Add java op class for relu derivative, and use in Activation ReLU (#207)

* Add java op class for relu derivative, and use in ACtivation ReLU

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

* Fix

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

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/functions/DifferentialFunctionFactory.java

@@ -133,23 +133,8 @@ import org.nd4j.linalg.api.ops.impl.reduce3.EuclideanDistance;
 import org.nd4j.linalg.api.ops.impl.reduce3.HammingDistance;
 import org.nd4j.linalg.api.ops.impl.reduce3.JaccardDistance;
 import org.nd4j.linalg.api.ops.impl.reduce3.ManhattanDistance;
-import org.nd4j.linalg.api.ops.impl.scalar.LeakyReLU;
+import org.nd4j.linalg.api.ops.impl.scalar.*;
-import org.nd4j.linalg.api.ops.impl.scalar.LogX;
 import org.nd4j.linalg.api.ops.impl.scalar.Pow;
-import org.nd4j.linalg.api.ops.impl.scalar.PowDerivative;
-import org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinear;
-import org.nd4j.linalg.api.ops.impl.scalar.Relu6;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarAdd;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarDivision;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarFMod;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarMax;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarMin;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarMultiplication;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarReverseDivision;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarReverseSubtraction;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarSet;
-import org.nd4j.linalg.api.ops.impl.scalar.ScalarSubtraction;
-import org.nd4j.linalg.api.ops.impl.scalar.Step;
 import org.nd4j.linalg.api.ops.impl.scalar.comparison.ScalarEquals;
 import org.nd4j.linalg.api.ops.impl.scalar.comparison.ScalarGreaterThan;
 import org.nd4j.linalg.api.ops.impl.scalar.comparison.ScalarGreaterThanOrEqual;
@@ -211,6 +196,7 @@ import org.nd4j.linalg.api.ops.impl.transforms.clip.ClipByValue;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndReplace;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.*;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Pow;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.segment.SegmentMax;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.segment.SegmentMean;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.segment.SegmentMin;
@@ -1340,6 +1326,10 @@ public class DifferentialFunctionFactory {
         return new RectifiedLinear(sameDiff(), iX, false, cutoff).outputVariable();
     }
 
+    public SDVariable reluDerivative(SDVariable input, SDVariable grad){
+        return new RectifiedLinearDerivative(sameDiff(), input, grad).outputVariable();
+    }
+
     public SDVariable relu6(SDVariable iX, double cutoff) {
         return new Relu6(sameDiff(), iX, false, cutoff).outputVariable();
     }

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

@@ -228,6 +228,7 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.scalar.Pow.class,
             org.nd4j.linalg.api.ops.impl.scalar.PowDerivative.class,
             org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinear.class,
+            org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinearDerivative.class,
             org.nd4j.linalg.api.ops.impl.scalar.Relu6.class,
             org.nd4j.linalg.api.ops.impl.scalar.ReplaceNans.class,
             org.nd4j.linalg.api.ops.impl.scalar.ScalarAdd.class,

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/activations/impl/ActivationReLU.java

@@ -18,6 +18,7 @@ package org.nd4j.linalg.activations.impl;
 
 import lombok.EqualsAndHashCode;
 import lombok.Getter;
+import org.nd4j.linalg.api.ops.impl.scalar.RectifiedLinearDerivative;
 import org.nd4j.linalg.api.ops.impl.scalar.Step;
 import org.nd4j.linalg.primitives.Pair;
 import org.nd4j.linalg.activations.BaseActivationFunction;
@@ -41,8 +42,7 @@ public class ActivationReLU extends BaseActivationFunction {
     @Override
     public Pair<INDArray, INDArray> backprop(INDArray in, INDArray epsilon) {
         assertShape(in, epsilon);
-        INDArray dLdz = Nd4j.getExecutioner().exec(new Step(in));
+        INDArray dLdz = Nd4j.exec(new RectifiedLinearDerivative(in, epsilon, in.ulike()))[0];
-        dLdz.muli(epsilon);
         return new Pair<>(dLdz, null);
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/scalar/RectifiedLinear.java

@@ -22,6 +22,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.BaseScalarOp;
 
 import java.util.Arrays;
+import java.util.Collections;
 import java.util.List;
 
 /**
@@ -77,8 +78,12 @@ public class RectifiedLinear extends BaseScalarOp {
 
     @Override
     public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable step = new Step(sameDiff, arg(), false, scalarValue.getDouble(0)).outputVariables()[0];
+        if(scalarValue.getDouble(0) == 0.0){
-        SDVariable ret = step.mul(i_v.get(0));
+            return Collections.singletonList(f().reluDerivative(arg(), i_v.get(0)));
-        return Arrays.asList(ret);
+        } else {
+            SDVariable step = new Step(sameDiff, arg(), false, scalarValue.getDouble(0)).outputVariables()[0];
+            SDVariable ret = step.mul(i_v.get(0));
+            return Collections.singletonList(ret);
+        }
     }
 }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/scalar/RectifiedLinearDerivative.java

@@ -0,0 +1,43 @@
+package org.nd4j.linalg.api.ops.impl.scalar;
+
+import lombok.NonNull;
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+import org.nd4j.shade.guava.base.Preconditions;
+
+import java.util.Collections;
+import java.util.List;
+
+public class RectifiedLinearDerivative extends DynamicCustomOp {
+
+    public RectifiedLinearDerivative(){ }
+
+    public RectifiedLinearDerivative(SameDiff sd, SDVariable input, SDVariable gradient){
+        super(sd, new SDVariable[]{input, gradient});
+    }
+
+    public RectifiedLinearDerivative(@NonNull INDArray input, @NonNull INDArray gradient, INDArray output){
+        super(new INDArray[]{input, gradient}, wrapOrNull(output));
+    }
+
+    @Override
+    public String opName(){
+        return "relu_bp";
+    }
+
+    @Override
+    public List<DataType> calculateOutputDataTypes(List<DataType> dataTypes) {
+        Preconditions.checkArgument(dataTypes != null && dataTypes.size() == 2, "Expected exactly 2 input datatypes, got %s", dataTypes);
+        Preconditions.checkArgument(dataTypes.get(0).isFPType() && dataTypes.get(1).isFPType(), "Input datatypes must be floating point, got %s", dataTypes);
+
+        return Collections.singletonList(dataTypes.get(0));
+    }
+
+    @Override
+    public List<SDVariable> doDiff(List<SDVariable> f1) {
+        throw new UnsupportedOperationException("Not supported");
+    }
+}