Playing with some new code 2 - clean build/test

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

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

@@ -57,6 +57,7 @@ public class AutoEncoder extends BasePretrainNetwork {
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
         this.setNetConfiguration(conf);
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        runInheritance();
         org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder ret =
                         new org.deeplearning4j.nn.layers.feedforward.autoencoder.AutoEncoder(lconf, networkDataType);
 

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

@@ -520,6 +520,7 @@ public abstract class BaseLayerConfiguration extends LayerConfiguration implemen
         if(this.regularizationBias == null) this.regularizationBias = conf.getRegularizationBias();
         if(this.regularization == null ) this.regularization = conf.getRegularization();
         if(this.gradientNormalization == null) this.gradientNormalization = conf.getGradientNormalization();
+        if(this.weightInit == null) this.weightInit = conf.getWeightInit();
     }
 
 }

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

@@ -92,8 +92,9 @@ public class BatchNormalization extends FeedForwardLayer {
     public Layer instantiate(NeuralNetConfiguration conf, Collection<TrainingListener> trainingListeners,
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
         this.setNetConfiguration(conf);
-
         LayerValidation.assertNOutSet("BatchNormalization", getLayerName(), layerIndex, getNOut());
+        runInheritance();
+
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
         org.deeplearning4j.nn.layers.normalization.BatchNormalization ret =
                         new org.deeplearning4j.nn.layers.normalization.BatchNormalization(lconf, networkDataType);

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

@@ -54,6 +54,7 @@ public class OutputLayer extends BaseOutputLayer {
                              int layerIndex, INDArray layerParamsView, boolean initializeParams, DataType networkDataType) {
         LayerValidation.assertNInNOutSet("OutputLayer", getLayerName(), layerIndex, getNIn(), getNOut());
         LayerConfiguration lconf = conf.getFlattenedLayerConfigurations().get(layerIndex);
+        runInheritance();
 
         org.deeplearning4j.nn.layers.OutputLayer ret = new org.deeplearning4j.nn.layers.OutputLayer(lconf, networkDataType);
         ret.addTrainingListeners(trainingListeners);

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

@@ -210,7 +210,7 @@ public class DefaultParamInitializer extends AbstractParamInitializer {
     }
 
     protected INDArray createWeightMatrix(long nIn, long nOut,
-                                            @NonNull IWeightInit weightInit,
+                                            IWeightInit weightInit,
                                           INDArray weightParamView, boolean initializeParameters) {
         val shape = new long[] {nIn, nOut};