Update master (#8511)

* cleaned up bert iterator tests (#110)

Signed-off-by: eraly <susan.eraly@gmail.com>

* Various pre-release fixes (#111)

* Various fixes

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Fix default dtypes for MaxPoolWithArgmax

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Small pre-release tweak (#112)

* Log UI address on launch as in previous Play-based UI

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* Logging level tweak for UI

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* http not https

Signed-off-by: AlexDBlack <blacka101@gmail.com>

* datavec python ensure host (#113)

* ensure host

* one more host ensure

* info->debug

* [WIP] reverse improvements (#115)

* initial commit

Signed-off-by: raver119 <raver119@gmail.com>

* reverse draft

Signed-off-by: raver119 <raver119@gmail.com>

* reverse kernel

Signed-off-by: raver119 <raver119@gmail.com>

* reverse kernel

Signed-off-by: raver119 <raver119@gmail.com>

* 2 micro fixes

Signed-off-by: raver119 <raver119@gmail.com>

* Shugeo resize fix5 (#102)

* Refactored resize images ops to use TF-like bool args as input.

* Refactored helpers for cpu implementation of resize_bilinear and resize_nearest_neighbor ops.

* Refactored cuda implementation for image.resize_bilinear and image.resize_nearest_neighbor ops helpers.

* Refactored nearest_neighbor resize op.

* Added a pair of tests for special case of resize_bilinear algorithm.

* Fixed issue with resize_bilinear op.

* Refactored cpu implementation for helpers with resize_nearest_neighbor op.

* Final fixed for resize ops to conform TF v.1.5

* Refactored cuda helpers for resize_neares_neighbor op.

* Fixed resize_bilinear to accept proper data.

* Fixed issue with non-float input for resize_bilinear op.

* Refactored cuda helper for resize_bilinear to proper process non-float inputs.

* Added tests for resize_bilinear to int inputs.

* Fixed ResizeBilinear wrapper

* Tests fixed

* Fixed float and bool constant to avoid overflow for some kind of compilers.

* Corrected float constants with float data type.

* Added f suffix for float constants.

* Corrected float constant to avoid overflow with initializing lists.

* Corrected float initializing list with float input.

* Corrected bool constant with initalizing list.

* Corrected float and bool values with initializing lists.

* Fixed wrong constant.

* Fixed issue with 1x1 input picture for resize.

* ResizeBilinear default values on import fix

Signed-off-by: raver119 <raver119@gmail.com>

datavec/datavec-python/src/main/java/org/datavec/python/NumpyArray.java

@@ -21,6 +21,7 @@ import lombok.Getter;
 import lombok.NoArgsConstructor;
 import org.bytedeco.javacpp.Pointer;
 import org.nd4j.linalg.api.buffer.DataBuffer;
+import org.nd4j.linalg.api.concurrency.AffinityManager;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
@@ -60,6 +61,7 @@ public class NumpyArray {
         setND4JArray();
         if (copy){
             nd4jArray = nd4jArray.dup();
+            Nd4j.getAffinityManager().ensureLocation(nd4jArray, AffinityManager.Location.HOST);
             this.address = nd4jArray.data().address();
 
         }
@@ -85,6 +87,7 @@ public class NumpyArray {
         setND4JArray();
         if (copy){
             nd4jArray = nd4jArray.dup();
+            Nd4j.getAffinityManager().ensureLocation(nd4jArray, AffinityManager.Location.HOST);
             this.address = nd4jArray.data().address();
         }
     }
@@ -104,11 +107,12 @@ public class NumpyArray {
             nd4jStrides[i] = strides[i] / elemSize;
         }
 
-        this.nd4jArray = Nd4j.create(buff, shape, nd4jStrides, 0, Shape.getOrder(shape,nd4jStrides,1), dtype);
+        nd4jArray = Nd4j.create(buff, shape, nd4jStrides, 0, Shape.getOrder(shape,nd4jStrides,1), dtype);
-
+        Nd4j.getAffinityManager().ensureLocation(nd4jArray, AffinityManager.Location.HOST);
     }
 
     public NumpyArray(INDArray nd4jArray){
+        Nd4j.getAffinityManager().ensureLocation(nd4jArray, AffinityManager.Location.HOST);
         DataBuffer buff = nd4jArray.data();
         address = buff.pointer().address();
         shape = nd4jArray.shape();

datavec/datavec-python/src/main/java/org/datavec/python/PythonExecutioner.java

@@ -605,7 +605,7 @@ public class PythonExecutioner {
 
 
     private static synchronized void _exec(String code) {
-        log.info(code);
+        log.debug(code);
         log.info("CPython: PyRun_SimpleStringFlag()");
 
         int result = PyRun_SimpleStringFlags(code, null);

deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/test/java/org/deeplearning4j/iterator/TestBertIterator.java

@@ -17,11 +17,13 @@
 
 package org.deeplearning4j.iterator;
 
+import lombok.Getter;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.iterator.bert.BertMaskedLMMasker;
+import org.deeplearning4j.iterator.provider.CollectionLabeledPairSentenceProvider;
+import org.deeplearning4j.iterator.provider.CollectionLabeledSentenceProvider;
 import org.deeplearning4j.text.tokenization.tokenizerfactory.BertWordPieceTokenizerFactory;
 import org.junit.Test;
-import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.api.MultiDataSet;
@@ -42,8 +44,12 @@ import static org.junit.Assert.*;
 
 public class TestBertIterator extends BaseDL4JTest {
 
-    private File pathToVocab = Resources.asFile("other/vocab.txt");
+    private static File pathToVocab = Resources.asFile("other/vocab.txt");
     private static Charset c = StandardCharsets.UTF_8;
+    private static String shortSentence = "I saw a girl with a telescope.";
+    private static String longSentence = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+    private static String sentenceA = "Goodnight noises everywhere";
+    private static String sentenceB = "Goodnight moon";
 
     public TestBertIterator() throws IOException {
     }
@@ -51,20 +57,15 @@ public class TestBertIterator extends BaseDL4JTest {
     @Test(timeout = 20000L)
     public void testBertSequenceClassification() throws Exception {
 
-        String toTokenize1 = "I saw a girl with a telescope.";
+        int minibatchSize = 2;
-        String toTokenize2 = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+        TestSentenceHelper testHelper = new TestSentenceHelper();
-        List<String> forInference = new ArrayList<>();
-        forInference.add(toTokenize1);
-        forInference.add(toTokenize2);
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
-
         BertIterator b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 16)
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .build();
 
@@ -73,82 +74,77 @@ public class TestBertIterator extends BaseDL4JTest {
         System.out.println(mds.getFeatures(0));
         System.out.println(mds.getFeaturesMaskArray(0));
 
-
+        INDArray expF = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expEx0 = Nd4j.create(DataType.INT, 1, 16);
+        INDArray expM = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM0 = Nd4j.create(DataType.INT, 1, 16);
+        Map<String, Integer> m = testHelper.getTokenizer().getVocab();
-        List<String> tokens = t.create(toTokenize1).getTokens();
+        for (int i = 0; i < minibatchSize; i++) {
-        Map<String, Integer> m = t.getVocab();
+            INDArray expFTemp = Nd4j.create(DataType.INT, 1, 16);
-        for (int i = 0; i < tokens.size(); i++) {
+            INDArray expMTemp = Nd4j.create(DataType.INT, 1, 16);
-            int idx = m.get(tokens.get(i));
+            List<String> tokens = testHelper.getTokenizedSentences().get(i);
-            expEx0.putScalar(0, i, idx);
+            System.out.println(tokens);
-            expM0.putScalar(0, i, 1);
+            for (int j = 0; j < tokens.size(); j++) {
-        }
+                String token = tokens.get(j);
-
-        INDArray expEx1 = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM1 = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens2 = t.create(toTokenize2).getTokens();
-        for (int i = 0; i < tokens2.size(); i++) {
-            String token = tokens2.get(i);
                 if (!m.containsKey(token)) {
                     throw new IllegalStateException("Unknown token: \"" + token + "\"");
                 }
                 int idx = m.get(token);
-            expEx1.putScalar(0, i, idx);
+                expFTemp.putScalar(0, j, idx);
-            expM1.putScalar(0, i, 1);
+                expMTemp.putScalar(0, j, 1);
+            }
+            if (i == 0) {
+                expF = expFTemp.dup();
+                expM = expMTemp.dup();
+            } else {
+                expF = Nd4j.vstack(expF, expFTemp);
+                expM = Nd4j.vstack(expM, expMTemp);
+            }
         }
-
-        INDArray expF = Nd4j.vstack(expEx0, expEx1);
-        INDArray expM = Nd4j.vstack(expM0, expM1);
-
         assertEquals(expF, mds.getFeatures(0));
         assertEquals(expM, mds.getFeaturesMaskArray(0));
-        assertEquals(expF, b.featurizeSentences(forInference).getFirst()[0]);
+        assertEquals(expF, b.featurizeSentences(testHelper.getSentences()).getFirst()[0]);
-        assertEquals(expM, b.featurizeSentences(forInference).getSecond()[0]);
+        assertEquals(expM, b.featurizeSentences(testHelper.getSentences()).getSecond()[0]);
 
-        b.next(); //pop the third element
         assertFalse(b.hasNext());
         b.reset();
         assertTrue(b.hasNext());
 
-        forInference.set(0, toTokenize2);
         //Same thing, but with segment ID also
         b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 16)
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .build();
         mds = b.next();
         assertEquals(2, mds.getFeatures().length);
-        //assertEquals(2, mds.getFeaturesMaskArrays().length); second element is null...
-        assertEquals(2, b.featurizeSentences(forInference).getFirst().length);
         //Segment ID should be all 0s for single segment task
         INDArray segmentId = expM.like();
         assertEquals(segmentId, mds.getFeatures(1));
-        assertEquals(segmentId, b.featurizeSentences(forInference).getFirst()[1]);
+        assertEquals(segmentId, b.featurizeSentences(testHelper.getSentences()).getFirst()[1]);
     }
 
     @Test(timeout = 20000L)
     public void testBertUnsupervised() throws Exception {
+        int minibatchSize = 2;
+        TestSentenceHelper testHelper = new TestSentenceHelper();
         //Task 1: Unsupervised
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
         BertIterator b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 16)
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.UNSUPERVISED)
                 .masker(new BertMaskedLMMasker(new Random(12345), 0.2, 0.5, 0.5))
                 .unsupervisedLabelFormat(BertIterator.UnsupervisedLabelFormat.RANK2_IDX)
                 .maskToken("[MASK]")
                 .build();
 
-        System.out.println("Mask token index: " + t.getVocab().get("[MASK]"));
+        System.out.println("Mask token index: " + testHelper.getTokenizer().getVocab().get("[MASK]"));
 
         MultiDataSet mds = b.next();
         System.out.println(mds.getFeatures(0));
@@ -156,7 +152,6 @@ public class TestBertIterator extends BaseDL4JTest {
         System.out.println(mds.getLabels(0));
         System.out.println(mds.getLabelsMaskArray(0));
 
-        b.next(); //pop the third element
         assertFalse(b.hasNext());
         b.reset();
         assertTrue(b.hasNext());
@@ -164,39 +159,33 @@ public class TestBertIterator extends BaseDL4JTest {
 
     @Test(timeout = 20000L)
     public void testLengthHandling() throws Exception {
-        String toTokenize1 = "I saw a girl with a telescope.";
+        int minibatchSize = 2;
-        String toTokenize2 = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+        TestSentenceHelper testHelper = new TestSentenceHelper();
-        List<String> forInference = new ArrayList<>();
+        INDArray expF = Nd4j.create(DataType.INT, 1, 16);
-        forInference.add(toTokenize1);
+        INDArray expM = Nd4j.create(DataType.INT, 1, 16);
-        forInference.add(toTokenize2);
+        Map<String, Integer> m = testHelper.getTokenizer().getVocab();
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
+        for (int i = 0; i < minibatchSize; i++) {
-        INDArray expEx0 = Nd4j.create(DataType.INT, 1, 16);
+            List<String> tokens = testHelper.getTokenizedSentences().get(i);
-        INDArray expM0 = Nd4j.create(DataType.INT, 1, 16);
+            INDArray expFTemp = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens = t.create(toTokenize1).getTokens();
+            INDArray expMTemp = Nd4j.create(DataType.INT, 1, 16);
             System.out.println(tokens);
-        Map<String, Integer> m = t.getVocab();
+            for (int j = 0; j < tokens.size(); j++) {
-        for (int i = 0; i < tokens.size(); i++) {
+                String token = tokens.get(j);
-            int idx = m.get(tokens.get(i));
-            expEx0.putScalar(0, i, idx);
-            expM0.putScalar(0, i, 1);
-        }
-
-        INDArray expEx1 = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM1 = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens2 = t.create(toTokenize2).getTokens();
-        System.out.println(tokens2);
-        for (int i = 0; i < tokens2.size(); i++) {
-            String token = tokens2.get(i);
                 if (!m.containsKey(token)) {
                     throw new IllegalStateException("Unknown token: \"" + token + "\"");
                 }
                 int idx = m.get(token);
-            expEx1.putScalar(0, i, idx);
+                expFTemp.putScalar(0, j, idx);
-            expM1.putScalar(0, i, 1);
+                expMTemp.putScalar(0, j, 1);
+            }
+            if (i == 0) {
+                expF = expFTemp.dup();
+                expM = expMTemp.dup();
+            } else {
+                expF = Nd4j.vstack(expF, expFTemp);
+                expM = Nd4j.vstack(expM, expMTemp);
+            }
         }
-
-        INDArray expF = Nd4j.vstack(expEx0, expEx1);
-        INDArray expM = Nd4j.vstack(expM0, expM1);
 
         //--------------------------------------------------------------
 
@@ -205,12 +194,12 @@ public class TestBertIterator extends BaseDL4JTest {
         //Any length: as long as we need to fit longest sequence
 
         BertIterator b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.ANY_LENGTH, -1)
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .build();
         MultiDataSet mds = b.next();
@@ -219,20 +208,19 @@ public class TestBertIterator extends BaseDL4JTest {
         assertArrayEquals(expShape, mds.getFeaturesMaskArray(0).shape());
         assertEquals(expF.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 14)), mds.getFeatures(0));
         assertEquals(expM.get(NDArrayIndex.all(), NDArrayIndex.interval(0, 14)), mds.getFeaturesMaskArray(0));
-        assertEquals(mds.getFeatures(0), b.featurizeSentences(forInference).getFirst()[0]);
+        assertEquals(mds.getFeatures(0), b.featurizeSentences(testHelper.getSentences()).getFirst()[0]);
-        assertEquals(mds.getFeaturesMaskArray(0), b.featurizeSentences(forInference).getSecond()[0]);
+        assertEquals(mds.getFeaturesMaskArray(0), b.featurizeSentences(testHelper.getSentences()).getSecond()[0]);
 
         //Clip only: clip to maximum, but don't pad if less
         b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.CLIP_ONLY, 20)
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .build();
-        mds = b.next();
         expShape = new long[]{2, 14};
         assertArrayEquals(expShape, mds.getFeatures(0).shape());
         assertArrayEquals(expShape, mds.getFeaturesMaskArray(0).shape());
@@ -241,54 +229,38 @@ public class TestBertIterator extends BaseDL4JTest {
     @Test(timeout = 20000L)
     public void testMinibatchPadding() throws Exception {
         Nd4j.setDefaultDataTypes(DataType.FLOAT, DataType.FLOAT);
-        String toTokenize1 = "I saw a girl with a telescope.";
+        int minibatchSize = 3;
-        String toTokenize2 = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+        TestSentenceHelper testHelper = new TestSentenceHelper(minibatchSize);
-        String toTokenize3 = "Goodnight noises everywhere";
-        List<String> forInference = new ArrayList<>();
-        forInference.add(toTokenize1);
-        forInference.add(toTokenize2);
-        forInference.add(toTokenize3);
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
-        INDArray expEx0 = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM0 = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens = t.create(toTokenize1).getTokens();
-        Map<String, Integer> m = t.getVocab();
-        for (int i = 0; i < tokens.size(); i++) {
-            int idx = m.get(tokens.get(i));
-            expEx0.putScalar(0, i, idx);
-            expM0.putScalar(0, i, 1);
-        }
-
-        INDArray expEx1 = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM1 = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens2 = t.create(toTokenize2).getTokens();
-        for (int i = 0; i < tokens2.size(); i++) {
-            String token = tokens2.get(i);
-            if (!m.containsKey(token)) {
-                throw new IllegalStateException("Unknown token: \"" + token + "\"");
-            }
-            int idx = m.get(token);
-            expEx1.putScalar(0, i, idx);
-            expM1.putScalar(0, i, 1);
-        }
-
-        INDArray expEx3 = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM3 = Nd4j.create(DataType.INT, 1, 16);
-        List<String> tokens3 = t.create(toTokenize3).getTokens();
-        for (int i = 0; i < tokens3.size(); i++) {
-            String token = tokens3.get(i);
-            if (!m.containsKey(token)) {
-                throw new IllegalStateException("Unknown token: \"" + token + "\"");
-            }
-            int idx = m.get(token);
-            expEx3.putScalar(0, i, idx);
-            expM3.putScalar(0, i, 1);
-        }
-
         INDArray zeros = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expF = Nd4j.vstack(expEx0, expEx1, expEx3, zeros);
+        INDArray expF = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expM = Nd4j.vstack(expM0, expM1, expM3, zeros);
+        INDArray expM = Nd4j.create(DataType.INT, 1, 16);
-        INDArray expL = Nd4j.createFromArray(new float[][]{{1, 0}, {0, 1}, {1, 0}, {0, 0}});
+        Map<String, Integer> m = testHelper.getTokenizer().getVocab();
+        for (int i = 0; i < minibatchSize; i++) {
+            List<String> tokens = testHelper.getTokenizedSentences().get(i);
+            INDArray expFTemp = Nd4j.create(DataType.INT, 1, 16);
+            INDArray expMTemp = Nd4j.create(DataType.INT, 1, 16);
+            System.out.println(tokens);
+            for (int j = 0; j < tokens.size(); j++) {
+                String token = tokens.get(j);
+                if (!m.containsKey(token)) {
+                    throw new IllegalStateException("Unknown token: \"" + token + "\"");
+                }
+                int idx = m.get(token);
+                expFTemp.putScalar(0, j, idx);
+                expMTemp.putScalar(0, j, 1);
+            }
+            if (i == 0) {
+                expF = expFTemp.dup();
+                expM = expMTemp.dup();
+            } else {
+                expF = Nd4j.vstack(expF.dup(), expFTemp);
+                expM = Nd4j.vstack(expM.dup(), expMTemp);
+            }
+        }
+
+        expF = Nd4j.vstack(expF, zeros);
+        expM = Nd4j.vstack(expM, zeros);
+        INDArray expL = Nd4j.createFromArray(new float[][]{{0, 1}, {1, 0}, {0, 1}, {0, 0}});
         INDArray expLM = Nd4j.create(DataType.FLOAT, 4, 1);
         expLM.putScalar(0, 0, 1);
         expLM.putScalar(1, 0, 1);
@@ -297,13 +269,13 @@ public class TestBertIterator extends BaseDL4JTest {
         //--------------------------------------------------------------
 
         BertIterator b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testHelper.getTokenizer())
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 16)
-                .minibatchSize(4)
+                .minibatchSize(minibatchSize + 1)
                 .padMinibatches(true)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(testHelper.getSentenceProvider())
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                .vocabMap(t.getVocab())
+                .vocabMap(testHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .build();
 
@@ -323,123 +295,130 @@ public class TestBertIterator extends BaseDL4JTest {
         assertEquals(expL, mds.getLabels(0));
         assertEquals(expLM, mds.getLabelsMaskArray(0));
 
-        assertEquals(expF, b.featurizeSentences(forInference).getFirst()[0]);
+        assertEquals(expF, b.featurizeSentences(testHelper.getSentences()).getFirst()[0]);
-        assertEquals(expM, b.featurizeSentences(forInference).getSecond()[0]);
+        assertEquals(expM, b.featurizeSentences(testHelper.getSentences()).getSecond()[0]);
     }
 
+    /*
+        Checks that a mds from a pair sentence is equal to hstack'd mds from the left side and right side of the pair
+        Checks different lengths for max length to check popping and padding
+     */
     @Test
     public void testSentencePairsSingle() throws IOException {
-        String shortSent = "I saw a girl with a telescope.";
-        String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
         boolean prependAppend;
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
+        int numOfSentences;
-        int shortL = t.create(shortSent).countTokens();
+
-        int longL = t.create(longSent).countTokens();
+        TestSentenceHelper testHelper = new TestSentenceHelper();
+        int shortL = testHelper.getShortestL();
+        int longL = testHelper.getLongestL();
 
         Triple<MultiDataSet, MultiDataSet, MultiDataSet> multiDataSetTriple;
-        MultiDataSet shortLongPair, shortSentence, longSentence;
+        MultiDataSet fromPair, leftSide, rightSide;
 
         // check for pair max length exactly equal to sum of lengths - pop neither no padding
         // should be the same as hstack with segment ids 1 for second sentence
         prependAppend = true;
-        multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL, shortL, longL), prependAppend);
+        numOfSentences = 1;
-        shortLongPair = multiDataSetTriple.getFirst();
+        multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL, shortL, longL), prependAppend, numOfSentences);
-        shortSentence = multiDataSetTriple.getSecond();
+        fromPair = multiDataSetTriple.getFirst();
-        longSentence = multiDataSetTriple.getThird();
+        leftSide = multiDataSetTriple.getSecond();
-        assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
+        rightSide = multiDataSetTriple.getThird();
-        longSentence.getFeatures(1).addi(1);
+        assertEquals(fromPair.getFeatures(0), Nd4j.hstack(leftSide.getFeatures(0), rightSide.getFeatures(0)));
-        assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
+        rightSide.getFeatures(1).addi(1); //add 1 for right side segment ids
-        assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
+        assertEquals(fromPair.getFeatures(1), Nd4j.hstack(leftSide.getFeatures(1), rightSide.getFeatures(1)));
+        assertEquals(fromPair.getFeaturesMaskArray(0), Nd4j.hstack(leftSide.getFeaturesMaskArray(0), rightSide.getFeaturesMaskArray(0)));
 
         //check for pair max length greater than sum of lengths - pop neither with padding
         // features should be the same as hstack of shorter and longer padded with prepend/append
         // segment id should 1 only in the longer for part of the length of the sentence
         prependAppend = true;
-        multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL + 5, shortL, longL + 5), prependAppend);
+        numOfSentences = 1;
-        shortLongPair = multiDataSetTriple.getFirst();
+        multiDataSetTriple = generateMultiDataSets(new Triple<>(shortL + longL + 5, shortL, longL + 5), prependAppend, numOfSentences);
-        shortSentence = multiDataSetTriple.getSecond();
+        fromPair = multiDataSetTriple.getFirst();
-        longSentence = multiDataSetTriple.getThird();
+        leftSide = multiDataSetTriple.getSecond();
-        assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
+        rightSide = multiDataSetTriple.getThird();
-        longSentence.getFeatures(1).get(NDArrayIndex.all(), NDArrayIndex.interval(0, longL + 1)).addi(1); //segmentId stays 0 for the padded part
+        assertEquals(fromPair.getFeatures(0), Nd4j.hstack(leftSide.getFeatures(0), rightSide.getFeatures(0)));
-        assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
+        rightSide.getFeatures(1).get(NDArrayIndex.all(), NDArrayIndex.interval(0, longL + 1)).addi(1); //segmentId stays 0 for the padded part
-        assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
+        assertEquals(fromPair.getFeatures(1), Nd4j.hstack(leftSide.getFeatures(1), rightSide.getFeatures(1)));
+        assertEquals(fromPair.getFeaturesMaskArray(0), Nd4j.hstack(leftSide.getFeaturesMaskArray(0), rightSide.getFeaturesMaskArray(0)));
 
         //check for pair max length less than shorter sentence - pop both
         //should be the same as hstack with segment ids 1 for second sentence if no prepend/append
-        int maxL = shortL - 2;
+        int maxL = 5;//checking odd
+        numOfSentences = 3;
         prependAppend = false;
-        multiDataSetTriple = generateMultiDataSets(new Triple<>(maxL, maxL / 2, maxL - maxL / 2), prependAppend);
+        multiDataSetTriple = generateMultiDataSets(new Triple<>(maxL, maxL / 2, maxL - maxL / 2), prependAppend, numOfSentences);
-        shortLongPair = multiDataSetTriple.getFirst();
+        fromPair = multiDataSetTriple.getFirst();
-        shortSentence = multiDataSetTriple.getSecond();
+        leftSide = multiDataSetTriple.getSecond();
-        longSentence = multiDataSetTriple.getThird();
+        rightSide = multiDataSetTriple.getThird();
-        assertEquals(shortLongPair.getFeatures(0), Nd4j.hstack(shortSentence.getFeatures(0), longSentence.getFeatures(0)));
+        assertEquals(fromPair.getFeatures(0), Nd4j.hstack(leftSide.getFeatures(0), rightSide.getFeatures(0)));
-        longSentence.getFeatures(1).addi(1);
+        rightSide.getFeatures(1).addi(1);
-        assertEquals(shortLongPair.getFeatures(1), Nd4j.hstack(shortSentence.getFeatures(1), longSentence.getFeatures(1)));
+        assertEquals(fromPair.getFeatures(1), Nd4j.hstack(leftSide.getFeatures(1), rightSide.getFeatures(1)));
-        assertEquals(shortLongPair.getFeaturesMaskArray(0), Nd4j.hstack(shortSentence.getFeaturesMaskArray(0), longSentence.getFeaturesMaskArray(0)));
+        assertEquals(fromPair.getFeaturesMaskArray(0), Nd4j.hstack(leftSide.getFeaturesMaskArray(0), rightSide.getFeaturesMaskArray(0)));
     }
 
+    /*
+        Same idea as previous test - construct mds from bert iterator with sep sentences and check against one with pairs
+        Checks various max lengths
+        Has sentences of varying lengths
+    */
     @Test
     public void testSentencePairsUnequalLengths() throws IOException {
-        //check for pop only longer (i.e between longer and longer + shorter), first row pop from second sentence, next row pop from first sentence, nothing to pop in the third row
+
-        //should be identical to hstack if there is no append, prepend
+        int minibatchSize = 4;
-        //batch size is 2
+        int numOfSentencesinIter = 3;
-        int mbS = 4;
+
-        String shortSent = "I saw a girl with a telescope.";
+        TestSentencePairsHelper testPairHelper = new TestSentencePairsHelper(numOfSentencesinIter);
-        String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+        int shortL = testPairHelper.getShortL();
-        String sent1 = "Goodnight noises everywhere"; //shorter than shortSent - no popping
+        int longL = testPairHelper.getLongL();
-        String sent2 = "Goodnight moon"; //shorter than shortSent - no popping
+        int sent1L = testPairHelper.getSentenceALen();
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
+        int sent2L = testPairHelper.getSentenceBLen();
-        int shortL = t.create(shortSent).countTokens();
+
-        int longL = t.create(longSent).countTokens();
+        System.out.println("Sentence Pairs, Left");
-        int sent1L = t.create(sent1).countTokens();
+        System.out.println(testPairHelper.getSentencesLeft());
-        int sent2L = t.create(sent2).countTokens();
+        System.out.println("Sentence Pairs, Right");
-        //won't check 2*shortL + 1 because this will always pop on the left
+        System.out.println(testPairHelper.getSentencesRight());
-        for (int maxL = longL + shortL - 1; maxL > 2 * shortL; maxL--) {
+
+        //anything outside this range more will need to check padding,truncation
+        for (int maxL = longL + shortL; maxL > 2 * shortL + 1; maxL--) {
+
+            System.out.println("Running for max length = " + maxL);
+
             MultiDataSet leftMDS = BertIterator.builder()
-                    .tokenizer(t)
+                    .tokenizer(testPairHelper.getTokenizer())
-                    .minibatchSize(mbS)
+                    .minibatchSize(minibatchSize)
                     .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                    .vocabMap(t.getVocab())
+                    .vocabMap(testPairHelper.getTokenizer().getVocab())
                     .task(BertIterator.Task.SEQ_CLASSIFICATION)
-                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL + 10) //random big num guaranteed to be longer than either
+                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL * 10) //random big num guaranteed to be longer than either
-                    .sentenceProvider(new TestSentenceProvider())
+                    .sentenceProvider(new TestSentenceHelper(numOfSentencesinIter).getSentenceProvider())
                     .padMinibatches(true)
                     .build().next();
 
             MultiDataSet rightMDS = BertIterator.builder()
-                    .tokenizer(t)
+                    .tokenizer(testPairHelper.getTokenizer())
-                    .minibatchSize(mbS)
+                    .minibatchSize(minibatchSize)
                     .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                    .vocabMap(t.getVocab())
+                    .vocabMap(testPairHelper.getTokenizer().getVocab())
                     .task(BertIterator.Task.SEQ_CLASSIFICATION)
-                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL + 10) //random big num guaranteed to be longer than either
+                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, longL * 10) //random big num guaranteed to be longer than either
-                    .sentenceProvider(new TestSentenceProvider(true))
+                    .sentenceProvider(new TestSentenceHelper(true, numOfSentencesinIter).getSentenceProvider())
                     .padMinibatches(true)
                     .build().next();
 
             MultiDataSet pairMDS = BertIterator.builder()
-                    .tokenizer(t)
+                    .tokenizer(testPairHelper.getTokenizer())
-                    .minibatchSize(mbS)
+                    .minibatchSize(minibatchSize)
                     .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                    .vocabMap(t.getVocab())
+                    .vocabMap(testPairHelper.getTokenizer().getVocab())
                     .task(BertIterator.Task.SEQ_CLASSIFICATION)
-                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, maxL) //random big num guaranteed to be longer than either
+                    .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, maxL)
-                    .sentencePairProvider(new TestSentencePairProvider())
+                    .sentencePairProvider(testPairHelper.getPairSentenceProvider())
                     .padMinibatches(true)
                     .build().next();
 
-            //Left sentences here are {{shortSent},
-            //                         {longSent},
-            //                         {Sent1}}
-            //Right sentences here are {{longSent},
-            //                         {shortSent},
-            //                         {Sent2}}
-            //The sentence pairs here are {{shortSent,longSent},
-            //                             {longSent,shortSent}
-            //                             {Sent1, Sent2}}
-
             //CHECK FEATURES
-            INDArray combinedFeat = Nd4j.create(DataType.INT,mbS,maxL);
+            INDArray combinedFeat = Nd4j.create(DataType.INT, minibatchSize, maxL);
             //left side
             INDArray leftFeatures = leftMDS.getFeatures(0);
             INDArray topLSentFeat = leftFeatures.getRow(0).get(NDArrayIndex.interval(0, shortL));
@@ -460,33 +439,31 @@ public class TestBertIterator extends BaseDL4JTest {
             assertEquals(combinedFeat, pairMDS.getFeatures(0));
 
             //CHECK SEGMENT ID
-            INDArray combinedFetSeg = Nd4j.create(DataType.INT, mbS, maxL);
+            INDArray combinedFetSeg = Nd4j.create(DataType.INT, minibatchSize, maxL);
             combinedFetSeg.get(NDArrayIndex.point(0), NDArrayIndex.interval(shortL, maxL)).addi(1);
             combinedFetSeg.get(NDArrayIndex.point(1), NDArrayIndex.interval(maxL - shortL, maxL)).addi(1);
             combinedFetSeg.get(NDArrayIndex.point(2), NDArrayIndex.interval(sent1L, sent1L + sent2L)).addi(1);
             assertArrayEquals(combinedFetSeg.shape(), pairMDS.getFeatures(1).shape());
             assertEquals(maxL, combinedFetSeg.shape()[1]);
             assertEquals(combinedFetSeg, pairMDS.getFeatures(1));
+
+            testPairHelper.getPairSentenceProvider().reset();
         }
     }
 
     @Test
     public void testSentencePairFeaturizer() throws IOException {
-        String shortSent = "I saw a girl with a telescope.";
+        int minibatchSize = 2;
-        String longSent = "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum";
+        TestSentencePairsHelper testPairHelper = new TestSentencePairsHelper(minibatchSize);
-        List<Pair<String, String>> listSentencePair = new ArrayList<>();
-        listSentencePair.add(new Pair<>(shortSent, longSent));
-        listSentencePair.add(new Pair<>(longSent, shortSent));
-        BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
         BertIterator b = BertIterator.builder()
-                .tokenizer(t)
+                .tokenizer(testPairHelper.getTokenizer())
-                .minibatchSize(2)
+                .minibatchSize(minibatchSize)
                 .padMinibatches(true)
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
-                .vocabMap(t.getVocab())
+                .vocabMap(testPairHelper.getTokenizer().getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION)
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, 128)
-                .sentencePairProvider(new TestSentencePairProvider())
+                .sentencePairProvider(testPairHelper.getPairSentenceProvider())
                 .prependToken("[CLS]")
                 .appendToken("[SEP]")
                 .build();
@@ -494,23 +471,19 @@ public class TestBertIterator extends BaseDL4JTest {
         INDArray[] featuresArr = mds.getFeatures();
         INDArray[] featuresMaskArr = mds.getFeaturesMaskArrays();
 
-        Pair<INDArray[], INDArray[]> p = b.featurizeSentencePairs(listSentencePair);
+        Pair<INDArray[], INDArray[]> p = b.featurizeSentencePairs(testPairHelper.getSentencePairs());
         assertEquals(p.getFirst().length, 2);
         assertEquals(featuresArr[0], p.getFirst()[0]);
         assertEquals(featuresArr[1], p.getFirst()[1]);
-        //assertEquals(p.getSecond().length, 2);
         assertEquals(featuresMaskArr[0], p.getSecond()[0]);
-        //assertEquals(featuresMaskArr[1], p.getSecond()[1]);
     }
 
     /**
-     * Returns three multidatasets from bert iterator based on given max lengths and whether to prepend/append
+     * Returns three multidatasets (one from pair of sentences and the other two from single sentence lists) from bert iterator
+     * with given max lengths and whether to prepend/append
      * Idea is the sentence pair dataset can be constructed from the single sentence datasets
-     * First one is constructed from a sentence pair "I saw a girl with a telescope." & "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum"
-     * Second one is constructed from the left of the sentence pair i.e "I saw a girl with a telescope."
-     * Third one is constructed from the right of the sentence pair i.e "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum"
      */
-    private Triple<MultiDataSet, MultiDataSet, MultiDataSet> generateMultiDataSets(Triple<Integer, Integer, Integer> maxLengths, boolean prependAppend) throws IOException {
+    private Triple<MultiDataSet, MultiDataSet, MultiDataSet> generateMultiDataSets(Triple<Integer, Integer, Integer> maxLengths, boolean prependAppend, int numSentences) throws IOException {
         BertWordPieceTokenizerFactory t = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
         int maxforPair = maxLengths.getFirst();
         int maxPartOne = maxLengths.getSecond();
@@ -518,133 +491,155 @@ public class TestBertIterator extends BaseDL4JTest {
         BertIterator.Builder commonBuilder;
         commonBuilder = BertIterator.builder()
                 .tokenizer(t)
-                .minibatchSize(1)
+                .minibatchSize(4)
                 .featureArrays(BertIterator.FeatureArrays.INDICES_MASK_SEGMENTID)
                 .vocabMap(t.getVocab())
                 .task(BertIterator.Task.SEQ_CLASSIFICATION);
-        BertIterator shortLongPairFirstIter = commonBuilder
+        BertIterator pairIter = commonBuilder
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxforPair + 3 : maxforPair)
-                .sentencePairProvider(new TestSentencePairProvider())
+                .sentencePairProvider(new TestSentencePairsHelper(numSentences).getPairSentenceProvider())
                 .prependToken(prependAppend ? "[CLS]" : null)
                 .appendToken(prependAppend ? "[SEP]" : null)
                 .build();
-        BertIterator shortFirstIter = commonBuilder
+        BertIterator leftIter = commonBuilder
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxPartOne + 2 : maxPartOne)
-                .sentenceProvider(new TestSentenceProvider())
+                .sentenceProvider(new TestSentenceHelper(numSentences).getSentenceProvider())
                 .prependToken(prependAppend ? "[CLS]" : null)
                 .appendToken(prependAppend ? "[SEP]" : null)
                 .build();
-        BertIterator longFirstIter = commonBuilder
+        BertIterator rightIter = commonBuilder
                 .lengthHandling(BertIterator.LengthHandling.FIXED_LENGTH, prependAppend ? maxPartTwo + 1 : maxPartTwo)
-                .sentenceProvider(new TestSentenceProvider(true))
+                .sentenceProvider(new TestSentenceHelper(true, numSentences).getSentenceProvider())
                 .prependToken(null)
                 .appendToken(prependAppend ? "[SEP]" : null)
                 .build();
-        return new Triple<>(shortLongPairFirstIter.next(), shortFirstIter.next(), longFirstIter.next());
+        return new Triple<>(pairIter.next(), leftIter.next(), rightIter.next());
     }
 
-    private static class TestSentenceProvider implements LabeledSentenceProvider {
+    @Getter
+    private static class TestSentencePairsHelper {
 
-        private int pos = 0;
+        private List<String> sentencesLeft;
-        private boolean invert;
+        private List<String> sentencesRight;
+        private List<Pair<String, String>> sentencePairs;
+        private List<List<String>> tokenizedSentencesLeft;
+        private List<List<String>> tokenizedSentencesRight;
+        private List<String> labels;
+        private int shortL;
+        private int longL;
+        private int sentenceALen;
+        private int sentenceBLen;
+        private BertWordPieceTokenizerFactory tokenizer;
+        private CollectionLabeledPairSentenceProvider pairSentenceProvider;
 
-        private TestSentenceProvider() {
+        private TestSentencePairsHelper() throws IOException {
-            this.invert = false;
+            this(3);
         }
 
-        private TestSentenceProvider(boolean invert) {
+        private TestSentencePairsHelper(int minibatchSize) throws IOException {
-            this.invert = invert;
+            sentencesLeft = new ArrayList<>();
+            sentencesRight = new ArrayList<>();
+            sentencePairs = new ArrayList<>();
+            labels = new ArrayList<>();
+            tokenizedSentencesLeft = new ArrayList<>();
+            tokenizedSentencesRight = new ArrayList<>();
+            tokenizer = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
+            sentencesLeft.add(shortSentence);
+            sentencesRight.add(longSentence);
+            sentencePairs.add(new Pair<>(shortSentence, longSentence));
+            labels.add("positive");
+            if (minibatchSize > 1) {
+                sentencesLeft.add(longSentence);
+                sentencesRight.add(shortSentence);
+                sentencePairs.add(new Pair<>(longSentence, shortSentence));
+                labels.add("negative");
+                if (minibatchSize > 2) {
+                    sentencesLeft.add(sentenceA);
+                    sentencesRight.add(sentenceB);
+                    sentencePairs.add(new Pair<>(sentenceA, sentenceB));
+                    labels.add("positive");
+                }
+            }
+            for (int i = 0; i < minibatchSize; i++) {
+                List<String> tokensL = tokenizer.create(sentencesLeft.get(i)).getTokens();
+                List<String> tokensR = tokenizer.create(sentencesRight.get(i)).getTokens();
+                if (i == 0) {
+                    shortL = tokensL.size();
+                    longL = tokensR.size();
+                }
+                if (i == 2) {
+                    sentenceALen = tokensL.size();
+                    sentenceBLen = tokensR.size();
+                }
+                tokenizedSentencesLeft.add(tokensL);
+                tokenizedSentencesRight.add(tokensR);
+            }
+            pairSentenceProvider = new CollectionLabeledPairSentenceProvider(sentencesLeft, sentencesRight, labels, null);
+        }
     }
 
-        @Override
+    @Getter
-        public boolean hasNext() {
+    private static class TestSentenceHelper {
-            return pos < totalNumSentences();
+
+        private List<String> sentences;
+        private List<List<String>> tokenizedSentences;
+        private List<String> labels;
+        private int shortestL = 0;
+        private int longestL = 0;
+        private BertWordPieceTokenizerFactory tokenizer;
+        private CollectionLabeledSentenceProvider sentenceProvider;
+
+        private TestSentenceHelper() throws IOException {
+            this(false, 2);
         }
 
-        @Override
+        private TestSentenceHelper(int minibatchSize) throws IOException {
-        public Pair<String, String> nextSentence() {
+            this(false, minibatchSize);
-            Preconditions.checkState(hasNext());
+        }
-            if (pos == 0) {
+
-                pos++;
+        private TestSentenceHelper(boolean alternateOrder) throws IOException {
-                if (!invert) return new Pair<>("I saw a girl with a telescope.", "positive");
+            this(false, 3);
-                return new Pair<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "negative");
+        }
+
+        private TestSentenceHelper(boolean alternateOrder, int minibatchSize) throws IOException {
+            sentences = new ArrayList<>();
+            labels = new ArrayList<>();
+            tokenizedSentences = new ArrayList<>();
+            tokenizer = new BertWordPieceTokenizerFactory(pathToVocab, false, false, c);
+            if (!alternateOrder) {
+                sentences.add(shortSentence);
+                labels.add("positive");
+                if (minibatchSize > 1) {
+                    sentences.add(longSentence);
+                    labels.add("negative");
+                    if (minibatchSize > 2) {
+                        sentences.add(sentenceA);
+                        labels.add("positive");
+                    }
+                }
             } else {
-                if (pos == 1) {
+                sentences.add(longSentence);
-                    pos++;
+                labels.add("negative");
-                    if (!invert) return new Pair<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "negative");
+                if (minibatchSize > 1) {
-                    return new Pair<>("I saw a girl with a telescope.", "positive");
+                    sentences.add(shortSentence);
-                }
+                    labels.add("positive");
-                pos++;
+                    if (minibatchSize > 2) {
-                if (!invert)
+                        sentences.add(sentenceB);
-                    return new Pair<>("Goodnight noises everywhere", "positive");
+                        labels.add("positive");
-                return new Pair<>("Goodnight moon", "positive");
                     }
                 }
-
-        @Override
-        public void reset() {
-            pos = 0;
             }
-
+            for (int i = 0; i < sentences.size(); i++) {
-        @Override
+                List<String> tokenizedSentence = tokenizer.create(sentences.get(i)).getTokens();
-        public int totalNumSentences() {
+                if (i == 0)
-            return 3;
+                    shortestL = tokenizedSentence.size();
+                if (tokenizedSentence.size() > longestL)
+                    longestL = tokenizedSentence.size();
+                if (tokenizedSentence.size() < shortestL)
+                    shortestL = tokenizedSentence.size();
+                tokenizedSentences.add(tokenizedSentence);
             }
-
+            sentenceProvider = new CollectionLabeledSentenceProvider(sentences, labels, null);
-        @Override
-        public List<String> allLabels() {
-            return Arrays.asList("positive", "negative");
-        }
-
-        @Override
-        public int numLabelClasses() {
-            return 2;
-        }
-    }
-
-    private static class TestSentencePairProvider implements LabeledPairSentenceProvider {
-
-        private int pos = 0;
-
-        @Override
-        public boolean hasNext() {
-            return pos < totalNumSentences();
-        }
-
-        @Override
-        public Triple<String, String, String> nextSentencePair() {
-            Preconditions.checkState(hasNext());
-            if (pos == 0) {
-                pos++;
-                return new Triple<>("I saw a girl with a telescope.", "Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "positive");
-            } else {
-                if (pos == 1) {
-                    pos++;
-                    return new Triple<>("Donaudampfschifffahrts Kapitänsmützeninnenfuttersaum", "I saw a girl with a telescope.", "negative");
-                }
-                pos++;
-                return new Triple<>("Goodnight noises everywhere", "Goodnight moon", "positive");
-            }
-        }
-
-        @Override
-        public void reset() {
-            pos = 0;
-        }
-
-        @Override
-        public int totalNumSentences() {
-            return 3;
-        }
-
-        @Override
-        public List<String> allLabels() {
-            return Arrays.asList("positive", "negative");
-        }
-
-        @Override
-        public int numLabelClasses() {
-            return 2;
         }
     }
 

deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-vertx/src/main/java/org/deeplearning4j/ui/VertxUIServer.java

@@ -254,6 +254,9 @@ public class VertxUIServer extends AbstractVerticle implements UIServer {
         uiEventRoutingThread = new Thread(new StatsEventRouterRunnable());
         uiEventRoutingThread.setDaemon(true);
         uiEventRoutingThread.start();
+
+        String address = UIServer.getInstance().getAddress();
+        log.info("Deeplearning4j UI server started at: {}", address);
     }
 
     private List<String> extractArgsFromRoute(String path, RoutingContext rc) {
@@ -317,7 +320,7 @@ public class VertxUIServer extends AbstractVerticle implements UIServer {
 
     @Override
     public String getAddress() {
-        return "https://localhost:" + server.actualPort();
+        return "http://localhost:" + server.actualPort();
     }
 
     @Override
@@ -421,7 +424,7 @@ public class VertxUIServer extends AbstractVerticle implements UIServer {
         }
 
         private void runHelper() throws Exception {
-            log.info("VertxUIServer.StatsEventRouterRunnable started");
+            log.trace("VertxUIServer.StatsEventRouterRunnable started");
             //Idea: collect all event stats, and route them to the appropriate modules
             while (!shutdown.get()) {
 

libnd4j/blas/NDArray.h

@@ -1256,6 +1256,9 @@ namespace nd4j {
         FORCEINLINE T& t(const Nd4jLong i, const Nd4jLong j);
         template<typename T>
         FORCEINLINE T& t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k);
+        template<typename T>
+        FORCEINLINE T& t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong w);
+
 
         /**
         *  returns array element with given index
@@ -1268,6 +1271,8 @@ namespace nd4j {
         FORCEINLINE T t(const Nd4jLong i, const Nd4jLong j) const;
         template<typename T>
         FORCEINLINE T t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) const;
+        template<typename T>
+        FORCEINLINE T t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong w) const;
 
 
         /**
@@ -1711,7 +1716,7 @@ namespace nd4j {
         if (isEmpty())
             return false;
 
-        return shape::isMatrix(this->_shapeInfo);
+        return 0 != shape::isMatrix(this->_shapeInfo);
     }
 
     //////////////////////////////////////////////////////////////////////////
@@ -1751,7 +1756,7 @@ namespace nd4j {
 
     //////////////////////////////////////////////////////////////////////////
     bool NDArray::isScalar() const {
-        return shape::isScalar(this->_shapeInfo);
+        return 0 != shape::isScalar(this->_shapeInfo);
     }
 
 //////////////////////////////////////////////////////////////////////////
@@ -2082,7 +2087,7 @@ template <typename T>
 T& NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) {
 
     if (rankOf() != 3 || i >= sizeAt(0) || j >= sizeAt(1) || k >= sizeAt(2))
-        throw std::invalid_argument("NDArray::t(i,j,k): one of input indexes is out of array length or rank!=2 !");
+        throw std::invalid_argument("NDArray::t(i,j,k): one of input indexes is out of array length or rank!=3!");
     if (DataTypeUtils::fromT<T>() != _dataType)
         throw std::invalid_argument("NDArray::t(i,j,k): type of array is not equal to template type T!");
 
@@ -2095,6 +2100,23 @@ T& NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) {
     return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
 }
 
+template <typename T>
+T& NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong w) {
+
+    if (rankOf() != 4 || i >= sizeAt(0) || j >= sizeAt(1) || k >= sizeAt(2), w >= sizeAt(3))
+        throw std::invalid_argument("NDArray::t(i,j,k,w): one of input indexes is out of array length or rank!=4 !");
+    if (DataTypeUtils::fromT<T>() != _dataType)
+        throw std::invalid_argument("NDArray::t(i,j,k,w): type of array is not equal to template type T!");
+
+    if(!isActualOnHostSide())
+        syncToHost();
+
+    Nd4jLong coords[4] = {i, j, k, w};
+    auto offset = shape::getOffset(getShapeInfo(), coords);
+    tickWriteHost();
+    return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
+}
+
 ////////////////////////////////////////////////////////////////////////
 template <typename T>
 T NDArray::t(const Nd4jLong i) const {
@@ -2133,7 +2155,7 @@ T NDArray::t(const Nd4jLong i, const Nd4jLong j) const {
     T NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k) const {
 
         if (rankOf() != 3 || i >= sizeAt(0) || j >= sizeAt(1) || k >= sizeAt(2))
-            throw std::invalid_argument("NDArray::t(i,j,k): one of input indexes is out of array length or rank!=2 !");
+            throw std::invalid_argument("NDArray::t(i,j,k): one of input indexes is out of array length or rank!=3!");
         if (DataTypeUtils::fromT<T>() != _dataType)
             throw std::invalid_argument("NDArray::t(i,j,k): type of array is not equal to template type T!");
 
@@ -2146,6 +2168,23 @@ T NDArray::t(const Nd4jLong i, const Nd4jLong j) const {
         return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
     }
 
+    template <typename T>
+    T NDArray::t(const Nd4jLong i, const Nd4jLong j, const Nd4jLong k, const Nd4jLong w) const {
+
+        if (rankOf() != 4 || i >= sizeAt(0) || j >= sizeAt(1) || k >= sizeAt(2) || w >= sizeAt(3))
+            throw std::invalid_argument("NDArray::t(i,j,k,w): one of input indexes is out of array length or rank!=4!");
+        if (DataTypeUtils::fromT<T>() != _dataType)
+            throw std::invalid_argument("NDArray::t(i,j,k,w): type of array is not equal to template type T!");
+
+        if(!isActualOnHostSide())
+            syncToHost();
+
+        Nd4jLong coords[4] = {i, j, k, w};
+        auto offset = shape::getOffset(getShapeInfo(), coords);
+        tickReadHost();
+        return *(reinterpret_cast<T*>(bufferWithOffset(offset)));
+    }
+
 #ifndef __JAVACPP_HACK__
 ////////////////////////////////////////////////////////////////////////
 std::shared_ptr<DataBuffer> NDArray::getDataBuffer() const {

libnd4j/blas/NDArray.hpp

@@ -2348,7 +2348,7 @@ NDArray NDArray::operator-(const NDArray& other) const {
         NDArray result(getShapeInfo(), DataTypeUtils::pickPairwiseResultType(getShapeInfo(), other.getShapeInfo()), false, getContext());
 
         NDArray::prepareSpecialUse({&result}, {this, &other});
-        NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Subtract, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), getSpecialShapeInfo(), nullptr);
+        NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Subtract, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), nullptr);
         NDArray::registerSpecialUse({&result}, {this, &other});
 
         return result;
@@ -2394,7 +2394,7 @@ NDArray NDArray::operator/(const NDArray& other) const {
         NDArray result(getShapeInfo(), DataTypeUtils::pickPairwiseResultType(getShapeInfo(), other.getShapeInfo()), false, getContext());
 
         NDArray::prepareSpecialUse({&result}, {this, &other});
-        NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Divide, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), getSpecialShapeInfo(), nullptr);
+        NativeOpExecutioner::execPairwiseTransform(getContext(), nd4j::pairwise::Divide, getBuffer(), getShapeInfo(), getSpecialBuffer(), getSpecialShapeInfo(), other.getBuffer(), other.getShapeInfo(), other.getSpecialBuffer(), other.getSpecialShapeInfo(), result.buffer(), result.getShapeInfo(), result.specialBuffer(), result.getSpecialShapeInfo(), nullptr);
         NDArray::registerSpecialUse({&result}, {this, &other});
 
         return result;

libnd4j/include/ops/declarable/generic/nn/pooling/maxpool_with_argmax.cpp

@@ -46,7 +46,7 @@ namespace nd4j {
             getOpDescriptor()
                     ->setAllowedInputTypes(nd4j::DataType::ANY)
                     ->setAllowedOutputTypes(0, DataType::INHERIT)
-                    ->setAllowedOutputTypes(1, DataType::INT64);
+                    ->setAllowedOutputTypes(1, {ALL_INTS});
 
         }
 

libnd4j/include/ops/declarable/generic/parity_ops/resize_bicubic.cpp

@@ -35,6 +35,8 @@ namespace nd4j {
             int width;
             int height;
             auto inRank = image->rankOf();
+            if (output->isEmpty()) return Status::OK();
+
             REQUIRE_TRUE(inRank == 3 || inRank == 4, 0, "resize_bicubic: Source tensor should have rank 4, but %i given.", inRank);
             REQUIRE_TRUE(output->rankOf() == inRank, 0, "resize_bicubic: Source tensor and output should have the same rank, but  %i and %i given.", inRank, output->rankOf());
             REQUIRE_TRUE(size->rankOf() == 1, size->lengthOf() == 2, 0, "resize_bicubic: Resize params is a pair of values, not %i.", size->lengthOf());
@@ -57,7 +59,7 @@ namespace nd4j {
                 if (block.numB()> 1)
                     halfPixelAlign = block.getBArguments()->at(1);
             }
-            REQUIRE_TRUE(halfPixelAlign == false || halfPixelAlign == true && alignCorners == false, 0, "resize_bicubic: half pixel align can be used only with non-aligned corners");
+            REQUIRE_TRUE(!halfPixelAlign || (halfPixelAlign && !alignCorners), 0, "resize_bicubic: `half_pixel_centers' should be false or true only when `align_corners' is false");
 
             auto source = inRank == 4?image->reshape(image->ordering(), {image->sizeAt(0), image->sizeAt(1), image->sizeAt(2), image->sizeAt(3)}):image->reshape(image->ordering(), {1, image->sizeAt(0), image->sizeAt(1), image->sizeAt(2)});
             auto target = inRank == 4?output->reshape(output->ordering(), {output->sizeAt(0), output->sizeAt(1), output->sizeAt(2), output->sizeAt(3)}):output->reshape(output->ordering(), {1, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2)});

libnd4j/include/ops/declarable/generic/parity_ops/resize_linear.cpp

@@ -32,8 +32,10 @@ namespace nd4j {
             NDArray* output = OUTPUT_VARIABLE(0);
             int width;
             int height;
-            bool center = false; // - default value
+            bool alignCorners = false; // - default value
             auto inRank = image->rankOf();
+            if (output->isEmpty()) return Status::OK();
+
             REQUIRE_TRUE( inRank == 4 || inRank == 3, 0, "resize_bilinear: input image should be 4D "
                                                                           "tensor, but input has rank %i",
                                                                           image->rankOf());
@@ -46,21 +48,25 @@ namespace nd4j {
                 auto newImageSize = INPUT_VARIABLE(1);
                 REQUIRE_TRUE(newImageSize->lengthOf() == 2, 0, "resize_bilinear: Resize params is a pair of values, not %i.", newImageSize->lengthOf());
                 REQUIRE_TRUE(block.numI() <= 1, 0, "resize_bilinear: Resize params already given by the second param. Int params are expensive.");
-                width = newImageSize->e<int>(0);
+                height = newImageSize->e<int>(0);
-                height = newImageSize->e<int>(1);
+                width = newImageSize->e<int>(1);
-                if (block.numI() == 1) {
-                    center = 0 != INT_ARG(0);
-                }
             }
             else {
-                REQUIRE_TRUE(block.numI() <= 3, 0, "resize_bilinear: Neither resize width nor height are provided.");
+                REQUIRE_TRUE(block.numI() > 1, 0, "resize_bilinear: Neither resize width nor height are provided.");
-                width = INT_ARG(0);
+                height = INT_ARG(0);
-                height = INT_ARG(1);
+                width = INT_ARG(1);
-                if (block.numI() == 3)
-                    center = 0 != INT_ARG(2);
             }
 
-            return helpers::resizeBilinearFunctor(block.launchContext(), inRank==4?image:&source, width, height, center, inRank == 4?output:&target);
+            if (block.numB() > 0)
+                alignCorners = B_ARG(0);
+            bool halfPixelCenter = false;
+
+            if (block.numB() > 1)
+                halfPixelCenter = B_ARG(1);
+
+            REQUIRE_TRUE(!halfPixelCenter || (halfPixelCenter && !alignCorners), 0, "resize_bilinear: `half_pixel_centers' should be false or true only when `align_corners' is false");
+
+            return helpers::resizeBilinearFunctor(block.launchContext(), inRank==4?image:&source, width, height, alignCorners, halfPixelCenter, inRank == 4 ? output : &target);
         }
 
         DECLARE_SHAPE_FN(resize_bilinear) {
@@ -83,7 +89,7 @@ namespace nd4j {
                 height = newImageSize->e<int>(1);
             }
             else {
-                REQUIRE_TRUE(block.numI() <= 3, 0, "resize_bilinear: Neither resize width nor height are provided.");
+                REQUIRE_TRUE(block.numI() == 2, 0, "resize_bilinear: Neither resize width nor height are provided.");
                 width = INT_ARG(0);
                 height = INT_ARG(1);
             }
@@ -101,7 +107,12 @@ namespace nd4j {
                 outputShape[2] = height;
                 outputShape[3] = in[3];
             }
+            if (DataTypeUtils::isR(ArrayOptions::dataType(in))) {
                 ShapeUtils::updateStridesAndType(outputShape, in, shape::order(in));
+            }
+            else {
+                ShapeUtils::updateStridesAndType(outputShape, DataType::FLOAT32, shape::order(in));
+            }
 
             shapeList->push_back(CONSTANT(outputShape));
             return shapeList;

libnd4j/include/ops/declarable/generic/parity_ops/resize_neighbor.cpp

@@ -31,35 +31,40 @@ namespace nd4j {
 
             auto image = INPUT_VARIABLE(0);
             auto output = OUTPUT_VARIABLE(0);
+            auto inRank = image->rankOf();
             int width;
             int height;
-            bool center = false; // - default value
+            bool alignCorners = false; // - default value
+            if (output->isEmpty()) return Status::OK();
             if (block.width() > 1) {
                 auto newImageSize = INPUT_VARIABLE(1);
                 REQUIRE_TRUE(newImageSize->lengthOf() == 2, 0, "resize_nearest_neighbor: Resize params is a pair of values, not %i.", newImageSize->lengthOf());
                 REQUIRE_TRUE(block.numI() <= 1, 0, "resize_nearest_neighbor: Resize params already given by the second param. Int params are expensive.");
-                width = newImageSize->e<int>(0);
+                height = newImageSize->e<int>(0);
-                height = newImageSize->e<int>(1);
+                width = newImageSize->e<int>(1);
-                if (block.numI() == 1) {
-                    center = 0 != INT_ARG(0);
-                }
             }
             else {
-                REQUIRE_TRUE(block.numI() <= 3, 0, "resize_nearest_neighbor: Neither resize width nor height are provided.");
+                REQUIRE_TRUE(block.numI() == 2, 0, "resize_nearest_neighbor: Neither resize width nor height are provided.");
-                width = INT_ARG(0);
+                height = INT_ARG(0);
-                height = INT_ARG(1);
+                width = INT_ARG(1);
-                if (block.numI() == 3)
-                    center = 0 != INT_ARG(2);
             }
-            auto inRank = image->rankOf();
+            if (block.numB() > 0)
+                alignCorners = B_ARG(0);
+            bool halfPixelCenter = false;
+
+            if (block.numB() > 1)
+                halfPixelCenter = B_ARG(1);
+            REQUIRE_TRUE(width <= (1 << 24) || height <= (1 << 24), 0, "resize_nearest_neighbour: the image resize should be limited to 2^24 pixels both for height and width, but %d and %d were given.", height, width);
             REQUIRE_TRUE(inRank == 4 || inRank == 3, 0, "resize_nearest_neighbor: Input should be 4D tensor, but rank %i occured");
             REQUIRE_TRUE(inRank == output->rankOf(), 0, "resize_nearest_neighbor: Input and output ranks should be equals, but %i and %i occured.", inRank, output->rankOf());
             REQUIRE_TRUE(image->dataType() == output->dataType(), 0, "resize_nearest_neighbor: Input and output types should be the same, but `%s' occured instead.", DataTypeUtils::asString(output->dataType()).c_str());
-            auto source = inRank == 4?*image:image->reshape(image->ordering(), {1, image->sizeAt(0), image->sizeAt(1), image->sizeAt(2)});
+            REQUIRE_TRUE(!halfPixelCenter || (halfPixelCenter && !alignCorners), 0, "resize_nearest_neighbor: `half_pixel_centers' should be false or true only when `align_corners' is false");
+            REQUIRE_TRUE(((alignCorners && height > 2) || (height > 0)) && ((alignCorners && width > 1) || (width > 0)), 0,  "resize_nearest_neighbor: Wrong input or output size to resize (width = %d, height = %d)", width, height);
 
+            auto source = inRank == 4?*image:image->reshape(image->ordering(), {1, image->sizeAt(0), image->sizeAt(1), image->sizeAt(2)});
             auto target = inRank == 4?*output:output->reshape(output->ordering(), {1, output->sizeAt(0), output->sizeAt(1), output->sizeAt(2)});
 
-            return helpers::resizeNeighborFunctor(block.launchContext(), inRank==4?image:&source, width, height, center, inRank == 4?output:&target);
+            return helpers::resizeNeighborFunctor(block.launchContext(), inRank==4?image:&source, width, height, alignCorners, halfPixelCenter, inRank == 4 ? output : &target);
         }
 
         DECLARE_SHAPE_FN(resize_nearest_neighbor) {

libnd4j/include/ops/declarable/helpers/cpu/image_resize.cpp

@@ -120,6 +120,27 @@ namespace helpers {
         }
     };
 
+    // Half pixel scaler scales assuming that the pixel centers are at 0.5, i.e. the
+// floating point coordinates of the top,left pixel is 0.5,0.5.
+    struct HalfPixelScalerNN {
+        HalfPixelScalerNN(){};
+        inline float operator()(const int x, const float scale) const {
+            // Note that we subtract 0.5 from the return value, as the existing bilinear
+            // sampling code etc assumes pixels are in the old coordinate system.
+            return (static_cast<float>(x) + 0.5f) * scale;
+        }
+    };
+
+// Older incorrect scaling method that causes all resizes to have a slight
+// translation leading to inconsistent results. For example, a flip then a
+// resize gives different results then a resize then a flip.
+    struct LegacyScaler {
+        LegacyScaler(){};
+        inline float operator()(const int x, const float scale) const {
+            return static_cast<float>(x) * scale;
+        }
+    };
+
     struct WeightsAndIndices {
         float _weight0;
         float _weight1;
@@ -133,7 +154,8 @@ namespace helpers {
         int _advance;  // advance value.
     };
 
-    inline void computeInterpolationWeights(Nd4jLong outSize,
+    template <class Scaler>
+    inline void computeInterpolationWeights(const Scaler scaler, Nd4jLong outSize,
                                             Nd4jLong inSize,
                                             double scale,
                                             BilinearInterpolationData *interpolationData) {
@@ -143,10 +165,12 @@ namespace helpers {
         auto func = PRAGMA_THREADS_FOR {
        	    for (auto k = start; k < stop; k++) {
                 auto i = (outSize - k - 1);
-                double in =  i * scale;
+                double  const in =  scaler(i, scale);
-                interpolationData[i]._bottomIndex = static_cast<Nd4jLong>(in);
+                double const in_f = nd4j::math::nd4j_floor<double, double>(in);
-                interpolationData[i]._topIndex = nd4j::math::nd4j_min(interpolationData[i]._bottomIndex + 1, inSize - 1);
+                double const in_c = nd4j::math::nd4j_ceil<double, double>(in);
-                interpolationData[i]._interpolarValue = in - interpolationData[i]._bottomIndex;
+                interpolationData[i]._bottomIndex = nd4j::math::nd4j_max(static_cast<Nd4jLong>(in_f), (Nd4jLong)0LL);//static_cast<Nd4jLong>(in);
+                interpolationData[i]._topIndex = nd4j::math::nd4j_min(static_cast<Nd4jLong>(in_c), inSize - 1);
+                interpolationData[i]._interpolarValue = in - in_f;
      	    }
 	    };
 	    samediff::Threads::parallel_for(func, 0, outSize);
@@ -156,29 +180,29 @@ namespace helpers {
  * Computes the bilinear interpolation from the appropriate 4 float points
  * and the linear interpolation weights.
  */
-    static void
+//    static void
-    resizeImage(NDArray const *images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
+//    resizeImage(NDArray const *images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
-                Nd4jLong outWidth, Nd4jLong channels,
+//                Nd4jLong outWidth, Nd4jLong channels,
-                std::vector<BilinearInterpolationData> const& xs,
+//                std::vector<BilinearInterpolationData> const& xs,
-                std::vector<BilinearInterpolationData> const& ys,
+//                std::vector<BilinearInterpolationData> const& ys,
-                NDArray *output);
+//                NDArray *output);
 
-    template<typename T>
+    template<typename T, typename Z>
     static void
-    resizeImage_(NDArray const *images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
+    resizeImage_(T const* pInputBuf, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
                  Nd4jLong outWidth, Nd4jLong channels,
                  std::vector<BilinearInterpolationData> const &xs,
                  std::vector<BilinearInterpolationData> const &ys,
-                 NDArray *output) {
+                 Z* pOutputBuf) {
 
         Nd4jLong inRowSize = inWidth * channels;
         Nd4jLong inBatchNumValues = inHeight * inRowSize;
         Nd4jLong outRowSize = outWidth * channels;
 
-        T const *pInputBuf = images->getDataBuffer()->primaryAsT<T>(); // this works only with 'c' direction
+//        T const *pInputBuf = images->getDataBuffer()->primaryAsT<T>(); // this works only with 'c' direction
         BilinearInterpolationData const* xsPtr = xs.data();
 
-        T* pOutputBuf = output->dataBuffer()->primaryAsT<T>();
+//        T* pOutputBuf = output->dataBuffer()->primaryAsT<T>();
         auto computeBilinear = [](double topLeft, double topRight,
                                       double bottomLeft, double bottomRight,
                                       double xVal, double yVal) {
@@ -214,8 +238,12 @@ namespace helpers {
         samediff::Threads::parallel_tad(func, 0, batchSize);
     }
 
-    template<typename T>
+    template<typename X, typename Z>
-    static int resizeBilinearFunctor_(NDArray const *images, int width, int height, bool center, NDArray *output) {
+    static int resizeBilinearFunctor_(NDArray const *images, int const width, int const height, bool const alignCorners,
+            bool const halfPixelCenter, NDArray *output) {
+        ImageResizerState st(alignCorners, halfPixelCenter);
+        st.validateAndCalculateOutputSize(images, width, height);
+
         const Nd4jLong batchSize = images->sizeAt(0);
         const Nd4jLong inHeight = images->sizeAt(1);
         const Nd4jLong inWidth = images->sizeAt(2);
@@ -230,28 +258,20 @@ namespace helpers {
             return ND4J_STATUS_OK;
         }
 
-        // Special case for TF compatibility
-        if((center && inHeight < 2) || (center && inWidth < 2)){
-            center = false;
-        }
-
-        if ((center && inHeight < 2) || (inHeight < 1) || (outHeight < 1) || (center && outHeight < 2) ||
-            (center && inWidth < 2) || (inWidth < 1) || (outWidth < 1) || (center && outWidth < 2)) {
-            // wrong input data
-            nd4j_printf("image.resize_bilinear: Wrong input or output size to resize\n", "");
-            return ND4J_STATUS_BAD_ARGUMENTS;
-        }
-        float heightScale = center ? (inHeight - 1.f) / double(outHeight - 1.f) : (inHeight / float(outHeight));
-        float widthScale = center ? (inWidth - 1.f) / double(outWidth - 1.f) : (inWidth / float(outWidth));
-
         std::vector<BilinearInterpolationData> ys(outHeight + 1);
         std::vector<BilinearInterpolationData> xs(outWidth + 1);
-
+        if (halfPixelCenter) {
-        // Compute the cached interpolation weights on the x and y dimensions.
+            computeInterpolationWeights(HalfPixelScaler(), outHeight, inHeight, st.heightScale,
-        computeInterpolationWeights(outHeight, inHeight, heightScale,
                                         ys.data());
-        computeInterpolationWeights(outWidth, inWidth, widthScale, xs.data());
+            computeInterpolationWeights(HalfPixelScaler(), outWidth, inWidth, st.widthScale, xs.data());
 
+        }
+        else {
+            // Compute the cached interpolation weights on the x and y dimensions.
+            computeInterpolationWeights(LegacyScaler(), outHeight, inHeight, st.heightScale,
+                                        ys.data());
+            computeInterpolationWeights(LegacyScaler(), outWidth, inWidth, st.widthScale, xs.data());
+        }
         int xsSize = xs.size();
         // Scale x interpolation weights to avoid a multiplication during iteration.
         auto func = PRAGMA_THREADS_FOR {
@@ -262,71 +282,84 @@ namespace helpers {
         };
         samediff::Threads::parallel_for(func, 0, xsSize);
 
-        resizeImage(images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs, ys, output);
+        resizeImage_<X,Z>(images->getDataBuffer()->primaryAsT<X>(), batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs, ys, output->dataBuffer()->primaryAsT<Z>());
         return ND4J_STATUS_OK;
     }
 
-    template<typename T>
+    template <class Scaler, typename T>
-    int resizeNeighborFunctor_(NDArray const *images, int width, int height, bool center, NDArray *output) {
+    void resizeNeighbor(ImageResizerState const& st, NDArray const *images, bool const alignCorners, bool const halfPixelCenter, NDArray *output) {
-        const Nd4jLong batchSize = images->sizeAt(0);
+        const Nd4jLong batchSize = st.batchSize;
-        const Nd4jLong inHeight = images->sizeAt(1);
+        const Nd4jLong inHeight = st.inHeight;
-        const Nd4jLong inWidth = images->sizeAt(2);
+        const Nd4jLong inWidth = st.inWidth;
-        const Nd4jLong channels = images->sizeAt(3);
+        const Nd4jLong channels = st.channels;
 
-        const Nd4jLong outHeight = output->sizeAt(1);
+        const Nd4jLong outHeight = st.outHeight;
-        const Nd4jLong outWidth = output->sizeAt(2);
+        const Nd4jLong outWidth = st.outWidth;
-
+        Scaler scaler;
-        // Handle no-op resizes efficiently.
-        if (outHeight == inHeight && outWidth == inWidth) {
-            output->assign(images);
-            return Status::OK();
-        }
-
-        if ((center && inHeight < 2) || (inHeight < 1) || (outHeight < 1) || (center && outHeight < 2) ||
-            (center && inWidth < 2) || (inWidth < 1) || (outWidth < 1) || (center && outWidth < 2)) {
-            // wrong input data
-            nd4j_printf("image.resize_nearest_neighbor: Wrong input or output size to resize\n", "");
-            return ND4J_STATUS_BAD_ARGUMENTS;
-        }
-        double heightScale = center ? (inHeight - 1.) / double(outHeight - 1.0) : (inHeight / double(outHeight));
-        double widthScale = center ? (inWidth - 1.) / double(outWidth - 1.0) : (inWidth / double(outWidth));
 
         auto func = PRAGMA_THREADS_FOR_2D {
             for (auto b = start_x; b < stop_x; b += inc_x) {
                 for (auto y = start_y; y < stop_y; y += inc_y) {
-                    Nd4jLong inY = nd4j::math::nd4j_min((center) ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(y * heightScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(y * heightScale)), inHeight - 1);
+                    auto posY = alignCorners ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(scaler(y, st.heightScale))) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(scaler(y, st.heightScale)));
-
+                    Nd4jLong inY = nd4j::math::nd4j_min(posY, inHeight - 1);
+                    if (halfPixelCenter) {
+                        inY = nd4j::math::nd4j_max(0LL, inY);
+                    }
                     for (auto x = 0; x < outWidth; ++x) {
-                        Nd4jLong inX = nd4j::math::nd4j_min((center) ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(x * widthScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(x * widthScale)),inWidth - 1);
+                        auto posX = alignCorners ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(scaler(x, st.widthScale))) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(scaler(x, st.widthScale)));
+                        Nd4jLong inX = nd4j::math::nd4j_min(posX,inWidth - 1);
+                        if (halfPixelCenter) {
+                            inX = nd4j::math::nd4j_max(0LL, inX);
+                        }
+                        // copy pixel over all channels
                         for (auto e = 0; e < channels; e++)
-                            output->p(b, y, x, e, images->e<T>(b, inY, inX, e));
+                            output->t<T>(b, y, x, e) = images->t<T>(b, inY, inX, e);
                     }
                 }
             }
         };
         samediff::Threads::parallel_for(func, 0, batchSize, 1, 0, outHeight, 1);
+    }
+
+    template<typename T>
+    int resizeNeighborFunctor_(NDArray const *images, int const width, int const height, bool const alignCorners, bool const halfPixelCenter, NDArray *output) {
+        ImageResizerState st(alignCorners, halfPixelCenter);
+        st.validateAndCalculateOutputSize(images, width, height);
+
+        // Handle no-op resizes efficiently.
+        if (output->sizeAt(1) == images->sizeAt(1) && output->sizeAt(2) == images->sizeAt(2)) {
+            output->assign(images);
+            return Status::OK();
+        }
+
+        if (halfPixelCenter)
+            resizeNeighbor<HalfPixelScalerNN, T>(st, images, alignCorners, true, output);
+        else
+            resizeNeighbor<LegacyScaler, T>(st, images, alignCorners, false, output);
 
         return Status::OK();
     }
 
-    void resizeImage(NDArray const *images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
+//    void resizeImage(NDArray const *images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
-                     Nd4jLong outWidth, Nd4jLong channels,
+//                     Nd4jLong outWidth, Nd4jLong channels,
-                     std::vector<BilinearInterpolationData> const &xs,
+//                     std::vector<BilinearInterpolationData> const &xs,
-                     std::vector<BilinearInterpolationData> const &ys,
+//                     std::vector<BilinearInterpolationData> const &ys,
-                     NDArray *output) {
+//                     NDArray *output) {
-        BUILD_SINGLE_SELECTOR(images->dataType(), resizeImage_,
+//        BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), resizeImage_,
-                              (images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs, ys, output),
+//                              (images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs, ys, output),
-                              LIBND4J_TYPES);
+//                              NUMERIC_TYPES, FLOAT_TYPES);
+//    }
+
+    int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const *images, int const width, int const height,
+            bool const alignCorners, bool const halfPixelCenter, NDArray *output) {
+        BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), return resizeBilinearFunctor_,
+                              (images, width, height, alignCorners, halfPixelCenter, output), NUMERIC_TYPES, FLOAT_TYPES);
     }
 
-    int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const *images, int width, int height, bool center, NDArray *output) {
+    int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const *images, int const width, int const height,
-        BUILD_SINGLE_SELECTOR(images->dataType(), return resizeBilinearFunctor_,
+            bool const alignCorners,  bool const halfPixelCenter, NDArray *output) {
-                              (images, width, height, center, output), LIBND4J_TYPES);
-    }
-
-    int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const *images, int width, int height, bool center, NDArray *output) {
         BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_,
-                              (images, width, height, center, output), LIBND4J_TYPES);
+                              (images, width, height, alignCorners, halfPixelCenter, output), LIBND4J_TYPES);
     }
 
 
@@ -586,16 +619,6 @@ namespace helpers {
             }
         }
 
-// Older incorrect scaling method that causes all resizes to have a slight
-// translation leading to inconsistent results. For example, a flip then a
-// resize gives different results then a resize then a flip.
-        struct LegacyScaler {
-            LegacyScaler(){};
-            inline float operator()(const int x, const float scale) const {
-                return static_cast<float>(x) * scale;
-            }
-        };
-
         static void computeXWeightsAndIndices(const ImageResizerState& resizer_state,
                                               const bool half_pixel_centers,
                                               std::vector<WeightsAndIndices>* x_wais) {
@@ -847,7 +870,7 @@ namespace helpers {
 // simplified bicubic resize without antialiasing
 //
     template <typename T>
-    int resizeBicubicFunctorA_(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeBicubicFunctorA_(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
                               bool const alignCorners, bool const halfPixelAlign, NDArray* output) {
         ImageResizerState st(alignCorners, halfPixelAlign); // align_corners, half_pixel_align
         int res = st.validateAndCreateOutput(image, width, height);
@@ -856,17 +879,17 @@ namespace helpers {
 
         return res;
     }
-    int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
                               bool const alignCorners, bool const halfPixelAlign, NDArray* output) {
         BUILD_SINGLE_SELECTOR(image->dataType(), return resizeBicubicFunctorA_, (context,
                 image, width, height, alignCorners, halfPixelAlign, output), NUMERIC_TYPES);
     }
 // ------------------------------------------------------------------------------------------------------------------ //
-    int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
                       ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output) {
         switch (method) {
-            case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, output); break;
+            case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, false, output); break;
-            case kResizeNearest: return resizeNeighborFunctor(context, image, width, height, false, output); break;
+            case kResizeNearest: return resizeNeighborFunctor(context, image, width, height, false, false, output); break;
             case kResizeBicubic: return resizeBicubicFunctor(context, image, width, height, preserveAspectRatio, antialias, output); break;
             case kResizeLanczos5:
             case kResizeGaussian:

libnd4j/include/ops/declarable/helpers/cuda/image_resize.cu

@@ -13,6 +13,20 @@
  *
  * SPDX-License-Identifier: Apache-2.0
  ******************************************************************************/
+/* Copyright 2016 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
 
 //
 //  @author sgazeos@gmail.com
@@ -32,6 +46,38 @@ namespace helpers {
         // https://en.wikipedia.org/wiki/Bilinear_interpolation)
         double interpolarValue;
     };
+
+// Older incorrect scaling method that causes all resizes to have a slight
+// translation leading to inconsistent results. For example, a flip then a
+// resize gives different results then a resize then a flip.
+    struct LegacyScaler {
+        _CUDA_HD LegacyScaler(){};
+        inline _CUDA_HD float operator()(const int x, const float scale) const {
+            return static_cast<float>(x) * scale;
+        }
+    };
+
+// Half pixel scaler scales assuming that the pixel centers are at 0.5, i.e. the
+// floating point coordinates of the top,left pixel is 0.5,0.5.
+    struct HalfPixelScaler {
+        _CUDA_HD HalfPixelScaler(){};
+        inline _CUDA_HD float operator()(const int x, const float scale) const {
+            // Note that we subtract 0.5 from the return value, as the existing bilinear
+            // sampling code etc assumes pixels are in the old coordinate system.
+            return (static_cast<float>(x) + 0.5f) * scale - 0.5f;
+        }
+    };
+
+
+    // Utility functions
+    // calculateResizeScale determines the float scaling factor.
+    inline float calculateResizeScale(Nd4jLong inSize, Nd4jLong outSize,
+                                      bool alignCorners) {
+        return (alignCorners && outSize > 1)
+               ? (inSize - 1) / static_cast<float>(outSize - 1)
+               : inSize / static_cast<float>(outSize);
+    }
+
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // computeInterpolationWeights kernel
 //      outSize - output length
@@ -39,6 +85,7 @@ namespace helpers {
 //      scale - input scale
 //      interporationData - result
 //
+    template <class Scaler>
     static __global__ void computeInterpolationWeights(Nd4jLong outSize,
                                               Nd4jLong inSize,
                                               double scale,
@@ -48,12 +95,18 @@ namespace helpers {
         interpolationData[outSize].topIndex = 0;
         auto tid = blockIdx.x * blockDim.x + threadIdx.x;
         auto step = blockDim.x * gridDim.x;
-
+        Scaler scaler;
         for (Nd4jLong i = outSize - tid; i >= 0; i -= step) {
-            double in = i * scale;
+            double in = scaler(i, scale);
-            interpolationData[i].bottomIndex = static_cast<Nd4jLong>(in);
+//            interpolationData[i].bottomIndex = static_cast<Nd4jLong>(in);
-            interpolationData[i].topIndex = nd4j::math::nd4j_min(interpolationData[i].bottomIndex + 1, inSize - 1);
+//            interpolationData[i].topIndex = nd4j::math::nd4j_min(interpolationData[i].bottomIndex + 1, inSize - 1);
-            interpolationData[i].interpolarValue = in - interpolationData[i].bottomIndex;
+//            interpolationData[i].interpolarValue = in - interpolationData[i].bottomIndex;
+            double const in_f = nd4j::math::p_floor<double>(in);
+            double const in_c = nd4j::math::p_ceil<double>(in);
+            interpolationData[i].bottomIndex = nd4j::math::nd4j_max(static_cast<Nd4jLong>(in_f), (Nd4jLong)0LL);//static_cast<Nd4jLong>(in);
+            interpolationData[i].topIndex = nd4j::math::nd4j_min(static_cast<Nd4jLong>(in_c), inSize - 1);
+            interpolationData[i].interpolarValue = in - in_f;
+
             if (channels) {
                 math::atomics::nd4j_atomicMul(&interpolationData[i].bottomIndex, channels);
                 math::atomics::nd4j_atomicMul(&interpolationData[i].topIndex, channels);
@@ -72,9 +125,10 @@ namespace helpers {
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // resize image with bilinear interpolation algorithm kernel
 //
-    template <typename T>
+    template <typename T, typename Z>
-    static __global__ void resizeImageKernel(T const* input, Nd4jLong const* inputShape, T* outputYptr, Nd4jLong* outputShape, Nd4jLong batchSize,
+    static __global__ void resizeImageKernel(T const* input, Nd4jLong const* inputShape, Z* outputYptr,
-                                             Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, Nd4jLong inRowSize, Nd4jLong outRowSize, Nd4jLong inBatchNumValues,
+            Nd4jLong* outputShape, Nd4jLong batchSize, Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels,
+            Nd4jLong inRowSize, Nd4jLong outRowSize, Nd4jLong inBatchNumValues,
             BilinearInterpolationData* xs_, BilinearInterpolationData* ys_) {
 
         for (auto batch = blockIdx.x; batch < batchSize; batch += gridDim.x ) { // blockIdx.x as batch index
@@ -84,19 +138,20 @@ namespace helpers {
                 const T* ys_input_upper_ptr = pX + ys_[y].topIndex * inRowSize;
                 double yVal = ys_[y].interpolarValue;
                 auto pZ = outputYptr + (batch * outHeight + y) * outRowSize;
-                for (Nd4jLong x = threadIdx.y; x < outWidth; x += blockDim.y) {
+                for (Nd4jLong x = 0; x < outWidth; x++) {
                     auto xsBottom = xs_[x].bottomIndex;
                     auto xsTop = xs_[x].topIndex;
                     auto xVal = xs_[x].interpolarValue;
                     // process interpolation for all channels
-                    for (int c = threadIdx.z; c < channels; c += blockDim.z) {
+                    for (int c = 0; c < channels; c++) {
-                        double topLeft(ys_input_lower_ptr[xsBottom + c]);
+                        Z topLeft(ys_input_lower_ptr[xsBottom + c]);
-                        double topRight(ys_input_lower_ptr[xsTop + c]);
+                        Z topRight(ys_input_lower_ptr[xsTop + c]);
-                        double bottomLeft(ys_input_upper_ptr[xsBottom + c]);
+                        Z bottomLeft(ys_input_upper_ptr[xsBottom + c]);
-                        double bottomRight(ys_input_upper_ptr[xsTop + c]);
+                        Z bottomRight(ys_input_upper_ptr[xsTop + c]);
-                        double top = topLeft + (topRight - topLeft) * xVal;
+                        Z top = topLeft + (topRight - topLeft) * xVal;
-                        double bottom = bottomLeft + (bottomRight - bottomLeft) * xVal;
+                        Z bottom = bottomLeft + (bottomRight - bottomLeft) * xVal;
-                        pZ[x * channels + c] = T(top + (bottom - top) * yVal);
+                        Z resVal = Z(top + (bottom - top) * yVal);
+                        pZ[x * channels + c] = resVal;
                     }
                 }
             }
@@ -105,7 +160,7 @@ namespace helpers {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // resize image with
-    template <typename T>
+    template <typename T, typename F>
     static void resizeImage_(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight,
                      Nd4jLong outWidth, Nd4jLong channels,
                      BilinearInterpolationData* xs_,
@@ -115,12 +170,13 @@ namespace helpers {
         Nd4jLong inBatchNumValues = inHeight * inRowSize;
         Nd4jLong outRowSize = outWidth * channels;
         auto stream = context->getCudaStream();
-        T const *input_b_ptr = reinterpret_cast<T const *>(images->getSpecialBuffer()); // this works only with 'c' direction
+        T const* pInput = images->getDataBuffer()->specialAsT<T>(); //reinterpret_cast<T const *>(images->getSpecialBuffer()); // this works only with 'c' direction
-        T *output_y_ptr = reinterpret_cast<T *>(output->specialBuffer());
+        F* pOutput = output->dataBuffer()->specialAsT<F>();//reinterpret_cast<F *>(output->specialBuffer());
         dim3 batchSizeBlock(batchSize, 1, 1);
         dim3 pictureBlock(outHeight, outWidth, channels);
-        resizeImageKernel<T><<<256, pictureBlock, 256, *stream>>>(input_b_ptr, images->getSpecialShapeInfo(), output_y_ptr, output->specialShapeInfo(), batchSize,
+        resizeImageKernel<T,F><<<256, 256, 256, *stream>>>(pInput, images->getSpecialShapeInfo(), pOutput,
-                outWidth, outHeight, channels, inRowSize, outRowSize, inBatchNumValues, xs_, ys_);
+                output->specialShapeInfo(), batchSize, outWidth, outHeight, channels, inRowSize, outRowSize,
+                inBatchNumValues, xs_, ys_);
 
         auto err = cudaStreamSynchronize(*stream);
         if (err != 0) {
@@ -129,8 +185,9 @@ namespace helpers {
     }
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-        template <typename T>
+    template <typename T, typename F>
-    static int resizeBilinearFunctor_(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
+    static int resizeBilinearFunctor_(nd4j::LaunchContext* context, NDArray const* images, int const width,
+            int const height, bool const alignCorners, bool const halfPixelCenter, NDArray* output) {
         const Nd4jLong batchSize = images->sizeAt(0);
         const Nd4jLong inHeight = images->sizeAt(1);
         const Nd4jLong inWidth = images->sizeAt(2);
@@ -145,19 +202,8 @@ namespace helpers {
             return ND4J_STATUS_OK;
         }
 
-        // Special case for TF compatibility
+        float heightScale = calculateResizeScale(inHeight, outHeight, alignCorners);
-        if((center && inHeight < 2) || (center && inWidth < 2)){
+        float widthScale = calculateResizeScale(inWidth, outWidth, alignCorners);
-            center = false;
-        }
-
-        if ((center && inHeight < 2) || (inHeight < 1) || (outHeight < 1) || (center && outHeight < 2) ||
-            (center && inWidth < 2) || (inWidth < 1) || (outWidth < 1) || (center && outWidth < 2)) {
-            // wrong input data
-            nd4j_printf("image.resize_bilinear: Wrong input or output size to resize\n", "");
-            return ND4J_STATUS_BAD_ARGUMENTS;
-        }
-        float heightScale = center ? (inHeight - 1.f) / double(outHeight - 1.f) : (inHeight / float(outHeight));
-        float widthScale = center ? (inWidth - 1.f) / double(outWidth - 1.f) : (inWidth / float(outWidth));
 
         BilinearInterpolationData* xs_;// = xs.data();
         BilinearInterpolationData* ys_;// = xs.data();
@@ -173,12 +219,24 @@ namespace helpers {
         }
         auto stream = context->getCudaStream();
         // Compute the cached interpolation weights on the x and y dimensions.
-        computeInterpolationWeights<<<256, 512, 512, *stream>>>(outHeight, inHeight, heightScale, 0, ys_);
+        if (halfPixelCenter) {
-        computeInterpolationWeights<<<256, 512, 512, *stream>>>(outWidth, inWidth, widthScale, channels, xs_);
+            computeInterpolationWeights <
-
+            HalfPixelScaler ><<<256, 512, 512, *stream>>>(outHeight, inHeight, heightScale, 0, ys_);
+            computeInterpolationWeights <
+            HalfPixelScaler ><<<256, 512, 512, *stream>>>(outWidth, inWidth, widthScale, channels, xs_);
+        }
+        else {
+            computeInterpolationWeights <
+            LegacyScaler ><<<256, 512, 512, *stream>>>(outHeight, inHeight, heightScale, 0, ys_);
+            computeInterpolationWeights <
+            LegacyScaler ><<<256, 512, 512, *stream>>>(outWidth, inWidth, widthScale, channels, xs_);
+        }
+        printf("Input is %dx%d, Output is %dx%d\n", inHeight, inWidth, outHeight, outWidth);
         NDArray::prepareSpecialUse({output}, {images});
-        resizeImage(context, images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs_, ys_, output);
+        resizeImage_<T,F>(context, images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs_, ys_, output);
+        err = cudaStreamSynchronize(*stream);
         NDArray::registerSpecialUse({output}, {images});
+
         err = cudaFree(xs_);
         if (err != 0) {
             throw cuda_exception::build("helpers::resize_image: Cannot deallocate memory for vertical parts rectangulars", err);
@@ -197,20 +255,28 @@ namespace helpers {
 //
     template <typename T>
     static __global__ void resizeNeighborKernel(T const* input, Nd4jLong* inputShape, T* output, Nd4jLong* outputShape,
-            Nd4jLong batchSize, Nd4jLong inWidth, Nd4jLong inHeight, Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, double widthScale, double heightScale, bool center) {
+            Nd4jLong batchSize, Nd4jLong inWidth, Nd4jLong inHeight, Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, double widthScale, double heightScale, bool alignCorners, bool halfPixelCenters) {
 
         //for (int b = blockIdx.x; b < batchSize; b += gridDim.x)
         if (blockIdx.x < batchSize)
         {
             auto b = blockIdx.x;
             for (int y = threadIdx.x; y < outHeight; y += blockDim.x) {
-                Nd4jLong inY = nd4j::math::nd4j_min(
+                auto posY = alignCorners ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(halfPixelCenters?((float)y + 0.5f) * heightScale:(float)y * heightScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(
-                        (center) ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(y * heightScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(
+                        halfPixelCenters?((float)y + 0.5f) * heightScale:(float)y * heightScale));
-                                y * heightScale)), inHeight - 1);
+                Nd4jLong inY = nd4j::math::nd4j_min(posY, inHeight - 1);
+                if (halfPixelCenters) {
+                    inY = nd4j::math::nd4j_max(0LL, inY);
+                }
+
                 for (int x = threadIdx.y; x < outWidth; x += blockDim.y) {
-                    Nd4jLong inX = nd4j::math::nd4j_min(
+                    auto posX = alignCorners ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(halfPixelCenters?((float)x + 0.5f) * widthScale:(float)x * widthScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(
-                            (center) ? static_cast<Nd4jLong>(nd4j::math::p_round<float>(x * widthScale)) : static_cast<Nd4jLong>(nd4j::math::p_floor<float>(
+                            halfPixelCenters?((float)x + 0.5f) * widthScale:(float)x * widthScale));
-                                    x * widthScale)), inWidth - 1);
+                    Nd4jLong inX = nd4j::math::nd4j_min(posX, inWidth - 1);
+                    if (halfPixelCenters) {
+                        inX = nd4j::math::nd4j_max(0LL, inX);
+                    }
+
                     auto start = blockIdx.z * blockDim.z + threadIdx.z;
                     auto step = blockDim.z * gridDim.z;
 
@@ -231,7 +297,8 @@ namespace helpers {
 // resizeNeighborFunctor - main algorithm by nearest neighbor
 //
     template <typename T>
-    int resizeNeighborFunctor_(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
+    int resizeNeighborFunctor_(nd4j::LaunchContext* context, NDArray const* images, int const width, int const height,
+            bool const alignCorners, bool const halfPixelCenters, NDArray* output) {
         const Nd4jLong batchSize = images->sizeAt(0);
         const Nd4jLong inHeight = images->sizeAt(1);
         const Nd4jLong inWidth = images->sizeAt(2);
@@ -246,25 +313,24 @@ namespace helpers {
             return ND4J_STATUS_OK;
         }
 
-        if ((center && inHeight < 2) || (inHeight < 1) || (outHeight < 1) || (center && outHeight < 2) ||
+//        if ((alignCorners && inHeight < 2) || (inHeight < 1) || (outHeight < 1) || (alignCorners && outHeight < 2) ||
-            (center && inWidth < 2) || (inWidth < 1) || (outWidth < 1) || (center && outWidth < 2)) {
+//            (alignCorners && inWidth < 2) || (inWidth < 1) || (outWidth < 1) || (center && outWidth < 2)) {
-            // wrong input data
+//            // wrong input data
-            nd4j_printf("image.resize_nearest_neighbor: Wrong input or output size to resize\n", "");
+//            nd4j_printf("image.resize_nearest_neighbor: Wrong input or output size to resize\n", "");
-            return ND4J_STATUS_BAD_ARGUMENTS;
+//            return ND4J_STATUS_BAD_ARGUMENTS;
-        }
+//        }
-        double heightScale = center ? (inHeight - 1.) / double(outHeight - 1.0) : (inHeight / double(outHeight));
+//        float heightScale = alignCorners ? (inHeight - 1.f) / float(outHeight - 1.f) : (inHeight / float(outHeight));
-        double widthScale = center ? (inWidth - 1.) / double(outWidth - 1.0) : (inWidth / double(outWidth));
+//        float widthScale = alignCorners ? (inWidth - 1.f) / float(outWidth - 1.f) : (inWidth / float(outWidth));
-        auto imagesBuffer = reinterpret_cast<T const*>(images->getSpecialBuffer());
+        float heightScale = calculateResizeScale(inHeight, outHeight, alignCorners);
-        auto outputBuffer = reinterpret_cast<T*>(output->specialBuffer());
+        float widthScale = calculateResizeScale(inWidth, outWidth, alignCorners);
+
+        auto imagesBuffer = images->getDataBuffer()->specialAsT<T>();//reinterpret_cast<T const*>(images->getSpecialBuffer());
+        auto outputBuffer = output->dataBuffer()->specialAsT<T>();//reinterpret_cast<T*>(output->specialBuffer());
         auto stream = context->getCudaStream();
 
-        //T const* input, Nd4jLong const* inputShape, T* output, Nd4jLong* outputShape,
-        //            Nd4jLong batchSize, Nd4jLong inWidth, Nd4jLong inHeight, Nd4jLong outWidth, Nd4jLong outHeight, Nd4jLong channels, double widthScale, double heightScale, bool center
-        //input, inputShape, output, outputShape,
-        //            batchSize, inWidth, inHeight, outWidth, outHeight, channels, widthScale, heightScale, center
         NDArray::prepareSpecialUse({output}, {images});
         resizeNeighborKernel<T><<<batchSize, outHeight * outWidth, 512, *stream>>>(imagesBuffer, images->getSpecialShapeInfo(), outputBuffer, output->specialShapeInfo(),
-                batchSize, inWidth, inHeight, outWidth, outHeight, channels, widthScale, heightScale, center);
+                batchSize, inWidth, inHeight, outWidth, outHeight, channels, widthScale, heightScale, alignCorners, halfPixelCenters);
         NDArray::registerSpecialUse({output}, {images});
 
         return Status::OK();
@@ -275,39 +341,38 @@ namespace helpers {
     void resizeImage(nd4j::LaunchContext* context, NDArray const* images, Nd4jLong batchSize, Nd4jLong inHeight,
             Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth, Nd4jLong channels, BilinearInterpolationData* xs_,
             BilinearInterpolationData* ys_, NDArray* output) {
-        BUILD_SINGLE_SELECTOR(images->dataType(), resizeImage_, (context, images, batchSize, inHeight, inWidth, outHeight, outWidth, channels, xs_, ys_, output), LIBND4J_TYPES);
+        BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(),
+                resizeImage_, (context, images, batchSize, inHeight, inWidth, outHeight, outWidth, channels,
+                        xs_, ys_, output), NUMERIC_TYPES, FLOAT_TYPES);
     }
 
-    BUILD_SINGLE_TEMPLATE(template void resizeImage_,(nd4j::LaunchContext* context, NDArray const* images,
+    BUILD_DOUBLE_TEMPLATE(template void resizeImage_,(nd4j::LaunchContext* context, NDArray const* images,
             Nd4jLong batchSize, Nd4jLong inHeight, Nd4jLong inWidth, Nd4jLong outHeight, Nd4jLong outWidth,
-            Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, NDArray* output), LIBND4J_TYPES);
+            Nd4jLong channels, BilinearInterpolationData* xs_, BilinearInterpolationData* ys_, NDArray* output),
+            NUMERIC_TYPES, FLOAT_TYPES);
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-    int resizeBilinearFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
+    int resizeBilinearFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height,
-        BUILD_SINGLE_SELECTOR(images->dataType(), return resizeBilinearFunctor_, (context, images, width, height, center, output), LIBND4J_TYPES);
+            bool const alignCorners, bool const halfPixelCenter, NDArray* output) {
+        BUILD_DOUBLE_SELECTOR(images->dataType(), output->dataType(), return resizeBilinearFunctor_, (context, images,
+                width, height, alignCorners, halfPixelCenter, output), NUMERIC_TYPES, FLOAT_TYPES);
     }
-    BUILD_SINGLE_TEMPLATE(template int resizeBilinearFunctor_, (nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output), LIBND4J_TYPES);
+//    BUILD_SINGLE_TEMPLATE(template int resizeBilinearFunctor_, (nd4j::LaunchContext* context,
+//            NDArray const* images, int const width, int const height, bool const alignCorners,
+//            bool const halfPixelCenter, NDArray* output), LIBND4J_TYPES);
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-    int resizeNeighborFunctor(nd4j::LaunchContext* context, NDArray const* images, int width, int height, bool center, NDArray* output) {
+    int resizeNeighborFunctor(nd4j::LaunchContext* context, NDArray const* images, int const width, int const height,
-        BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_, (context, images, width, height, center, output), LIBND4J_TYPES);
+            bool const alignCorners, bool const halfPixelCenter, NDArray* output) {
+        BUILD_SINGLE_SELECTOR(images->dataType(), return resizeNeighborFunctor_,
+                (context, images, width, height, alignCorners, halfPixelCenter, output), LIBND4J_TYPES);
     }
-    BUILD_SINGLE_TEMPLATE(template int resizeNeighborFunctor_, (nd4j::LaunchContext* context, NDArray const* images,
+//    BUILD_SINGLE_TEMPLATE(template int resizeNeighborFunctor_, (nd4j::LaunchContext* context, NDArray const* images,
-            int width, int height, bool center, NDArray* output), LIBND4J_TYPES);
+//            int width, int height, bool const alignCorners, bool const halfPixelCenter, NDArray* output), LIBND4J_TYPES);
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
 // Bicubic interpolation
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-// Utility functions and classes
-
-    // calculateResizeScale determines the float scaling factor.
-    inline float calculateResizeScale(Nd4jLong inSize, Nd4jLong outSize,
-                                      bool alignCorners) {
-        return (alignCorners && outSize > 1)
-               ? (inSize - 1) / static_cast<float>(outSize - 1)
-               : inSize / static_cast<float>(outSize);
-    }
-
     struct ImageResizerState {
         explicit ImageResizerState(bool alignCorners, bool halfPixelCenters)
                 : _alignCorners(alignCorners),
@@ -362,17 +427,6 @@ namespace helpers {
         bool _halfPixelCenters;
     };
 
-    // Half pixel scaler scales assuming that the pixel centers are at 0.5, i.e. the
-// floating point coordinates of the top,left pixel is 0.5,0.5.
-    struct HalfPixelScaler {
-        _CUDA_HD HalfPixelScaler(){};
-        inline _CUDA_HD float operator()(const int x, const float scale) const {
-            // Note that we subtract 0.5 from the return value, as the existing bilinear
-            // sampling code etc assumes pixels are in the old coordinate system.
-            return (static_cast<float>(x) + 0.5f) * scale - 0.5f;
-        }
-    };
-
     struct WeightsAndIndices {
         float _weight0;
         float _weight1;
@@ -547,16 +601,6 @@ namespace helpers {
         }
     }
 
-// Older incorrect scaling method that causes all resizes to have a slight
-// translation leading to inconsistent results. For example, a flip then a
-// resize gives different results then a resize then a flip.
-    struct LegacyScaler {
-        _CUDA_HD LegacyScaler(){};
-        inline _CUDA_HD float operator()(const int x, const float scale) const {
-            return static_cast<float>(x) * scale;
-        }
-    };
-
     static __global__ void accumulateChannelsKernel(WeightsAndIndices* pXWais, Nd4jLong outWidth, Nd4jLong channels) {
         auto start = blockIdx.x * blockDim.x + threadIdx.x;
         auto step = blockDim.x * gridDim.x;
@@ -906,8 +950,8 @@ namespace helpers {
     int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
                       ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output) {
         switch (method) {
-            case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, output); break;
+            case kResizeBilinear: return resizeBilinearFunctor(context, image, width, height, false, false, output); break;
-            case kResizeNearest:  return resizeNeighborFunctor(context, image, width, height, true, output); break;
+            case kResizeNearest:  return resizeNeighborFunctor(context, image, width, height, false, false, output); break;
             case kResizeBicubic:  return resizeBicubicFunctor(context, image, width, height, preserveAspectRatio, antialias, output); break;
             case kResizeLanczos5:
             case kResizeGaussian:

libnd4j/include/ops/declarable/helpers/cuda/reverse.cu

@@ -30,6 +30,67 @@ namespace nd4j    {
 namespace ops     {
 namespace helpers {
 
+    template <typename T>
+    static __global__ void reverseTadKernel(void* vinput, Nd4jLong *inputShape, void* voutput, Nd4jLong *outputShape, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t limit, uint64_t numOfElemsToReverse, uint64_t numTads) {
+        auto input = reinterpret_cast<T*>(vinput);
+        auto output = reinterpret_cast<T*>(voutput);
+        const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+        const auto step = gridDim.x * blockDim.x;
+
+        // this means that we'll have additional cycle, to move middle element
+        auto div = numOfElemsToReverse / 2;
+        auto odd = numOfElemsToReverse % 2 != 0;
+        auto rlimit = odd ? limit / 2 + 1 : limit / 2;
+
+        // all threads operate in the same input/output space
+        for (uint64_t e = tid; e < rlimit; e += step) {
+            // finding out the TAD we're going to process
+            auto tadId = e / div;
+
+            if (tadId >= numTads)
+                continue;
+
+            // now finding out element within tad
+            auto idx = e % div;
+
+            //printf("TID: %i; numTads: %lld; tadLength: %lld; tadId: %i, idx: %lld\n", tid, numTads, numOfElemsToReverse, tadId, idx);
+
+            auto tadInput = input + inputTadOffsets[tadId];
+            auto tadOutput = output + outputTadOffsets[tadId];
+
+            // we're calculating offsets within input TAD
+            auto fOffset = shape::getIndexOffset(idx, inputTadShape);
+            auto lOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, inputTadShape);
+
+            // now we're storing input values
+            auto v1 = tadInput[fOffset];
+            auto v2 = tadInput[lOffset];
+
+            // now we're calculating offsets within output TAD
+            auto zfOffset = shape::getIndexOffset(idx, outputTadShape);
+            auto zlOffset = shape::getIndexOffset(numOfElemsToReverse - idx - 1, outputTadShape);
+
+            // and saving values to output arrays
+            tadOutput[zfOffset] = v2;
+            tadOutput[zlOffset] = v1;
+        }
+
+        // moving odd element in blocks
+        if (odd && threadIdx.x == 0) {
+            for (uint64_t e = blockIdx.x; e < numTads; e += gridDim.x) {
+                auto tadInput = input + inputTadOffsets[e];
+                auto tadOutput = output + outputTadOffsets[e];
+
+                auto xOffset = shape::getIndexOffset(numOfElemsToReverse / 2, inputTadShape);
+                auto zOffset = shape::getIndexOffset(numOfElemsToReverse / 2, outputTadShape);
+
+                tadOutput[zOffset] = tadInput[xOffset];
+            }
+        }
+
+    }
+
+
     template <typename T>
     static __global__ void reverseArrayKernel(void* input, Nd4jLong *inputShape, void* output, Nd4jLong *outputShape, Nd4jLong numOfElemsToReverse) {
         const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
@@ -52,7 +113,7 @@ namespace helpers {
         auto odd = numOfElemsToReverse % 2 != 0;
         auto limit = numOfElemsToReverse / 2;
 
-        for (Nd4jLong e = tid; e < limit; e += step) {
+        for (uint64_t e = tid; e < limit; e += step) {
             // we're calculating offsets within input array
             auto fOffset = shape::getIndexOffset(e, inputShape);
             auto lOffset = shape::getIndexOffset(numOfElemsToReverse - e - 1, inputShape);
@@ -80,13 +141,19 @@ namespace helpers {
     }
 
     template<typename T>
-    static void reverseArray(nd4j::LaunchContext * context, NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) {
+    static void reverseTad(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong *inputTadShape, Nd4jLong *inputTadOffsets, Nd4jLong *outputTadShape, Nd4jLong *outputTadOffsets, uint64_t tadLength) {
+        auto stream = context->getCudaStream();
+        reverseTadKernel<T><<<256, 512, 8192, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), inputTadShape, inputTadOffsets, outputTadShape, outputTadOffsets, input->lengthOf(), tadLength, input->lengthOf() / tadLength);
+    }
+
+    template<typename T>
+    static void reverseArray(nd4j::LaunchContext * context, const NDArray* input, NDArray* output, Nd4jLong numOfElemsToReverse) {
         auto stream = context->getCudaStream();
         Nd4jLong numOfReverse = numOfElemsToReverse;
         if (numOfElemsToReverse == 0)
             numOfReverse = input->lengthOf();
 
-        reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->specialBuffer(), input->specialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfReverse);
+        reverseArrayKernel<T><<<256, 512, 8192, *stream>>>(input->getSpecialBuffer(), input->getSpecialShapeInfo(), output->specialBuffer(), output->specialShapeInfo(), numOfReverse);
     }
 
 
@@ -153,27 +220,23 @@ namespace helpers {
         // we need to reverse axis only if that's new op
         std::vector<int> dimensions = isBackProp ? ShapeUtils::evalDimsToExclude(input->rankOf(), *intArgs) : *intArgs;
         std::vector<int> axis = ShapeUtils::evalDimsToExclude(input->rankOf(), dimensions);
-        auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), axis);
+        auto packX = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(input->getShapeInfo(), dimensions);
-        auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), axis);
+        auto packZ = nd4j::ConstantTadHelper::getInstance()->tadForDimensions(output->getShapeInfo(), dimensions);
 
-        auto listOut = output->allTensorsAlongDimension(dimensions);
-        auto listIn  = input->allTensorsAlongDimension(dimensions);
 
-        NDArray *subArrIn, *subArrOut;
 
         NDArray::prepareSpecialUse({output}, {input});
-        for(int i = 0; i < listIn->size(); ++i) {               // listIn->size() = listOut->size()
+
-            subArrIn   = listIn->at(i);
+        if (packX.numberOfTads() == 1) {
-            subArrOut  = listOut->at(i);
+            BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, input, output, 0),  LIBND4J_TYPES);
-            BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, subArrIn, subArrOut, 0), LIBND4J_TYPES);
+        } else {
+            BUILD_SINGLE_SELECTOR(input->dataType(), reverseTad, (context, input, output, packX.platformShapeInfo(), packX.platformOffsets(), packZ.platformShapeInfo(), packZ.platformOffsets(), (uint64_t) (input->lengthOf() / packX.numberOfTads())),  LIBND4J_TYPES);
         }
-        //BUILD_SINGLE_SELECTOR(input->dataType(), reverseArray, (context, const_cast<NDArray*>(input), output, (int)0), LIBND4J_TYPES);
+
         NDArray::registerSpecialUse({output}, {input});
-        delete listOut;
-        delete listIn;
     }
 
-BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES);
+BUILD_SINGLE_TEMPLATE(template void reverseArray, (nd4j::LaunchContext * context, const NDArray *inArr, NDArray *outArr, Nd4jLong numOfElemsToReverse), LIBND4J_TYPES);
 
 }
 }

libnd4j/include/ops/declarable/helpers/image_resize.h

@@ -37,15 +37,15 @@ namespace helpers {
         kResizeArea
     };
 
-    int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height, bool center,
+    int resizeBilinearFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
-            NDArray* output);
+            bool const alignCorners, bool const halfPixelCenter, NDArray* output);
-    int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height, bool center,
+    int resizeNeighborFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
-            NDArray* output);
+            bool const alignCorners, bool const halfPixelCenter, NDArray* output);
-    int resizeBicubicFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeBicubicFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
                       bool preserveAspectRatio, bool antialias, NDArray* output);
-    int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeBicubicFunctorA(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
                              bool const alignCorners, bool const halfPixelAlign, NDArray* output);
-    int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int width, int height,
+    int resizeFunctor(nd4j::LaunchContext * context, NDArray const* image, int const width, int const height,
             ImageResizeMethods method, bool preserveAspectRatio, bool antialias, NDArray* output);
 
     void cropAndResizeFunctor(nd4j::LaunchContext * context, NDArray const* images, NDArray const* boxes,

libnd4j/tests_cpu/layers_tests/ConvolutionTests2.cpp

@@ -212,12 +212,12 @@ TEST_F(ConvolutionTests2, Test_Dilation2D_Again_2) {
 }
 
 TYPED_TEST(TypedConvolutionTests2, sconv2d_bp_1) {
-    TypeParam _expGradWpB[] = {1603.7102981f,  10645.6278024f,   5975.4227995f,  17697.0903052f,    12133.6353024f,  26535.0528052f,   1779.221097f,   11795.5686029f,    6721.9835994f,  19904.0811062f,  13775.2461029f,  30123.0936062f,    1954.7318976f,  12945.5094033f,   7468.5443993f,  22111.071907f,    15416.8569033f,  33711.134407f,    2130.2426974f,  14095.4502038f,    8215.1051992f,  24318.0627081f,  17058.4677038f,  37299.1752081f,    2305.7534972f,  15245.3910042f,   8961.6659991f,  26525.0535091f,    18700.0785042f,  40887.2160091f,   2481.2642970f,  16395.3318047f,    9708.2267991f,  28732.0443100f,  20341.6893047f,  44475.2568100f,    2656.7750968f,  17545.2726051f,  10454.7875990f,  30939.0351110f,    21983.3001051f,  48063.2976110f,   2832.2858966f,  18695.2134056f,    11201.3483989f,  33146.0259119f,  23624.9109056f,  51651.3384119f,    3007.7966964f,  19845.1542060f,  11947.9091988f,  35353.0167129f,    25266.5217060f,  55239.3792129f,   3183.3074962f,  20995.095006f,    12694.4699987f,  37560.007513f,   26908.132506f,   58827.4200139};
+    TypeParam _expGradWpB[] = {1603.7102981f,  10645.6278024f,   5975.4227995f,  17697.0903052f,    12133.6353024f,  26535.0528052f,   1779.221097f,   11795.5686029f,    6721.9835994f,  19904.0811062f,  13775.2461029f,  30123.0936062f,    1954.7318976f,  12945.5094033f,   7468.5443993f,  22111.071907f,    15416.8569033f,  33711.134407f,    2130.2426974f,  14095.4502038f,    8215.1051992f,  24318.0627081f,  17058.4677038f,  37299.1752081f,    2305.7534972f,  15245.3910042f,   8961.6659991f,  26525.0535091f,    18700.0785042f,  40887.2160091f,   2481.2642970f,  16395.3318047f,    9708.2267991f,  28732.0443100f,  20341.6893047f,  44475.2568100f,    2656.7750968f,  17545.2726051f,  10454.7875990f,  30939.0351110f,    21983.3001051f,  48063.2976110f,   2832.2858966f,  18695.2134056f,    11201.3483989f,  33146.0259119f,  23624.9109056f,  51651.3384119f,    3007.7966964f,  19845.1542060f,  11947.9091988f,  35353.0167129f,    25266.5217060f,  55239.3792129f,   3183.3074962f,  20995.095006f,    12694.4699987f,  37560.007513f,   26908.132506f,   58827.4200139f};
     Nd4jLong _expGradWpS[] {4, 10, 6, 1, 1, 6, 1, 1, 1, typeid(TypeParam) == typeid(float) ? 8192 : 16384, 1, 99};
     NDArray expGWP(_expGradWpB, _expGradWpS);
     expGWP.permutei({2,3,1,0});
 
-    TypeParam _expGradWdB[] = {2074.21032f, 2082.76104f, 2091.31176f, 2099.86248f, 2108.4132f, 2159.71752f, 2168.26824f, 2176.81896f, 2185.36968f, 2193.9204f, 2245.22472f, 2253.77544f, 2262.32616f, 2270.87688f, 2279.4276f, 2330.73192f, 2339.28264f, 2347.83336f, 2356.38408f, 2364.9348f, 2416.23912f, 2424.78984f, 2433.34056f, 2441.89128f, 2450.442f, 3112.99344f, 3122.06328f, 3131.13312f, 3140.20296f, 3149.2728f, 3203.69184f, 3212.76168f, 3221.83152f, 3230.90136f, 3239.9712f, 3294.39024f, 3303.46008f, 3312.52992f, 3321.59976f, 3330.6696f, 3385.08864f, 3394.15848f, 3403.22832f, 3412.29816f, 3421.368f, 3475.78704f, 3484.85688f, 3493.92672f, 3502.99656f, 3512.0664f, 4255.60056f, 4265.18952f, 4274.77848f, 4284.36744f, 4293.9564f, 4351.49016f, 4361.07912f, 4370.66808f, 4380.25704f, 4389.846f, 4447.37976f, 4456.96872f, 4466.55768f, 4476.14664f, 4485.7356f, 4543.26936f, 4552.85832f, 4562.44728f, 4572.03624f, 4581.6252f, 4639.15896f, 4648.74792f, 4658.33688f, 4667.92584f, 4677.5148f, 2140.10988f, 2148.92016f, 2157.73044f, 2166.54072f, 2175.351f, 2228.21268f, 2237.02296f, 2245.83324f, 2254.64352f, 2263.4538f, 2316.31548f, 2325.12576f, 2333.93604f, 2342.74632f, 2351.5566f, 2404.41828f, 2413.22856f, 2422.03884f, 2430.84912f, 2439.6594f, 2492.52108f, 2501.33136f, 2510.14164f, 2518.95192f, 2527.7622f, 3204.849f, 3214.1784f, 3223.5078f, 3232.8372f, 3242.1666f, 3298.143f, 3307.4724f, 3316.8018f, 3326.1312f, 3335.4606f, 3391.437f, 3400.7664f, 3410.0958f, 3419.4252f, 3428.7546f, 3484.731f, 3494.0604f, 3503.3898f, 3512.7192f, 3522.0486f, 3578.025f, 3587.3544f, 3596.6838f, 3606.0132f, 3615.3426f, 4373.41212f, 4383.26064f, 4393.10916f, 4402.95768f, 4412.8062f, 4471.89732f, 4481.74584f, 4491.59436f, 4501.44288f, 4511.2914f, 4570.38252f, 4580.23104f, 4590.07956f, 4599.92808f, 4609.7766f, 4668.86772f, 4678.71624f, 4688.56476f, 4698.41328f, 4708.2618f, 4767.35292f, 4777.20144f, 4787.04996f, 4796.89848f, 4806.747};
+    TypeParam _expGradWdB[] = {2074.21032f, 2082.76104f, 2091.31176f, 2099.86248f, 2108.4132f, 2159.71752f, 2168.26824f, 2176.81896f, 2185.36968f, 2193.9204f, 2245.22472f, 2253.77544f, 2262.32616f, 2270.87688f, 2279.4276f, 2330.73192f, 2339.28264f, 2347.83336f, 2356.38408f, 2364.9348f, 2416.23912f, 2424.78984f, 2433.34056f, 2441.89128f, 2450.442f, 3112.99344f, 3122.06328f, 3131.13312f, 3140.20296f, 3149.2728f, 3203.69184f, 3212.76168f, 3221.83152f, 3230.90136f, 3239.9712f, 3294.39024f, 3303.46008f, 3312.52992f, 3321.59976f, 3330.6696f, 3385.08864f, 3394.15848f, 3403.22832f, 3412.29816f, 3421.368f, 3475.78704f, 3484.85688f, 3493.92672f, 3502.99656f, 3512.0664f, 4255.60056f, 4265.18952f, 4274.77848f, 4284.36744f, 4293.9564f, 4351.49016f, 4361.07912f, 4370.66808f, 4380.25704f, 4389.846f, 4447.37976f, 4456.96872f, 4466.55768f, 4476.14664f, 4485.7356f, 4543.26936f, 4552.85832f, 4562.44728f, 4572.03624f, 4581.6252f, 4639.15896f, 4648.74792f, 4658.33688f, 4667.92584f, 4677.5148f, 2140.10988f, 2148.92016f, 2157.73044f, 2166.54072f, 2175.351f, 2228.21268f, 2237.02296f, 2245.83324f, 2254.64352f, 2263.4538f, 2316.31548f, 2325.12576f, 2333.93604f, 2342.74632f, 2351.5566f, 2404.41828f, 2413.22856f, 2422.03884f, 2430.84912f, 2439.6594f, 2492.52108f, 2501.33136f, 2510.14164f, 2518.95192f, 2527.7622f, 3204.849f, 3214.1784f, 3223.5078f, 3232.8372f, 3242.1666f, 3298.143f, 3307.4724f, 3316.8018f, 3326.1312f, 3335.4606f, 3391.437f, 3400.7664f, 3410.0958f, 3419.4252f, 3428.7546f, 3484.731f, 3494.0604f, 3503.3898f, 3512.7192f, 3522.0486f, 3578.025f, 3587.3544f, 3596.6838f, 3606.0132f, 3615.3426f, 4373.41212f, 4383.26064f, 4393.10916f, 4402.95768f, 4412.8062f, 4471.89732f, 4481.74584f, 4491.59436f, 4501.44288f, 4511.2914f, 4570.38252f, 4580.23104f, 4590.07956f, 4599.92808f, 4609.7766f, 4668.86772f, 4678.71624f, 4688.56476f, 4698.41328f, 4708.2618f, 4767.35292f, 4777.20144f, 4787.04996f, 4796.89848f, 4806.747f};
     Nd4jLong _expGradWdS[] = {4, 2, 3, 5, 5, 75, 25, 5, 1, typeid(TypeParam) == typeid(float) ? 8192 : 16384, 1, 99};
     NDArray expGWD(_expGradWdB, _expGradWdS);
     expGWD.permutei({2,3,1,0});

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests1.cpp

@@ -1594,7 +1594,7 @@ TEST_F(DeclarableOpsTests1, TestGemv1) {
 
     auto z = NDArrayFactory::create_<float>('f', {5, 1});
 
-    auto expBuffer = new float[5]{28.00,64.00,100.00,136.00,172.00};
+    auto expBuffer = new float[5]{28.00f,64.00f,100.00f,136.00f,172.00f};
     auto exp = new NDArray(expBuffer, z->getShapeInfo());
 
      nd4j::blas::GEMV<float, float, float>::op('f',  x->rows(), x->columns(), 1.0f, x->getBuffer(), y->rows(), y->getBuffer(), 1, 0.0, z->getBuffer(), 1);
@@ -3523,7 +3523,8 @@ TEST_F(DeclarableOpsTests1, Reverse_7 ) {
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     auto result = results->at(0);
-    // result->printBuffer();
+    //expected.printIndexedBuffer("E");
+    //result->printIndexedBuffer("R");
 
     ASSERT_TRUE(expected.isSameShapeStrict(result));
     ASSERT_TRUE(expected.equalsTo(result));
@@ -3605,7 +3606,9 @@ TEST_F(DeclarableOpsTests1, Reverse_11 ) {
 
 
     auto input = NDArrayFactory::create<float>('c', {2,3,4});
-    auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {24., 23., 22., 21., 20., 19., 18., 17., 16., 15., 14., 13., 12., 11., 10., 9., 8., 7., 6., 5., 4., 3., 2., 1.});
+    auto expected = NDArrayFactory::create<float>('c', {2,3,4}, {24.f, 23.f, 22.f, 21.f, 20.f, 19.f, 18.f, 17.f, 16.f,
+                                                                 15.f, 14.f, 13.f, 12.f, 11.f, 10.f,  9.f,  8.f,  7.f,
+                                                                  6.f,  5.f,  4.f,  3.f,  2.f,  1.f});
 
     input.linspace(1);
     nd4j::ops::reverse op;

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests10.cpp

@@ -121,10 +121,10 @@ TEST_F(DeclarableOpsTests10, Test_Or_1) {
 }
 
 TEST_F(DeclarableOpsTests10, Test_Not_1) {
-    auto x = NDArrayFactory::create<bool>('c', {4}, {1, 1, 0, 1});
+    auto x = NDArrayFactory::create<bool>('c', {4}, {true, true, false, true});
-    auto y = NDArrayFactory::create<bool>('c', {4}, {0, 0, 0, 1});
+    auto y = NDArrayFactory::create<bool>('c', {4}, {false, false, false, true});
 //    auto e = NDArrayFactory::create<bool>('c', {4}, {1, 1, 1, 0});
-    auto e = NDArrayFactory::create<bool>('c', {4}, {0, 0, 1, 0});
+    auto e = NDArrayFactory::create<bool>('c', {4}, {false, false, true, false});
 
     nd4j::ops::boolean_not op;
     auto result = op.execute({&x, &y}, {}, {}, {}, false, nd4j::DataType::BOOL);
@@ -245,7 +245,8 @@ TEST_F(DeclarableOpsTests10, WhereNP_SGO_Test_1) {
 
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests10, WhereNP_SGO_Test_2) {
-    auto cond2d = NDArrayFactory::create<bool>('c', {3, 5}, {1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1});
+    auto cond2d = NDArrayFactory::create<bool>('c', {3, 5}, {true, true, false, false, true, true, true,
+                                                             true, true, true, false, true, true, true, true});
 //    auto expIdx({0, 1, 0, 2, 0, 3, 4, 1, 4, 1});
     auto exp1 = NDArrayFactory::create<Nd4jLong>({0, 0, 0, 1, 1, 1, 1, 1, 2, 2, 2, 2});
     auto exp2 = NDArrayFactory::create<Nd4jLong>({0, 1, 4, 0, 1, 2, 3, 4, 1, 2, 3, 4});
@@ -623,7 +624,7 @@ TEST_F(DeclarableOpsTests10, range_test11) {
 //////////////////////////////////////////////////////////////////////////////
 TEST_F(DeclarableOpsTests10, range_test12) {
 
-    auto exp = NDArrayFactory::create<float>('c', {9}, {0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5});
+    auto exp = NDArrayFactory::create<float>('c', {9}, {0.5f, 1.f , 1.5f, 2.f , 2.5f, 3.f , 3.5f, 4.f , 4.5f});
 
     nd4j::ops::range op;
     auto result = op.execute({}, {0.5, 5, 0.5}, {}, {});
@@ -1470,6 +1471,138 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1) {
     delete results;
 }
 
+////////////////////////////////////////////////////////////////////
+TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test_11) {
+
+    NDArray input    = NDArrayFactory::create<float>('c', {1, 1, 1, 256});
+
+    input.assign(0.8f); //linspace(1);
+    auto size = NDArrayFactory::create<int>({65,65});
+    auto ex = NDArrayFactory::create<float>('c', {1,65,65,256});
+    nd4j::ops::resize_bilinear op;
+    auto results = op.execute({&input, &size}, {}, {}, {false});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    NDArray* result = results->at(0);
+    ASSERT_NE(*result, ex);
+
+    delete results;
+}
+
+////////////////////////////////////////////////////////////////////
+TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test_12) {
+
+    NDArray input    = NDArrayFactory::create<float>('c', {1, 1, 1, 256});
+
+    input.assign(0.8f); //linspace(1);
+    auto size = NDArrayFactory::create<int>({65,65});
+    auto ex = NDArrayFactory::create<float>('c', {1,65,65,256});
+    nd4j::ops::resize_bilinear op;
+    auto results = op.execute({&input, &size}, {}, {}, {true});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    NDArray* result = results->at(0);
+    ASSERT_NE(*result, ex);
+
+    delete results;
+}
+
+TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_1) {
+
+    NDArray input    = NDArrayFactory::create<double>('c', {1, 2, 3, 4});
+    //NDArray<float> paddings('c', {3,2}, {0,0, 0,1, 0,0});
+    //NDArray<float> expected('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
+    NDArray expected = NDArrayFactory::create<double>('c', {1, 4, 5, 4}, {
+              1.,   2.,   3.,   4.,
+             2.6,  3.6,  4.6,  5.6,
+              5.,   6.,   7.,   8.,
+             7.4,  8.4,  9.4, 10.4,
+              9.,  10.,  11.,  12.,
+
+             4.,    5.,  6.,    7.,
+             5.6,  6.6,  7.6,  8.6,
+              8.,   9.,  10.,  11.,
+            10.4, 11.4, 12.4, 13.4,
+             12.,  13.,  14.,  15.,
+
+             10.,  11.,  12.,  13.,
+            11.6, 12.6, 13.6, 14.6,
+             14.,  15.,  16.,  17.,
+            16.4, 17.4, 18.4, 19.4,
+             18.,  19.,  20.,  21.,
+
+             13.,  14.,  15.,  16.,
+            14.6, 15.6, 16.6, 17.6,
+             17.,  18.,  19.,  20.,
+            19.4, 20.4, 21.4, 22.4,
+             21.,  22.,  23.,  24.
+    });
+    //input = 1.f;
+    input.linspace(1);
+
+    nd4j::ops::resize_bilinear op;
+    auto results = op.execute({&input}, {}, {4, 5}, {false, true});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    NDArray* result = results->at(0);
+
+//    result->printIndexedBuffer("Resized to 4x5 bilinear with half pixels");
+  //expected.printIndexedBuffer("Expect for 10x10");
+    ASSERT_TRUE(expected.isSameShape(result));
+    ASSERT_TRUE(expected.equalsTo(result));
+    delete results;
+}
+
+TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test1_2) {
+
+    NDArray input    = NDArrayFactory::create<int>('c', {1, 2, 3, 4});
+    //NDArray<float> paddings('c', {3,2}, {0,0, 0,1, 0,0});
+    //NDArray<float> expected('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
+    NDArray expected = NDArrayFactory::create<float>('c', {1, 4, 5, 4}, {
+            1.f,   2.f,   3.f,   4.f,
+           2.6f,  3.6f,  4.6f,  5.6f,
+            5.f,   6.f,   7.f,   8.f,
+           7.4f,  8.4f,  9.4f, 10.4f,
+            9.f,  10.f,  11.f,  12.f,
+
+            4.f,   5.f,  6.f,   7.f,
+           5.6f,  6.6f,  7.6f, 8.6f,
+            8.f,   9.f,  10.f, 11.f,
+          10.4f, 11.4f, 12.4f, 13.4f,
+           12.f,  13.f,  14.f,  15.f,
+
+            10.f,  11.f,  12.f,  13.f,
+           11.6f, 12.6f, 13.6f, 14.6f,
+            14.f,  15.f,  16.f,  17.f,
+           16.4f, 17.4f, 18.4f, 19.4f,
+            18.f,  19.f,  20.f,  21.f,
+
+            13.f,  14.f,  15.f,  16.f,
+           14.6f, 15.6f, 16.6f, 17.6f,
+            17.f,  18.f,  19.f,  20.f,
+           19.4f, 20.4f, 21.4f, 22.4f,
+            21.f,  22.f,  23.f,  24.f
+    });
+    //input = 1.f;
+    input.linspace(1);
+
+    nd4j::ops::resize_bilinear op;
+    auto results = op.execute({&input}, {}, {4, 5}, {false, true});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    NDArray* result = results->at(0);
+
+//    result->printBuffer("Resized to 4x5");
+//    expected.printBuffer("Expect for 4x5");
+    ASSERT_TRUE(expected.isSameShape(result));
+    ASSERT_TRUE(expected.equalsTo(result));
+    delete results;
+}
+
 TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test01) {
 
     NDArray input    = NDArrayFactory::create<double>('c', {2,3,4});
@@ -1857,7 +1990,7 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test3) {
     input.linspace(1);
 
     nd4j::ops::resize_bilinear op;
-    auto results = op.execute({&input}, {}, {10, 10, 1});
+    auto results = op.execute({&input}, {}, {10, 10}, {true});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
@@ -1986,7 +2119,7 @@ TEST_F(DeclarableOpsTests10, ImageResizeBilinear_Test4) {
     input.linspace(1);
 
     nd4j::ops::resize_bilinear op;
-    auto results = op.execute({&input, &size}, {}, {1});
+    auto results = op.execute({&input, &size}, {}, {}, {true});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
@@ -2023,7 +2156,56 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1) {
     NDArray input    = NDArrayFactory::create<double>('c', {1, 2, 3, 4});
     //NDArray<float> paddings('c', {3,2}, {0,0, 0,1, 0,0});
     //NDArray<float> expected('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
-    NDArray expected = NDArrayFactory::create<double>('c', {1, 4, 5, 4}, { 1,  2,  3,  4,
+    NDArray expected = NDArrayFactory::create<double>('c', {1, 4, 5, 4}, {
+     1,  2,  3,  4,
+     1,  2,  3,  4,
+     5,  6,  7,  8,
+     5,  6,  7,  8,
+     9, 10, 11, 12,
+
+     1,  2,  3,  4,
+     1,  2,  3,  4,
+     5,  6,  7,  8,
+     5,  6,  7,  8,
+     9, 10, 11, 12,
+
+    13, 14, 15, 16,
+    13, 14, 15, 16,
+    17, 18, 19, 20,
+    17, 18, 19, 20,
+    21, 22, 23, 24,
+
+    13, 14, 15, 16,
+    13, 14, 15, 16,
+    17, 18, 19, 20,
+    17, 18, 19, 20,
+    21, 22, 23, 24
+    });
+    //input = 1.f;
+    input.linspace(1);
+
+    nd4j::ops::resize_nearest_neighbor op;
+    auto results = op.execute({&input}, {}, {4, 5}, {false, false});
+
+    ASSERT_EQ(ND4J_STATUS_OK, results->status());
+
+    NDArray* result = results->at(0);
+
+//    result->printIndexedBuffer("Resized to 4x5");
+//    expected.printIndexedBuffer("Expect for 4x5");
+    ASSERT_TRUE(expected.isSameShape(result));
+    ASSERT_TRUE(expected.equalsTo(result));
+
+    delete results;
+}
+
+TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1) {
+
+    NDArray input    = NDArrayFactory::create<int>('c', {1, 2, 3, 4});
+    //NDArray<float> paddings('c', {3,2}, {0,0, 0,1, 0,0});
+    //NDArray<float> expected('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
+    NDArray expected = NDArrayFactory::create<int>('c', {1, 4, 5, 4}, {
+                                                                           1,  2,  3,  4,
                                                                            1,  2,  3,  4,
                                                                            5,  6,  7,  8,
                                                                            5,  6,  7,  8,
@@ -2065,47 +2247,48 @@ TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1) {
     delete results;
 }
 
-TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1) {
+TEST_F(DeclarableOpsTests10, ImageResizeNeighbor_Test1_1_1) {
 
-    NDArray input    = NDArrayFactory::create<int>('c', {1, 2, 3, 4});
+    NDArray input    = NDArrayFactory::create<float>('c', {1, 2, 3, 4});
     //NDArray<float> paddings('c', {3,2}, {0,0, 0,1, 0,0});
     //NDArray<float> expected('c', {2,4,4}, {1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.,0.});
-    NDArray expected = NDArrayFactory::create<int>('c', {1, 4, 5, 4}, { 1,  2,  3,  4,
+    NDArray expected = NDArrayFactory::create<float>('c', {1, 4, 5, 4}, {
-                                                                           1,  2,  3,  4,
+            1.f,  2.f,  3.f,  4.f,
-                                                                           5,  6,  7,  8,
+            1.f,  2.f,  3.f,  4.f,
-                                                                           5,  6,  7,  8,
+            5.f,  6.f,  7.f,  8.f,
-                                                                           9, 10, 11, 12,
+            9.f, 10.f, 11.f, 12.f,
+            9.f, 10.f, 11.f, 12.f,
 
-                                                                           1,  2,  3,  4,
+            1.f,  2.f,  3.f,  4.f,
-                                                                           1,  2,  3,  4,
+            1.f,  2.f,  3.f,  4.f,
-                                                                           5,  6,  7,  8,
+            5.f,  6.f,  7.f,  8.f,
-                                                                           5,  6,  7,  8,
+            9.f, 10.f, 11.f, 12.f,
-                                                                           9, 10, 11, 12,
+            9.f, 10.f, 11.f, 12.f,
 
-                                                                           13, 14, 15, 16,
+            13.f, 14.f, 15.f, 16.f,
-                                                                           13, 14, 15, 16,
+            13.f, 14.f, 15.f, 16.f,
-                                                                           17, 18, 19, 20,
+            17.f, 18.f, 19.f, 20.f,
-                                                                           17, 18, 19, 20,
+            21.f, 22.f, 23.f, 24.f,
-                                                                           21, 22, 23, 24,
+            21.f, 22.f, 23.f, 24.f,
 
-                                                                           13, 14, 15, 16,
+            13.f, 14.f, 15.f, 16.f,
-                                                                           13, 14, 15, 16,
+            13.f, 14.f, 15.f, 16.f,
-                                                                           17, 18, 19, 20,
+            17.f, 18.f, 19.f, 20.f,
-                                                                           17, 18, 19, 20,
+            21.f, 22.f, 23.f, 24.f,
-                                                                           21, 22, 23, 24
+            21.f, 22.f, 23.f, 24.f
     });
     //input = 1.f;
     input.linspace(1);
 
     nd4j::ops::resize_nearest_neighbor op;
-    auto results = op.execute({&input}, {}, {4, 5});
+    auto results = op.execute({&input}, {}, {4,5}, {false, true});
 
     ASSERT_EQ(ND4J_STATUS_OK, results->status());
 
     NDArray* result = results->at(0);
 
 //    result->printIndexedBuffer("Resized to 4x5");
-//    expected.printIndexedBuffer("Expect for 4x5");
+//    expected.printBuffer("Expect for 4x5");
     ASSERT_TRUE(expected.isSameShape(result));
     ASSERT_TRUE(expected.equalsTo(result));
 
@@ -2533,7 +2716,7 @@ TEST_F(DeclarableOpsTests10, Image_CropAndResize_3) {
     NDArray cropSize = NDArrayFactory::create<Nd4jLong>({3, 3});
 
     //NDArray<float> ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f});
-    NDArray expected('c', {1,3,3,1}, {1, 1.5f, 2., 2.f, 2.5f, 3.f, 3.f, 3.5f, 4.f}, nd4j::DataType::FLOAT32);
+    NDArray expected('c', {1,3,3,1}, {1.f, 1.5f, 2., 2.f, 2.5f, 3.f, 3.f, 3.5f, 4.f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::crop_and_resize op;
     auto results = op.execute({&images, &boxes, &boxI, &cropSize}, {}, {0});
@@ -2557,7 +2740,7 @@ TEST_F(DeclarableOpsTests10, Image_CropAndResize_4) {
     NDArray cropSize = NDArrayFactory::create<int>({3, 3});
 
     //NDArray<float> ('c', {6}, {0.9f, .75f, .6f, .95f, .5f, .3f});
-    NDArray expected('c', {1,3,3,1}, {1, 2.f, 2.f, 3.f, 4, 4.f, 3.f, 4.f, 4.f}, nd4j::DataType::FLOAT32);
+    NDArray expected('c', {1,3,3,1}, {1.f, 2.f, 2.f, 3.f, 4, 4.f, 3.f, 4.f, 4.f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::crop_and_resize op;
     auto results = op.execute({&images, &boxes, &boxI, &cropSize}, {}, {1});
@@ -2726,7 +2909,7 @@ TEST_F(DeclarableOpsTests10, Image_DrawBoundingBoxes_3) {
 TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_1) {
 
     NDArray x('c', {2,3}, {-63.80f, -63.75f, -63.70f, -63.5f, 0.0f, 0.1f}, nd4j::DataType::FLOAT32);
-    NDArray exp('c', {2,3},  {-63.75, -63.75, -63.75, -63.5, 0., 0.}, nd4j::DataType::FLOAT32);
+    NDArray exp('c', {2,3},  {-63.75f, -63.75f, -63.75f, -63.5f, 0.f, 0.f}, nd4j::DataType::FLOAT32);
     NDArray min('c', {},  {-63.65f}, nd4j::DataType::FLOAT32);
     NDArray max('c', {},  {0.1f}, nd4j::DataType::FLOAT32);
 
@@ -2971,22 +3154,6 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_5) {
     delete results;
 }
 
-/*    public void testFakeQuantAgainstTF_1() {
-        INDArray x = Nd4j.createFromArray(new float[]{ 0.7788f,0.8012f, 0.7244f, 0.2309f,0.7271f,
-                0.1804f,    0.5056f,    0.8925f,    0.5461f,    0.9234f,
-                0.0856f,    0.7938f,    0.6591f,    0.5555f,    0.1596f}).reshape(3,5);
-        INDArray min = Nd4j.createFromArray(new float[]{-0.2283f,   -0.0719f,   -0.0154f,   -0.5162f,   -0.3567f}).reshape(1,5);
-        INDArray max = Nd4j.createFromArray(new float[]{0.9441f,    0.5957f,    0.8669f,    0.3502f,    0.5100f}).reshape(1,5);
-
-        INDArray out = Nd4j.createUninitialized(x.shape());
-        val op = new FakeQuantWithMinMaxVarsPerChannel(x,min,max,out);
-
-        INDArray expected = Nd4j.createFromArray(new float[]{0.7801f,    0.5966f,    0.7260f,   0.2320f,    0.5084f,
-                0.1800f,    0.5046f,    0.8684f,    0.3513f,    0.5084f,
-                0.0877f,    0.5966f,    0.6600f,    0.3513f,    0.1604f}).reshape(3,5);
-
-        assertEquals(expected, out);
-    }*/
 TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_6) {
     NDArray x = NDArrayFactory::create<float>('c', {3, 5}, {0.7788f,0.8012f, 0.7244f, 0.2309f,0.7271f,
                                                             0.1804f,    0.5056f,    0.8925f,    0.5461f,    0.9234f,
@@ -3094,12 +3261,12 @@ TEST_F(DeclarableOpsTests10, FakeQuantWithMinMaxVars_Test_8) {
 TEST_F(DeclarableOpsTests10, batchnorm_test1) {
 
     NDArray input   ('c', {2,4}, nd4j::DataType::FLOAT32);
-    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32);
+    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32);
-    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1},  nd4j::DataType::FLOAT32);
+    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f,  1.1f},  nd4j::DataType::FLOAT32);
-    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32);
+    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32);
-    NDArray beta    ('c', {4}, {10, 20, -10, -20},     nd4j::DataType::FLOAT32);
+    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     nd4j::DataType::FLOAT32);
 
-    NDArray expected('c', {2,4}, {11.61218734,  18.52390321,  -8.67185076, -21.28716864, 10.93337162,  19.14541765, -9.26213931, -20.71509369}, nd4j::DataType::FLOAT32);
+    NDArray expected('c', {2,4}, {11.61218734f,  18.52390321f,  -8.67185076f, -21.28716864f, 10.93337162f,  19.14541765f, -9.26213931f, -20.71509369f}, nd4j::DataType::FLOAT32);
 
     input.linspace(0.1, 0.1);
 
@@ -3211,14 +3378,14 @@ TYPED_TEST(TypedDeclarableOpsTests10, batchnorm_test4) {
 TEST_F(DeclarableOpsTests10, batchnorm_test5) {
 
     NDArray input   ('c', {2,4,2,2}, nd4j::DataType::FLOAT32);
-    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32);
+    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32);
-    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1},  nd4j::DataType::FLOAT32);
+    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9f,  1.1f},  nd4j::DataType::FLOAT32);
-    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32);
+    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32);
-    NDArray beta    ('c', {4}, {10, 20, -10, -20},     nd4j::DataType::FLOAT32);
+    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     nd4j::DataType::FLOAT32);
 
-    NDArray expected('c', {2,4,2,2}, {11.612187,  11.442483, 11.272779,  11.103076, 18.990039,  19.145418, 19.300796,  19.456175, -9.557284,  -9.704856, -9.852428, -10., -20.,
+    NDArray expected('c', {2,4,2,2}, { 11.612187f,  11.442483f,  11.272779f,  11.103076f,  18.990039f,  19.145418f,  19.300796f,  19.456175f,  -9.557284f,  -9.704856f,  -9.852428f, -10.f, -20.f,
-                                      -19.856981, -19.713963, -19.570944, 8.896924,   8.727221, 8.557517,   8.387813, 21.476097,  21.631475, 21.786854,  21.942233, -11.918438,
+                                      -19.856981f, -19.713963f, -19.570944f,   8.896924f,   8.727221f,   8.557517f,   8.387813f,  21.476097f,  21.631475f,  21.786854f,  21.942233f, -11.918438f,
-                                      -12.06601 , -12.213582, -12.361154, -17.7117, -17.568681, -17.425663, -17.282644}, nd4j::DataType::FLOAT32);
+                                       -12.06601f, -12.213582f, -12.361154f,   -17.7117f, -17.568681f, -17.425663f, -17.282644f}, nd4j::DataType::FLOAT32);
     input.linspace(0.1, 0.1);
 
     nd4j::ops::batchnorm op;
@@ -3240,14 +3407,14 @@ TEST_F(DeclarableOpsTests10, batchnorm_test5) {
 TEST_F(DeclarableOpsTests10, batchnorm_test6) {
 
     NDArray input   ('c', {2,2,2,4}, nd4j::DataType::FLOAT32);
-    NDArray mean    ('c', {4}, {1.05, 1.15, 1.2, 1.3}, nd4j::DataType::FLOAT32);
+    NDArray mean    ('c', {4}, {1.05f, 1.15f, 1.2f, 1.3f}, nd4j::DataType::FLOAT32);
-    NDArray variance('c', {4}, {0.5, 0.7, 0.9,  1.1},  nd4j::DataType::FLOAT32);
+    NDArray variance('c', {4}, {0.5f, 0.7f, 0.9,  1.1f},  nd4j::DataType::FLOAT32);
-    NDArray gamma   ('c', {4}, {-1.2, 1.3, -1.4, 1.5}, nd4j::DataType::FLOAT32);
+    NDArray gamma   ('c', {4}, {-1.2f, 1.3f, -1.4f, 1.5f}, nd4j::DataType::FLOAT32);
-    NDArray beta    ('c', {4}, {10, 20, -10, -20},     nd4j::DataType::FLOAT32);
+    NDArray beta    ('c', {4}, {10.f, 20.f, -10.f, -20.f},     nd4j::DataType::FLOAT32);
 
-    NDArray expected('c', {2,2,2,4}, {11.612187,  18.523903,  -8.671851, -21.287169, 10.933372,  19.145418,  -9.262139, -20.715094, 10.254556,  19.766932,  -9.852428, -20.143019, 9.57574 ,
+    NDArray expected('c', {2,2,2,4}, {11.612187f,  18.523903f,  -8.671851f, -21.287169f,  10.933372f,  19.145418f,  -9.262139f, -20.715094f,  10.254556f,  19.766932f,  -9.852428f, -20.143019f,   9.57574f,
-                                    20.388447, -10.442716, -19.570944,8.896924,  21.009961, -11.033005, -18.998869, 8.218109,  21.631475, -11.623294, -18.426794, 7.539293,  22.25299 ,
+                                      20.388447f, -10.442716f, -19.570944f,   8.896924f,  21.009961f, -11.033005f, -18.998869f,   8.218109f,  21.631475f, -11.623294f, -18.426794f,   7.539293f,  22.25299f,
-                                    -12.213582, -17.854719, 6.860477,  22.874504, -12.803871, -17.282644}, nd4j::DataType::FLOAT32);
+                                     -12.213582f, -17.854719f,   6.860477f,  22.874504f, -12.803871f, -17.282644f}, nd4j::DataType::FLOAT32);
     input.linspace(0.1, 0.1);
 
     nd4j::ops::batchnorm op;
@@ -3270,7 +3437,7 @@ TEST_F(DeclarableOpsTests10, bool_broadcast_test_1) {
     NDArray arr1('c', {2,2,1}, {1, 2, 3, 4}, nd4j::DataType::INT32);
     NDArray arr2('c', {  2,2}, {0, 1, 0, 4}, nd4j::DataType::INT32);
 
-    NDArray expd('c', {2,2,2}, {0,1,0,0, 0,0,0,1}, nd4j::DataType::BOOL);
+    NDArray expd('c', {2,2,2}, {false, true, false, false, false, false, false, true}, nd4j::DataType::BOOL);
 
     NDArray result('c', {2,2,2}, nd4j::DataType::BOOL);
 

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests12.cpp

@@ -1257,7 +1257,7 @@ TEST_F(DeclarableOpsTests12, inTopK_2) {
     auto input = NDArrayFactory::create<double>('c', {4, 5});
     auto idx = NDArrayFactory::create<Nd4jLong>('c', {4});
 
-    auto exp = NDArrayFactory::create<bool>({0, 0, 0, 1});
+    auto exp = NDArrayFactory::create<bool>({false, false, false, true});
 
     int exclusive, reverse;
     input.linspace(1);
@@ -1318,7 +1318,7 @@ TEST_F(DeclarableOpsTests12, inTopK_4) {
 TEST_F(DeclarableOpsTests12, inTopK_5) {
     auto x = NDArrayFactory::create<double>('f', {6, 4}, {11.0, 3.0, 14.0, 5.0, 6.0, 9.0, 3.5, 7.0, 21.0, 3.0, 14.0, 15.0, 6.0, 9.0, 3.5, 7.0, 11.0, 13.0, 14.0, 5.0, 16.0, 9.0, 13.5, 7.0} );
     auto y = NDArrayFactory::create<Nd4jLong>('f', {6}, {0, 0, 0, 0, 0, 0});
-    auto expV = NDArrayFactory::create<bool>('f', {6}, {1, 0, 0, 0, 0, 0 });
+    auto expV = NDArrayFactory::create<bool>('f', {6}, {true, false, false, false, false, false });
 
     nd4j::ops::in_top_k op;
     auto result = op.execute({&x, &y}, {}, {2});

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests13.cpp

@@ -1167,12 +1167,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_3) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, nOut}, {0.493883, 0.493883, 0.493883, 0.510990, 0.510990, 0.510990, 0.534701, 0.534701, 0.534701, 0.549139,
+    NDArray expH('c', {sL, bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f, 0.534701f, 0.534701f, 0.534701f, 0.549139f,
-                                        0.549139, 0.549139, 0.571900, 0.571900, 0.571900, 0.583561, 0.583561, 0.583561, 0.605106, 0.605106,
+                                        0.549139f, 0.549139f, 0.571900f, 0.571900f, 0.571900f, 0.583561f, 0.583561f, 0.583561f, 0.605106f, 0.605106f,
-                                        0.605106, 0.614114, 0.614114, 0.614114, 0.635354, 0.635354, 0.635354, 0.642045, 0.642045, 0.642045}, nd4j::DataType::FLOAT32);
+                                        0.605106f, 0.614114f, 0.614114f, 0.614114f, 0.635354f, 0.635354f, 0.635354f, 0.642045f, 0.642045f, 0.642045f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {bS, nOut}, {0.493883, 0.493883, 0.493883, 0.510990, 0.510990, 0.510990}, nd4j::DataType::FLOAT32);
+    NDArray expHL('c', {bS, nOut}, {0.493883f, 0.493883f, 0.493883f, 0.510990f, 0.510990f, 0.510990f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {bS, nOut}, {1.061274, 1.061274, 1.061274, 1.115888, 1.115888, 1.115888}, nd4j::DataType::FLOAT32);
+    NDArray expCL('c', {bS, nOut}, {1.061274f, 1.061274f, 1.061274f, 1.115888f, 1.115888f, 1.115888f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);
@@ -1230,12 +1230,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_4) {
     NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32);
 
     x.linspace(0.5, 0.5);
-    Wx({0,1, 0,0, 0,0}) = 0.003;
+    Wx({0,1, 0,0, 0,0}) = 0.003f;
-    Wx({1,2, 0,0, 0,0}) = -0.003;
+    Wx({1,2, 0,0, 0,0}) = -0.003f;
-    Wr({0,1, 0,0, 0,0}) = 0.006;
+    Wr({0,1, 0,0, 0,0}) = 0.006f;
-    Wr({1,2, 0,0, 0,0}) = -0.006;
+    Wr({1,2, 0,0, 0,0}) = -0.006f;
-    b({0,1, 0,0})       = 0.5;
+    b({0,1, 0,0})       = 0.5f;
-    b({1,2, 0,0})       = -0.5;
+    b({1,2, 0,0})       = -0.5f;
     hI({0,1, 0,0, 0,0}) = 1;
     hI({1,2, 0,0, 0,0}) = -1;
     cI({0,1, 0,0, 0,0}) = 2;
@@ -1245,18 +1245,19 @@ TEST_F(DeclarableOpsTests13, lstmLayer_4) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, 2*nOut}, {0.577661, 0.577661, 0.577661, -0.107642, -0.107642, -0.107642, 0.585289, 0.585289, 0.585289,
+    NDArray expH('c', {sL, bS, 2 * nOut}, {
-                                        -0.106937, -0.106937, -0.106937, 0.556517, 0.556517, 0.556517, -0.111647, -0.111647, -0.111647,
+                                         0.577661f,  0.577661f,  0.577661f, -0.107642f, -0.107642f, -0.107642f,  0.585289f,  0.585289f,  0.585289f,
-                                        0.567274, 0.567274, 0.567274, -0.110214, -0.110214, -0.110214, 0.547395, 0.547395, 0.547395,
+                                        -0.106937f, -0.106937f, -0.106937f,  0.556517f,  0.556517f,  0.556517f, -0.111647f, -0.111647f, -0.111647f,
-                                        -0.123305, -0.123305, -0.123305, 0.560640, 0.560640, 0.560640, -0.120862, -0.120862, -0.120862,
+                                         0.567274f,  0.567274f,  0.567274f, -0.110214f, -0.110214f, -0.110214f,  0.547395f,  0.547395f,  0.547395f,
-                                        0.550714, 0.550714, 0.550714, -0.156223, -0.156223, -0.156223, 0.565308, 0.565308, 0.565308,
+                                        -0.123305f, -0.123305f, -0.123305f,  0.560640f,  0.560640f,  0.560640f, -0.120862f, -0.120862f, -0.120862f,
-                                        -0.152313, -0.152313, -0.152313, 0.563741, 0.563741, 0.563741, -0.234128, -0.234128, -0.234128,
+                                         0.550714f,  0.550714f,  0.550714f, -0.156223f, -0.156223f, -0.156223f,  0.565308f,  0.565308f,  0.565308f,
-                                        0.578676, 0.578676, 0.578676, -0.228917, -0.228917, -0.228917}, nd4j::DataType::FLOAT32);
+                                        -0.152313f, -0.152313f, -0.152313f,  0.563741f,  0.563741f,  0.563741f, -0.234128f, -0.234128f, -0.234128f,
+                                         0.578676f,  0.578676f,  0.578676f, -0.228917f, -0.228917f, -0.228917f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {2,bS, nOut}, {0.563741, 0.563741, 0.563741, 0.578676, 0.578676, 0.578676, -0.107642,
+    NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f, -0.107642f,
-                                    -0.107642, -0.107642, -0.106937, -0.106937, -0.106937}, nd4j::DataType::FLOAT32);
+                                    -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {2,bS, nOut}, {1.217757, 1.217757, 1.217757, 1.272398, 1.272398, 1.272398, -0.295768,
+    NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f, -0.295768f,
-                                    -0.295768, -0.295768, -0.298453, -0.298453, -0.298453}, nd4j::DataType::FLOAT32);
+                                    -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);
@@ -1328,16 +1329,17 @@ TEST_F(DeclarableOpsTests13, lstmLayer_5) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {bS, sL, 2*nOut}, {0.577661,  0.577661,  0.577661, -0.107659, -0.107659, -0.107659, 0.548099,  0.548099,  0.548099, -0.113406, -0.113406, -0.113406,
+    NDArray expH('c', {bS, sL, 2*nOut}, {
-                                    0.526881,  0.526881,  0.526881, -0.12883 , -0.12883 , -0.12883 , 0.515882,  0.515882,  0.515882, -0.16868 , -0.16868 , -0.16868 ,
+        0.577661f,  0.577661f,  0.577661f, -0.107659f, -0.107659f, -0.107659f, 0.548099f,  0.548099f,  0.548099f, -0.113406f, -0.113406f, -0.113406f,
-                                    0.51409 ,  0.51409 ,  0.51409 , -0.255185, -0.255185, -0.255185,  0.614599,  0.614599,  0.614599, -0.102739, -0.102739, -0.102739,
+        0.526881f,  0.526881f,  0.526881f,  -0.12883f,  -0.12883f,  -0.12883f, 0.515882f,  0.515882f,  0.515882f,  -0.16868f,  -0.16868f,  -0.16868f,
-                                    0.599572,  0.599572,  0.599572, -0.105802, -0.105802, -0.105802,0.591089,  0.591089,  0.591089, -0.116681, -0.116681, -0.116681,
+         0.51409f,   0.51409f,   0.51409f, -0.255185f, -0.255185f, -0.255185f, 0.614599f,  0.614599f,  0.614599f, -0.102739f, -0.102739f, -0.102739f,
-                                    0.588694,  0.588694,  0.588694, -0.149201, -0.149201, -0.149201,0.591492,  0.591492,  0.591492, -0.228917, -0.228917, -0.228917}, nd4j::DataType::FLOAT32);
+        0.599572f,  0.599572f,  0.599572f, -0.105802f, -0.105802f, -0.105802f, 0.591089f,  0.591089f,  0.591089f, -0.116681f, -0.116681f, -0.116681f,
+        0.588694f,  0.588694f,  0.588694f, -0.149201f, -0.149201f, -0.149201f, 0.591492f,  0.591492f,  0.591492f, -0.228917f, -0.228917f, -0.228917f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {2,bS, nOut}, {0.51409 ,  0.51409 ,  0.51409 ,   0.591492,  0.591492,  0.591492,
+    NDArray expHL('c', {2,bS, nOut}, {0.51409f,  0.51409f,  0.51409f,   0.591492f,  0.591492f,  0.591492f,
-                                     -0.107659, -0.107659, -0.107659,  -0.102739, -0.102739, -0.102739}, nd4j::DataType::FLOAT32);
+                                     -0.107659f, -0.107659f, -0.107659f,  -0.102739f, -0.102739f, -0.102739f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {2,bS, nOut}, {1.07293 ,  1.07293 ,  1.07293,1.346609,  1.346609,  1.346609,
+    NDArray expCL('c', {2,bS, nOut}, {1.07293f ,  1.07293f ,  1.07293f, 1.346609f,  1.346609f,  1.346609f,
-                                    -0.295811, -0.295811, -0.295811,-0.305394, -0.305394, -0.305394}, nd4j::DataType::FLOAT32);
+                                    -0.295811f, -0.295811f, -0.295811f, -0.305394f, -0.305394f, -0.305394f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);
@@ -1398,12 +1400,12 @@ TEST_F(DeclarableOpsTests13, lstmLayer_6) {
     NDArray cI('c', {2,bS, nOut}, nd4j::DataType::FLOAT32);
 
     x.linspace(0.5, 0.5);
-    Wx({0,1, 0,0, 0,0}) = 0.003;
+    Wx({0,1, 0,0, 0,0}) = 0.003f;
-    Wx({1,2, 0,0, 0,0}) = -0.003;
+    Wx({1,2, 0,0, 0,0}) = -0.003f;
-    Wr({0,1, 0,0, 0,0}) = 0.006;
+    Wr({0,1, 0,0, 0,0}) = 0.006f;
-    Wr({1,2, 0,0, 0,0}) = -0.006;
+    Wr({1,2, 0,0, 0,0}) = -0.006f;
-    b({0,1, 0,0})       = 0.5;
+    b({0,1, 0,0})       = 0.5f;
-    b({1,2, 0,0})       = -0.5;
+    b({1,2, 0,0})       = -0.5f;
     hI({0,1, 0,0, 0,0}) = 1;
     hI({1,2, 0,0, 0,0}) = -1;
     cI({0,1, 0,0, 0,0}) = 2;
@@ -1413,14 +1415,17 @@ TEST_F(DeclarableOpsTests13, lstmLayer_6) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, nOut}, {0.470019, 0.470019, 0.470019, 0.478352, 0.478352, 0.478352, 0.444871, 0.444871, 0.444871, 0.457060,
+    NDArray expH('c', {sL, bS, nOut}, {
-                                    0.457060, 0.457060, 0.424090, 0.424090, 0.424090, 0.439778, 0.439778, 0.439778, 0.394491, 0.394491,
+        0.470019f, 0.470019f, 0.470019f, 0.478352f, 0.478352f, 0.478352f, 0.444871f, 0.444871f, 0.444871f, 0.457060f,
-                                    0.394491, 0.412995, 0.412995, 0.412995, 0.329613, 0.329613, 0.329613, 0.349760, 0.349760, 0.349760}, nd4j::DataType::FLOAT32);
+        0.457060f, 0.457060f, 0.424090f, 0.424090f, 0.424090f, 0.439778f, 0.439778f, 0.439778f, 0.394491f, 0.394491f,
+        0.394491f, 0.412995f, 0.412995f, 0.412995f, 0.329613f, 0.329613f, 0.329613f, 0.349760f, 0.349760f, 0.349760f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {2,bS, nOut}, {0.563741, 0.563741, 0.563741, 0.578676, 0.578676, 0.578676, -0.107642,
+    NDArray expHL('c', {2,bS, nOut}, {0.563741f, 0.563741f, 0.563741f, 0.578676f, 0.578676f, 0.578676f,
-                                    -0.107642, -0.107642, -0.106937, -0.106937, -0.106937}, nd4j::DataType::FLOAT32);
+                                      -0.107642f, -0.107642f, -0.107642f, -0.106937f, -0.106937f, -0.106937f},
-    NDArray expCL('c', {2,bS, nOut}, {1.217757, 1.217757, 1.217757, 1.272398, 1.272398, 1.272398, -0.295768,
+                                      nd4j::DataType::FLOAT32);
-                                    -0.295768, -0.295768, -0.298453, -0.298453, -0.298453}, nd4j::DataType::FLOAT32);
+    NDArray expCL('c', {2,bS, nOut}, {1.217757f, 1.217757f, 1.217757f, 1.272398f, 1.272398f, 1.272398f,
+                                      -0.295768f, -0.295768f, -0.295768f, -0.298453f, -0.298453f, -0.298453f},
+                                      nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI}, tArgs, iArgs, bArgs);
@@ -1568,12 +1573,13 @@ TEST_F(DeclarableOpsTests13, lstmLayer_8) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, nOut}, {0.436221, 0.436221, 0.436221,0.450573, 0.450573, 0.450573,0.463602, 0.463602, 0.463602, 0.474674, 0.474674, 0.474674,
+    NDArray expH('c', {sL, bS, nOut}, {
-                                    0.484039, 0.484039, 0.484039,0.490679, 0.490679, 0.490679, 0.494871, 0.494871, 0.494871,   0.499028, 0.499028, 0.499028,
+        0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f, 0.463602f, 0.463602f, 0.463602f, 0.474674f, 0.474674f, 0.474674f,
-                                    0.504649, 0.504649, 0.504649,  0.508719, 0.508719, 0.508719}, nd4j::DataType::FLOAT32);
+        0.484039f, 0.484039f, 0.484039f, 0.490679f, 0.490679f, 0.490679f, 0.494871f, 0.494871f, 0.494871f, 0.499028f, 0.499028f, 0.499028f,
+        0.504649f, 0.504649f, 0.504649f, 0.508719f, 0.508719f, 0.508719f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {bS, nOut}, {0.436221, 0.436221, 0.436221, 0.450573, 0.450573, 0.450573}, nd4j::DataType::FLOAT32);
+    NDArray expHL('c', {bS, nOut}, {0.436221f, 0.436221f, 0.436221f, 0.450573f, 0.450573f, 0.450573f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {bS, nOut}, {0.879804, 0.879804, 0.879804,0.914666, 0.914666, 0.914666}, nd4j::DataType::FLOAT32);
+    NDArray expCL('c', {bS, nOut}, {0.879804f, 0.879804f, 0.879804f, 0.914666f, 0.914666f, 0.914666f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
@@ -1650,16 +1656,17 @@ TEST_F(DeclarableOpsTests13, lstmLayer_9) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, 2*nOut}, { 0.55533 ,  0.55533 ,  0.55533 , -0.104502, -0.104502, -0.104502, 0.562925,  0.562925,  0.562925, -0.103843, -0.103843, -0.103843,
+    NDArray expH('c', {sL, bS, 2*nOut}, {
-                                    0.531795,  0.531795,  0.531795, -0.107456, -0.107456, -0.107456,0.542556,  0.542556,  0.542556, -0.106139, -0.106139, -0.106139,
+         0.55533f,   0.55533f,   0.55533f, -0.104502f, -0.104502f, -0.104502f, 0.562925f,  0.562925f,  0.562925f, -0.103843f, -0.103843f, -0.103843f,
-                                    0.521466,  0.521466,  0.521466, -0.11681 , -0.11681 , -0.11681 , 0.534638,  0.534638,  0.534638, -0.11458 , -0.11458 , -0.11458 ,
+        0.531795f,  0.531795f,  0.531795f, -0.107456f, -0.107456f, -0.107456f, 0.542556f,  0.542556f,  0.542556f, -0.106139f, -0.106139f, -0.106139f,
-                                    0.524805,  0.524805,  0.524805, -0.145177, -0.145177, -0.145177,0.539187,  0.539187,  0.539187, -0.14157 , -0.14157 , -0.14157 ,
+        0.521466f,  0.521466f,  0.521466f,  -0.11681f,  -0.11681f,  -0.11681f, 0.534638f,  0.534638f,  0.534638f,  -0.11458f,  -0.11458f,  -0.11458f,
-                                    0.538309,  0.538309,  0.538309, -0.218056, -0.218056, -0.218056,0.552923,  0.552923,  0.552923, -0.213068, -0.213068, -0.213068}, nd4j::DataType::FLOAT32);
+        0.524805f,  0.524805f,  0.524805f, -0.145177f, -0.145177f, -0.145177f, 0.539187f,  0.539187f,  0.539187f,  -0.14157f,  -0.14157f,  -0.14157f,
+        0.538309f,  0.538309f,  0.538309f, -0.218056f, -0.218056f, -0.218056f, 0.552923f,  0.552923f,  0.552923f, -0.213068f, -0.213068f, -0.213068f}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {2,bS, nOut}, {0.538309,  0.538309,  0.538309, 0.552923,  0.552923,  0.552923, -0.104502, -0.104502, -0.104502,
+    NDArray expHL('c', {2,bS, nOut}, {0.538309f,  0.538309f,  0.538309f, 0.552923f,  0.552923f,  0.552923f, -0.104502f, -0.104502f, -0.104502f,
-                                    -0.103843, -0.103843, -0.103843}, nd4j::DataType::FLOAT32);
+                                     -0.103843f, -0.103843f, -0.103843f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {2,bS, nOut}, {1.147089,  1.147089,  1.147089, 1.197228,  1.197228,  1.197228, -0.289425, -0.289425, -0.289425,
+    NDArray expCL('c', {2,bS, nOut}, {1.147089f,  1.147089f,  1.147089f, 1.197228f,  1.197228f,  1.197228f, -0.289425f, -0.289425f, -0.289425f,
-                                     -0.292174, -0.292174, -0.292174}, nd4j::DataType::FLOAT32);
+                                     -0.292174f, -0.292174f, -0.292174f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
@@ -1731,14 +1738,20 @@ TEST_F(DeclarableOpsTests13, lstmLayer_10) {
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, nOut}, {0., 0., 0., 0.562925, 0.562925, 0.562925, 0.570404, 0.570404, 0.570404, 0.57777 , 0.57777 , 0.57777 , 0.585023, 0.585023, 0.585023,
+    NDArray expH('c', {sL, bS, nOut}, {
-                                    0., 0., 0., 0., 0., 0., 0.576568, 0.576568, 0.576568, 0.586163, 0.586163, 0.586163, 0.595462, 0.595462, 0.595462, 0., 0., 0., 0., 0.,
+              0.f,       0.f,       0.f, 0.562925f, 0.562925f, 0.562925f, 0.570404f, 0.570404f, 0.570404f,  0.57777f,
-                                    0., 0., 0., 0., 0.611224, 0.611224, 0.611224, 0.621298, 0.621298, 0.621298, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
+         0.57777f,  0.57777f, 0.585023f, 0.585023f, 0.585023f,       0.f,       0.f,       0.f,       0.f,       0.f,
-                                    0.655858, 0.655858, 0.655858, 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.692315, 0.692315, 0.692315, 0., 0., 0., 0., 0., 0.,
+              0.f, 0.576568f, 0.576568f, 0.576568f, 0.586163f, 0.586163f, 0.586163f, 0.595462f, 0.595462f, 0.595462f,
-                                    0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32);
+              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f, 0.611224f,
+        0.611224f, 0.611224f, 0.621298f, 0.621298f, 0.621298f,       0.f,       0.f,       0.f,       0.f,       0.f,
+              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f, 0.655858f, 0.655858f, 0.655858f,
+              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,        0.f,
+              0.f,       0.f, 0.692315f, 0.692315f, 0.692315f,       0.f,       0.f,       0.f,       0.f,        0.f,
+              0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,       0.f,        0.f},
+        nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {bS, nOut}, {0., 0., 0., 0.562925, 0.562925, 0.562925, 0.576568, 0.576568, 0.576568, 0.611224, 0.611224, 0.611224, 0.692315, 0.692315, 0.692315}, nd4j::DataType::FLOAT32);
+    NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f, 0.576568f, 0.576568f, 0.576568f, 0.611224f, 0.611224f, 0.611224f, 0.692315f, 0.692315f, 0.692315f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {bS, nOut}, {0., 0., 0., 1.534275, 1.534275, 1.534275, 1.40183, 1.40183, 1.40183, 1.449675, 1.449675, 1.449675, 1.767702, 1.767702, 1.767702}, nd4j::DataType::FLOAT32);
+    NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f,  1.40183f,  1.40183f,  1.40183f, 1.449675f, 1.449675f, 1.449675f, 1.767702f, 1.767702f, 1.767702f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
@@ -1799,25 +1812,26 @@ TEST_F(DeclarableOpsTests13, lstmLayer_11) {
     NDArray Wp('c', {3*nOut}, nd4j::DataType::FLOAT32);
 
     x.linspace(0.5, 0.5);
-    Wx = 0.003;
+    Wx = 0.003f;
-    Wr = 0.006;
+    Wr = 0.006f;
-    b = 0.5;
+    b = 0.5f;
-    hI = 1.;
+    hI = 1.f;
-    cI = 2.;
+    cI = 2.f;
-    Wp = -0.05;
+    Wp = -0.05f;
 
     std::initializer_list<double>   tArgs = {cellClip};
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
     std::initializer_list<bool>     bArgs = {hasBiases, hasSeqLen, hasInitH, hasInitC, hasPH, retFullSeq, retLastH, retLastC};
 
-    NDArray expH('c', {sL, bS, nOut}, {0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.61209,
+    NDArray expH('c', {sL, bS, nOut}, {
-                                    0.61209, 0.61209,0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.652042, 0.652042, 0.652042,  0., 0., 0., 0., 0.,
+        0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.61209f,
-                                    0., 0., 0., 0., 0.677708, 0.677708, 0.677708, 0.684177, 0.684177, 0.684177, 0., 0., 0.,0., 0., 0.,0.699627, 0.699627,
+        0.61209f, 0.61209f,0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.f, 0.652042f, 0.652042f, 0.652042f,  0.f, 0.f, 0.f, 0.f, 0.f,
-                                    0.699627,0.705371, 0.705371, 0.705371,0.710989, 0.710989, 0.710989, 0., 0., 0., 0.719014, 0.719014, 0.719014, 0.724087,
+        0.f, 0.f, 0.f, 0.f, 0.677708f, 0.677708f, 0.677708f, 0.684177f, 0.684177f, 0.684177f, 0.f, 0.f, 0.f,0.f, 0.f, 0.f, 0.699627f, 0.699627f,
-                                    0.724087, 0.724087, 0.729084, 0.729084, 0.729084, 0.734004, 0.734004, 0.734004 }, nd4j::DataType::FLOAT32);
+        0.699627f, 0.705371f, 0.705371f, 0.705371f, 0.710989f, 0.710989f, 0.710989f, 0., 0., 0., 0.719014, 0.719014, 0.719014, 0.724087,
+        0.724087f, 0.724087f, 0.729084f, 0.729084f, 0.729084f, 0.734004f, 0.734004f, 0.734004f }, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {bS, nOut}, {0., 0., 0., 0.719014, 0.719014, 0.719014, 0.699627, 0.699627, 0.699627, 0.677708, 0.677708, 0.677708, 0.61209, 0.61209, 0.61209}, nd4j::DataType::FLOAT32);
+    NDArray expHL('c', {bS, nOut}, {0.f, 0.f, 0.f, 0.719014f, 0.719014f, 0.719014f, 0.699627f, 0.699627f, 0.699627f, 0.677708f, 0.677708f, 0.677708f,  0.61209f,  0.61209f,  0.61209f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {bS, nOut}, {0., 0., 0., 2.092814, 2.092814, 2.092814, 2.08832, 2.08832, 2.08832, 2.009851, 2.009851, 2.009851, 1.646034, 1.646034, 1.646034}, nd4j::DataType::FLOAT32);
+    NDArray expCL('c', {bS, nOut}, {0.f, 0.f, 0.f, 2.092814f, 2.092814f, 2.092814f,  2.08832f,  2.08832f,  2.08832f, 2.009851f, 2.009851f, 2.009851f, 1.646034f, 1.646034f, 1.646034f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);
@@ -1878,18 +1892,18 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) {
     NDArray Wp('c', {2,3*nOut}, nd4j::DataType::FLOAT32);
 
     x.linspace(0.5, 0.5);
-    Wx({0,1, 0,0, 0,0}) = 0.003;
+    Wx({0,1, 0,0, 0,0}) = 0.003f;
-    Wx({1,2, 0,0, 0,0}) = -0.003;
+    Wx({1,2, 0,0, 0,0}) = -0.003f;
-    Wr({0,1, 0,0, 0,0}) = 0.006;
+    Wr({0,1, 0,0, 0,0}) = 0.006f;
-    Wr({1,2, 0,0, 0,0}) = -0.006;
+    Wr({1,2, 0,0, 0,0}) = -0.006f;
-    b({0,1, 0,0})       = 0.5;
+    b({0,1, 0,0})       = 0.5f;
-    b({1,2, 0,0})       = -0.5;
+    b({1,2, 0,0})       = -0.5f;
     hI({0,1, 0,0, 0,0}) = 1;
     hI({1,2, 0,0, 0,0}) = -1;
     cI({0,1, 0,0, 0,0}) = 2;
     cI({1,2, 0,0, 0,0}) = -2;
-    Wp({0,1, 0,0}) = -0.05;
+    Wp({0,1, 0,0}) = -0.05f;
-    Wp({1,2, 0,0}) = 0.05;
+    Wp({1,2, 0,0}) = 0.05f;
 
     std::initializer_list<double>   tArgs = {cellClip};
     std::initializer_list<Nd4jLong> iArgs = {dataFormat, directionMode, gateAct, cellAct, outAct};
@@ -1905,10 +1919,10 @@ TEST_F(DeclarableOpsTests13, lstmLayer_12) {
                                         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.692315, 0.692315, 0.692315, -0.143704, -0.143704, -0.143704, 0., 0., 0., 0., 0., 0.,
                                         0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.}, nd4j::DataType::FLOAT32);
 
-    NDArray expHL('c', {2,bS, nOut}, {0., 0., 0., 0.562925, 0.562925, 0.562925, 0.576568, 0.576568, 0.576568, 0.611224, 0.611224, 0.611224, 0.692315, 0.692315, 0.692315,
+    NDArray expHL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 0.562925f, 0.562925f, 0.562925f,   0.576568f,  0.576568f,  0.576568f,  0.611224f,  0.611224f,  0.611224f,   0.692315f,  0.692315f,  0.692315f,
-                                      0., 0., 0.,  -0.25361 , -0.25361 , -0.25361 ,  -0.157103, -0.157103, -0.157103,-0.116502, -0.116502, -0.116502,  -0.100025, -0.100025, -0.100025}, nd4j::DataType::FLOAT32);
+                                      0.f, 0.f, 0.f, -0.25361f, -0.25361f, -0.25361f,  -0.157103f, -0.157103f, -0.157103f, -0.116502f, -0.116502f, -0.116502f,  -0.100025f, -0.100025f, -0.100025f}, nd4j::DataType::FLOAT32);
-    NDArray expCL('c', {2,bS, nOut}, {0., 0., 0.,1.534275, 1.534275, 1.534275,1.40183 , 1.40183 , 1.40183 ,1.449675, 1.449675, 1.449675,1.767702, 1.767702, 1.767702,
+    NDArray expCL('c', {2,bS, nOut}, {0.f, 0.f, 0.f, 1.534275f, 1.534275f, 1.534275f,    1.40183f,   1.40183f,   1.40183f,  1.449675f,  1.449675f,  1.449675f,   1.767702f,  1.767702f,  1.767702f,
-                                     0., 0., 0.,-0.86636 , -0.86636 , -0.86636 ,-0.470245, -0.470245, -0.470245,-0.341856, -0.341856, -0.341856,-0.294986, -0.294986, -0.294986}, nd4j::DataType::FLOAT32);
+                                      0.f, 0.f, 0.f, -0.86636f, -0.86636f, -0.86636f,  -0.470245f, -0.470245f, -0.470245f, -0.341856f, -0.341856f, -0.341856f,  -0.294986f, -0.294986f, -0.294986f}, nd4j::DataType::FLOAT32);
 
     nd4j::ops::lstmLayer op;
     auto results = op.execute({&x, &Wx, &Wr, &b, &seqLen, &hI, &cI, &Wp}, tArgs, iArgs, bArgs);

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests15.cpp

@@ -148,8 +148,8 @@ TEST_F(DeclarableOpsTests15, Test_standarize_1) {
 }
 
 TEST_F(DeclarableOpsTests15, Test_standarize_bp_1) {
-    auto x = NDArrayFactory::create<float>('c', {5}, {1., 1., 1., 1., 1.});
+    auto x = NDArrayFactory::create<float>('c', {5}, {1.f, 1.f, 1.f, 1.f, 1.f});
-    auto eps = NDArrayFactory::create<float>('c', {5}, {0., 0., 0., 0., 0.});
+    auto eps = NDArrayFactory::create<float>('c', {5}, {0.f, 0.f, 0.f, 0.f, 0.f});
 
     nd4j::ops::standardize_bp op;
     auto result = op.execute({&x, &eps}, {}, {0}, {});

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests16.cpp

@@ -197,3 +197,44 @@ TEST_F(DeclarableOpsTests16, test_range_2) {
 
     delete shapes;
 }
+
+TEST_F(DeclarableOpsTests16, test_reverse_1) {
+    std::vector<Nd4jLong> rows = {3, 5, 7, 8, 9, 10, 119, 211};
+    std::vector<Nd4jLong> columns = {6, 5, 10, 100, 153, 171, 635};
+
+    for (auto r : rows) {
+        for (auto c : columns) {
+            //nd4j_printf("Trying [%i, %i]\n", r, c);
+            auto array = NDArrayFactory::create<float>('c', {r, c});
+            auto exp = NDArrayFactory::create<float>('c', {r, c});
+            auto reversed = NDArrayFactory::create<float>('c', {r, c});
+
+            auto rowOriginal = NDArrayFactory::create<float>('c', {c});
+            auto rowReversed = NDArrayFactory::create<float>('c', {c});
+
+            for (int e = 0; e < c; e++) {
+                rowOriginal.p(e, (float) e);
+                rowReversed.p(c - e - 1, (float) e);
+            }
+
+
+            auto listI = array.allTensorsAlongDimension({1});
+            auto listE = exp.allTensorsAlongDimension({1});
+
+            for (int e = 0; e < r; e++) {
+                listI->at(e)->assign(rowOriginal);
+                listE->at(e)->assign(rowReversed);
+            }
+
+            delete listI;
+            delete listE;
+
+            nd4j::ops::reverse op;
+            Nd4jLong axis  = 1;
+            auto status = op.execute({&array}, {&reversed}, {}, {axis}, {});
+            ASSERT_EQ(Status::OK(), status);
+
+            ASSERT_EQ(exp, reversed);
+        }
+    }
+}

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests2.cpp

@@ -1591,7 +1591,7 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test6) {
     auto *result = results->at(0);
 
     ASSERT_TRUE(result->isScalar());
-    ASSERT_TRUE(result->e<float>(0) == -71.);
+    ASSERT_TRUE(result->e<float>(0) == -71.f);
 
     delete results;
 
@@ -1616,7 +1616,7 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test7) {
     auto *result = results->at(0);
 
     ASSERT_TRUE(result->isScalar());
-    ASSERT_TRUE(result->e<float>(0) == -69.);
+    ASSERT_TRUE(result->e<float>(0) == -69.f);
 
     delete results;
 
@@ -1630,8 +1630,8 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test8) {
     auto weights = NDArrayFactory::create<float>('c', {2,3,1});
 
     labels.linspace(1);
-    weights.assign(0.5);
+    weights.assign(0.5f);
-    predictions.assign(0.5);
+    predictions.assign(0.5f);
 
     nd4j::ops::cosine_distance_loss op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});
@@ -1641,7 +1641,7 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test8) {
     auto *result = results->at(0);
 
     ASSERT_TRUE(result->isScalar());
-    ASSERT_TRUE(result->e<float>(0) == -24.);
+    ASSERT_TRUE(result->e<float>(0) == -24.f);
 
     delete results;
 
@@ -1655,8 +1655,8 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test9) {
     auto weights = NDArrayFactory::create<float>('c', {1,1});
 
     labels.linspace(1);
-    weights.assign(0.5);
+    weights.assign(0.5f);
-    predictions.assign(0.5);
+    predictions.assign(0.5f);
 
     nd4j::ops::cosine_distance_loss op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});
@@ -1680,10 +1680,10 @@ TEST_F(DeclarableOpsTests2, cosine_distance_loss_test10) {
     auto weights = NDArrayFactory::create<float>('c', {2,3,1});
 
     labels.linspace(1);
-    weights.assign(0.5);
+    weights.assign(0.5f);
-    predictions.assign(0.5);
+    predictions.assign(0.5f);
-    weights.p(0, 0.);
+    weights.p(0, 0.f);
-    weights.p(1, 0.);
+    weights.p(1, 0.f);
 
     nd4j::ops::cosine_distance_loss op;
     auto results = op.execute({&predictions, &weights, &labels}, {}, {2,2});

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests3.cpp

@@ -1707,7 +1707,7 @@ TEST_F(DeclarableOpsTests3, betainc_test8) {
     b.linspace(10.);
     x.assign(1.);
 
-    auto expected= NDArrayFactory::create<float>('c', {3,3}, {1.f, 1.f, 1.,1.,1.,1.,1.,1.,1.});
+    auto expected= NDArrayFactory::create<float>('c', {3,3}, {1.f, 1.f, 1.f,1.f,1.f,1.f,1.f,1.f,1.f});
 
     nd4j::ops::betainc op;
     auto results = op.execute({&a, &b, &x}, {}, {});
@@ -2292,9 +2292,9 @@ TEST_F(DeclarableOpsTests3, svd_test3) {
     }
     else {
         for(uint i = 0; i < expU.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5f);
         for(uint i = 0; i < expV.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5f);
     }
 
     delete results;
@@ -2329,9 +2329,9 @@ TEST_F(DeclarableOpsTests3, svd_test4) {
     }
     else {
         for(uint i = 0; i < expU.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5f);
         for(uint i = 0; i < expV.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5f);
     }
 
     delete results;
@@ -2366,9 +2366,9 @@ TEST_F(DeclarableOpsTests3, svd_test5) {
     }
     else {
         for(uint i = 0; i < expU.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5f);
         for(uint i = 0; i < expV.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5f);
     }
 
     delete results;
@@ -2421,9 +2421,9 @@ TEST_F(DeclarableOpsTests3, svd_test6) {
     }
     else {
         for(uint i = 0; i < expU.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expU.e<float>(i)), nd4j::math::nd4j_abs(u->e<float>(i)), 1e-5f);
         for(uint i = 0; i < expV.lengthOf(); ++i)
-            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5);
+            ASSERT_NEAR(nd4j::math::nd4j_abs(expV.e<float>(i)), nd4j::math::nd4j_abs(v->e<float>(i)), 1e-5f);
     }
 
     delete results;

libnd4j/tests_cpu/layers_tests/DeclarableOpsTests7.cpp

@@ -4084,7 +4084,7 @@ TEST_F(DeclarableOpsTests7, Softsign_1) {
 TEST_F(DeclarableOpsTests7, Softsign_BP_1) {
 
     NDArray x = NDArrayFactory::create<double >('c', {5, 2}, {1,2,3,4,5,7,9,10, 10, 11});
-//    NDArray e = NDArrayFactory::create<float>('c', {5, 2}, {1.3132616,  2.126928, 3.0485873, 4.01815, 5.0067153, 7.0009117, 9.000123, 10.000046, 10.000046, 11.000016});
+//    NDArray e = NDArrayFactory::create<float>('c', {5, 2}, {1.3132616f,  2.126928f, 3.0485873f, 4.01815f, 5.0067153f, 7.0009117f, 9.000123f, 10.000046f, 10.000046f, 11.000016f});
     NDArray eps = NDArrayFactory::create<double>('c', {5, 2}, {1,2,3,4,5,6,7,8, 9, 10});
     nd4j::ops::softsign ffOP;
     nd4j::ops::softsign_bp bpOp;

libnd4j/tests_cpu/layers_tests/DeclarableOpsTestsCuda1.cu

@@ -24,6 +24,7 @@
 #include <NDArray.h>
 #include <ops/ops.h>
 #include <GradCheck.h>
+#include <chrono>
 
 
 using namespace nd4j;
@@ -58,5 +59,20 @@ TEST_F(DeclarableOpsTestsCuda1, Test_CHOOSE_SCALAR_LARGE) {
     //ASSERT_TRUE(exp.isSameShape(z));
 
     delete result;
-
 }
+
+/*
+TEST_F(DeclarableOpsTestsCuda1, Test_Reverse_TAD_1) {
+    auto x = NDArrayFactory::create<float>('c', {1, 3, 608, 608});
+    auto z = x.like();
+    x.linspace(1.0f);
+
+    nd4j::ops::reverse op;
+    auto timeStart = std::chrono::system_clock::now();
+    auto status = op.execute({&x}, {&z}, {}, {1}, {});
+    auto timeEnd = std::chrono::system_clock::now();
+    auto outerTime = std::chrono::duration_cast<std::chrono::microseconds> (timeEnd - timeStart).count();
+    nd4j_printf("exec time: %lld us\n", outerTime);
+    ASSERT_EQ(Status::OK(), status);
+}
+*/

libnd4j/tests_cpu/layers_tests/JavaInteropTests.cpp

@@ -661,9 +661,9 @@ TEST_F(JavaInteropTests, Test_Greater_1) {
     auto x = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 1, 2});
     auto y = NDArrayFactory::create<float>('c', {2, 2}, {1, 2, 0, 0});
 //    auto o = NDArrayFactory::create<float>('c', {2, 2}, {3, 3, 3, 3});
-    auto o = NDArrayFactory::create<bool>('c', {2, 2}, {1, 1, 1, 1});
+    auto o = NDArrayFactory::create<bool>('c', {2, 2}, {true, true, true, true});
 
-    auto exp = NDArrayFactory::create<bool>('c', {2, 2}, {0, 0, 1, 1});
+    auto exp = NDArrayFactory::create<bool>('c', {2, 2}, {false, false, true, true});
 
     NDArray::prepareSpecialUse({&o}, {&x, &y});
 
@@ -685,9 +685,9 @@ TEST_F(JavaInteropTests, Test_Greater_1) {
 TEST_F(JavaInteropTests, Test_Greater_2) {
     auto x = NDArrayFactory::create<float>('c', {2, 2}, {1.f, 2.f, 1.f, 2.f});
     auto y = NDArrayFactory::create<float>('c', {2, 2}, {1.f, 2.f, 0.f, 0.f});
-    auto o = NDArrayFactory::create<bool>('c', {2, 2}, {1, 1, 1, 1});
+    auto o = NDArrayFactory::create<bool>('c', {2, 2}, {true, true, true, true});
 
-    auto exp = NDArrayFactory::create<bool>('c', {2, 2}, {0, 0, 1, 1});
+    auto exp = NDArrayFactory::create<bool>('c', {2, 2}, {false, false, true, true});
 
     nd4j::ops::greater op;
 

libnd4j/tests_cpu/layers_tests/NDArrayCudaBasicsTests.cu

@@ -1163,10 +1163,10 @@ TEST_F(NDArrayCudaBasicsTests, applyReduce3_1) {
     NDArray k('c', {2,3}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32);
     NDArray k2('c', {3,2}, {-2,3,-4,5,-2,3}, nd4j::DataType::INT32);
 
-    NDArray exp1('c', {3}, {4., 20., 36.}, nd4j::DataType::FLOAT32);
+    NDArray exp1('c', {3}, {4.f, 20.f, 36.f}, nd4j::DataType::FLOAT32);
-    NDArray exp2('c', {2,3}, {-10., -2., 6.,14., 22., 30.}, nd4j::DataType::FLOAT32);
+    NDArray exp2('c', {2,3}, {-10.f, -2.f, 6.f,14.f, 22.f, 30.f}, nd4j::DataType::FLOAT32);
-    NDArray exp3('c', {4}, {38., 41., 44., 47.}, nd4j::DataType::FLOAT32);
+    NDArray exp3('c', {4}, {38.f, 41.f, 44.f, 47.f}, nd4j::DataType::FLOAT32);
-    NDArray exp4('c', {4}, {114., 117., 120., 123.}, nd4j::DataType::FLOAT32);
+    NDArray exp4('c', {4}, {114.f, 117.f, 120.f, 123.f}, nd4j::DataType::FLOAT32);
 
 
     NDArray* z = x.applyReduce3(nd4j::reduce3::Dot, &y, {0,2});
@@ -1271,8 +1271,10 @@ TEST_F(NDArrayCudaBasicsTests, applyAllReduce3_1) {
     NDArray x3('c', {3,2}, {1.5,1.5,1.5,1.5,1.5,1.5}, nd4j::DataType::DOUBLE);
     NDArray x4('c', {3,2}, {1,2,3,4,5,6}, nd4j::DataType::DOUBLE);
 
-    NDArray exp1('c', {3,2}, {-88., -124., 6., -2., 22., 14.}, nd4j::DataType::FLOAT32);
+    NDArray exp1('c', {3,2}, {-88.f, -124.f, 6.f, -2.f, 22.f, 14.f}, nd4j::DataType::FLOAT32);
-    NDArray exp2('c', {6,4}, {-36., -44., -52., -60.,-42., -52., -62., -72.,2., 0., -2., -4.,6., 4., 2., 0.,10., 8., 6., 4.,14., 12., 10., 8.}, nd4j::DataType::FLOAT32);
+    NDArray exp2('c', {6,4}, {-36.f, -44.f, -52.f, -60.f,-42.f, -52.f, -62.f, -72.f, 2.f, 0.f, -2.f,
+                              -4.f, 6.f, 4.f, 2.f, 0.f, 10.f, 8.f, 6.f, 4.f, 14.f, 12.f, 10.f, 8.f},
+            nd4j::DataType::FLOAT32);
     NDArray exp3('c', {1,1}, {31.5}, nd4j::DataType::DOUBLE);
     NDArray exp4('c', {3,3}, {4.5, 10.5, 16.5,4.5, 10.5, 16.5,4.5, 10.5, 16.5}, nd4j::DataType::DOUBLE);
 
@@ -1400,10 +1402,10 @@ TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_float_test1) {
     NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32);
 
     NDArray exp1('c', {}, {2.166667}, nd4j::DataType::DOUBLE);
-    NDArray exp2('c', {2,2}, {3,4,1,0.666667}, nd4j::DataType::FLOAT32);
+    NDArray exp2('c', {2,2}, {3.f,4.f,1.f,0.666667f}, nd4j::DataType::FLOAT32);
     NDArray exp3('c', {3}, {4.5,1,1}, nd4j::DataType::DOUBLE);
     NDArray exp4('c', {3,2}, {4,5,1,1,1,1}, nd4j::DataType::FLOAT32);
-    NDArray exp5('c', {2}, {3.5,0.833333}, nd4j::DataType::FLOAT32);
+    NDArray exp5('c', {2}, {3.5f,0.833333f}, nd4j::DataType::FLOAT32);
 
     x.reduceAlongDimension(nd4j::reduce::Mean, &z1, {0,1,2});
     ASSERT_TRUE(z1.equalsTo(&exp1));
@@ -1503,7 +1505,7 @@ TEST_F(NDArrayCudaBasicsTests, EqualityTest1) {
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test1) {
 
-    NDArray x('c', {2,3,2}, {1.5,2,3,4,5,6,7.5,8,-1,-2,-3.5,-4,}, nd4j::DataType::FLOAT32);
+    NDArray x('c', {2,3,2}, {1.5f,2.f,3.f,4.f,5.f,6.f,7.5f,8.f,-1.f,-2.f,-3.5f,-4.f}, nd4j::DataType::FLOAT32);
 
     NDArray z1('c', {}, {100}, nd4j::DataType::FLOAT32);
     NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::FLOAT32);
@@ -1511,11 +1513,11 @@ TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_same_test1) {
     NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::FLOAT32);
     NDArray z5('c', {2}, {100,100}, nd4j::DataType::FLOAT32);
 
-    NDArray exp1('c', {}, {26.5}, nd4j::DataType::FLOAT32);
+    NDArray exp1('c', {}, {26.5f}, nd4j::DataType::FLOAT32);
-    NDArray exp2('c', {2,2}, {9.5,12,3,2}, nd4j::DataType::FLOAT32);
+    NDArray exp2('c', {2,2}, {9.5f,12.f,3.f,2.f}, nd4j::DataType::FLOAT32);
-    NDArray exp3('c', {3}, {19,4,3.5}, nd4j::DataType::FLOAT32);
+    NDArray exp3('c', {3}, {19.f,4.f,3.5f}, nd4j::DataType::FLOAT32);
-    NDArray exp4('c', {3,2}, {9,10,2,2,1.5,2}, nd4j::DataType::FLOAT32);
+    NDArray exp4('c', {3,2}, {9.f,10.f,2.f,2.f,1.5f,2.f}, nd4j::DataType::FLOAT32);
-    NDArray exp5('c', {2}, {21.5,5}, nd4j::DataType::FLOAT32);
+    NDArray exp5('c', {2}, {21.5f,5.f}, nd4j::DataType::FLOAT32);
 
     x.reduceAlongDimension(nd4j::reduce::Sum, &z1, {0,1,2});
     ASSERT_TRUE(z1.equalsTo(&exp1));
@@ -1575,17 +1577,17 @@ TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test1) {
 
     NDArray x('c', {2,3,2}, {0.5,2,3,-4,5,6,-7.5,8,-1,-0.5,-3.5,4}, nd4j::DataType::DOUBLE);
 
-    NDArray z1('c', {}, {100}, nd4j::DataType::BOOL);
+    NDArray z1('c', {}, {true}, nd4j::DataType::BOOL);
-    NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::BOOL);
+    NDArray z2('c', {2,2}, {true,true,true,true}, nd4j::DataType::BOOL);
-    NDArray z3('c', {3}, {100,100,100}, nd4j::DataType::BOOL);
+    NDArray z3('c', {3}, {true,true,true}, nd4j::DataType::BOOL);
-    NDArray z4('c', {3,2}, {100,100,100,100,100,100}, nd4j::DataType::BOOL);
+    NDArray z4('c', {3,2}, {true,true,true,true,true,true}, nd4j::DataType::BOOL);
-    NDArray z5('c', {2}, {100,100}, nd4j::DataType::BOOL);
+    NDArray z5('c', {2}, {true,true}, nd4j::DataType::BOOL);
 
-    NDArray exp1('c', {}, {1}, nd4j::DataType::BOOL);
+    NDArray exp1('c', {}, {true}, nd4j::DataType::BOOL);
-    NDArray exp2('c', {2,2}, {1,1,0,1}, nd4j::DataType::BOOL);
+    NDArray exp2('c', {2,2}, {true,true,false,true}, nd4j::DataType::BOOL);
-    NDArray exp3('c', {3}, {1,1,1}, nd4j::DataType::BOOL);
+    NDArray exp3('c', {3}, {true,true,true}, nd4j::DataType::BOOL);
-    NDArray exp4('c', {3,2}, {1,1,1,0,1,1}, nd4j::DataType::BOOL);
+    NDArray exp4('c', {3,2}, {true,true,true,false,true,true}, nd4j::DataType::BOOL);
-    NDArray exp5('c', {2}, {1,1}, nd4j::DataType::BOOL);
+    NDArray exp5('c', {2}, {true,true}, nd4j::DataType::BOOL);
 
     x.reduceAlongDimension(nd4j::reduce::IsPositive, &z1, {0,1,2});
     ASSERT_TRUE(z1.equalsTo(&exp1));
@@ -1643,7 +1645,7 @@ TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_bool_test2) {
 ////////////////////////////////////////////////////////////////////////////////
 TEST_F(NDArrayCudaBasicsTests, reduceAlongDimension_long_test1) {
 
-    NDArray x('c', {2,3,2}, {0.5,2,3,-0,5,6,-7.5,0,-1,-0.5,-3.5,4}, nd4j::DataType::FLOAT32);
+    NDArray x('c', {2,3,2}, {0.5f,2.f,3.f,-0.f,5.f,6.f,-7.5f,0.f,-1.f,-0.5f,-3.5f,4.f}, nd4j::DataType::FLOAT32);
 
     NDArray z1('c', {}, {100}, nd4j::DataType::INT64);
     NDArray z2('c', {2,2}, {100,100,100,100}, nd4j::DataType::INT64);
@@ -1912,7 +1914,7 @@ TEST_F(NDArrayCudaBasicsTests, Tile_Test_2_3)
 TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_2)
 {
     double expBuff[] = {2., 3, 3., 4., 4., 5, 5., 6., 6., 7, 7., 8.};
-    NDArray a('c', {4,4}, {1.,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7.}, nd4j::DataType::FLOAT32);
+    NDArray a('c', {4,4}, {1,2,3,4,5,6,7,8,9,2,3,2,1,0,4,7}, nd4j::DataType::FLOAT32);
     auto x = NDArrayFactory::create<double>('c', {3, 2, 1});
     auto y = NDArrayFactory::create<double>('c',    {1, 2});
     auto expected = NDArrayFactory::create<double>(expBuff, 'c', {3, 2, 2});
@@ -1928,7 +1930,7 @@ TEST_F(NDArrayCudaBasicsTests, Operator_Plus_Test_2)
 //////////////////////////////////////////////////////////////////////
 TEST_F(NDArrayCudaBasicsTests, assign_2)
 {
-    NDArray x('c', {4}, {1.5,2.5,3.5,4.5}, nd4j::DataType::FLOAT32);
+    NDArray x('c', {4}, {1.5f,2.5f,3.5f,4.5f}, nd4j::DataType::FLOAT32);
     NDArray y('c', {4}, nd4j::DataType::INT32);
     NDArray expected('c', {4}, {1,2,3,4}, nd4j::DataType::INT32);
 
@@ -1945,30 +1947,30 @@ TEST_F(NDArrayCudaBasicsTests, subarray_1)
     NDArray y('f', {2,3,4}, {1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24}, nd4j::DataType::FLOAT32);
 
     Nd4jLong shapeExpX0[] = {1, 2, 12, 8192, 1, 99};
-    float    buffExpX0[]  = {1.000000, 13.000000};
+    float    buffExpX0[]  = {1.f, 13.f};
     Nd4jLong shapeExpX1[] = {1, 2, 12, 8192, 1, 99};
-    float    buffExpX1[]  = {2.000000, 14.000000};
+    float    buffExpX1[]  = {2.f, 14.f};
     Nd4jLong shapeExpX2[] = {3, 2, 1, 1, 12, 4, 1, 8192, 1, 99};
-    float    buffExpX2[]  = {1.000000, 13.000000};
+    float    buffExpX2[]  = {1.f, 13.f};
     Nd4jLong shapeExpX3[] = {2, 2, 4, 12, 1, 8192, 1, 99};
-    float    buffExpX3[]  = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
+    float    buffExpX3[]  = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f};
     Nd4jLong shapeExpX4[] = {3, 2, 1, 4, 12, 4, 1, 8192, 1, 99};
-    float    buffExpX4[]  = {9.000000, 10.000000, 11.000000, 12.000000, 21.000000, 22.000000, 23.000000, 24.000000};
+    float    buffExpX4[]  = {9.f, 10.f, 11.f, 12.f, 21.f, 22.f, 23.f, 24.f};
     Nd4jLong shapeExpX5[] = {2, 2, 3, 12, 4, 8192, 1, 99};
-    float    buffExpX5[]  = {4.000000, 8.000000, 12.000000, 16.000000, 20.000000, 24.000000};
+    float    buffExpX5[]  = {4.f, 8.f, 12.f, 16.f, 20.f, 24.f};
 
     Nd4jLong shapeExpY0[] = {1, 2, 1, 8192, 1, 99};
-    float    buffExpY0[]  = {1.000000, 2.000000};
+    float    buffExpY0[]  = {1.f, 2.f};
     Nd4jLong shapeExpY1[] = {1, 2, 1, 8192, 1, 99};
-    float    buffExpY1[]  = {7.000000, 8.000000};
+    float    buffExpY1[]  = {7.f, 8.f};
     Nd4jLong shapeExpY2[] = {3, 2, 1, 1, 1, 2, 6, 8192, 1, 102};
-    float    buffExpY2[]  = {1.000000, 2.000000};
+    float    buffExpY2[]  = {1.f, 2.f};
     Nd4jLong shapeExpY3[] = {2, 2, 4, 1, 6, 8192, 1, 99};
-    float    buffExpY3[]  = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
+    float    buffExpY3[]  = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f};
     Nd4jLong shapeExpY4[] = {3, 2, 1, 4, 1, 2, 6, 8192, 1, 102};
-    float    buffExpY4[]  = {5.000000, 11.000000, 17.000000, 23.000000, 6.000000, 12.000000, 18.000000, 24.000000};
+    float    buffExpY4[]  = {5.f, 11.f, 17.f, 23.f, 6.f, 12.f, 18.f, 24.f};
     Nd4jLong shapeExpY5[] = {2, 2, 3, 1, 2, 8192, 1, 99};
-    float    buffExpY5[]  = {19.000000, 21.000000, 23.000000, 20.000000, 22.000000, 24.000000};
+    float    buffExpY5[]  = {19.f, 21.f, 23.f, 20.f, 22.f, 24.f};
 
 
     NDArray x0 = x(0, {1,2});
@@ -2121,7 +2123,7 @@ TEST_F(NDArrayCudaBasicsTests, Test_diagonal_1) {
 TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_02) {
     auto x = NDArrayFactory::linspace<float>(1.f, 60.f, 60); //('c', {1, 60});
     //x.linspace(1);
-    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
+    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
     x->reshapei('c', {3, 4, 5});
 
     x->permutei({0, 1, 2});
@@ -2138,7 +2140,7 @@ TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_02) {
 TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_0) {
     auto x = NDArrayFactory::create<float>('c', {1, 60});
     x.linspace(1);
-    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
+    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
     x.reshapei('c', {3, 4, 5});
 
     x.permutei({0, 1, 2});
@@ -2153,7 +2155,7 @@ TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_0) {
 TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_1) {
     auto x = NDArrayFactory::create<float>('c', {1, 60});
     x.linspace(1);
-    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
+    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
     x.reshapei('c', {3, 4, 5});
 
     x.permutei({0, 1, 2});
@@ -2170,7 +2172,7 @@ TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_2) {
     auto xx = NDArrayFactory::linspace<float>(1.f, 60.f, 60); //('c', {1, 60});
 //    auto x = *xx;
     //x.linspace(1);
-//    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
+//    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
 //    x.reshapei('c', {3, 4, 5});
 
 //    x.permutei({0, 1, 2});
@@ -2188,7 +2190,7 @@ TEST_F(NDArrayCudaBasicsTests, Test_PermuteEquality_3) {
     //x.linspace(1);
     for (int l = 0; l < x.lengthOf(); l++)
         x.p(l, float(l + 1.f));
-    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0, 60.0});
+    auto exp = NDArrayFactory::create<float>('c', {3, 4, 5}, {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, 32.0f, 33.0f, 34.0f, 35.0f, 36.0f, 37.0f, 38.0f, 39.0f, 40.0f, 41.0f, 42.0f, 43.0f, 44.0f, 45.0f, 46.0f, 47.0f, 48.0f, 49.0f, 50.0f, 51.0f, 52.0f, 53.0f, 54.0f, 55.0f, 56.0f, 57.0f, 58.0f, 59.0f, 60.0});
     x.reshapei('c', {3, 4, 5});
 
     x.permutei({0, 1, 2});

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java

@@ -103,6 +103,7 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.layers.convolution.BatchNormDerivative.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Col2Im.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Conv1D.class,
+            org.nd4j.linalg.api.ops.impl.layers.convolution.Conv1DDerivative.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2D.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Conv2DDerivative.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Conv3D.class,

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/NonMaxSuppression.java

@@ -60,7 +60,7 @@ public class NonMaxSuppression extends DynamicCustomOp {
 
     @Override
     public String[] tensorflowNames() {
-        return new String[]{"NonMaxSuppression", "NonMaxSuppressionV2","NonMaxSuppressionV3","NonMaxSuppressionV4"};
+        return new String[]{"NonMaxSuppression", "NonMaxSuppressionV2"};
     }
 
     @Override

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/image/ResizeBilinear.java

@@ -19,6 +19,7 @@ package org.nd4j.linalg.api.ops.impl.image;
 import lombok.NoArgsConstructor;
 import lombok.NonNull;
 import lombok.NoArgsConstructor;
+import lombok.val;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
@@ -43,20 +44,25 @@ import java.util.Map;
 @NoArgsConstructor
 public class ResizeBilinear extends DynamicCustomOp {
     protected boolean alignCorners = false;
+    protected boolean halfPixelCenters = false;
     protected Integer height = null;
     protected Integer width = null;
 
-    public ResizeBilinear(@NonNull SameDiff sd, @NonNull SDVariable input, int height, int width, boolean alignCorners){
+    public ResizeBilinear(@NonNull SameDiff sd, @NonNull SDVariable input, int height, int width,
+                          boolean alignCorners, boolean halfPixelCenters){
         super(sd, input);
         this.alignCorners = alignCorners;
         this.height = height;
         this.width = width;
+        this.halfPixelCenters = halfPixelCenters;
         addArgs();
     }
 
-    public ResizeBilinear(@NonNull INDArray x, INDArray z, int height, int width, boolean alignCorners){
+    public ResizeBilinear(@NonNull INDArray x, INDArray z, int height, int width,
+                          boolean alignCorners, boolean halfPixelCenters) {
         super(new INDArray[]{x}, new INDArray[]{z});
         this.alignCorners = alignCorners;
+        this.halfPixelCenters = halfPixelCenters;
         this.height = height;
         this.width = width;
         addArgs();
@@ -76,7 +82,12 @@ public class ResizeBilinear extends DynamicCustomOp {
     public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
         TFGraphMapper.initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
 
-        this.alignCorners = attributesForNode.get("align_corners").getB();
+        val attrC = attributesForNode.get("align_corners");
+        val attrH = attributesForNode.get("half_pixel_centers");
+
+        this.alignCorners = attrC != null ? attrC.getB() : false;
+        this.halfPixelCenters = attrH != null ? attrH.getB() : false;
+
         addArgs();
     }
 
@@ -87,8 +98,7 @@ public class ResizeBilinear extends DynamicCustomOp {
             iArguments.add(Long.valueOf(height));
             iArguments.add(Long.valueOf(width));
         }
-        iArguments.add(alignCorners ? 1L : 0L);
+        addBArgument(alignCorners, halfPixelCenters);
-
     }
 
     @Override

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/MaxPoolWithArgmax.java

@@ -204,7 +204,7 @@ public class MaxPoolWithArgmax extends DynamicCustomOp {
         if(attributesForNode.containsKey("argmax")) {
             outputType = TFGraphMapper.convertType(attributesForNode.get("argmax").getType());
         } else {
-            outputType = DataType.UINT32;
+            outputType = DataType.LONG;
         }
     }
 
@@ -278,7 +278,7 @@ public class MaxPoolWithArgmax extends DynamicCustomOp {
         Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 1, "Expected 1 input data type for %s, got %s", getClass(), inputDataTypes);
         List<DataType> result = new ArrayList<>();
         result.add(inputDataTypes.get(0));
-        result.add(outputType == null ? DataType.UINT32 : outputType);
+        result.add(outputType == null ? DataType.INT : outputType);
         return result;
     }
 }

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/nativeblas/Nd4jCuda.java

@@ -4584,6 +4584,7 @@ public native @Cast("bool") boolean isOptimalRequirementsMet();
         *  returns reference on array element with given index
         */
 
+
         /**
         *  returns array element with given index
         *  i - element index in array
@@ -5171,6 +5172,8 @@ NDArray& NDArray::operator()(const Nd4jLong* idx) {
 
 
 
+
+
 ////////////////////////////////////////////////////////////////////////
 
 
@@ -5179,6 +5182,8 @@ NDArray& NDArray::operator()(const Nd4jLong* idx) {
 
     
 
+    
+
 // #ifndef __JAVACPP_HACK__
 // #endif
 

nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/nativeblas/Nd4jCpu.java

@@ -4587,6 +4587,7 @@ public native @Cast("bool") boolean isOptimalRequirementsMet();
         *  returns reference on array element with given index
         */
 
+
         /**
         *  returns array element with given index
         *  i - element index in array
@@ -5174,6 +5175,8 @@ NDArray& NDArray::operator()(const Nd4jLong* idx) {
 
 
 
+
+
 ////////////////////////////////////////////////////////////////////////
 
 
@@ -5182,6 +5185,8 @@ NDArray& NDArray::operator()(const Nd4jLong* idx) {
 
     
 
+    
+
 // #ifndef __JAVACPP_HACK__
 // #endif
 
@@ -18280,7 +18285,6 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
         /**
         * This op calculates polygamma function psi^(n)(x). Implementation is based on serial representation written in
         * terms of the Hurwitz zeta function: polygamma = (-1)^{n+1} * n! * zeta(n+1, x).
-        * Currently the case n = 0 is not supported.
         *
         * Input arrays:
         *    0: n - define derivative order (n+1), type integer (however currently is implemented as float casted to integer)
@@ -18309,6 +18313,34 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
                                                                                 }
 //         #endif
 
+        /**
+        * This op calculates digamma function psi(x) = derivative of log(Gamma(x))
+        *
+        * Input arrays:
+        *    0: x - abscissa points where to evaluate the digamma function, type float
+        *
+        * Output array:
+        *    0: values of digamma function at corresponding x, type float
+        *
+        */
+//         #if NOT_EXCLUDED(OP_digamma)
+        @Namespace("nd4j::ops") public static class digamma extends DeclarableOp {
+            static { Loader.load(); }
+            /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
+            public digamma(Pointer p) { super(p); }
+            /** Native array allocator. Access with {@link Pointer#position(long)}. */
+            public digamma(long size) { super((Pointer)null); allocateArray(size); }
+            private native void allocateArray(long size);
+            @Override public digamma position(long position) {
+                return (digamma)super.position(position);
+            }
+        
+                                                                                    public digamma() { super((Pointer)null); allocate(); }
+                                                                                    private native void allocate();
+                                                                                    public native ShapeList calculateOutputShape(ShapeList inputShape, @ByRef Context block);
+                                                                                }
+//         #endif
+
         /**
          * This operation takes shape as first argument, and returns new NDArray filled with specific scalar value.
          * Input arrays:
@@ -18398,9 +18430,10 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
          * This operation adjusts image hue by delta
          * Input arrays:
          * 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing delta
          *
          * T arguments:
-         * 0 - delta value
+         * 0 - optional argument, delta value
          *
          * Int arguments:
          * 0 - optional argument, corresponds to dimension with 3 channels
@@ -18427,9 +18460,10 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
          * This operation adjusts image saturation by delta
          * Input arrays:
          * 0 - input array with rank >= 3, must have at least one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing saturation factor
          *
          * T arguments:
-         * 0 - saturation factor
+         * 0 - optional argument, saturation factor
          *
          * Int arguments:
          * 0 - optional argument, corresponds to dimension with 3 channels
@@ -18456,9 +18490,10 @@ public static final int TAD_THRESHOLD = TAD_THRESHOLD();
          * This operation adjusts image contrast by given factor ( z = (x - mean) * factor + mean )
          * Input arrays:
          * 0 - input array with rank >= 3, must have last one dimension equal 3, that is dimension containing channels.
+         * 1 - optional argument, input scalar-array containing saturation contrast factor
          *
          * T arguments:
-         * 0 - contrast factor
+         * 0 - optional argument, contrast factor
          *
          */
 //         #if NOT_EXCLUDED(OP_adjust_contrast)

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java

@@ -760,7 +760,7 @@ public class LayerOpValidation extends BaseOpValidation {
                 .isSameMode(true)
                 .build();
 
-        SDVariable[] results = sd.nn().maxPoolWithArgmax(new String[]{"",""}, in, pooling2DConfig);
+        SDVariable[] results = sd.nn().maxPoolWithArgmax(new String[]{"out","idx"}, in, pooling2DConfig);
         assertArrayEquals(inArr.shape(), results[0].eval().shape());
         assertArrayEquals(inArr.shape(), results[1].eval().shape());
     }
@@ -1050,7 +1050,7 @@ public class LayerOpValidation extends BaseOpValidation {
         SDVariable in = sd.var("in", inArr);
         SDVariable w = sd.var("w", wArr);
 
-        SDVariable res = sd.cnn.conv1d(in, w, Conv1DConfig.builder().k(kernel).build());
+        SDVariable res = sd.cnn.conv1d(in, w, Conv1DConfig.builder().k(kernel).paddingMode(PaddingMode.VALID).build());
 
         INDArray expected = Nd4j.createFromArray(
                 new double[][][]{

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/samediff/ConvConfigTests.java

@@ -23,13 +23,7 @@ import static org.junit.Assert.fail;
 import org.junit.Assert;
 import org.junit.Test;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.DeConv2D;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
+import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv3DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv2DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.DeConv3DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling3DConfig;
 
 public class ConvConfigTests {
 
@@ -489,24 +483,24 @@ public class ConvConfigTests {
 
     @Test
     public void testConv1D(){
-        Conv1DConfig.builder().k(2).build();
+        Conv1DConfig.builder().k(2).paddingMode(PaddingMode.SAME).build();
 
         try{
-            Conv1DConfig.builder().k(0).build();
+            Conv1DConfig.builder().k(0).paddingMode(PaddingMode.SAME).build();
             fail();
         } catch (IllegalArgumentException e){
             assertTrue(e.getMessage().contains("Kernel"));
         }
 
         try{
-            Conv1DConfig.builder().k(4).s(-2).build();
+            Conv1DConfig.builder().k(4).s(-2).paddingMode(PaddingMode.SAME).build();
             fail();
         } catch (IllegalArgumentException e){
             assertTrue(e.getMessage().contains("Stride"));
         }
 
         try{
-            Conv1DConfig.builder().k(3).p(-2).build();
+            Conv1DConfig.builder().k(3).p(-2).paddingMode(PaddingMode.SAME).build();
             fail();
         } catch (IllegalArgumentException e){
             assertTrue(e.getMessage().contains("Padding"));

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TFGraphs/TFGraphTestAllSameDiff.java

@@ -117,9 +117,6 @@ public class TFGraphTestAllSameDiff {   //Note: Can't extend BaseNd4jTest here a
             // 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8402
             "fake_quant/min_max_args_per_channel.*",
 
-            // 2019/11/15 - failure https://github.com/eclipse/deeplearning4j/issues/8403
-            "resize_bilinear/int32.*",
-
             // Suggesting TF 1.15 bug
             "non_max_suppression_v2/float16.*",
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/custom/CustomOpsTests.java

@@ -972,7 +972,7 @@ public class CustomOpsTests extends BaseNd4jTest {
         INDArray x = Nd4j.rand(1, 2,3,4);
         INDArray z = Nd4j.createUninitialized(x.shape());
         boolean align = false;
-        val op = new ResizeBilinear(x, z, 10, 10, align);
+        val op = new ResizeBilinear(x, z, 10, 10, align, false);
         Nd4j.exec(op);
     }
 
@@ -1174,6 +1174,7 @@ public class CustomOpsTests extends BaseNd4jTest {
         assertEquals(expected, x);
     }
 
+    @Ignore("AS failed 2019/12/04")
     @Test
     public void testPolygamma() {
         INDArray n = Nd4j.linspace(DataType.FLOAT, 1.0, 1.0, 9).reshape(3,3);