Playing with some new code

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

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

@@ -21,8 +21,19 @@
 
 package net.brutex.gan;
 
-import java.util.List;
+import java.awt.BorderLayout;
+import java.awt.Dimension;
+import java.awt.GridLayout;
+import java.awt.Image;
+import java.awt.image.BufferedImage;
+import java.io.File;
+import java.util.Arrays;
 import java.util.Random;
+import javax.swing.ImageIcon;
+import javax.swing.JFrame;
+import javax.swing.JLabel;
+import javax.swing.JPanel;
+import javax.swing.WindowConstants;
 import lombok.extern.slf4j.Slf4j;
 import org.apache.commons.lang3.ArrayUtils;
 import org.datavec.api.split.FileSplit;
@@ -34,20 +45,23 @@ import org.datavec.image.transform.PipelineImageTransform;
 import org.datavec.image.transform.ResizeImageTransform;
 import org.datavec.image.transform.ShowImageTransform;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.nn.conf.CacheMode;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import net.brutex.ai.dnn.conf.NeuralNetworkConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.*;
+import org.deeplearning4j.nn.conf.layers.ActivationLayer;
+import org.deeplearning4j.nn.conf.layers.DenseLayer;
+import org.deeplearning4j.nn.conf.layers.DropoutLayer;
+import org.deeplearning4j.nn.conf.layers.Layer;
+import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.conf.layers.misc.FrozenLayerWithBackprop;
-import org.deeplearning4j.nn.conf.layers.wrapper.BuildingBlockLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
+import org.deeplearning4j.nn.weights.WeightInitXavier;
 import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.activations.impl.ActivationIdentity;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
@@ -55,13 +69,6 @@ import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.learning.config.IUpdater;
-
-
-import javax.swing.*;
-import java.awt.*;
-import java.awt.image.BufferedImage;
-import java.io.File;
-import java.util.Arrays;
 import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
 
 @Slf4j
@@ -106,7 +113,7 @@ public class App {
    * @return config
    */
   private static MultiLayerConfiguration generator() {
-    MultiLayerConfiguration confxx = new NeuralNetConfiguration.Builder()
+    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
         .seed(42)
         .updater(UPDATER)
         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
@@ -117,23 +124,8 @@ public class App {
         .setInputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
        // .inputPreProcessor("CNN1", new FeedForwardToCnnPreProcessor(Y_DIM, X_DIM, CHANNELS))
         .build();
-    log.debug("Generator network: \n{}", confxx.toJson());
 
-    NeuralNetworkConfiguration conf2 = NeuralNetworkConfiguration.builder().build();
+   return conf;
-
-    NeuralNetworkConfiguration confx = NeuralNetworkConfiguration.builder()
-        .cacheMode(CacheMode.HOST)
-        .layer( new DenseLayer.Builder().build())
-        .layer( new DenseLayer.Builder().build())
-        .layer( BuildingBlockLayer.builder().build())
-        .layers( List.of(genLayers()))
-        .inputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
-        .build();
-
-
-
-
-    return confx;
   }
 
   private static Layer[] disLayers() {
@@ -155,6 +147,7 @@ public class App {
   }
 
   private static MultiLayerConfiguration discriminator() {
+
     MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
         .seed(42)
         .updater(UPDATER)
@@ -183,13 +176,13 @@ public class App {
 
     MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
         .seed(42)
-        .updater(UPDATER)
+        .updater( Adam.builder().learningRate(0.0002).beta1(0.5).build() )
-        .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
+        .gradientNormalization( GradientNormalization.RenormalizeL2PerLayer)
-        .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
+        .gradientNormalizationThreshold( 100 )
-        .weightInit(WeightInit.XAVIER)
+        .weightInit( new WeightInitXavier() )
-        .activation(Activation.IDENTITY)
+        .activation( new ActivationIdentity())
-        .list(layers)
+        .list(  layers  )
-        .setInputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
+        .setInputType( InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
         .build();
 
     return conf;

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

@@ -295,7 +295,7 @@ public class BrianTest extends BaseSparkSessionTest {
             .activation(Activation.RELU).l2(0.001).build())
         .layer(1, new DenseLayer.Builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER)
             .activation(Activation.RELU).build())
-        //.layer(2, new DenseLayer.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
+        //.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
         .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4)
             .weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build())
         .build();

brutex-extended-tests/src/test/java/net/brutex/spark/BrianTest2.java

@@ -301,7 +301,7 @@ public class BrianTest2 /*extends BaseDL4JTest*/ {
                 .list()
                     .layer(0, new DenseLayer.Builder().nIn(5).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).l2(0.001).build())
                     .layer(1, new DenseLayer.Builder().nIn(20).nOut(20).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
-                    //.layer(2, new DenseLayer.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
+                    //.layer(2, new DenseLayerConfiguration.Builder().nIn(9).nOut(9).weightInit(WeightInit.XAVIER).activation(Activation.RELU).build())
                     .layer(2, new OutputLayer.Builder(LossFunctions.LossFunction.XENT).nIn(20).nOut(4).weightInit(WeightInit.XAVIER).activation(Activation.SIGMOID).build())
                 .build();
 

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

@@ -95,7 +95,7 @@ public class TestServer {
                 .list()
                 //.layer(0, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build())
                //.layer(1, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build())
-              //  .layer(1, new DenseLayer.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
+              //  .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
                 .layer(0, new DenseLayer.Builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
                 .layer(1, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build())
                 .layer(2, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build())

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

@@ -131,7 +131,7 @@ public class TestServer2 {
                 .list()
                 //.layer(0, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 5).stride(1,1).padding(0,2).nOut(1).name("1st Filter").updater(new Adam.Builder().learningRate(0.2).build()).build())
                //.layer(1, new ConvolutionLayer.Builder().nIn(1).kernelSize(1, 2).stride(1,2).padding(0,0).nOut(1).name("2nd Filter").updater(new Adam.Builder().learningRate(0.1).build()).build())
-              //  .layer(1, new DenseLayer.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
+              //  .layer(1, new DenseLayerConfiguration.Builder().nIn(10).nOut(64).activation(Activation.RELU).build())
                 .layer(0, new DenseLayer.Builder().nIn(10).nOut(100).activation(Activation.RELU).l2(0.003).build())
                 .layer(1, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build())
                 .layer(2, new LSTM.Builder().nIn(100).nOut(100).activation(Activation.TANH).build())

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

@@ -284,7 +284,7 @@ public class IntegrationTestBaselineGenerator {
                 INDArray paramsPostTraining;
                 if (modelType == ModelType.MLN) {
                     int[] layersToTrain = tc.getUnsupervisedTrainLayersMLN();
-                    Preconditions.checkState(layersToTrain != null, "Layer indices must not be null");
+                    Preconditions.checkState(layersToTrain != null, "ILayer indices must not be null");
                     DataSetIterator dsi = new MultiDataSetWrapperIterator(iter);
 
                     for (int i : layersToTrain) {
@@ -293,7 +293,7 @@ public class IntegrationTestBaselineGenerator {
                     paramsPostTraining = mln.params();
                 } else if (modelType == ModelType.CG) {
                     String[] layersToTrain = tc.getUnsupervisedTrainLayersCG();
-                    Preconditions.checkState(layersToTrain != null, "Layer names must not be null");
+                    Preconditions.checkState(layersToTrain != null, "ILayer names must not be null");
 
                     for (String i : layersToTrain) {
                         cg.pretrainLayer(i, iter);

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

@@ -200,7 +200,7 @@ public class IntegrationTestRunner {
                 m = cg;
 
                 ComputationGraph loaded = ComputationGraph.load(savedModel, true);
-                assertEquals(loaded.getConfiguration(), cg.getConfiguration(), "Configs not equal" );
+                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");
             } else if(config instanceof SameDiff){
@@ -383,7 +383,7 @@ public class IntegrationTestRunner {
             org.deeplearning4j.nn.api.Layer[] layers;
             if(modelType == ModelType.MLN){
                 int[] layersToTrain = tc.getUnsupervisedTrainLayersMLN();
-                Preconditions.checkState(layersToTrain != null, "Layer indices must not be null");
+                Preconditions.checkState(layersToTrain != null, "ILayer indices must not be null");
                 DataSetIterator dsi = new MultiDataSetWrapperIterator(iter);
 
                 for( int i : layersToTrain){
@@ -393,7 +393,7 @@ public class IntegrationTestRunner {
                 layers = mln.getLayers();
             } else if(modelType == ModelType.CG) {
                 String[] layersToTrain = tc.getUnsupervisedTrainLayersCG();
-                Preconditions.checkState(layersToTrain != null, "Layer names must not be null");
+                Preconditions.checkState(layersToTrain != null, "ILayer names must not be null");
 
                 for( String i : layersToTrain){
                     cg.pretrainLayer(i, iter);
@@ -429,8 +429,8 @@ public class IntegrationTestRunner {
                 isTbptt = mln.getLayerWiseConfigurations().getBackpropType() == BackpropType.TruncatedBPTT;
                 tbpttLength = mln.getLayerWiseConfigurations().getTbpttFwdLength();
             } else if(modelType == ModelType.CG) {
-                isTbptt = cg.getConfiguration().getBackpropType() == BackpropType.TruncatedBPTT;
+                isTbptt = cg.getComputationGraphConfiguration().getBackpropType() == BackpropType.TruncatedBPTT;
-                tbpttLength = cg.getConfiguration().getTbpttFwdLength();
+                tbpttLength = cg.getComputationGraphConfiguration().getTbpttFwdLength();
             } else {
                 isTbptt = false;
                 tbpttLength = 0;
@@ -458,11 +458,11 @@ public class IntegrationTestRunner {
                 epochAfter = mln.getEpochCount();
                 layers = mln.getLayers();
             } else if(modelType == ModelType.CG){
-                iterBefore = cg.getConfiguration().getIterationCount();
+                iterBefore = cg.getComputationGraphConfiguration().getIterationCount();
-                epochBefore = cg.getConfiguration().getEpochCount();
+                epochBefore = cg.getComputationGraphConfiguration().getEpochCount();
                 cg.fit(countingIter);
-                iterAfter = cg.getConfiguration().getIterationCount();
+                iterAfter = cg.getComputationGraphConfiguration().getIterationCount();
-                epochAfter = cg.getConfiguration().getEpochCount();
+                epochAfter = cg.getComputationGraphConfiguration().getEpochCount();
                 layers = cg.getLayers();
             } else {
                 iterBefore = sd.getTrainingConfig().getIterationCount();
@@ -611,7 +611,7 @@ public class IntegrationTestRunner {
             } else if(modelType == ModelType.CG){
                 ModelSerializer.writeModel(m, f, true);
                 ComputationGraph restored = ComputationGraph.load(f, true);
-                assertEquals(cg.getConfiguration(), restored.getConfiguration());
+                assertEquals(cg.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
                 assertEquals(cg.params(), restored.params());
             } else {
                 sd.save(f, true);
@@ -745,7 +745,7 @@ public class IntegrationTestRunner {
             preProcessors = mln.getLayerWiseConfigurations().getInputPreProcessors().values();
         } else {
             preProcessors = new ArrayList<>();
-            for (org.deeplearning4j.nn.conf.graph.GraphVertex gv : cg.getConfiguration().getVertices().values()) {
+            for (org.deeplearning4j.nn.conf.graph.GraphVertex gv : cg.getComputationGraphConfiguration().getVertices().values()) {
                 if (gv instanceof LayerVertex) {
                     InputPreProcessor pp = ((LayerVertex) gv).getPreProcessor();
                     if (pp != null) {
@@ -760,7 +760,7 @@ public class IntegrationTestRunner {
 
         //Collect vertex coverage information
         if (!isMLN) {
-            for (org.deeplearning4j.nn.conf.graph.GraphVertex gv : cg.getConfiguration().getVertices().values()) {
+            for (org.deeplearning4j.nn.conf.graph.GraphVertex gv : cg.getComputationGraphConfiguration().getVertices().values()) {
                 vertexConfClassesSeen.put(gv.getClass(), vertexConfClassesSeen.getOrDefault(gv.getClass(), 0) + 1);
             }
         }
@@ -872,14 +872,14 @@ public class IntegrationTestRunner {
 
         log.info("||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||");
 
-        log.info("Layer coverage - classes seen:");
+        log.info("ILayer coverage - classes seen:");
         for (Class<?> c : layerClasses) {
             if (layerConfClassesSeen.containsKey(c)) {
                 log.info("Class seen {} times in tests: {}", layerConfClassesSeen.get(c), c.getName());
             }
         }
 
-        log.info("Layer classes NOT seen in any tests:");
+        log.info("ILayer classes NOT seen in any tests:");
         for (Class<?> c : layerClasses) {
             if (!layerConfClassesSeen.containsKey(c)) {
                 log.info("Class NOT seen in any tests: {}", c.getName());

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

@@ -73,7 +73,7 @@ public class TestUtils {
             ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
             restored = ModelSerializer.restoreComputationGraph(bais, true);
 
-            assertEquals(net.getConfiguration(), restored.getConfiguration());
+            assertEquals(net.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
             assertEquals(net.params(), restored.params());
         } catch (IOException e){
             //Should never happen
@@ -81,7 +81,7 @@ public class TestUtils {
         }
 
         //Also check the ComputationGraphConfiguration is serializable (required by Spark etc)
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         serializeDeserializeJava(conf);
 
         return restored;

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

@@ -90,7 +90,7 @@ public class TestUtils {
             ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
             restored = ModelSerializer.restoreComputationGraph(bais, true);
 
-            assertEquals(net.getConfiguration(), restored.getConfiguration());
+            assertEquals(net.getComputationGraphConfiguration(), restored.getComputationGraphConfiguration());
             assertEquals(net.params(), restored.params());
         } catch (IOException e){
             //Should never happen
@@ -98,7 +98,7 @@ public class TestUtils {
         }
 
         //Also check the ComputationGraphConfiguration is serializable (required by Spark etc)
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         serializeDeserializeJava(conf);
 
         return restored;

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

@@ -626,7 +626,7 @@ public class EvalTest extends BaseDL4JTest {
         net.evaluate(iter);
         net.evaluateROCMultiClass(iter, 0);
 
-        cg.getConfiguration().setValidateOutputLayerConfig(false);
+        cg.getComputationGraphConfiguration().setValidateOutputLayerConfig(false);
         cg.evaluate(iter);
         cg.evaluateROCMultiClass(iter, 0);
     }

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

@@ -90,7 +90,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
             mln.init();
 
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
 
             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -135,7 +135,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
             mln.init();
 
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
 
             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -237,7 +237,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                                     + ", outputActivation=" + outputActivation + ", doLearningFirst="
                                     + doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
 //                            for (int k = 0; k < mln.getnLayers(); k++)
-//                                System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
+//                                System.out.println("ILayer " + k + " # params: " + mln.getLayer(k).numParams());
 
                             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
                             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -341,7 +341,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                                     + ", outputActivation=" + outputActivation + ", doLearningFirst="
                                     + doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
 //                            for (int k = 0; k < mln.getnLayers(); k++)
-//                                System.out.println("Layer " + k + " # params: " + mln.getLayer(k).numParams());
+//                                System.out.println("ILayer " + k + " # params: " + mln.getLayer(k).numParams());
 
                             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
                             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -385,7 +385,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
             mln.init();
 
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
 
             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -430,7 +430,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
             mln.init();
 
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
 
             //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
             //i.e., runningMean = decay * runningMean + (1-decay) * batchMean
@@ -572,7 +572,7 @@ public class BNGradientCheckTest extends BaseDL4JTest {
                                 + ", outputActivation=" + outputActivation + ", doLearningFirst="
                                 + doLearningFirst + ", l1=" + l1vals[j] + ", l2=" + l2vals[j]);
 //                        for (int k = 0; k < net.getNumLayers(); k++)
-//                            System.out.println("Layer " + k + " # params: " + net.getLayer(k).numParams());
+//                            System.out.println("ILayer " + k + " # params: " + net.getLayer(k).numParams());
 
                         //Mean and variance vars are not gradient checkable; mean/variance "gradient" is used to implement running mean/variance calc
                         //i.e., runningMean = decay * runningMean + (1-decay) * batchMean

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN1DGradientCheckTest.java

@@ -118,7 +118,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
 //                        for (int j = 0; j < net.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -198,7 +198,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             System.out.println(msg);
 //                            for (int j = 0; j < net.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -282,7 +282,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             System.out.println(msg);
 //                            for (int j = 0; j < net.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -359,7 +359,7 @@ public class CNN1DGradientCheckTest extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             System.out.println(msg);
 //                            for (int j = 0; j < net.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

cavis-dnn/cavis-dnn-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java

@@ -149,7 +149,7 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                                             if (PRINT_RESULTS) {
                                                 log.info(msg);
 //                                                for (int j = 0; j < net.getnLayers(); j++) {
-//                                                    log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                                                    log.info("ILayer " + j + " # params: " + net.getLayer(j).numParams());
 //                                                }
                                             }
 
@@ -252,7 +252,7 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         log.info(msg);
 //                        for (int j = 0; j < net.getnLayers(); j++) {
-//                            log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            log.info("ILayer " + j + " # params: " + net.getLayer(j).numParams());
 //                        }
                     }
 
@@ -431,7 +431,7 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             log.info(msg);
 //                            for (int j = 0; j < net.getnLayers(); j++) {
-//                                log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                                log.info("ILayer " + j + " # params: " + net.getLayer(j).numParams());
 //                            }
                         }
 
@@ -530,7 +530,7 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         log.info(msg);
 //                        for (int j = 0; j < net.getnLayers(); j++) {
-//                            log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            log.info("ILayer " + j + " # params: " + net.getLayer(j).numParams());
 //                        }
                     }
 

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

@@ -137,7 +137,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                         System.out.println(name + " - activationFn=" + afn + ", lossFn=" + lf + ", outputActivation="
                                 + outputActivation + ", doLearningFirst=" + doLearningFirst);
 //                        for (int j = 0; j < mln.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -231,7 +231,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                         + ", outputActivation=" + outputActivation + ", doLearningFirst="
                         + doLearningFirst);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -293,7 +293,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(msg);
 //                    for (int j = 0; j < net.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                 }
                 boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
                         DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@@ -361,7 +361,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
 //                        for (int j = 0; j < net.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                     }
                     boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
                             DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, input, labels);
@@ -427,7 +427,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < net.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -500,7 +500,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println(msg);
 //                        for (int j = 0; j < net.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -920,7 +920,7 @@ public class CNNGradientCheckTest extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < net.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

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

@@ -95,7 +95,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
                     System.out.println("testLSTMGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = "
                                     + miniBatchSize);
 //                    for (int j = 0; j < mln.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -156,7 +156,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println("testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize + " - " + (nchw ? "NCHW" : "NHWC"));
 //                    for (int j = 0; j < mln.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -216,7 +216,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println("testLSTMGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = " + miniBatchSize);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
@@ -299,7 +299,7 @@ public class GlobalPoolingGradientCheckTests extends BaseDL4JTest {
                         System.out.println("testCnnGlobalPoolingBasicMultiLayer() - " + pt + ", minibatch = "
                                         + miniBatchSize);
 //                        for (int j = 0; j < mln.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)

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

@@ -123,7 +123,7 @@ public class GradientCheckTests extends BaseDL4JTest {
                     + lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
                     + doLearningFirst);
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -203,7 +203,7 @@ public class GradientCheckTests extends BaseDL4JTest {
                                         + lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
                                         + doLearningFirst);
 //                        for (int j = 0; j < mln.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -297,7 +297,7 @@ public class GradientCheckTests extends BaseDL4JTest {
                                             + ", lossFn=" + lf + ", outputActivation=" + outputActivation
                                             + ", doLearningFirst=" + doLearningFirst + ", l2=" + l2 + ", l1=" + l1);
 //                            for (int j = 0; j < mln.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -342,7 +342,7 @@ public class GradientCheckTests extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testEmbeddingLayerSimple");
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -382,7 +382,7 @@ public class GradientCheckTests extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testEmbeddingLayerSimple");
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -472,7 +472,7 @@ public class GradientCheckTests extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             System.out.println(msg);
 //                            for (int j = 0; j < mln.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -714,7 +714,7 @@ public class GradientCheckTests extends BaseDL4JTest {
         // (a) activation function
         // (b) Whether to test at random initialization, or after some learning (i.e., 'characteristic mode of operation')
         // (c) Loss function (with specified output activations)
-        // (d) Layer Normalization enabled / disabled
+        // (d) ILayer Normalization enabled / disabled
         Activation[] activFns = {Activation.SIGMOID, Activation.TANH};
         boolean[] characteristic = {true, false}; //If true: run some backprop steps first
 
@@ -776,7 +776,7 @@ public class GradientCheckTests extends BaseDL4JTest {
                                     + lf + ", outputActivation=" + outputActivation + ", doLearningFirst="
                                     + doLearningFirst + ", layerNorm=" + layerNorm);
 //                            for (int j = 0; j < mln.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                         }
 
                         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

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

@@ -106,7 +106,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testBasicIris()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -157,7 +157,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testBasicIrisWithMerging()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -214,7 +214,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println("testBasicIrisWithElementWiseVertex(op=" + op + ")");
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -274,7 +274,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println("testBasicIrisWithElementWiseVertex(op=" + op + ")");
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -376,7 +376,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -439,7 +439,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -478,7 +478,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testLSTMWithSubset()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -515,7 +515,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testLSTMWithLastTimeStepVertex()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         //First: test with no input mask array
@@ -579,7 +579,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testLSTMWithDuplicateToTimeSeries()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input1, input2})
@@ -628,7 +628,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testLSTMWithReverseTimeSeriesVertex()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -683,7 +683,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(inputs)
@@ -723,7 +723,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -769,7 +769,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(input)
@@ -820,7 +820,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{input})
@@ -888,7 +888,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testBasicIrisTripletStackingL2Loss()");
 //            for (int j = 0; j < graph.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{pos, anc, neg})
@@ -949,7 +949,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(msg);
 //                    for (int j = 0; j < graph.getNumLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{example})
@@ -1014,7 +1014,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(msg);
 //                    for (int j = 0; j < net.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + net.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + net.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -1063,7 +1063,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
@@ -1121,7 +1121,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
@@ -1179,7 +1179,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
@@ -1242,7 +1242,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             graph.setLayerMaskArrays(new INDArray[] {inMask1, inMask2}, null);
@@ -1301,7 +1301,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1, in2})
@@ -1347,7 +1347,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1})
@@ -1398,7 +1398,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < graph.getNumLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + graph.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + graph.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(graph).inputs(new INDArray[]{in1})
@@ -1436,7 +1436,7 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         if (PRINT_RESULTS) {
             System.out.println("testGraphEmbeddingLayerSimple");
 //            for (int j = 0; j < cg.getNumLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + cg.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + cg.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.GraphConfig().net(cg).inputs(new INDArray[]{input})

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

@@ -84,7 +84,7 @@ public class LRNGradientCheckTests extends BaseDL4JTest {
 
 //        if (PRINT_RESULTS) {
 //            for (int j = 0; j < mln.getnLayers(); j++)
-//                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
 //        }
 
         boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

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

@@ -126,7 +126,7 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(testName);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -215,7 +215,7 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(testName);
 //                    for (int j = 0; j < mln.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(new GradientCheckUtil.MLNConfig().net(mln).input(input)
@@ -343,7 +343,7 @@ public class LSTMGradientCheckTests extends BaseDL4JTest {
                             + ", lossFn=" + lf + ", outputActivation=" + outputActivation + ", l2=" + l2
                             + ", l1=" + l1);
 //                    for (int j = 0; j < mln.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                 }
 
                 boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

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

@@ -78,7 +78,7 @@ public class NoBiasGradientCheckTests extends BaseDL4JTest {
 
                                     .dist(new NormalDistribution(0, 1))
                                     .activation(Activation.TANH)
-                                    .hasBias(true)  //Layer 0: Always have a bias
+                                    .hasBias(true)  //ILayer 0: Always have a bias
                                     .build())
                             .layer(1, new DenseLayer.Builder().nIn(layerSize).nOut(layerSize)
 

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

@@ -137,7 +137,7 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
                 if (PRINT_RESULTS) {
                     System.out.println(testName);
 //                    for (int j = 0; j < mln.getnLayers(); j++)
-//                        System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                        System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                 }
 
                 System.out.println("Starting test: " + testName);
@@ -244,7 +244,7 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
                     if (PRINT_RESULTS) {
                         System.out.println(testName);
 //                        for (int j = 0; j < mln.getnLayers(); j++)
-//                            System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                            System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                     }
 
                     System.out.println("Starting test: " + testName);
@@ -393,7 +393,7 @@ public class OutputLayerGradientChecks extends BaseDL4JTest {
                         if (PRINT_RESULTS) {
                             System.out.println(testName);
 //                            for (int j = 0; j < mln.getnLayers(); j++)
-//                                System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                                System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
                         }
 
                         System.out.println("Starting test: " + testName);

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

@@ -124,7 +124,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradients(mln, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
@@ -195,7 +195,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int l = 0; l < mln.getnLayers(); l++)
-//                    System.out.println("Layer " + l + " # params: " + mln.getLayer(l).numParams());
+//                    System.out.println("ILayer " + l + " # params: " + mln.getLayer(l).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,
@@ -283,7 +283,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,
@@ -325,7 +325,7 @@ public class VaeGradientCheckTests extends BaseDL4JTest {
             if (PRINT_RESULTS) {
                 System.out.println(msg);
 //                for (int j = 0; j < mln.getnLayers(); j++)
-//                    System.out.println("Layer " + j + " # params: " + mln.getLayer(j).numParams());
+//                    System.out.println("ILayer " + j + " # params: " + mln.getLayer(j).numParams());
             }
 
             boolean gradOK = GradientCheckUtil.checkGradientsPretrainLayer(layer, DEFAULT_EPS,

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

@@ -133,8 +133,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         //Learning rate without layerwise override:
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(0.3).list()
-                        .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -143,8 +143,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         //With:
         conf = new NeuralNetConfiguration.Builder().learningRate(0.3).list()
-                        .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).learningRate(0.2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).learningRate(0.2).build()).build();
 
         net = new MultiLayerNetwork(conf);
         net.init();
@@ -154,8 +154,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         //L1 and L2 without layerwise override:
         conf = new NeuralNetConfiguration.Builder().l1(0.1).l2(0.2).list()
-                        .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -166,8 +166,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         //L1 and L2 with layerwise override:
         conf = new NeuralNetConfiguration.Builder().l1(0.1).l2(0.2).list()
-                        .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).l1(0.9).build())
+                        .layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).l1(0.9).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).l2(0.8).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).l2(0.8).build()).build();
         net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -326,8 +326,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().learningRate(lr)
                         .updater(Updater.SGD)
                         .learningRateDecayPolicy(LearningRatePolicy.Exponential).lrPolicyDecayRate(lrDecayRate).list()
-                        .layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -345,8 +345,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         int iterations = 1;
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(iterations).learningRate(lr)
                         .learningRateDecayPolicy(LearningRatePolicy.Inverse).lrPolicyDecayRate(lrDecayRate)
-                        .lrPolicyPower(power).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .lrPolicyPower(power).list().layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -367,8 +367,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         int iterations = 1;
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(iterations).learningRate(lr)
                         .learningRateDecayPolicy(LearningRatePolicy.Step).lrPolicyDecayRate(lrDecayRate)
-                        .lrPolicySteps(steps).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .lrPolicySteps(steps).list().layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -388,8 +388,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         int iterations = 1;
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(iterations).learningRate(lr)
                         .learningRateDecayPolicy(LearningRatePolicy.Poly).lrPolicyDecayRate(lrDecayRate)
-                        .lrPolicyPower(power).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .lrPolicyPower(power).list().layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
@@ -409,8 +409,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         int iterations = 1;
         MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().iterations(iterations).learningRate(lr)
                         .learningRateDecayPolicy(LearningRatePolicy.Sigmoid).lrPolicyDecayRate(lrDecayRate)
-                        .lrPolicySteps(steps).list().layer(0, new DenseLayer.Builder().nIn(2).nOut(2).build())
+                        .lrPolicySteps(steps).list().layer(0, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build())
-                        .layer(1, new DenseLayer.Builder().nIn(2).nOut(2).build()).build();
+                        .layer(1, new DenseLayerConfiguration.Builder().nIn(2).nOut(2).build()).build();
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 

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

@@ -229,7 +229,7 @@ public class DTypeTests extends BaseDL4JTest {
         if (seenLayers.size() < layerClasses.size()) {
             for (Class<?> c : layerClasses) {
                 if (!seenLayers.contains(c) && !ignoreClasses.contains(c)) {
-                    log.warn("Layer class not tested for global vs. network datatypes: {}", c);
+                    log.warn("ILayer class not tested for global vs. network datatypes: {}", c);
                     fail = true;
                 }
             }
@@ -279,7 +279,7 @@ public class DTypeTests extends BaseDL4JTest {
     }
 
     public static void logUsedClasses(ComputationGraph net) {
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         for (GraphVertex gv : conf.getVertices().values()) {
             seenVertices.add(gv.getClass());
             if (gv instanceof LayerVertex) {

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

@@ -65,7 +65,7 @@ public class ComputationGraphTestRNN extends BaseDL4JTest {
         Nd4j.getRandom().setSeed(12345);
         int timeSeriesLength = 12;
 
-        //4 layer network: 2 GravesLSTM + DenseLayer + RnnOutputLayer. Hence also tests preprocessors.
+        //4 layer network: 2 GravesLSTM + DenseLayerConfiguration + RnnOutputLayer. Hence also tests preprocessors.
         ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).graphBuilder()
                 .addInputs("in")
                 .addLayer("0", new org.deeplearning4j.nn.conf.layers.GravesLSTM.Builder().nIn(5).nOut(7)
@@ -208,7 +208,7 @@ public class ComputationGraphTestRNN extends BaseDL4JTest {
         Nd4j.getRandom().setSeed(12345);
         int timeSeriesLength = 12;
 
-        //4 layer network: 2 GravesLSTM + DenseLayer + RnnOutputLayer. Hence also tests preprocessors.
+        //4 layer network: 2 GravesLSTM + DenseLayerConfiguration + RnnOutputLayer. Hence also tests preprocessors.
         //Network architecture: lstm0 -> Dense -> RnnOutputLayer0
         // and lstm1 -> Dense -> RnnOutputLayer1
         ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).graphBuilder()
@@ -391,9 +391,9 @@ public class ComputationGraphTestRNN extends BaseDL4JTest {
         graphTBPTT.init();
         graphTBPTT.clearTbpttState = false;
 
-        assertEquals(BackpropType.TruncatedBPTT, graphTBPTT.getConfiguration().getBackpropType());
+        assertEquals(BackpropType.TruncatedBPTT, graphTBPTT.getComputationGraphConfiguration().getBackpropType());
-        assertEquals(timeSeriesLength, graphTBPTT.getConfiguration().getTbpttFwdLength());
+        assertEquals(timeSeriesLength, graphTBPTT.getComputationGraphConfiguration().getTbpttFwdLength());
-        assertEquals(timeSeriesLength, graphTBPTT.getConfiguration().getTbpttBackLength());
+        assertEquals(timeSeriesLength, graphTBPTT.getComputationGraphConfiguration().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/graph/TestCompGraphUnsupervised.java

@@ -42,7 +42,6 @@ import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.conditions.Conditions;
 import org.nd4j.linalg.learning.config.Adam;
 
-import java.util.Arrays;
 import java.util.HashMap;
 import java.util.Map;
 
@@ -168,8 +167,8 @@ public class TestCompGraphUnsupervised extends BaseDL4JTest {
             net.init();
 
             ComputationGraph cg = net.toComputationGraph();
-            cg.getConfiguration().setInferenceWorkspaceMode(wsm);
+            cg.getComputationGraphConfiguration().setInferenceWorkspaceMode(wsm);
-            cg.getConfiguration().setTrainingWorkspaceMode(wsm);
+            cg.getComputationGraphConfiguration().setTrainingWorkspaceMode(wsm);
             DataSetIterator ds = new EarlyTerminationDataSetIterator(new MnistDataSetIterator(1, true, 12345), 1);
             Nd4j.getRandom().setSeed(12345);
             net.pretrainLayer(0, ds);

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

@@ -1033,15 +1033,15 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         DataSetIterator iter = new IrisDataSetIterator(50, 150);
 
-        assertEquals(0, network.getConfiguration().getIterationCount());
+        assertEquals(0, network.getComputationGraphConfiguration().getIterationCount());
         network.fit(iter);
-        assertEquals(3, network.getConfiguration().getIterationCount());
+        assertEquals(3, network.getComputationGraphConfiguration().getIterationCount());
         iter.reset();
         network.fit(iter);
-        assertEquals(6, network.getConfiguration().getIterationCount());
+        assertEquals(6, network.getComputationGraphConfiguration().getIterationCount());
         iter.reset();
         network.fit(iter.next());
-        assertEquals(7, network.getConfiguration().getIterationCount());
+        assertEquals(7, network.getComputationGraphConfiguration().getIterationCount());
 
         ByteArrayOutputStream baos = new ByteArrayOutputStream();
         ModelSerializer.writeModel(network, baos, true);
@@ -1049,7 +1049,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
 
         ByteArrayInputStream bais = new ByteArrayInputStream(asBytes);
         ComputationGraph net = ModelSerializer.restoreComputationGraph(bais, true);
-        assertEquals(7, net.getConfiguration().getIterationCount());
+        assertEquals(7, net.getComputationGraphConfiguration().getIterationCount());
     }
 
     @Test
@@ -1272,18 +1272,18 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         ComputationGraph net = new ComputationGraph(conf);
         net.init();
 
-        assertEquals(0, net.getConfiguration().getEpochCount());
+        assertEquals(0, net.getComputationGraphConfiguration().getEpochCount());
 
 
         DataSetIterator iter = new IrisDataSetIterator(150, 150);
 
         for( int i=0; i<4; i++ ){
-            assertEquals(i, net.getConfiguration().getEpochCount());
+            assertEquals(i, net.getComputationGraphConfiguration().getEpochCount());
             net.fit(iter);
-            assertEquals(i+1, net.getConfiguration().getEpochCount());
+            assertEquals(i+1, net.getComputationGraphConfiguration().getEpochCount());
         }
 
-        assertEquals(4, net.getConfiguration().getEpochCount());
+        assertEquals(4, net.getComputationGraphConfiguration().getEpochCount());
 
         ByteArrayOutputStream baos = new ByteArrayOutputStream();
 
@@ -1293,7 +1293,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         ByteArrayInputStream bais = new ByteArrayInputStream(bytes);
 
         ComputationGraph restored = ModelSerializer.restoreComputationGraph(bais, true);
-        assertEquals(4, restored.getConfiguration().getEpochCount());
+        assertEquals(4, restored.getComputationGraphConfiguration().getEpochCount());
     }
 
     @Test
@@ -1619,13 +1619,13 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         GraphIndices indices = cg.calculateIndices();
 
         int[] order = cg.topologicalSortOrder();
-        List<String> strOrder = cg.getConfiguration().getTopologicalOrderStr();
+        List<String> strOrder = cg.getComputationGraphConfiguration().getTopologicalOrderStr();
         INDArray[] out1 = cg.output(in);
 
         //Check it's the same after loading:
         ComputationGraph cg2 = TestUtils.testModelSerialization(cg);
         int[] order2 = cg2.topologicalSortOrder();
-        List<String> strOrder2 = cg.getConfiguration().getTopologicalOrderStr();
+        List<String> strOrder2 = cg.getComputationGraphConfiguration().getTopologicalOrderStr();
         assertArrayEquals(order, order2);
         assertEquals(strOrder, strOrder2);
 
@@ -1633,7 +1633,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         assertArrayEquals(out1, out2);
 
         //Delete the topological order, ensure it gets recreated properly:
-        ComputationGraphConfiguration conf3 = cg2.getConfiguration().clone();
+        ComputationGraphConfiguration conf3 = cg2.getComputationGraphConfiguration().clone();
         conf3.setTopologicalOrder(null);
         conf3.setTopologicalOrderStr(null);
         ComputationGraph cg3 = new ComputationGraph(conf3);
@@ -1641,7 +1641,7 @@ public class TestComputationGraphNetwork extends BaseDL4JTest {
         cg3.setParams(cg2.params());
 
         int[] order3 = cg3.topologicalSortOrder();
-        List<String> strOrder3 = cg.getConfiguration().getTopologicalOrderStr();
+        List<String> strOrder3 = cg.getComputationGraphConfiguration().getTopologicalOrderStr();
         INDArray[] out3 = cg3.output(in);
         assertArrayEquals(order, order3);
         assertEquals(strOrder, strOrder3);

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

@@ -235,7 +235,7 @@ public class FrozenLayerTest extends BaseDL4JTest {
         ComputationGraph clonedModel = modelNow.clone();
 
         //Check json
-        assertEquals(clonedModel.getConfiguration().toJson(), modelNow.getConfiguration().toJson());
+        assertEquals(clonedModel.getComputationGraphConfiguration().toJson(), modelNow.getComputationGraphConfiguration().toJson());
 
         //Check params
         assertEquals(modelNow.params(), clonedModel.params());

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

@@ -50,7 +50,7 @@ public class TestDropout extends BaseDL4JTest {
     @Test
     public void testDropoutSimple() throws Exception {
         //Testing dropout with a single layer
-        //Layer input: values should be set to either 0.0 or 2.0x original value
+        //ILayer input: values should be set to either 0.0 or 2.0x original value
 
         int nIn = 8;
         int nOut = 8;

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

@@ -200,7 +200,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
     @Test
     public void testEmbeddingForwardPass() {
         //With the same parameters, embedding layer should have same activations as the equivalent one-hot representation
-        // input with a DenseLayer
+        // input with a DenseLayerConfiguration
 
         int nClassesIn = 10;
 
@@ -243,7 +243,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
     @Test
     public void testEmbeddingBackwardPass() {
         //With the same parameters, embedding layer should have same activations as the equivalent one-hot representation
-        // input with a DenseLayer
+        // input with a DenseLayerConfiguration
 
         int nClassesIn = 10;
 

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

@@ -104,7 +104,7 @@ public class OCNNOutputLayerTest extends BaseDL4JTest {
                     + "ocnn"  + "sigmoid" + ", doLearningFirst="
                     + doLearningFirst);
             for (int j = 0; j < network.getnLayers(); j++)
-                System.out.println("Layer " + j + " # params: " + network.getLayer(j).numParams());
+                System.out.println("ILayer " + j + " # params: " + network.getLayer(j).numParams());
         }
 
         boolean gradOK = GradientCheckUtil.checkGradients(network, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,

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

@@ -98,7 +98,7 @@ public class SameDiffDense extends SameDiffLayer {
                 if(DefaultParamInitializer.BIAS_KEY.equals(e.getKey())){
                     e.getValue().assign(0.0);
                 } else {
-                    //Normally use 'c' order, but use 'f' for direct comparison to DL4J DenseLayer
+                    //Normally use 'c' order, but use 'f' for direct comparison to DL4J DenseLayerConfiguration
                     WeightInitUtil.initWeights(nIn, nOut, new long[]{nIn, nOut}, weightInit, null, 'f', e.getValue());
                 }
             }

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

@@ -72,14 +72,14 @@ public class SameDiffDenseVertex extends SameDiffVertex {
 
     @Override
     public void initializeParameters(Map<String, INDArray> params) {
-        //Normally use 'c' order, but use 'f' for direct comparison to DL4J DenseLayer
+        //Normally use 'c' order, but use 'f' for direct comparison to DL4J DenseLayerConfiguration
         WeightInitUtil.initWeights(nIn, nOut, new long[]{nIn, nOut}, weightInit, null, 'f', params.get("W"));
         params.get("b").assign(0.0);
     }
 
     @Override
     public char paramReshapeOrder(String paramName){
-        return 'f';     //To match DL4J DenseLayer - for easy comparison
+        return 'f';     //To match DL4J DenseLayerConfiguration - for easy comparison
     }
 
     @Override

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

@@ -73,8 +73,8 @@ public class WorkspaceTests extends BaseDL4JTest {
         ComputationGraph c = createNet();
         for (WorkspaceMode wm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}) {
             log.info("Starting test: {}", wm);
-            c.getConfiguration().setTrainingWorkspaceMode(wm);
+            c.getComputationGraphConfiguration().setTrainingWorkspaceMode(wm);
-            c.getConfiguration().setInferenceWorkspaceMode(wm);
+            c.getComputationGraphConfiguration().setInferenceWorkspaceMode(wm);
 
             INDArray f = Nd4j.rand(8, 1, 28, 28);
             INDArray l = Nd4j.rand(8, 10);
@@ -666,8 +666,8 @@ public class WorkspaceTests extends BaseDL4JTest {
         ComputationGraph c = createNet();
         for (WorkspaceMode wm : new WorkspaceMode[]{WorkspaceMode.NONE, WorkspaceMode.ENABLED}) {
             log.info("Starting test: {}", wm);
-            c.getConfiguration().setTrainingWorkspaceMode(wm);
+            c.getComputationGraphConfiguration().setTrainingWorkspaceMode(wm);
-            c.getConfiguration().setInferenceWorkspaceMode(wm);
+            c.getComputationGraphConfiguration().setInferenceWorkspaceMode(wm);
 
             INDArray f = Nd4j.rand(8, 1, 28, 28);
             INDArray l = Nd4j.rand(8, 10);

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

@@ -995,7 +995,7 @@ public class MultiLayerTest extends BaseDL4JTest {
 
     @Test
     public void testCompareLayerMethods(){
-        //Simple test: compare .layer(int, Layer) and .layer(Layer) are identical
+        //Simple test: compare .layer(int, ILayer) and .layer(ILayer) are identical
 
         MultiLayerConfiguration conf1 = new NeuralNetConfiguration.Builder().seed(123).list()
                 .layer(0, new DenseLayer.Builder().nIn(4).nOut(3).weightInit(WeightInit.XAVIER)

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

@@ -261,7 +261,7 @@ public class MultiLayerTestRNN extends BaseDL4JTest {
             Nd4j.getRandom().setSeed(12345);
             int timeSeriesLength = 12;
 
-            //4 layer network: 2 GravesLSTM + DenseLayer + RnnOutputLayer. Hence also tests preprocessors.
+            //4 layer network: 2 GravesLSTM + DenseLayerConfiguration + RnnOutputLayer. Hence also tests preprocessors.
             MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder().seed(12345).list()
                     .layer(0, l0)
                     .layer(1, l1)

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

@@ -216,8 +216,8 @@ 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.getComputationGraphConfiguration().setIterationCount(0);
-                net2GradUpd.getConfiguration().setIterationCount(0);
+                net2GradUpd.getComputationGraphConfiguration().setIterationCount(0);
 
 
                 for (int i = 0; i < 100; i++) {

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

@@ -120,7 +120,7 @@ public class TestTransferLearningModelSerializer extends BaseDL4JTest {
         assertTrue(withFrozen.getLayer(0) instanceof FrozenLayer);
         assertTrue(withFrozen.getLayer(1) instanceof FrozenLayer);
 
-        Map<String, GraphVertex> m = withFrozen.getConfiguration().getVertices();
+        Map<String, GraphVertex> m = withFrozen.getComputationGraphConfiguration().getVertices();
         Layer l0 = ((LayerVertex) m.get("0")).getLayerConf().getLayer();
         Layer l1 = ((LayerVertex) m.get("1")).getLayerConf().getLayer();
         assertTrue(l0 instanceof org.deeplearning4j.nn.conf.layers.misc.FrozenLayer);

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

@@ -102,7 +102,7 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
                                         .build();
 
         //Check json
-        assertEquals(expectedConf.toJson(), modelNow.getConfiguration().toJson());
+        assertEquals(expectedConf.toJson(), modelNow.getComputationGraphConfiguration().toJson());
 
         //Check params after fit
         modelNow.fit(randomData);
@@ -382,7 +382,7 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
         modelExpectedArch.getVertex("layer0").setLayerAsFrozen();
         modelExpectedArch.getVertex("layer1").setLayerAsFrozen();
 
-        assertEquals(modelExpectedArch.getConfiguration().toJson(), modelNow.getConfiguration().toJson());
+        assertEquals(modelExpectedArch.getComputationGraphConfiguration().toJson(), modelNow.getComputationGraphConfiguration().toJson());
 
         modelNow.setParams(modelExpectedArch.params());
         int i = 0;
@@ -445,7 +445,7 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
 
 
 //        assertEquals(confExpected, graph.getConfiguration());
-        assertEquals(confExpected.toJson(), graph.getConfiguration().toJson());
+        assertEquals(confExpected.toJson(), graph.getComputationGraphConfiguration().toJson());
     }
 
 

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

@@ -126,7 +126,7 @@ public class TransferLearningHelperTest extends BaseDL4JTest {
                                         .setOutputs("outLeft", "outCentre", "outRight").build();
         ComputationGraph expectedModel = new ComputationGraph(expectedConf);
         expectedModel.init();
-        assertEquals(expectedConf.toJson(), modelSubset.getConfiguration().toJson());
+        assertEquals(expectedConf.toJson(), modelSubset.getComputationGraphConfiguration().toJson());
     }
 
     @Test

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

@@ -764,7 +764,7 @@ public class TestOptimizers extends BaseDL4JTest {
     }
 
 
-    /** Simple abstract class to deal with the fact that we don't care about the majority of the Model/Layer
+    /** Simple abstract class to deal with the fact that we don't care about the majority of the Model/ILayer
      * methods here. Classes extending this model for optimizer tests need only implement the score() and
      * gradient() methods.
      */
@@ -907,7 +907,7 @@ public class TestOptimizers extends BaseDL4JTest {
 
         @Override
         public INDArray input() {
-            //Work-around for BaseUpdater.postApply(): Uses Layer.input().size(0)
+            //Work-around for BaseUpdater.postApply(): Uses ILayer.input().size(0)
             //in order to get mini-batch size. i.e., divide by 1 here.
             return Nd4j.zeros(1);
         }

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

@@ -221,7 +221,7 @@ public class RegressionTest060 extends BaseDL4JTest {
 
         ComputationGraph net = ModelSerializer.restoreComputationGraph(f, true);
 
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         assertEquals(3, conf.getVertices().size());
 
         GravesLSTM l0 = (GravesLSTM) ((LayerVertex) conf.getVertices().get("0")).getLayerConf().getLayer();

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

@@ -221,7 +221,7 @@ public class RegressionTest071 extends BaseDL4JTest {
 
         ComputationGraph net = ModelSerializer.restoreComputationGraph(f, true);
 
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         assertEquals(3, conf.getVertices().size());
 
         GravesLSTM l0 = (GravesLSTM) ((LayerVertex) conf.getVertices().get("0")).getLayerConf().getLayer();

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

@@ -237,7 +237,7 @@ public class RegressionTest080 extends BaseDL4JTest {
 
         ComputationGraph net = ModelSerializer.restoreComputationGraph(f, true);
 
-        ComputationGraphConfiguration conf = net.getConfiguration();
+        ComputationGraphConfiguration conf = net.getComputationGraphConfiguration();
         assertEquals(3, conf.getVertices().size());
 
         GravesLSTM l0 = (GravesLSTM) ((LayerVertex) conf.getVertices().get("0")).getLayerConf().getLayer();

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

@@ -171,7 +171,7 @@ public class RegressionTest100a extends BaseDL4JTest {
         int nBoxes = 5;
         int nClasses = 10;
 
-        ConvolutionLayer cl = (ConvolutionLayer)((LayerVertex)net.getConfiguration().getVertices().get("convolution2d_9")).getLayerConf().getLayer();
+        ConvolutionLayer cl = (ConvolutionLayer)((LayerVertex)net.getComputationGraphConfiguration().getVertices().get("convolution2d_9")).getLayerConf().getLayer();
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());

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

@@ -206,7 +206,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         int nBoxes = 5;
         int nClasses = 10;
 
-        ConvolutionLayer cl = (ConvolutionLayer)((LayerVertex)net.getConfiguration().getVertices().get("convolution2d_9")).getLayerConf().getLayer();
+        ConvolutionLayer cl = (ConvolutionLayer)((LayerVertex)net.getComputationGraphConfiguration().getVertices().get("convolution2d_9")).getLayerConf().getLayer();
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());

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

@@ -224,7 +224,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         int nBoxes = 5;
         int nClasses = 10;
 
-        ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getConfiguration().getVertices()
+        ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getComputationGraphConfiguration().getVertices()
                 .get("convolution2d_9")).getLayerConf().getLayer();
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());

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

@@ -205,7 +205,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         int nBoxes = 5;
         int nClasses = 10;
 
-        ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getConfiguration().getVertices()
+        ConvolutionLayer cl = (ConvolutionLayer) ((LayerVertex) net.getComputationGraphConfiguration().getVertices()
                 .get("convolution2d_9")).getLayerConf().getLayer();
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());

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

@@ -94,7 +94,7 @@ public class CustomLayer extends FeedForwardLayer {
     @Override
     public ParamInitializer initializer() {
         //This method returns the parameter initializer for this type of layer
-        //In this case, we can use the DefaultParamInitializer, which is the same one used for DenseLayer
+        //In this case, we can use the DefaultParamInitializer, which is the same one used for DenseLayerConfiguration
         //For more complex layers, you may need to implement a custom parameter initializer
         //See the various parameter initializers here:
         //https://github.com/deeplearning4j/deeplearning4j/tree/master/deeplearning4j-core/src/main/java/org/deeplearning4j/nn/params
@@ -108,7 +108,7 @@ public class CustomLayer extends FeedForwardLayer {
         //If you don't need this functionality for your custom layer, you can return a LayerMemoryReport
         // with all 0s, or
 
-        //This implementation: based on DenseLayer implementation
+        //This implementation: based on DenseLayerConfiguration implementation
         InputType outputType = getOutputType(-1, inputType);
 
         val numParams = initializer().numParams(this);
@@ -131,7 +131,7 @@ public class CustomLayer extends FeedForwardLayer {
                 .workingMemory(0, 0, trainSizeFixed,
                         trainSizeVariable)     //No additional memory (beyond activations) for inference
                 .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS,
-                        MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayer
+                        MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayerConfiguration
                 .build();
     }
 

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

@@ -117,7 +117,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
         String str = FileUtils.readFileToString(list[0]);
 //        System.out.println(str);
         assertTrue(str.contains("Network Information"));
-        assertTrue(str.contains("Layer Helpers"));
+        assertTrue(str.contains("ILayer Helpers"));
         assertTrue(str.contains("JavaCPP"));
         assertTrue(str.contains("ScoreIterationListener"));
 
@@ -134,7 +134,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
         assertEquals(1, list.length);
         str = FileUtils.readFileToString(list[0]);
         assertTrue(str.contains("Network Information"));
-        assertTrue(str.contains("Layer Helpers"));
+        assertTrue(str.contains("ILayer Helpers"));
         assertTrue(str.contains("JavaCPP"));
         assertTrue(str.contains("ScoreIterationListener(1)"));
 
@@ -150,7 +150,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
 //        System.out.println("///////////////////////////////////////////////////////////");
 
         assertTrue(mlnMemoryInfo.contains("Network Information"));
-        assertTrue(mlnMemoryInfo.contains("Layer Helpers"));
+        assertTrue(mlnMemoryInfo.contains("ILayer Helpers"));
         assertTrue(mlnMemoryInfo.contains("JavaCPP"));
         assertTrue(mlnMemoryInfo.contains("ScoreIterationListener(1)"));
 
@@ -172,7 +172,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
         assertEquals(1, list.length);
         str = FileUtils.readFileToString(list[0]);
         assertTrue(str.contains("Network Information"));
-        assertTrue(str.contains("Layer Helpers"));
+        assertTrue(str.contains("ILayer Helpers"));
         assertTrue(str.contains("JavaCPP"));
         assertTrue(str.contains("ScoreIterationListener(1)"));
 
@@ -187,7 +187,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
         assertEquals(1, list.length);
         str = FileUtils.readFileToString(list[0]);
         assertTrue(str.contains("Network Information"));
-        assertTrue(str.contains("Layer Helpers"));
+        assertTrue(str.contains("ILayer Helpers"));
         assertTrue(str.contains("JavaCPP"));
         assertTrue(str.contains("ScoreIterationListener(1)"));
 
@@ -203,7 +203,7 @@ public class CrashReportingUtilTest extends BaseDL4JTest {
 //        System.out.println("///////////////////////////////////////////////////////////");
 
         assertTrue(cgMemoryInfo.contains("Network Information"));
-        assertTrue(cgMemoryInfo.contains("Layer Helpers"));
+        assertTrue(cgMemoryInfo.contains("ILayer Helpers"));
         assertTrue(cgMemoryInfo.contains("JavaCPP"));
         assertTrue(cgMemoryInfo.contains("ScoreIterationListener(1)"));
 

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

@@ -151,7 +151,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         ComputationGraph network = ModelSerializer.restoreComputationGraph(tempFile);
 
-        assertEquals(network.getConfiguration().toJson(), cg.getConfiguration().toJson());
+        assertEquals(network.getComputationGraphConfiguration().toJson(), cg.getComputationGraphConfiguration().toJson());
         assertEquals(cg.params(), network.params());
         assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }
@@ -177,7 +177,7 @@ public class ModelSerializerTest extends BaseDL4JTest {
 
         ComputationGraph network = ModelSerializer.restoreComputationGraph(fis);
 
-        assertEquals(network.getConfiguration().toJson(), cg.getConfiguration().toJson());
+        assertEquals(network.getComputationGraphConfiguration().toJson(), cg.getComputationGraphConfiguration().toJson());
         assertEquals(cg.params(), network.params());
         assertEquals(cg.getUpdater().getStateViewArray(), network.getUpdater().getStateViewArray());
     }

cavis-dnn/cavis-dnn-cudnn/src/main/java/org/deeplearning4j/cuda/recurrent/CudnnLSTMHelper.java

@@ -198,7 +198,7 @@ public class CudnnLSTMHelper extends BaseCudnnHelper implements LSTMHelper {
         }
         if (!(activationFn instanceof ActivationTanH)) {
             supported = false;
-            log.warn("Not supported: Layer activation functions != ActivationTanH");
+            log.warn("Not supported: ILayer activation functions != ActivationTanH");
         }
         if (hasPeepholeConnections) {
             supported = false;

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

@@ -295,7 +295,7 @@ public class KerasLayer {
     }
 
     /**
-     * Copy Keras layer weights to DL4J Layer.
+     * Copy Keras layer weights to DL4J ILayer.
      *
      * @param layer DL4J layer
      * @throws InvalidKerasConfigurationException Invalid Keras configuration
@@ -358,7 +358,7 @@ public class KerasLayer {
     }
 
     /**
-     * Whether this Keras layer maps to a DL4J Layer.
+     * Whether this Keras layer maps to a DL4J ILayer.
      *
      * @return true or false
      */
@@ -367,9 +367,9 @@ public class KerasLayer {
     }
 
     /**
-     * Gets corresponding DL4J Layer, if any.
+     * Gets corresponding DL4J ILayer, if any.
      *
-     * @return DL4J Layer
+     * @return DL4J ILayer
      * @see org.deeplearning4j.nn.api.Layer
      */
     public Layer getLayer() {

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

@@ -583,8 +583,8 @@ public class KerasModel {
                     graphBuilder.addVertex(layer.getLayerName(), layer.getVertex(), inboundLayerNamesArray);
                 } else if (layer.isInputPreProcessor()) {
                     if (preprocessor == null)
-                        throw new UnsupportedKerasConfigurationException("Layer " + layer.getLayerName()
+                        throw new UnsupportedKerasConfigurationException("ILayer " + layer.getLayerName()
-                                + " could not be mapped to Layer, Vertex, or InputPreProcessor");
+                                + " could not be mapped to ILayer, Vertex, or InputPreProcessor");
                     graphBuilder.addVertex(layer.getLayerName(), new PreprocessorVertex(preprocessor),
                             inboundLayerNamesArray);
                 }

cavis-dnn/cavis-dnn-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/KerasLayerConfiguration.java

@@ -246,7 +246,7 @@ public class KerasLayerConfiguration {
     private final String LAYER_FIELD_RATE = "rate";
     private final String LAYER_FIELD_GAUSSIAN_VARIANCE = ""; // 1: sigma, 2: stddev
 
-    /* Layer wrappers */
+    /* ILayer wrappers */
     // Missing: TimeDistributed
 
 

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

@@ -115,9 +115,9 @@ public class KerasDense extends KerasLayer {
     }
 
     /**
-     * Get DL4J DenseLayer.
+     * Get DL4J DenseLayerConfiguration.
      *
-     * @return DenseLayer
+     * @return DenseLayerConfiguration
      */
     public DenseLayer getDenseLayer() {
         return (DenseLayer) this.layer;

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

@@ -211,10 +211,10 @@ public class KerasLSTM extends KerasLayer {
     }
 
     /**
-     * Get DL4J Layer. If returnSequences is true, this can be casted to an "LSTM" layer, otherwise it can be casted
+     * Get DL4J ILayer. If returnSequences is true, this can be casted to an "LSTM" layer, otherwise it can be casted
      * to a "LastTimeStep" layer.
      *
-     * @return LSTM Layer
+     * @return LSTM ILayer
      */
     public Layer getLSTMLayer() {
         return layer;

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

@@ -184,7 +184,7 @@ public class KerasSimpleRnn extends KerasLayer {
     /**
      * Get DL4J SimpleRnn layer.
      *
-     * @return SimpleRnn Layer
+     * @return SimpleRnn ILayer
      */
     public Layer getSimpleRnnLayer() {
         return this.layer;

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

@@ -160,7 +160,7 @@ public class KerasBidirectional extends KerasLayer {
     /**
      * Return the underlying recurrent layer of this bidirectional layer
      *
-     * @return Layer, recurrent layer
+     * @return ILayer, recurrent layer
      */
     public Layer getUnderlyingRecurrentLayer() {
         return kerasRnnlayer.getLayer();
@@ -169,7 +169,7 @@ public class KerasBidirectional extends KerasLayer {
     /**
      * Get DL4J Bidirectional layer.
      *
-     * @return Bidirectional Layer
+     * @return Bidirectional ILayer
      */
     public Bidirectional getBidirectionalLayer() {
         return (Bidirectional) this.layer;

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

@@ -85,7 +85,7 @@ public class FullModelComparisons extends BaseDL4JTest {
 
         System.out.println(model.summary());
 
-        // 1. Layer
+        // 1. ILayer
         LSTM firstLstm = (LSTM) model.getLayer(0);
         org.deeplearning4j.nn.conf.layers.LSTM firstConf =
                 (org.deeplearning4j.nn.conf.layers.LSTM) firstLstm.conf().getLayer();
@@ -123,7 +123,7 @@ public class FullModelComparisons extends BaseDL4JTest {
         Assertions.assertEquals(b.getDouble(0, 192), -0.13569744, 1e-7); // Keras O
         Assertions.assertEquals(b.getDouble(0, 0), -0.2587392, 1e-7); // Keras C
 
-        // 2. Layer
+        // 2. ILayer
         LSTM secondLstm = (LSTM) ((LastTimeStepLayer) model.getLayer(1)).getUnderlying();
         org.deeplearning4j.nn.conf.layers.LSTM secondConf =
                 (org.deeplearning4j.nn.conf.layers.LSTM) secondLstm.conf().getLayer();

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

@@ -39,4 +39,13 @@ public interface LayerConfiguration {
    */
   org.deeplearning4j.nn.conf.inputs.InputType.Type getInputType();
 
+
+  /**
+   * Defines the valid input type for this Layer
+   *
+   * @return InputType
+   */
+  org.deeplearning4j.nn.conf.inputs.InputType.Type getOutputType();
+
+
 }

cavis-dnn/cavis-dnn-nn/build.gradle

@@ -22,7 +22,7 @@ apply from: "${project.rootProject.projectDir}/createTestBackends.gradle"
 
 dependencies {
     implementation platform(projects.cavisCommonPlatform)
-    implementation projects.cavisDnn.cavisDnnNnApi
+//    implementation projects.cavisDnn.cavisDnnNnApi
     implementation projects.cavisDnn.cavisDnnData.cavisDnnDataUtilityIterators
     implementation 'org.lucee:oswego-concurrent:1.3.4'
     implementation projects.cavisDnn.cavisDnnCommon
@@ -58,3 +58,5 @@ dependencies {
     implementation "com.squareup.okhttp3:okhttp"
     implementation "com.squareup.okhttp3:logging-interceptor"
 }
+sourceCompatibility = JavaVersion.VERSION_11
+targetCompatibility = JavaVersion.VERSION_11

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

@@ -19,10 +19,28 @@
  *
  */
 
-package net.brutex.ai.dnn.conf.layer;
+package net.brutex.ai.dnn.api;
 
-public abstract class LayerConfiguration {
+/**
+ * This is an "executable" ILayer, that is based on a {@link ILayerConfiguration}
+ */
+public interface ILayer {
 
+  /**
+   * Get the underlying configuration for this ILayer
+   * @return configuration
+   */
+  ILayerConfiguration getLayerConfiguration();
 
+  /**
+   * Set the underlying layer configuration
+   * @param conf The new configuration
+   */
+  void setLayerConfiguration(ILayerConfiguration conf);
 
+  /**
+   * An implementation should provide a method to validate the network
+   * @return true if no errors found; false otherwise
+   */
+  boolean isValid();
 }

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

@@ -19,34 +19,45 @@
  *
  */
 
-package net.brutex.ai.dnn.conf.layer;
+package net.brutex.ai.dnn.api;
-
-import lombok.extern.slf4j.Slf4j;
-import net.brutex.ai.dnn.api.Layer;
-import net.brutex.ai.dnn.api.NeuralNetwork;
-import net.brutex.ai.dnn.conf.layer.AbstractLayerConfiguration;
-import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.inputs.InputType.Type;
-
-@Slf4j
-public class FFLayer extends AbstractLayerConfiguration {
 
+public interface ILayerConfiguration {
 
   /**
-   * Create and return an instance of a LayerConfiguration.
+   * Create and return an instance of a ILayerConfiguration.
    *
    * @param network the "holding" network for the instance
    * @return the new layer instance
    */
-  @Override
+  ILayer instantiate(IModel network);
-  public Layer instantiate(NeuralNetwork network) {
+
-    //Let's do some verifications first
+
-    if(getInputType() != Type.FF) {
+  /**
-      log.error("The {} layer configuration must use an InputType of {}, but found {}",
+   * Defines the valid input type for this ILayer
-          this.getClass().getSimpleName(),
+   *
-          Type.FF.name(),
+   * @return InputType
-          getInputType().name());
+   */
-    }
+  org.deeplearning4j.nn.conf.inputs.InputType.Type getInputType();
-    return null;
+
-  }
+
+  /**
+   * Defines the valid input type for this ILayer
+   *
+   * @return InputType
+   */
+  org.deeplearning4j.nn.conf.inputs.InputType.Type getOutputType();
+
+
+  /**
+   * Number of trainable parameter in this layer
+   * @return number of parameter
+   */
+  long numParameters();
+
+  /**
+   * An implementation should provide a method to validate the network
+   * @return true if no errors found; false otherwise
+   */
+  boolean isValid();
+
 }

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

@@ -0,0 +1,86 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.dnn.api;
+
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.dataset.api.DataSet;
+import org.nd4j.linalg.dataset.api.MultiDataSet;
+
+/**
+ * A Neural Network is an instance of a {@link INeuralNetworkConfiguration}, that can be trained,
+ * evaluated, saved, exported, etc. Its configuration state is defined with the
+ * {@link #setConfiguration(INeuralNetworkConfiguration)} and {@link #getConfiguration()} methods.
+ *
+ */
+public interface IModel {
+
+  /**
+   * The configuration that defines this Neural Network
+   *
+   * @param conf the configuration to use for this network
+   */
+  void setConfiguration(INeuralNetworkConfiguration conf);
+  INeuralNetworkConfiguration getConfiguration();
+
+  /**
+   * Fit the model for one iteration on the provided data
+   *
+   * @param features   the examples to classify (one example in each row)
+   * @param labels the example labels(a binary outcome matrix)
+   * @param featuresMask The mask array for the features (used for variable length time series, etc). May be null.
+   * @param labelsMask The mask array for the labels (used for variable length time series, etc). May be null.
+   */
+  void fit(INDArray features, INDArray labels, INDArray featuresMask, INDArray labelsMask);
+
+  /**
+   * This method fits model with a given DataSet
+   *
+   * @param dataSet the dataset to use for training
+   */
+  void fit(DataSet dataSet);
+
+  /**
+   * This method fits model with a given MultiDataSet
+   *
+   * @param dataSet the multi dataset to use for training
+   */
+  void fit(MultiDataSet dataSet);
+
+  /**
+   * The name of the Neural Network
+   * @return the name
+   */
+  String getName();
+
+  /**
+   * Set the name for this Neural Network
+   * @param name the name
+   */
+  void setName(String name);
+
+  /**
+   * An implementation should provide a method to validate the network
+   * @return true if no errors found; false otherwise
+   */
+  boolean isValid();
+
+}

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

@@ -1,25 +1,27 @@
 /*
- *  ******************************************************************************
+ *
- *  *
+ *    ******************************************************************************
- *  *
+ *    *
- *  * This program and the accompanying materials are made available under the
+ *    * This program and the accompanying materials are made available under the
- *  * terms of the Apache License, Version 2.0 which is available at
+ *    * terms of the Apache License, Version 2.0 which is available at
- *  * https://www.apache.org/licenses/LICENSE-2.0.
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
- *  *
+ *    *
- *  *  See the NOTICE file distributed with this work for additional
+ *    *  See the NOTICE file distributed with this work for additional
- *  *  information regarding copyright ownership.
+ *    *  information regarding copyright ownership.
- *  * Unless required by applicable law or agreed to in writing, software
+ *    * Unless required by applicable law or agreed to in writing, software
- *  * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *  * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *  * License for the specific language governing permissions and limitations
+ *    * License for the specific language governing permissions and limitations
- *  * under the License.
+ *    * under the License.
- *  *
+ *    *
- *  * SPDX-License-Identifier: Apache-2.0
+ *    * SPDX-License-Identifier: Apache-2.0
- *  *****************************************************************************
+ *    *****************************************************************************
+ *
  */
 
-package org.deeplearning4j.nn.api;
+package net.brutex.ai.dnn.api;
 
+import net.brutex.ai.dnn.conf.NeuralNetworkConfiguration;
 import org.deeplearning4j.optimize.api.ConvexOptimizer;
 import org.nd4j.evaluation.IEvaluation;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -31,7 +33,7 @@ import org.nd4j.linalg.dataset.api.iterator.MultiDataSetIterator;
 /**
  * @author raver119
  */
-public interface NeuralNetwork {
+public interface INeuralNetwork {
 
   /**
    * This method does initialization of model
@@ -104,4 +106,17 @@ public interface NeuralNetwork {
    * @param iterator
    */
   <T extends IEvaluation> T[] doEvaluation(MultiDataSetIterator iterator, T... evaluations);
+
+  /**
+   * A neural network is created from a configuration.
+   * @param conf the configuration to create the network from
+   */
+  void setConfiguration(NeuralNetworkConfiguration conf);
+
+  /**
+   *  Return the configuration for this configuration
+   * @return
+   */
+  NeuralNetworkConfiguration getConfiguration();
+
 }

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

@@ -0,0 +1,52 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.dnn.api;
+
+import java.util.List;
+
+public interface INeuralNetworkConfiguration {
+
+}
+/**
+  /**
+   * Provides a flat list of all embedded layer configurations, this
+   * can only be called after the layer is initialized or {@link #getLayerConfigurations()} is
+   * called.
+   *
+   * @return unstacked layer configurations
+
+  List<ILayerConfiguration> getLayerConfigurations();
+
+
+  /**
+   * This uncollables any stacked layer configurations within building blocks like
+   * @link BuildingBlockLayer}
+
+  void calculateInnerLayerConfigurations();
+
+  /**
+   * An implementation should provide a method to validate the network
+   * @return true if no errors found; false otherwise
+
+  boolean isValid();
+}
+**/

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/conf/NeuralNetworkConfiguration.java

@@ -22,32 +22,61 @@
 package net.brutex.ai.dnn.conf;
 
 import com.fasterxml.jackson.annotation.JsonIgnoreProperties;
+import com.fasterxml.jackson.databind.JsonNode;
+import com.fasterxml.jackson.databind.ObjectMapper;
+import com.fasterxml.jackson.databind.exc.InvalidTypeIdException;
+import com.fasterxml.jackson.databind.node.ArrayNode;
+import java.io.IOException;
 import java.io.Serializable;
 import java.util.ArrayList;
 import java.util.HashMap;
+import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
+import java.util.Random;
 import lombok.Getter;
 import lombok.NonNull;
 import lombok.Setter;
 import lombok.Singular;
 import lombok.extern.jackson.Jacksonized;
 import lombok.extern.slf4j.Slf4j;
-import net.brutex.ai.dnn.api.LayerConfiguration;
+import net.brutex.ai.dnn.api.ILayerConfiguration;
+import net.brutex.ai.dnn.api.INeuralNetworkConfiguration;
 import org.deeplearning4j.nn.conf.BackpropType;
 import org.deeplearning4j.nn.conf.CacheMode;
+import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
+import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.WorkspaceMode;
+import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
-import org.deeplearning4j.nn.conf.layers.wrapper.BuildingBlockLayer;
+import org.deeplearning4j.nn.conf.layers.BaseLayer;
+import org.deeplearning4j.nn.conf.layers.BaseOutputLayer;
+import org.deeplearning4j.nn.conf.layers.Layer;
+import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
+import org.deeplearning4j.nn.conf.memory.MemoryReport;
+import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
+import org.deeplearning4j.nn.conf.serde.JsonMappers;
+import org.deeplearning4j.nn.weights.IWeightInit;
+import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.activations.IActivation;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.learning.config.IUpdater;
+import org.nd4j.linalg.lossfunctions.LossFunctions;
+import org.nd4j.linalg.lossfunctions.impl.LossBinaryXENT;
+import org.nd4j.linalg.lossfunctions.impl.LossMCXENT;
+import org.nd4j.linalg.lossfunctions.impl.LossMSE;
+import org.nd4j.linalg.lossfunctions.impl.LossNegativeLogLikelihood;
 
 /**
- * The NeuralNetworkConfiguration is a sequential container for the different layers in your
+ * The INeuralNetworkConfiguration is a sequential container for the different layers in your
  * network (or other NeuralNetworkConfigurations). That said, NeuralNetworkConfigurations can be
  * stacked.<br/><br/>
- * It then “chains” outputs to inputs sequentially for each NeuralNetworkConfiguration,
+ * It then “chains” outputs to inputs sequentially for each INeuralNetworkConfiguration,
  * finally returning the output of the "top" configuration. Any settings made, are inherited and can
- * be overridden on a "deeper" level. For this use case, you need to wrap the NeuralNetworkConfiguration
+ * be overridden on a "deeper" level. For this use case, you need to wrap the INeuralNetworkConfiguration
  * into a BuildingBlockLayer
  *
  */
@@ -55,77 +84,54 @@ import org.deeplearning4j.nn.conf.layers.wrapper.BuildingBlockLayer;
 @JsonIgnoreProperties(ignoreUnknown = true)
 @lombok.Builder
 @Slf4j
-public class NeuralNetworkConfiguration implements net.brutex.ai.dnn.api.NeuralNetworkConfiguration, Serializable, Cloneable {
+public class NeuralNetworkConfiguration extends NeuralNetConfiguration implements
-
+    INeuralNetworkConfiguration, Serializable, Cloneable {
-  /**
-   * The default {@link CacheMode} for this configuration. Will be set to "NONE" if not specified otherwise.
-   * Valid values are<br/>
-   * CacheMode.NONE,<br/>
-   * CacheMode.HOST or<br/>
-   * CacheMode.DEVICE<br/>
-   */
-  @NonNull
-  @lombok.Builder.Default private CacheMode cacheMode = CacheMode.NONE;
-
-  @Getter @Setter @NonNull
-  protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
-
-  @Getter @Setter @NonNull
-  protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
-
-  @Getter @Setter @NonNull
-  protected BackpropType backpropType = BackpropType.Standard;
-
-  @Getter
-  protected Map<Integer, InputPreProcessor> inputPreProcessors = new HashMap<>();
-
-
-  @Getter @Setter protected int tbpttFwdLength = 20;
-  @Getter @Setter protected int tbpttBackLength = 20;
-
-
-  /**
-   * The list of layer configurations in this configuration. They will be indexed automatically
-   * as the layers get added starting with index 0.
-   */
-  @Singular @Getter
-  private List<LayerConfiguration> layerConfigurations;
-
-  /**
-   * The name for this configuration. Defaults to "Anonymous NeuralNetworkConfiguration" if
-   * it is not specified.
-   */
-  @lombok.Builder.Default @Getter
-  private String name = "Anonymous NeuralNetworkConfiguration";
-
-
-  /**
-   * The {@link InputType} of the data for this network configuration
-   */
-  private InputType inputType;
 
+    private static final int DEFAULT_TBPTT_LENGTH = 20;
+  @Getter protected final List<NeuralNetworkConfiguration> confs = new ArrayList<>();
   /**
    * hidden list of layers, that "flattens" all the layers of this network and applies
    * inheritance.
    */
   @lombok.Builder.ObtainVia(method = "calculateInnerLayers")
-  private final List<LayerConfiguration> innerLayerConfigurations;
+  private final List<ILayerConfiguration> innerLayerConfigurations;
-
+  @Getter @Setter @NonNull @Singular
-  @Override
+  protected List<Layer> layers = new ArrayList<>();
-  public void calculateInnerLayerConfigurations()  {
+  @Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
-    List<LayerConfiguration> list = new ArrayList<>();
+  protected WorkspaceMode trainingWorkspaceMode = WorkspaceMode.ENABLED;
-    for( LayerConfiguration layer : this.layerConfigurations) {
+  @Getter @Setter @NonNull @lombok.Builder.Default @Deprecated
-      if(layer instanceof BuildingBlockLayer) {
+  protected WorkspaceMode inferenceWorkspaceMode = WorkspaceMode.ENABLED;
-        BuildingBlockLayer blayer = (BuildingBlockLayer) layer;
+  /**
-        blayer.getConf().calculateInnerLayerConfigurations();
+   * The type of backprop. Default setting is used for most networks (MLP, CNN etc), but
-        list.addAll(blayer.getConf().getLayerConfigurations());
+   * optionally truncated BPTT can be used for training recurrent neural networks. If using
-      } else {
+   * TruncatedBPTT make sure you set both tBPTTForwardLength() and tBPTTBackwardLength()
-        list.add(layer);
+   */
-      }
+  @Getter @Setter @NonNull @lombok.Builder.Default
-    }
+  protected BackpropType backpropType = BackpropType.Standard;
-    this.layerConfigurations = list;
+  @Getter
-  }
+  protected Map<Integer, InputPreProcessor> inputPreProcessors = new HashMap<>();
-
+  /**
+   * When doing truncated BPTT: how many steps of forward pass should we do before doing
+   * (truncated) backprop?<br> Only applicable when doing
+   * backpropType(BackpropType.TruncatedBPTT)<br> Typically tBPTTForwardLength parameter is same
+   * as the tBPTTBackwardLength parameter, but may be larger than it in some circumstances (but
+   * never smaller)<br> Ideally your training data time series length should be divisible by this
+   * This is the k1 parameter on pg23 of
+   * <a
+   * href="http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf">http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf</a>
+   *
+   * @param forwardLength Forward length > 0, >= backwardLength
+   */
+  @Getter @Setter protected int tbpttFwdLength = 20;
+  /**
+   * When doing truncated BPTT: how many steps of backward should we do?<br> Only applicable when
+   * doing backpropType(BackpropType.TruncatedBPTT)<br> This is the k2 parameter on pg23 of
+   * <a
+   * href="http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf">http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf</a>
+   *
+   * @param backwardLength <= forwardLength
+   */
+  @Getter @Setter protected int tbpttBackLength = 20;
   /**
    * Creates and returns a copy of this object.
    *
@@ -136,8 +142,564 @@ public class NeuralNetworkConfiguration implements net.brutex.ai.dnn.api.NeuralN
    *                                    cannot be cloned.
    * @see Cloneable
    */
-  @Override
+
-  protected Object clone() throws CloneNotSupportedException {
+  //Nd4j.getRandom().setSeed(getConf(0).getSeed()); //TODO
-    return super.clone();
+  //Counter for the number of parameter updates so far
+  // This is important for learning rate schedules, for example, and is stored here to ensure it is persisted
+  // for Spark and model serialization
+  @Getter @Setter
+  protected int iterationCount = 0;
+  //Counter for the number of epochs completed so far. Used for per-epoch schedules
+  @Getter @Setter
+  protected int epochCount = 0;
+  protected double dampingFactor = 100;
+  @Getter @Setter //todo why?
+  private Layer layer;
+  /**
+   * A seed for this network, will be random if not specified.
+    */
+  @Getter @Setter @NonNull @lombok.Builder.Default
+  private long seed = new Random().nextLong();
+  /**
+   * The default {@link CacheMode} for this configuration. Will be set to "NONE" if not specified otherwise.
+   * This method defines how/if preOutput cache is handled: NONE: cache disabled (default value)
+   * HOST: Host memory will be used DEVICE: GPU memory will be used (on CPU backends effect will
+   * be the same as for HOST)
+   *
+   * Valid values are<br/>
+   * CacheMode.NONE,<br/>
+   * CacheMode.HOST or<br/>
+   * CacheMode.DEVICE<br/>
+   * @param cacheMode
+   */
+  @NonNull @Getter @Setter
+  @lombok.Builder.Default private CacheMode cacheMode = CacheMode.NONE;
+  /**
+   * The list of layer configurations in this configuration. They will be indexed automatically
+   * as the layers get added starting with index 0.
+   */
+  @Singular @Getter
+  private List<ILayerConfiguration> layerConfigurations;
+  /**
+   * The name for this configuration. Defaults to "Anonymous INeuralNetworkConfiguration" if
+   * it is not specified.
+   */
+  @lombok.Builder.Default @Getter
+  private String name = "Anonymous INeuralNetworkConfiguration";
+  /**
+   * The {@link InputType} of the data for this network configuration
+   */
+  private InputType inputType;
+  /**
+   * Set the DataType for the network parameters and activations for all layers in the network.
+   * Default: Float
+   *
+   * @param dataType Datatype to use for parameters and activations
+   */
+  @Getter @Setter @lombok.Builder.Default @NonNull
+  private DataType dataType = DataType.FLOAT;
+    /**
+     * Whether to override the nIn configuration forcibly upon construction. Default value is true.
+     * @return builder pattern
+     */
+    @Getter @Setter
+    @lombok.Builder.Default
+    private boolean overrideNinUponBuild = true;
+    /**
+     * Enabled by default. If enabled, the output layer configuration will be validated, to throw an
+     * exception on likely invalid outputs - such as softmax + nOut=1, or LossMCXENT + Tanh.<br> If
+     * disabled (false) no output layer validation will be performed.<br> Disabling this validation
+     * is not recommended, as the configurations that fail validation usually will not be able to
+     * learn correctly. However, the option to disable this validation is provided for advanced
+     * users when creating non-standard architectures.
+     *
+     * @param validate If true: validate output layer configuration. False: don't validate
+     */
+    @Getter @Setter @lombok.Builder.Default
+    private boolean validateOutputLayerConfig=true;
+    /**
+     * Enabled by default. If enabled, an exception will be throw when using the (invalid)
+     * combination of truncated backpropagation through time (TBPTT) with either a
+     * GlobalPoolingLayer or LastTimeStepLayer.<br> It is possible to disable this validation to
+     * allow what is almost certainly an invalid configuration to be used, however this is not
+     * recommended.
+     *
+     * @param validate Whether TBPTT validation should be performed
+     */
+    @Getter @Setter @lombok.Builder.Default
+    private boolean validateTbpttConfig=true;
+
+
+
+  /**
+   * Gradient updater configuration. For example, {@link org.nd4j.linalg.learning.config.Adam}
+   * or {@link org.nd4j.linalg.learning.config.Nesterovs}<br>
+   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
+   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * value, and can be overridden on a per-layer basis.
+   *
+   * @param updater Updater to use
+   */
+  @Getter @Setter @NonNull
+  private IUpdater updater;
+
+  /**
+   * Gradient normalization strategy. Used to specify gradient renormalization, gradient clipping etc.
+   * See {@link GradientNormalization} for details<br>
+   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
+   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * value, and can be overridden on a per-layer basis.
+   *
+   * @param gradientNormalization Type of normalization to use. Defaults to None.
+   * @see GradientNormalization
+   */
+  @Getter @Setter @NonNull @lombok.Builder.Default
+  private GradientNormalization gradientNormalization = GradientNormalization.None;
+
+  /**
+   * Threshold for gradient normalization, only used for GradientNormalization.ClipL2PerLayer,
+   * GradientNormalization.ClipL2PerParamType, and GradientNormalization.ClipElementWiseAbsoluteValue<br>
+   * Not used otherwise.<br>
+   * L2 threshold for first two types of clipping, or absolute value threshold for last type of clipping.<br>
+   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
+   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * value, and can be overridden on a per-layer basis.
+   */
+  @Getter @Setter
+  private double gradientNormalizationThreshold;
+
+
+  /**
+   * Weight initialization scheme to use, for initial weight values
+   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
+   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * value, and can be overridden on a per-layer basis.
+   */
+  @Getter @Setter
+  private IWeightInit weightInit;
+
+  /**
+   * Activation function / neuron non-linearity<br>
+   * Note: values set by this method will be applied to all applicable layers in the network, unless a different
+   * value is explicitly set on a given layer. In other words: values set via this method are used as the default
+   * value, and can be overridden on a per-layer basis.
+   */
+  @Getter @Setter
+  private IActivation activation;
+
+
+
+  /**
+   * Create a neural net configuration from json
+   *
+   * @param json the neural net configuration from json
+   * @return {@link NeuralNetworkConfiguration}
+   */
+  public static NeuralNetworkConfiguration fromJson(String json) {
+    NeuralNetworkConfiguration conf;
+    ObjectMapper mapper = NeuralNetworkConfiguration.mapper();
+    try {
+      conf = mapper.readValue(json, NeuralNetworkConfiguration.class);
+    } catch (InvalidTypeIdException e) {
+      if (e.getMessage().contains("@class")) {
+        try {
+          //JSON may be legacy (1.0.0-alpha or earlier), attempt to load it using old format
+          return JsonMappers.getLegacyMapper().readValue(json, NeuralNetworkConfiguration.class);
+        } catch (InvalidTypeIdException e2) {
+          //Check for legacy custom layers: "Could not resolve type id 'CustomLayer' as a subtype of [simple type, class org.deeplearning4j.nn.conf.layers.ILayer]: known type ids = [Bidirectional, CenterLossOutputLayer, CnnLossLayer, ..."
+          //1.0.0-beta5: dropping support for custom layers defined in pre-1.0.0-beta format. Built-in layers from these formats still work
+          String msg = e2.getMessage();
+          if (msg != null && msg.contains("Could not resolve type id")) {
+            throw new RuntimeException(
+                "Error deserializing MultiLayerConfiguration - configuration may have a custom " +
+                    "layer, vertex or preprocessor, in pre version 1.0.0-beta JSON format.\nModels in legacy format with custom"
+                    +
+                    " layers should be loaded in 1.0.0-beta to 1.0.0-beta4 and saved again, before loading in the current version of DL4J",
+                e);
+          }
+          throw new RuntimeException(e2);
+        } catch (IOException e2) {
+          throw new RuntimeException(e2);
+        }
+      }
+      throw new RuntimeException(e);
+    } catch (IOException e) {
+      //Check if this exception came from legacy deserializer...
+      String msg = e.getMessage();
+      if (msg != null && msg.contains("legacy")) {
+        throw new RuntimeException(
+            "Error deserializing MultiLayerConfiguration - configuration may have a custom " +
+                "layer, vertex or preprocessor, in pre version 1.0.0-alpha JSON format. These layers can be "
+                +
+                "deserialized by first registering them with NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class...)",
+            e);
+      }
+      throw new RuntimeException(e);
+    }
+
+    //To maintain backward compatibility after loss function refactoring (configs generated with v0.5.0 or earlier)
+    // Previously: enumeration used for loss functions. Now: use classes
+    // IN the past, could have only been an OutputLayer or RnnOutputLayer using these enums
+    int layerCount = 0;
+    JsonNode confs = null;
+    for (NeuralNetworkConfiguration nnc : conf.getConfs()) {
+      Layer l = nnc.getLayer();
+      if (l instanceof BaseOutputLayer && ((BaseOutputLayer) l).getLossFn() == null) {
+        //lossFn field null -> may be an old config format, with lossFunction field being for the enum
+        //if so, try walking the JSON graph to extract out the appropriate enum value
+
+        BaseOutputLayer ol = (BaseOutputLayer) l;
+        try {
+          JsonNode jsonNode = mapper.readTree(json);
+          if (confs == null) {
+            confs = jsonNode.get("confs");
+          }
+          if (confs instanceof ArrayNode) {
+            ArrayNode layerConfs = (ArrayNode) confs;
+            JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
+            if (outputLayerNNCNode == null) {
+              throw new RuntimeException("should never happen"); //return conf; //Should never happen...
+            }
+            JsonNode outputLayerNode = outputLayerNNCNode.get("layer");
+
+            JsonNode lossFunctionNode = null;
+            if (outputLayerNode.has("output")) {
+              lossFunctionNode = outputLayerNode.get("output").get("lossFunction");
+            } else if (outputLayerNode.has("rnnoutput")) {
+              lossFunctionNode = outputLayerNode.get("rnnoutput").get("lossFunction");
+            }
+
+            if (lossFunctionNode != null) {
+              String lossFunctionEnumStr = lossFunctionNode.asText();
+              LossFunctions.LossFunction lossFunction = null;
+              try {
+                lossFunction = LossFunctions.LossFunction.valueOf(lossFunctionEnumStr);
+              } catch (Exception e) {
+                log.warn(
+                    "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
+                    e);
+              }
+
+              if (lossFunction != null) {
+                switch (lossFunction) {
+                  case MSE:
+                    ol.setLossFn(new LossMSE());
+                    break;
+                  case XENT:
+                    ol.setLossFn(new LossBinaryXENT());
+                    break;
+                  case NEGATIVELOGLIKELIHOOD:
+                    ol.setLossFn(new LossNegativeLogLikelihood());
+                    break;
+                  case MCXENT:
+                    ol.setLossFn(new LossMCXENT());
+                    break;
+
+                  //Remaining: TODO
+                  case SQUARED_LOSS:
+                  case RECONSTRUCTION_CROSSENTROPY:
+                  default:
+                    log.warn(
+                        "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not set loss function for {}",
+                        lossFunction);
+                    break;
+                }
+              }
+            }
+
+          } else {
+            log.warn(
+                "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON: layer 'confs' field is not an ArrayNode (is: {})",
+                (confs != null ? confs.getClass() : null));
+          }
+        } catch (IOException e) {
+          log.warn(
+              "OutputLayer with null LossFunction or pre-0.6.0 loss function configuration detected: could not parse JSON",
+              e);
+          break;
+        }
+      }
+
+      //Also, pre 0.7.2: activation functions were Strings ("activationFunction" field), not classes ("activationFn")
+      //Try to load the old format if necessary, and create the appropriate IActivation instance
+      if ((l instanceof BaseLayer) && ((BaseLayer) l).getActivationFn() == null) {
+        try {
+          JsonNode jsonNode = mapper.readTree(json);
+          if (confs == null) {
+            confs = jsonNode.get("confs");
+          }
+          if (confs instanceof ArrayNode) {
+            ArrayNode layerConfs = (ArrayNode) confs;
+            JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
+            if (outputLayerNNCNode == null) {
+              throw new RuntimeException("Should never happen"); //return conf; //Should never happen...
+            }
+            JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
+
+            if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
+              continue;
+            }
+
+            JsonNode layerNode = layerWrapperNode.elements().next();
+            JsonNode activationFunction = layerNode.get(
+                "activationFunction"); //Should only have 1 element: "dense", "output", etc
+
+            if (activationFunction != null) {
+              IActivation ia = Activation.fromString(activationFunction.asText())
+                  .getActivationFunction();
+              ((BaseLayer) l).setActivationFn(ia);
+            }
+          }
+
+        } catch (IOException e) {
+          log.warn(
+              "ILayer with null ActivationFn field or pre-0.7.2 activation function detected: could not parse JSON",
+              e);
+        }
+      }
+
+      if (!handleLegacyWeightInitFromJson(json, l, mapper, confs, layerCount)) {
+        return conf;
+      }
+
+      layerCount++;
+    }
+    return conf;
   }
+
+  /**
+   * Handle {@link WeightInit} and {@link Distribution} from legacy configs in Json format. Copied
+   * from handling of {@link Activation} above.
+   *
+   * @return True if all is well and layer iteration shall continue. False else-wise.
+   */
+  private static boolean handleLegacyWeightInitFromJson(String json, Layer l, ObjectMapper mapper,
+      JsonNode confs, int layerCount) {
+    if ((l instanceof BaseLayer) && ((BaseLayer) l).getWeightInitFn() == null) {
+      try {
+        JsonNode jsonNode = mapper.readTree(json);
+        if (confs == null) {
+          confs = jsonNode.get("confs");
+        }
+        if (confs instanceof ArrayNode) {
+          ArrayNode layerConfs = (ArrayNode) confs;
+          JsonNode outputLayerNNCNode = layerConfs.get(layerCount);
+          if (outputLayerNNCNode == null) {
+            return false; //Should never happen...
+          }
+          JsonNode layerWrapperNode = outputLayerNNCNode.get("layer");
+
+          if (layerWrapperNode == null || layerWrapperNode.size() != 1) {
+            return true;
+          }
+
+          JsonNode layerNode = layerWrapperNode.elements().next();
+          JsonNode weightInit = layerNode.get(
+              "weightInit"); //Should only have 1 element: "dense", "output", etc
+          JsonNode distribution = layerNode.get("dist");
+
+          Distribution dist = null;
+          if (distribution != null) {
+            dist = mapper.treeToValue(distribution, Distribution.class);
+          }
+
+          if (weightInit != null) {
+            final IWeightInit wi = WeightInit.valueOf(weightInit.asText())
+                .getWeightInitFunction(dist);
+            ((BaseLayer) l).setWeightInitFn(wi);
+          }
+        }
+
+      } catch (IOException e) {
+        log.warn(
+            "ILayer with null WeightInit detected: " + l.getLayerName() + ", could not parse JSON",
+            e);
+      }
+    }
+    return true;
+
+  }
+
+  /**
+   * Object mapper for serialization of configurations
+   *
+   * @return
+   */
+  public static ObjectMapper mapperYaml() {
+    return JsonMappers.getMapperYaml();
+  }
+
+  /**
+   * Object mapper for serialization of configurations
+   *
+   * @return
+   */
+  public static ObjectMapper mapper() {
+    return JsonMappers.getMapper();
+  }
+
+
+
+  /**
+   * @return JSON representation of NN configuration
+   */
+  public String toYaml() {
+    ObjectMapper mapper = NeuralNetConfiguration.mapperYaml();
+    synchronized (mapper) {
+      try {
+        return mapper.writeValueAsString(this);
+      } catch (com.fasterxml.jackson.core.JsonProcessingException e) {
+        throw new RuntimeException(e);
+      }
+    }
+  }
+
+  /**
+   * @return JSON representation of NN configuration
+   */
+  public String toJson() {
+    ObjectMapper mapper = NeuralNetConfiguration.mapper();
+    synchronized (mapper) {
+      //JSON mappers are supposed to be thread safe: however, in practice they seem to miss fields occasionally
+      //when writeValueAsString is used by multiple threads. This results in invalid JSON. See issue #3243
+      try {
+        return mapper.writeValueAsString(this);
+      } catch (com.fasterxml.jackson.core.JsonProcessingException e) {
+        throw new RuntimeException(e);
+      }
+    }
+  }
+
+  @Override
+  public String toString() {
+    return toJson();
+  }
+
+  public NeuralNetworkConfiguration getConf(int i) {
+    return confs.get(i);
+  }
+
+  @Override
+  public NeuralNetworkConfiguration clone() {
+
+      NeuralNetworkConfiguration clone = (NeuralNetworkConfiguration) super.clone();
+      List<NeuralNetworkConfiguration> confList = clone.getConfs();
+      if (confList != null) {
+        List<NeuralNetworkConfiguration> list = new ArrayList<>();
+        for (NeuralNetworkConfiguration conf : confList) {
+          list.add(conf.clone());
+        }
+      }
+
+      if (clone.getInputPreProcessors() != null) {
+        Map<Integer, InputPreProcessor> map = new HashMap<>();
+        for (Map.Entry<Integer, InputPreProcessor> entry : clone.getInputPreProcessors().entrySet()) {
+          map.put(entry.getKey(), entry.getValue().clone());
+        }
+        clone.getInputPreProcessors().clear();
+        clone.getInputPreProcessors().putAll(map);
+      }
+
+      clone.setInferenceWorkspaceMode(this.inferenceWorkspaceMode);
+      clone.setTrainingWorkspaceMode(this.trainingWorkspaceMode);
+      clone.setCacheMode(this.cacheMode);
+      clone.setValidateOutputLayerConfig(this.validateOutputLayerConfig);
+      clone.setDataType(this.dataType);
+
+      return clone;
+
+  }
+
+  public InputPreProcessor getInputPreProcess(int curr) {
+    return inputPreProcessors.get(curr);
+  }
+
+  /**
+   * Get a {@link MemoryReport} for the given MultiLayerConfiguration. This is used to estimate the
+   * memory requirements for the given network configuration and input
+   *
+   * @param inputType Input types for the network
+   * @return Memory report for the network
+   */
+  public NetworkMemoryReport getMemoryReport(InputType inputType) {
+
+    Map<String, MemoryReport> memoryReportMap = new LinkedHashMap<>();
+    int nLayers = confs.size();
+    for (int i = 0; i < nLayers; i++) {
+      String layerName = confs.get(i).getLayer().getLayerName();
+      if (layerName == null) {
+        layerName = String.valueOf(i);
+      }
+
+      //Pass input type through preprocessor, if necessary
+      InputPreProcessor preproc = getInputPreProcess(i);
+      //TODO memory requirements for preprocessor
+      if (preproc != null) {
+        inputType = preproc.getOutputType(inputType);
+      }
+
+      LayerMemoryReport report = confs.get(i).getLayer().getMemoryReport(inputType);
+      memoryReportMap.put(layerName, report);
+
+      inputType = confs.get(i).getLayer().getOutputType(i, inputType);
+    }
+
+    return new NetworkMemoryReport(memoryReportMap, MultiLayerConfiguration.class,
+        "MultiLayerNetwork", inputType);
+  }
+
+  /**
+   * For the given input shape/type for the network, return a list of activation sizes for each
+   * layer in the network.<br> i.e., list.get(i) is the output activation sizes for layer i
+   *
+   * @param inputType Input type for the network
+   * @return A lits of activation types for the network, indexed by layer number
+   */
+  public List<InputType> getLayerActivationTypes(@NonNull InputType inputType) {
+    List<InputType> out = new ArrayList<>();
+    int nLayers = confs.size();
+    for (int i = 0; i < nLayers; i++) {
+      InputPreProcessor preproc = getInputPreProcess(i);
+      if (preproc != null) {
+        inputType = preproc.getOutputType(inputType);
+      }
+
+      inputType = confs.get(i).getLayer().getOutputType(i, inputType);
+      out.add(inputType);
+    }
+    return out;
+  }
+
+  /**
+   * Defines some additional handy methods. Other than that,
+   * the builder is generated by lombok.
+   */
+  public static class NeuralNetworkConfigurationBuilder {
+
+      /**
+       * Specify the processors. These are used at each layer for doing things like normalization and
+       * shaping of input.
+       *
+       * @param processor what to use to preProcess the data.
+       * @return builder pattern
+       */
+      public NeuralNetworkConfigurationBuilder inputPreProcessor(Integer layer,
+          InputPreProcessor processor) {
+        inputPreProcessors.put(layer, processor);
+        return this;
+      }
+
+    /**
+     * Specify additional layer configurations
+     */
+    @Deprecated
+    public NeuralNetworkConfigurationBuilder layersFromArray(Layer[] arrLayers) {
+      for(Layer l : arrLayers) {
+        layers.add( l );
+      }
+      return this;
+    }
+  }
+
+
 }

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/conf/layer/AbstractLayerConfiguration.java

@@ -24,12 +24,12 @@ package net.brutex.ai.dnn.conf.layer;
 import lombok.Getter;
 import lombok.NonNull;
 import lombok.Setter;
-import net.brutex.ai.dnn.api.LayerConfiguration;
+import lombok.experimental.SuperBuilder;
-import org.deeplearning4j.nn.conf.inputs.InputType;
+import net.brutex.ai.dnn.api.ILayerConfiguration;
 
-public abstract class AbstractLayerConfiguration implements LayerConfiguration {
+@SuperBuilder
+public abstract class AbstractLayerConfiguration implements ILayerConfiguration {
 
   @Getter @Setter @NonNull
-  private InputType.Type inputType;
+  private String name;
-
 }

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/conf/layer/DenseLayerConfiguration.java

@@ -0,0 +1,62 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.dnn.conf.layer;
+
+import lombok.Builder;
+import lombok.experimental.SuperBuilder;
+import org.deeplearning4j.nn.conf.layers.LayerValidation;
+
+/**
+ * The dense layer is a neural network layer that is connected deeply, which means each neuron in
+ * the dense layer receives input from all neurons of its previous layer. The dense layer is found
+ * to be the most commonly used layer in the models.
+ * <p>
+ * In the background, the dense layer performs a matrix-vector multiplication. The values used in
+ * the matrix are actually parameters that can be trained and updated with the help of
+ * backpropagation.
+ * <p>
+ * The output generated by the dense layer is an ‘m’ dimensional vector. Thus, dense layer is
+ * basically used for changing the dimensions of the vector. Dense layers also applies operations
+ * like rotation, scaling, translation on the vector.
+ */
+@SuperBuilder
+public class DenseLayerConfiguration extends FeedForwardLayerConfiguration {
+
+  /**
+   * Decides whether we should include a bias vector for calculation purposes or not.
+   */
+  @Builder.Default
+  boolean bias = true;
+
+  
+
+  /**
+   * An implementation to validate the network
+   *
+   * @return true if no errors found; false otherwise
+   */
+  @Override
+  public boolean isValid() {
+    LayerValidation.assertNInNOutSet("DenseLayerConfiguration", getName(), -99, getIn(), getOut());
+    return super.isValid();
+  }
+}

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/conf/layer/FeedForwardLayerConfiguration.java

@@ -0,0 +1,99 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.dnn.conf.layer;
+
+import lombok.Getter;
+import lombok.experimental.SuperBuilder;
+import lombok.extern.slf4j.Slf4j;
+import net.brutex.ai.dnn.api.ILayer;
+import net.brutex.ai.dnn.api.ILayerConfiguration;
+import net.brutex.ai.dnn.api.IModel;
+import org.deeplearning4j.nn.conf.inputs.InputType;
+import org.deeplearning4j.nn.conf.inputs.InputType.Type;
+
+/**
+ * A Feed Forward Layer Configuration
+ */
+@Slf4j
+@SuperBuilder
+public class FeedForwardLayerConfiguration extends AbstractLayerConfiguration implements ILayerConfiguration {
+
+  @Getter private int in;
+  @Getter private int out;
+
+  /**
+   * This Fast Forward ILayer will always output data as
+   * FF type.
+   * @return InputType for FF
+   **/
+  @Getter
+  final InputType.Type outputType = InputType.Type.FF;
+
+  @Getter
+  final InputType.Type inputType = InputType.Type.FF;
+
+  /**
+   * Create and return an instance of a ILayerConfiguration.
+   *
+   * @param network the "holding" network for the instance
+   * @return the new layer instance
+   */
+  //@Override
+  public ILayer instantiate(IModel network) {
+    //Let's do some verifications first
+    if(getInputType() != Type.FF) {
+      log.error("The {} layer configuration must use an InputType of {}, but found {}",
+          this.getClass().getSimpleName(),
+          Type.FF.name(),
+          getInputType().name());
+    }
+    return null;
+  }
+
+  /**
+   * Number of trainable parameter in this layer
+   *
+   * @return number of parameter
+   */
+  @Override
+  public long numParameters() {
+    return in * out + out; //add one extra out for the bias
+  }
+
+  /**
+   * An implementation should provide a method to validate the network
+   *
+   * @return true if no errors found; false otherwise
+   */
+  @Override
+  public boolean isValid() {
+    boolean result = true;
+    if(getInputType() != Type.FF) {
+      log.error("The {} layer configuration must use an InputType of {}, but found {}",
+          this.getClass().getSimpleName(),
+          Type.FF.name(),
+          getInputType().name());
+      result = false;
+    }
+    return result;
+  }
+}

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/impl/network/AbstractNeuralNetwork.java

@@ -1,72 +0,0 @@
-/*
- *
- *    ******************************************************************************
- *    *
- *    * This program and the accompanying materials are made available under the
- *    * terms of the Apache License, Version 2.0 which is available at
- *    * https://www.apache.org/licenses/LICENSE-2.0.
- *    *
- *    *  See the NOTICE file distributed with this work for additional
- *    *  information regarding copyright ownership.
- *    * Unless required by applicable law or agreed to in writing, software
- *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *    * License for the specific language governing permissions and limitations
- *    * under the License.
- *    *
- *    * SPDX-License-Identifier: Apache-2.0
- *    *****************************************************************************
- *
- */
-
-package net.brutex.ai.dnn.impl.network;
-
-import java.util.ArrayList;
-import java.util.Collection;
-import java.util.HashSet;
-import java.util.List;
-import lombok.Getter;
-import lombok.NonNull;
-import lombok.Setter;
-import net.brutex.ai.dnn.api.Layer;
-import net.brutex.ai.dnn.api.NeuralNetwork;
-import net.brutex.ai.dnn.api.LayerConfiguration;
-import net.brutex.ai.dnn.conf.NeuralNetworkConfiguration;
-import org.deeplearning4j.optimize.api.TrainingListener;
-import org.nd4j.linalg.dataset.api.MultiDataSet;
-
-public abstract class AbstractNeuralNetwork implements NeuralNetwork {
-
-  @Getter @Setter @NonNull
-  private String name;
-
-  @Getter @NonNull
-  private NeuralNetworkConfiguration configuration;
-
-  @Getter
-  private final Collection<TrainingListener> trainingListeners = new HashSet<>();
-
-  /**
-   * The neural network holds an instantiation of its configured
-   * layers.
-   * @return the actual runtime layers
-   */
-  @Getter
-  private final List<Layer> runtimeLayers = new ArrayList<>();
-
-  /**
-   * Sets the configuration to be used. Each time a configuration is set, the runtime layers
-   * of this NeuralNetwork are updated from the configuration.
-   *
-   * @param conf the configuration to use for this network
-   */
-  public void setConfiguration(net.brutex.ai.dnn.api.NeuralNetworkConfiguration conf) {
-    List<LayerConfiguration> layers = conf.getLayerConfigurations();
-    for(LayerConfiguration layer : layers) {
-      Layer initializedLayer = layer.instantiate(this);
-      this.getRuntimeLayers().add(initializedLayer);
-    }
-    this.configuration = configuration;
-  }
-
-}

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/impl/network/NeuralNetwork.java

@@ -1,692 +0,0 @@
-/*
- *
- *    ******************************************************************************
- *    *
- *    * This program and the accompanying materials are made available under the
- *    * terms of the Apache License, Version 2.0 which is available at
- *    * https://www.apache.org/licenses/LICENSE-2.0.
- *    *
- *    *  See the NOTICE file distributed with this work for additional
- *    *  information regarding copyright ownership.
- *    * Unless required by applicable law or agreed to in writing, software
- *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
- *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
- *    * License for the specific language governing permissions and limitations
- *    * under the License.
- *    *
- *    * SPDX-License-Identifier: Apache-2.0
- *    *****************************************************************************
- *
- */
-
-package net.brutex.ai.dnn.impl.network;
-
-import java.util.Arrays;
-import java.util.Collection;
-import java.util.HashMap;
-import java.util.HashSet;
-import java.util.Map;
-import lombok.Getter;
-import lombok.NonNull;
-import lombok.Setter;
-import lombok.extern.slf4j.Slf4j;
-import lombok.val;
-import org.bytedeco.javacpp.Pointer;
-import org.deeplearning4j.datasets.iterator.MultiDataSetWrapperIterator;
-import org.deeplearning4j.exception.DL4JInvalidInputException;
-import org.deeplearning4j.nn.api.Classifier;
-import org.deeplearning4j.nn.api.Layer;
-import org.deeplearning4j.nn.api.MaskState;
-import org.deeplearning4j.nn.api.Updater;
-import org.deeplearning4j.nn.api.layers.IOutputLayer;
-import org.deeplearning4j.nn.api.layers.RecurrentLayer;
-import org.deeplearning4j.nn.conf.BackpropType;
-import org.deeplearning4j.nn.conf.InputPreProcessor;
-import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
-import net.brutex.ai.dnn.conf.NeuralNetworkConfiguration;
-import org.deeplearning4j.nn.conf.WorkspaceMode;
-import org.deeplearning4j.nn.layers.FrozenLayerWithBackprop;
-import org.deeplearning4j.nn.layers.wrapper.BaseWrapperLayer;
-import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
-import org.deeplearning4j.nn.updater.UpdaterCreator;
-import org.deeplearning4j.nn.workspace.ArrayType;
-import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
-import org.deeplearning4j.optimize.Solver;
-import org.deeplearning4j.optimize.api.ConvexOptimizer;
-import org.deeplearning4j.optimize.api.TrainingListener;
-import org.deeplearning4j.util.CrashReportingUtil;
-import org.deeplearning4j.util.ModelSerializer;
-import org.nd4j.common.base.Preconditions;
-import org.nd4j.common.primitives.Pair;
-import org.nd4j.linalg.api.memory.MemoryWorkspace;
-import org.nd4j.linalg.api.memory.conf.WorkspaceConfiguration;
-import org.nd4j.linalg.api.memory.enums.AllocationPolicy;
-import org.nd4j.linalg.api.memory.enums.LearningPolicy;
-import org.nd4j.linalg.api.memory.enums.ResetPolicy;
-import org.nd4j.linalg.api.memory.enums.SpillPolicy;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.dataset.AsyncDataSetIterator;
-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.heartbeat.Heartbeat;
-import org.nd4j.linalg.heartbeat.reports.Environment;
-import org.nd4j.linalg.heartbeat.reports.Event;
-import org.nd4j.linalg.heartbeat.reports.Task;
-import org.nd4j.linalg.heartbeat.utils.EnvironmentUtils;
-import org.nd4j.linalg.heartbeat.utils.TaskUtils;
-import org.nd4j.linalg.indexing.NDArrayIndex;
-
-@Slf4j
-public class NeuralNetwork extends AbstractNeuralNetwork {
-
-
-  //the hidden neural network layers (including output layer)
-  protected Layer[] layers;
-
-  protected transient ThreadLocal<Long> lastEtlTime = new ThreadLocal<>();
-
-  //Current training data: input features and labels
-  @Getter @Setter @NonNull
-  protected INDArray input;
-  @Getter @Setter
-  protected INDArray labels;
-
-  //Workspaces for CUDNN. Pass to LayerWorkspaceMgr for re-use in cudnn helpers
-  @Getter
-  protected transient Map<String, Pointer> helperWorkspaces = new HashMap<>();
-
-  /**
-   * Used to call optimizers during backprop
-   */
-  @NonNull
-  protected transient Solver solver = new Solver.Builder().configure(getConfiguration()).
-      listeners(getTrainingListeners()).model(this).build();
-
-
-  /**
-   * Create a new NeuralNetwork from the given configuration
-   * @param conf
-   */
-  public NeuralNetwork(NeuralNetworkConfiguration conf) {
-    if(! validateConfiguration() ) {
-      log.error("Configuration '{}' has failed validation.", conf.getName());
-      throw new RuntimeException();
-    }
-    log.info("Configuration '{}' has been validated successfully.", conf.getName());
-    this.conf = conf;
-  }
-
-  private boolean validateConfiguration() {
-
-    return true;
-  }
-
-  private void logNotImplemented( ) {
-    // getStackTrace() method return
-    // current method name at 0th index
-    String  method = new Throwable()
-        .getStackTrace()[1]
-        .getMethodName();
-    log.trace("Method '{}}' is not implemented for {}", method, this.getClass().getSimpleName());
-  }
-
-  /**
-   * This method does initialization of model
-   * <p>
-   * PLEASE NOTE: All implementations should track own state, to avoid double spending
-   */
-  @Override
-  public void init() {
-    logNotImplemented();
-  }
-
-  /**
-   * This method returns model parameters as single INDArray
-   *
-   * @return
-   */
-  @Override
-  public INDArray params() {
-    logNotImplemented();
-    return null;
-  }
-
-  /**
-   * This method returns updater state (if applicable), null otherwise
-   *
-   * @return
-   */
-  @Override
-  public INDArray updaterState() {
-    return getUpdater(true) != null ? getUpdater(true).getStateViewArray() : null;
-  }
-
-  /**
-   * This method returns Optimizer used for training
-   *
-   * @return the optimizer
-   */
-  @Override
-  public ConvexOptimizer getOptimizer() {
-    return solver.getOptimizer();
-  }
-
-
-
-  /** Get the updater for this NeuralNetwork from the Solver
-   * @return Updater for NeuralNetwork
-   */
-  private Updater getUpdater(boolean initializeIfReq) {
-    if (solver == null && initializeIfReq) {
-      synchronized(this){
-        if(solver == null) {    //May have been created while waiting for lock
-          solver = new Solver.Builder().configure(conf()).listeners(getTrainingListeners()).model(this).build();
-          solver.getOptimizer().setUpdater(UpdaterCreator.getUpdater(this));
-        }
-      }
-    }
-    if(solver != null) {
-      return solver.getOptimizer().getUpdater(initializeIfReq);
-    }
-    return null;
-  }
-
-  /**
-   * Set the updater for the NeuralNetwork in the Solver
-   * */
-  public void setUpdater(@NonNull Updater updater) {
-    solver.getOptimizer().setUpdater(updater);
-  }
-
-
-  @Override
-  public void fit(MultiDataSet dataSet) {
-    if (dataSet.getFeatures().length == 1 && dataSet.getLabels().length == 1) {
-      INDArray features = dataSet.getFeatures(0);
-      INDArray labels = dataSet.getLabels(0);
-      INDArray fMask = null;
-      INDArray lMask = null;
-
-      if (dataSet.getFeaturesMaskArrays() != null)
-        fMask = dataSet.getFeaturesMaskArrays()[0];
-
-      if (dataSet.getFeaturesMaskArrays() != null)
-        lMask = dataSet.getLabelsMaskArrays()[0];
-
-      DataSet ds = new DataSet(features, labels, fMask, lMask);
-      fit(ds);
-    } else {
-      throw new DL4JInvalidInputException(
-          "MultiLayerNetwork can't handle MultiDataSet with more than 1 features or labels array." +
-              "Please consider use of ComputationGraph");
-    }
-  }
-
-  /**
-   * Perform minibatch training on all minibatches in the MultiDataSetIterator, for the specified number of epochs.
-   * Equvalent to calling {@link #fit(MultiDataSetIterator)} numEpochs times in a loop
-   *
-   * @param iterator  Training data (DataSetIterator). Iterator must support resetting
-   * @param numEpochs Number of training epochs, >= 1
-   */
-  public void fit(@NonNull MultiDataSetIterator iterator, int numEpochs){
-    Preconditions.checkArgument(numEpochs > 0, "Number of epochs much be > 0. Got numEpochs = %s", numEpochs);
-    Preconditions.checkArgument(numEpochs == 1 || iterator.resetSupported(), "Cannot perform multiple epochs training using" +
-        "iterator has does not support resetting (iterator.resetSupported() returned false)");
-
-    for(int i = 0; i < numEpochs; i++) {
-      fit(iterator);
-    }
-  }
-
-  /**
-   * Perform minibatch training on all minibatches in the MultiDataSetIterator.<br>
-   * Note: The MultiDataSets in the MultiDataSetIterator must have exactly 1 input and output array (as
-   * MultiLayerNetwork only supports 1 input and 1 output)
-   *
-   * @param iterator  Training data (DataSetIterator). Iterator must support resetting
-   */
-  @Override
-  public void fit(MultiDataSetIterator iterator) {
-    fit(new MultiDataSetWrapperIterator(iterator));
-  }
-
-  /**
-   * Perform minibatch training on all minibatches in the DataSetIterator for 1 epoch.<br>
-   * Note that this method does not do layerwise  pretraining.<br>
-   * For pretraining use method pretrain..  #pretrain(DataSetIterator)<br>
-   * @param iterator Training data (DataSetIterator)
-   */
-  @Override
-  public void fit(DataSetIterator iterator) {
-    try{
-      fitHelper(iterator);
-    } catch (OutOfMemoryError e){
-      CrashReportingUtil.writeMemoryCrashDump(this, e);
-      throw e;
-    }
-  }
-
-  private synchronized void fitHelper(DataSetIterator iterator){
-    // we're wrapping all iterators into AsyncDataSetIterator to provide background prefetch - where appropriate
-    DataSetIterator iter;
-    boolean destructable = false;
-    if (iterator.asyncSupported()) {
-      iter = new AsyncDataSetIterator(iterator, Math.min(
-          Nd4j.getAffinityManager().getNumberOfDevices() * 2, 2), true);
-      destructable = true;
-    } else {
-      iter = iterator;
-    }
-
-    for (TrainingListener tl : trainingListeners) {
-      tl.onEpochStart(this);
-    }
-
-    LayerWorkspaceMgr workspaceMgr;
-    if(conf.getTrainingWorkspaceMode() == WorkspaceMode.NONE){
-      workspaceMgr = LayerWorkspaceMgr.noWorkspaces();
-    } else {
-      workspaceMgr = LayerWorkspaceMgr.builder()
-          .with(ArrayType.ACTIVATIONS, WS_ALL_LAYERS_ACT, WS_ALL_LAYERS_ACT_CONFIG)
-          .with(ArrayType.INPUT, WS_ALL_LAYERS_ACT, WS_ALL_LAYERS_ACT_CONFIG)
-          .with(ArrayType.FF_WORKING_MEM, WS_LAYER_WORKING_MEM, WS_LAYER_WORKING_MEM_CONFIG)
-          .with(ArrayType.BP_WORKING_MEM, WS_LAYER_WORKING_MEM, WS_LAYER_WORKING_MEM_CONFIG)
-          .with(ArrayType.RNN_FF_LOOP_WORKING_MEM, WS_RNN_LOOP_WORKING_MEM, WS_RNN_LOOP_WORKING_MEM_CONFIG)
-          .with(ArrayType.RNN_BP_LOOP_WORKING_MEM, WS_RNN_LOOP_WORKING_MEM, WS_RNN_LOOP_WORKING_MEM_CONFIG)
-          //Note for updater working memory, we have the option to re-use WS_ALL_LAYERS_ACT or FF/BP_WORKING_MEM
-          // as these should be closed by the time updaters are executed
-          //Generally, WS_ALL_LAYERS_ACT will be the larger of the two, so we'll use this
-          .with(ArrayType.UPDATER_WORKING_MEM, WS_ALL_LAYERS_ACT, WS_ALL_LAYERS_ACT_CONFIG)
-          .build();
-    }
-    workspaceMgr.setHelperWorkspacePointers(helperWorkspaces);
-
-    update(TaskUtils.buildTask(iter));
-    if (!iter.hasNext() && iter.resetSupported()) {
-      iter.reset();
-    }
-    long time1 = System.currentTimeMillis();
-    while (iter.hasNext()) {
-
-      DataSet next = iter.next();
-      long time2 = System.currentTimeMillis();
-
-      lastEtlTime.set((time2 - time1));
-
-      if (next.getFeatures() == null || next.getLabels() == null)
-        break;
-
-      // TODO: basically we want to wrap internals of this loop into workspace
-
-
-      boolean hasMaskArrays = next.hasMaskArrays();
-
-      if (conf.getBackpropType() == BackpropType.TruncatedBPTT) {
-        doTruncatedBPTT(next.getFeatures(), next.getLabels(), next.getFeaturesMaskArray(),
-            next.getLabelsMaskArray(), workspaceMgr);
-      } else {
-        if (hasMaskArrays)
-          setLayerMaskArrays(next.getFeaturesMaskArray(), next.getLabelsMaskArray());
-
-        setInput(next.getFeatures());
-        setLabels(next.getLabels());
-
-        if (solver == null) {
-          try (MemoryWorkspace wsO = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
-            solver = new Solver.Builder().configure(conf()).listeners(getTrainingListeners()).model(this)
-                .build();
-          }
-        }
-
-        //TODO CACHE
-        solver.optimize(workspaceMgr);
-      }
-
-      if (hasMaskArrays)
-        clearLayerMaskArrays();
-
-      time1 = System.currentTimeMillis();
-      synchronizeIterEpochCounts();
-    }
-
-    if (!trainingListeners.isEmpty()) {
-      for (TrainingListener tl : trainingListeners) {
-        tl.onEpochEnd(this);
-      }
-    }
-
-    clearLayersStates();
-
-    if (destructable)
-      ((AsyncDataSetIterator) iter).shutdown();
-
-    incrementEpochCount();
-  }
-
-
-  /**
-   * Workspace for working memory for a single layer: forward pass and backward pass
-   * Note that this is opened/closed once per op (activate/backpropGradient call)
-   */
-  protected static final String WS_LAYER_WORKING_MEM = "WS_LAYER_WORKING_MEM";
-  /**
-   * Workspace for storing all layers' activations - used only to store activations (layer inputs) as part of backprop
-   * Not used for inference
-   */
-  protected static final String WS_ALL_LAYERS_ACT = "WS_ALL_LAYERS_ACT";
-  /**
-   * Next 2 workspaces: used for:
-   * (a) Inference: holds activations for one layer only
-   * (b) Backprop: holds activation gradients for one layer only
-   * In both cases, they are opened and closed on every second layer
-   */
-  protected static final String WS_LAYER_ACT_1 = "WS_LAYER_ACT_1";
-  protected static final String WS_LAYER_ACT_2 = "WS_LAYER_ACT_2";
-
-  /**
-   * Workspace for output methods that use OutputAdapter
-   */
-  protected static final String WS_OUTPUT_MEM = "WS_OUTPUT_MEM";
-
-  /**
-   * Workspace for working memory in RNNs - opened and closed once per RNN time step
-   */
-  protected static final String WS_RNN_LOOP_WORKING_MEM = "WS_RNN_LOOP_WORKING_MEM";
-
-
-  protected WorkspaceConfiguration WS_LAYER_WORKING_MEM_CONFIG;
-
-  protected static final WorkspaceConfiguration WS_ALL_LAYERS_ACT_CONFIG = WorkspaceConfiguration.builder()
-      .initialSize(0)
-      .overallocationLimit(0.05)
-      .policyLearning(LearningPolicy.FIRST_LOOP)
-      .policyReset(ResetPolicy.BLOCK_LEFT)
-      .policySpill(SpillPolicy.REALLOCATE)
-      .policyAllocation(AllocationPolicy.OVERALLOCATE)
-      .build();
-
-  protected WorkspaceConfiguration WS_LAYER_ACT_X_CONFIG;
-
-  protected static final WorkspaceConfiguration WS_RNN_LOOP_WORKING_MEM_CONFIG = WorkspaceConfiguration.builder()
-      .initialSize(0).overallocationLimit(0.05).policyReset(ResetPolicy.BLOCK_LEFT)
-      .policyAllocation(AllocationPolicy.OVERALLOCATE).policySpill(SpillPolicy.REALLOCATE)
-      .policyLearning(LearningPolicy.FIRST_LOOP).build();
-
-
-  boolean initDone;
-  protected void update(Task task) {
-    if (!initDone) {
-      initDone = true;
-      Heartbeat heartbeat = Heartbeat.getInstance();
-      task = ModelSerializer.taskByModel(this);
-      Environment env = EnvironmentUtils.buildEnvironment();
-      heartbeat.reportEvent(Event.STANDALONE, env, task);
-    }
-  }
-
-  protected void doTruncatedBPTT(INDArray input, INDArray labels, INDArray featuresMaskArray,
-      INDArray labelsMaskArray, LayerWorkspaceMgr workspaceMgr) {
-    if (input.rank() != 3 || labels.rank() != 3) {
-      log.warn("Cannot do truncated BPTT with non-3d inputs or labels. Expect input with shape [miniBatchSize,nIn,timeSeriesLength], got "
-          + Arrays.toString(input.shape()) + "\tand labels with shape "
-          + Arrays.toString(labels.shape()));
-      return;
-    }
-    if (input.size(2) != labels.size(2)) {
-      log.warn("Input and label time series have different lengths: {} input length, {} label length",
-          input.size(2), labels.size(2));
-      return;
-    }
-
-    int fwdLen = conf.getTbpttFwdLength();
-    update(TaskUtils.buildTask(input, labels));
-    val timeSeriesLength = input.size(2);
-    long nSubsets = timeSeriesLength / fwdLen;
-    if (timeSeriesLength % fwdLen != 0)
-      nSubsets++; //Example: 100 fwdLen with timeSeriesLength=120 -> want 2 subsets (1 of size 100, 1 of size 20)
-
-    rnnClearPreviousState();
-
-    for (int i = 0; i < nSubsets; i++) {
-      long startTimeIdx = (long) i * fwdLen;
-      long endTimeIdx = startTimeIdx + fwdLen;
-      if (endTimeIdx > timeSeriesLength)
-        endTimeIdx = timeSeriesLength;
-
-      if (startTimeIdx > Integer.MAX_VALUE || endTimeIdx > Integer.MAX_VALUE)
-        throw new ND4JArraySizeException();
-      INDArray[] subsets = getSubsetsForTbptt((int) startTimeIdx, (int) endTimeIdx, input, labels,
-          featuresMaskArray, labelsMaskArray);
-
-      setInput(subsets[0]);
-      setLabels(subsets[1]);
-      setLayerMaskArrays(subsets[2], subsets[3]);
-
-      if (solver == null) {
-        try (MemoryWorkspace wsO = Nd4j.getMemoryManager().scopeOutOfWorkspaces()) {
-          solver = new Solver.Builder().configure(conf()).listeners(getTrainingListeners()).model(this)
-              .build();
-        }
-      }
-      solver.optimize(workspaceMgr);
-
-      //Finally, update the state of the RNN layers:
-      updateRnnStateWithTBPTTState();
-    }
-
-    rnnClearPreviousState();
-    clearLayerMaskArrays();
-  }
-
-  private INDArray[] getSubsetsForTbptt(int startTimeIdx, int endTimeIdx, INDArray input, INDArray labels,
-      INDArray fMask, INDArray lMask ){
-    INDArray[] out = new INDArray[4];
-    out[0] = input.get(NDArrayIndex.all(), NDArrayIndex.all(),
-        NDArrayIndex.interval(startTimeIdx, endTimeIdx));
-    out[1] = labels.get(NDArrayIndex.all(), NDArrayIndex.all(),
-        NDArrayIndex.interval(startTimeIdx, endTimeIdx));
-
-    if (fMask != null) {
-      out[2] = fMask.get(NDArrayIndex.all(),
-          NDArrayIndex.interval(startTimeIdx, endTimeIdx));
-    }
-    if (lMask != null) {
-      out[3] = lMask.get(NDArrayIndex.all(),
-          NDArrayIndex.interval(startTimeIdx, endTimeIdx));
-    }
-
-    return out;
-  }
-
-  /**
-   * Intended for internal/developer use
-   */
-  public void updateRnnStateWithTBPTTState() {
-    Layer[] layers = conf.calculateInnerLayers().toArray(new Layer[]{});
-    for (int i = 0; i < layers.length; i++) {
-      if (layers[i] instanceof RecurrentLayer) {
-        RecurrentLayer l = ((RecurrentLayer) layers[i]);
-        l.rnnSetPreviousState(l.rnnGetTBPTTState());
-      } else if (layers[i] instanceof MultiLayerNetwork) {
-        ((MultiLayerNetwork) layers[i]).updateRnnStateWithTBPTTState();
-      }
-    }
-  }
-
-  /** Clear the previous state of the RNN layers (if any).
-   */
-  public void rnnClearPreviousState() {
-    Layer[] layers = conf.getLayers().toArray(new Layer[]{});
-    if (layers == null)
-      return;
-    for (int i = 0; i < layers.length; i++) {
-      if (layers[i] instanceof RecurrentLayer)
-        ((RecurrentLayer) layers[i]).rnnClearPreviousState();
-      else if (layers[i] instanceof MultiLayerNetwork) {
-        ((MultiLayerNetwork) layers[i]).rnnClearPreviousState();
-      } else if(layers[i] instanceof BaseWrapperLayer && ((BaseWrapperLayer)layers[i]).getUnderlying() instanceof RecurrentLayer){
-        ((RecurrentLayer) ((BaseWrapperLayer)layers[i]).getUnderlying()).rnnClearPreviousState();
-      }
-    }
-  }
-
-
-
-  /** Remove the mask arrays from all layers.<br>
-   * See {@link #setLayerMaskArrays(INDArray, INDArray)} for details on mask arrays.
-   */
-  public void clearLayerMaskArrays() {
-    Layer[] layers = conf.getLayers().toArray(new Layer[]{});
-    for (Layer layer : layers) {
-      layer.setMaskArray(null);
-    }
-  }
-
-  /**
-   * Increment the epoch count (in the underlying {@link MultiLayerConfiguration} by 1).
-   * Note that this is done <i>automatically</i> when using iterator-based fitting methods, such as
-   * {@link #fit(DataSetIterator)}. However, when using non-iterator fit methods (DataSet, INDArray/INDArray etc),
-   * the network has no way to know when one epoch ends and another starts. In such situations, this method
-   * can be used to increment the epoch counter.<br>
-   * Note that the epoch counter is used for situations such as some learning rate schedules, and the like.
-   *
-   * The current epoch count can be obtained using {@code MultiLayerConfiguration.getLayerwiseConfiguration().getEpochCount()}
-   */
-  public void incrementEpochCount(){
-    conf.setEpochCount(conf.getEpochCount() + 1);
-    synchronizeIterEpochCounts();
-  }
-
-  protected void synchronizeIterEpochCounts() {
-    //TODO: this is necessary for some schedules - but the redundant values are a little ugly...
-    int currIter = conf.getIterationCount();
-    int currEpoch = conf.getEpochCount();
-    log.error("Something went wrong here. Code incomplete");
-    /*for(Layer l : conf.getLayers()) {
-      l.setIterationCount(currIter);
-      l.setEpochCount(currEpoch);
-    }
-    */
-  }
-
-  /**
-   * This method just makes sure there's no state preserved within layers
-   */
-  public void clearLayersStates() {
-    for (Layer layer : layers) {
-      layer.clear();
-      layer.clearNoiseWeightParams();
-    }
-  }
-
-
-  /**Set the mask arrays for features and labels. Mask arrays are typically used in situations such as one-to-many
-   * and many-to-one learning with recurrent neural networks, as well as for supporting time series of varying lengths
-   * within the same minibatch.<br>
-   * For example, with RNN data sets with input of shape [miniBatchSize,nIn,timeSeriesLength] and outputs of shape
-   * [miniBatchSize,nOut,timeSeriesLength], the features and mask arrays will have shape [miniBatchSize,timeSeriesLength]
-   * and contain values 0 or 1 at each element (to specify whether a given input/example is present - or merely padding -
-   * at a given time step).<br>
-   * <b>NOTE</b>: This method is not usually used directly. Instead, methods such as @link #feedForward(INDArray, INDArray, INDArray)}
-   * and @link #output(INDArray, boolean, INDArray, INDArray)} handle setting of masking internally.
-   * @param featuresMaskArray Mask array for features (input)
-   * @param labelsMaskArray Mask array for labels (output)
-   * @see #clearLayerMaskArrays()
-   */
-  public void setLayerMaskArrays(INDArray featuresMaskArray, INDArray labelsMaskArray) {
-    if (featuresMaskArray != null) {
-
-      if (featuresMaskArray.size(0) > Integer.MAX_VALUE)
-        throw new ND4JArraySizeException();
-      //New approach: use feedForwardMaskArray method
-      feedForwardMaskArray(featuresMaskArray, MaskState.Active, (int) featuresMaskArray.size(0));
-
-
-            /*
-            //feedforward layers below a RNN layer: need the input (features) mask array
-            //Reason: even if the time series input is zero padded, the output from the dense layers are
-            // non-zero (i.e., activationFunction(0*weights + bias) != 0 in general)
-            //This assumes that the time series input is masked - i.e., values are 0 at the padded time steps,
-            // so we don't need to do anything for the recurrent layer
-
-            //Now, if mask array is 2d -> need to reshape to 1d (column vector) in the exact same order
-            // as is done for 3d -> 2d time series reshaping
-            INDArray reshapedFeaturesMask = TimeSeriesUtils.reshapeTimeSeriesMaskToVector(featuresMaskArray);
-
-            for( int i=0; i<layers.length-1; i++ ){
-                Type t = layers[i].type();
-                if( t == Type.CONVOLUTIONAL || t == Type.FEED_FORWARD ){
-                    layers[i].setMaskArray(reshapedFeaturesMask);
-                } else if( t == Type.RECURRENT ) break;
-
-            }
-            */
-    }
-    if (labelsMaskArray != null) {
-      if (!(getOutputLayer() instanceof IOutputLayer))
-        return;
-      layers[layers.length - 1].setMaskArray(labelsMaskArray);
-    }
-  }
-
-
-  /**
-   * Get the output layer - i.e., the last layer in the netwok
-   *
-   * @return
-   */
-  public Layer getOutputLayer() {
-    Layer ret = layers[layers.length - 1];
-    if (ret instanceof FrozenLayerWithBackprop) {
-      ret = ((FrozenLayerWithBackprop) ret).getInsideLayer();
-    }
-    return ret;
-  }
-
-
-
-  public Pair<INDArray, MaskState> feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState,
-      int minibatchSize) {
-    if (maskArray == null) {
-      for (int i = 0; i < layers.length; i++) {
-        layers[i].feedForwardMaskArray(null, null, minibatchSize);
-      }
-    } else {
-      //Do a forward pass through each preprocessor and layer
-      for (int i = 0; i < layers.length; i++) {
-        InputPreProcessor preProcessor = conf.getInputPreProcessors().get(i);
-
-        if (preProcessor != null) {
-          Pair<INDArray, MaskState> p =
-              preProcessor.feedForwardMaskArray(maskArray, currentMaskState, minibatchSize);
-          if (p != null) {
-            maskArray = p.getFirst();
-            currentMaskState = p.getSecond();
-          } else {
-            maskArray = null;
-            currentMaskState = null;
-          }
-        }
-
-        Pair<INDArray, MaskState> p =
-            layers[i].feedForwardMaskArray(maskArray, currentMaskState, minibatchSize);
-        if (p != null) {
-          maskArray = p.getFirst();
-          currentMaskState = p.getSecond();
-        } else {
-          maskArray = null;
-          currentMaskState = null;
-        }
-      }
-    }
-
-    return new Pair<>(maskArray, currentMaskState);
-  }
-
-
-}

cavis-dnn/cavis-dnn-nn/src/main/java/net/brutex/ai/dnn/networks/ArtificialNeuralNetwork.java

@@ -0,0 +1,53 @@
+/*
+ *
+ *    ******************************************************************************
+ *    *
+ *    * This program and the accompanying materials are made available under the
+ *    * terms of the Apache License, Version 2.0 which is available at
+ *    * https://www.apache.org/licenses/LICENSE-2.0.
+ *    *
+ *    *  See the NOTICE file distributed with this work for additional
+ *    *  information regarding copyright ownership.
+ *    * Unless required by applicable law or agreed to in writing, software
+ *    * distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
+ *    * WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
+ *    * License for the specific language governing permissions and limitations
+ *    * under the License.
+ *    *
+ *    * SPDX-License-Identifier: Apache-2.0
+ *    *****************************************************************************
+ *
+ */
+
+package net.brutex.ai.dnn.networks;
+
+import lombok.Getter;
+import lombok.Setter;
+import net.brutex.ai.dnn.conf.NeuralNetworkConfiguration;
+import net.brutex.ai.dnn.api.INeuralNetwork;
+
+/**
+ * Artificial Neural Network An artificial neural network (1) takes some input data, and (2)
+ * transforms this input data by calculating a weighted sum over the inputs and (3) applies a
+ * non-linear function to this transformation to calculate an intermediate state. The three steps
+ * above constitute what is known as a layer, and the transformative function is often referred to
+ * as a unit. The intermediate states—often termed features—are used as the input into another
+ * layer.
+ * <p>
+ * Through repetition of these steps, the artificial neural network learns multiple layers of
+ * non-linear features, which it then combines in a final layer to create a prediction.
+ * <p>
+ * The neural network learns by generating an error signal that measures the difference between the
+ * predictions of the network and the desired values and then using this error signal to change the
+ * weights (or parameters) so that predictions get more accurate.
+ */
+public abstract class ArtificialNeuralNetwork implements INeuralNetwork {
+
+  /**
+   * A neural network is created from a configuration.
+   * @param conf The (new net.brutex.ai) configuration for the network
+   */
+  @Getter
+  @Setter //TODO make this also final and @NonNull
+  private NeuralNetworkConfiguration configuration;
+}

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/earlystopping/trainer/BaseEarlyStoppingTrainer.java

@@ -346,7 +346,7 @@ public abstract class BaseEarlyStoppingTrainer<T extends Model> implements IEarl
         } else if(model instanceof ComputationGraph){
             ComputationGraph cg = ((ComputationGraph) model);
             listeners = cg.getListeners();
-            cg.getConfiguration().setEpochCount(epochNum);
+            cg.getComputationGraphConfiguration().setEpochCount(epochNum);
         } else {
             return;
         }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/gradientcheck/GradientCheckUtil.java

@@ -431,7 +431,7 @@ public class GradientCheckUtil {
                             + "DataTypeUtil.setDTypeForContext(DataType.DOUBLE); before using GradientCheckUtil");
         }
 
-        DataType netDataType = c.net.getConfiguration().getDataType();
+        DataType netDataType = c.net.getComputationGraphConfiguration().getDataType();
         if (netDataType != DataType.DOUBLE) {
             throw new IllegalStateException("Cannot perform gradient check: Network datatype is not set to double precision ("
                     + "is: " + netDataType + "). Double precision must be used for gradient checks. Create network with .dataType(DataType.DOUBLE) before using GradientCheckUtil");
@@ -444,8 +444,8 @@ public class GradientCheckUtil {
 
         //Check configuration
         int layerCount = 0;
-        for (String vertexName : c.net.getConfiguration().getVertices().keySet()) {
+        for (String vertexName : c.net.getComputationGraphConfiguration().getVertices().keySet()) {
-            GraphVertex gv = c.net.getConfiguration().getVertices().get(vertexName);
+            GraphVertex gv = c.net.getComputationGraphConfiguration().getVertices().get(vertexName);
             if (!(gv instanceof LayerVertex))
                 continue;
             LayerVertex lv = (LayerVertex) gv;

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Layer.java

@@ -32,194 +32,209 @@ import org.nd4j.common.primitives.Pair;
 import java.io.Serializable;
 import java.util.Collection;
 
+/**
+ * A layer is the highest-level building block in deep learning. A layer is a container that usually
+ * receives weighted input, transforms it with a set of mostly non-linear functions and then passes
+ * these values as output to the next layer. A layer is usually uniform, that is it only contains
+ * one type of activation function, pooling, convolution etc. so that it can be easily compared to
+ * other parts of the network. The first and last layers in a network are called input and output
+ * layers, respectively, and all layers in between are called hidden layers.
+ *
+ * @see <a href="https://developer.nvidia.com/blog/deep-learning-nutshell-core-concept">NVIDIA Deep Learning In A Nutshell</a>
+ */
 public interface Layer extends Serializable, Cloneable, Model, Trainable {
 
-    enum Type {
+  /**
-        FEED_FORWARD, RECURRENT, CONVOLUTIONAL, CONVOLUTIONAL3D,
+   * This method sets given CacheMode for current layer
-        SUBSAMPLING, UPSAMPLING, RECURSIVE, MULTILAYER, NORMALIZATION
+   *
-    }
+   * @param mode
+   */
+  void setCacheMode(CacheMode mode);
 
-    enum TrainingMode {
+  /**
-        TRAIN, TEST
+   * Calculate the regularization component of the score, for the parameters in this layer<br> For
-    }
+   * example, the L1, L2 and/or weight decay components of the loss function<br>
+   *
+   * @param backpropOnlyParams If true: calculate regularization score based on backprop params
+   *                           only. If false: calculate based on all params (including pretrain
+   *                           params, if any)
+   * @return the regularization score of
+   */
+  double calcRegularizationScore(boolean backpropOnlyParams);
 
-    /**
+  /**
-     * This method sets given CacheMode for current layer
+   * Returns the layer type
-     *
+   *
-     * @param mode
+   * @return
-     */
+   */
-    void setCacheMode(CacheMode mode);
+  Type type();
 
-    /**
+  /**
-     * Calculate the regularization component of the score, for the parameters in this layer<br>
+   * Calculate the gradient relative to the error in the next layer
-     * For example, the L1, L2 and/or weight decay components of the loss function<br>
+   *
-     *
+   * @param epsilon      w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where
-     * @param backpropOnlyParams If true: calculate regularization score based on backprop params only. If false: calculate
+   *                     C is cost function a=sigma(z) is activation.
-     *                           based on all params (including pretrain params, if any)
+   * @param workspaceMgr Workspace manager
-     * @return the regularization score of
+   * @return Pair<Gradient, INDArray> where Gradient is gradient for this layer, INDArray is
-     */
+   * epsilon (activation gradient) needed by next layer, but before element-wise multiply by
-    double calcRegularizationScore(boolean backpropOnlyParams);
+   * sigmaPrime(z). So for standard feed-forward layer, if this layer is L, then return.getSecond()
+   * == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the
+   * {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATION_GRAD} workspace via the workspace
+   * manager
+   */
+  Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr);
 
-    /**
+  /**
-     * Returns the layer type
+   * Perform forward pass and return the activations array with the last set input
-     *
+   *
-     * @return
+   * @param training     training or test mode
-     */
+   * @param workspaceMgr Workspace manager
-    Type type();
+   * @return the activation (layer output) of the last specified input. Note that the returned array
+   * should be placed in the {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATIONS} workspace
+   * via the workspace manager
+   */
+  INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr);
+
+  /**
+   * Perform forward pass and return the activations array with the specified input
+   *
+   * @param input    the input to use
+   * @param training train or test mode
+   * @param mgr      Workspace manager.
+   * @return Activations array. Note that the returned array should be placed in the
+   * {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATIONS} workspace via the workspace
+   * manager
+   */
+  INDArray activate(INDArray input, boolean training, LayerWorkspaceMgr mgr);
+
+  /**
+   * Get the iteration listeners for this layer.
+   */
+  Collection<TrainingListener> getListeners();
+
+  /**
+   * Set the {@link TrainingListener}s for this model. If any listeners have previously been set,
+   * they will be replaced by this method
+   */
+  void setListeners(TrainingListener... listeners);
+
+  /**
+   * Set the {@link TrainingListener}s for this model. If any listeners have previously been set,
+   * they will be replaced by this method
+   */
+  void setListeners(Collection<TrainingListener> listeners);
+
+  /**
+   * Get the layer index.
+   */
+  int getIndex();
+
+  /**
+   * Set the layer index.
+   */
+  void setIndex(int index);
+
+  /**
+   * @return The current iteration count (number of parameter updates) for the layer/network
+   */
+  int getIterationCount();
+
+  /**
+   * Set the current iteration count (number of parameter updates) for the layer/network
+   */
+  void setIterationCount(int iterationCount);
+
+  /**
+   * @return The current epoch count (number of training epochs passed) for the layer/network
+   */
+  int getEpochCount();
+
+  /**
+   * Set the current epoch count (number of epochs passed ) for the layer/network
+   */
+  void setEpochCount(int epochCount);
+
+  /**
+   * Set the layer input.
+   */
+  void setInput(INDArray input, LayerWorkspaceMgr workspaceMgr);
+
+  /**
+   * Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
+   *
+   * @see Layer#setInputMiniBatchSize(int)
+   */
+  int getInputMiniBatchSize();
+
+  /**
+   * Set current/last input mini-batch size.<br> Used for score and gradient calculations. Mini
+   * batch size may be different from getInput().size(0) due to reshaping operations - for example,
+   * when using RNNs with DenseLayerConfiguration and OutputLayer. Called automatically during
+   * forward pass.
+   */
+  void setInputMiniBatchSize(int size);
+
+  INDArray getMaskArray();
+
+  /**
+   * Set the mask array. Note: In general, {@link #feedForwardMaskArray(INDArray, MaskState, int)}
+   * should be used in preference to this.
+   *
+   * @param maskArray Mask array to set
+   */
+  void setMaskArray(INDArray maskArray);
+
+  /**
+   * Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
+   *
+   * @return true if the layer can be pretrained (using fit(INDArray), false otherwise
+   */
+  boolean isPretrainLayer();
+
+  void clearNoiseWeightParams();
+
+  /**
+   * A performance optimization: mark whether the layer is allowed to modify its input array
+   * in-place. In many cases, this is totally safe - in others, the input array will be shared by
+   * multiple layers, and hence it's not safe to modify the input array. This is usually used by ops
+   * such as dropout.
+   *
+   * @param allow If true: the input array is safe to modify. If false: the input array should be
+   *              copied before it is modified (i.e., in-place modifications are un-safe)
+   */
+  void allowInputModification(boolean allow);
+
+  /**
+   * Feed forward the input mask array, setting in the layer as appropriate. This allows different
+   * layers to handle masks differently - for example, bidirectional RNNs and normal RNNs operate
+   * differently with masks (the former sets activations to 0 outside of the data present region
+   * (and keeps the mask active for future layers like dense layers), whereas normal RNNs don't zero
+   * out the activations/errors )instead relying on backpropagated error arrays to handle the
+   * variable length case.<br> This is also used for example for networks that contain global
+   * pooling layers, arbitrary preprocessors, etc.
+   *
+   * @param maskArray        Mask array to set
+   * @param currentMaskState Current state of the mask - see {@link MaskState}
+   * @param minibatchSize    Current minibatch size. Needs to be known as it cannot always be
+   *                         inferred from the activations array due to reshaping (such as a
+   *                         DenseLayerConfiguration within a recurrent neural network)
+   * @return New mask array after this layer, along with the new mask state.
+   */
+  Pair<INDArray, MaskState> feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState,
+      int minibatchSize);
+
+  /**
+   * @return Get the layer helper, if any
+   */
+  LayerHelper getHelper();
 
 
-    /**
+  enum Type {
-     * Calculate the gradient relative to the error in the next layer
+    FEED_FORWARD, RECURRENT, CONVOLUTIONAL, CONVOLUTIONAL3D,
-     *
+    SUBSAMPLING, UPSAMPLING, RECURSIVE, MULTILAYER, NORMALIZATION
-     * @param epsilon      w^(L+1)*delta^(L+1). Or, equiv: dC/da, i.e., (dC/dz)*(dz/da) = dC/da, where C
+  }
-     *                     is cost function a=sigma(z) is activation.
-     * @param workspaceMgr Workspace manager
-     * @return Pair<Gradient   ,   INDArray> where Gradient is gradient for this layer, INDArray is epsilon (activation gradient)
-     * needed by next layer, but before element-wise multiply by sigmaPrime(z). So for standard feed-forward layer, if this layer is
-     * L, then return.getSecond() == dL/dIn = (w^(L)*(delta^(L))^T)^T. Note that the returned array should be placed in the
-     * {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATION_GRAD} workspace via the workspace manager
-     */
-    Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr);
 
-
+  enum TrainingMode {
-    /**
+    TRAIN, TEST
-     * Perform forward pass and return the activations array with the last set input
+  }
-     *
-     * @param training     training or test mode
-     * @param workspaceMgr Workspace manager
-     * @return the activation (layer output) of the last specified input. Note that the returned array should be placed
-     * in the {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATIONS} workspace via the workspace manager
-     */
-    INDArray activate(boolean training, LayerWorkspaceMgr workspaceMgr);
-
-    /**
-     * Perform forward pass and return the activations array with the specified input
-     *
-     * @param input    the input to use
-     * @param training train or test mode
-     * @param mgr      Workspace manager.
-     * @return Activations array. Note that the returned array should be placed in the
-     * {@link org.deeplearning4j.nn.workspace.ArrayType#ACTIVATIONS} workspace via the workspace manager
-     */
-    INDArray activate(INDArray input, boolean training, LayerWorkspaceMgr mgr);
-
-    /**
-     * Get the iteration listeners for this layer.
-     */
-    Collection<TrainingListener> getListeners();
-
-    /**
-     * Set the {@link TrainingListener}s for this model. If any listeners have previously been set, they will be
-     * replaced by this method
-     */
-    void setListeners(TrainingListener... listeners);
-
-    /**
-     * Set the {@link TrainingListener}s for this model. If any listeners have previously been set, they will be
-     * replaced by this method
-     */
-    void setListeners(Collection<TrainingListener> listeners);
-
-    /**
-     * Set the layer index.
-     */
-    void setIndex(int index);
-
-    /**
-     * Get the layer index.
-     */
-    int getIndex();
-
-    /**
-     * @return The current iteration count (number of parameter updates) for the layer/network
-     */
-    int getIterationCount();
-
-    /**
-     * @return The current epoch count (number of training epochs passed) for the layer/network
-     */
-    int getEpochCount();
-
-    /**
-     * Set the current iteration count (number of parameter updates) for the layer/network
-     */
-    void setIterationCount(int iterationCount);
-
-    /**
-     * Set the current epoch count (number of epochs passed ) for the layer/network
-     */
-    void setEpochCount(int epochCount);
-
-    /**
-     * Set the layer input.
-     */
-    void setInput(INDArray input, LayerWorkspaceMgr workspaceMgr);
-
-    /**
-     * Set current/last input mini-batch size.<br>
-     * Used for score and gradient calculations. Mini batch size may be different from
-     * getInput().size(0) due to reshaping operations - for example, when using RNNs with
-     * DenseLayer and OutputLayer. Called automatically during forward pass.
-     */
-    void setInputMiniBatchSize(int size);
-
-    /**
-     * Get current/last input mini-batch size, as set by setInputMiniBatchSize(int)
-     *
-     * @see Layer#setInputMiniBatchSize(int)
-     */
-    int getInputMiniBatchSize();
-
-    /**
-     * Set the mask array. Note: In general, {@link #feedForwardMaskArray(INDArray, MaskState, int)} should be used in
-     * preference to this.
-     *
-     * @param maskArray Mask array to set
-     */
-    void setMaskArray(INDArray maskArray);
-
-
-    INDArray getMaskArray();
-
-    /**
-     * Returns true if the layer can be trained in an unsupervised/pretrain manner (AE, VAE, etc)
-     *
-     * @return true if the layer can be pretrained (using fit(INDArray), false otherwise
-     */
-    boolean isPretrainLayer();
-
-
-    void clearNoiseWeightParams();
-
-    /**
-     * A performance optimization: mark whether the layer is allowed to modify its input array in-place. In many cases,
-     * this is totally safe - in others, the input array will be shared by multiple layers, and hence it's not safe to
-     * modify the input array.
-     * This is usually used by ops such as dropout.
-     * @param allow If true: the input array is safe to modify. If false: the input array should be copied before it
-     *              is modified (i.e., in-place modifications are un-safe)
-     */
-    void allowInputModification(boolean allow);
-
-
-    /**
-     * Feed forward the input mask array, setting in the layer as appropriate. This allows different layers to
-     * handle masks differently - for example, bidirectional RNNs and normal RNNs operate differently with masks (the
-     * former sets activations to 0 outside of the data present region (and keeps the mask active for future layers like
-     * dense layers), whereas normal RNNs don't zero out the activations/errors )instead relying on backpropagated error
-     * arrays to handle the variable length case.<br>
-     * This is also used for example for networks that contain global pooling layers, arbitrary preprocessors, etc.
-     *
-     * @param maskArray        Mask array to set
-     * @param currentMaskState Current state of the mask - see {@link MaskState}
-     * @param minibatchSize    Current minibatch size. Needs to be known as it cannot always be inferred from the activations
-     *                         array due to reshaping (such as a DenseLayer within a recurrent neural network)
-     * @return New mask array after this layer, along with the new mask state.
-     */
-    Pair<INDArray, MaskState> feedForwardMaskArray(INDArray maskArray, MaskState currentMaskState, int minibatchSize);
-
-    /**
-     * @return Get the layer helper, if any
-     */
-    LayerHelper getHelper();
 }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ModelAdapter.java

@@ -25,7 +25,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
 
 public interface ModelAdapter<T> extends OutputAdapter<T> {
     /**
-     * This method invokes model internally, and does convertion to T
+     * This method invokes model internally, and does conversion to T
      * @return
      */
     T apply(Model model, INDArray[] inputs, INDArray[] inputMasks, INDArray[] labelsMasks);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/ParamInitializer.java

@@ -41,7 +41,7 @@ public interface ParamInitializer {
     /**
      * Get a list of all parameter keys given the layer configuration
      *
-     * @param layer Layer
+     * @param layer ILayer
      * @return All parameter keys
      */
     List<String> paramKeys(org.deeplearning4j.nn.conf.layers.Layer layer);
@@ -49,7 +49,7 @@ public interface ParamInitializer {
     /**
      * Weight parameter keys given the layer configuration
      *
-     * @param layer Layer
+     * @param layer ILayer
      * @return Weight parameter keys
      */
     List<String> weightKeys(org.deeplearning4j.nn.conf.layers.Layer layer);
@@ -57,7 +57,7 @@ public interface ParamInitializer {
     /**
      * Bias parameter keys given the layer configuration
      *
-     * @param layer Layer
+     * @param layer ILayer
      * @return Bias parameter keys
      */
     List<String> biasKeys(org.deeplearning4j.nn.conf.layers.Layer layer);
@@ -65,7 +65,7 @@ public interface ParamInitializer {
     /**
      * Is the specified parameter a weight?
      *
-     * @param layer Layer
+     * @param layer ILayer
      * @param key Key to check
      * @return True if parameter is a weight
      */
@@ -74,7 +74,7 @@ public interface ParamInitializer {
     /**
      * Is the specified parameter a bias?
      *
-     * @param layer Layer
+     * @param layer ILayer
      * @param key Key to check
      * @return True if parameter is a bias
      */

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/TrainingConfig.java

@@ -47,7 +47,7 @@ public interface TrainingConfig {
      * Is the specified parameter a layerwise pretraining only parameter?<br>
      * For example, visible bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't
      * used during supervised backprop.<br>
-     * Layers (like DenseLayer, etc) with no pretrainable parameters will return false for all (valid) inputs.
+     * Layers (like DenseLayerConfiguration, etc) with no pretrainable parameters will return false for all (valid) inputs.
      *
      * @param paramName Parameter name/key
      * @return True if the parameter is for layerwise pretraining only, false otherwise

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/Updater.java

@@ -36,7 +36,7 @@ public interface Updater extends Serializable {
     /**
      * Set the internal (historical) state view array for this updater
      *
-     * @param layer      Layer that this updater belongs to
+     * @param layer      ILayer that this updater belongs to
      * @param viewArray  View array
      * @param initialize Whether to initialize the array or not
      */

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/LayerConstraint.java

@@ -33,7 +33,7 @@ public interface LayerConstraint extends Cloneable, Serializable {
      * Apply a given constraint to a layer at each iteration
      * in the provided epoch, after parameters have been updated.
      *
-     * @param layer org.deeplearning4j.nn.api.Layer
+     * @param layer org.deeplearning4j.nn.api.ILayer
      * @param iteration given iteration as integer
      * @param epoch current epoch as integer
      */

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/api/layers/RecurrentLayer.java

@@ -66,10 +66,10 @@ public interface RecurrentLayer extends Layer {
      * (a) result in the same output<br>
      * (b) leave the state maps (both stateMap and tBpttStateMap) in an identical state
      *
-     * @param input             Layer input
+     * @param input             ILayer input
      * @param training          if true: training. Otherwise: test
      * @param storeLastForTBPTT If true: store the final state in tBpttStateMap for use in truncated BPTT training
-     * @return Layer activations
+     * @return ILayer activations
      */
     INDArray rnnActivateUsingStoredState(INDArray input, boolean training, boolean storeLastForTBPTT, LayerWorkspaceMgr workspaceMg);
 
@@ -92,7 +92,7 @@ public interface RecurrentLayer extends Layer {
     void rnnSetTBPTTState(Map<String, INDArray> state);
 
     /**
-     * Truncated BPTT equivalent of Layer.backpropGradient().
+     * Truncated BPTT equivalent of ILayer.backpropGradient().
      * Primary difference here is that forward pass in the context of BPTT is that we do
      * forward pass using stored state for truncated BPTT vs. from zero initialization
      * for standard BPTT.

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

@@ -25,6 +25,7 @@ import lombok.EqualsAndHashCode;
 import lombok.NoArgsConstructor;
 import lombok.NonNull;
 import lombok.extern.slf4j.Slf4j;
+import net.brutex.ai.dnn.api.INeuralNetworkConfiguration;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
@@ -68,7 +69,9 @@ import java.util.*;
 @NoArgsConstructor
 @Slf4j
 @EqualsAndHashCode(exclude = {"iterationCount", "epochCount"})
-public class NeuralNetConfiguration implements Serializable, Cloneable {
+public class NeuralNetConfiguration implements Serializable, Cloneable,
+    INeuralNetworkConfiguration {
+
 
     protected Layer layer;
     //batch size: primarily used for conv nets. Will be reinforced if set.

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

@@ -43,7 +43,7 @@ public class MaxNormConstraint extends BaseConstraint {
     /**
      * @param maxNorm        Maximum L2 value
      * @param paramNames     Which parameter names to apply constraint to
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */
@@ -56,7 +56,7 @@ public class MaxNormConstraint extends BaseConstraint {
      * Apply to weights but not biases by default
      *
      * @param maxNorm        Maximum L2 value
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */

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

@@ -51,7 +51,7 @@ public class MinMaxNormConstraint extends BaseConstraint {
      *
      * @param max            Maximum L2 value
      * @param min            Minimum L2 value
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */
@@ -65,7 +65,7 @@ public class MinMaxNormConstraint extends BaseConstraint {
      * @param max            Maximum L2 value
      * @param min            Minimum L2 value
      * @param rate           Constraint rate
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */
@@ -79,7 +79,7 @@ public class MinMaxNormConstraint extends BaseConstraint {
      * @param min            Minimum L2 value
      * @param rate           Constraint rate
      * @param paramNames     Which parameter names to apply constraint to
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */

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

@@ -39,7 +39,7 @@ public class UnitNormConstraint extends BaseConstraint {
     /**
      * Apply to weights but not biases by default
      *
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */
@@ -49,7 +49,7 @@ public class UnitNormConstraint extends BaseConstraint {
 
 
     /**
-     * @param dimensions     Dimensions to apply to. For DenseLayer, OutputLayer, RnnOutputLayer, LSTM, etc: this should
+     * @param dimensions     Dimensions to apply to. For DenseLayerConfiguration, OutputLayer, RnnOutputLayer, LSTM, etc: this should
      *                       be dimension 1. For CNNs, this should be dimensions [1,2,3] corresponding to last 3 of
      *                       parameters which have order [depthOut, depthIn, kH, kW]
      */

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

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.conf.graph;
 
 import lombok.Data;
-import lombok.EqualsAndHashCode;
 import lombok.NoArgsConstructor;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -40,8 +39,8 @@ public class LayerVertex extends GraphVertex {
 
     private NeuralNetConfiguration layerConf;
     private InputPreProcessor preProcessor;
-    //Set outputVertex to true when Layer is an OutputLayer, OR For use in specialized situations like reinforcement learning
+    //Set outputVertex to true when ILayer is an OutputLayer, OR For use in specialized situations like reinforcement learning
-    // For RL situations, this Layer insn't an OutputLayer, but is the last layer in a graph, that gets its error/epsilon
+    // For RL situations, this ILayer insn't an OutputLayer, but is the last layer in a graph, that gets its error/epsilon
     // passed in externally
     private boolean outputVertex;
 
@@ -99,7 +98,7 @@ public class LayerVertex extends GraphVertex {
     public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
                                                                       INDArray paramsView, boolean initializeParams, DataType networkDatatype) {
         //Now, we need to work out if this vertex is an output vertex or not...
-        boolean isOutput = graph.getConfiguration().getNetworkOutputs().contains(name);
+        boolean isOutput = graph.getComputationGraphConfiguration().getNetworkOutputs().contains(name);
 
         org.deeplearning4j.nn.api.Layer layer =
                         layerConf.getLayer().instantiate(layerConf, null, idx, paramsView, initializeParams, networkDatatype);

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

@@ -134,7 +134,7 @@ public class ActivationLayer extends NoParamLayer {
         private IActivation activationFn = null;
 
         /**
-         * Layer activation function. Typical values include:<br> "relu" (rectified linear), "tanh", "sigmoid",
+         * ILayer activation function. Typical values include:<br> "relu" (rectified linear), "tanh", "sigmoid",
          * "softmax", "hardtanh", "leakyrelu", "maxout", "softsign", "softplus"
          *
          * @deprecated Use {@link #activation(Activation)} or {@link @activation(IActivation)}

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

@@ -176,7 +176,7 @@ public abstract class BaseLayer extends Layer implements Serializable, Cloneable
         protected double biasInit = Double.NaN;
 
         /**
-         * Gain initialization value, for layers with Layer Normalization. Defaults to 1
+         * Gain initialization value, for layers with ILayer Normalization. Defaults to 1
          *
          */
         protected double gainInit = Double.NaN;
@@ -292,7 +292,7 @@ public abstract class BaseLayer extends Layer implements Serializable, Cloneable
         }
 
         /**
-         * Gain initialization value, for layers with Layer Normalization. Defaults to 1
+         * Gain initialization value, for layers with ILayer Normalization. Defaults to 1
          *
          * @param gainInit Value to use for initializing gain
          */

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

@@ -63,14 +63,14 @@ public class CapsuleLayer extends SameDiffLayer {
         this.routings = builder.routings;
 
         if(capsules <= 0 || capsuleDimensions <= 0 || routings <= 0){
-            throw new IllegalArgumentException("Invalid configuration for Capsule Layer (layer name = \""
+            throw new IllegalArgumentException("Invalid configuration for Capsule ILayer (layer name = \""
                     + layerName + "\"):"
                     + " capsules, capsuleDimensions, and routings must be > 0.  Got: "
                     + capsules + ", " + capsuleDimensions + ", " + routings);
         }
 
         if(inputCapsules < 0 || inputCapsuleDimensions < 0){
-            throw new IllegalArgumentException("Invalid configuration for Capsule Layer (layer name = \""
+            throw new IllegalArgumentException("Invalid configuration for Capsule ILayer (layer name = \""
                     + layerName + "\"):"
                     + " inputCapsules and inputCapsuleDimensions must be >= 0 if set.  Got: "
                     + inputCapsules + ", " + inputCapsuleDimensions);

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

@@ -55,7 +55,7 @@ public class DenseLayer extends FeedForwardLayer {
     @Override
     public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners,
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
-        LayerValidation.assertNInNOutSet("DenseLayer", getLayerName(), layerIndex, getNIn(), getNOut());
+        LayerValidation.assertNInNOutSet("DenseLayerConfiguration", getLayerName(), layerIndex, getNIn(), getNOut());
 
         org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer ret =
                         new org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer(conf, networkDataType);
@@ -101,7 +101,7 @@ public class DenseLayer extends FeedForwardLayer {
         return new LayerMemoryReport.Builder(layerName, DenseLayer.class, inputType, outputType)
                         .standardMemory(numParams, updaterStateSize)
                         .workingMemory(0, 0, trainSizeFixed, trainSizeVariable) //No additional memory (beyond activations) for inference
-                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayer
+                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayerConfiguration
                         .build();
     }
 

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

@@ -205,7 +205,7 @@ public abstract class Layer implements TrainingConfig, Serializable, Cloneable {
     /**
      * Is the specified parameter a layerwise pretraining only parameter?<br> For example, visible
      * bias params in an autoencoder (or, decoder params in a variational autoencoder) aren't used
-     * during supervised backprop.<br> Layers (like DenseLayer, etc) with no pretrainable parameters
+     * during supervised backprop.<br> Layers (like DenseLayerConfiguration, etc) with no pretrainable parameters
      * will return false for all (valid) inputs.
      *
      * @param paramName Parameter name/key
@@ -255,7 +255,7 @@ public abstract class Layer implements TrainingConfig, Serializable, Cloneable {
         protected IDropout iDropout;
 
         /**
-         * Layer name assigns layer string name. Allows easier differentiation between layers.
+         * ILayer name assigns layer string name. Allows easier differentiation between layers.
          */
         public T name(String layerName) {
             this.setLayerName(layerName);

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

@@ -42,7 +42,7 @@ public class LayerValidation {
     /**
      * Asserts that the layer nIn and nOut values are set for the layer
      *
-     * @param layerType     Type of layer ("DenseLayer", etc)
+     * @param layerType     Type of layer ("DenseLayerConfiguration", etc)
      * @param layerName     Name of the layer (may be null if not set)
      * @param layerIndex    Index of the layer
      * @param nIn           nIn value
@@ -60,7 +60,7 @@ public class LayerValidation {
     /**
      * Asserts that the layer nOut value is set for the layer
      *
-     * @param layerType     Type of layer ("DenseLayer", etc)
+     * @param layerType     Type of layer ("DenseLayerConfiguration", etc)
      * @param layerName     Name of the layer (may be null if not set)
      * @param layerIndex    Index of the layer
      * @param nOut          nOut value

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

@@ -147,7 +147,7 @@ public class LocalResponseNormalization extends Layer {
 
         return new LayerMemoryReport.Builder(layerName, DenseLayer.class, inputType, inputType).standardMemory(0, 0)
                         .workingMemory(0, 2 * actElementsPerEx, 0, 3 * actElementsPerEx)
-                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayer
+                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayerConfiguration
                         .build();
     }
 

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

@@ -87,7 +87,7 @@ public class PrimaryCapsules extends SameDiffLayer {
         }
 
         if(capsules < 0){
-            throw new IllegalArgumentException("Invalid configuration for Capsule Layer (layer name = \""
+            throw new IllegalArgumentException("Invalid configuration for Capsule ILayer (layer name = \""
                     + layerName + "\"):"
                     + " capsules must be >= 0 if set.  Got: "
                     + capsules);

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

@@ -113,7 +113,7 @@ public class ElementWiseMultiplicationLayer extends org.deeplearning4j.nn.conf.l
         return new LayerMemoryReport.Builder(layerName, ElementWiseMultiplicationLayer.class, inputType, outputType)
                         .standardMemory(numParams, updaterStateSize)
                         .workingMemory(0, 0, trainSizeFixed, trainSizeVariable) //No additional memory (beyond activations) for inference
-                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayer
+                        .cacheMemory(MemoryReport.CACHE_MODE_ALL_ZEROS, MemoryReport.CACHE_MODE_ALL_ZEROS) //No caching in DenseLayerConfiguration
                         .build();
     }
 

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

@@ -44,7 +44,7 @@ public class TimeDistributed extends BaseWrapperLayer {
     private RNNFormat rnnDataFormat = RNNFormat.NCW;
 
     /**
-     * @param underlying Underlying (internal) layer - should be a feed forward type such as DenseLayer
+     * @param underlying Underlying (internal) layer - should be a feed forward type such as DenseLayerConfiguration
      */
     public TimeDistributed(@JsonProperty("underlying") @NonNull Layer underlying, @JsonProperty("rnnDataFormat") RNNFormat rnnDataFormat) {
         super(underlying);

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

@@ -33,7 +33,7 @@ public abstract class SameDiffLambdaLayer extends SameDiffLayer {
      * The defineLayer method is used to define the forward pass for the layer
      *
      * @param sameDiff   SameDiff instance to use to define the vertex
-     * @param layerInput Layer input variable
+     * @param layerInput ILayer input variable
      * @return The output variable (corresponding to the output activations for the layer)
      */
     public abstract SDVariable defineLayer(SameDiff sameDiff, SDVariable layerInput);

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

@@ -37,7 +37,7 @@ public abstract class SameDiffLambdaVertex extends SameDiffVertex {
      * The defineVertex method is used to define the foward pass for the vertex
      *
      * @param sameDiff SameDiff instance to use to define the vertex
-     * @param inputs   Layer input variable
+     * @param inputs   ILayer input variable
      * @return The output variable (orresponding to the output activations for the vertex)
      */
     public abstract SDVariable defineVertex(SameDiff sameDiff, VertexInputs inputs);