diff --git a/cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerValidation.java b/cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerValidation.java
index 55713cc08..2a5f16be6 100644
--- a/cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerValidation.java
+++ b/cavis-dnn/cavis-dnn-nn/src/main/java/org/deeplearning4j/nn/conf/layers/LayerValidation.java
@@ -26,6 +26,8 @@ import org.deeplearning4j.nn.api.layers.LayerConstraint;
 import org.deeplearning4j.nn.conf.dropout.IDropout;
 import org.deeplearning4j.nn.conf.layers.misc.FrozenLayer;
 import org.deeplearning4j.nn.conf.layers.recurrent.Bidirectional;
+import org.nd4j.linalg.learning.regularization.L1Regularization;
+import org.nd4j.linalg.learning.regularization.L2Regularization;
 import org.nd4j.linalg.learning.regularization.Regularization;
 
 import java.util.ArrayList;
@@ -126,17 +128,84 @@ public class LayerValidation {
         }
     }
 
-    private static void configureBaseLayer(String layerName, BaseLayer bLayer, IDropout iDropout, List<Regularization> regularization,
-                                           List<Regularization> regularizationBias) {
-        if (regularization != null && !regularization.isEmpty()) {
-            bLayer.setRegularization(regularization);
-        }
-        if (regularizationBias != null && !regularizationBias.isEmpty()) {
-            bLayer.setRegularizationBias(regularizationBias);
-        }
+	private static void configureBaseLayer(String layerName, BaseLayer bLayer, IDropout iDropout,
+			List<Regularization> regularization, List<Regularization> regularizationBias) {
+		if (regularization != null && !regularization.isEmpty()) {
 
-        if (bLayer.getIDropout() == null) {
-            bLayer.setIDropout(iDropout);
-        }
-    }
+			final List<Regularization> bLayerRegs = bLayer.getRegularization();
+			if (bLayerRegs == null || bLayerRegs.isEmpty()) {
+
+				bLayer.setRegularization(regularization);
+			} else {
+
+				boolean hasL1 = false;
+				boolean hasL2 = false;
+				final List<Regularization> regContext = regularization;
+				for (final Regularization reg : bLayerRegs) {
+
+					if (reg instanceof L1Regularization) {
+
+						hasL1 = true;
+					} else if (reg instanceof L2Regularization) {
+
+						hasL2 = true;
+					}
+				}
+				for (final Regularization reg : regContext) {
+
+					if (reg instanceof L1Regularization) {
+
+						if (!hasL1)
+							bLayerRegs.add(reg);
+					} else if (reg instanceof L2Regularization) {
+
+						if (!hasL2)
+							bLayerRegs.add(reg);
+					} else
+						bLayerRegs.add(reg);
+				}
+			}
+		}
+		if (regularizationBias != null && !regularizationBias.isEmpty()) {
+
+			final List<Regularization> bLayerRegs = bLayer.getRegularizationBias();
+			if (bLayerRegs == null || bLayerRegs.isEmpty()) {
+
+				bLayer.setRegularizationBias(regularizationBias);
+			} else {
+
+				boolean hasL1 = false;
+				boolean hasL2 = false;
+				final List<Regularization> regContext = regularizationBias;
+				for (final Regularization reg : bLayerRegs) {
+
+					if (reg instanceof L1Regularization) {
+
+						hasL1 = true;
+					} else if (reg instanceof L2Regularization) {
+
+						hasL2 = true;
+					}
+				}
+				for (final Regularization reg : regContext) {
+
+					if (reg instanceof L1Regularization) {
+
+						if (!hasL1)
+							bLayerRegs.add(reg);
+					} else if (reg instanceof L2Regularization) {
+
+						if (!hasL2)
+							bLayerRegs.add(reg);
+					} else
+						bLayerRegs.add(reg);
+				}
+			}
+		}
+
+		if (bLayer.getIDropout() == null) {
+
+			bLayer.setIDropout(iDropout);
+		}
+	}
 }