Playing with some new code 2 - clean build/test

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

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

@@ -118,7 +118,7 @@ public class App {
         .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
         .gradientNormalizationThreshold(GRADIENT_THRESHOLD)
         //.weightInit(WeightInit.XAVIER)
-        .weightInitFn(new WeightInitXavier())
+        .weightInit(WeightInit.XAVIER)
         .activation(Activation.IDENTITY)
         .layersFromArray(genLayers())
         .inputType(InputType.convolutional(X_DIM, Y_DIM, CHANNELS))

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

@@ -74,7 +74,7 @@ public class LayerBuilderTest extends BaseDL4JTest {
         checkSerialization(layer);
 
         assertEquals(act, layer.getActivationFn());
-        assertEquals(weight.getWeightInitFunction(), layer.getWeightInitFn());
+        assertEquals(weight.getWeightInitFunction(), layer.getWeightInit());
         assertEquals(new Dropout(dropOut), layer.getIDropout());
         assertEquals(updater, layer.getIUpdater());
         assertEquals(gradNorm, layer.getGradientNormalization());

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

@@ -99,8 +99,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInit());
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInit());
 
         assertEquals(1, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
         assertEquals(1, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getBiasInit(), 0.0);
@@ -117,8 +117,8 @@ public class LayerConfigTest extends BaseDL4JTest {
         net = new MultiLayerNetwork(conf);
         net.init();
 
-        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(defaultDistribution), ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getWeightInit());
-        assertEquals(new WeightInitDistribution(overriddenDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInitFn());
+        assertEquals(new WeightInitDistribution(overriddenDistribution), ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getWeightInit());
 
         assertEquals(1, ((BaseLayerConfiguration) conf.getConf(0).getLayer()).getBiasInit(), 0.0);
         assertEquals(0, ((BaseLayerConfiguration) conf.getConf(1).getLayer()).getBiasInit(), 0.0);

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

@@ -185,7 +185,7 @@ public class LayerConfigValidationTest extends BaseDL4JTest {
         layerConf1 = (BaseLayerConfiguration) net.getLayer(1).getLayerConfiguration();
         assertEquals(expectedAdamMeanDecay, ((Adam) layerConf1.getIUpdater()).getBeta1(), 1e-3);
         assertEquals(expectedAdamVarDecay, ((Adam) layerConf1.getIUpdater()).getBeta2(), 1e-3);
-        assertEquals(new WeightInitDistribution(expectedDist), layerConf1.getWeightInitFn());
+        assertEquals(new WeightInitDistribution(expectedDist), layerConf1.getWeightInit());
         assertNull(TestUtils.getL1Reg(layerConf1.getRegularization()));
         assertNull(TestUtils.getL2Reg(layerConf1.getRegularization()));
 

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

@@ -157,7 +157,7 @@ public class SameDiffConv extends SameDiffLayer {
     public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
         NeuralNetConfiguration clone = globalConfig.clone().build();
         if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(clone.getActivationFn());
+            activation = SameDiffLayerUtils.fromIActivation(clone.getActivation());
         }
         if (cm == null) {
             cm = clone.getConvolutionMode();

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

@@ -119,7 +119,7 @@ public class SameDiffDense extends SameDiffLayer {
     public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
         NeuralNetConfiguration clone = globalConfig.clone().build();
         if(activation == null){
-            activation = SameDiffLayerUtils.fromIActivation(clone.getActivationFn());
+            activation = SameDiffLayerUtils.fromIActivation(clone.getActivation());
         }
     }
 

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

@@ -141,9 +141,9 @@ public class TransferLearningCompGraphTest extends BaseDL4JTest {
         BaseLayerConfiguration bl0 = ((BaseLayerConfiguration) modelNow.getLayer("layer0").getLayerConfiguration());
         BaseLayerConfiguration bl1 = ((BaseLayerConfiguration) modelNow.getLayer("layer1").getLayerConfiguration());
         BaseLayerConfiguration bl3 = ((BaseLayerConfiguration) modelNow.getLayer("layer3").getLayerConfiguration());
-        assertEquals(bl0.getWeightInitFn(), new WeightInitDistribution(new NormalDistribution(1, 1e-1)));
+        assertEquals(bl0.getWeightInit(), new WeightInitDistribution(new NormalDistribution(1, 1e-1)));
-        assertEquals(bl1.getWeightInitFn(), new WeightInitXavier());
+        assertEquals(bl1.getWeightInit(), new WeightInitXavier());
-        assertEquals(bl1.getWeightInitFn(), new WeightInitXavier());
+        assertEquals(bl1.getWeightInit(), new WeightInitXavier());
 
         ComputationGraph modelExpectedArch = new ComputationGraph(overallConf.graphBuilder().addInputs("layer0In")
                         .addLayer("layer0", new DenseLayer.Builder().nIn(4).nOut(3).build(), "layer0In")

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

@@ -163,14 +163,14 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         BaseLayerConfiguration bl0 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(0).getLayer());
         BaseLayerConfiguration bl1 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(1).getLayer());
         BaseLayerConfiguration bl3 = ((BaseLayerConfiguration) modelNow.getNetConfiguration().getConf(3).getLayer());
-        assertEquals(bl0.getWeightInitFn().getClass(), WeightInitXavier.class);
+        assertEquals(bl0.getWeightInit().getClass(), WeightInitXavier.class);
         try {
-            assertEquals(JsonMappers.getMapper().writeValueAsString(bl1.getWeightInitFn()),
+            assertEquals(JsonMappers.getMapper().writeValueAsString(bl1.getWeightInit()),
                     JsonMappers.getMapper().writeValueAsString(new WeightInitDistribution(new NormalDistribution(1, 1e-1))));
         } catch (JsonProcessingException e) {
             throw new RuntimeException(e);
         }
-        assertEquals(bl3.getWeightInitFn(), new WeightInitXavier());
+        assertEquals(bl3.getWeightInit(), new WeightInitXavier());
 
         //modelNow should have the same architecture as modelExpectedArch
         assertArrayEquals(modelExpectedArch.getModelParams().shape(), modelNow.getModelParams().shape());
@@ -506,13 +506,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         BaseLayerConfiguration l0 = (BaseLayerConfiguration) net.getLayer(0).getLayerConfiguration();
         assertEquals(new Adam(1e-4), l0.getIUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(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(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
-        assertEquals(new WeightInitRelu(), l1.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l1.getWeightInit());
         assertEquals(0.2, TestUtils.getL2(l1), 1e-6);
 
         assertEquals(BackpropType.Standard, conf.getBackpropType());
@@ -521,13 +521,13 @@ public class TransferLearningMLNTest extends BaseDL4JTest {
         l0 = (BaseLayerConfiguration) net2.getLayer(0).getLayerConfiguration();
         assertEquals(new Adam(2e-2), l0.getIUpdater());
         assertEquals(Activation.TANH.getActivationFunction(), l0.getActivationFn());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(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(Activation.HARDSIGMOID.getActivationFunction(), l1.getActivationFn());
-        assertEquals(new WeightInitRelu(), l1.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l1.getWeightInit());
         assertEquals(0.2, TestUtils.getL2(l1), 1e-6);
 
         assertEquals(BackpropType.TruncatedBPTT, net2.getNetConfiguration().getBackpropType());

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

@@ -37,6 +37,7 @@ import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
 import org.deeplearning4j.nn.params.PretrainParamInitializer;
 import org.deeplearning4j.nn.updater.graph.ComputationGraphUpdater;
+import org.deeplearning4j.nn.weights.WeightInit;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 import org.nd4j.linalg.activations.Activation;
@@ -940,7 +941,9 @@ public class TestUpdaters extends BaseDL4JTest {
 
         List<UpdaterBlock> blocks;
         NeuralNetConfiguration conf =
-                        NeuralNetConfiguration.builder().updater(new Adam(0.5)).list()
+                        NeuralNetConfiguration.builder()
+                                .updater(new Adam(0.5))
+                                .weightInit(WeightInit.NORMAL)
                                         .layer(0, new VariationalAutoencoder.Builder().nIn(8).nOut(12)
                                                         .encoderLayerSizes(10, 11).decoderLayerSizes(13, 14).build())
                                         .build();

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

@@ -72,7 +72,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals("relu", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
         assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
 
@@ -81,7 +81,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMCXENT);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new Nesterovs(0.15, 0.9), l1.getIUpdater());
         assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
@@ -106,7 +106,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationLReLU);
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getIDropout());
@@ -118,7 +118,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMSE);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l1.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getIDropout());
@@ -145,7 +145,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertEquals("tanh", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
@@ -165,7 +165,7 @@ public class RegressionTest050 extends BaseDL4JTest {
         assertTrue(l2.getLossFn() instanceof LossNegativeLogLikelihood);
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
 

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

@@ -74,7 +74,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals("relu", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
         assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
 
@@ -83,7 +83,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMCXENT);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new Nesterovs(0.15, 0.9), l1.getIUpdater());
         assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
@@ -108,7 +108,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationLReLU);
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getIDropout());
@@ -122,7 +122,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMSE);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l1.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getIDropout());
@@ -151,7 +151,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertEquals("tanh", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
@@ -171,7 +171,7 @@ public class RegressionTest060 extends BaseDL4JTest {
         assertTrue(l2.getLossFn() instanceof LossNegativeLogLikelihood); //TODO
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
 

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

@@ -75,7 +75,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals("relu", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new Nesterovs(0.15, 0.9), l0.getIUpdater());
         assertEquals(0.15, ((Nesterovs)l0.getIUpdater()).getLearningRate(), 1e-6);
 
@@ -84,7 +84,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMCXENT);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
         assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
@@ -109,7 +109,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationLReLU);
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l0.getIDropout());
@@ -123,7 +123,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMSE);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        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(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertEquals(new Dropout(0.6), l1.getIDropout());
@@ -152,7 +152,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertEquals("tanh", l0.getActivationFn().toString());
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
         assertArrayEquals(new int[] {2, 2}, l0.getKernelSize());
@@ -172,7 +172,7 @@ public class RegressionTest071 extends BaseDL4JTest {
         assertTrue(l2.getLossFn() instanceof LossNegativeLogLikelihood); //TODO
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertEquals(new RmsProp(0.15, 0.96, RmsProp.DEFAULT_RMSPROP_EPSILON), l0.getIUpdater());
         assertEquals(0.15, ((RmsProp)l0.getIUpdater()).getLearningRate(), 1e-6);
 

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

@@ -74,7 +74,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationReLU);
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertTrue(l0.getIUpdater() instanceof Nesterovs);
         Nesterovs n = (Nesterovs) l0.getIUpdater();
         assertEquals(0.9, n.getMomentum(), 1e-6);
@@ -87,7 +87,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMCXENT);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertTrue(l1.getIUpdater() instanceof Nesterovs);
         assertEquals(0.9, ((Nesterovs)l1.getIUpdater()).getMomentum(), 1e-6);
         assertEquals(0.15, ((Nesterovs)l1.getIUpdater()).getLearningRate(), 1e-6);
@@ -113,7 +113,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationLReLU);
         assertEquals(3, l0.getNIn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInitFn());
+        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l0.getWeightInit());
         assertTrue(l0.getIUpdater() instanceof RmsProp);
         RmsProp r = (RmsProp) l0.getIUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
@@ -130,7 +130,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l1.getLossFn() instanceof LossMSE);
         assertEquals(4, l1.getNIn());
         assertEquals(5, l1.getNOut());
-        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l1.getWeightInitFn());
+        assertEquals(new WeightInitDistribution(new NormalDistribution(0.1, 1.2)), l1.getWeightInit());
         assertTrue(l1.getIUpdater() instanceof RmsProp);
         r = (RmsProp) l1.getIUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
@@ -162,7 +162,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l0.getActivationFn() instanceof ActivationTanH);
         assertEquals(3, l0.getNIn());
         assertEquals(3, l0.getNOut());
-        assertEquals(new WeightInitRelu(), l0.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l0.getWeightInit());
         assertTrue(l0.getIUpdater() instanceof RmsProp);
         RmsProp r = (RmsProp) l0.getIUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);
@@ -185,7 +185,7 @@ public class RegressionTest080 extends BaseDL4JTest {
         assertTrue(l2.getLossFn() instanceof LossNegativeLogLikelihood);
         assertEquals(26 * 26 * 3, l2.getNIn());
         assertEquals(5, l2.getNOut());
-        assertEquals(new WeightInitRelu(), l2.getWeightInitFn());
+        assertEquals(new WeightInitRelu(), l2.getWeightInit());
         assertTrue(l2.getIUpdater() instanceof RmsProp);
         r = (RmsProp) l2.getIUpdater();
         assertEquals(0.96, r.getRmsDecay(), 1e-6);

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

@@ -89,21 +89,21 @@ public class RegressionTest100a extends BaseDL4JTest {
         GravesLSTM l0 = (GravesLSTM) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l0.getActivationFn());
         assertEquals(200, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new RmsProp(0.1), l0.getIUpdater());
 
         GravesLSTM l1 = (GravesLSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l1));
         assertEquals(new RmsProp(0.1), l1.getIUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
-        assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new WeightDecay(0.001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new RmsProp(0.1), l0.getIUpdater());
 
@@ -139,7 +139,7 @@ public class RegressionTest100a extends BaseDL4JTest {
         assertEquals(32, l0.getNOut());
         assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
-                assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+                assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new Adam(0.05), l0.getIUpdater());
 
@@ -175,7 +175,7 @@ public class RegressionTest100a extends BaseDL4JTest {
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());
-        assertEquals(new WeightInitXavier(), cl.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), cl.getWeightInit());
         assertArrayEquals(new int[]{1,1}, cl.getKernelSize());
         assertArrayEquals(new int[]{1,1}, cl.getKernelSize());
 

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

@@ -124,21 +124,21 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         LSTM l0 = (LSTM) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l0.getActivationFn());
         assertEquals(200, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l1));
         assertEquals(new Adam(0.005), l1.getIUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
-        assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new WeightDecay(0.0001, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
 
@@ -174,7 +174,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         assertEquals(32, l0.getNOut());
         assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
-                assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+                assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new WeightDecay(1e-4, false), TestUtils.getWeightDecayReg(l0));
         assertEquals(new Adam(1e-3), l0.getIUpdater());
 
@@ -210,7 +210,7 @@ public class RegressionTest100b3 extends BaseDL4JTest {
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());
-        assertEquals(new WeightInitXavier(), cl.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), cl.getWeightInit());
         assertArrayEquals(new int[]{1,1}, cl.getKernelSize());
         assertArrayEquals(new int[]{1,1}, cl.getKernelSize());
 

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

@@ -142,21 +142,21 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         LSTM l0 = (LSTM) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l0.getActivationFn());
         assertEquals(200, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
         assertEquals(new Adam(0.005), l1.getIUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
-        assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
         assertEquals(new Adam(0.005), l2.getIUpdater());
 
@@ -192,7 +192,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(32, l0.getNOut());
         assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(1e-3), l0.getIUpdater());
 
@@ -229,7 +229,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());
-        assertEquals(new WeightInitXavier(), cl.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), cl.getWeightInit());
         assertArrayEquals(new int[]{1, 1}, cl.getKernelSize());
 
         INDArray outExp;
@@ -260,7 +260,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         ConvolutionLayer l0 = (ConvolutionLayer) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l0.getActivationFn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l0.getKernelSize());
@@ -271,7 +271,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         SeparableConvolution2D l1 = (SeparableConvolution2D) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l1.getActivationFn());
         assertEquals(8, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
         assertEquals(new Adam(0.005), l1.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l1.getKernelSize());
@@ -297,7 +297,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         DepthwiseConvolution2D l5 = (DepthwiseConvolution2D) net.getLayer(5).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l5.getActivationFn());
         assertEquals(16, l5.getNOut());
-        assertEquals(new WeightInitXavier(), l5.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l5.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
         assertEquals(new Adam(0.005), l5.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l5.getKernelSize());
@@ -318,7 +318,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
 
         ConvolutionLayer l8 = (ConvolutionLayer) net.getLayer(8).getLayerConfiguration();
         assertEquals(4, l8.getNOut());
-        assertEquals(new WeightInitXavier(), l8.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l8.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8));
         assertEquals(new Adam(0.005), l8.getIUpdater());
         assertArrayEquals(new int[]{4, 4}, l8.getKernelSize());
@@ -327,7 +327,7 @@ public class RegressionTest100b4 extends BaseDL4JTest {
         assertArrayEquals(new int[]{0, 0}, l8.getPadding());
 
         CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).getLayerConfiguration();
-        assertEquals(new WeightInitXavier(), l9.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l9.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9));
         assertEquals(new Adam(0.005), l9.getIUpdater());
         assertEquals(new LossMAE(), l9.getLossFn());

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

@@ -124,21 +124,21 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         LSTM l0 = (LSTM) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l0.getActivationFn());
         assertEquals(200, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
 
         LSTM l1 = (LSTM) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationTanH(), l1.getActivationFn());
         assertEquals(200, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
         assertEquals(new Adam(0.005), l1.getIUpdater());
 
         RnnOutputLayer l2 = (RnnOutputLayer) net.getLayer(2).getLayerConfiguration();
         assertEquals(new ActivationSoftmax(), l2.getActivationFn());
         assertEquals(77, l2.getNOut());
-        assertEquals(new WeightInitXavier(), l2.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l2.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l2));
         assertEquals(new Adam(0.005), l2.getIUpdater());
 
@@ -174,7 +174,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(32, l0.getNOut());
         assertArrayEquals(new int[]{256, 256}, l0.getEncoderLayerSizes());
         assertArrayEquals(new int[]{256, 256}, l0.getDecoderLayerSizes());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(1e-3), l0.getIUpdater());
 
@@ -210,7 +210,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertEquals(nBoxes * (5 + nClasses), cl.getNOut());
         assertEquals(new ActivationIdentity(), cl.getActivationFn());
         assertEquals(ConvolutionMode.Same, cl.getConvolutionMode());
-        assertEquals(new WeightInitXavier(), cl.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), cl.getWeightInit());
         assertArrayEquals(new int[]{1, 1}, cl.getKernelSize());
 
         INDArray outExp;
@@ -240,7 +240,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         ConvolutionLayer l0 = (ConvolutionLayer) net.getLayer(0).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l0.getActivationFn());
         assertEquals(4, l0.getNOut());
-        assertEquals(new WeightInitXavier(), l0.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l0.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l0));
         assertEquals(new Adam(0.005), l0.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l0.getKernelSize());
@@ -251,7 +251,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         SeparableConvolution2D l1 = (SeparableConvolution2D) net.getLayer(1).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l1.getActivationFn());
         assertEquals(8, l1.getNOut());
-        assertEquals(new WeightInitXavier(), l1.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l1.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l1));
         assertEquals(new Adam(0.005), l1.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l1.getKernelSize());
@@ -277,7 +277,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         DepthwiseConvolution2D l5 = (DepthwiseConvolution2D) net.getLayer(5).getLayerConfiguration();
         assertEquals(new ActivationReLU(), l5.getActivationFn());
         assertEquals(16, l5.getNOut());
-        assertEquals(new WeightInitXavier(), l5.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l5.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l5));
         assertEquals(new Adam(0.005), l5.getIUpdater());
         assertArrayEquals(new int[]{3, 3}, l5.getKernelSize());
@@ -298,7 +298,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
 
         ConvolutionLayer l8 = (ConvolutionLayer) net.getLayer(8).getLayerConfiguration();
         assertEquals(4, l8.getNOut());
-        assertEquals(new WeightInitXavier(), l8.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l8.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l8));
         assertEquals(new Adam(0.005), l8.getIUpdater());
         assertArrayEquals(new int[]{4, 4}, l8.getKernelSize());
@@ -307,7 +307,7 @@ public class RegressionTest100b6 extends BaseDL4JTest {
         assertArrayEquals(new int[]{0, 0}, l8.getPadding());
 
         CnnLossLayer l9 = (CnnLossLayer) net.getLayer(9).getLayerConfiguration();
-        assertEquals(new WeightInitXavier(), l9.getWeightInitFn());
+        assertEquals(new WeightInitXavier(), l9.getWeightInit());
         assertEquals(new L2Regularization(0.0001), TestUtils.getL2Reg(l9));
         assertEquals(new Adam(0.005), l9.getIUpdater());
         assertEquals(new LossMAE(), l9.getLossFn());

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

@@ -167,7 +167,7 @@ public class KerasInitilizationTest extends BaseDL4JTest {
         layerConfig.put(conf.getLAYER_FIELD_KERAS_VERSION(), kerasVersion);
 
         DenseLayer layer = new KerasDense(layerConfig, false).getDenseLayer();
-        assertEquals(dl4jInitializer, layer.getWeightInitFn());
+        assertEquals(dl4jInitializer, layer.getWeightInit());
 
     }
 }

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/advanced/activation/KerasPReLUTest.java

@@ -79,7 +79,7 @@ public class KerasPReLUTest extends BaseDL4JTest {
 
         PReLULayer layer = kerasPReLU.getPReLULayer();
         assertArrayEquals(layer.getInputShape(), new long[] {3, 5, 4});
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
 
         assertEquals(layerName, layer.getLayerName());
     }

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution1DTest.java

@@ -100,7 +100,7 @@ public class KerasAtrousConvolution1DTest extends BaseDL4JTest {
         Convolution1DLayer layer = new KerasAtrousConvolution1D(layerConfig).getAtrousConvolution1D();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasAtrousConvolution2DTest.java

@@ -114,7 +114,7 @@ public class KerasAtrousConvolution2DTest extends BaseDL4JTest {
         ConvolutionLayer layer = new KerasAtrousConvolution2D(layerConfig).getAtrousConvolution2D();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution1DTest.java

@@ -122,7 +122,7 @@ public class KerasConvolution1DTest extends BaseDL4JTest {
         Convolution1DLayer layer = new KerasConvolution1D(layerConfig).getConvolution1DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution2DTest.java

@@ -123,7 +123,7 @@ public class KerasConvolution2DTest extends BaseDL4JTest {
         ConvolutionLayer layer = new KerasConvolution2D(layerConfig).getConvolution2DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasConvolution3DTest.java

@@ -119,7 +119,7 @@ public class KerasConvolution3DTest extends BaseDL4JTest {
         ConvolutionLayer layer = new KerasConvolution3D(layerConfig).getConvolution3DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasDeconvolution2DTest.java

@@ -123,7 +123,7 @@ public class KerasDeconvolution2DTest extends BaseDL4JTest {
         Deconvolution2D layer = new KerasDeconvolution2D(layerConfig).getDeconvolution2DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasDepthwiseConvolution2DTest.java

@@ -128,7 +128,7 @@ public class KerasDepthwiseConvolution2DTest extends BaseDL4JTest {
         DepthwiseConvolution2D layer = kerasLayer.getDepthwiseConvolution2DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(DEPTH_MULTIPLIER, layer.getDepthMultiplier());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/convolution/KerasSeparableConvolution2DTest.java

@@ -130,7 +130,7 @@ public class KerasSeparableConvolution2DTest extends BaseDL4JTest {
         SeparableConvolution2D layer = new KerasSeparableConvolution2D(layerConfig).getSeparableConvolution2DLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(DEPTH_MULTIPLIER, layer.getDepthMultiplier());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/core/KerasDenseTest.java

@@ -89,7 +89,7 @@ public class KerasDenseTest extends BaseDL4JTest {
         DenseLayer layer = new KerasDense(layerConfig, false).getDenseLayer();
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasLSTMTest.java

@@ -38,7 +38,6 @@ import org.deeplearning4j.nn.weights.WeightInitXavier;
 import org.junit.jupiter.api.Assertions;
 import org.junit.jupiter.api.Test;
 
-import java.util.Arrays;
 import java.util.Collections;
 import java.util.HashMap;
 import java.util.Map;
@@ -131,7 +130,7 @@ public class KerasLSTMTest extends BaseDL4JTest {
         }
         assertEquals(ACTIVATION_DL4J, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

cavis-dnn/cavis-dnn-modelimport/src/test/java/org/deeplearning4j/nn/modelimport/keras/layers/recurrent/KerasSimpleRnnTest.java

@@ -101,7 +101,7 @@ public class KerasSimpleRnnTest extends BaseDL4JTest {
                 (SimpleRnn) ((LastTimeStep) new KerasSimpleRnn(layerConfig).getSimpleRnnLayer()).getUnderlying();
         assertEquals(ACTIVATION, layer.getActivationFn().toString());
         assertEquals(LAYER_NAME, layer.getLayerName());
-        assertEquals(INIT_DL4J, layer.getWeightInitFn());
+        assertEquals(INIT_DL4J, layer.getWeightInit());
         assertEquals(L1_REGULARIZATION, KerasTestUtils.getL1(layer), 0.0);
         assertEquals(L2_REGULARIZATION, KerasTestUtils.getL2(layer), 0.0);
         assertEquals(new Dropout(DROPOUT_DL4J), layer.getIDropout());

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

@@ -28,6 +28,7 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 public interface INeuralNetworkConfiguration extends Serializable, Cloneable {
 
   INeuralNetworkConfiguration clone();
+
   void init();
 
   /**
@@ -36,27 +37,3 @@ public interface INeuralNetworkConfiguration extends Serializable, Cloneable {
    */
   IModel getNet();
   }
-/**
-  /**
-   * Provides a flat list of all embedded layer configurations, this
-   * can only be called after the layer is initialized or {@link #getLayerConfigurations()} is
-   * called.
-   *
-   * @return unstacked layer configurations
-
-  List<ILayerConfiguration> getLayerConfigurations();
-
-
-  /**
-   * This uncollables any stacked layer configurations within building blocks like
-   * @link BuildingBlockLayer}
-
-  void calculateInnerLayerConfigurations();
-
-  /**
-   * An implementation should provide a method to validate the network
-   * @return true if no errors found; false otherwise
-
-  boolean isValid();
-}
-**/

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

@@ -259,7 +259,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
      */
     private static void handleLegacyWeightInitFromJson(String json, LayerConfiguration layer, ObjectMapper mapper, JsonNode vertices) {
         if (layer instanceof BaseLayerConfiguration
-            && ((BaseLayerConfiguration) layer).getWeightInitFn() == null) {
+            && ((BaseLayerConfiguration) layer).getWeightInit() == null) {
             String layerName = layer.getLayerName();
 
             try {
@@ -291,7 +291,7 @@ public class ComputationGraphConfiguration implements Serializable, Cloneable {
 
                 if (weightInit != null) {
                     final IWeightInit wi = WeightInit.valueOf(weightInit.asText()).getWeightInitFunction(dist);
-                    ((BaseLayerConfiguration) layer).setWeightInitFn(wi);
+                    ((BaseLayerConfiguration) layer).setWeightInit(wi);
                 }
 
             } catch (IOException e) {

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

@@ -35,15 +35,11 @@ import java.util.LinkedHashMap;
 import java.util.List;
 import java.util.Map;
 import java.util.stream.Collectors;
-import lombok.Data;
+
-import lombok.EqualsAndHashCode;
+import lombok.*;
-import lombok.Getter;
-import lombok.NonNull;
-import lombok.Setter;
 import lombok.experimental.SuperBuilder;
 import lombok.extern.jackson.Jacksonized;
 import lombok.extern.slf4j.Slf4j;
-import lombok.val;
 import net.brutex.ai.dnn.api.IModel;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.api.layers.LayerConstraint;
@@ -67,9 +63,9 @@ import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
 import org.deeplearning4j.nn.conf.memory.MemoryReport;
 import org.deeplearning4j.nn.conf.memory.NetworkMemoryReport;
 import org.deeplearning4j.nn.conf.serde.JsonMappers;
+import org.deeplearning4j.nn.conf.stepfunctions.DefaultStepFunction;
 import org.deeplearning4j.nn.conf.stepfunctions.StepFunction;
 import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
-import org.deeplearning4j.nn.conf.weightnoise.WeightNoise;
 import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.util.OutputLayerUtil;
@@ -319,16 +315,14 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   private boolean validateTbpttConfig = true;
   /**
    * Gradient updater configuration. For example, {@link org.nd4j.linalg.learning.config.Adam} or
-   * {@link org.nd4j.linalg.learning.config.Nesterovs}<br> Note: values set by this method will be
+   * {@link org.nd4j.linalg.learning.config.Nesterovs}<br>
-   * applied to all applicable layers in the network, unless a different value is explicitly set on
+   * Note: values set by this method will be applied to all applicable layers in the network, unless
-   * a given layer. In other words: values set via this method are used as the default value, and
+   * a different value is explicitly set on a given layer. In other words: values set via this
-   * can be overridden on a per-layer basis.
+   * method are used as the default value, and can be overridden on a per-layer basis.
    *
    * @param updater Updater to use
    */
-  @Getter
+  @Getter @Setter @Builder.Default private IUpdater updater = new Sgd();
-  @Setter
-  private IUpdater updater;
   /**
    * Gradient normalization strategy. Used to specify gradient renormalization, gradient clipping
    * etc. See {@link GradientNormalization} for details<br> Note: values set by this method will be
@@ -357,19 +351,9 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   @Setter
   private double gradientNormalizationThreshold;
 
-  /**
-   * Activation function / neuron non-linearity<br> Note: values set by this method will be applied
-   * to all applicable layers in the network, unless a different value is explicitly set on a given
-   * layer. In other words: values set via this method are used as the default value, and can be
-   * overridden on a per-layer basis.
-   */
-  @Getter
-  @Setter
-  private IActivation activation;
   // whether to constrain the gradient to unit norm or not
-  @Getter
+  @Getter @Setter @Builder.Default private StepFunction stepFunction = new DefaultStepFunction();
-  @Setter
+
-  private StepFunction stepFunction;
   @Getter
   @Setter
   @lombok.Builder.Default
@@ -400,13 +384,10 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   @Getter
   @lombok.Builder.Default
   private List<Regularization> regularizationBias = new ArrayList<>();
-  @Getter
+
-  @Setter
-  @lombok.Builder.Default
-  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.
@@ -420,7 +401,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   @Getter
   @Setter
   @lombok.Builder.Default
-  private IActivation activationFn = new ActivationSigmoid();
+  private IActivation activation = new ActivationSigmoid();
 
   /**
    * Sets the convolution mode for convolutional layers, which impacts padding and output sizes.
@@ -698,7 +679,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
   private static boolean handleLegacyWeightInitFromJson(String json, LayerConfiguration l,
       ObjectMapper mapper,
       JsonNode confs, int layerCount) {
-    if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getWeightInitFn() == null) {
+    if ((l instanceof BaseLayerConfiguration) && ((BaseLayerConfiguration) l).getWeightInit() == null) {
       try {
         JsonNode jsonNode = mapper.readTree(json);
         if (confs == null) {
@@ -729,7 +710,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
           if (weightInit != null) {
             final IWeightInit wi = WeightInit.valueOf(weightInit.asText())
                 .getWeightInitFunction(dist);
-            ((BaseLayerConfiguration) l).setWeightInitFn(wi);
+            ((BaseLayerConfiguration) l).setWeightInit(wi);
           }
         }
 
@@ -851,8 +832,8 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
      * that do not have an individual setting (nor a default)
      */
     for(LayerConfiguration lconf : this.getFlattenedLayerConfigurations()) {
-      if(lconf.getActivationFn() == null ) lconf.setActivationFn(this.getActivationFn());
+      if(lconf.getActivationFn() == null ) lconf.setActivationFn(this.getActivation());
-      if(lconf.getIUpdater() == null ) lconf.setIUpdater( this.getIUpdater() );
+      if(lconf.getIUpdater() == null ) lconf.setIUpdater( this.getUpdater() );
       if(lconf.getIDropout() == null ) lconf.setIDropout( this.getIdropOut() );
       if(lconf.getWeightNoise() == null ) lconf.setWeightNoise( this.getWeightNoise());
 
@@ -1108,13 +1089,19 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
    */
   public List<LayerConfiguration> getFlattenedLayerConfigurations(NeuralNetConfiguration conf) {
     List<LayerConfiguration> ret = new ArrayList<>(); //create the final return list
-    for( Object obj : conf.getInnerConfigurations().stream().skip(1) //don't include self
+    //When properly initialized, _this_ configuration is set first in the list, however we
-        .collect(Collectors.toList())) {
+    //can find cases where this is not true, thus the first configuration is another net or layer configuration
+    //and should not be skipped. In essence, skip first configuration if that is "this".
+    int iSkip = 0;
+    if(conf.getInnerConfigurations().size()>0 && conf.getInnerConfigurations().get(0).equals(this)) { iSkip=1;}
+    conf.getInnerConfigurations().stream().skip(iSkip)
+            .forEach(obj -> {
               //if Layer Config, include in list and inherit parameters from this conf
               //else if neural net configuration, call self recursively to resolve layer configurations
-      if (obj instanceof LayerConfiguration)
+              if (obj instanceof LayerConfiguration) {
+                ((LayerConfiguration) obj).setNetConfiguration(conf);
                 ret.add((LayerConfiguration) obj);
-      else if (obj instanceof NeuralNetConfiguration)
+                } else if (obj instanceof NeuralNetConfiguration)
                 ret.addAll(getFlattenedLayerConfigurations(
                         (NeuralNetConfiguration) obj));
               else {
@@ -1122,15 +1109,7 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
                         "The list of layers and neural network configurations does contain an object of {}. Element will be ignored.",
                         obj.getClass().getSimpleName());
               }
-    }
+            });
-            /**
-            LayerConfiguration lc = ((LayerConfiguration) lc).getType().getClazz().cast(obj);
-            switch(lc.getType()) {
-              case FC: { //fully connected layer
-                ((FeedForwardLayer) lc).setWeightInitFn(this.getWeightInitFn());
-            }
-            if(lc instanceof FeedForwardLayer && ((FeedForwardLayer) lc).getWeightInitFn() == null) {
-            **/
     return ret;
   }
 
@@ -1143,17 +1122,6 @@ public class NeuralNetConfiguration extends NeuralNetBaseBuilderConfiguration {
     return getFlattenedLayerConfigurations(this);
   }
 
-
-  /**
-   * Get the configuration of the first layer
-   * @return layer configuration
-   */
-  /**
-  public LayerConfiguration getFirstLayer() {
-    return getFlattenedLayerConfigurations().get(0);
-  }
-**/
-
   /**
    * Add a new layer to the first position
    * @param layer configuration

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

@@ -23,6 +23,7 @@ package org.deeplearning4j.nn.conf.layers;
 import lombok.*;
 import org.deeplearning4j.nn.api.ITraininableLayerConfiguration;
 import org.deeplearning4j.nn.conf.GradientNormalization;
+import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.Updater;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.weightnoise.IWeightNoise;
@@ -30,6 +31,7 @@ import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.nn.weights.WeightInitDistribution;
 import org.deeplearning4j.util.NetworkUtils;
+import org.jetbrains.annotations.NotNull;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.IActivation;
 import org.nd4j.linalg.api.buffer.DataType;
@@ -52,7 +54,7 @@ import java.util.List;
 public abstract class BaseLayerConfiguration extends LayerConfiguration implements ITraininableLayerConfiguration, Serializable, Cloneable {
 
     @NonNull
-    protected IWeightInit weightInitFn;
+    protected IWeightInit weightInit;
     protected double biasInit = 0.0;
     protected double gainInit = 0.0;
     protected List<Regularization> regularization;
@@ -68,7 +70,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
     public BaseLayerConfiguration(Builder builder) {
         super(builder);
         this.layerName = builder.layerName;
-        this.weightInitFn = builder.weightInitFn;
+        this.weightInit = builder.weightInit;
         this.biasInit = builder.biasInit;
         this.gainInit = builder.gainInit;
         this.regularization = builder.regularization;
@@ -89,7 +91,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
     public void resetLayerDefaultConfig() {
         //clear the learning related params for all layers in the origConf and set to defaults
         this.setIUpdater(null);
-        this.setWeightInitFn(null);
+        this.setWeightInit(null);
         this.setBiasInit(Double.NaN);
         this.setGainInit(Double.NaN);
         this.regularization = null;
@@ -103,9 +105,6 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
     @Override
     public BaseLayerConfiguration clone() {
         BaseLayerConfiguration clone = (BaseLayerConfiguration) super.clone();
-        if (clone.iDropout != null) {
-            clone.iDropout = clone.iDropout.clone();
-        }
         if(regularization != null){
             //Regularization fields are _usually_ thread safe and immutable, but let's clone to be sure
             clone.regularization = new ArrayList<>(regularization.size());
@@ -170,7 +169,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
          *
          * @see IWeightInit
          */
-        protected IWeightInit weightInitFn = null;
+        protected IWeightInit weightInit = null;
 
         /**
          * Bias initialization value, for layers with biases. Defaults to 0
@@ -255,7 +254,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
          * @see IWeightInit
          */
         public T weightInit(IWeightInit weightInit) {
-            this.setWeightInitFn(weightInit);
+            this.setWeightInit(weightInit);
             return (T) this;
         }
 
@@ -270,7 +269,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
                                 "Not supported!, Use weightInit(Distribution distribution) instead!");
             }
 
-            this.setWeightInitFn(weightInit.getWeightInitFunction());
+            this.setWeightInit(weightInit.getWeightInitFunction());
             return (T) this;
         }
 
@@ -508,4 +507,19 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
         }
     }
 
+    /**
+     * Inherit setting from neural network for those settings, that are not already set or do have
+     * a layer(type) specific default.
+     * @param conf the neural net configration to inherit parameters from
+     */
+    @Override
+    public void runInheritance(@NotNull NeuralNetConfiguration conf) {
+        super.runInheritance(conf);
+        if(this.biasUpdater == null ) this.biasUpdater = conf.getBiasUpdater();
+        if(this.iUpdater == null ) this.iUpdater = conf.getUpdater();
+        if(this.regularizationBias == null) this.regularizationBias = conf.getRegularizationBias();
+        if(this.regularization == null ) this.regularization = conf.getRegularization();
+        if(this.gradientNormalization == null) this.gradientNormalization = conf.getGradientNormalization();
+    }
+
 }

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

@@ -172,6 +172,7 @@ public class ConvolutionLayer extends FeedForwardLayer {
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
         setNetConfiguration(conf);
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        lconf.runInheritance();
 
         LayerValidation.assertNInNOutSet("ConvolutionLayer", getLayerName(), layerIndex, getNIn(), getNOut());
 
@@ -404,9 +405,10 @@ public class ConvolutionLayer extends FeedForwardLayer {
 
         /**
          * Set the convolution mode for the Convolution layer. See {@link ConvolutionMode} for more details
+         * Default is {@link ConvolutionMode}.Truncate.
          *
          */
-        protected ConvolutionMode convolutionMode;
+        protected ConvolutionMode convolutionMode = ConvolutionMode.Truncate;
 
         /**
          * Kernel dilation. Default: {1, 1}, which is standard convolutions. Used for implementing dilated convolutions,

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

@@ -62,19 +62,18 @@ public class DenseLayer extends FeedForwardLayer {
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
 
         LayerValidation.assertNInNOutSet("DenseLayerConfiguration", getLayerName(), layerIndex, getNIn(), getNOut());
-
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        lconf.runInheritance();
 
         org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer ret =
                         new org.deeplearning4j.nn.layers.feedforward.dense.DenseLayer(lconf, networkDataType);
-        if(getWeightInitFn() == null) setWeightInitFn(new WeightInitXavier());
+
+        if(getWeightInit() == null) setWeightInit(new WeightInitXavier());
         ret.addTrainingListeners(trainingListeners);
         ret.setIndex(layerIndex);
         ret.setParamsViewArray(layerParamsView);
         Map<String, INDArray> paramTable = initializer().init(this, layerParamsView, initializeParams);
         ret.setParamTable(paramTable);
-        ret.setLayerConfiguration(lconf);
-
         return ret;
     }
 

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

@@ -217,14 +217,14 @@ public class EmbeddingSequenceLayer extends FeedForwardLayer {
             return this;
         }
 
-        @Override
+
         public void setWeightInitFn(IWeightInit weightInit){
             if(weightInit instanceof WeightInitEmbedding){
                 long[] shape = ((WeightInitEmbedding) weightInit).shape();
                 nIn(shape[0]);
                 nOut(shape[1]);
             }
-            this.weightInitFn = weightInit;
+            this.weightInit = weightInit;
         }
 
         /**

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

@@ -66,28 +66,29 @@ import org.nd4j.linalg.learning.regularization.Regularization;
 @Slf4j
 public abstract class LayerConfiguration implements ILayerConfiguration, Serializable, Cloneable { // ITraininableLayerConfiguration
 
-    protected String layerName = "noname";
+    protected String layerName;
     @Getter
     protected List<String> variables = new ArrayList<>();
-    public void addVariable(String s) {variables.add(s);}
-
-    protected IDropout iDropout;
     protected List<LayerConstraint> constraints;
     protected IWeightNoise weightNoise;
+    private IDropout iDropout;
     /**
      * The type of the layer, basically defines the base class and its properties
      */
     @Getter @Setter @NonNull
     private LayerType type = LayerType.UNKNOWN;
-
     @Getter @Setter
     private NeuralNetConfiguration netConfiguration;
+  @Getter @Setter
+  private IActivation activationFn;
 
     public LayerConfiguration(Builder builder) {
         this.layerName = builder.layerName;
         this.iDropout = builder.iDropout;
     }
 
+    public void addVariable(String s) {variables.add(s);}
+
     public String toJson() {
         throw new RuntimeException("toJson is not implemented for LayerConfiguration");
     }
@@ -151,6 +152,7 @@ public abstract class LayerConfiguration implements ILayerConfiguration, Seriali
     public LayerConfiguration getLayer() {
         return this;
     }
+
     @Override
     public LayerConfiguration clone() {
         try {
@@ -218,7 +220,6 @@ public abstract class LayerConfiguration implements ILayerConfiguration, Seriali
      */
     public abstract void setNIn(InputType inputType, boolean override);
 
-
     /**
      * For the given type of input to this layer, what preprocessor (if any) is required?<br>
      * Returns null if no preprocessor is required, otherwise returns an appropriate {@link
@@ -263,11 +264,11 @@ public abstract class LayerConfiguration implements ILayerConfiguration, Seriali
                         "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(), getLayerName());
     }
@@ -285,15 +286,33 @@ public abstract class LayerConfiguration implements ILayerConfiguration, Seriali
       this.variables.clear();
   }
 
-  @Getter @Setter
+    /**
-  private IActivation activationFn;
+     * Inherit setting from neural network for those settings, that are not already set or do have
+     * a layer(type) specific default. This implementation does not require the neural network configuration to be
+     * the same as the one returned from this layers {@link #getNetConfiguration()}.
+     *
+     * @param conf a neural net configration to inherit parameters from
+     *
+     */
+    public void runInheritance(@NonNull NeuralNetConfiguration conf) {
+      if(this.activationFn == null ) this.activationFn = conf.getActivation();
+      if(this.iDropout == null ) this.iDropout = conf.getIdropOut();
+      if(this.weightNoise == null) this.weightNoise = conf.getWeightNoise();
+    }
+
+    /** Runs {@link #runInheritance(NeuralNetConfiguration)} using the layers configurations embedded neural net
+     * configuration (the one returned from {@link #getNetConfiguration()}.
+     */
+    public void runInheritance() {
+        runInheritance(getNetConfiguration());
+    }
 
   @SuppressWarnings("unchecked")
     @Getter
     @Setter
     public abstract static class Builder<T extends Builder<T>> {
 
-        protected String layerName = "noname";
+        protected String layerName;
 
         protected List<LayerConstraint> allParamConstraints;
 

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

@@ -215,7 +215,7 @@ public class LocallyConnected1D extends SameDiffLayer {
     public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
         NeuralNetConfiguration global_conf = globalConfig.build();
         if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(global_conf.getActivationFn());
+            activation = SameDiffLayerUtils.fromIActivation(global_conf.getActivation());
         }
         if (cm == null) {
             cm = global_conf.getConvolutionMode();

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

@@ -232,7 +232,7 @@ public class LocallyConnected2D extends SameDiffLayer {
     public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
         NeuralNetConfiguration gconf = globalConfig.build();
         if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(gconf.getActivationFn());
+            activation = SameDiffLayerUtils.fromIActivation(gconf.getActivation());
         }
         if (cm == null) {
             cm = gconf.getConvolutionMode();

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

@@ -117,7 +117,7 @@ public class PReLULayer extends BaseLayerConfiguration {
 
         public Builder(){
             //Default to 0s, and don't inherit global default
-            this.weightInitFn = new WeightInitConstant(0);
+            this.weightInit = new WeightInitConstant(0);
         }
 
         /**

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

@@ -152,7 +152,7 @@ public class RecurrentAttentionLayer extends SameDiffLayer {
     @Override
     public void applyGlobalConfigToLayer(NeuralNetConfiguration.NeuralNetConfigurationBuilder globalConfig) {
         if (activation == null) {
-            activation = SameDiffLayerUtils.fromIActivation(globalConfig.build().getActivationFn());
+            activation = SameDiffLayerUtils.fromIActivation(globalConfig.build().getActivation());
         }
     }
 

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

@@ -196,7 +196,7 @@ public abstract class AbstractSameDiffLayer extends LayerConfiguration {
             regularizationBias = bConf.getRegularizationBias();
         }
         if (updater == null) {
-            updater = bConf.getIUpdater();
+            updater = bConf.getUpdater();
         }
         if (biasUpdater == null) {
             biasUpdater = bConf.getBiasUpdater();

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

@@ -156,7 +156,7 @@ public abstract class SameDiffVertex extends GraphVertex implements ITraininable
             regularizationBias = b_conf.getRegularizationBias();
         }
         if (updater == null) {
-            updater = b_conf.getIUpdater();
+            updater = b_conf.getUpdater();
         }
         if (biasUpdater == null) {
             biasUpdater = b_conf.getBiasUpdater();

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

@@ -72,6 +72,7 @@ public class VariationalAutoencoder extends BasePretrainNetwork {
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
         org.deeplearning4j.nn.layers.variational.VariationalAutoencoder ret =
                         new org.deeplearning4j.nn.layers.variational.VariationalAutoencoder(lconf, networkDataType);
+        lconf.runInheritance();
 
         ret.addTrainingListeners(trainingListeners);
         ret.setIndex(layerIndex);

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

@@ -98,7 +98,7 @@ public abstract class BaseNetConfigDeserializer<T> extends StdDeserializer<T> im
     protected boolean requiresWeightInitFromLegacy(LayerConfiguration[] layers){
         for(LayerConfiguration l : layers){
             if(l instanceof BaseLayerConfiguration
-                && ((BaseLayerConfiguration)l).getWeightInitFn() == null){
+                && ((BaseLayerConfiguration)l).getWeightInit() == null){
                 return true;
             }
         }
@@ -254,7 +254,7 @@ public abstract class BaseNetConfigDeserializer<T> extends StdDeserializer<T> im
                         d = NeuralNetConfiguration.mapper().readValue(dist, Distribution.class);
                     }
                     IWeightInit iwi = w.getWeightInitFunction(d);
-                    baseLayerConfiguration.setWeightInitFn(iwi);
+                    baseLayerConfiguration.setWeightInit(iwi);
                 } catch (Throwable t){
                     log.warn("Failed to infer weight initialization from legacy JSON format",t);
                 }

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

@@ -129,7 +129,7 @@ public class ComputationGraphConfigurationDeserializer
                     }
 
                     if(requiresLegacyWeightInitHandling && layers[layerIdx] instanceof BaseLayerConfiguration
-                        && ((BaseLayerConfiguration)layers[layerIdx]).getWeightInitFn() == null){
+                        && ((BaseLayerConfiguration)layers[layerIdx]).getWeightInit() == null){
                         handleWeightInitBackwardCompatibility((BaseLayerConfiguration)layers[layerIdx], (ObjectNode)next);
                     }
 
@@ -160,7 +160,7 @@ public class ComputationGraphConfigurationDeserializer
                     layerIdx++;
                 } else if("org.deeplearning4j.nn.conf.graph.LayerVertex".equals(cls)){
                     if(requiresLegacyWeightInitHandling && layers[layerIdx] instanceof BaseLayerConfiguration
-                        && ((BaseLayerConfiguration)layers[layerIdx]).getWeightInitFn() == null) {
+                        && ((BaseLayerConfiguration)layers[layerIdx]).getWeightInit() == null) {
                         //Post JSON format change for subclasses, but before WeightInit was made a class
                         confNode = (ObjectNode) next.get("layerConf");
                         next = confNode.get("layer");

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

@@ -141,7 +141,7 @@ public class NeuralNetConfigurationDeserializer extends BaseNetConfigDeserialize
                 }
 
                 if(requiresLegacyWeightInitHandling && layers[i] instanceof BaseLayerConfiguration
-                    && ((BaseLayerConfiguration) layers[i]).getWeightInitFn() == null) {
+                    && ((BaseLayerConfiguration) layers[i]).getWeightInit() == null) {
                     handleWeightInitBackwardCompatibility((BaseLayerConfiguration) layers[i], on);
                 }
 

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

@@ -88,14 +88,19 @@ public abstract class AbstractLayer<LayerConf_T extends LayerConfiguration> impl
       cacheMode = layerConfiguration.getNetConfiguration().getCacheMode();
     }
     this.dataType = dataType;
+    if (layerConfiguration.getNetConfiguration() == null) {
+      throw new RuntimeException("You cannot create a layer from a layer configuration, that is not part of any neural network configuration.");
+      }
     this.net = layerConfiguration.getNetConfiguration().getNet();
   }
 
   public void addTrainingListeners(TrainingListener... listeners) {
+    if(listeners != null)
     trainingListeners.addAll(List.of(listeners));
   }
 
   public void addTrainingListeners(Collection<TrainingListener> listeners) {
+    if(listeners != null)
     trainingListeners.addAll(listeners);
   }
 

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

@@ -77,7 +77,6 @@ public abstract class BaseLayer<LayerConfT extends BaseLayerConfiguration>
    * INDArray params;
    */
   public BaseLayer(LayerConfiguration conf, DataType dataType) {
-
     super(conf, dataType);
   }
 

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

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.layers.ocnn;
 
 import lombok.val;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.params.DefaultParamInitializer;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -154,7 +153,7 @@ public class OCNNParamInitializer extends DefaultParamInitializer {
                                           boolean initializeParameters) {
 
         org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer ocnnOutputLayer = ( org.deeplearning4j.nn.conf.ocnn.OCNNOutputLayer) configuration;
-        IWeightInit weightInit = ocnnOutputLayer.getWeightInitFn();
+        IWeightInit weightInit = ocnnOutputLayer.getWeightInit();
         if (initializeParameters) {
             INDArray ret = weightInit.init(weightParamView.size(0), //Fan in
                     weightParamView.size(1), //Fan out

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

@@ -92,7 +92,7 @@ public class VariationalAutoencoder implements Layer {
     protected int epochCount;
     @Getter @Setter @NonNull
     private LayerConfiguration layerConfiguration;
-    private @Getter @Setter Collection<TrainingListener> trainingListeners;
+  private @Getter @Setter Collection<TrainingListener> trainingListeners = new HashSet<>();
 
     public VariationalAutoencoder(@NonNull LayerConfiguration layerConfiguration, DataType dataType) {
         this.layerConfiguration = layerConfiguration;
@@ -113,6 +113,27 @@ public class VariationalAutoencoder implements Layer {
                         .getNumSamples();
     }
 
+    /**
+     * Replace the TrainingListeners for this model
+     *
+     * @param listeners new listeners
+     */
+    @Override
+    public void addTrainingListeners(TrainingListener... listeners) {
+        if(listeners != null)
+            trainingListeners.addAll(List.of(listeners));
+    }
+
+/**
+*
+   * @param listeners
+  */
+    @Override
+    public void addTrainingListeners(Collection<TrainingListener> listeners) {
+        if(listeners != null)
+            trainingListeners.addAll(listeners);
+    }
+
     /**
      * Get a reference to the network this layer is part of.
      *
@@ -1214,24 +1235,6 @@ public class VariationalAutoencoder implements Layer {
         //No-op for individual layers
     }
 
-    /**
-     * Replace the TrainingListeners for this model
-     *
-     * @param listeners new listeners
-     */
-    @Override
-    public void addTrainingListeners(TrainingListener... listeners) {
-        trainingListeners.addAll(List.of(listeners));
-    }
-
-/**
-*
-   * @param listeners
-  */
-    @Override
-    public void addTrainingListeners(Collection<TrainingListener> listeners) {
-        trainingListeners.addAll(listeners);
-    }
 
     @AllArgsConstructor
     @Data

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/params/Convolution3DParamInitializer.java

@@ -22,7 +22,6 @@ package org.deeplearning4j.nn.params;
 
 
 import lombok.val;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.Convolution3D;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -131,7 +130,7 @@ public class Convolution3DParamInitializer extends ConvolutionParamInitializer {
 
             val weightsShape = new long[]{outputDepth, inputDepth, kernel[0], kernel[1], kernel[2]};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c',
                     weightView);
         } else {
             int[] kernel = layerConf.getKernelSize();

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

@@ -180,7 +180,7 @@ public class ConvolutionParamInitializer extends AbstractParamInitializer {
 
             val weightsShape = new long[] {outputDepth, inputDepth, kernel[0], kernel[1]};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c', weightView);
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c', weightView);
         } else {
             int[] kernel = layerConf.getKernelSize();
             return WeightInitUtil.reshapeWeights(

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/params/Deconvolution3DParamInitializer.java

@@ -22,7 +22,6 @@ package org.deeplearning4j.nn.params;
 
 
 import lombok.val;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.Deconvolution3D;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -130,7 +129,7 @@ public class Deconvolution3DParamInitializer extends ConvolutionParamInitializer
             //libnd4j: [kD, kH, kW, oC, iC]
             val weightsShape = new long[]{kernel[0], kernel[1], kernel[2], outputDepth, inputDepth};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c', weightView);
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c', weightView);
         } else {
             int[] kernel = layerConf.getKernelSize();
             return WeightInitUtil.reshapeWeights(

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

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.params;
 
 import lombok.val;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -61,7 +60,7 @@ public class DeconvolutionParamInitializer extends ConvolutionParamInitializer {
 
             val weightsShape = new long[] {inputDepth, outputDepth, kernel[0], kernel[1]};
 
-            INDArray weights = layerConf.getWeightInitFn().init(
+            INDArray weights = layerConf.getWeightInit().init(
                     fanIn, fanOut, weightsShape, 'c', weightView);
 
             return weights;

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

@@ -196,13 +196,13 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
                         (org.deeplearning4j.nn.conf.layers.FeedForwardLayer) conf;
 
         if (initializeParameters) {
-            if( layerConf.getWeightInitFn() == null) {
+            if( layerConf.getWeightInit() == null) {
                 // set a default and set warning
-                layerConf.setWeightInitFn(new WeightInitXavier());
+                layerConf.setWeightInit(new WeightInitXavier());
                 log.warn("Weight Initializer function was not set on layer {} of class {}, it will default to {}", conf.getLayerName(),
                     conf.getClass().getSimpleName(), WeightInitXavier.class.getSimpleName());
             }
-            return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), layerConf.getWeightInitFn(),
+            return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), layerConf.getWeightInit(),
                             weightParamView, true);
         } else {
             return createWeightMatrix(layerConf.getNIn(), layerConf.getNOut(), null, weightParamView, false);

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

@@ -23,8 +23,6 @@ package org.deeplearning4j.nn.params;
 
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DepthwiseConvolution2D;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -193,7 +191,7 @@ public class DepthwiseConvolutionParamInitializer extends AbstractParamInitializ
 
             val weightsShape = new long[] {kernel[0], kernel[1], inputDepth, depthMultiplier};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c',
                             weightView);
         } else {
             int[] kernel = layerConf.getKernelSize();

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

@@ -22,8 +22,6 @@ package org.deeplearning4j.nn.params;
 
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -159,14 +157,14 @@ public class GravesBidirectionalLSTMParamInitializer extends AbstractParamInitia
             val inputWShape = new long[]{nLast, 4 * nL};
             val recurrentWShape = new long[]{nL, 4 * nL + 3};
 
-            params.put(INPUT_WEIGHT_KEY_FORWARDS, layerConf.getWeightInitFn().init(fanIn, fanOut, inputWShape,
+            params.put(INPUT_WEIGHT_KEY_FORWARDS, layerConf.getWeightInit().init(fanIn, fanOut, inputWShape,
                     IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, iwF));
-            params.put(RECURRENT_WEIGHT_KEY_FORWARDS, layerConf.getWeightInitFn().init(fanIn, fanOut, recurrentWShape,
+            params.put(RECURRENT_WEIGHT_KEY_FORWARDS, layerConf.getWeightInit().init(fanIn, fanOut, recurrentWShape,
                     IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, rwF));
             params.put(BIAS_KEY_FORWARDS, bF);
-            params.put(INPUT_WEIGHT_KEY_BACKWARDS, layerConf.getWeightInitFn().init(fanIn, fanOut, inputWShape,
+            params.put(INPUT_WEIGHT_KEY_BACKWARDS, layerConf.getWeightInit().init(fanIn, fanOut, inputWShape,
                     IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, iwR));
-            params.put(RECURRENT_WEIGHT_KEY_BACKWARDS, layerConf.getWeightInitFn().init(fanIn, fanOut, recurrentWShape,
+            params.put(RECURRENT_WEIGHT_KEY_BACKWARDS, layerConf.getWeightInit().init(fanIn, fanOut, recurrentWShape,
                     IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, rwR));
             params.put(BIAS_KEY_BACKWARDS, bR);
         } else {

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

@@ -22,8 +22,6 @@ package org.deeplearning4j.nn.params;
 
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -124,10 +122,10 @@ public class GravesLSTMParamInitializer extends AbstractParamInitializer {
             if(layerConf.getWeightInitFnRecurrent() != null){
                 rwInit = layerConf.getWeightInitFnRecurrent();
             } else {
-                rwInit = layerConf.getWeightInitFn();
+                rwInit = layerConf.getWeightInit();
             }
 
-            params.put(INPUT_WEIGHT_KEY,layerConf.getWeightInitFn().init(fanIn, fanOut, inputWShape,
+            params.put(INPUT_WEIGHT_KEY,layerConf.getWeightInit().init(fanIn, fanOut, inputWShape,
                             IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, inputWeightView));
             params.put(RECURRENT_WEIGHT_KEY, rwInit.init(fanIn, fanOut, recurrentWShape,
                             IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, recurrentWeightView));

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

@@ -27,7 +27,6 @@ import java.util.List;
 import java.util.Map;
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.conf.layers.LSTM;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -132,10 +131,10 @@ public class LSTMParamInitializer extends AbstractParamInitializer {
             if(layerConf.getWeightInitFnRecurrent() != null){
                 rwInit = layerConf.getWeightInitFnRecurrent();
             } else {
-                rwInit = layerConf.getWeightInitFn();
+                rwInit = layerConf.getWeightInit();
             }
 
-            params.put(INPUT_WEIGHT_KEY, layerConf.getWeightInitFn().init(fanIn, fanOut, inputWShape,
+            params.put(INPUT_WEIGHT_KEY, layerConf.getWeightInit().init(fanIn, fanOut, inputWShape,
                     IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, inputWeightView));
             params.put(RECURRENT_WEIGHT_KEY, rwInit.init(fanIn, fanOut, recurrentWShape, IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, recurrentWeightView));
             biasView.put(new INDArrayIndex[] {NDArrayIndex.interval(0,0,true), NDArrayIndex.interval(nL, 2 * nL)},

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

@@ -133,7 +133,7 @@ public class PReLUParamInitializer extends AbstractParamInitializer {
 
         PReLULayer layerConf = (PReLULayer) conf;
         if (initializeParameters) {
-            return layerConf.getWeightInitFn().init(layerConf.getNIn(), layerConf.getNOut(),
+            return layerConf.getWeightInit().init(layerConf.getNIn(), layerConf.getNOut(),
                     weightShape, IWeightInit.DEFAULT_WEIGHT_INIT_ORDER, weightParamView);
         } else {
             return WeightInitUtil.reshapeWeights(weightShape, weightParamView);

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

@@ -23,8 +23,6 @@ package org.deeplearning4j.nn.params;
 
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.SeparableConvolution2D;
 import org.deeplearning4j.nn.weights.WeightInitUtil;
@@ -220,7 +218,7 @@ public class SeparableConvolutionParamInitializer extends AbstractParamInitializ
 
             val weightsShape = new long[] {depthMultiplier, inputDepth, kernel[0], kernel[1]};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c',
                             weightView);
         } else {
             int[] kernel = layerConf.getKernelSize();
@@ -249,7 +247,7 @@ public class SeparableConvolutionParamInitializer extends AbstractParamInitializ
 
             val weightsShape = new long[] {outputDepth, depthMultiplier * inputDepth, 1, 1};
 
-            return layerConf.getWeightInitFn().init(fanIn, fanOut, weightsShape, 'c',
+            return layerConf.getWeightInit().init(fanIn, fanOut, weightsShape, 'c',
                     weightView);
         } else {
             return WeightInitUtil.reshapeWeights(

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

@@ -22,8 +22,6 @@ package org.deeplearning4j.nn.params;
 
 import lombok.val;
 import org.deeplearning4j.nn.api.AbstractParamInitializer;
-import org.deeplearning4j.nn.api.ParamInitializer;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.recurrent.SimpleRnn;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -102,14 +100,14 @@ public class SimpleRnnParamInitializer extends AbstractParamInitializer {
 
         if (initializeParams) {
             m = getSubsets(paramsView, nIn, nOut, false, hasLayerNorm(c));
-            INDArray w = c.getWeightInitFn().init(nIn, nOut, new long[]{nIn, nOut}, 'f', m.get(WEIGHT_KEY));
+            INDArray w = c.getWeightInit().init(nIn, nOut, new long[]{nIn, nOut}, 'f', m.get(WEIGHT_KEY));
             m.put(WEIGHT_KEY, w);
 
             IWeightInit rwInit;
             if (c.getWeightInitFnRecurrent() != null) {
                 rwInit = c.getWeightInitFnRecurrent();
             } else {
-                rwInit = c.getWeightInitFn();
+                rwInit = c.getWeightInit();
             }
 
             INDArray rw = rwInit.init(nOut, nOut, new long[]{nOut, nOut}, 'f', m.get(RECURRENT_WEIGHT_KEY));

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

@@ -21,7 +21,6 @@
 package org.deeplearning4j.nn.params;
 
 import lombok.val;
-import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.LayerConfiguration;
 import org.deeplearning4j.nn.conf.layers.variational.VariationalAutoencoder;
 import org.deeplearning4j.nn.weights.IWeightInit;
@@ -200,7 +199,7 @@ public class VariationalAutoencoderParamInitializer extends DefaultParamInitiali
         int[] encoderLayerSizes = layer.getEncoderLayerSizes();
         int[] decoderLayerSizes = layer.getDecoderLayerSizes();
 
-        IWeightInit weightInit = layer.getWeightInitFn();
+        IWeightInit weightInit = layer.getWeightInit();
 
         int soFar = 0;
         for (int i = 0; i < encoderLayerSizes.length; i++) {

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

@@ -164,7 +164,7 @@ public class FineTuneConfiguration {
             bl.setActivationFn(activationFn);
         }
         if (weightInitFn != null) {
-            bl.setWeightInitFn(weightInitFn);
+            bl.setWeightInit(weightInitFn);
         }
         if (biasInit != null) {
             bl.setBiasInit(biasInit);
@@ -264,10 +264,10 @@ public class FineTuneConfiguration {
     NeuralNetConfiguration.NeuralNetConfigurationBuilder confBuilder = NeuralNetConfiguration.builder();
 
       if (activationFn != null) {
-          confBuilder.activationFn(activationFn);
+          confBuilder.activation(activationFn);
       }
       if (weightInitFn != null) {
-          confBuilder.weightInitFn(weightInitFn);
+          confBuilder.weightInit(weightInitFn);
       }
       if (biasInit != null) {
           confBuilder.biasInit(biasInit);

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

@@ -462,7 +462,7 @@ public class TransferLearning {
             Preconditions.checkArgument(layerImpl instanceof FeedForwardLayer, "nInReplace can only be applide on FeedForward layers;" +
                     "got layer of type %s", layerImpl.getClass().getSimpleName());
             FeedForwardLayer layerImplF = (FeedForwardLayer) layerImpl;
-            layerImplF.setWeightInitFn(init);
+            layerImplF.setWeightInit(init);
             layerImplF.setNIn(nIn);
             long numParams = layerImpl.initializer().numParams(layerConf);
             INDArray params = Nd4j.create(origModel.getNetConfiguration().getDataType(), 1, numParams);
@@ -480,7 +480,7 @@ public class TransferLearning {
             Preconditions.checkArgument(layerImpl instanceof FeedForwardLayer, "nOutReplace can only be applide on FeedForward layers;" +
                     "got layer of type %s", layerImpl.getClass().getSimpleName());
             FeedForwardLayer layerImplF = (FeedForwardLayer) layerImpl;
-            layerImplF.setWeightInitFn(scheme);
+            layerImplF.setWeightInit(scheme);
             layerImplF.setNOut(nOut);
             long numParams = layerImpl.initializer().numParams(layerConf);
             INDArray params = Nd4j.create(origModel.getNetConfiguration().getDataType(), 1, numParams);
@@ -492,7 +492,7 @@ public class TransferLearning {
                 layerImpl = layerConf; //modify in place
                 if(layerImpl instanceof FeedForwardLayer) {
                     layerImplF = (FeedForwardLayer) layerImpl;
-                    layerImplF.setWeightInitFn(schemeNext);
+                    layerImplF.setWeightInit(schemeNext);
                     layerImplF.setNIn(nOut);
                     numParams = layerImpl.initializer().numParams(layerConf);
                     if (numParams > 0) {
@@ -738,7 +738,7 @@ public class TransferLearning {
 
             layerImpl.resetLayerDefaultConfig();
             FeedForwardLayer layerImplF = (FeedForwardLayer) layerImpl;
-            layerImplF.setWeightInitFn(scheme);
+            layerImplF.setWeightInit(scheme);
             layerImplF.setNIn(nIn);
 
             if(editedVertices.contains(layerName) && editedConfigBuilder.getVertices().get(layerName) instanceof LayerVertex
@@ -767,7 +767,7 @@ public class TransferLearning {
                 LayerConfiguration layerImpl = layerConf.clone();
                 layerImpl.resetLayerDefaultConfig();
                 FeedForwardLayer layerImplF = (FeedForwardLayer) layerImpl;
-                layerImplF.setWeightInitFn(scheme);
+                layerImplF.setWeightInit(scheme);
                 layerImplF.setNOut(nOut);
 
                 if(editedVertices.contains(layerName) && editedConfigBuilder.getVertices().get(layerName) instanceof LayerVertex
@@ -806,7 +806,7 @@ public class TransferLearning {
                         continue;
                     layerImpl = layerConf.clone();
                     layerImplF = (FeedForwardLayer) layerImpl;
-                    layerImplF.setWeightInitFn(schemeNext);
+                    layerImplF.setWeightInit(schemeNext);
                     layerImplF.setNIn(nOut);
 
                     nInFromNewConfig.put(fanoutVertexName, nOut);

cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/updater/BaseMultiLayerUpdater.java

@@ -207,10 +207,11 @@ public abstract class BaseMultiLayerUpdater<T extends IModel> implements Updater
      */
     public void setStateViewArray(INDArray viewArray) {
         if(this.updaterStateViewArray == null){
-            if(viewArray == null)
+            if(viewArray == null || viewArray.length()==0)
                 return; //No op - for example, SGD and NoOp updater - i.e., no stored state
             else {
-                throw new IllegalStateException("Attempting to set updater state view array with null value");
+                //this.updaterStateViewArray.set
+               // throw new IllegalStateException("Attempting to set updater state view array with null value");
             }
         }
         if (this.updaterStateViewArray.length() != viewArray.length())
@@ -296,7 +297,7 @@ public abstract class BaseMultiLayerUpdater<T extends IModel> implements Updater
         //PRE apply (gradient clipping, etc): done on a per-layer basis
         for (Map.Entry<String, Gradient> entry : layerGradients.entrySet()) {
             String layerName = entry.getKey();
-            ITrainableLayer layer = layersByName.get(layerName);
+            ITrainableLayer layer = layersByName.get(layerName); //Todo Layers may have the same name!?
 
             preApply(layer, layerGradients.get(layerName), iteration);
         }

cavis-dnn/cavis-dnn-nn/src/test/java/net/brutex/ai/dnn/api/dnnTest.java

@@ -29,7 +29,6 @@ import org.apache.commons.lang3.RandomUtils;
 import org.deeplearning4j.datasets.iterator.FloatsDataSetIterator;
 import org.deeplearning4j.nn.conf.GradientNormalization;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
-import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.ActivationLayer;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
@@ -39,7 +38,6 @@ import org.deeplearning4j.optimize.listeners.ScoreToChartListener;
 import org.junit.jupiter.api.Test;
 import org.nd4j.common.primitives.Pair;
 import org.nd4j.linalg.activations.Activation;
-import org.nd4j.linalg.activations.impl.ActivationIdentity;
 import org.nd4j.linalg.activations.impl.ActivationLReLU;
 import org.nd4j.linalg.activations.impl.ActivationSigmoid;
 import org.nd4j.linalg.learning.config.Adam;
@@ -85,8 +83,8 @@ class dnnTest {
             .updater(Adam.builder().learningRate(0.0002).beta1(0.5).build())
             .gradientNormalization(GradientNormalization.RenormalizeL2PerLayer)
             .gradientNormalizationThreshold(100)
-            .weightInitFn(new WeightInitXavier())
+            .weightInit(new WeightInitXavier())
-            .activationFn(new ActivationSigmoid())
+            .activation(new ActivationSigmoid())
             // .inputType(InputType.convolutional(28, 28, 1))
             .layer(new DenseLayer.Builder().nIn(6).nOut(20).build())
             .layer(new ActivationLayer.Builder(new ActivationLReLU(0.2)).build())

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

@@ -1182,7 +1182,7 @@ public class TrainModule implements UIModule {
                                 String.valueOf(nParams)});
                         if (nParams > 0) {
                             try {
-                                String str = JsonMappers.getMapper().writeValueAsString(bl.getWeightInitFn());
+                                String str = JsonMappers.getMapper().writeValueAsString(bl.getWeightInit());
                                 layerInfoRows.add(new String[]{
                                         i18N.getMessage("train.model.layerinfotable.layerWeightInit"), str});
                             } catch (JsonProcessingException e) {

cavis-zoo/cavis-zoo-models/src/main/java/org/deeplearning4j/zoo/model/Darknet19.java

@@ -29,6 +29,7 @@ import org.deeplearning4j.nn.conf.ComputationGraphConfiguration.GraphBuilder;
 import org.deeplearning4j.nn.conf.inputs.InputType;
 import org.deeplearning4j.nn.conf.layers.*;
 import org.deeplearning4j.nn.graph.ComputationGraph;
+import org.deeplearning4j.nn.weights.IWeightInit;
 import org.deeplearning4j.nn.weights.WeightInit;
 import org.deeplearning4j.zoo.ModelMetaData;
 import org.deeplearning4j.zoo.PretrainedType;

cavis-zoo/cavis-zoo-models/src/main/java/org/deeplearning4j/zoo/model/ResNet50.java

@@ -176,7 +176,7 @@ public class ResNet50 extends ZooModel {
                         .activation(Activation.IDENTITY)
                         .optimizationAlgo(OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT)
                         .updater(updater)
-                        .weightInitFn(weightInit)
+                        .weightInit(weightInit)
                         .l1(1e-7)
                         .l2(5e-5)
                         .miniBatch(true)