Various ND4J/DL4J fixes and improvements (#87)

* Reshape and reallocate - small fixes

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

* Reshape and reallocate - small fixes

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

* #6488 ElementWiseVertex broadcast support

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

* Constructors and broadcast supported it Transforms.max/min

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

* #8054 ElementWiseVertex now supports broadcast inputs

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

* #8057 Nd4j.create overload dtype fix

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

* #7551 ND4J Shape validation fix

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

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/GradientCheckTestsComputationGraph.java

@@ -16,6 +16,7 @@
 
 package org.deeplearning4j.gradientcheck;
 
+import lombok.extern.slf4j.Slf4j;
 import org.deeplearning4j.BaseDL4JTest;
 import org.deeplearning4j.TestUtils;
 import org.deeplearning4j.datasets.iterator.impl.IrisDataSetIterator;
@@ -53,9 +54,9 @@ import java.util.Arrays;
 import java.util.Map;
 import java.util.Random;
 
-import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.*;
-import static org.junit.Assert.assertTrue;
 
+@Slf4j
 public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
 
     public static final boolean PRINT_RESULTS = true;
@@ -287,6 +288,56 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         }
     }
 
+    @Test
+    public void testElementWiseVertexBroadcast(){
+
+        ElementWiseVertex.Op[] ops =
+                new ElementWiseVertex.Op[] {ElementWiseVertex.Op.Add, ElementWiseVertex.Op.Average,
+                        ElementWiseVertex.Op.Subtract, ElementWiseVertex.Op.Max, ElementWiseVertex.Op.Product};
+
+        for(boolean firstSmaller : new boolean[]{false, true}) {
+            for (ElementWiseVertex.Op op : ops) {
+                ComputationGraphConfiguration conf = new NeuralNetConfiguration.Builder()
+                        .updater(new NoOp())
+                        .dataType(DataType.DOUBLE)
+                        .activation(Activation.TANH)
+                        .seed(12345)
+                        .graphBuilder()
+                        .addInputs("in")
+                        .setOutputs("out")
+                        .layer("l1", new DenseLayer.Builder().nIn(3).nOut(firstSmaller ? 1 : 3).build(), "in")   //[mb,3]
+                        .layer("l2", new DenseLayer.Builder().nIn(3).nOut(firstSmaller ? 3 : 1).build(), "in")   //[mb,1]
+                        .addVertex("ew", new ElementWiseVertex(op), "l1", "l2")
+                        .layer("out", new OutputLayer.Builder().nIn(3).nOut(2).lossFunction(LossFunctions.LossFunction.MCXENT).activation(Activation.SOFTMAX).build(), "ew")
+                        .build();
+
+                ComputationGraph graph = new ComputationGraph(conf);
+                graph.init();
+
+                for (int mb : new int[]{1, 5}) {
+                    String msg = (firstSmaller ? "first smaller, " : "second smaller, ") + "mb=" + mb + ", op=" + op;
+
+                    log.info("Test: {}", msg);
+
+                    INDArray in = Nd4j.rand(DataType.FLOAT, mb, 3);
+
+                    INDArray out = graph.outputSingle(in);
+                    assertArrayEquals(new long[]{mb, 2}, out.shape());
+
+                    INDArray labels = TestUtils.randomOneHot(mb, 2);
+
+                    graph.fit(new DataSet(in, labels));
+
+                    boolean gradOK = GradientCheckUtil.checkGradients(graph, DEFAULT_EPS, DEFAULT_MAX_REL_ERROR,
+                            DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS, RETURN_ON_FIRST_FAILURE, new INDArray[]{in},
+                            new INDArray[]{labels});
+                    assertTrue(msg, gradOK);
+                    TestUtils.testModelSerialization(graph);
+                }
+            }
+        }
+    }
+
     @Test
     public void testCnnDepthMerge() {
 

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/conf/graph/ElementWiseVertexTest.java

@@ -17,6 +17,7 @@
 package org.deeplearning4j.nn.conf.graph;
 
 import org.deeplearning4j.BaseDL4JTest;
+import org.deeplearning4j.TestUtils;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.conf.ComputationGraphConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
@@ -34,6 +35,7 @@ import org.nd4j.linalg.activations.impl.ActivationTanH;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.rng.distribution.impl.UniformDistribution;
+import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Sgd;
 import org.nd4j.linalg.lossfunctions.LossFunctions.LossFunction;
@@ -42,6 +44,8 @@ import org.nd4j.linalg.primitives.Pair;
 
 import java.util.Map;
 
+import static org.junit.Assert.assertArrayEquals;
+
 /**
  * Created by binesh on 6/14/2017.
  */
@@ -690,6 +694,7 @@ public class ElementWiseVertexTest extends BaseDL4JTest {
         Assert.assertEquals(0, mse(nullsafe(gradients.get("dense2_b")), dEdb2), this.epsilon);
     }
 
+
     private static double mse(INDArray output, INDArray target) {
         double mse_expect = Transforms.pow(output.sub(target), 2.0).sumNumber().doubleValue()
                         / (output.columns() * output.rows());

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/vertex/impl/ElementWiseVertex.java

@@ -27,15 +27,20 @@ import org.nd4j.linalg.api.memory.MemoryWorkspace;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.CustomOp;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastTo;
 import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.SubOp;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Or;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldSubOp;
+import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.conditions.Conditions;
+import org.nd4j.linalg.ops.transforms.Transforms;
 import org.nd4j.linalg.primitives.Pair;
 import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 
+import java.util.Arrays;
+
 /** An ElementWiseVertex is used to combine the activations of two or more layer in an element-wise manner<br>
  * For example, the activations may be combined by addition, subtraction or multiplication or by selecting the maximum.
  * Addition, Average, Product and Max may use an arbitrary number of input arrays. Note that in the case of subtraction, only two inputs may be used.
@@ -80,17 +85,44 @@ public class ElementWiseVertex extends BaseGraphVertex {
         if (inputs.length == 1)
             return workspaceMgr.dup(ArrayType.ACTIVATIONS, inputs[0]);
 
+        boolean isBc = false;
+        for(int i=1; i<inputs.length; i++ ){
+            if(!inputs[0].equalShapes(inputs[i])){
+                isBc = true;
+                break;
+            }
+        }
+
+        long[] outShape;
+        if(!isBc){
+            outShape = inputs[0].shape();
+        } else {
+            outShape = Shape.broadcastOutputShape(inputs[0].shape(), inputs[1].shape());
+            for( int i=2; i<inputs.length; i++ ){
+                outShape = Shape.broadcastOutputShape(outShape, inputs[i].shape());
+            }
+        }
+
         switch (op) {
             case Add:
-                INDArray sum =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, inputs[0].shape());
+                INDArray sum =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, outShape);
+                if(isBc && !Arrays.equals(outShape, inputs[0].shape())){
+                    Nd4j.exec(new BroadcastTo(inputs[0], outShape, sum));
+                } else {
                     sum.assign(inputs[0]);
+                }
+
                 for (int i = 1; i < inputs.length; i++) {
                     sum.addi(inputs[i].castTo(dataType));
                 }
                 return sum;
             case Average:
-                INDArray average =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, inputs[0].shape());
+                INDArray average =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, outShape);
+                if(isBc && !Arrays.equals(outShape, inputs[0].shape())){
+                    Nd4j.exec(new BroadcastTo(inputs[0], outShape, average));
+                } else {
                     average.assign(inputs[0]);
+                }
                 for (int i = 1; i < inputs.length; i++) {
                     average.addi(inputs[i].castTo(dataType));
                 }
@@ -98,15 +130,28 @@ public class ElementWiseVertex extends BaseGraphVertex {
             case Subtract:
                 if (inputs.length != 2)
                     throw new IllegalArgumentException("ElementWise subtraction only supports 2 inputs");
-                return Nd4j.getExecutioner().exec(new OldSubOp(inputs[0], inputs[1], workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, inputs[0].dataType(), inputs[0].shape())));
+                return Nd4j.exec(new SubOp(inputs, new INDArray[]{workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, inputs[0].dataType(), outShape)}))[0];
             case Product:
-                INDArray product =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, inputs[0].shape());
+                INDArray product =  workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, dataType, outShape);
+
+                if(isBc && !Arrays.equals(outShape, inputs[0].shape())){
+                    Nd4j.exec(new BroadcastTo(inputs[0], outShape, product));
+                } else {
                     product.assign(inputs[0]);
+                }
+
                 for (int i = 1; i < inputs.length; i++) {
                     product.muli(inputs[i].castTo(dataType));
                 }
                 return product;
             case Max:
+                boolean isBroadcast = false;
+                for(int i=1; i<inputs.length; i++ ){
+                    isBroadcast |= !inputs[0].equalShapes(inputs[i]);
+                    if(isBroadcast)
+                        break;
+                }
+                if(!isBroadcast) {
                     INDArray max = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS, inputs[0].dataType(), inputs[0].shape(), inputs[0].ordering());
                     CustomOp op = DynamicCustomOp.builder("mergemax")
                             .addInputs(inputs)
@@ -115,6 +160,19 @@ public class ElementWiseVertex extends BaseGraphVertex {
                             .build();
                     Nd4j.getExecutioner().exec(op);
                     return max;
+                } else {
+                    //AB 20190729 mergemax doesn't support broadcast at this point
+                    if(inputs.length == 1){
+                        return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, inputs[0]);
+                    } else {
+                        INDArray max = Transforms.max(inputs[0], inputs[1], true);
+                        for( int i=2; i<inputs.length; i++ ){
+                            max = Transforms.max(max, inputs[i], false);
+                        }
+                        return workspaceMgr.leverageTo(ArrayType.ACTIVATIONS, max);
+                    }
+                }
+
             default:
                 throw new UnsupportedOperationException("Unknown op: " + this.op);
         }
@@ -128,40 +186,119 @@ public class ElementWiseVertex extends BaseGraphVertex {
         if (nInForwardPass == 1)
             return new Pair<>(null, new INDArray[] {workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon)});
 
+        boolean broadcastCase = false;
+        for( int i=1; i<nInForwardPass; i++ ){
+            broadcastCase |= !inputs[0].equalShapes(inputs[i]);
+        }
+
         switch (op) {
             case Add:
                 //If x=sum_i a_i then dL/da_i = dL/dx * dx/da_i = dL/dx
                 INDArray[] out = new INDArray[nInForwardPass];
-                for (int i = 0; i < nInForwardPass; i++)
+                for (int i = 0; i < nInForwardPass; i++) {
+                    if(!broadcastCase) {
+                        //Standard case
                         out[i] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
+                    } else {
+                        //For broadcast case, we need to sum along the broadcast dimensions
+                        //So if [mb,3]+[mb,1] -> input 0 backprops epsilon, input 1 backprops epsilon.sum(1,keepDim=true)
+                        if(inputs[i].equalShapes(epsilon)){
+                            out[i] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
+                        } else {
+                            int[] bcDim = Shape.getBroadcastDimensions(inputs[i].shape(), epsilon.shape());
+                            try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)){
+                                out[i] = epsilon.sum(true, bcDim);
+                            }
+                        }
+                    }
+                }
                 return new Pair<>(null, out);
             case Average:
                 INDArray[] outAverage = new INDArray[nInForwardPass];
                 try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)){
-                    for (int i = 0; i < nInForwardPass; i++)
+                    for (int i = 0; i < nInForwardPass; i++) {
+                        if(inputs[i].equalShapes(epsilon)){
                             outAverage[i] = epsilon.div(nInForwardPass);
+                        } else {
+                            int[] bcDim = Shape.getBroadcastDimensions(inputs[i].shape(), epsilon.shape());
+                            outAverage[i] = epsilon.div(nInForwardPass).sum(true, bcDim);
+                        }
+                    }
                 }
                 return new Pair<>(null, outAverage);
             case Subtract:
                 INDArray[] out2 = new INDArray[2];
+                if(!broadcastCase){
                     out2[0] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
                     out2[1] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon).negi();
+                } else {
+                    if(inputs[0].equalShapes(epsilon)){
+                        //Second input is smaller/broadcast
+                        out2[0] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
+                        int[] bcDim = Shape.getBroadcastDimensions(inputs[1].shape(), epsilon.shape());
+                        try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)) {
+                            out2[1] = epsilon.sum(true, bcDim).negi();
+                        }
+                    } else {
+                        //First input is smaller/broadcast
+                        int[] bcDim = Shape.getBroadcastDimensions(inputs[0].shape(), epsilon.shape());
+                        try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)) {
+                            out2[0] = epsilon.sum(true, bcDim);
+                        }
+                        out2[1] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon).negi();
+                    }
+                }
                 return new Pair<>(null, out2);
             case Product:
                 INDArray[] out_product = new INDArray[nInForwardPass];
+                INDArray[] inBc = inputs;
+                if(broadcastCase){
+                    inBc = new INDArray[inputs.length];
+                    for( int i=0; i<inputs.length; i++ ){
+                        if(inputs[i].equalShapes(epsilon)){
+                            inBc[i] = inputs[i];
+                        } else {
+                            inBc[i] = epsilon.ulike();
+                            Nd4j.exec(new BroadcastTo(inputs[i], epsilon.shape(), inBc[i]));
+                        }
+                    }
+                }
+
                 for (int i = 0; i < nInForwardPass; i++) {
                     out_product[i] = workspaceMgr.dup(ArrayType.ACTIVATION_GRAD, epsilon);
                     for (int j = 0; j < nInForwardPass; ++j) {
                         if (i != j)
-                            out_product[i].muli(inputs[j]);
+                            out_product[i].muli(inBc[j]);
+                    }
+
+                    if(!inputs[i].equalShapes(epsilon)){
+                        int[] bcDim = Shape.getBroadcastDimensions(inputs[i].shape(), epsilon.shape());
+                        try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)) {
+                            out_product[i] = out_product[i].sum(true, bcDim);
+                        }
                     }
                 }
                 return new Pair<>(null, out_product);
             case Max:
                 INDArray[] outMax = new INDArray[nInForwardPass];
                 INDArray maxIndices = workspaceMgr.createUninitialized(ArrayType.BP_WORKING_MEM, DataType.INT, epsilon.shape(), epsilon.ordering());
+
+                INDArray[] bcIn = inputs;
+                if(broadcastCase){
+                    //Broadcast to right shape...
+                    bcIn = new INDArray[inputs.length];
+                    for( int i=0; i<inputs.length; i++ ){
+                        if(inputs[i].equalShapes(epsilon)){
+                            bcIn[i] = inputs[i];
+                        } else {
+                            bcIn[i] = epsilon.ulike();
+                            Nd4j.exec(new BroadcastTo(inputs[i], epsilon.shape(), bcIn[i]));
+                        }
+                    }
+                }
+
                 CustomOp op = DynamicCustomOp.builder("mergemaxindex")
-                        .addInputs(inputs)
+                        .addInputs(bcIn)
                         .addOutputs(maxIndices)
                         .callInplace(false)
                         .build();
@@ -172,7 +309,17 @@ public class ElementWiseVertex extends BaseGraphVertex {
                     //generate a mask with 1s and 0s in the right places and muli with epsilon
                     MatchConditionTransform nd4jop = new MatchConditionTransform(maxIndices, outMax[i], Conditions.equals(i));
                     Nd4j.getExecutioner().exec(nd4jop);
-                    outMax[i] = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, outMax[i].castTo(Nd4j.defaultFloatingPointType())).muli(epsilon);
+                    if(broadcastCase && !epsilon.equalShapes(inputs[i])){
+                        //Broadcast  for ths input
+                        outMax[i] = outMax[i].castTo(epsilon.dataType()).mul(epsilon);
+                        int[] bcDim = Shape.getBroadcastDimensions(inputs[i].shape(), epsilon.shape());
+                        try(MemoryWorkspace ws = workspaceMgr.notifyScopeBorrowed(ArrayType.ACTIVATION_GRAD)) {
+                            outMax[i] = outMax[i].sum(true, bcDim);
+                        }
+                    } else {
+                        //Standard case
+                        outMax[i] = workspaceMgr.leverageTo(ArrayType.ACTIVATION_GRAD, outMax[i].castTo(epsilon.dataType()).muli(epsilon));
+                    }
                 }
                 return new Pair<>(null, outMax);
             default:

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ndarray/BaseNDArray.java

@@ -2476,6 +2476,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
         // length/data.length can be different in case of Threshold conversion
         if(isEmpty() || isS())
             return false;
+
         return Shape.offset(jvmShapeInfo.javaShapeInformation) > 0
                 || (length() < data().length() && data.dataType() != DataType.INT)
                 || data().originalDataBuffer() != null;
@@ -4577,7 +4578,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
             return ret;
         } else {
             INDArray ret = this.dup(order);
-            return ret.reshape(order, shape);
+            return Nd4j.create(ret.data(), shape);
         }
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/Max.java

@@ -44,6 +44,10 @@ public class Max extends BaseDynamicTransformOp {
         super(sameDiff, args, inPlace);
     }
 
+    public Max( INDArray first, INDArray second, INDArray out){
+        super(new INDArray[]{first, second}, out == null ? null : new INDArray[]{out});
+    }
+
     public Max( INDArray[] inputs, INDArray[] outputs) {
         super(inputs, outputs);
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/Min.java

@@ -44,6 +44,10 @@ public class Min extends BaseDynamicTransformOp {
         super(sameDiff, args, inPlace);
     }
 
+    public Min( INDArray first, INDArray second, INDArray out){
+        super(new INDArray[]{first, second}, out == null ? null : new INDArray[]{out});
+    }
+
     public Min( INDArray[] inputs, INDArray[] outputs) {
         super(inputs, outputs);
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/same/Max.java

@@ -26,7 +26,7 @@ import java.util.Collections;
 import java.util.List;
 
 /**
- * Calculate the absolute minimum over a vector
+ * Calculate the maximum value between two arrays in an elementwise fashion, broadcasting if required
  *
  * @author raver119@gmail.com
  */

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/same/Min.java

@@ -26,7 +26,7 @@ import java.util.Collections;
 import java.util.List;
 
 /**
- * Calculate the absolute minimum over a vector
+ * Calculate the minimum value between two arrays in an elementwise fashion, broadcasting if required
  *
  * @author raver119@gmail.com
  */

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/Nd4j.java

@@ -538,7 +538,7 @@ public class Nd4j {
     public static INDArray create(int[] sliceShape, float[]... arrays) {
         //TODO: Remove duplicate code.
         int slices = arrays.length;
-        INDArray ret = Nd4j.create(ArrayUtil.combine(new int[] {slices}, sliceShape));
+        INDArray ret = Nd4j.createUninitialized(DataType.FLOAT, ArrayUtil.toLongArray(ArrayUtil.combine(new int[] {slices}, sliceShape)));
         for (int i = 0; i < ret.slices(); i++)
             ret.putSlice(i, Nd4j.create(arrays[i]).reshape(ArrayUtil.toLongArray(sliceShape)));
         return ret;
@@ -572,7 +572,7 @@ public class Nd4j {
      */
     public static INDArray create(int[] sliceShape, double[]... arrays) {
         int slices = arrays.length;
-        INDArray ret = Nd4j.create(ArrayUtil.combine(new int[] {slices}, sliceShape));
+        INDArray ret = Nd4j.createUninitialized(DataType.DOUBLE, ArrayUtil.toLongArray(ArrayUtil.combine(new int[] {slices}, sliceShape)));
         for (int i = 0; i < ret.slices(); i++)
             ret.putSlice(i, Nd4j.create(arrays[i]).reshape(ArrayUtil.toLongArray(sliceShape)));
         return ret;
@@ -3984,6 +3984,7 @@ public class Nd4j {
      * @return the created ndarray.
      */
     public static INDArray create(int[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -3991,6 +3992,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(long[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -3998,6 +4000,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(double[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -4005,6 +4008,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(float[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return  INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -4012,6 +4016,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(short[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -4019,6 +4024,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(byte[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -4026,6 +4032,7 @@ public class Nd4j {
      * See {@link #create(int[], long[], DataType)}
      */
     public static INDArray create(boolean[] data, long[] shape, DataType type) {
+        checkShapeValues(data.length, shape);
         return INSTANCE.create(data, shape, Nd4j.getStrides(shape), type, Nd4j.getMemoryManager().getCurrentWorkspace());
     }
 
@@ -5165,17 +5172,17 @@ public class Nd4j {
     protected static void checkShapeValues(int length, int... shape) {
         checkShapeValues(shape);
 
-        if (ArrayUtil.prodLong(shape) > length)
+        if (ArrayUtil.prodLong(shape) != length && !(length == 1 && shape.length == 0))
             throw new ND4JIllegalStateException("Shape of the new array " + Arrays.toString(shape)
-                    + " doesn't match data length: " + length);
+                    + " doesn't match data length: " + length + " - prod(shape) must equal the number of values provided");
     }
 
     protected static void checkShapeValues(int length, long... shape) {
         checkShapeValues(shape);
 
-        if (ArrayUtil.prodLong(shape) > length)
+        if (ArrayUtil.prodLong(shape) != length && !(length == 1 && shape.length == 0))
             throw new ND4JIllegalStateException("Shape of the new array " + Arrays.toString(shape)
-                    + " doesn't match data length: " + length);
+                    + " doesn't match data length: " + length + " - prod(shape) must equal the number of values provided");
     }
 
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/ops/transforms/Transforms.java

@@ -45,9 +45,11 @@ import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Xor;
 import org.nd4j.linalg.api.ops.impl.transforms.same.*;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.*;
 import org.nd4j.linalg.api.shape.LongShapeDescriptor;
+import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.inverse.InvertMatrix;
 
+import java.util.Arrays;
 import java.util.List;
 
 /**
@@ -858,11 +860,11 @@ public class Transforms {
      * @return
      */
     public static INDArray max(INDArray first, INDArray second, boolean dup) {
-        INDArray result = first;
+        long[] outShape = broadcastResultShape(first, second);   //Also validates
-        if (dup) {
+        Preconditions.checkState(dup || Arrays.equals(outShape, first.shape()), "Cannot do inplace max operation when first input is not equal to result shape (%ndShape vs. result %s)",
-            result = first.ulike();
+                first, outShape);
-        }
+        INDArray out = dup ? Nd4j.create(first.dataType(), outShape) : first;
-        return exec(new OldMax(first, second, result));
+        return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.Max(first, second, out))[0];
     }
 
     /**
@@ -908,10 +910,11 @@ public class Transforms {
      * @return
      */
     public static INDArray min(INDArray first, INDArray second, boolean dup) {
-        if (dup) {
+        long[] outShape = broadcastResultShape(first, second);   //Also validates
-            first = first.dup();
+        Preconditions.checkState(dup || Arrays.equals(outShape, first.shape()), "Cannot do inplace min operation when first input is not equal to result shape (%ndShape vs. result %s)",
-        }
+                first, outShape);
-        return exec(new OldMin(second, first, first));
+        INDArray out = dup ? Nd4j.create(first.dataType(), outShape) : first;
+        return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.transforms.custom.Min(first, second, out))[0];
     }
 
     /**
@@ -1179,4 +1182,15 @@ public class Transforms {
         }
     }
 
+
+    protected static long[] broadcastResultShape(INDArray first, INDArray second){
+        if(first.equalShapes(second)){
+            return first.shape();
+        } else if(Shape.areShapesBroadcastable(first.shape(), second.shape())){
+            return Shape.broadcastOutputShape(first.shape(), second.shape());
+        } else {
+            throw new IllegalStateException("Array shapes are not broadcastable: " + Arrays.toString(first.shape()) +
+                    " vs. " + Arrays.toString(second.shape()));
+        }
+    }
 }

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/Nd4jTestsC.java

@@ -2699,6 +2699,21 @@ public class Nd4jTestsC extends BaseNd4jTest {
         assertEquals(expected, actual);
     }
 
+    @Test
+    public void testBroadcastDiv2(){
+        INDArray arr = Nd4j.ones(DataType.DOUBLE, 1, 64, 125, 125).muli(2);
+        INDArray vec = Nd4j.ones(DataType.DOUBLE, 64).muli(2);
+
+        INDArray exp = Nd4j.ones(DataType.DOUBLE, 1, 64, 125, 125);
+        INDArray out = arr.like();
+
+        for( int i=0; i<10; i++ ) {
+            out.assign(0.0);
+            Nd4j.getExecutioner().exec(new BroadcastDivOp(arr, vec, out, 1));
+            assertEquals(exp, out);
+        }
+    }
+
 
     @Test
     public void testBroadcastMult() {
@@ -7417,7 +7432,8 @@ public class Nd4jTestsC extends BaseNd4jTest {
 
         INDArray arr1a = Nd4j.create(new long[]{2,3}, 'c').get(NDArrayIndex.all(), NDArrayIndex.interval(0,2));
         INDArray arr3 = arr1a.reshape('c', false, 4,1);
-        assertFalse(arr3.isView());     //Should be copy
+        boolean isView = arr3.isView();
+        assertFalse(isView);     //Should be copy
 
         try{
             INDArray arr4 = arr1a.reshape('c', true, 4,1);
@@ -7861,6 +7877,54 @@ public class Nd4jTestsC extends BaseNd4jTest {
         final INDArray arr2 = arr1.reshape(3,1);
         assertEquals("Incorrect type!", DataType.FLOAT, arr1.mmul(arr2).dataType());
     }
+
+
+    @Test
+    public void testCreateDtypes() {
+        int[] sliceShape = new int[] {9};
+        float[] arrays = new float[] {1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, 8.0f, 9.0f};
+        double [] arrays_double = new double[] {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
+
+        INDArray x = Nd4j.create( sliceShape, arrays, arrays );
+        assertEquals(DataType.FLOAT, x.dataType());
+
+        INDArray xd = Nd4j.create( sliceShape, arrays_double, arrays_double  );
+        assertEquals(DataType.DOUBLE, xd.dataType());
+    }
+
+
+    @Test
+    public void testCreateShapeValidation(){
+        try {
+            Nd4j.create(new double[]{1, 2, 3}, new int[]{1, 1});
+            fail();
+        } catch (Exception t){
+            assertTrue(t.getMessage().contains("length"));
+        }
+
+        try {
+            Nd4j.create(new float[]{1, 2, 3}, new int[]{1, 1});
+            fail();
+        } catch (Exception t){
+            assertTrue(t.getMessage().contains("length"));
+        }
+
+        try {
+            Nd4j.create(new byte[]{1, 2, 3}, new long[]{1, 1}, DataType.BYTE);
+            fail();
+        } catch (Exception t){
+            assertTrue(t.getMessage().contains("length"));
+        }
+
+        try {
+            Nd4j.create(new double[]{1, 2, 3}, new int[]{1, 1}, 'c');
+            fail();
+        } catch (Exception t){
+            assertTrue(t.getMessage().contains("length"));
+        }
+    }
+
+
     ///////////////////////////////////////////////////////
     protected static void fillJvmArray3D(float[][][] arr) {
         int cnt = 1;

nd4j/nd4j-buffer/src/main/java/org/nd4j/linalg/api/buffer/BaseDataBuffer.java

@@ -2601,7 +2601,8 @@ public abstract class BaseDataBuffer implements DataBuffer {
         }
 
         Pointer.memcpy(pointer, oldPointer, this.length() * getElementSize());
-        //this.underlyingLength = length;
+        this.underlyingLength = length;
+        this.length = length;
         return this;
     }