Fixing tests

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

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

@@ -217,14 +217,14 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
 
         netCopy.fit(data);
-        IUpdater expectedUpdater = ((BaseLayer) netCopy.conf().getLayer()).getIUpdater();
+        IUpdater expectedUpdater = ((BaseLayer) netCopy.conf().getLayer()).getUpdater();
-        double expectedLR = ((Nesterovs)((BaseLayer) netCopy.conf().getLayer()).getIUpdater()).getLearningRate();
+        double expectedLR = ((Nesterovs)((BaseLayer) netCopy.conf().getLayer()).getUpdater()).getLearningRate();
-        double expectedMomentum = ((Nesterovs)((BaseLayer) netCopy.conf().getLayer()).getIUpdater()).getMomentum();
+        double expectedMomentum = ((Nesterovs)((BaseLayer) netCopy.conf().getLayer()).getUpdater()).getMomentum();
 
-        IUpdater actualUpdater = ((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getIUpdater();
+        IUpdater actualUpdater = ((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getUpdater();
         sparkNet.fit(sparkData);
-        double actualLR = ((Nesterovs)((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getIUpdater()).getLearningRate();
+        double actualLR = ((Nesterovs)((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getUpdater()).getLearningRate();
-        double actualMomentum = ((Nesterovs)((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getIUpdater()).getMomentum();
+        double actualMomentum = ((Nesterovs)((BaseLayer) sparkNet.getNetwork().conf().getLayer()).getUpdater()).getMomentum();
 
         assertEquals(expectedUpdater, actualUpdater);
         assertEquals(expectedLR, actualLR, 0.01);

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

@@ -47,6 +47,7 @@ import org.datavec.image.transform.ShowImageTransform;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.distribution.NormalDistribution;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
@@ -77,10 +78,10 @@ public class App {
   private static final double LEARNING_RATE = 0.000002;
   private static final double GRADIENT_THRESHOLD = 100.0;
 
-  private static final int X_DIM = 28;
+  private static final int X_DIM = 20 ;
-  private static final int Y_DIM = 28;
+  private static final int Y_DIM = 20;
   private static final int CHANNELS = 1;
-  private static final int batchSize = 9;
+  private static final int batchSize = 10;
   private static final int INPUT = 128;
 
   private static final int OUTPUT_PER_PANEL = 4;
@@ -97,12 +98,13 @@ public class App {
     return new LayerConfiguration[] {
         DenseLayer.builder().nIn(INPUT).nOut(X_DIM*Y_DIM*CHANNELS).weightInit(WeightInit.NORMAL).build(),
         ActivationLayer.builder(Activation.LEAKYRELU).build(),
+
         DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
         DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM).build(),
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
-        DenseLayer.builder().nIn(X_DIM*Y_DIM).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH)
+
-        .build()
+        DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM*CHANNELS).activation(Activation.TANH).build()
     };
  }
 
@@ -131,32 +133,36 @@ public class App {
 
   private static LayerConfiguration[] disLayers() {
     return new LayerConfiguration[]{
-        DenseLayer.builder().nOut(X_DIM*Y_DIM*CHANNELS*2).build(), //input is set by setInputType on the network
+        DenseLayer.builder().name("1.Dense").nOut(X_DIM*Y_DIM*CHANNELS).build(), //input is set by setInputType on the network
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
         DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().nIn(X_DIM * Y_DIM*CHANNELS*2).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
+        DenseLayer.builder().name("2.Dense").nIn(X_DIM * Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM*CHANNELS*4).build(), //HxBxC
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
         DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
+        DenseLayer.builder().name("3.Dense").nIn(X_DIM*Y_DIM*CHANNELS*4).nOut(X_DIM*Y_DIM*CHANNELS).build(),
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
         DropoutLayer.builder(1 - 0.5).build(),
-        DenseLayer.builder().nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
+        DenseLayer.builder().name("4.Dense").nIn(X_DIM*Y_DIM*CHANNELS).nOut(X_DIM*Y_DIM).build(),
         ActivationLayer.builder(new ActivationLReLU(0.2)).build(),
         DropoutLayer.builder(1 - 0.5).build(),
-        OutputLayer.builder().lossFunction(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
+
+        OutputLayer.builder().name("dis-output").lossFunction(LossFunction.XENT).nIn(X_DIM*Y_DIM).nOut(1).activation(Activation.SIGMOID).build()
     };
   }
 
   private static NeuralNetConfiguration discriminator() {
 
-    NeuralNetConfiguration conf = NeuralNetConfiguration.builder()
+    NeuralNetConfiguration conf =
+        NeuralNetConfiguration.builder()
             .seed(42)
             .updater(UPDATER)
             .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
             .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
             .weightInit(WeightInit.XAVIER)
-        //.weightInitFn(new WeightInitXavier())
+            //.weightNoise(new WeightNoise(new NormalDistribution(0.5, 0.5)))
-        //.activationFn(new ActivationIdentity())
+                .weightNoise(null)
+            // .weightInitFn(new WeightInitXavier())
+            // .activationFn(new ActivationIdentity())
             .activation(Activation.IDENTITY)
             .layersFromArray(disLayers())
             .inputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))
@@ -171,7 +177,7 @@ public class App {
     LayerConfiguration[] disLayers = Arrays.stream(disLayers())
         .map((layer) -> {
          if (layer instanceof DenseLayer || layer instanceof OutputLayer) {
-          return FrozenLayerWithBackprop.builder(layer);
+          return FrozenLayerWithBackprop.builder(layer).name("frozen-for-"+layer.getName()).build();
           } else {
             return layer;
           }
@@ -204,7 +210,7 @@ public class App {
   public static void main(String... args) throws Exception {
 
     log.info("\u001B[32m  Some \u001B[1m green \u001B[22m text \u001B[0m \u001B[7m Inverted\u001B[0m   ");
-    Nd4j.getMemoryManager().setAutoGcWindow(15 * 1000);
+    Nd4j.getMemoryManager().setAutoGcWindow(500);
 
 //    MnistDataSetIterator trainData = new MnistDataSetIterator(128, true, 45);
   //  FileSplit fileSplit = new FileSplit(new File("c:/users/brian/downloads/flowers"), NativeImageLoader.getALLOWED_FORMATS());
@@ -236,10 +242,10 @@ public class App {
 
     copyParams(gen, dis, gan);
 
-    gen.addTrainingListeners(new PerformanceListener(10, true));
+    gen.addTrainingListeners(new PerformanceListener(15, true));
-    dis.addTrainingListeners(new PerformanceListener(10, true));
+    //dis.addTrainingListeners(new PerformanceListener(10, true));
-    gan.addTrainingListeners(new PerformanceListener(10, true));
+    //gan.addTrainingListeners(new PerformanceListener(10, true));
-    gan.addTrainingListeners(new ScoreToChartListener("gan"));
+    //gan.addTrainingListeners(new ScoreToChartListener("gan"));
     //dis.setListeners(new ScoreToChartListener("dis"));
 
     System.out.println(gan.toString());

cavis-dnn/cavis-dnn-api/src/main/java/org/nd4j/linalg/api/memory/deallocation/DeallocatorService.java

@@ -107,6 +107,9 @@ public class DeallocatorService {
             boolean canRun = true;
             long cnt = 0;
             while (canRun) {
+                log.trace("Starting deallocator threat with name '{}'. isPeriodicGc: {}, AutoGcWindow: {}. Current allocated memory: {}"
+                ,this.getName(), Nd4j.getMemoryManager().isPeriodicGcActive()
+                , Nd4j.getMemoryManager().getAutoGcWindow(), Nd4j.getMemoryManager().allocatedMemory(deviceId));
                 // if periodicGc is enabled, only first thread will call for it
                 if (Nd4j.getMemoryManager().isPeriodicGcActive() && threadIdx == 0 && Nd4j.getMemoryManager().getAutoGcWindow() > 0) {
                     val reference = (DeallocatableReference) queue.poll();
@@ -120,6 +123,7 @@ public class DeallocatorService {
                         }
                     } else {
                         // invoking deallocator
+                        log.trace("Deallocate reference {}", reference.getId());
                         reference.getDeallocator().deallocate();
                         referenceMap.remove(reference.getId());
                     }

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

@@ -498,7 +498,7 @@ public class TestEarlyStopping extends BaseDL4JTest {
         assertEquals(net.getNetConfiguration().getOptimizationAlgo(), mln.getNetConfiguration().getOptimizationAlgo());
         BaseLayerConfiguration bl = (BaseLayerConfiguration) net.getLayerConfiguration();
         assertEquals(bl.getActivationFn().toString(), ((BaseLayerConfiguration) mln.getLayerConfiguration()).getActivationFn().toString());
-        assertEquals(bl.getIUpdater(), ((BaseLayerConfiguration) mln.getLayerConfiguration()).getIUpdater());
+        assertEquals(bl.getUpdater(), ((BaseLayerConfiguration) mln.getLayerConfiguration()).getUpdater());
     }
 
     @Test

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

@@ -306,7 +306,7 @@ public class AttentionLayerTest extends BaseDL4JTest {
                         .activation(Activation.IDENTITY)
                         .updater(new NoOp())
                         .weightInit(WeightInit.XAVIER)
-                        .list()
+
                         .layer(LSTM.builder().nOut(layerSize).build())
                         .layer(RecurrentAttentionLayer.builder().nIn(layerSize).nOut(layerSize).nHeads(1).projectInput(false).hasBias(false).build())
                         .layer(GlobalPoolingLayer.builder().poolingType(PoolingType.AVG).build())

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

@@ -76,7 +76,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
         assertEquals(act, layer.getActivationFn());
         assertEquals(weight.getWeightInitFunction(), layer.getWeightInit());
         assertEquals(new Dropout(dropOut), layer.getDropOut());
-        assertEquals(updater, layer.getIUpdater());
+        assertEquals(updater, layer.getUpdater());
         assertEquals(gradNorm, layer.getGradientNormalization());
         assertEquals(gradNormThreshold, layer.getGradientNormalizationThreshold(), 0.0);
     }

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

@@ -213,8 +213,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
-        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
 
         Map<Integer, Double> testMomentumAfter2 = new HashMap<>();
         testMomentumAfter2.put(0, 0.2);
@@ -227,8 +227,8 @@ public class LayerConfigTest extends BaseDL4JTest {
 
         net = new MultiLayerNetwork(conf);
         net.init();
-        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.1, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
-        assertEquals(0.2, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
+        assertEquals(0.2, ((Nesterovs)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getMomentumISchedule().valueAt(0,0), 0.0);
     }
 
     @Test
@@ -239,10 +239,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater() instanceof AdaDelta);
-        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater() instanceof AdaDelta);
-        assertEquals(0.5, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertEquals(0.5, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getRho(), 0.0);
-        assertEquals(0.01, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertEquals(0.01, ((AdaDelta)((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getRho(), 0.0);
 
         conf = NeuralNetConfiguration.builder().updater(new RmsProp(1.0, 2.0, RmsProp.DEFAULT_RMSPROP_EPSILON))
                         .layer(0, DenseLayer.builder().nIn(2).nOut(2).updater(new RmsProp(1.0, 1.0, RmsProp.DEFAULT_RMSPROP_EPSILON)).build())
@@ -252,10 +252,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater() instanceof RmsProp);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater() instanceof RmsProp);
-        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater() instanceof AdaDelta);
+        assertTrue(((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater() instanceof AdaDelta);
-        assertEquals(1.0, ((RmsProp) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getRmsDecay(), 0.0);
+        assertEquals(1.0, ((RmsProp) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getRmsDecay(), 0.0);
-        assertEquals(0.5, ((AdaDelta) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getRho(), 0.0);
+        assertEquals(0.5, ((AdaDelta) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getRho(), 0.0);
     }
 
 
@@ -270,10 +270,10 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getBeta1(), 0.0);
+        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getBeta1(), 0.0);
-        assertEquals(0.6, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getBeta1(), 0.0);
+        assertEquals(0.6, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getBeta1(), 0.0);
-        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getIUpdater()).getBeta2(), 0.0);
+        assertEquals(0.5, ((Adam) ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getUpdater()).getBeta2(), 0.0);
-        assertEquals(0.7, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getIUpdater()).getBeta2(), 0.0);
+        assertEquals(0.7, ((Adam) ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getUpdater()).getBeta2(), 0.0);
     }
 
     @Test

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

@@ -163,12 +163,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         net.init();
 
         BaseLayerConfiguration layerConf = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
-        assertEquals(expectedMomentum, ((Nesterovs) layerConf.getIUpdater()).getMomentum(), 1e-3);
+        assertEquals(expectedMomentum, ((Nesterovs) layerConf.getUpdater()).getMomentum(), 1e-3);
         assertNull(TestUtils.getL1Reg(layerConf.getRegularization()));
         assertEquals(0.5, TestUtils.getL2(layerConf), 1e-3);
 
         BaseLayerConfiguration layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
-        assertEquals(0.4, ((Nesterovs) layerConf1.getIUpdater()).getMomentum(), 1e-3);
+        assertEquals(0.4, ((Nesterovs) layerConf1.getUpdater()).getMomentum(), 1e-3);
 
         // Adam Updater
         conf = NeuralNetConfiguration.builder().updater(new Adam(0.3))
@@ -183,8 +183,8 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         assertEquals(0.5, TestUtils.getL2(layerConf), 1e-3);
 
         layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
-        assertEquals(expectedAdamMeanDecay, ((Adam) layerConf1.getIUpdater()).getBeta1(), 1e-3);
+        assertEquals(expectedAdamMeanDecay, ((Adam) layerConf1.getUpdater()).getBeta1(), 1e-3);
-        assertEquals(expectedAdamVarDecay, ((Adam) layerConf1.getIUpdater()).getBeta2(), 1e-3);
+        assertEquals(expectedAdamVarDecay, ((Adam) layerConf1.getUpdater()).getBeta2(), 1e-3);
         assertEquals(new WeightInitDistribution(expectedDist), layerConf1.getWeightInit());
         assertNull(TestUtils.getL1Reg(layerConf1.getRegularization()));
         assertNull(TestUtils.getL2Reg(layerConf1.getRegularization()));
@@ -197,12 +197,12 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         net.init();
 
         layerConf = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
-        assertEquals(expectedRmsDecay, ((RmsProp) layerConf.getIUpdater()).getRmsDecay(), 1e-3);
+        assertEquals(expectedRmsDecay, ((RmsProp) layerConf.getUpdater()).getRmsDecay(), 1e-3);
         assertNull(TestUtils.getL1Reg(layerConf.getRegularization()));
         assertNull(TestUtils.getL2Reg(layerConf.getRegularization()));
 
         layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
-        assertEquals(0.4, ((RmsProp) layerConf1.getIUpdater()).getRmsDecay(), 1e-3);
+        assertEquals(0.4, ((RmsProp) layerConf1.getUpdater()).getRmsDecay(), 1e-3);
 
 
     }

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

@@ -164,16 +164,4 @@ public class SameDiffConv extends SameDiffLayer {
         return activation.asSameDiff("out", sameDiff, conv);
     }
 
-    @Override
-    public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-        NeuralNetConfiguration clone = globalConfig.clone().build();
-        if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(clone.getActivation());
-        }
-        if (convolutionMode == null) {
-            convolutionMode = clone.getConvolutionMode();
-        }
-    }
-
-
 }

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

@@ -114,14 +114,6 @@ public class SameDiffDense extends SameDiffLayer {
     return activation.asSameDiff("out", sd, z);
   }
 
-  @Override
-  public void applyGlobalConfigToLayer(
-      NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-    NeuralNetConfiguration clone = globalConfig.clone().build();
-    if (activation == null) {
-      activation = SameDiffLayerUtils.fromIActivation(clone.getActivation());
-    }
-  }
 
   public char paramReshapeOrder(String param) {
     // To match DL4J for easy comparison

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

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.layers.samediff.testlayers;
 
 import java.util.Map;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.samediff.SDLayerParams;
 import org.deeplearning4j.nn.conf.layers.samediff.SameDiffOutputLayer;
@@ -87,8 +86,4 @@ public class SameDiffMSEOutputLayer extends SameDiffOutputLayer {
     // To match DL4J for easy comparison
     return 'f';
   }
-
-  @Override
-  public void applyGlobalConfigToLayer(
-      NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {}
 }

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

@@ -99,7 +99,7 @@ public class TransferLearningComplex extends BaseDL4JTest {
 
             //Also check config:
             BaseLayerConfiguration bl = ((BaseLayerConfiguration) l.getLayerConfiguration());
-            assertEquals(new Adam(2e-2), bl.getIUpdater());
+            assertEquals(new Adam(2e-2), bl.getUpdater());
             assertEquals(Activation.LEAKYRELU.getActivationFunction(), bl.getActivationFn());
         }
         assertTrue(cFound);

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

@@ -92,7 +92,7 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
 
         for (org.deeplearning4j.nn.api.Layer l : modelNow.getLayers()) {
             BaseLayerConfiguration bl = ((BaseLayerConfiguration) l.getLayerConfiguration());
-            assertEquals(new RmsProp(0.5), bl.getIUpdater());
+            assertEquals(new RmsProp(0.5), bl.getUpdater());
         }
 
 
@@ -504,13 +504,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
 
         //Check original net isn't modified:
         BaseLayerConfiguration l0 = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
-        assertEquals(new Adam(1e-4), l0.getIUpdater());
+        assertEquals(new Adam(1e-4), l0.getUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
 
         BaseLayerConfiguration l1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
-        assertEquals(new Adam(1e-4), l1.getIUpdater());
+        assertEquals(new Adam(1e-4), l1.getUpdater());
         assertEquals(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
         assertEquals(new WeightInitRelu(), l1.getWeightInit());
         assertEquals(0.2, TestUtils.getL2(l1), 1e-6);
@@ -519,13 +519,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
 
         //Check new net has only the appropriate things modified (i.e., LR)
         l0 = (BaseLayerConfiguration) net2.getLayer(0).getLayerConfiguration();
-        assertEquals(new Adam(2e-2), l0.getIUpdater());
+        assertEquals(new Adam(2e-2), l0.getUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
 
         l1 = (BaseLayerConfiguration) net2.getLayer(1).getLayerConfiguration();
-        assertEquals(new Adam(2e-2), l1.getIUpdater());
+        assertEquals(new Adam(2e-2), l1.getUpdater());
         assertEquals(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
         assertEquals(new WeightInitRelu(), l1.getWeightInit());
         assertEquals(0.2, TestUtils.getL2(l1), 1e-6);

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

@@ -100,7 +100,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -145,7 +145,7 @@ public class TestUpdaters extends BaseDL4JTest {
                 msdx.put(key, msdxTmp);
                 count++;
             }
-            assertEquals(rho, ((AdaDelta)layer.getTypedLayerConfiguration().getIUpdater()).getRho(), 1e-4);
+            assertEquals(rho, ((AdaDelta)layer.getTypedLayerConfiguration().getUpdater()).getRho(), 1e-4);
         }
 
         assertEquals(4, count);
@@ -166,7 +166,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -186,7 +186,7 @@ public class TestUpdaters extends BaseDL4JTest {
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
             count++;
         }
-        assertEquals(lr, ((AdaGrad)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((AdaGrad)layer.getTypedLayerConfiguration().getUpdater()).getLearningRate(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -210,7 +210,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -246,8 +246,8 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(beta1, ((Adam)layer.getTypedLayerConfiguration().getIUpdater()).getBeta1(), 1e-4);
+        assertEquals(beta1, ((Adam)layer.getTypedLayerConfiguration().getUpdater()).getBeta1(), 1e-4);
-        assertEquals(beta2, ((Adam)layer.getTypedLayerConfiguration().getIUpdater()).getBeta2(), 1e-4);
+        assertEquals(beta2, ((Adam)layer.getTypedLayerConfiguration().getUpdater()).getBeta2(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -274,7 +274,7 @@ public class TestUpdaters extends BaseDL4JTest {
         layer.setBackpropGradientsViewArray(gradients);
 
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -363,7 +363,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -399,8 +399,8 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(beta1, ((AdaMax)layer.getTypedLayerConfiguration().getIUpdater()).getBeta1(), 1e-4);
+        assertEquals(beta1, ((AdaMax)layer.getTypedLayerConfiguration().getUpdater()).getBeta1(), 1e-4);
-        assertEquals(beta2, ((AdaMax)layer.getTypedLayerConfiguration().getIUpdater()).getBeta2(), 1e-4);
+        assertEquals(beta2, ((AdaMax)layer.getTypedLayerConfiguration().getUpdater()).getBeta2(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -419,7 +419,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -444,7 +444,7 @@ public class TestUpdaters extends BaseDL4JTest {
             count++;
         }
 
-        assertEquals(mu, ((Nesterovs)layer.getTypedLayerConfiguration().getIUpdater()).getMomentum(), 1e-4);
+        assertEquals(mu, ((Nesterovs)layer.getTypedLayerConfiguration().getUpdater()).getMomentum(), 1e-4);
         assertEquals(2, count);
     }
 
@@ -466,7 +466,7 @@ public class TestUpdaters extends BaseDL4JTest {
         BaseLayer layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         Updater updater = UpdaterCreator.getUpdater(layer);
-        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getIUpdater().stateSize(numParams);
+        int updaterStateSize = (int) layer.getTypedLayerConfiguration().getUpdater().stateSize(numParams);
         INDArray updaterState = Nd4j.create(1, updaterStateSize);
         updater.setStateViewArray(layer, updaterState, true);
 
@@ -496,7 +496,7 @@ public class TestUpdaters extends BaseDL4JTest {
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
             lastG.put(key, lastGTmp);
         }
-        assertEquals(rmsDecay, ((RmsProp)layer.getTypedLayerConfiguration().getIUpdater()).getRmsDecay(), 1e-4);
+        assertEquals(rmsDecay, ((RmsProp)layer.getTypedLayerConfiguration().getUpdater()).getRmsDecay(), 1e-4);
     }
 
     @Test
@@ -528,7 +528,7 @@ public class TestUpdaters extends BaseDL4JTest {
             gradExpected = val.mul(lr);
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
         }
-        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getUpdater()).getLearningRate(), 1e-4);
     }
 
 
@@ -770,7 +770,7 @@ public class TestUpdaters extends BaseDL4JTest {
             gradExpected = val.mul(lr);
             assertEquals(gradExpected, gradient.getGradientFor(entry.getKey()));
         }
-        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getUpdater()).getLearningRate(), 1e-4);
 
 
         //Test with pretrain == false
@@ -798,7 +798,7 @@ public class TestUpdaters extends BaseDL4JTest {
         layer = (BaseLayer) conf.getFirstLayer().instantiate(conf, null, 0, params, true, params.dataType());
         layer.setBackpropGradientsViewArray(gradients);
         updater = UpdaterCreator.getUpdater(layer);
-        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getIUpdater()).getLearningRate(), 1e-4);
+        assertEquals(lr, ((Sgd)layer.getTypedLayerConfiguration().getUpdater()).getLearningRate(), 1e-4);
     }
 
     @Test

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

@@ -61,18 +61,18 @@ public class TestCustomUpdater extends BaseDL4JTest {
                         .build();
 
         //First: Check updater config
-        assertTrue(((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getIUpdater() instanceof CustomIUpdater);
+        assertTrue(((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getUpdater() instanceof CustomIUpdater);
-        assertTrue(((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getIUpdater() instanceof CustomIUpdater);
+        assertTrue(((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getUpdater() instanceof CustomIUpdater);
-        assertTrue(((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getIUpdater() instanceof Sgd);
+        assertTrue(((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getUpdater() instanceof Sgd);
-        assertTrue(((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getIUpdater() instanceof Sgd);
+        assertTrue(((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getUpdater() instanceof Sgd);
 
-        CustomIUpdater u0_0 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getIUpdater();
+        CustomIUpdater u0_0 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(0).getLayer()).getUpdater();
-        CustomIUpdater u0_1 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getIUpdater();
+        CustomIUpdater u0_1 = (CustomIUpdater) ((BaseLayerConfiguration) conf1.getConf(1).getLayer()).getUpdater();
         assertEquals(lr, u0_0.getLearningRate(), 1e-6);
         assertEquals(lr, u0_1.getLearningRate(), 1e-6);
 
-        Sgd u1_0 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getIUpdater();
+        Sgd u1_0 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(0).getLayer()).getUpdater();
-        Sgd u1_1 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getIUpdater();
+        Sgd u1_1 = (Sgd) ((BaseLayerConfiguration) conf2.getConf(1).getLayer()).getUpdater();
         assertEquals(lr, u1_0.getLearningRate(), 1e-6);
         assertEquals(lr, u1_1.getLearningRate(), 1e-6);
 

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

@@ -73,8 +73,8 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
-        assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
+        assertEquals(new Nesterovs(0.15, 0.9), l0.getUpdater());
-        assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l0.getUpdater()).getLearningRate(), 1e-6);
 
         OutputLayer l1 = (OutputLayer) conf.getConf(1).getLayer();
         assertEquals("softmax", l1.getActivationFn().toString());
@@ -82,9 +82,9 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
-        assertEquals(new Nesterovs(0.15, 0.9), l1.getIUpdater());
+        assertEquals(new Nesterovs(0.15, 0.9), l1.getUpdater());
-        assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
+        assertEquals(0.9, ((Nesterovs)l1.getUpdater()).getMomentum(), 1e-6);
-        assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l1.getUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
         assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
@@ -107,8 +107,8 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
         assertEquals(new WeightDecay(0.2, false), TestUtils.getWeightDecayReg(l0));
@@ -119,8 +119,8 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getUpdater());
-        assertEquals(0.15, ((RmsProp)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l1.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l1), 1e-6);
         assertEquals(new WeightDecay(0.2, false), TestUtils.getWeightDecayReg(l1));
@@ -146,8 +146,8 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
         assertArrayEquals(new int[] {1, 1}, l0.getStride());
         assertArrayEquals(new int[] {0, 0}, l0.getPadding());
@@ -166,8 +166,8 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
         assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());

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

@@ -75,8 +75,8 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
-        assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
+        assertEquals(new Nesterovs(0.15, 0.9), l0.getUpdater());
-        assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l0.getUpdater()).getLearningRate(), 1e-6);
 
         OutputLayer l1 = (OutputLayer) conf.getConf(1).getLayer();
         assertEquals("softmax", l1.getActivationFn().toString());
@@ -84,9 +84,9 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
-        assertEquals(new Nesterovs(0.15, 0.9), l1.getIUpdater());
+        assertEquals(new Nesterovs(0.15, 0.9), l1.getUpdater());
-        assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
+        assertEquals(0.9, ((Nesterovs)l1.getUpdater()).getMomentum(), 1e-6);
-        assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l1.getUpdater()).getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
         assertEquals(Nd4j.linspace(1, numParams, numParams, Nd4j.dataType()).reshape(1,numParams), net.getModelParams());
@@ -109,8 +109,8 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
         assertEquals(new WeightDecay(0.2, false), TestUtils.getWeightDecayReg(l0));
@@ -123,8 +123,8 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getUpdater());
-        assertEquals(0.15, ((RmsProp)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l1.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l1), 1e-6);
         assertEquals(new WeightDecay(0.2,false), TestUtils.getWeightDecayReg(l1));
@@ -152,8 +152,8 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
         assertArrayEquals(new int[] {1, 1}, l0.getStride());
         assertArrayEquals(new int[] {0, 0}, l0.getPadding());
@@ -172,8 +172,8 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
 
         assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
 

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

@@ -76,8 +76,8 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
-        assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
+        assertEquals(new Nesterovs(0.15, 0.9), l0.getUpdater());
-        assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l0.getUpdater()).getLearningRate(), 1e-6);
 
         OutputLayer l1 = (OutputLayer) conf.getConf(1).getLayer();
         assertEquals("softmax", l1.getActivationFn().toString());
@@ -85,9 +85,9 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
-        assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
+        assertEquals(0.9, ((Nesterovs)l1.getUpdater()).getMomentum(), 1e-6);
-        assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
+        assertEquals(0.9, ((Nesterovs)l1.getUpdater()).getMomentum(), 1e-6);
-        assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l1.getUpdater()).getLearningRate(), 1e-6);
 
         long numParams = (int)net.numParams();
         assertEquals(Nd4j.linspace(1, numParams, numParams).reshape(1,numParams), net.getModelParams());
@@ -110,8 +110,8 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
         assertEquals(new WeightDecay(0.2,false), TestUtils.getWeightDecayReg(l0));
@@ -124,8 +124,8 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l1.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l1), 1e-6);
         assertEquals(new WeightDecay(0.2,false), TestUtils.getWeightDecayReg(l1));
@@ -153,8 +153,8 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
         assertArrayEquals(new int[] {1, 1}, l0.getStride());
         assertArrayEquals(new int[] {0, 0}, l0.getPadding());
@@ -173,8 +173,8 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
+        assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getUpdater());
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
 
         assertTrue(conf.getInputPreProcess(2) instanceof CnnToFeedForwardPreProcessor);
 

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

@@ -75,10 +75,10 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
-        assertTrue(l0.getIUpdater() instanceof Nesterovs);
+        assertTrue(l0.getUpdater() instanceof Nesterovs);
-        Nesterovs n = (Nesterovs) l0.getIUpdater();
+        Nesterovs n = (Nesterovs) l0.getUpdater();
         assertEquals(0.9, n.getMomentum(), 1e-6);
-        assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(0.15, n.getLearningRate(), 1e-6);
 
 
@@ -88,9 +88,9 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
-        assertTrue(l1.getIUpdater() instanceof Nesterovs);
+        assertTrue(l1.getUpdater() instanceof Nesterovs);
-        assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
+        assertEquals(0.9, ((Nesterovs)l1.getUpdater()).getMomentum(), 1e-6);
-        assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((Nesterovs)l1.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(0.15, n.getLearningRate(), 1e-6);
 
         int numParams = (int)net.numParams();
@@ -114,11 +114,11 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
-        assertTrue(l0.getIUpdater() instanceof RmsProp);
+        assertTrue(l0.getUpdater() instanceof RmsProp);
-        RmsProp r = (RmsProp) l0.getIUpdater();
+        RmsProp r = (RmsProp) l0.getUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
         assertEquals(0.15, r.getLearningRate(), 1e-6);
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l0), 1e-6);
         assertEquals(new WeightDecay(0.2,false), TestUtils.getWeightDecayReg(l0));
@@ -131,11 +131,11 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
         assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l1.getWeightInit());
-        assertTrue(l1.getIUpdater() instanceof RmsProp);
+        assertTrue(l1.getUpdater() instanceof RmsProp);
-        r = (RmsProp) l1.getIUpdater();
+        r = (RmsProp) l1.getUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
         assertEquals(0.15, r.getLearningRate(), 1e-6);
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getDropOut());
         assertEquals(0.1, TestUtils.getL1(l1), 1e-6);
         assertEquals(new WeightDecay(0.2, false), TestUtils.getWeightDecayReg(l1));
@@ -163,11 +163,11 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
         assertEquals(new WeightInitRelu(), l0.getWeightInit());
-        assertTrue(l0.getIUpdater() instanceof RmsProp);
+        assertTrue(l0.getUpdater() instanceof RmsProp);
-        RmsProp r = (RmsProp) l0.getIUpdater();
+        RmsProp r = (RmsProp) l0.getUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
         assertEquals(0.15, r.getLearningRate(), 1e-6);
-        assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
+        assertEquals(0.15, ((RmsProp)l0.getUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
         assertArrayEquals(new int[] {1, 1}, l0.getStride());
         assertArrayEquals(new int[] {0, 0}, l0.getPadding());
@@ -186,8 +186,8 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
         assertEquals(new WeightInitRelu(), l2.getWeightInit());
-        assertTrue(l2.getIUpdater() instanceof RmsProp);
+        assertTrue(l2.getUpdater() instanceof RmsProp);
-        r = (RmsProp) l2.getIUpdater();
+        r = (RmsProp) l2.getUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
         assertEquals(0.15, r.getLearningRate(), 1e-6);
 

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

@@ -91,21 +91,21 @@ public class RegressionTest100a extends BaseDL4JTest {
         assertEquals(200, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new RmsProp(0.1), l0.getIUpdater());
+        assertEquals(new RmsProp(0.1), l0.getUpdater());
 
         GravesLSTM l1 = (GravesLSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l1));
-        assertEquals(new RmsProp(0.1), l1.getIUpdater());
+        assertEquals(new RmsProp(0.1), l1.getUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
         assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new RmsProp(0.1), l0.getIUpdater());
+        assertEquals(new RmsProp(0.1), l0.getUpdater());
 
         assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
         assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
@@ -141,7 +141,7 @@ public class RegressionTest100a extends BaseDL4JTest {
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
                 assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new Adam(0.05), l0.getIUpdater());
+        assertEquals(new Adam(0.05), l0.getUpdater());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100a/VaeMNISTAnomaly_Output_100a.bin");

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

@@ -75,12 +75,12 @@ public class RegressionTest100b3 extends BaseDL4JTest {
             DenseLayer l0 = (DenseLayer) net.getLayer(0).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l0.getActivationFn());
             assertEquals(new WeightDecay(0.03, false), TestUtils.getWeightDecayReg(l0));
-            assertEquals(new RmsProp(0.95), l0.getIUpdater());
+            assertEquals(new RmsProp(0.95), l0.getUpdater());
 
             CustomLayer l1 = (CustomLayer) net.getLayer(1).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l1.getActivationFn());
             assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
-            assertEquals(new RmsProp(0.95), l1.getIUpdater());
+            assertEquals(new RmsProp(0.95), l1.getUpdater());
 
 
             INDArray outExp;
@@ -126,21 +126,21 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         assertEquals(200, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l1));
-        assertEquals(new Adam(0.005), l1.getIUpdater());
+        assertEquals(new Adam(0.005), l1.getUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
         assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
 
         assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
         assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
@@ -176,7 +176,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
                 assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
-        assertEquals(new Adam(1e-3), l0.getIUpdater());
+        assertEquals(new Adam(1e-3), l0.getUpdater());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b3/VaeMNISTAnomaly_Output_100b3.bin");

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

@@ -94,12 +94,12 @@ public class RegressionTest100b4 extends BaseDL4JTest {
             DenseLayer l0 = (DenseLayer) net.getLayer(0).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l0.getActivationFn());
             assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0));
-            assertEquals(new RmsProp(0.95), l0.getIUpdater());
+            assertEquals(new RmsProp(0.95), l0.getUpdater());
 
             CustomLayer l1 = (CustomLayer) net.getLayer(1).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l1.getActivationFn());
             assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
-            assertEquals(new RmsProp(0.95), l1.getIUpdater());
+            assertEquals(new RmsProp(0.95), l1.getUpdater());
 
             INDArray outExp;
             File f2 = Resources
@@ -144,21 +144,21 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(200, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
-        assertEquals(new Adam(0.005), l1.getIUpdater());
+        assertEquals(new Adam(0.005), l1.getUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
         assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
-        assertEquals(new Adam(0.005), l2.getIUpdater());
+        assertEquals(new Adam(0.005), l2.getUpdater());
 
         assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
         assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
@@ -194,7 +194,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(1e-3), l0.getIUpdater());
+        assertEquals(new Adam(1e-3), l0.getUpdater());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b4/VaeMNISTAnomaly_Output_100b4.bin");
@@ -262,7 +262,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l0.getKernelSize());
         assertArrayEquals(new int[]{2, 1}, l0.getStride());
         assertArrayEquals(new int[]{1, 1}, l0.getDilation());
@@ -273,7 +273,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(8, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
-        assertEquals(new Adam(0.005), l1.getIUpdater());
+        assertEquals(new Adam(0.005), l1.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l1.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l1.getStride());
         assertArrayEquals(new int[]{1, 1}, l1.getDilation());
@@ -299,7 +299,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(16, l5.getNOut());
         assertEquals(new WeightInitXavier(), l5.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
-        assertEquals(new Adam(0.005), l5.getIUpdater());
+        assertEquals(new Adam(0.005), l5.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l5.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l5.getStride());
         assertArrayEquals(new int[]{1, 1}, l5.getDilation());
@@ -320,7 +320,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(4, l8.getNOut());
         assertEquals(new WeightInitXavier(), l8.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8));
-        assertEquals(new Adam(0.005), l8.getIUpdater());
+        assertEquals(new Adam(0.005), l8.getUpdater());
         assertArrayEquals(new int[]{4, 4}, l8.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l8.getStride());
         assertArrayEquals(new int[]{1, 1}, l8.getDilation());
@@ -329,7 +329,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).getLayerConfiguration();
         assertEquals(new WeightInitXavier(), l9.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9));
-        assertEquals(new Adam(0.005), l9.getIUpdater());
+        assertEquals(new Adam(0.005), l9.getUpdater());
         assertEquals(new LossMAE(), l9.getLossFunction());
 
         INDArray outExp;

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

@@ -76,12 +76,12 @@ public class RegressionTest100b6 extends BaseDL4JTest {
             DenseLayer l0 = (DenseLayer) net.getLayer(0).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l0.getActivationFn());
             assertEquals(new L2Regularization(0.03), TestUtils.getL2Reg(l0));
-            assertEquals(new RmsProp(0.95), l0.getIUpdater());
+            assertEquals(new RmsProp(0.95), l0.getUpdater());
 
             CustomLayer l1 = (CustomLayer) net.getLayer(1).getLayerConfiguration();
             assertEquals(new ActivationTanH(), l1.getActivationFn());
             assertEquals(new ActivationSigmoid(), l1.getSecondActivationFunction());
-            assertEquals(new RmsProp(0.95), l1.getIUpdater());
+            assertEquals(new RmsProp(0.95), l1.getUpdater());
 
             INDArray outExp;
             File f2 = Resources
@@ -126,21 +126,21 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(200, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
-        assertEquals(new Adam(0.005), l1.getIUpdater());
+        assertEquals(new Adam(0.005), l1.getUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
         assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
-        assertEquals(new Adam(0.005), l2.getIUpdater());
+        assertEquals(new Adam(0.005), l2.getUpdater());
 
         assertEquals(BackpropType.TruncatedBPTT, net.getNetConfiguration().getBackpropType());
         assertEquals(50, net.getNetConfiguration().getTbpttBackLength());
@@ -176,7 +176,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(1e-3), l0.getIUpdater());
+        assertEquals(new Adam(1e-3), l0.getUpdater());
 
         INDArray outExp;
         File f2 = Resources.asFile("regression_testing/100b6/VaeMNISTAnomaly_Output_100b6.bin");
@@ -242,7 +242,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(4, l0.getNOut());
         assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
-        assertEquals(new Adam(0.005), l0.getIUpdater());
+        assertEquals(new Adam(0.005), l0.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l0.getKernelSize());
         assertArrayEquals(new int[]{2, 1}, l0.getStride());
         assertArrayEquals(new int[]{1, 1}, l0.getDilation());
@@ -253,7 +253,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(8, l1.getNOut());
         assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
-        assertEquals(new Adam(0.005), l1.getIUpdater());
+        assertEquals(new Adam(0.005), l1.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l1.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l1.getStride());
         assertArrayEquals(new int[]{1, 1}, l1.getDilation());
@@ -279,7 +279,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(16, l5.getNOut());
         assertEquals(new WeightInitXavier(), l5.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
-        assertEquals(new Adam(0.005), l5.getIUpdater());
+        assertEquals(new Adam(0.005), l5.getUpdater());
         assertArrayEquals(new int[]{3, 3}, l5.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l5.getStride());
         assertArrayEquals(new int[]{1, 1}, l5.getDilation());
@@ -300,7 +300,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(4, l8.getNOut());
         assertEquals(new WeightInitXavier(), l8.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8));
-        assertEquals(new Adam(0.005), l8.getIUpdater());
+        assertEquals(new Adam(0.005), l8.getUpdater());
         assertArrayEquals(new int[]{4, 4}, l8.getKernelSize());
         assertArrayEquals(new int[]{1, 1}, l8.getStride());
         assertArrayEquals(new int[]{1, 1}, l8.getDilation());
@@ -309,7 +309,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).getLayerConfiguration();
         assertEquals(new WeightInitXavier(), l9.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9));
-        assertEquals(new Adam(0.005), l9.getIUpdater());
+        assertEquals(new Adam(0.005), l9.getUpdater());
         assertEquals(new LossMAE(), l9.getLossFunction());
 
         INDArray outExp;

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

@@ -113,7 +113,7 @@ public class CustomLayer extends FeedForwardLayer {
     InputType outputType = getOutputType(-1, inputType);
 
     val numParams = initializer().numParams(this);
-    int updaterStateSize = (int) getIUpdater().stateSize(numParams);
+    int updaterStateSize = (int) getUpdater().stateSize(numParams);
 
     int trainSizeFixed = 0;
     int trainSizeVariable = 0;

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

@@ -235,7 +235,7 @@ public class GradientCheckUtil {
     for (LayerConfiguration n : c.net.getNetConfiguration().getFlattenedLayerConfigurations()) {
       if (n instanceof BaseLayerConfiguration) {
         BaseLayerConfiguration bl = (BaseLayerConfiguration) n;
-        IUpdater u = bl.getIUpdater();
+        IUpdater u = bl.getUpdater();
         if (u instanceof Sgd) {
           // Must have LR of 1.0
           double lr = ((Sgd) u).getLearningRate();
@@ -540,7 +540,7 @@ public class GradientCheckUtil {
 
       if (lv.getLayerConfiguration() instanceof BaseLayerConfiguration) {
         BaseLayerConfiguration bl = (BaseLayerConfiguration) lv.getLayerConfiguration();
-        IUpdater u = bl.getIUpdater();
+        IUpdater u = bl.getUpdater();
         if (u instanceof Sgd) {
           // Must have LR of 1.0
           double lr = ((Sgd) u).getLearningRate();

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

@@ -322,7 +322,7 @@ public abstract class NeuralNetBaseBuilderConfiguration implements INeuralNetwor
   @Getter @Setter @lombok.Builder.Default @JsonIgnore private IUpdater iUpdater = new Sgd();
   /**
    * Gradient updater configuration, for the biases only. If not set, biases will use the updater as
-   * set by {@link #setIUpdater(IUpdater)}<br>
+   * set by {@link #setUpdater(IUpdater)}<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.

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

@@ -65,7 +65,7 @@ public class ActivationLayer extends NoParamLayer {
   }
 
   @Override
-  public IUpdater getIUpdater() {
+  public IUpdater getUpdater() {
     return null;
   }
 

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

@@ -88,7 +88,7 @@ public class AutoEncoder extends BasePretrainNetwork {
 
         val actElementsPerEx = outputType.arrayElementsPerExample() + inputType.arrayElementsPerExample();
         val numParams = initializer().numParams(this);
-        val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+        val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
         int trainSizePerEx = 0;
         if (getDropOut() != null) {

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

@@ -95,7 +95,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
    * Gradient updater. For example, {@link org.nd4j.linalg.learning.config.Adam} or {@link
    * org.nd4j.linalg.learning.config.Nesterovs}
    */
-  @Getter @Setter
+  @Getter
   protected IUpdater updater;
   /**
    * Gradient updater configuration, for the biases only. If not set, biases will use the updater as
@@ -134,7 +134,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
    */
   public void resetLayerDefaultConfig() {
     // clear the learning related params for all layers in the origConf and set to defaults
-    this.setIUpdater(null);
+    this.setUpdater( (IUpdater) null);
     this.setWeightInit(null);
     this.setBiasInit(Double.NaN);
     this.setGainInit(Double.NaN);
@@ -142,10 +142,16 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
     this.regularizationBias = null;
     this.setGradientNormalization(GradientNormalization.None);
     this.setGradientNormalizationThreshold(1.0);
-    this.updater = null;
     this.biasUpdater = null;
   }
 
+    public void setUpdater(Updater updater) {
+        setUpdater(updater.getIUpdaterWithDefaultConfig());
+    }
+    public void setUpdater(IUpdater iUpdater) {
+        this.updater=iUpdater;
+    }
+
   @Override
   public BaseLayerConfiguration clone() {
     BaseLayerConfiguration clone = (BaseLayerConfiguration) super.clone();
@@ -203,6 +209,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration
     if (this.updater == null) this.updater = conf.getUpdater();
     if (this.regularizationBias == null) this.regularizationBias = conf.getRegularizationBias();
     if (this.regularization == null) this.regularization = conf.getRegularization();
+    if( this.weightInit == null) this.weightInit = conf.getWeightInit();
     if (this.gradientNormalization == null)
       this.gradientNormalization = conf.getGradientNormalization();
     // if(this.weightInit == null) this.weightInit = conf.getWeightInit();

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

@@ -56,7 +56,7 @@ public abstract class BaseOutputLayer extends FeedForwardLayer {
         InputType outputType = getOutputType(-1, inputType);
 
         val numParams = initializer().numParams(this);
-        val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+        val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
         int trainSizeFixed = 0;
         int trainSizeVariable = 0;

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

@@ -235,7 +235,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
   @Override
   public LayerMemoryReport getMemoryReport(InputType inputType) {
     val paramSize = initializer().numParams(this);
-    val updaterStateSize = (int) getIUpdater().stateSize(paramSize);
+    val updaterStateSize = (int) getUpdater().stateSize(paramSize);
 
     InputType.InputTypeConvolutional c = (InputType.InputTypeConvolutional) inputType;
     InputType.InputTypeConvolutional outputType =

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

@@ -60,7 +60,7 @@ public class DenseLayer extends FeedForwardLayer {
     LayerValidation.assertNInNOutSet(
         "DenseLayerConfiguration", getName(), layerIndex, getNIn(), getNOut());
     LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
-    lconf.runInheritance();
+    runInheritance();
 
     org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer ret =
         new org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer(lconf, networkDataType);
@@ -84,7 +84,7 @@ public class DenseLayer extends FeedForwardLayer {
     InputType outputType = getOutputType(-1, inputType);
 
     val numParams = initializer().numParams(this);
-    val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+    val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
     int trainSizeFixed = 0;
     int trainSizeVariable = 0;

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

@@ -96,7 +96,7 @@ public class EmbeddingLayer extends FeedForwardLayer {
 
     val actElementsPerEx = outputType.arrayElementsPerExample();
     val numParams = initializer().numParams(this);
-    val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+    val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
     // Embedding layer does not use caching.
     // Inference: no working memory - just activations (pullRows)

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

@@ -162,7 +162,7 @@ extends FeedForwardLayerBuilder<C, B> {
 
         val actElementsPerEx = outputType.arrayElementsPerExample();
         val numParams = initializer().numParams(this);
-        val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+        val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
         return new LayerMemoryReport.Builder(name, EmbeddingSequenceLayer.class, inputType, outputType)
                         .standardMemory(numParams, updaterStateSize).workingMemory(0, 0, 0, actElementsPerEx)

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

@@ -34,6 +34,7 @@ import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
+import org.deeplearning4j.nn.conf.Updater;
 import org.deeplearning4j.nn.conf.dropout.Dropout;
 import org.deeplearning4j.nn.conf.dropout.IDropout;
 import org.deeplearning4j.nn.conf.inputs.InputType;
@@ -281,16 +282,6 @@ public abstract class LayerConfiguration
         "Not supported: all layers with parameters should override this method");
   }
 
-  public IUpdater getIUpdater() {
-    throw new UnsupportedOperationException(
-        "Not supported: all layers with parameters should override this method");
-  }
-
-  public void setIUpdater(IUpdater iUpdater) {
-    log.warn(
-        "Setting an IUpdater on {} with name {} has no effect.", getClass().getSimpleName(), name);
-  }
-
   /**
    * This is a report of the estimated memory consumption for the given layer
    *
@@ -316,6 +307,7 @@ public abstract class LayerConfiguration
     if (this.activation == null) this.activation = conf.getActivation();
     if (this.dropOut == null) this.dropOut = conf.getIdropOut();
     if (this.weightNoise == null) this.weightNoise = conf.getWeightNoise();
+
   }
 
   /**
@@ -326,6 +318,24 @@ public abstract class LayerConfiguration
     runInheritance(getNetConfiguration());
   }
 
+  /**
+   * This will always return no-Op updater.
+   * @return
+   */
+  public IUpdater getUpdater() {
+    log.warn("Calling getUpdater() in {} will always return no-Op Updater.", LayerConfiguration.class.getSimpleName());
+    return Updater.NONE.getIUpdaterWithDefaultConfig();
+  }
+  @Deprecated
+  public void setUpdater(Updater updater) {
+    setUpdater(updater.getIUpdaterWithDefaultConfig());
+  }
+
+  public void setUpdater(IUpdater iUpdater) {
+    throw new RuntimeException("When " + this.getName() + " wants to you an Updater, it needs to override the "
+    + "Getter/ Setter for the Updater and not rely on LayerConfiguration class.");
+  }
+
   public abstract static class LayerConfigurationBuilder<
       C extends LayerConfiguration, B extends LayerConfigurationBuilder<C, B>> {
 

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected1D.java

@@ -249,17 +249,6 @@ public class LocallyConnected1D extends SameDiffLayer {
     }
   }
 
-  @Override
-  public void applyGlobalConfigToLayer(
-      NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-    NeuralNetConfiguration global_conf = globalConfig.build();
-    if (activation == null) {
-      activation = SameDiffLayerUtils.fromIActivation(global_conf.getActivation());
-    }
-    if (convolutionMode == null) {
-      convolutionMode = global_conf.getConvolutionMode();
-    }
-  }
 
   private static final class LocallyConnected1DBuilderImpl
       extends LocallyConnected1DBuilder<LocallyConnected1D, LocallyConnected1DBuilderImpl> {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LocallyConnected2D.java

@@ -305,17 +305,6 @@ public class LocallyConnected2D extends SameDiffLayer {
     }
   }
 
-  @Override
-  public void applyGlobalConfigToLayer(
-      NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-    NeuralNetConfiguration gconf = globalConfig.build();
-    if (activation == null) {
-      activation = SameDiffLayerUtils.fromIActivation(gconf.getActivation());
-    }
-    if (convolutionMode == null) {
-      convolutionMode = gconf.getConvolutionMode();
-    }
-  }
 
   private static final class LocallyConnected2DBuilderImpl
       extends LocallyConnected2DBuilder<LocallyConnected2D, LocallyConnected2DBuilderImpl> {

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

@@ -56,10 +56,11 @@ public abstract class NoParamLayer extends LayerConfiguration {
   }
 
   /**
+   * Will always return no-Op updater.
    * @return
    */
   @Override
-  public IUpdater getIUpdater() {
+  public IUpdater getUpdater() {
     return Updater.NONE.getIUpdaterWithDefaultConfig();
   }
 

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

@@ -40,9 +40,7 @@ import org.nd4j.linalg.lossfunctions.LossFunctions;
 @SuperBuilder(builderMethodName = "innerBuilder")
 public class OutputLayer extends BaseOutputLayer {
 
-  { // Set default activation function to softmax (to match default loss function MCXENT)
+
-    setActivation(Activation.SOFTMAX.getActivationFunction());
-  }
 
   public static OutputLayerBuilder<?, ?> builder() {
     return innerBuilder();

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

@@ -120,7 +120,7 @@ public class PReLULayer extends BaseLayerConfiguration {
     InputType outputType = getOutputType(-1, inputType);
 
     val numParams = initializer().numParams(this);
-    val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+    val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
     return new LayerMemoryReport.Builder(name, PReLULayer.class, inputType, outputType)
         .standardMemory(numParams, updaterStateSize)

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

@@ -22,6 +22,7 @@ package org.deeplearning4j.nn.conf.layers;
 
 import lombok.*;
 import lombok.experimental.SuperBuilder;
+import org.deeplearning4j.nn.api.ITraininableLayerConfiguration;
 import org.deeplearning4j.nn.conf.InputPreProcessor;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.RNNFormat;
@@ -35,6 +36,7 @@ import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.common.base.Preconditions;
 import org.nd4j.linalg.activations.Activation;
+import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
@@ -44,7 +46,9 @@ import java.util.Map;
 @Data
 @EqualsAndHashCode(callSuper = true)
 @SuperBuilder
-public class RecurrentAttentionLayer extends SameDiffLayer {
+public class RecurrentAttentionLayer extends SameDiffLayer implements ITraininableLayerConfiguration {
+
+private DataType dataType;
 
     private static final class RecurrentAttentionLayerBuilderImpl extends RecurrentAttentionLayerBuilder<RecurrentAttentionLayer, RecurrentAttentionLayerBuilderImpl> {
         public RecurrentAttentionLayer build() {
@@ -190,13 +194,6 @@ public class RecurrentAttentionLayer extends SameDiffLayer {
         }
     }
 
-    @Override
-    public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-        if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(globalConfig.build().getActivation());
-        }
-    }
-
     @Override
     public void validateInput(INDArray input) {
         final long inputLength = input.size(2);

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

@@ -91,7 +91,7 @@ public class ElementWiseMultiplicationLayer extends org.deeplearning4j.nn.conf.l
         InputType outputType = getOutputType(-1, inputType);
 
         val numParams = initializer().numParams(this);
-        val updaterStateSize = (int) getIUpdater().stateSize(numParams);
+        val updaterStateSize = (int) getUpdater().stateSize(numParams);
 
         int trainSizeFixed = 0;
         int trainSizeVariable = 0;

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

@@ -82,6 +82,7 @@ public class FrozenLayerWithBackprop extends BaseWrapperLayerConfiguration {
         org.deeplearning4j.nn.api.Layer newUnderlyingLayer = underlying.instantiate(conf, trainingListeners,
                         layerIndex, layerParamsView, initializeParams, networkDataType);
 
+        runInheritance();
         newUnderlyingLayer.setLayerConfiguration(underlying); //Fix a problem, where the embedded layer gets the conf of the frozen layer, rather than its own
         NeuralNetConfiguration nncUnderlying = underlying.getNetConfiguration();
 
@@ -130,4 +131,6 @@ public class FrozenLayerWithBackprop extends BaseWrapperLayerConfiguration {
         this.constraints = constraints;
         this.underlying.setConstraints(constraints);
     }
+
+
 }

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

@@ -38,6 +38,7 @@ import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
 import org.deeplearning4j.optimize.api.TrainingListener;
 import org.deeplearning4j.util.NetworkUtils;
+import org.jetbrains.annotations.NotNull;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.factory.Nd4j;
@@ -93,6 +94,21 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
 @Getter @Setter
   private SDLayerParams layerParams;
 
+  @Override
+  public void runInheritance(@NotNull NeuralNetConfiguration conf) {
+    super.runInheritance(conf);
+    if (this.biasUpdater == null ) this.biasUpdater = conf.getBiasUpdater();
+    if (this.updater == null) this.updater = conf.getUpdater();
+    if (this.regularizationBias == null || regularizationBias.isEmpty()) this.regularizationBias = conf.getRegularizationBias();
+    if (this.regularization == null || regularization.isEmpty()) this.regularization = conf.getRegularization();
+   // if( this.weightInit == null) this.weightInit = conf.getWeightInit();
+    if (this.gradientNormalization == null)
+      this.gradientNormalization = conf.getGradientNormalization();
+    // if(this.weightInit == null) this.weightInit = conf.getWeightInit();
+    if (Double.isNaN(gradientNormalizationThreshold)) {
+      this.gradientNormalizationThreshold = conf.getGradientNormalizationThreshold();
+    }
+  }
   @Override
   public List<Regularization> getRegularizationByParam(String paramName) {
     if (layerParams.isWeightParam(paramName)) {
@@ -122,10 +138,6 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
     return null;
   }
 
-  public void applyGlobalConfigToLayer(
-      NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
-    // Default implementation: no op
-  }
 
   /**
    * Define the parameters for the network. Use {@link SDLayerParams#addWeightParam(String,
@@ -195,29 +207,6 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
         fanIn, fanOut, array.shape(), weightInit, null, paramReshapeOrder(null), array);
   }
 
-  public void applyGlobalConfig(NeuralNetConfiguration.NeuralNetConfigurationBuilder b) {
-    NeuralNetConfiguration bConf = b.build();
-    if (regularization == null || regularization.isEmpty()) {
-      regularization = bConf.getRegularization();
-    }
-    if (regularizationBias == null || regularizationBias.isEmpty()) {
-      regularizationBias = bConf.getRegularizationBias();
-    }
-    if (updater == null) {
-      updater = bConf.getUpdater();
-    }
-    if (biasUpdater == null) {
-      biasUpdater = bConf.getBiasUpdater();
-    }
-    if (gradientNormalization == null) {
-      gradientNormalization = bConf.getGradientNormalization();
-    }
-    if (Double.isNaN(gradientNormalizationThreshold)) {
-      gradientNormalizationThreshold = bConf.getGradientNormalizationThreshold();
-    }
-
-    applyGlobalConfigToLayer(b);
-  }
 
   /**
    * This method generates an "all ones" mask array for use in the SameDiff model when none is

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

@@ -137,7 +137,7 @@ public class VariationalAutoencoder extends BasePretrainNetwork {
 
     val actElementsPerEx = outputType.arrayElementsPerExample();
     val numParams = initializer().numParams(this);
-    int updaterStateSize = (int) getIUpdater().stateSize(numParams);
+    int updaterStateSize = (int) getUpdater().stateSize(numParams);
 
     int inferenceWorkingMemSizePerEx = 0;
     // Forward pass size through the encoder:

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

@@ -123,17 +123,17 @@ public abstract class BaseWrapperLayerConfiguration extends LayerConfiguration {
   /**
    * @return
    */
-  @Override
+
-  public IUpdater getIUpdater() {
+  public IUpdater getUpdater() {
-    return underlying.getIUpdater();
+    return underlying.getUpdater();
   }
 
   /**
    * @param iUpdater
    */
-  @Override
+
-  public void setIUpdater(IUpdater iUpdater) {
+  public void setUpdater(IUpdater iUpdater) {
-    underlying.setIUpdater(iUpdater);
+    underlying.setUpdater(iUpdater);
   }
 
   @Override

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

@@ -68,7 +68,7 @@ public abstract class BaseNetConfigDeserializer<T> extends StdDeserializer<T> im
         for(LayerConfiguration l : layers){
             if(l instanceof BaseLayerConfiguration){
                 BaseLayerConfiguration bl = (BaseLayerConfiguration)l;
-                if(bl.getIUpdater() == null && bl.initializer().numParams(bl) > 0){
+                if(bl.getUpdater() == null && bl.initializer().numParams(bl) > 0){
                     return true;
                 }
             }
@@ -200,7 +200,7 @@ public abstract class BaseNetConfigDeserializer<T> extends StdDeserializer<T> im
                         break;
                 }
 
-                layer.setIUpdater(iu);
+                layer.setUpdater(iu);
             }
         }
     }

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

@@ -116,7 +116,7 @@ public class ComputationGraphConfigurationDeserializer
                     }
 
                     if(attemptIUpdaterFromLegacy && layers[layerIdx] instanceof BaseLayerConfiguration
-                        && ((BaseLayerConfiguration)layers[layerIdx]).getIUpdater() == null){
+                        && ((BaseLayerConfiguration)layers[layerIdx]).getUpdater() == null){
                         handleUpdaterBackwardCompatibility((BaseLayerConfiguration)layers[layerIdx], (ObjectNode)next);
                     }
 

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

@@ -87,7 +87,7 @@ public class NeuralNetConfigurationDeserializer extends BaseNetConfigDeserialize
                 ObjectNode on = (ObjectNode) confsNode.get(i);
                 ObjectNode confNode = null;
                 if(layers[i] instanceof BaseLayerConfiguration
-                    && ((BaseLayerConfiguration)layers[i]).getIUpdater() == null){
+                    && ((BaseLayerConfiguration)layers[i]).getUpdater() == null){
                     //layer -> (first/only child) -> updater
                     if(on.has("layer")){
                         confNode = on;

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

@@ -77,6 +77,7 @@ public class WeightNoise implements IWeightNoise {
                 (applyToBias && init.isBiasParam(layer.getLayerConfiguration(), paramKey))) {
 
             org.nd4j.linalg.api.rng.distribution.Distribution dist = Distributions.createDistribution(distribution);
+
             INDArray noise = dist.sample(param.ulike());
             INDArray out = workspaceMgr.createUninitialized(ArrayType.INPUT, param.dataType(), param.shape(), param.ordering());
 

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

@@ -577,7 +577,8 @@ public abstract class AbstractLayer<LayerConf_T extends LayerConfiguration> impl
    */
   @Override
   public Map<String, INDArray> getParamTable(boolean isBackprop) {
-    throw new RuntimeException("Not implemented");
+//    throw new RuntimeException("Not implemented");
+    return null;
   }
 
   /**

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java

@@ -340,6 +340,6 @@ public class GlobalPoolingLayer extends AbstractLayer<org.deeplearning4j.nn.conf
      */
     @Override
     public void setParamTable(Map<String, INDArray> paramTable) {
-        throw new RuntimeException("Not implemented.");
+
     }
 }

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

@@ -768,7 +768,7 @@ public class LSTMHelpers {
         InputType outputType = lstmLayer.getOutputType(-1, inputType);
 
         val numParams = lstmLayer.initializer().numParams(lstmLayer);
-        int updaterSize = (int) lstmLayer.getIUpdater().stateSize(numParams);
+        int updaterSize = (int) lstmLayer.getUpdater().stateSize(numParams);
 
         //Memory use during forward pass:
         //ifogActivations: nTimeSteps * [minibatch,4*layerSize] (not cached during inference fwd pass)

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

@@ -565,7 +565,7 @@ public class MultiLayerNetwork extends ArtificialNeuralNetwork
    * inference/backprop are excluded from the returned list.
    *
    * @param backpropParamsOnly If true, return backprop params only. If false: return all params
-   * @return Parameters for the network
+   * @return Parameters for the network, empty Map if no parameters present in the neural network
    */
   @Override
   public Map<String, INDArray> getParamTable(boolean backpropParamsOnly) {
@@ -573,10 +573,11 @@ public class MultiLayerNetwork extends ArtificialNeuralNetwork
     Map<String, INDArray> allParams = new LinkedHashMap<>();
     for (int i = 0; i < layers.length; i++) {
       Map<String, INDArray> paramMap = layers[i].getParamTable(backpropParamsOnly);
+      if(paramMap!=null){
       for (Map.Entry<String, INDArray> entry : paramMap.entrySet()) {
         String newKey = i + "_" + entry.getKey();
         allParams.put(newKey, entry.getValue());
-      }
+      }}
     }
     return allParams;
   }

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/params/DefaultParamInitializer.java

@@ -94,6 +94,7 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
         if (!(conf instanceof org.deeplearning4j.nn.conf.layers.FeedForwardLayer))
             throw new IllegalArgumentException("unsupported layer type: " + conf.getClass().getName());
 
+        INDArray reshapedParamsView = paramsView.reshape(paramsView.length());
         Map<String, INDArray> params = Collections.synchronizedMap(new LinkedHashMap<String, INDArray>());
 
         val length = numParams(conf);
@@ -107,14 +108,14 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
         val nOut = layerConf.getNOut();
 
         val nWeightParams = nIn * nOut;
-        INDArray weightView = paramsView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nWeightParams));
+        INDArray weightView = reshapedParamsView.get(NDArrayIndex.interval(0, nWeightParams));
 
         params.put(WEIGHT_KEY, createWeightMatrix(layerConf, weightView, initializeParams));
         layerConf.addVariable(WEIGHT_KEY);
 
         long offset = nWeightParams;
         if(hasBias(layerConf)){
-            INDArray biasView = paramsView.get(NDArrayIndex.interval(0,0,true),
+            INDArray biasView = reshapedParamsView.get(
                     NDArrayIndex.interval(offset, offset + nOut));
             params.put(BIAS_KEY, createBias(layerConf, biasView, initializeParams));
             layerConf.addVariable(BIAS_KEY);
@@ -122,7 +123,7 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
         }
 
         if(hasLayerNorm(layerConf)){
-            INDArray gainView = paramsView.get(NDArrayIndex.interval(0,0,true),
+            INDArray gainView = reshapedParamsView.get(
                     NDArrayIndex.interval(offset, offset + nOut));
             params.put(GAIN_KEY, createGain(conf, gainView, initializeParams));
             conf.addVariable(GAIN_KEY);
@@ -138,8 +139,9 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
         val nIn = layerConf.getNIn();
         val nOut = layerConf.getNOut();
         val nWeightParams = nIn * nOut;
+        INDArray gradientViewReshaped = gradientView.reshape(gradientView.length());
 
-        INDArray weightGradientView = gradientView.get(NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(0, nWeightParams))
+        INDArray weightGradientView = gradientViewReshaped.get(NDArrayIndex.interval(0, nWeightParams))
                 .reshape('f', nIn, nOut);
 
         Map<String, INDArray> out = new LinkedHashMap<>();
@@ -147,14 +149,14 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
 
         long offset = nWeightParams;
         if(hasBias(layerConf)){
-            INDArray biasView = gradientView.get(NDArrayIndex.interval(0,0,true),
+            INDArray biasView = gradientViewReshaped.get(
                     NDArrayIndex.interval(offset, offset + nOut)); //Already a row vector
             out.put(BIAS_KEY, biasView);
             offset += nOut;
         }
 
         if(hasLayerNorm(layerConf)){
-            INDArray gainView = gradientView.get(NDArrayIndex.interval(0,0,true),
+            INDArray gainView = gradientViewReshaped.get(
                     NDArrayIndex.interval(offset, offset + nOut)); //Already a row vector
             out.put(GAIN_KEY, gainView);
         }
@@ -196,12 +198,6 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
                         (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf;
 
         if (initializeParameters) {
-            if( layerConf.getWeightInit() == null) {
-                // set a default and set warning
-                layerConf.setWeightInit(new WeightInitXavier());
-                log.warn("Weight Initializer function was not set on layer {} of class {}, it will default to {}", conf.getName(),
-                    conf.getClass().getSimpleName(), WeightInitXavier.class.getSimpleName());
-            }
                  return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), layerConf.getWeightInit(),
                             weightParamView, true);
         } else {

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/transferlearning/FineTuneConfiguration.java

@@ -200,7 +200,7 @@ public class FineTuneConfiguration {
             bl.setGradientNormalizationThreshold(gradientNormalizationThreshold);
         }
       if (updater != null) {
-        bl.setIUpdater(updater);
+        bl.setUpdater(updater);
       }
       if (biasUpdater != null) {
         bl.setBiasUpdater(biasUpdater);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/util/NetworkUtils.java

@@ -125,7 +125,7 @@ public class NetworkUtils {
         LayerConfiguration l = net.getLayer(layerNumber).getLayerConfiguration();
         if (l instanceof BaseLayerConfiguration) {
             BaseLayerConfiguration bl = (BaseLayerConfiguration) l;
-            IUpdater u = bl.getIUpdater();
+            IUpdater u = bl.getUpdater();
             if (u != null && u.hasLearningRate()) {
                 if (newLrSchedule != null) {
                     u.setLrAndSchedule(Double.NaN, newLrSchedule);
@@ -207,7 +207,7 @@ public class NetworkUtils {
         int epoch = net.getEpochCount();
         if (l instanceof BaseLayerConfiguration) {
             BaseLayerConfiguration bl = (BaseLayerConfiguration) l;
-            IUpdater u = bl.getIUpdater();
+            IUpdater u = bl.getUpdater();
             if (u != null && u.hasLearningRate()) {
                 double d = u.getLearningRate(iter, epoch);
                 if (Double.isNaN(d)) {
@@ -247,7 +247,7 @@ public class NetworkUtils {
         LayerConfiguration l = net.getLayer(layerName).getLayerConfiguration();
         if (l instanceof BaseLayerConfiguration) {
             BaseLayerConfiguration bl = (BaseLayerConfiguration) l;
-            IUpdater u = bl.getIUpdater();
+            IUpdater u = bl.getUpdater();
             if (u != null && u.hasLearningRate()) {
                 if (newLrSchedule != null) {
                     u.setLrAndSchedule(Double.NaN, newLrSchedule);
@@ -329,7 +329,7 @@ public class NetworkUtils {
         int epoch = net.getComputationGraphConfiguration().getEpochCount();
         if (l instanceof BaseLayerConfiguration) {
             BaseLayerConfiguration bl = (BaseLayerConfiguration) l;
-            IUpdater u = bl.getIUpdater();
+            IUpdater u = bl.getUpdater();
             if (u != null && u.hasLearningRate()) {
                 double d = u.getLearningRate(iter, epoch);
                 if (Double.isNaN(d)) {

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

@@ -210,14 +210,14 @@ public class TestSparkMultiLayerParameterAveraging extends BaseSparkTest {
         MultiLayerNetwork netCopy = sparkNet.getNetwork().clone();
 
         netCopy.fit(data);
-        IUpdater expectedUpdater = ((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getIUpdater();
+        IUpdater expectedUpdater = ((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getUpdater();
-        double expectedLR = ((Nesterovs)((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getIUpdater()).getLearningRate();
+        double expectedLR = ((Nesterovs)((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getUpdater()).getLearningRate();
-        double expectedMomentum = ((Nesterovs)((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getIUpdater()).getMomentum();
+        double expectedMomentum = ((Nesterovs)((BaseLayerConfiguration) netCopy.getLayerConfiguration()).getUpdater()).getMomentum();
 
-        IUpdater actualUpdater = ((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getIUpdater();
+        IUpdater actualUpdater = ((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getUpdater();
         sparkNet.fit(sparkData);
-        double actualLR = ((Nesterovs)((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getIUpdater()).getLearningRate();
+        double actualLR = ((Nesterovs)((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getUpdater()).getLearningRate();
-        double actualMomentum = ((Nesterovs)((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getIUpdater()).getMomentum();
+        double actualMomentum = ((Nesterovs)((BaseLayerConfiguration) sparkNet.getNetwork().getLayerConfiguration()).getUpdater()).getMomentum();
 
         assertEquals(expectedUpdater, actualUpdater);
         assertEquals(expectedLR, actualLR, 0.01);

cavis-native/cavis-native-jcublas/src/main/java/org/nd4j/linalg/jcublas/ops/executioner/CudaExecutioner.java

@@ -1580,10 +1580,11 @@ public class CudaExecutioner extends DefaultOpExecutioner {
                         zb, (LongPointer) hostZShapeInfo, (LongPointer) AtomicAllocator.getInstance().getPointer(z.shapeInfoDataBuffer(), context),
                          AtomicAllocator.getInstance().getPointer(op.extraArgsDataBuff(z.dataType()), context));
         }
-
+        int errorCode = nativeOps.lastErrorCode();
-        if (nativeOps.lastErrorCode() != 0)
+        if (errorCode != 0) {
-            throw new RuntimeException(nativeOps.lastErrorMessage() + " error code: " + nativeOps.lastErrorCode());
+              throw new RuntimeException(
-
+          nativeOps.lastErrorMessage() + " error code: " + errorCode);
+        }
         profilingConfigurableHookOut(op, oc, st);
 
         return z;

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

@@ -1189,7 +1189,7 @@ public class TrainModule implements UIModule {
                                 throw new RuntimeException(e);
                             }
 
-                            IUpdater u = bl.getIUpdater();
+                            IUpdater u = bl.getUpdater();
                             String us = (u == null ? "" : u.getClass().getSimpleName());
                             layerInfoRows.add(new String[]{i18N.getMessage("train.model.layerinfotable.layerUpdater"),
                                     us});