diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/BaseLayer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/BaseLayer.java
index 405889f0e..00ca7e7c4 100755
--- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/BaseLayer.java
+++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/BaseLayer.java
@@ -93,7 +93,7 @@ public abstract class BaseLayer<LayerConfT extends org.deeplearning4j.nn.conf.la
             INDArray g = getParam(DefaultParamInitializer.GAIN_KEY);
 
             INDArray dldg = gradientViews.get(DefaultParamInitializer.GAIN_KEY);
-            Nd4j.getExecutioner().exec(new LayerNormBp(preNorm, g, delta, delta, dldg, 1));
+            Nd4j.getExecutioner().exec(new LayerNormBp(preNorm, g, delta, delta, dldg, true, 1));
             ret.gradientForVariable().put(DefaultParamInitializer.GAIN_KEY, dldg);
 
         }
@@ -318,7 +318,7 @@ public abstract class BaseLayer<LayerConfT extends org.deeplearning4j.nn.conf.la
         INDArray preNorm = ret;
         if(hasLayerNorm()){
             preNorm = (forBackprop ? ret.dup(ret.ordering()) : ret);
-            Nd4j.getExecutioner().exec(new LayerNorm(preNorm, g, ret, 1));
+            Nd4j.getExecutioner().exec(new LayerNorm(preNorm, g, ret, true, 1));
         }
 
         if(hasBias()){
diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java
index 5704e57d3..e4bdcf56f 100644
--- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java
+++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/SimpleRnn.java
@@ -158,7 +158,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
                 dldnCurrent = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, dldzCurrent.dataType(), dldzCurrent.shape());
                 INDArray ggCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, gg.dataType(), gxg.shape());
                 INDArray bgCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, bg.dataType(), bg.shape());
-                Nd4j.getExecutioner().exec(new LayerNormBp(nCurrent, gx, b, dldzCurrent, dldnCurrent, ggCur, bgCur, 1));
+                Nd4j.getExecutioner().exec(new LayerNormBp(nCurrent, gx, b, dldzCurrent, dldnCurrent, ggCur, bgCur, true, 1));
                 gxg.addi(ggCur);
                 bg.addi(bgCur);
             }else{
@@ -177,7 +177,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
             if(hasLayerNorm() && i > end){
                 dldzNext = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, dldzCurrent.dataType(), dldzCurrent.shape());
                 INDArray ggCur = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, gg.dataType(), grg.shape());
-                Nd4j.getExecutioner().exec(new LayerNormBp(rCurrent, gr, dldzCurrent, dldzNext, ggCur, 1));
+                Nd4j.getExecutioner().exec(new LayerNormBp(rCurrent, gr, dldzCurrent, dldzNext, ggCur, true, 1));
                 grg.addi(ggCur);
             }else{
                 dldzNext = dldzCurrent;
@@ -256,7 +256,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
             if(hasLayerNorm()){
                 INDArray currOutPreNorm = (forBackprop ? outPreNorm : out).get(all(), all(), point(i));
                 Nd4j.gemm(currIn, w, currOutPreNorm, false, false, 1.0, 0.0);
-                Nd4j.getExecutioner().exec(new LayerNorm(currOutPreNorm, gx, b, currOut, 1));
+                Nd4j.getExecutioner().exec(new LayerNorm(currOutPreNorm, gx, b, currOut, true, 1));
             }else{
                 Nd4j.gemm(currIn, w, currOut, false, false, 1.0, 1.0);  //beta = 1.0 to keep previous contents (bias)
             }
@@ -266,7 +266,7 @@ public class SimpleRnn extends BaseRecurrentLayer<org.deeplearning4j.nn.conf.lay
                     INDArray currRecPreNorm = forBackprop ? recPreNorm.get(all(), all(), point(i)) : workspaceMgr.createUninitialized(ArrayType.FF_WORKING_MEM, currOut.dataType(), currOut.shape(), 'f');;
                     Nd4j.gemm(prevStepOut, rw, currRecPreNorm, false, false, 1.0, 0.0);
                     INDArray recNorm = workspaceMgr.createUninitialized(ArrayType.FF_WORKING_MEM, currOut.dataType(), currOut.shape(), 'f');
-                    Nd4j.getExecutioner().exec(new LayerNorm(currRecPreNorm, gr, recNorm, 1));
+                    Nd4j.getExecutioner().exec(new LayerNorm(currRecPreNorm, gr, recNorm, true, 1));
                     currOut.addi(recNorm);
                 }else {
                     Nd4j.gemm(prevStepOut, rw, currOut, false, false, 1.0, 1.0);    //beta = 1.0 to keep previous contents
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LayerNorm.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LayerNorm.java
index 65222fe98..137bc6693 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LayerNorm.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/LayerNorm.java
@@ -54,17 +54,14 @@ public class LayerNorm extends DynamicCustomOp {
         this(sameDiff, input, gain, null, channelsFirst, dimensions);
     }
 
-    public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, int... dimensions) {
-        super("layer_norm", new INDArray[]{input, gain, bias}, new INDArray[]{result});
-        Preconditions.checkArgument(bias != null, "LayerNorm: Use different constructor if bias is null.");
-
+    public LayerNorm(INDArray input, INDArray gain, INDArray bias, INDArray result, boolean channelsFirst, int... dimensions) {
+        super("layer_norm", wrapFilterNull(input, gain, bias), wrapOrNull(result));
+        this.channelsFirst = channelsFirst;
         setDimensions(dimensions);
     }
 
-    public LayerNorm(INDArray input, INDArray gain, INDArray result, int... dimensions) {
-        super("layer_norm", new INDArray[]{input, gain}, new INDArray[]{result});
-        noBias = true;
-        setDimensions(dimensions);
+    public LayerNorm(INDArray input, INDArray gain, INDArray result, boolean channelsFirst, int... dimensions) {
+        this(input, gain, null, result, channelsFirst, dimensions);
     }
 
     @Override