diff --git a/deeplearning4j-core/src/main/java/org/deeplearning4j/datasets/canova/SequenceRecordReaderDataSetIterator.java b/deeplearning4j-core/src/main/java/org/deeplearning4j/datasets/canova/SequenceRecordReaderDataSetIterator.java
index a1b429d23c47..460d935f53c1 100644
--- a/deeplearning4j-core/src/main/java/org/deeplearning4j/datasets/canova/SequenceRecordReaderDataSetIterator.java
+++ b/deeplearning4j-core/src/main/java/org/deeplearning4j/datasets/canova/SequenceRecordReaderDataSetIterator.java
@@ -161,8 +161,9 @@ private DataSet nextSingleSequenceReader(int num){
 
         if(minLength == maxLength){
             for (int i = 0; i < listFeatures.size(); i++) {
-                featuresOut.tensorAlongDimension(i, 1, 2).assign(listFeatures.get(i));
-                labelsOut.tensorAlongDimension(i, 1, 2).assign(listLabels.get(i));
+                //Note: this TAD gives us shape [vectorSize,tsLength] whereas we need a [vectorSize,timeSeriesLength] matrix (that listFeatures contains)
+                featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0).assign(listFeatures.get(i));
+                labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0).assign(listLabels.get(i));
             }
         } else {
             featuresMask = Nd4j.ones(listFeatures.size(),maxLength);
@@ -171,8 +172,10 @@ private DataSet nextSingleSequenceReader(int num){
                 INDArray f = listFeatures.get(i);
                 int tsLength = f.size(0);
 
-                featuresOut.tensorAlongDimension(i, 1, 2).put(new INDArrayIndex[]{NDArrayIndex.interval(0, tsLength), NDArrayIndex.all()}, f);
-                labelsOut.tensorAlongDimension(i, 1, 2).put(new INDArrayIndex[]{NDArrayIndex.interval(0, tsLength), NDArrayIndex.all()}, listLabels.get(i));
+                featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
+                        .put(new INDArrayIndex[]{NDArrayIndex.interval(0, tsLength), NDArrayIndex.all()}, f);
+                labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
+                        .put(new INDArrayIndex[]{NDArrayIndex.interval(0, tsLength), NDArrayIndex.all()}, listLabels.get(i));
                 for( int j=tsLength; j<maxLength; j++ ){
                     featuresMask.put(i,j,0.0);
                     labelsMask.put(i,j,0.0);
@@ -222,8 +225,10 @@ private DataSet nextMultipleSequenceReaders(int num){
             featuresOut = Nd4j.create(featureShape,'f');
             labelsOut = Nd4j.create(labelShape,'f');
             for (int i = 0; i < featureList.size(); i++) {
-                featuresOut.tensorAlongDimension(i, 1, 2).assign(featureList.get(i));
-                labelsOut.tensorAlongDimension(i, 1, 2).assign(labelList.get(i));
+                featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
+                        .assign(featureList.get(i));
+                labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
+                        .assign(labelList.get(i));
             }
         } else if( alignmentMode == AlignmentMode.ALIGN_START ){
             int longestTimeSeries = 0;
@@ -251,9 +256,10 @@ private DataSet nextMultipleSequenceReaders(int num){
                 INDArray f = featureList.get(i);
                 INDArray l = labelList.get(i);
 
-                featuresOut.tensorAlongDimension(i, 1, 2)
+                //Again, permute is to put [timeSeriesLength,vectorSize] into a [vectorSize,timeSeriesLength] matrix
+                featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                         .put(new INDArrayIndex[]{NDArrayIndex.interval(0, f.size(0)), NDArrayIndex.all()}, f);
-                labelsOut.tensorAlongDimension(i, 1, 2)
+                labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                         .put(new INDArrayIndex[]{NDArrayIndex.interval(0, l.size(0)), NDArrayIndex.all()}, l);
                 for( int j=f.size(0); j<longestTimeSeries; j++ ){
                     featuresMask.putScalar(i,j,0.0);
@@ -294,9 +300,9 @@ private DataSet nextMultipleSequenceReaders(int num){
 
                 if(fLen >= lLen){
                     //Align labels with end of features (features are longer)
-                    featuresOut.tensorAlongDimension(i, 1, 2)
+                    featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                             .put(new INDArrayIndex[]{NDArrayIndex.interval(0, fLen), NDArrayIndex.all()}, f);
-                    labelsOut.tensorAlongDimension(i, 1, 2)
+                    labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                             .put(new INDArrayIndex[]{NDArrayIndex.interval(fLen-lLen, fLen), NDArrayIndex.all()}, l);
 
                     for( int j=fLen; j<longestTimeSeries; j++ ){
@@ -312,9 +318,9 @@ private DataSet nextMultipleSequenceReaders(int num){
                     }
                 } else {
                     //Align features with end of labels (labels are longer)
-                    featuresOut.tensorAlongDimension(i, 1, 2)
+                    featuresOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                             .put(new INDArrayIndex[]{NDArrayIndex.interval(lLen-fLen, lLen), NDArrayIndex.all()}, f);
-                    labelsOut.tensorAlongDimension(i, 1, 2)
+                    labelsOut.tensorAlongDimension(i, 1, 2).permutei(1,0)
                             .put(new INDArrayIndex[]{NDArrayIndex.interval(0, lLen), NDArrayIndex.all()}, l);
 
                     //features mask: component before features
diff --git a/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/canova/RecordReaderDataSetiteratorTest.java b/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/canova/RecordReaderDataSetiteratorTest.java
index 152c2e5d082d..cc254e618b47 100644
--- a/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/canova/RecordReaderDataSetiteratorTest.java
+++ b/deeplearning4j-core/src/test/java/org/deeplearning4j/datasets/canova/RecordReaderDataSetiteratorTest.java
@@ -416,24 +416,24 @@ public void testVariableLengthSequence() throws Exception{
         expF0.tensorAlongDimension(1, 1).assign(Nd4j.create(new double[]{10, 11, 12}));
         expF0.tensorAlongDimension(2, 1).assign(Nd4j.create(new double[]{20, 21, 22}));
         expF0.tensorAlongDimension(3, 1).assign(Nd4j.create(new double[]{30, 31, 32}));
-        assertEquals(dsListAlignStart.get(0).getFeatureMatrix(), expF0);
-        assertEquals(dsListAlignEnd.get(0).getFeatureMatrix(), expF0);
+        assertEquals(expF0, dsListAlignStart.get(0).getFeatureMatrix());
+        assertEquals(expF0, dsListAlignEnd.get(0).getFeatureMatrix());
 
         INDArray expF1 = Nd4j.create(1, 3, 4);
         expF1.tensorAlongDimension(0, 1).assign(Nd4j.create(new double[]{100, 101, 102}));
         expF1.tensorAlongDimension(1, 1).assign(Nd4j.create(new double[]{110, 111, 112}));
         expF1.tensorAlongDimension(2, 1).assign(Nd4j.create(new double[]{120, 121, 122}));
         expF1.tensorAlongDimension(3, 1).assign(Nd4j.create(new double[]{130, 131, 132}));
-        assertEquals(dsListAlignStart.get(1).getFeatureMatrix(), expF1);
-        assertEquals(dsListAlignEnd.get(1).getFeatureMatrix(), expF1);
+        assertEquals(expF1, dsListAlignStart.get(1).getFeatureMatrix());
+        assertEquals(expF1, dsListAlignEnd.get(1).getFeatureMatrix());
 
         INDArray expF2 = Nd4j.create(1, 3, 4);
         expF2.tensorAlongDimension(0, 1).assign(Nd4j.create(new double[]{200, 201, 202}));
         expF2.tensorAlongDimension(1, 1).assign(Nd4j.create(new double[]{210, 211, 212}));
         expF2.tensorAlongDimension(2, 1).assign(Nd4j.create(new double[]{220, 221, 222}));
         expF2.tensorAlongDimension(3, 1).assign(Nd4j.create(new double[]{230, 231, 232}));
-        assertEquals(dsListAlignStart.get(2).getFeatureMatrix(), expF2);
-        assertEquals(dsListAlignEnd.get(2).getFeatureMatrix(), expF2);
+        assertEquals(expF2, dsListAlignStart.get(2).getFeatureMatrix());
+        assertEquals(expF2, dsListAlignEnd.get(2).getFeatureMatrix());
 
         //Check features mask array:
         INDArray featuresMaskExpected = Nd4j.ones(1,4); //1 example, 4 values: same for both start/end align here