diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java
index fc3b155c2..433ce7c2e 100644
--- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java
+++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java
@@ -296,7 +296,7 @@ public class GlobalPoolingLayer extends AbstractLayer<org.deeplearning4j.nn.conf
 
         switch (poolingType) {
             case MAX:
-                INDArray isMax = Nd4j.exec(new IsMax(inputArray.dup(), poolDim))[0];
+                INDArray isMax = Nd4j.exec(new IsMax(inputArray, inputArray.ulike(), poolDim))[0];
                 return Nd4j.getExecutioner().exec(new BroadcastMulOp(isMax, epsilon, isMax, broadcastDims));
             case AVG:
                 //if out = avg(in,dims) then dL/dIn = 1/N * dL/dOut
diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java
index bf3b1fd38..4c1207d32 100644
--- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java
+++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/ConvolutionUtils.java
@@ -639,7 +639,7 @@ public class ConvolutionUtils {
 
         INDArray output = Nd4j.createUninitialized(new int[]{(int)in.size(0), 1, outH, 1}, 'c');
 
-        DynamicCustomOp op = new MaxPooling2D(in, output, Pooling2DConfig.builder()
+        DynamicCustomOp op = new MaxPooling2D(reshaped4d, output, Pooling2DConfig.builder()
                 .kH(k[0]).kW(k[1])
                 .sH(s[0]).sW(s[1])
                 .pH(pad == null ? 0 : pad[0]).pW(pad == null ? 0 : pad[1])
diff --git a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/MaskedReductionUtil.java b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/MaskedReductionUtil.java
index 5ea4fc8c3..23083f3a4 100644
--- a/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/MaskedReductionUtil.java
+++ b/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/MaskedReductionUtil.java
@@ -136,7 +136,7 @@ public class MaskedReductionUtil {
                 Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, 0, 2));
                 //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op
 
-                INDArray isMax = Nd4j.exec(new IsMax(withInf, 2))[0];
+                INDArray isMax = Nd4j.exec(new IsMax(withInf, withInf.ulike(), 2))[0];
 
                 return Nd4j.getExecutioner().exec(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
             case AVG:
@@ -296,7 +296,7 @@ public class MaskedReductionUtil {
                 Nd4j.getExecutioner().exec(new BroadcastAddOp(input, negInfMask, withInf, dimensions));
                 //At this point: all the masked out steps have value -inf, hence can't be the output of the MAX op
 
-                INDArray isMax = Nd4j.exec(new IsMax(withInf, 2, 3))[0];
+                INDArray isMax = Nd4j.exec(new IsMax(withInf, withInf.ulike(), 2, 3))[0];
 
                 return Nd4j.getExecutioner().exec(new BroadcastMulOp(isMax, epsilon2d, isMax, 0, 1));
             case AVG:
diff --git a/deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/BaseDL4JTest.java b/deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/BaseDL4JTest.java
index ef26f2848..05d0957fb 100644
--- a/deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/BaseDL4JTest.java
+++ b/deeplearning4j/deeplearning4j-nn/src/test/java/org/deeplearning4j/BaseDL4JTest.java
@@ -22,7 +22,6 @@ import org.junit.After;
 import org.junit.Before;
 import org.junit.Rule;
 import org.junit.rules.TestName;
-import org.nd4j.linalg.api.buffer.DataBuffer;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
 import org.nd4j.linalg.api.ops.executioner.OpExecutioner;