diff --git a/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/fetchers/MnistDataFetcher.java b/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/fetchers/MnistDataFetcher.java
index bd8726fed..3be8401d7 100755
--- a/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/fetchers/MnistDataFetcher.java
+++ b/deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/fetchers/MnistDataFetcher.java
@@ -22,10 +22,12 @@ import org.deeplearning4j.base.MnistFetcher;
 import org.deeplearning4j.common.resources.DL4JResources;
 import org.deeplearning4j.common.resources.ResourceType;
 import org.deeplearning4j.datasets.mnist.MnistManager;
+import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.iterator.fetcher.BaseDataFetcher;
 import org.nd4j.linalg.factory.Nd4j;
+import org.nd4j.linalg.indexing.NDArrayIndex;
 import org.nd4j.linalg.util.MathUtils;
 
 import java.io.File;
@@ -167,14 +169,19 @@ public class MnistDataFetcher extends BaseDataFetcher {
         this(true);
     }
 
+    private float[][] featureData = null;
+
     @Override
     public void fetch(int numExamples) {
         if (!hasMore()) {
             throw new IllegalStateException("Unable to get more; there are no more images");
         }
 
-        float[][] featureData = new float[numExamples][0];
-        float[][] labelData = new float[numExamples][0];
+        INDArray labels = Nd4j.zeros(DataType.FLOAT, numExamples, numOutcomes);
+
+        if(featureData == null || featureData.length < numExamples){
+            featureData = new float[numExamples][28*28];
+        }
 
         int actualExamples = 0;
         byte[] working = null;
@@ -202,33 +209,33 @@ public class MnistDataFetcher extends BaseDataFetcher {
                 label--;
             }
 
-            float[] featureVec = new float[img.length];
-            featureData[actualExamples] = featureVec;
-            labelData[actualExamples] = new float[numOutcomes];
-            labelData[actualExamples][label] = 1.0f;
+            labels.put(actualExamples, label, 1.0f);
 
-            for (int j = 0; j < img.length; j++) {
-                float v = ((int) img[j]) & 0xFF; //byte is loaded as signed -> convert to unsigned
-                if (binarize) {
-                    if (v > 30.0f)
-                        featureVec[j] = 1.0f;
-                    else
-                        featureVec[j] = 0.0f;
-                } else {
-                    featureVec[j] = v / 255.0f;
-                }
+            for(int j = 0 ; j < img.length ; j++) {
+                featureData[actualExamples][j] = ((int) img[j]) & 0xFF;
             }
 
             actualExamples++;
         }
 
-        if (actualExamples < numExamples) {
-            featureData = Arrays.copyOfRange(featureData, 0, actualExamples);
-            labelData = Arrays.copyOfRange(labelData, 0, actualExamples);
+        INDArray features;
+
+        if(featureData.length == actualExamples){
+            features = Nd4j.create(featureData);
+        } else {
+            features = Nd4j.create(Arrays.copyOfRange(featureData, 0, actualExamples));
+        }
+
+        if (actualExamples < numExamples) {
+            labels = labels.get(NDArrayIndex.interval(0, actualExamples), NDArrayIndex.all());
+        }
+
+        if(binarize){
+            features = features.gt(30.0).castTo(DataType.FLOAT);
+        } else {
+            features.divi(255.0);
         }
 
-        INDArray features = Nd4j.create(featureData);
-        INDArray labels = Nd4j.create(labelData);
         curr = new DataSet(features, labels);
     }