newListeners.toArray(new TrainingListener[]{}));
 
         } else if (model instanceof ComputationGraph) {
             Collection<TrainingListener> listeners = ((ComputationGraph) model).getListeners();
             Collection<TrainingListener> newListeners = new LinkedList<>(listeners);
             newListeners.add(trainerListener);
-            model.setListeners(newListeners);
+            model.setListeners(newListeners.toArray(new TrainingListener[]{}));
         }
 
         this.wrapper = new ParallelWrapper.Builder<>(model).workers(workers).prefetchBuffer(prefetchBuffer)

cavis-dnn/cavis-dnn-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/InplaceParallelInference.java

@@ -204,7 +204,7 @@ public class InplaceParallelInference extends ParallelInference {
                     if (loadBalanceMode == LoadBalanceMode.FIFO)
                         queue.add(model);
                 } else if (sourceModel instanceof MultiLayerNetwork) {
-                    val model = new MultiLayerNetwork(NeuralNetConfiguration.fromJson(((MultiLayerNetwork) sourceModel).getConfiguration().toJson()));
+                    val model = new MultiLayerNetwork(NeuralNetConfiguration.fromJson(((MultiLayerNetwork) sourceModel).getNetConfiguration().toJson()));
                     model.init(params, false);
                     Nd4j.getExecutioner().commit();
 

cavis-dnn/cavis-dnn-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelInference.java

@@ -472,7 +472,7 @@ public class ParallelInference {
             } else if (protoModel instanceof MultiLayerNetwork) {
                 if (!rootDevice) {
                     this.replicatedModel = new MultiLayerNetwork(NeuralNetConfiguration.fromJson(
-                            ((MultiLayerNetwork) protoModel).getConfiguration().toJson()));
+                            ((MultiLayerNetwork) protoModel).getNetConfiguration().toJson()));
                     this.replicatedModel.init();
 
                     synchronized (locker) {

cavis-dnn/cavis-dnn-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/ParallelWrapper.java

@@ -957,10 +957,10 @@ public class ParallelWrapper implements AutoCloseable {
             List<TrainingListener> modelListeners = null;
             if (model instanceof MultiLayerNetwork) {
                 modelListeners = new ArrayList<>(((MultiLayerNetwork) model).getListeners());
-                model.setListeners(Collections.emptyList());
+                model.setListeners(new TrainingListener[]{});
             } else if (model instanceof ComputationGraph) {
                 modelListeners = new ArrayList<>(((ComputationGraph) model).getListeners());
-                model.setListeners(Collections.emptyList());
+                model.setListeners(new TrainingListener[]{});
             }
 
             if (modelListeners != null && !modelListeners.isEmpty()) {

cavis-dnn/cavis-dnn-parallelwrapper/src/main/java/org/deeplearning4j/parallelism/trainer/DefaultTrainer.java

@@ -278,7 +278,7 @@ public class DefaultTrainer extends Thread implements Trainer {
         }
         configureListeners(uuid, oldListeners, replicatedListeners);
 
-        this.replicatedModel.setListeners(replicatedListeners);
+        this.replicatedModel.setListeners(replicatedListeners.toArray(new TrainingListener[]{}));
     }
 
     @Override
@@ -296,7 +296,7 @@ public class DefaultTrainer extends Thread implements Trainer {
             if (originalModel instanceof MultiLayerNetwork) {
                 if (!onRootModel) {
                     NeuralNetConfiguration conf = NeuralNetConfiguration.fromJson(
-                                    ((MultiLayerNetwork) originalModel).getConfiguration().toJson());
+                                    ((MultiLayerNetwork) originalModel).getNetConfiguration().toJson());
                     conf.setTrainingWorkspaceMode(workspaceMode);
                     this.replicatedModel = new MultiLayerNetwork(conf);
 
@@ -323,7 +323,7 @@ public class DefaultTrainer extends Thread implements Trainer {
                     if (!((MultiLayerNetwork) replicatedModel).isInitCalled())
                         this.replicatedModel.init();
 
-                    ((MultiLayerNetwork) replicatedModel).getConfiguration()
+                    ((MultiLayerNetwork) replicatedModel).getNetConfiguration()
                                     .setTrainingWorkspaceMode(workspaceMode);
                 }
             } else if (originalModel instanceof ComputationGraph) {

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/main/java/org/deeplearning4j/spark/impl/multilayer/SparkDl4jMultiLayer.java

@@ -122,7 +122,7 @@ public class SparkDl4jMultiLayer extends SparkListenable {
     public SparkDl4jMultiLayer(JavaSparkContext javaSparkContext, MultiLayerNetwork network,
                     TrainingMaster<?, ?> trainingMaster) {
         sc = javaSparkContext;
-        this.conf = network.getConfiguration().clone();
+        this.conf = network.getNetConfiguration().clone();
         this.network = network;
         if (!network.isInitCalled())
             network.init();
@@ -315,8 +315,8 @@ public class SparkDl4jMultiLayer extends SparkListenable {
      * @return the multi layer network that was fitDataSet
      */
     public MultiLayerNetwork fitLabeledPoint(JavaRDD<LabeledPoint> rdd) {
-        int nLayers = network.getConfiguration().getFlattenedLayerConfigurations().size();
+        int nLayers = network.getNetConfiguration().getFlattenedLayerConfigurations().size();
-        FeedForwardLayer ffl = (FeedForwardLayer) network.getConfiguration().getFlattenedLayerConfigurations().get(nLayers - 1);
+        FeedForwardLayer ffl = (FeedForwardLayer) network.getNetConfiguration().getFlattenedLayerConfigurations().get(nLayers - 1);
         JavaRDD<DataSet> ds = MLLibUtil.fromLabeledPoint(sc, rdd, ffl.getNOut());
         return fit(ds);
     }

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/main/java/org/deeplearning4j/spark/impl/paramavg/ParameterAveragingTrainingMaster.java

@@ -275,7 +275,7 @@ public class ParameterAveragingTrainingMaster
 
     @Override
     public ParameterAveragingTrainingWorker getWorkerInstance(SparkDl4jMultiLayer network) {
-        NetBroadcastTuple tuple = new NetBroadcastTuple(network.getNetwork().getConfiguration(),
+        NetBroadcastTuple tuple = new NetBroadcastTuple(network.getNetwork().getNetConfiguration(),
                         network.getNetwork().params(), network.getNetwork().getUpdater().getStateViewArray());
 
         if (collectTrainingStats)
@@ -727,7 +727,7 @@ public class ParameterAveragingTrainingMaster
         if (params != null) {
             //Params may be null for edge case (empty RDD)
             if (network != null) {
-                NeuralNetConfiguration conf = network.getNetwork().getConfiguration();
+                NeuralNetConfiguration conf = network.getNetwork().getNetConfiguration();
                 int numUpdates = averagingFrequency;
                 conf.setIterationCount(conf.getIterationCount() + numUpdates);
             } else {

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/main/java/org/deeplearning4j/spark/impl/paramavg/ParameterAveragingTrainingWorker.java

@@ -172,9 +172,9 @@ public class ParameterAveragingTrainingWorker extends BaseTrainingWorker<Paramet
                 list.add(l); //Don't need to clone listeners: not from broadcast, so deserialization handles
             }
             if (m instanceof MultiLayerNetwork)
-                m.setListeners(list);
+                m.setListeners(list.toArray(new TrainingListener[]{}));
             else
-                m.setListeners(list);
+                m.setListeners(list.toArray(new TrainingListener[]{}));
         }
     }
 

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/test/java/org/deeplearning4j/spark/impl/customlayer/layer/CustomLayer.java

@@ -27,6 +27,7 @@ import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.FeedForwardLayer;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
 import org.deeplearning4j.optimize.api.TrainingListener;
@@ -53,13 +54,14 @@ public class CustomLayer extends FeedForwardLayer {
     public org.deeplearning4j.nn.api.Layer instantiate(NeuralNetConfiguration conf,
                                                        Collection<TrainingListener> trainingListeners, int layerIndex, INDArray layerParamsView,
                                                        boolean initializeParams, DataType networkDataType) {
-        CustomLayerImpl ret = new CustomLayerImpl(conf, networkDataType);
+        LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        CustomLayerImpl ret = new CustomLayerImpl(lconf, networkDataType);
         ret.setListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);
         ret.setParamTable(paramTable);
-        ret.setLayerConfiguration(conf);
+        ret.setLayerConfiguration(lconf);
         return ret;
     }
 

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/test/java/org/deeplearning4j/spark/impl/customlayer/layer/CustomLayerImpl.java

@@ -21,11 +21,12 @@
 package org.deeplearning4j.spark.impl.customlayer.layer;
 
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.layers.BaseLayer;
 import org.nd4j.linalg.api.buffer.DataType;
 
 public class CustomLayerImpl extends BaseLayer<CustomLayer> {
-    public CustomLayerImpl(NeuralNetConfiguration conf, DataType dataType) {
+    public CustomLayerImpl(LayerConfiguration conf, DataType dataType) {
         super(conf, dataType);
     }
 

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/test/java/org/deeplearning4j/spark/impl/misc/TestFrozenLayers.java

@@ -154,7 +154,7 @@ public class TestFrozenLayers extends BaseSparkTest {
         ComputationGraph withFrozen = new TransferLearning.GraphBuilder(origModel).fineTuneConfiguration(finetune)
                         .setFeatureExtractor("1").build();
 
-        Map<String, INDArray> m = withFrozen.paramTable();
+        Map<String, INDArray> m = withFrozen.getParamTable();
         Map<String, INDArray> pCopy = new HashMap<>();
         for (Map.Entry<String, INDArray> entry : m.entrySet()) {
             pCopy.put(entry.getKey(), entry.getValue().dup());
@@ -190,7 +190,7 @@ public class TestFrozenLayers extends BaseSparkTest {
 
         ComputationGraph fitted = sNet.getNetwork();
 
-        Map<String, INDArray> fittedParams = fitted.paramTable();
+        Map<String, INDArray> fittedParams = fitted.getParamTable();
 
         for (Map.Entry<String, INDArray> entry : fittedParams.entrySet()) {
             INDArray orig = pCopy.get(entry.getKey());

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-core/src/test/java/org/deeplearning4j/spark/impl/paramavg/TestSparkMultiLayerParameterAveraging.java

@@ -784,13 +784,13 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
 
             JavaRDD<DataSet> rdd = sc.parallelize(list);
 
-            assertEquals(0, sparkNet.getNetwork().getConfiguration().getIterationCount());
+            assertEquals(0, sparkNet.getNetwork().getNetConfiguration().getIterationCount());
             sparkNet.fit(rdd);
             assertEquals(minibatchesPerWorkerPerEpoch,
-                            sparkNet.getNetwork().getConfiguration().getIterationCount());
+                            sparkNet.getNetwork().getNetConfiguration().getIterationCount());
             sparkNet.fit(rdd);
             assertEquals(2 * minibatchesPerWorkerPerEpoch,
-                            sparkNet.getNetwork().getConfiguration().getIterationCount());
+                            sparkNet.getNetwork().getNetConfiguration().getIterationCount());
 
             sparkNet.getTrainingMaster().deleteTempFiles(sc);
         }
@@ -1074,11 +1074,11 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
 
 
         for(int i=0; i<3; i++ ){
-            assertEquals(i, sn1.getNetwork().getConfiguration().getEpochCount());
+            assertEquals(i, sn1.getNetwork().getNetConfiguration().getEpochCount());
             assertEquals(i, sn2.getNetwork().getComputationGraphConfiguration().getEpochCount());
             sn1.fit(rdd);
             sn2.fit(rdd);
-            assertEquals(i+1, sn1.getNetwork().getConfiguration().getEpochCount());
+            assertEquals(i+1, sn1.getNetwork().getNetConfiguration().getEpochCount());
             assertEquals(i+1, sn2.getNetwork().getComputationGraphConfiguration().getEpochCount());
         }
     }

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-parameterserver/src/main/java/org/deeplearning4j/spark/parameterserver/pw/SharedTrainingWrapper.java

@@ -239,7 +239,7 @@ public class SharedTrainingWrapper {
 
                 List<TrainingListener> listeners = worker.getListeners();
                 if(listeners != null){
-                    model.setListeners(listeners);
+                    model.setListeners(listeners.toArray(new TrainingListener[]{}));
                     StatsStorageRouter r = worker.getRouter();
                     if(r != null){
                         for(TrainingListener l : listeners){
@@ -425,7 +425,7 @@ public class SharedTrainingWrapper {
                                         .setTrainingWorkspaceMode(trainingConfiguration.getWorkspaceMode());
                         ((ComputationGraph) originalModel).setGradientsAccumulator(accumulator);
                     } else if (model instanceof MultiLayerNetwork) {
-                        ((MultiLayerNetwork) originalModel).getConfiguration()
+                        ((MultiLayerNetwork) originalModel).getNetConfiguration()
                                         .setTrainingWorkspaceMode(trainingConfiguration.getWorkspaceMode());
                         ((MultiLayerNetwork) originalModel).setGradientsAccumulator(accumulator);
                     }

cavis-dnn/cavis-dnn-spark/cavis-dnn-spark-parameterserver/src/main/java/org/deeplearning4j/spark/parameterserver/training/SharedTrainingMaster.java

@@ -262,7 +262,7 @@ public class SharedTrainingMaster extends BaseTrainingMaster<SharedTrainingResul
         /*
             Here we're going create our worker, which will be passed into corresponding FlatMapFunction
          */
-        NetBroadcastTuple tuple = new NetBroadcastTuple(network.getNetwork().getConfiguration(),
+        NetBroadcastTuple tuple = new NetBroadcastTuple(network.getNetwork().getNetConfiguration(),
                         network.getNetwork().params(), network.getNetwork().getUpdater().getStateViewArray());
 
         voidConfiguration.setUnicastControllerPort(voidConfiguration.getPortSupplier().getPort());

cavis-dnn/cavis-dnn-tsne/src/main/java/org/deeplearning4j/plot/BarnesHutTsne.java

@@ -20,6 +20,7 @@ package org.deeplearning4j.plot;
 import com.google.common.util.concurrent.AtomicDouble;
 import lombok.AllArgsConstructor;
 import lombok.Data;
+import lombok.NonNull;
 import lombok.Setter;
 import lombok.extern.slf4j.Slf4j;
 import net.brutex.ai.dnn.api.IModel;
@@ -29,15 +30,21 @@ import org.deeplearning4j.clustering.sptree.SpTree;
 import org.deeplearning4j.clustering.vptree.VPTree;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.WorkspaceMode;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.gradient.DefaultGradient;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.deeplearning4j.optimize.api.ConvexOptimizer;
 import org.deeplearning4j.optimize.api.TrainingListener;
+import org.nd4j.evaluation.IEvaluation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.conf.WorkspaceConfiguration;
 import org.nd4j.linalg.api.memory.enums.*;
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.api.DataSet;
+import org.nd4j.linalg.dataset.api.MultiDataSet;
+import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
+import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.BooleanIndexing;
 import org.nd4j.linalg.indexing.conditions.Conditions;
@@ -302,32 +309,104 @@ public class BarnesHutTsne implements IModel {
         return x;
     }
 
+    /**
+     * This method returns updater state (if applicable), null otherwise
+     *
+     * @return
+     */
+    @Override
+    public INDArray updaterState() {
+        return null;
+    }
+
     @Override
     public ConvexOptimizer getOptimizer() {
         return null;
     }
 
+    /**
+     * This method fits model with a given DataSet
+     *
+     * @param dataSet
+     */
+    @Override
+    public void fit(DataSet dataSet) {
+
+    }
+
+    /**
+     * This method fits model with a given MultiDataSet
+     *
+     * @param dataSet
+     */
+    @Override
+    public void fit(MultiDataSet dataSet) {
+
+    }
+
+    /**
+     * This method fits model with a given DataSetIterator
+     *
+     * @param iterator
+     */
+    @Override
+    public void fit(DataSetIterator iterator) {
+
+    }
+
+    /**
+     * This method fits model with a given MultiDataSetIterator
+     *
+     * @param iterator
+     */
+    @Override
+    public void fit(MultiDataSetIterator iterator) {
+
+    }
+
+    /**
+     * This method executes evaluation of the model against given iterator and evaluation
+     * implementations
+     *
+     * @param iterator
+     * @param evaluations
+     */
+    @Override
+    public <T extends IEvaluation> T[] doEvaluation(DataSetIterator iterator, T... evaluations) {
+        return null;
+    }
+
+    /**
+     * This method executes evaluation of the model against given iterator and evaluation
+     * implementations
+     *
+     * @param iterator
+     * @param evaluations
+     */
+    @Override
+    public <T extends IEvaluation> T[] doEvaluation(MultiDataSetIterator iterator,
+        T... evaluations) {
+        return null;
+    }
+
     @Override
     public INDArray getParam(String param) {
         return null;
     }
 
     @Override
-    public void addListeners(TrainingListener... listener) {
+    public void addListeners(TrainingListener... listener) {//no op
-        // no-op
     }
 
-    @Override
+    public Map<String, INDArray> getParamTable() {
-    public Map<String, INDArray> paramTable() {
         return null;
     }
 
-    @Override
+    public Map<String, INDArray> getParamTable(boolean backprapParamsOnly) {
-    public Map<String, INDArray> paramTable(boolean backprapParamsOnly) {
         return null;
     }
 
-    @Override
+
     public void setParamTable(Map<String, INDArray> paramTable) {
 
     }
@@ -490,7 +569,7 @@ public class BarnesHutTsne implements IModel {
      *
      * @param listeners
      */
-    @Override
+
     public void setListeners(Collection<org.deeplearning4j.optimize.api.TrainingListener> listeners) {
 
     }
@@ -901,8 +980,15 @@ public class BarnesHutTsne implements IModel {
         return null;
     }
 
+    /**
+     * @param netConfiguration
+     */
     @Override
-    public void setLayerConfiguration(NeuralNetConfiguration layerConfiguration) {
+    public void setNetConfiguration(@NonNull NeuralNetConfiguration netConfiguration) {
+
+    }
+
+    public void setLayerConfiguration(LayerConfiguration layerConfiguration) {
 
     }
 
@@ -1060,4 +1146,14 @@ public class BarnesHutTsne implements IModel {
     public void close(){
         //No-op
     }
+
+    /**
+     * Get the TrainingListeners
+     *
+     * @return training listener
+     */
+    @Override
+    public Collection<TrainingListener> getListeners() {
+        return null;
+    }
 }

cavis-ui/cavis-ui-model/src/main/java/org/deeplearning4j/ui/model/stats/BaseStatsListener.java

@@ -30,6 +30,7 @@ import org.deeplearning4j.core.storage.StorageMetaData;
 import org.deeplearning4j.core.storage.listener.RoutingIterationListener;
 import org.deeplearning4j.nn.api.Layer;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
@@ -426,10 +427,10 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
                 //Need to append "0_", "1_" etc to param names from layers...
                 int layerIdx = 0;
                 for (Layer l : ((MultiLayerNetwork) model).getLayers()) {
-                    NeuralNetConfiguration conf = l.getNetConfiguration();
+                    LayerConfiguration conf = l.getLayerConfiguration();
                     List<String> paramkeys = l.getLayerConfiguration().initializer().paramKeys(l.getLayerConfiguration());
                     for (String s : paramkeys) {
-                        double lr = conf.getFirstLayer().getUpdaterByParam(s).getLearningRate(l.getIterationCount(), l.getEpochCount());
+                        double lr = conf.getUpdaterByParam(s).getLearningRate(l.getIterationCount(), l.getEpochCount());
                         if (Double.isNaN(lr)) {
                             //Edge case: No-Op updater, AdaDelta etc - don't have a LR hence return NaN for IUpdater.getLearningRate
                             lr = 0.0;
@@ -440,11 +441,11 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
                 }
             } else if (model instanceof ComputationGraph) {
                 for (Layer l : ((ComputationGraph) model).getLayers()) {
-                    NeuralNetConfiguration conf = l.getNetConfiguration();
+                    LayerConfiguration conf = l.getLayerConfiguration();
-                    String layerName = conf.getFirstLayer().getLayerName();
+                    String layerName = conf.getLayerName();
                     List<String> paramkeys = l.getLayerConfiguration().initializer().paramKeys(l.getLayerConfiguration());
                     for (String s : paramkeys) {
-                        double lr = conf.getFirstLayer().getUpdaterByParam(s).getLearningRate(l.getIterationCount(), l.getEpochCount());
+                        double lr = conf.getUpdaterByParam(s).getLearningRate(l.getIterationCount(), l.getEpochCount());
                         if (Double.isNaN(lr)) {
                             //Edge case: No-Op updater, AdaDelta etc - don't have a LR hence return NaN for IUpdater.getLearningRate
                             lr = 0.0;
@@ -467,7 +468,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
         //--- Histograms ---
 
         if (updateConfig.collectHistograms(StatsType.Parameters)) {
-            Map<String, Histogram> paramHistograms = getHistograms(model.paramTable(backpropParamsOnly),
+            Map<String, Histogram> paramHistograms = getHistograms(model.getParamTable(backpropParamsOnly),
                     updateConfig.numHistogramBins(StatsType.Parameters));
             report.reportHistograms(StatsType.Parameters, paramHistograms);
         }
@@ -490,7 +491,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
         //--- Summary Stats: Mean, Variance, Mean Magnitudes ---
 
         if (updateConfig.collectMean(StatsType.Parameters)) {
-            Map<String, Double> meanParams = calculateSummaryStats(model.paramTable(backpropParamsOnly), StatType.Mean);
+            Map<String, Double> meanParams = calculateSummaryStats(model.getParamTable(backpropParamsOnly), StatType.Mean);
             report.reportMean(StatsType.Parameters, meanParams);
         }
 
@@ -511,7 +512,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
 
         if (updateConfig.collectStdev(StatsType.Parameters)) {
             Map<String, Double> stdevParams =
-                    calculateSummaryStats(model.paramTable(backpropParamsOnly), StatType.Stdev);
+                    calculateSummaryStats(model.getParamTable(backpropParamsOnly), StatType.Stdev);
             report.reportStdev(StatsType.Parameters, stdevParams);
         }
 
@@ -532,7 +533,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
 
         if (updateConfig.collectMeanMagnitudes(StatsType.Parameters)) {
             Map<String, Double> meanMagParams =
-                    calculateSummaryStats(model.paramTable(backpropParamsOnly), StatType.MeanMagnitude);
+                    calculateSummaryStats(model.getParamTable(backpropParamsOnly), StatType.MeanMagnitude);
             report.reportMeanMagnitudes(StatsType.Parameters, meanMagParams);
         }
 
@@ -652,7 +653,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
             long numParams;
             if (model instanceof MultiLayerNetwork) {
                 MultiLayerNetwork net = ((MultiLayerNetwork) model);
-                jsonConf = net.getConfiguration().toJson();
+                jsonConf = net.getNetConfiguration().toJson();
                 numLayers = net.getnLayers();
                 numParams = net.numParams();
             } else if (model instanceof ComputationGraph) {
@@ -670,7 +671,7 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
                         + (model == null ? null : model.getClass()));
             }
 
-            Map<String, INDArray> paramMap = model.paramTable(backpropParamsOnly);
+            Map<String, INDArray> paramMap = model.getParamTable(backpropParamsOnly);
             String[] paramNames = new String[paramMap.size()];
             int i = 0;
             for (String s : paramMap.keySet()) { //Assuming sensible iteration order - LinkedHashMaps are used in MLN/CG for example

cavis-ui/cavis-ui-vertx/src/main/java/org/deeplearning4j/ui/module/train/TrainModule.java

@@ -1129,8 +1129,8 @@ public class TrainModule implements UIModule {
                     NeuralNetConfiguration conf = NeuralNetConfiguration.fromJson(configJson);
                     int confIdx = layerIdx - 1; //-1 because of input
                     if (confIdx >= 0) {
-                        nnc = conf.getNetConfigurations().get(confIdx);
+                        layer = conf.getFlattenedLayerConfigurations().get(confIdx);
-                        layer = nnc.getFirstLayer();
+                        nnc = layer.getNetConfiguration();
                     } else {
                         //Input layer
                         layerType = "Input";
@@ -1144,7 +1144,7 @@ public class TrainModule implements UIModule {
                     if (vertices.containsKey(vertexName) && vertices.get(vertexName) instanceof LayerVertex) {
                         LayerVertex lv = (LayerVertex) vertices.get(vertexName);
                         nnc = lv.getNetConfiguration();
-                        layer = nnc.getFirstLayer();
+                        layer = lv.getLayerConfiguration();
                     } else if (conf.getNetworkInputs().contains(vertexName)) {
                         layerType = "Input";
                     } else {
@@ -1177,7 +1177,7 @@ public class TrainModule implements UIModule {
                     if (layer instanceof BaseLayer) {
                         BaseLayer bl = (BaseLayer) layer;
                         activationFn = bl.getActivationFn().toString();
-                        long nParams = layer.initializer().numParams(nnc.getFirstLayer());
+                        long nParams = layer.initializer().numParams(bl.getLayer());
                         layerInfoRows.add(new String[]{i18N.getMessage("train.model.layerinfotable.layerNParams"),
                                 String.valueOf(nParams)});
                         if (nParams > 0) {

cavis-ui/cavis-ui-vertx/src/main/java/org/deeplearning4j/ui/module/train/TrainModuleUtils.java

@@ -62,24 +62,24 @@ public class TrainModuleUtils {
         layerInfo.add(Collections.emptyMap());
 
 
-        List<NeuralNetConfiguration> list = config.getNetConfigurations();
+        List<LayerConfiguration> list = config.getFlattenedLayerConfigurations();
         int layerIdx = 1;
-        for (NeuralNetConfiguration c : list) {
+        for (LayerConfiguration c : list) {
-            LayerConfiguration layer = c.getFirstLayer();
+            LayerConfiguration layer = c;
             String layerName = layer.getLayerName();
             if (layerName == null)
                 layerName = "layer" + layerIdx;
             vertexNames.add(layerName);
             originalVertexName.add(String.valueOf(layerIdx - 1));
 
-            String layerType = c.getFirstLayer().getClass().getSimpleName().replaceAll("Layer$", "");
+            String layerType = c.getClass().getSimpleName().replaceAll("Layer$", "");
             layerTypes.add(layerType);
 
             layerInputs.add(Collections.singletonList(layerIdx - 1));
             layerIdx++;
 
             //Extract layer info
-            Map<String, String> map = getLayerInfo(c, layer);
+            Map<String, String> map = getLayerInfo(c.getNetConfiguration(), layer);
             layerInfo.add(map);
         }
 

cavis-ui/cavis-ui-vertx/src/main/resources/templates/SameDiffUI.html

@@ -143,7 +143,7 @@
                     <label class="btn btn-secondary" onclick="setLayout('dagre')">
                         <input type="radio" name="options" id="option3" autocomplete="off">Alt</label>
                     <label class="btn btn-secondary" onclick="setLayout('cose-bilkent')">
-                        <input type="radio" name="options" id="option3" autocomplete="off">Spread</label>
+                        <input type="radio" name="options" id="option4" autocomplete="off">Spread</label>
                 </div>
                 <br>
                 <br>

cavis-zoo/cavis-zoo-models/src/test/java/org/deeplearning4j/zoo/TestUtils.java

@@ -45,7 +45,7 @@ public class TestUtils {
             ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
             MultiLayerNetwork restored = ModelSerializer.restoreMultiLayerNetwork(bais, true);
 
-            assertEquals(net.getConfiguration(), restored.getConfiguration());
+            assertEquals(net.getNetConfiguration(), restored.getNetConfiguration());
             assertEquals(net.params(), restored.params());
 
             return restored;