[WIP] Various fixes, mostly SameDiff/Nd4j (#110)

* Nd4j pad update

Signed-off-by: Ryan Nett <rnett@skymind.io>

* switched from guava Immutables to Collections.unmodifiableList/Map

Signed-off-by: Ryan Nett <rnett@skymind.io>

* javadoc

Signed-off-by: Ryan Nett <rnett@skymind.io>

* use new pad

Signed-off-by: Ryan Nett <rnett@skymind.io>

* conv tests use OpValidation

Signed-off-by: Ryan Nett <rnett@skymind.io>

* deconv3d overrides

Signed-off-by: Ryan Nett <rnett@skymind.io>

* test fix for the new pad method

Signed-off-by: Ryan Nett <rnett@skymind.io>

* more test fixes

Signed-off-by: Ryan Nett <rnett@skymind.io>

* more test fixes

Signed-off-by: Ryan Nett <rnett@skymind.io>

* rename SameDiff function methods to op (except for the actual SameDiff function ones)

Signed-off-by: Ryan Nett <rnett@skymind.io>

* more pad overloads, test fix

Signed-off-by: Ryan Nett <rnett@skymind.io>

* test updates

Signed-off-by: Ryan Nett <rnett@skymind.io>

* conv1d test

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove Conv1D tf import (there isn't a TF conv1d op)

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove numThreads from Nd4j

Signed-off-by: Ryan Nett <rnett@skymind.io>

* replace Old ops with their newer versions, deprecate ones that haven't already been deprecated

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove use of setNumThreads

Signed-off-by: Ryan Nett <rnett@skymind.io>

* fix for Reverse and ATan2

Signed-off-by: Ryan Nett <rnett@skymind.io>

* fix test for wrong equals type

Signed-off-by: Ryan Nett <rnett@skymind.io>

* well it works now

Signed-off-by: Ryan Nett <rnett@skymind.io>

* better javadocs

Signed-off-by: Ryan Nett <rnett@skymind.io>

* NonNulls

Signed-off-by: Ryan Nett <rnett@skymind.io>

* better array literal

Signed-off-by: Ryan Nett <rnett@skymind.io>

* re-add tf import stuff (will remove later)

Signed-off-by: Ryan Nett <rnett@skymind.io>

* conv1d config load fix

Signed-off-by: Ryan Nett <rnett@skymind.io>

* partial config usage changes

Signed-off-by: Ryan Nett <rnett@skymind.io>

* remove Old op classes

Signed-off-by: Ryan Nett <rnett@skymind.io>

* config property fixes

Signed-off-by: Ryan Nett <rnett@skymind.io>

* removed one too many ops

Signed-off-by: Ryan Nett <rnett@skymind.io>

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/AlphaDropout.java

@@ -24,11 +24,10 @@ import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.random.impl.AlphaDropOut;
 import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
 import org.nd4j.linalg.factory.Nd4j;
-import org.nd4j.linalg.ops.transforms.Transforms;
 import org.nd4j.linalg.schedule.ISchedule;
 import org.nd4j.shade.jackson.annotation.JsonIgnoreProperties;
 import org.nd4j.shade.jackson.annotation.JsonProperty;
@@ -139,7 +138,7 @@ public class AlphaDropout implements IDropout {
         //a * (x * d + alphaPrime * (1-d)) + b
         INDArray inverseMask = mask.rsub(1.0);
         INDArray aPOneMinusD = inverseMask.muli(alphaPrime);
-        Nd4j.getExecutioner().exec(new OldMulOp(inputActivations, mask, output));   //out = x * d
+        Nd4j.getExecutioner().exec(new MulOp(inputActivations, mask, output));   //out = x * d
         output.addi(aPOneMinusD).muli(a).addi(b);
 
         //Nd4j.getExecutioner().exec(new AlphaDropOut(inputActivations, output, p, a, alphaPrime, b));
@@ -152,7 +151,7 @@ public class AlphaDropout implements IDropout {
         //dL/dIn = dL/dOut * dOut/dIn
         // dOut/dIn = 0 if dropped (d=0), or a otherwise (d=1)
         mask.muli(a);
-        Nd4j.getExecutioner().exec(new OldMulOp(gradAtOutput, mask, gradAtInput));
+        Nd4j.getExecutioner().exec(new MulOp(gradAtOutput, mask, gradAtInput));
         mask = null;
         return gradAtInput;
     }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/Dropout.java

@@ -24,7 +24,7 @@ import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.random.impl.DropOutInverted;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.schedule.ISchedule;
@@ -153,7 +153,7 @@ public class Dropout implements IDropout {
 
         mask = workspaceMgr.createUninitialized(ArrayType.INPUT, output.dataType(), output.shape(), output.ordering()).assign(1.0);
         Nd4j.getExecutioner().exec(new DropOutInverted(mask, mask, currP));
-        Nd4j.getExecutioner().exec(new OldMulOp(inputCast, mask, output));
+        Nd4j.getExecutioner().exec(new MulOp(inputCast, mask, output));
         return output;
     }
 
@@ -171,7 +171,7 @@ public class Dropout implements IDropout {
         if(m.dataType() != gradAtInput.dataType()){
             m = m.castTo(gradAtInput.dataType());
         }
-        Nd4j.getExecutioner().exec(new OldMulOp(gradAtOutput, m, gradAtInput));
+        Nd4j.getExecutioner().exec(new MulOp(gradAtOutput, m, gradAtInput));
         mask = null;
         return gradAtInput;
     }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianDropout.java

@@ -22,7 +22,7 @@ import org.deeplearning4j.nn.workspace.ArrayType;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.schedule.ISchedule;
@@ -88,7 +88,7 @@ public class GaussianDropout implements IDropout {
         noise = workspaceMgr.createUninitialized(ArrayType.INPUT, output.dataType(), inputActivations.shape(), inputActivations.ordering());
         Nd4j.getExecutioner().exec(new GaussianDistribution(noise, 1.0, stdev));
 
-        return Nd4j.getExecutioner().exec(new OldMulOp(inputActivations, noise, output));
+        return Nd4j.getExecutioner().exec(new MulOp(inputActivations, noise, output))[0];
     }
 
     @Override
@@ -96,7 +96,7 @@ public class GaussianDropout implements IDropout {
         Preconditions.checkState(noise != null, "Cannot perform backprop: GaussianDropout noise array is absent (already cleared?)");
         //out = in*y, where y ~ N(1, stdev)
         //dL/dIn = dL/dOut * dOut/dIn = y * dL/dOut
-        Nd4j.getExecutioner().exec(new OldMulOp(gradAtOutput, noise, gradAtInput));
+        Nd4j.getExecutioner().exec(new MulOp(gradAtOutput, noise, gradAtInput));
         noise = null;
         return gradAtInput;
     }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/GaussianNoise.java

@@ -19,7 +19,7 @@ package org.deeplearning4j.nn.conf.dropout;
 import lombok.Data;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAddOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
 import org.nd4j.linalg.api.ops.random.impl.GaussianDistribution;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.schedule.ISchedule;
@@ -69,7 +69,7 @@ public class GaussianNoise implements IDropout {
         INDArray noise = Nd4j.createUninitialized(output.dataType(), inputActivations.shape(), inputActivations.ordering());
         Nd4j.getExecutioner().exec(new GaussianDistribution(noise, 0, currS));
 
-        Nd4j.getExecutioner().exec(new OldAddOp(inputActivations, noise, output));
+        Nd4j.getExecutioner().exec(new AddOp(inputActivations, noise, output));
         return output;
     }
 

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/variational/BernoulliReconstructionDistribution.java

@@ -24,7 +24,7 @@ import org.nd4j.linalg.activations.impl.ActivationHardSigmoid;
 import org.nd4j.linalg.activations.impl.ActivationSigmoid;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldLessThan;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThan;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.BooleanIndexing;
 import org.nd4j.linalg.indexing.conditions.Conditions;
@@ -144,7 +144,7 @@ public class BernoulliReconstructionDistribution implements ReconstructionDistri
 
         INDArray out = Nd4j.createUninitialized(DataType.BOOL, p.shape());
 
-        Nd4j.getExecutioner().execAndReturn(new OldLessThan(rand, p, out));
+        Nd4j.getExecutioner().execAndReturn(new LessThan(rand, p, out));
         return out.castTo(DataType.FLOAT);
     }
 

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/weightnoise/WeightNoise.java

@@ -22,8 +22,8 @@ import org.deeplearning4j.nn.api.ParamInitializer;
 import org.deeplearning4j.nn.conf.distribution.Distribution;
 import org.deeplearning4j.nn.conf.distribution.Distributions;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAddOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.factory.Nd4j;
 import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;
 import org.deeplearning4j.nn.workspace.ArrayType;
@@ -86,9 +86,9 @@ public class WeightNoise implements IWeightNoise {
             INDArray out = workspaceMgr.createUninitialized(ArrayType.INPUT, param.dataType(), param.shape(), param.ordering());
 
             if (additive) {
-                Nd4j.getExecutioner().exec(new OldAddOp(param, noise,out));
+                Nd4j.getExecutioner().exec(new AddOp(param, noise,out));
             } else {
-                Nd4j.getExecutioner().exec(new OldMulOp(param, noise, out));
+                Nd4j.getExecutioner().exec(new MulOp(param, noise, out));
             }
             return out;
         }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/BatchNormalization.java

@@ -34,8 +34,8 @@ import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastAddOp;
 import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastDivOp;
 import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastMulOp;
 import org.nd4j.linalg.api.ops.impl.broadcast.BroadcastSubOp;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldDivOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.DivOp;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldSubOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.SubOp;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.exception.ND4JOpProfilerException;
 import org.nd4j.linalg.factory.Nd4j;
@@ -205,7 +205,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
                 INDArray batchMean = helper.getMeanCache(dataType);
                 INDArray batchVar = helper.getVarCache(dataType);
 
-                Nd4j.getExecutioner().exec(new OldSubOp(globalMean, batchMean, dGlobalMeanView));   //deltaGlobalMean = globalMean[t] - batchMean
+                Nd4j.getExecutioner().exec(new SubOp(globalMean, batchMean, dGlobalMeanView));   //deltaGlobalMean = globalMean[t] - batchMean
                 dGlobalMeanView.muli(1-layerConf().getDecay());
 
                 if(layerConf().isUseLogStd()){
@@ -219,12 +219,12 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
 
                     double decay = layerConf().getDecay();
                     INDArray varip1 = vari.mul(decay).addi(batchVar.mul(1-decay));
-                    Nd4j.getExecutioner().exec(new OldDivOp(vari, varip1, dGlobalLog10StdView));
+                    Nd4j.getExecutioner().exec(new DivOp(vari, varip1, dGlobalLog10StdView));
                     Transforms.log(dGlobalLog10StdView, false);
                     dGlobalLog10StdView.muli(ONE_ON_2LOGE_10);
                 } else {
                     //Use variance estimate parameterization. This was only option up to and including 1.0.0-beta3
-                    Nd4j.getExecutioner().exec(new OldSubOp(globalVar, batchVar, dGlobalVarView));      //deltaGlobalVar = globalVar[t] - batchVar
+                    Nd4j.getExecutioner().exec(new SubOp(globalVar, batchVar, dGlobalVarView));      //deltaGlobalVar = globalVar[t] - batchVar
                     dGlobalVarView.muli(1 - layerConf().getDecay());
                 }
 
@@ -343,7 +343,7 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
         And use the same idea for global variance estimate
          */
 
-        Nd4j.getExecutioner().exec(new OldSubOp(globalMean, batchMean, dGlobalMeanView));   //deltaGlobalMean = globalMean[t] - batchMean
+        Nd4j.getExecutioner().exec(new SubOp(globalMean, batchMean, dGlobalMeanView));   //deltaGlobalMean = globalMean[t] - batchMean
         dGlobalMeanView.muli(1-layerConf().getDecay());
 
         if(layerConf().isUseLogStd()){
@@ -357,12 +357,12 @@ public class BatchNormalization extends BaseLayer<org.deeplearning4j.nn.conf.lay
 
             double decay = layerConf().getDecay();
             INDArray varip1 = vari.mul(decay).addi(batchVar.mul(1-decay));
-            Nd4j.getExecutioner().exec(new OldDivOp(vari, varip1, dGlobalLog10StdView));
+            Nd4j.getExecutioner().exec(new DivOp(vari, varip1, dGlobalLog10StdView));
             Transforms.log(dGlobalLog10StdView, false);
             dGlobalLog10StdView.muli(ONE_ON_2LOGE_10);
         } else {
             //Use variance estimate parameterization. This was only option up to and including 1.0.0-beta3
-            Nd4j.getExecutioner().exec(new OldSubOp(globalVar, batchVar, dGlobalVarView));      //deltaGlobalVar = globalVar[t] - batchVar
+            Nd4j.getExecutioner().exec(new SubOp(globalVar, batchVar, dGlobalVarView));      //deltaGlobalVar = globalVar[t] - batchVar
             dGlobalVarView.muli(1 - layerConf().getDecay());
         }
 

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/normalization/LocalResponseNormalization.java

@@ -23,10 +23,9 @@ import org.deeplearning4j.nn.gradient.DefaultGradient;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.layers.AbstractLayer;
 import org.deeplearning4j.nn.layers.LayerHelper;
-import org.deeplearning4j.nn.layers.mkldnn.MKLDNNLocalResponseNormalizationHelper;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.exception.ND4JOpProfilerException;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.INDArrayIndex;
@@ -187,7 +186,7 @@ public class LocalResponseNormalization
 
         // gx = gy * unitScale**-beta - 2 * alpha * beta * sumPart/unitScale * a^i_{x,y}    - rearranged for more in-place ops
         INDArray nextEpsilon = workspaceMgr.createUninitialized(ArrayType.ACTIVATION_GRAD, epsilon.dataType(), epsilon.shape(), epsilon.ordering());
-        Nd4j.getExecutioner().exec(new OldMulOp(epsilon, scale, nextEpsilon));
+        Nd4j.getExecutioner().exec(new MulOp(epsilon, scale, nextEpsilon));
         nextEpsilon.subi(sumPart.muli(input).divi(unitScale).muli(2 * alpha * beta));
         return new Pair<>(retGradient, nextEpsilon);
     }
@@ -257,7 +256,7 @@ public class LocalResponseNormalization
             unitScale = sumPart.mul(alpha).addi(k);
             // y = x * unitScale**-beta
             scale = Transforms.pow(unitScale, -beta, true);
-            Nd4j.getExecutioner().exec(new OldMulOp(input, scale, activations));
+            Nd4j.getExecutioner().exec(new MulOp(input, scale, activations));
         } else {
             // unitScale = (k + alpha * sum_{j=max(0, i - n/2)}^{max(N-1, i + n/2)} (a^j_{x,y})^2 )
             sumPart.muli(alpha, activations).addi(k);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/recurrent/LSTMHelpers.java

@@ -35,7 +35,7 @@ import org.nd4j.linalg.activations.IActivation;
 import org.nd4j.linalg.activations.impl.ActivationSigmoid;
 import org.nd4j.linalg.api.memory.MemoryWorkspace;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.impl.transforms.same.TimesOneMinus;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
@@ -554,7 +554,7 @@ public class LSTMHelpers {
 
                 //Normally would use zo.dup() in above line, but won't be using zo again (for this time step). Ditto for zf, zg, zi
                 INDArray deltao = deltaoNext;
-                Nd4j.getExecutioner().exec(new OldMulOp(nablaOut, sigmahOfS, deltao));
+                Nd4j.getExecutioner().exec(new MulOp(nablaOut, sigmahOfS, deltao));
                 if (sigmoidGates) {
                     INDArray sigmaoPrimeOfZo = Nd4j.getExecutioner().exec(new TimesOneMinus(ao.dup('f'))); //Equivalent to sigmoid deriv on zo
                     deltao.muli(sigmaoPrimeOfZo);
@@ -607,7 +607,7 @@ public class LSTMHelpers {
                     deltag.muli(ai);
                     deltag.muli(nablaCellState);
                 } else {
-                    INDArray temp2 = Nd4j.getExecutioner().exec(new OldMulOp(ai, nablaCellState, Nd4j.createUninitialized(inputWeights.dataType(), ai.shape(), 'f')));
+                    INDArray temp2 = Nd4j.getExecutioner().exec(new MulOp(ai, nablaCellState, Nd4j.createUninitialized(inputWeights.dataType(), ai.shape(), 'f')))[0];
                     deltag.assign(gateActivationFn.backprop(fwdPass.gz[time], temp2).getFirst());
                     //TODO activation functions with params; optimize (no assign)
                 }
@@ -616,7 +616,7 @@ public class LSTMHelpers {
                 //Network input delta:
                 INDArray zi = fwdPass.iz[time];
                 INDArray deltai = deltaiNext;
-                temp = Nd4j.getExecutioner().exec(new OldMulOp(ag, nablaCellState, Nd4j.createUninitialized(inputWeights.dataType(), deltai.shape(), 'f')));
+                temp = Nd4j.getExecutioner().exec(new MulOp(ag, nablaCellState, Nd4j.createUninitialized(inputWeights.dataType(), deltai.shape(), 'f')))[0];
                 deltai.assign(afn.backprop(zi, temp).getFirst());
                 //TODO activation functions with params; also: optimize this (no assign)
                 //Shape: [m,n^L]

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

@@ -26,7 +26,6 @@ import onnx.OnnxProto3;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
-import org.nd4j.graph.DataType;
 import org.nd4j.imports.converters.DifferentialFunctionClassHolder;
 import org.nd4j.imports.descriptors.properties.AttributeAdapter;
 import org.nd4j.imports.descriptors.properties.PropertyMapping;
@@ -520,7 +519,7 @@ public abstract class DifferentialFunction {
      * @return the arguments for a given function
      */
     public  SDVariable[] args() {
-        return sameDiff.getInputVariablesForFunction(this);
+        return sameDiff.getInputVariablesForOp(this);
     }
 
     /**
@@ -661,7 +660,7 @@ public abstract class DifferentialFunction {
             }
 
             if(sameDiff != null && !(this instanceof SDVariable))
-                sameDiff.putFunctionForId(ownName,this);
+                sameDiff.putOpForId(ownName,this);
         }
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/EvaluationRecord.java

@@ -23,6 +23,7 @@ import com.google.common.collect.Lists;
 
 import java.util.ArrayList;
 import java.util.Collection;
+import java.util.Collections;
 import java.util.HashMap;
 import java.util.List;
 import java.util.Map;
@@ -39,12 +40,12 @@ import org.nd4j.evaluation.IMetric;
 @Getter
 public class EvaluationRecord {
 
-    private ImmutableMap<String, List<IEvaluation>> evaluations;
+    private Map<String, List<IEvaluation>> evaluations;
     private Map<Class<? extends IEvaluation>, IEvaluation> classEvaluations = new HashMap<>();
     private boolean isEmpty = true;
 
     public EvaluationRecord(Map<String, List<IEvaluation>> evaluations) {
-        this.evaluations = ImmutableMap.copyOf(evaluations);
+        this.evaluations = Collections.unmodifiableMap(evaluations);
 
         for (List<IEvaluation> le : evaluations.values()) {
             for (IEvaluation e : le) {
@@ -68,7 +69,7 @@ public class EvaluationRecord {
     /**
      * Get all evaluations
      */
-    public ImmutableMap<String, List<IEvaluation>> evaluations() {
+    public Map<String, List<IEvaluation>> evaluations() {
         return evaluations;
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/History.java

@@ -16,8 +16,8 @@
 
 package org.nd4j.autodiff.listeners.records;
 
-import com.google.common.collect.ImmutableList;
 import java.util.ArrayList;
+import java.util.Collections;
 import java.util.List;
 
 import lombok.Getter;
@@ -49,11 +49,11 @@ public class History {
 
     public History(List<EvaluationRecord> training, List<EvaluationRecord> validation, LossCurve loss,
             long trainingTimeMillis, List<Long> validationTimesMillis){
-        trainingHistory = ImmutableList.copyOf(training);
+        trainingHistory = Collections.unmodifiableList(training);
-        validationHistory = ImmutableList.copyOf(validation);
+        validationHistory = Collections.unmodifiableList(validation);
         this.lossCurve = loss;
         this.trainingTimeMillis = trainingTimeMillis;
-        this.validationTimesMillis = ImmutableList.copyOf(validationTimesMillis);
+        this.validationTimesMillis = Collections.unmodifiableList(validationTimesMillis);
     }
 
     /**

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/records/LossCurve.java

@@ -16,8 +16,8 @@
 
 package org.nd4j.autodiff.listeners.records;
 
-import com.google.common.collect.ImmutableList;
 import java.util.ArrayList;
+import java.util.Collections;
 import java.util.List;
 import lombok.Getter;
 import lombok.NonNull;
@@ -35,7 +35,7 @@ public class LossCurve {
     private INDArray lossValues;
 
     public LossCurve(List<Loss> losses){
-        lossNames = ImmutableList.copyOf(losses.get(0).getLossNames());
+        lossNames = Collections.unmodifiableList(losses.get(0).getLossNames());
         int numLossValues = losses.get(0).lossValues().length;
         lossValues = Nd4j.create(DataType.FLOAT, losses.size(), losses.get(0).lossValues().length);
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/SameDiff.java

@@ -466,8 +466,11 @@ public class SameDiff extends SDBaseOps {
     }
 
     /**
-     * Set the current {@link Listener} instances.
+     * Set the current SameDiff-wide {@link Listener} instances.
-     * Note that
+     *
+     * Note that this will overwrite the current listener list.
+     * If you want to use additional listeners for a single operation,
+     * use the listener arguments in those methods (e.g. {@link #fit()} and {@link FitConfig#listeners(Listener...)}).
      *
      * @param listeners Listeners
      */
@@ -476,19 +479,37 @@ public class SameDiff extends SDBaseOps {
         addListeners(listeners);
     }
 
+    /**
+     * See {@link #setListeners(Listener...)}.
+     */
     public void setListeners(Collection<? extends Listener> listeners) {
         this.listeners.clear();
         addListeners(listeners);
     }
 
+
+    /**
+     * Add SameDiff-wide {@link Listener} instances.
+     *
+     * If you want to use additional listeners for a single operation,
+     * use the listener arguments in those methods (e.g. {@link #fit()} and {@link FitConfig#listeners(Listener...)}).
+     *
+     * @param listeners Listeners
+     */
     public void addListeners(Listener... listeners) {
         addListeners(Arrays.asList(listeners));
     }
 
+    /**
+     * See {@link #addListeners(Listener...)}.
+     */
     public void addListeners(Collection<? extends Listener> listeners) {
         this.listeners.addAll(listeners);
     }
 
+    /**
+     * Gets the current SameDiff-wide listeners.
+     */
     public List<Listener> getListeners() {
         return listeners;
     }
@@ -585,6 +606,9 @@ public class SameDiff extends SDBaseOps {
     }
 
 
+    /**
+     * Gets all operations in a given name scope.
+     */
     public List<SameDiffOp> getOpsInScope(NameScope scope) {
         ArrayList<SameDiffOp> ops = new ArrayList<>();
         for (SameDiffOp v : this.ops.values()) {
@@ -594,6 +618,16 @@ public class SameDiff extends SDBaseOps {
         return ops;
     }
 
+    /**
+     * See {@link #getOpsInScope(NameScope)}.
+     */
+    public List<SameDiffOp> getOpsInScope(String scope){
+        return getOpsInScope(new NameScope(this, scope));
+    }
+
+    /**
+     * Gets all variables in a given name scope.
+     */
     public List<SDVariable> getVariablesInScope(NameScope scope) {
         ArrayList<SDVariable> vars = new ArrayList<>();
         for (SDVariable v : variables()) {
@@ -603,6 +637,13 @@ public class SameDiff extends SDBaseOps {
         return vars;
     }
 
+    /**
+     * See {@link #getVariablesInScope(NameScope)}.
+     */
+    public List<SDVariable> getVariablesInScope(String scope){
+        return getVariablesInScope(new NameScope(this, scope));
+    }
+
     /**
      * @param sameDiff
      * @return
@@ -638,8 +679,8 @@ public class SameDiff extends SDBaseOps {
                     function.getSameDiff());
             clone.setSameDiff(sameDiff);
             clone.setOwnName(function.getOwnName());
-            if (sameDiff.functionExists(function.getOwnName()))
+            if (sameDiff.opExists(function.getOwnName()))
-                sameDiff.putFunctionForId(function.getOwnName(), function);
+                sameDiff.putOpForId(function.getOwnName(), function);
             newFunctions.put(function.getOwnName(), clone);
 
             val argsForFunction = function.args();
@@ -672,17 +713,21 @@ public class SameDiff extends SDBaseOps {
      * @param id the function id to test for
      * @return true if the function id exists, false otherwise
      */
-    public boolean functionExists(String id) {
+    public boolean opExists(String id) {
         return ops.containsKey(id);
     }
 
-    public DifferentialFunction functionOutputFor(String varName) {
+    /**
-        if (variables.get(varName).getOutputOfOp() == null)
+     * Get the differential function (if any) that this variable is the output for
+     *
+     * @param variableName Name of the variable
+     * @return The differential function that this variable is an output of, or null if it is not the output of a function
+     */
+    public DifferentialFunction getVariableOutputOp(String variableName) {
+        Preconditions.checkState(variables.containsKey(variableName), "No variable with name \"%s\" found in graph", variableName);
+        if (variables.get(variableName).getOutputOfOp() == null)
             return null;
-        String outName = variables.get(varName).getOutputOfOp();
+        return ops.get(variables.get(variableName).getOutputOfOp()).getOp();
-        if (outName == null)
-            return null;
-        return ops.get(outName).getOp();
     }
 
     /**
@@ -691,7 +736,7 @@ public class SameDiff extends SDBaseOps {
      * @param id the id of the function
      * @return the function for the given id if it exists
      */
-    public DifferentialFunction getFunctionById(@NonNull String id) {
+    public DifferentialFunction getOpById(@NonNull String id) {
         if (!ops.containsKey(id)) {
             throw new ND4JIllegalStateException("No function with id " + id + " found!");
         }
@@ -705,7 +750,7 @@ public class SameDiff extends SDBaseOps {
      * @param id       the id of the function
      * @param function the function
      */
-    public void putFunctionForId(String id, DifferentialFunction function) {
+    public void putOpForId(String id, DifferentialFunction function) {
         if (ops.containsKey(id) && ops.get(id).getOp() == null) {
             throw new ND4JIllegalStateException("Function by id already exists!");
         } else if (function instanceof SDVariable) {
@@ -726,7 +771,7 @@ public class SameDiff extends SDBaseOps {
      * @param function the function to get the inputs for
      * @return the input ids for a given function
      */
-    public String[] getInputsForFunction(DifferentialFunction function) {
+    public String[] getInputsForOp(DifferentialFunction function) {
         if (!ops.containsKey(function.getOwnName()))
             throw new ND4JIllegalStateException("Illegal function instance id found " + function.getOwnName());
         List<String> inputs = ops.get(function.getOwnName()).getInputsToOp();
@@ -739,7 +784,7 @@ public class SameDiff extends SDBaseOps {
      * @param function the function to get the outputs for
      * @return the outputs ids for a given function
      */
-    public String[] getOutputsForFunction(DifferentialFunction function) {
+    public String[] getOutputsForOp(DifferentialFunction function) {
         if (!ops.containsKey(function.getOwnName()))
             throw new ND4JIllegalStateException("Illegal function instance id found " + function.getOwnName());
         List<String> outputs = ops.get(function.getOwnName()).getOutputsOfOp();
@@ -753,8 +798,8 @@ public class SameDiff extends SDBaseOps {
      * @param function the function reference to get the output variable(s) for
      * @return the output variables for the given function
      */
-    public SDVariable[] getOutputVariablesForFunction(DifferentialFunction function) {
+    public SDVariable[] getOutputVariablesForOp(DifferentialFunction function) {
-        val inputs = getOutputsForFunction(function);
+        val inputs = getOutputsForOp(function);
         if (inputs == null) {
             throw new ND4JIllegalStateException("No inputs found for function " + function);
         }
@@ -774,8 +819,8 @@ public class SameDiff extends SDBaseOps {
      * @param function the function reference to get the input variable(s) for
      * @return the input variables for the given function
      */
-    public SDVariable[] getInputVariablesForFunction(DifferentialFunction function) {
+    public SDVariable[] getInputVariablesForOp(DifferentialFunction function) {
-        val inputs = getInputsForFunction(function);
+        val inputs = getInputsForOp(function);
         if (inputs == null) {
             throw new ND4JIllegalStateException("No inputs found for function " + function);
         }
@@ -792,6 +837,10 @@ public class SameDiff extends SDBaseOps {
     }
 
 
+    /**
+     * Set the stored {@link INDArray} for a variable.  Only works if the variable is of type
+     * {@link VariableType#CONSTANT}, {@link VariableType#PLACEHOLDER}, or {@link VariableType#VARIABLE}.
+     */
     public void setArrayForVariable(@NonNull String varName, @NonNull INDArray arr) {
         Preconditions.checkState(variables.containsKey(varName), "No variable with name \"%s\" exists", varName);
 
@@ -830,6 +879,9 @@ public class SameDiff extends SDBaseOps {
         return variableNameToShape.get(varName);
     }
 
+    /**
+     * See {@link #getShapeForVarName(String)}, but returns the shape descriptor.
+     */
     public LongShapeDescriptor getShapeDescriptorForVarName(String varName) {
         if (getVariable(varName).getArr() != null) {
             return getVariable(varName).getArr().shapeDescriptor();
@@ -861,6 +913,9 @@ public class SameDiff extends SDBaseOps {
     }
 
 
+    /**
+     * Sets the shape descriptor for a variable.
+     */
     public void putShapeForVarName(String varName, LongShapeDescriptor shape) {
         val v = getVariable(varName);
         putShapeForVarName(varName, shape.getShape());
@@ -1559,19 +1614,6 @@ public class SameDiff extends SDBaseOps {
 
     }
 
-    /**
-     * Get the differential function (if any) that this variable is the output for
-     *
-     * @param variableName Name of the variable
-     * @return The differential function that this variable is an output of, or null if it is not the output of a function
-     */
-    public DifferentialFunction getVariableOutputFunction(String variableName) {
-        Preconditions.checkState(variables.containsKey(variableName), "No variable with name \"%s\" found in graph", variableName);
-        if (variables.get(variableName).getOutputOfOp() == null)
-            return null;
-        return ops.get(variables.get(variableName).getOutputOfOp()).getOp();
-    }
-
 
     /**
      * Returns true if this function already has defined arguments
@@ -1628,7 +1670,7 @@ public class SameDiff extends SDBaseOps {
      *
      * @return Array of differential functions
      */
-    public DifferentialFunction[] functions() {
+    public DifferentialFunction[] ops() {
         List<DifferentialFunction> out = new ArrayList<>(ops.size());
         for (SameDiffOp op : ops.values()) {
             out.add(op.getOp());
@@ -3143,10 +3185,18 @@ public class SameDiff extends SDBaseOps {
                 placeholders, batch, requiredActivations, activeListeners, at);
     }
 
+    /**
+     * See {@link #one(String, DataType, int...)}.
+     * Uses the DataType of the Nd4j default floating point type ({@link Nd4j#defaultFloatingPointType()}).
+     */
     public SDVariable one(String name, int... shape) {
         return one(name, Nd4j.defaultFloatingPointType(), shape);
     }
 
+    /**
+     * See {@link #one(String, DataType, long...)}.
+     * Uses the DataType of the Nd4j default floating point type ({@link Nd4j#defaultFloatingPointType()}).
+     */
     public SDVariable one(String name, long... shape) {
         return one(name, Nd4j.defaultFloatingPointType(), shape);
     }
@@ -3174,11 +3224,18 @@ public class SameDiff extends SDBaseOps {
         return var(name, new ConstantInitScheme('f', 1.0), dataType, shape);
     }
 
-
+    /**
+     * See {@link #zero(String, DataType, long...)}.
+     * Uses the DataType of the Nd4j default floating point type ({@link Nd4j#defaultFloatingPointType()}).
+     */
     public SDVariable zero(String name, long... shape) {
         return zero(name, Nd4j.defaultFloatingPointType(), shape);
     }
 
+    /**
+     * See {@link #zero(String, DataType, int...)}.
+     * Uses the DataType of the Nd4j default floating point type ({@link Nd4j#defaultFloatingPointType()}).
+     */
     public SDVariable zero(String name, int... shape) {
         return zero(name, Nd4j.defaultFloatingPointType(), shape);
     }
@@ -3293,6 +3350,18 @@ public class SameDiff extends SDBaseOps {
     }
 
     //TODO only allowing null datatype for TF import (it's fixed in a later step) - don't want this in the public API!
+
+    /**
+     * Variable initialization with a specified {@link WeightInitScheme}
+     * This method creates VARIABLE type SDVariable - i.e., must be floating point, and is a trainable parameter. See {@link VariableType} for more details.
+     *
+     * @param name             the name of the variable
+     * @param variableType     the SameDiff variable type of the variable (e.g. CONSTANT, PLACEHOLDER, etc.)
+     * @param weightInitScheme the weight initialization scheme
+     * @param dataType         the data type of the variable (float, int, etc)
+     * @param shape            the shape of the array to be created
+     * @return the created variable
+     */
     public SDVariable var(@NonNull String name, @NonNull VariableType variableType, WeightInitScheme weightInitScheme,
                           org.nd4j.linalg.api.buffer.DataType dataType, long... shape) {
 
@@ -3932,7 +4001,7 @@ public class SameDiff extends SDBaseOps {
      * @param varName  the variable name to remove
      * @param function the function to remove the argument from
      */
-    public void removeArgFromFunction(String varName, DifferentialFunction function) {
+    public void removeArgFromOp(String varName, DifferentialFunction function) {
         val args = function.args();
 
         for (int i = 0; i < args.length; i++) {
@@ -4324,7 +4393,7 @@ public class SameDiff extends SDBaseOps {
             }
 
             //Update the internal state: outgoing variables for function
-            if (getOutputsForFunction(function) == null)
+            if (getOutputsForOp(function) == null)
                 addOutgoingFor(ret, function);
 
             return ret;
@@ -4357,7 +4426,7 @@ public class SameDiff extends SDBaseOps {
 
 
             //Update the internal state: outgoing variables for function
-            if (getOutputsForFunction(function) == null)
+            if (getOutputsForOp(function) == null)
                 addOutgoingFor(ret, function);
 
             return ret;
@@ -4428,7 +4497,9 @@ public class SameDiff extends SDBaseOps {
                 .build();
     }
 
-
+    /**
+     * Create a new TensorArray.
+     */
     public TensorArray tensorArray(DataType dataType) {
         TensorArray ta = new TensorArray(this, dataType);
         SDVariable[] outVars = ta.outputVariables();
@@ -4439,7 +4510,6 @@ public class SameDiff extends SDBaseOps {
      * @param functionName
      * @param with
      */
-
     public SDVariable invokeFunctionOn(String functionName, SameDiff with) {
         SameDiff instance = sameDiffFunctionInstances.get(functionName);
         SDVariable ret = instance.invokeGraphOn(with);
@@ -5746,6 +5816,13 @@ public class SameDiff extends SDBaseOps {
         return bufferBuilder.dataBuffer();
     }
 
+    /**
+     * See {@link #asFlatGraph(long, ExecutorConfiguration, boolean)}.
+     *
+     * Uses the default {@link ExecutorConfiguration} with output mode as
+     * {@link OutputMode#VARIABLE_SPACE}, execution mode as {@link ExecutionMode#SEQUENTIAL},
+     * with profiling disabled and gather timings enabled.
+     */
     public FlatGraph asFlatGraph(boolean includeUpdaterState) {
         return FlatGraph.getRootAsFlatGraph(this.asFlatBuffers(includeUpdaterState));
     }
@@ -5765,6 +5842,10 @@ public class SameDiff extends SDBaseOps {
      * This method exports the current SameDiff instance into FlatBuffers format, returning the array ops and
      * all arrays as a ByteBuffer containing the FlatBuffers format data
      *
+     * Uses the default {@link ExecutorConfiguration} with output mode as
+     * {@link OutputMode#VARIABLE_SPACE}, execution mode as {@link ExecutionMode#SEQUENTIAL},
+     * with profiling disabled and gather timings enabled.
+     *
      * @param includeUpdaterState If true: include the updater state (state for updaters such as Adam, Nesterov, AdaGrad etc)
      * @return a ByteBuffer holding the exported FlatBuffers representation of the graph
      */
@@ -5870,7 +5951,11 @@ public class SameDiff extends SDBaseOps {
 
     /**
      * This method converts SameDiff instance to FlatBuffers and saves it to file which can be restored later<br>
-     * This includes the updater state, if applicable
+     * This includes the updater state, if applicable.
+     *
+     * Uses the default {@link ExecutorConfiguration} with output mode as
+     * {@link OutputMode#VARIABLE_SPACE}, execution mode as {@link ExecutionMode#SEQUENTIAL},
+     * with profiling disabled and gather timings enabled.
      *
      * @param file File to save the FlatBuffers serialized graph (including arrays) to
      */
@@ -5878,6 +5963,13 @@ public class SameDiff extends SDBaseOps {
         asFlatFile(file, true);
     }
 
+    /**
+     * See {@link #asFlatFile(File, ExecutorConfiguration, boolean)}.
+     *
+     * Uses the default {@link ExecutorConfiguration} with output mode as
+     * {@link OutputMode#VARIABLE_SPACE}, execution mode as {@link ExecutionMode#SEQUENTIAL},
+     * with profiling disabled and gather timings enabled.
+     */
     public void asFlatFile(@NonNull File file, boolean withUpdaterState) throws IOException {
         val fb = asFlatBuffers(withUpdaterState);
         val offset = fb.position();
@@ -5943,6 +6035,8 @@ public class SameDiff extends SDBaseOps {
      * instance from a byte buffers
      * instance.
      *
+     * See {@link #fromFlatBuffers(ByteBuffer, boolean)}.  Loads updater state (loadUpdaterState is true).
+     *
      * @param bbIn the input byte buffer
      * @return the created samediff instance
      * @throws IOException
@@ -5951,6 +6045,16 @@ public class SameDiff extends SDBaseOps {
         return fromFlatBuffers(bbIn, true);
     }
 
+    /**
+     * Create a {@link SameDiff}
+     * instance from a byte buffers
+     * instance.
+     *
+     * @param bbIn the input byte buffer
+     * @param loadUpdaterState If true, load the updater state (Adam etc state). For training, use true. For inference, use false
+     * @return the created samediff instance
+     * @throws IOException
+     */
     public static SameDiff fromFlatBuffers(ByteBuffer bbIn, boolean loadUpdaterState) throws IOException {
 
         FlatGraph fg = FlatGraph.getRootAsFlatGraph(bbIn);
@@ -6287,7 +6391,7 @@ public class SameDiff extends SDBaseOps {
     public String summary() {
 
         Map<String, SDVariable> varMap = variableMap();
-        DifferentialFunction[] functions = functions();
+        DifferentialFunction[] functions = ops();
 
 
         int countVarsWithArrays = 0;
@@ -6324,7 +6428,7 @@ public class SameDiff extends SDBaseOps {
             if (outputOf == null) {
                 outputOf = "<none>";
             } else {
-                DifferentialFunction d = getFunctionById(outputOf);
+                DifferentialFunction d = getOpById(outputOf);
                 outputOf = d.getOwnName() + "(" + d.opName() + ")";
             }
             outputOfFn.put(s, outputOf);
@@ -6412,7 +6516,7 @@ public class SameDiff extends SDBaseOps {
             for (Map.Entry<String, SameDiff> e : sameDiffFunctionInstances.entrySet()) {
                 SameDiff sd = e.getValue();
                 int vars = sd.variableMap().size();
-                int fns = (sd.functions() == null ? 0 : sd.functions().length);
+                int fns = (sd.ops() == null ? 0 : sd.ops().length);
                 int defFns = sd.definedFunctionNames().size();
 
                 sb.append(String.format(format, e.getKey(), String.valueOf(vars), String.valueOf(fns), String.valueOf(defFns))).append("\n");
@@ -6422,11 +6526,16 @@ public class SameDiff extends SDBaseOps {
         return sb.toString();
     }
 
-
+    /**
+     * Calculate data types for the variables in the graph
+     */
     public Map<String, org.nd4j.linalg.api.buffer.DataType> calculateOutputDataTypes() {
         return calculateOutputDataTypes(false);
     }
 
+    /**
+     * Calculate data types for the variables in the graph
+     */
     public Map<String, org.nd4j.linalg.api.buffer.DataType> calculateOutputDataTypes(boolean dynamicUpdate) {
         List<String> allVars = new ArrayList<>(variables.keySet());
         DataTypesSession session = new DataTypesSession(this, dynamicUpdate);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/internal/AbstractSession.java

@@ -325,7 +325,7 @@ public abstract class AbstractSession<T, O> {
                 }
 
 
-            } else if (sameDiff.getVariableOutputFunction(varToExec.getVariable()) != null) {
+            } else if (sameDiff.getVariableOutputOp(varToExec.getVariable()) != null) {
                 //Variable is the output of an op -> execute op
                 String opName = sameDiff.getVariables().get(varToExec.getVariable()).getOutputOfOp();
 
@@ -336,7 +336,7 @@ public abstract class AbstractSession<T, O> {
 
 
                 //Post execution: work out what is now available for exec
-                String[] opOutputVarNames = sameDiff.getFunctionById(opName).outputVariablesNames();
+                String[] opOutputVarNames = sameDiff.getOpById(opName).outputVariablesNames();
 
                 Preconditions.checkState(opOutputValues.length == opOutputVarNames.length, "Unexpected number of outputs from executed op %s:" +
                                 " got %s outputs when %s outputs were expected (%s)", parameterizedOp.getClass().getSimpleName(), opOutputValues.length,
@@ -423,10 +423,10 @@ public abstract class AbstractSession<T, O> {
         //Note subgraph initially should include placeholders and constants
         while (!processingQueue.isEmpty()) {
             String varName = processingQueue.remove();
-            String opName = (sameDiff.getVariableOutputFunction(varName) == null ? null : sameDiff.getVariableOutputFunction(varName).getOwnName());
+            String opName = (sameDiff.getVariableOutputOp(varName) == null ? null : sameDiff.getVariableOutputOp(varName).getOwnName());
 
             if (!subgraph.contains(varName)) {
-                String[] opInputs = opName == null ? null : sameDiff.getInputsForFunction(sameDiff.getFunctionById(opName));
+                String[] opInputs = opName == null ? null : sameDiff.getInputsForOp(sameDiff.getOpById(opName));
                 List<String> controlDeps = sameDiff.getVariables().get(varName).getControlDeps();
                 int numInputs = (opInputs == null ? 0 : opInputs.length);
                 if (controlDeps != null) {
@@ -457,7 +457,7 @@ public abstract class AbstractSession<T, O> {
 
             if (opName != null) {
                 //To execute op - and hence get this variable: need inputs to that op
-                String[] inputs = sameDiff.getInputsForFunction(sameDiff.getFunctionById(opName));
+                String[] inputs = sameDiff.getInputsForOp(sameDiff.getOpById(opName));
                 for (String s2 : inputs) {
                     if (!subgraph.contains(s2)) {
                         processingQueue.add(s2);
@@ -501,7 +501,7 @@ public abstract class AbstractSession<T, O> {
         if (inputForOps != null) {
             for (String opName : inputForOps) {
 
-                DifferentialFunction fn = sameDiff.getFunctionById(opName);
+                DifferentialFunction fn = sameDiff.getOpById(opName);
                 if (fn instanceof Merge) {
                     //Merge op: available for execution when *any* of its inputs are available. But only mark it for exec once...
                     List<String> opOutputs = sameDiff.getOps().get(opName).getOutputsOfOp();
@@ -888,7 +888,7 @@ public abstract class AbstractSession<T, O> {
             //Mark that outVar needs this specific executedVar (i.e., specific frame/iteration)
             //However, in the case of enter nodes, they are available for ALL iterations (used in loop conditions, for example)
             Variable v = sameDiff.getVariables().get(inputVar.getVariable());
-            boolean isEnter = sameDiff.getVariableOutputFunction(v.getVariable().getVarName()) instanceof Enter;
+            boolean isEnter = sameDiff.getVariableOutputOp(v.getVariable().getVarName()) instanceof Enter;
 
             if(isEnter){
                 VarId iter0 = forVariable;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/internal/DataTypesSession.java

@@ -59,7 +59,7 @@ public class DataTypesSession extends AbstractSession<DataType, DataTypesSession
 
     @Override
     public DataTypeCalc getAndParameterizeOp(String opName, FrameIter frameIter, Set<VarId> inputs, Set<VarId> allIterInputs, Set<String> constAndPhInputs, Map<String, DataType> placeholderValues) {
-        DifferentialFunction df = sameDiff.getFunctionById(opName);
+        DifferentialFunction df = sameDiff.getOpById(opName);
         List<DataType> inputDataTypes = new ArrayList<>();
         for(SDVariable v : df.args()){
             DataType dt = v.dataType();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/internal/InferenceSession.java

@@ -16,7 +16,6 @@
 
 package org.nd4j.autodiff.samediff.internal;
 
-import com.google.common.collect.ImmutableMap;
 import lombok.NonNull;
 import lombok.extern.slf4j.Slf4j;
 import org.nd4j.autodiff.functions.DifferentialFunction;
@@ -121,12 +120,12 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
         if(listeners != null && listeners.size() > 0){
             SameDiffOp sdOp = sameDiff.getOps().get(op.getOwnName());
 
-            ImmutableMap.Builder<String, INDArray> namedOutsBuilder = ImmutableMap.builder();
+            Map<String, INDArray> namedOutsBuilder = new HashMap<>();
 
             for(int i = 0 ; i < out.length ; i++)
                 namedOutsBuilder.put(sdOp.outputsOfOp.get(i), out[i]);
 
-            Map<String, INDArray> namedOuts = namedOutsBuilder.build();
+            Map<String, INDArray> namedOuts = Collections.unmodifiableMap(namedOutsBuilder);
 
             for(Listener l : listeners){
                 if(l.isActive(at.operation())) {
@@ -223,7 +222,7 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
             //Merge avairable for forward pass when any of its inputs are available. When multiple are available, behaviour
             // is undefined
             Merge m = (Merge) op;
-            String[] in = sameDiff.getInputsForFunction(op);
+            String[] in = sameDiff.getInputsForOp(op);
             for (String s : in) {
                 VarId vid = newVarId(s, outputFrameIter);
                 if (nodeOutputs.containsKey(vid)) {
@@ -275,10 +274,10 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
 
                 Preconditions.checkState(v != null, "Could not find input %s", inTensorArray.getVarName());
 
-                while(sameDiff.getVariableOutputFunction(inTensorArray.getVarName()) instanceof Enter){
+                while(sameDiff.getVariableOutputOp(inTensorArray.getVarName()) instanceof Enter){
                     //Handle the Enter case: this is like TensorArray -> Enter -> TensorArrayRead
                     //TODO also TensorArrayWrite, scatter, etc??
-                    inTensorArray = sameDiff.getVariableOutputFunction(inTensorArray.getVarName()).arg();
+                    inTensorArray = sameDiff.getVariableOutputOp(inTensorArray.getVarName()).arg();
                     v = newVarId(inTensorArray.getVarName(), v.getParentFrame());
                 }
 
@@ -300,10 +299,10 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
 
                 Preconditions.checkState(tArr != null, "Could not find input %s", inTensorArray.getVarName());
 
-                while(sameDiff.getVariableOutputFunction(inTensorArray.getVarName()) instanceof Enter){
+                while(sameDiff.getVariableOutputOp(inTensorArray.getVarName()) instanceof Enter){
                     //Handle the Enter case: this is like TensorArray -> Enter -> TensorArrayWrite
                     //TODO also TensorArrayScatter, etc??
-                    inTensorArray = sameDiff.getVariableOutputFunction(inTensorArray.getVarName()).arg();
+                    inTensorArray = sameDiff.getVariableOutputOp(inTensorArray.getVarName()).arg();
                     tArr = newVarId(inTensorArray.getVarName(), tArr.getParentFrame());
                 }
 
@@ -405,7 +404,7 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
                 //Input 2: The values to scatter
 
                 SDVariable inTensorArray = op.arg(0);   //Dummy variable representing the tensor array
-                TensorArray ta = (TensorArray) sameDiff.getVariableOutputFunction(inTensorArray.getVarName());
+                TensorArray ta = (TensorArray) sameDiff.getVariableOutputOp(inTensorArray.getVarName());
                 VarId tArr = (opInputs == null ? null : lookup(inTensorArray.getVarName(), opInputs, false));
                 if(tArr == null && allIterInputs != null){
                     tArr = lookup(inTensorArray.getVarName(), allIterInputs, false);
@@ -526,7 +525,7 @@ public class InferenceSession extends AbstractSession<INDArray,DifferentialFunct
     public DifferentialFunction getAndParameterizeOp(String opName, FrameIter frameIter, Set<VarId> opInputs, Set<VarId> allIterInputs,
                                                      Set<String> constAndPhInputs, Map<String,INDArray> placeholderValues) {
 
-        DifferentialFunction df = sameDiff.getFunctionById(opName);
+        DifferentialFunction df = sameDiff.getOpById(opName);
 
         //TODO We should clone these ops - probably - as we don't want them shared between threads/sessions!
         //But let's only clone them *once* and cache in inference session - not on every exec

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDBaseOps.java

@@ -18,7 +18,6 @@ package org.nd4j.autodiff.samediff.ops;
 
 import com.google.common.collect.Sets;
 import java.util.HashMap;
-import java.util.HashSet;
 import java.util.Map;
 import java.util.Set;
 import lombok.NonNull;
@@ -27,7 +26,6 @@ import org.nd4j.autodiff.samediff.ArgumentInterceptor;
 import org.nd4j.autodiff.samediff.NameScope;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.autodiff.samediff.SameDiffFunctionDefinition;
 import org.nd4j.autodiff.samediff.SameDiffLambda;
 import org.nd4j.autodiff.samediff.SameDiffNoArgSingleLambda;
 import org.nd4j.autodiff.samediff.SameDiffSingleLambda;
@@ -3377,7 +3375,7 @@ public abstract class SDBaseOps {
 
             for(SameDiffOp op : sd().getOpsInScope(ifScope)) {
                 for(String in : op.getInputsToOp()){
-                    sd().removeArgFromFunction(in, op.getOp());
+                    sd().removeArgFromOp(in, op.getOp());
                 }
                 sd().getOps().remove(op.getName());
             }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDCNN.java

@@ -385,6 +385,29 @@ public class SDCNN extends SDOps {
         return updateVariableNameAndReference(ret, name);
     }
 
+    /**
+     * 3D CNN deconvolution operation with or without optional bias
+     *
+     * @param input   Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
+     * @param weights Weights array - shape [kD, kH, kW, oC, iC]
+     * @param bias    Bias array - optional, may be null. If non-null, must have shape [outputChannels]
+     * @param config  Configuration
+     */
+    public SDVariable deconv3d(SDVariable input, SDVariable weights, SDVariable bias, DeConv3DConfig config) {
+        return deconv3d(null, input, weights, bias, config);
+    }
+
+    /**
+     * 3D CNN deconvolution operation with no bias
+     *
+     * @param input   Input array - shape [bS, iD, iH, iW, iC] (NDHWC) or [bS, iC, iD, iH, iW] (NCDHW)
+     * @param weights Weights array - shape [kD, kH, kW, oC, iC]
+     * @param config  Configuration
+     */
+    public SDVariable deconv3d(SDVariable input, SDVariable weights, DeConv3DConfig config) {
+        return deconv3d(input, weights, null, config);
+    }
+
     /**
      * Convolution 2d layer batch to space operation on 4d input.<br>
      * Reduces input channels dimension by rearranging data into a larger spatial dimensions<br>

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/serde/LegacyOpMapper.java

@@ -199,7 +199,7 @@ public class LegacyOpMapper {
             case 25:
                 return Or.class;
             case 26:
-                return OldAtan2Op.class;
+                throw new UnsupportedOperationException("OldATan2 (op number " + opNum + ") is no longer supported.");
             case 27:
                 return LogicalOr.class;
             case 28:
@@ -243,7 +243,7 @@ public class LegacyOpMapper {
             case 18:
                 return Floor.class;
             case 20:
-                return OldReverse.class;
+                throw new UnsupportedOperationException("OldReverse (op number " + opNum + ") is no longer supported.");
             default:
                 throw new UnsupportedOperationException("No known transform same op for op number: " + opNum);
         }
@@ -581,19 +581,19 @@ public class LegacyOpMapper {
     public static Class<?> pairwiseOpClass(int opNum){
         switch (opNum){
         case 0:
-                return OldAddOp.class;
+                throw new UnsupportedOperationException("OldFModOp (op number " + opNum + ") is no longer supported.");
         case 1:
                 return CopyOp.class;
         case 2:
-                return OldDivOp.class;
+                throw new UnsupportedOperationException("OldDivOp (op number " + opNum + ") is no longer supported.");
         case 3:
-                return OldEqualTo.class;
+                throw new UnsupportedOperationException("OldEqualTo (op number " + opNum + ") is no longer supported.");
         case 4:
-                return OldGreaterThan.class;
+                throw new UnsupportedOperationException("OldGreaterThan (op number " + opNum + ") is no longer supported.");
         case 5:
-                return OldLessThan.class;
+                throw new UnsupportedOperationException("OldLessThan (op number " + opNum + ") is no longer supported.");
         case 6:
-                return OldMulOp.class;
+                throw new UnsupportedOperationException("OldMulOp (op number " + opNum + ") is no longer supported.");
         case 7:
                 return Pow.class;
         case 8:
@@ -603,15 +603,15 @@ public class LegacyOpMapper {
         case 10:
                 return Eps.class;
         case 11:
-                return OldGreaterThanOrEqual.class;
+                throw new UnsupportedOperationException("OldGreaterThanOrEqual (op number " + opNum + ") is no longer supported.");
         case 12:
-                return OldLessThanOrEqual.class;
+                throw new UnsupportedOperationException("OldLessThanOrEqual (op number " + opNum + ") is no longer supported.");
         case 13:
-                return OldMax.class;
+                throw new UnsupportedOperationException("OldMax (op number " + opNum + ") is no longer supported.");
         case 14:
-                return OldMin.class;
+                throw new UnsupportedOperationException("OldMin (op number " + opNum + ") is no longer supported.");
         case 15:
-                return OldNotEqualTo.class;
+                throw new UnsupportedOperationException("OldNotEqualTo (op number " + opNum + ") is no longer supported.");
         case 16:
                 return Set.class;
         case 17:
@@ -631,11 +631,11 @@ public class LegacyOpMapper {
         case 59:
                 return RemainderOp.class;
         case 60:
-                return OldFModOp.class;
+                throw new UnsupportedOperationException("OldFModOp (op number " + opNum + ") is no longer supported.");
         case 69:
-                return OldAtan2Op.class;
+                throw new UnsupportedOperationException("OldATan2 (op number " + opNum + ") is no longer supported.");
         case 20:
-                return OldFloorDivOp.class;
+                throw new UnsupportedOperationException("OldFloorDivOp (op number " + opNum + ") is no longer supported.");
         case 26:
                 return RelativeError.class;
         case 27:

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/transform/GraphTransformUtil.java

@@ -78,7 +78,7 @@ public class GraphTransformUtil {
                 if (oldInputsForOps != null) {
                     List<String> newInputsForOps = new ArrayList<>();
                     for (String s : oldInputsForOps) {
-                        DifferentialFunction df = sd.getFunctionById(s);
+                        DifferentialFunction df = sd.getOpById(s);
                         if (!allSubGraphFns.contains(df)) {
                             newInputsForOps.add(s);
                         }
@@ -141,7 +141,7 @@ public class GraphTransformUtil {
                         // (1) variable is (was) input to op that has been removed - just remove from list
                         // (2) variable is now connected directly as an output: (A->B->C) becomes (A->C)
                         // For the latter case, this
-                        DifferentialFunction df = sd.getFunctionById(opName);
+                        DifferentialFunction df = sd.getOpById(opName);
                         if (allSubGraphFns.contains(df)) {
                             newInputsForOp.remove(opName);
                         }
@@ -178,7 +178,7 @@ public class GraphTransformUtil {
      */
     public static List<SubGraph> getSubgraphsMatching(SameDiff sd, SubGraphPredicate p) {
         List<SubGraph> out = new ArrayList<>();
-        for (DifferentialFunction df : sd.functions()) {
+        for (DifferentialFunction df : sd.ops()) {
             if (p.matches(sd, df)) {
                 SubGraph sg = p.getSubGraph(sd, df);
                 out.add(sg);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/transform/SubGraph.java

@@ -20,7 +20,6 @@ import lombok.AllArgsConstructor;
 import lombok.Builder;
 import lombok.Data;
 import lombok.NoArgsConstructor;
-import org.apache.commons.lang3.builder.Diff;
 import org.nd4j.autodiff.functions.DifferentialFunction;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
@@ -68,7 +67,7 @@ public class SubGraph {
             boolean allInSubgraph = true;
             if(inputsFor != null){
                 for(String opOwnName : inputsFor) {
-                    if (!inSubgraph(sameDiff.getFunctionById(opOwnName))){
+                    if (!inSubgraph(sameDiff.getOpById(opOwnName))){
                         allInSubgraph = false;
                         break;
                     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/transform/SubGraphPredicate.java

@@ -77,7 +77,7 @@ public class SubGraphPredicate extends OpPredicate {
                 }
 
                 SDVariable in = inputs[inNum];
-                DifferentialFunction df = sameDiff.getVariableOutputFunction(in.getVarName());
+                DifferentialFunction df = sameDiff.getVariableOutputOp(in.getVarName());
                 if (df == null || !e.getValue().matches(sameDiff, df)) {
                     return false;
                 }
@@ -103,7 +103,7 @@ public class SubGraphPredicate extends OpPredicate {
             for(Map.Entry<Integer,OpPredicate> entry : opInputSubgraphPredicates.entrySet()){
                 OpPredicate p2 = entry.getValue();
                 SDVariable arg = rootFn.arg(entry.getKey());
-                DifferentialFunction df = sd.getVariableOutputFunction(arg.getVarName());
+                DifferentialFunction df = sd.getVariableOutputOp(arg.getVarName());
                 if(df != null){
                     childNodes.add(df);
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/GradCheckUtil.java

@@ -28,7 +28,6 @@ import org.nd4j.autodiff.samediff.internal.SameDiffOp;
 import org.nd4j.autodiff.samediff.internal.Variable;
 import org.nd4j.autodiff.validation.listeners.NonInplaceValidationListener;
 import org.nd4j.base.Preconditions;
-import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.iter.NdIndexIterator;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -106,7 +105,7 @@ public class GradCheckUtil {
         }
 
         Set<String> fnOutputs = new HashSet<>();
-        for(DifferentialFunction f : sd.functions()){
+        for(DifferentialFunction f : sd.ops()){
             for(SDVariable s : f.outputVariables()){
                 fnOutputs.add(s.getVarName());
             }
@@ -593,7 +592,7 @@ public class GradCheckUtil {
         4. Gradient function: should contain all of the existing functions, and more
          */
 
-        DifferentialFunction[] dfs = sd.functions();
+        DifferentialFunction[] dfs = sd.ops();
         List<SDVariable> vars = sd.variables();
 
         Set<String> varSetStr = new HashSet<>();
@@ -661,7 +660,7 @@ public class GradCheckUtil {
 
             //Check that all original functions are present in the gradient function
             for(DifferentialFunction dfOrig : dfs){
-                Preconditions.checkNotNull(gradFn.getFunctionById(dfOrig.getOwnName()), "DifferentialFunction " + dfOrig.getOwnName()
+                Preconditions.checkNotNull(gradFn.getOpById(dfOrig.getOwnName()), "DifferentialFunction " + dfOrig.getOwnName()
                         + " from original SameDiff instance not present in grad fn");
             }
         }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/validation/OpValidation.java

@@ -79,7 +79,6 @@ import org.nd4j.linalg.api.ops.impl.transforms.gradient.*;
 import org.nd4j.linalg.api.ops.impl.transforms.gradient.SigmoidDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.gradient.TanhDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Not;
-import org.nd4j.linalg.api.ops.impl.transforms.segment.UnsortedSegmentMax;
 import org.nd4j.linalg.api.ops.impl.transforms.segment.bp.*;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.GELUDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.PreciseGELUDerivative;
@@ -94,7 +93,6 @@ import org.nd4j.linalg.api.ops.random.impl.Linspace;
 import org.nd4j.linalg.api.shape.LongShapeDescriptor;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.function.Function;
-import org.nd4j.linalg.indexing.NDArrayIndex;
 import org.nd4j.linalg.indexing.conditions.Conditions;
 import org.nd4j.linalg.primitives.Pair;
 import org.tensorflow.framework.OpDef;
@@ -464,7 +462,7 @@ public class OpValidation {
         //i.e., don't double count if a SameDiff instance has multiple copies of the same op type
 
         //Collect coverage information for backprop:
-        DifferentialFunction[] functions = sd.functions();
+        DifferentialFunction[] functions = sd.ops();
         Set<Class> backpropSeen = new HashSet<>();
         for (DifferentialFunction df : functions) {
             backpropSeen.add(df.getClass());
@@ -481,7 +479,7 @@ public class OpValidation {
         if (testCase.fwdTestFns() != null) {
             for (String s : testCase.fwdTestFns().keySet()) {
                 //Determine the differential function that this variable is the output of, if any
-                DifferentialFunction df = sd.getVariableOutputFunction(s);
+                DifferentialFunction df = sd.getVariableOutputOp(s);
                 if (df != null) {
                     if (seen == null)
                         seen = new HashSet<>();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/ROC.java

@@ -31,7 +31,7 @@ import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.Op;
 import org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.INDArrayIndex;
 import org.nd4j.linalg.indexing.NDArrayIndex;
@@ -708,8 +708,8 @@ public class ROC extends BaseEvaluation<ROC> {
                     itp = isTruePositive;
                     ifp = isFalsePositive;
                 } else {
-                    isTruePositive = Nd4j.getExecutioner().exec(new OldMulOp(predictedClass1, positiveActualClassColumn, itp));
+                    isTruePositive = Nd4j.getExecutioner().exec(new MulOp(predictedClass1, positiveActualClassColumn, itp))[0];
-                    isFalsePositive = Nd4j.getExecutioner().exec(new OldMulOp(predictedClass1, negativeActualClassColumn, ifp));
+                    isFalsePositive = Nd4j.getExecutioner().exec(new MulOp(predictedClass1, negativeActualClassColumn, ifp))[0];
                 }
 
                 //Counts for this batch:

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

@@ -68,17 +68,6 @@ public class DifferentialFunctionClassHolder {
         add("sameDiff");
         add("ownName");
     }};
-    private static final Set<String> classesWithConfig = new LinkedHashSet<String>(){{
-        add(AvgPooling2D.class.getName());
-        add(Conv2D.class.getName());
-        add(Conv3D.class.getName());
-        add(LocalResponseNormalization.class.getName());
-        add(MaxPooling2D.class.getName());
-        add(Pooling2D.class.getName());
-        add(Pooling3D.class.getName());
-        add(DepthwiseConv2D.class.getName());
-        add(DeConv2DTF.class.getName());
-    }};
     //When determining fields/properties, where should we terminate the search?
     //We don't wan to include every single field from every single superclass
     private static final Set<Class> classesToIgnore = new HashSet<>(Arrays.<Class>asList(
@@ -165,16 +154,37 @@ public class DifferentialFunctionClassHolder {
                 Map<String, Field> fieldNames = new LinkedHashMap<>();
                 Class<? extends DifferentialFunction> current = df.getClass();
                 val fields = new ArrayList<Field>();
+                boolean isFirst = true;
+
                 while (current.getSuperclass() != null && !classesToIgnore.contains(current.getSuperclass())) {
-                    if (classesWithConfig.contains(current.getName())) {
 
-                        val fieldName = "config";
+                    if (df.isConfigProperties() && isFirst) {
 
-                        val configField = current.getDeclaredField(fieldName);
+                        String fieldName = df.configFieldName();
-                        if (configField == null) {
+
-                            continue;
+                        if(fieldName == null)
+                            fieldName = "config";
+
+                        Field configField = null;
+                        try{
+                            configField = current.getDeclaredField(fieldName);
+                        } catch (NoSuchFieldException e){
+                            Class<?> currentConfig = current.getSuperclass();
+
+                            // find a config field in superclasses
+                            while(currentConfig.getSuperclass() != null){
+                                try {
+                                    configField = currentConfig.getDeclaredField(fieldName);
+                                    break;
+                                } catch (NoSuchFieldException e2){
+                                    currentConfig = currentConfig.getSuperclass();
+                                }
+                            }
                         }
 
+                        if(configField == null)
+                            continue;
+
                         val configFieldClass = configField.getType();
 
                         for (val field : configFieldClass.getDeclaredFields()) {
@@ -206,6 +216,7 @@ public class DifferentialFunctionClassHolder {
 
                     // do something with current's fields
                     current = (Class<? extends DifferentialFunction>) current.getSuperclass();
+                    isFirst = false;
 
                 }
 

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

@@ -347,14 +347,6 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndReplace.class,
             org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet.class,
             org.nd4j.linalg.api.ops.impl.transforms.comparison.Eps.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldEqualTo.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldGreaterThan.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldGreaterThanOrEqual.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldLessThan.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldLessThanOrEqual.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMax.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMin.class,
-            org.nd4j.linalg.api.ops.impl.transforms.comparison.OldNotEqualTo.class,
             org.nd4j.linalg.api.ops.impl.transforms.custom.ATan2.class,
             org.nd4j.linalg.api.ops.impl.transforms.custom.Assign.class,
             org.nd4j.linalg.api.ops.impl.transforms.custom.BatchToSpace.class,
@@ -453,15 +445,6 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MergeAddOp.class,
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp.class,
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.ModOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAddOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAtan2Op.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldDivOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldFModOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldFloorDivOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldRDivOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldRSubOp.class,
-            org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldSubOp.class,
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.PowPairwise.class,
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.RDivOp.class,
             org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.RSubOp.class,
@@ -493,8 +476,6 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.transforms.same.Max.class,
             org.nd4j.linalg.api.ops.impl.transforms.same.Min.class,
             org.nd4j.linalg.api.ops.impl.transforms.same.Negative.class,
-            org.nd4j.linalg.api.ops.impl.transforms.same.OldIdentity.class,
-            org.nd4j.linalg.api.ops.impl.transforms.same.OldReverse.class,
             org.nd4j.linalg.api.ops.impl.transforms.same.OneMinus.class,
             org.nd4j.linalg.api.ops.impl.transforms.same.Reciprocal.class,
             org.nd4j.linalg.api.ops.impl.transforms.same.Round.class,

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/BaseGraphMapper.java

@@ -361,7 +361,7 @@ public abstract class BaseGraphMapper<GRAPH_TYPE, NODE_TYPE, ATTR_TYPE, TENSOR_T
     }
 
     protected void initOutputVariables(SameDiff sd, DifferentialFunction df) {
-        String[] outNames = sd.getOutputsForFunction(df);
+        String[] outNames = sd.getOutputsForOp(df);
         SDVariable[] outVars;
         if (outNames == null) {
             outVars = sd.generateOutputVariableForOp(df, df.getOwnName() != null ? df.getOwnName() : df.opName(), true);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/onnx/OnnxGraphMapper.java

@@ -409,7 +409,7 @@ public class OnnxGraphMapper extends BaseGraphMapper<OnnxProto3.GraphProto, Onnx
             newInstance.setSameDiff(importState.getSameDiff());
 
             newInstance.initFromOnnx(tfNode,diff,getAttrMap(tfNode),importState.getGraph());
-            importState.getSameDiff().putFunctionForId(newInstance.getOwnName(),newInstance);
+            importState.getSameDiff().putOpForId(newInstance.getOwnName(),newInstance);
             //ensure we can track node name to function instance later.
             diff.setBaseNameForFunctionInstanceId(tfNode.getName(),newInstance);
             //diff.addVarNameForImport(tfNode.getName());

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/tf/TFGraphMapper.java

@@ -16,14 +16,11 @@
 
 package org.nd4j.imports.graphmapper.tf;
 
-import com.github.os72.protobuf351.Descriptors;
 import com.github.os72.protobuf351.Message;
 import com.google.common.primitives.Floats;
 import com.google.common.primitives.Ints;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
-import org.bytedeco.javacpp.indexer.Bfloat16Indexer;
-import org.bytedeco.javacpp.indexer.HalfIndexer;
 import org.nd4j.autodiff.functions.DifferentialFunction;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
@@ -41,19 +38,14 @@ import org.nd4j.imports.graphmapper.OpImportFilter;
 import org.nd4j.imports.graphmapper.OpImportOverride;
 import org.nd4j.imports.graphmapper.tf.tensors.TFTensorMapper;
 import org.nd4j.imports.graphmapper.tf.tensors.TFTensorMappers;
-import org.nd4j.linalg.api.buffer.*;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.controlflow.IfImportState;
-import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
 import org.nd4j.linalg.exception.ND4JIllegalStateException;
-import org.nd4j.linalg.factory.Nd4j;
-import org.nd4j.linalg.util.ArrayUtil;
 import org.tensorflow.framework.*;
 import org.tensorflow.framework.DataType;
 
 import java.io.*;
 import java.nio.ByteBuffer;
-import java.nio.ByteOrder;
 import java.util.*;
 
 /**
@@ -661,7 +653,7 @@ public class TFGraphMapper extends BaseGraphMapper<GraphDef,NodeDef,AttrValue,No
 
                     newInstance.initFromTensorFlow(tfNode, diff, getAttrMap(tfNode), importState.getGraph());
                     mapProperties(newInstance, tfNode, importState.getGraph(), importState.getSameDiff(), newInstance.mappingsForFunction());
-                    importState.getSameDiff().putFunctionForId(newInstance.getOwnName(), newInstance);
+                    importState.getSameDiff().putOpForId(newInstance.getOwnName(), newInstance);
                     //ensure we can track node name to function instance later.
                     diff.setBaseNameForFunctionInstanceId(tfNode.getName(), newInstance);
                 } catch (Exception e) {

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

@@ -61,6 +61,7 @@ import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.EqualTo;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThan;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThan;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.NotEqualTo;
 import org.nd4j.linalg.api.ops.impl.transforms.same.Negative;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.*;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.*;
@@ -1638,7 +1639,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
     public INDArray lt(INDArray other) {
         validateNumericalArray("less than (lt)", false);
         if (Shape.shapeEquals(this.shape(), other.shape())) {
-            return Nd4j.getExecutioner().exec(new OldLessThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())));
+            return Nd4j.getExecutioner().exec(new LessThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
         } else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
             return Nd4j.exec(new LessThan(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
         } else
@@ -1655,13 +1656,13 @@ public abstract class BaseNDArray implements INDArray, Iterable {
     @Override
     public INDArray neq(INDArray other) {
         Preconditions.checkState(!isEmpty(), "Cannot perform operation neq (not equal) on empty array");
-        return Nd4j.getExecutioner().exec(new OldNotEqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())));
+        return Nd4j.getExecutioner().exec(new NotEqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
     }
 
     @Override
     public INDArray eq(INDArray other) {
         if (Shape.shapeEquals(this.shape(), other.shape())) {
-            return Nd4j.getExecutioner().exec(new OldEqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())));
+            return Nd4j.getExecutioner().exec(new EqualTo(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
         } else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
             return Nd4j.exec(new EqualTo(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
         } else
@@ -1672,7 +1673,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
     public INDArray gt(INDArray other) {
         validateNumericalArray("greater than (gt)", false);
         if (Shape.shapeEquals(this.shape(), other.shape())) {
-            return Nd4j.getExecutioner().exec(new OldGreaterThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())));
+            return Nd4j.getExecutioner().exec(new GreaterThan(this, other, Nd4j.createUninitialized(DataType.BOOL, this.shape(), this.ordering())))[0];
         } else if (Shape.areShapesBroadcastable(this.shape(), other.shape())) {
             return Nd4j.exec(new GreaterThan(new INDArray[]{this, other}, new INDArray[]{Nd4j.createUninitialized(DataType.BOOL, Shape.broadcastOutputShape(this.shape(), other.shape()))}))[0];
         } else
@@ -5989,7 +5990,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
 
             return result;
         } else {
-            OldFModOp op = new OldFModOp(this, denominator, result);
+            FModOp op = new FModOp(this, denominator, result);
             Nd4j.getExecutioner().exec(op);
             return result;
         }
@@ -6011,7 +6012,7 @@ public abstract class BaseNDArray implements INDArray, Iterable {
     @Override
     public INDArray fmodi(INDArray denominator) {
         validateNumericalArray("fmodi", false);
-        OldFModOp op = new OldFModOp(this, denominator, this);
+        FModOp op = new FModOp(this, denominator, this);
         Nd4j.getExecutioner().exec(op);
         return this;
     }

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

@@ -274,7 +274,7 @@ public abstract class BaseOp extends DifferentialFunction implements Op {
     @Override
     public SDVariable[] outputVariables(String baseName) {
         if(zVertexId == null)  {
-            val outputNames = sameDiff.getOutputsForFunction(this);
+            val outputNames = sameDiff.getOutputsForOp(this);
             //no need to dynamically create if already exists
             if(outputNames != null) {
                 zVertexId = sameDiff.getVariable(outputNames[0]).getVarName();
@@ -293,13 +293,13 @@ public abstract class BaseOp extends DifferentialFunction implements Op {
 
                 sameDiff.setArrayForVariable(newVars[0].getVarName(),inputArr);
                 z = inputArr;
-                if(sameDiff.getOutputsForFunction(this) == null)
+                if(sameDiff.getOutputsForOp(this) == null)
                     sameDiff.addOutgoingFor(newVars,this);
                 return newVars;
             }
 
             SDVariable[] newVars = sameDiff.generateOutputVariableForOp(this, baseName, false);
-            if (sameDiff.getOutputsForFunction(this) == null)
+            if (sameDiff.getOutputsForOp(this) == null)
                 sameDiff.addOutgoingFor(newVars, this);
             return newVars;
         }

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

@@ -25,11 +25,9 @@ import onnx.OnnxProto3;
 import org.nd4j.autodiff.functions.DifferentialFunction;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.base.Preconditions;
 import org.nd4j.imports.NoOpNameFoundException;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.shape.LongShapeDescriptor;
-import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.exception.ND4JIllegalStateException;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.util.ArrayUtil;
@@ -208,7 +206,7 @@ public class DynamicCustomOp extends DifferentialFunction implements CustomOp {
     @Override
     public SDVariable[] outputVariables(String baseName) {
         if (this.outputVariables == null) {
-            val outputNames = sameDiff.getOutputsForFunction(this);
+            val outputNames = sameDiff.getOutputsForOp(this);
             //no need to dynamically create if already exists
             if (outputNames != null) {
                 outputVariables = new SDVariable[outputNames.length];
@@ -233,7 +231,7 @@ public class DynamicCustomOp extends DifferentialFunction implements CustomOp {
             }
 
             outputVariables = newVars;
-            if (sameDiff.getOutputsForFunction(this) == null)
+            if (sameDiff.getOutputsForOp(this) == null)
                 sameDiff.addOutgoingFor(outputVariables, this);
             return newVars;
         }
@@ -524,7 +522,7 @@ public class DynamicCustomOp extends DifferentialFunction implements CustomOp {
                 throw new ND4JIllegalStateException("Op [" + opName() + "] failure for [" + this.getOwnName() + "]: Number of inputs is invalid for execution. "
                         + numInputArguments() + " were provided but " + descriptor.getNumInputs() + " are required for execution");
             } else {
-                String[] inputNames = sameDiff.getInputsForFunction(this);
+                String[] inputNames = sameDiff.getInputsForOp(this);
                 String[] arrayShapes = new String[inputNames.length];
                 for( int i=0; i<inputNames.length; i++ ){
                     INDArray arr = sameDiff.getVariable(inputNames[i]).getArr();

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/If.java

@@ -107,7 +107,7 @@ public class If extends DifferentialFunction implements CustomOp {
               SameDiffFunctionDefinition falseBody) {
 
         this.sameDiff = parent;
-        parent.putFunctionForId(getOwnName(),this);
+        parent.putOpForId(getOwnName(),this);
         this.inputVars = inputVars;
         this.predicate = predicate;
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/controlflow/While.java

@@ -136,7 +136,7 @@ public class While extends DifferentialFunction implements CustomOp {
         this.trueBody = trueBody;
         this.blockName = blockName;
         this.dummyResult =  parent.var("dummyresult-" + UUID.randomUUID().toString(),new ZeroInitScheme('f'), DataType.FLOAT, 1);
-        parent.putFunctionForId(getOwnName(),this);
+        parent.putOpForId(getOwnName(),this);
 
         parent.addArgsFor(inputVars,this);
         parent.addOutgoingFor(new SDVariable[]{dummyResult},this);
@@ -457,7 +457,7 @@ public class While extends DifferentialFunction implements CustomOp {
 
         //the output of the condition should always be a singular scalar
         //this is a safe assumption
-        val conditionVars = scopeCondition.functions();
+        val conditionVars = scopeCondition.ops();
         if(conditionVars.length < 1) {
             throw new ND4JIllegalArgumentException("No functions found!");
         }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv1D.java

@@ -22,7 +22,6 @@ import lombok.NoArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import onnx.OnnxProto3;
-import org.nd4j.autodiff.functions.DifferentialFunction;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
@@ -40,7 +39,6 @@ import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv2DConfig;
 import org.nd4j.linalg.util.ArrayUtil;
 import org.tensorflow.framework.AttrValue;
 import org.tensorflow.framework.GraphDef;
@@ -71,7 +69,7 @@ public class Conv1D extends DynamicCustomOp {
         this.config = config;
         Preconditions.checkState(config.getS() >= 1 && config.getP() >= 0, INVALID_CONFIGURATION, config.getS(), config.getP());
         addArgs();
-        sameDiff.putFunctionForId(this.getOwnName(), this);
+        sameDiff.putOpForId(this.getOwnName(), this);
         sameDiff.addArgsFor(inputFunctions, this);
     }
 
@@ -113,12 +111,6 @@ public class Conv1D extends DynamicCustomOp {
         return config.toProperties();
     }
 
-    @Override
-    public void initFromTensorFlow(NodeDef nodeDef, SameDiff initWith, Map<String, AttrValue> attributesForNode, GraphDef graph) {
-        TFGraphMapper.getInstance().initFunctionFromProperties(nodeDef.getOp(), this, attributesForNode, nodeDef, graph);
-        addArgs();
-    }
-
     @Override
     public boolean isConfigProperties() {
         return true;
@@ -129,107 +121,6 @@ public class Conv1D extends DynamicCustomOp {
         return "config";
     }
 
-    @Override
-    public void initFromOnnx(OnnxProto3.NodeProto node, SameDiff initWith, Map<String, OnnxProto3.AttributeProto> attributesForNode, OnnxProto3.GraphProto graph) {
-        OnnxGraphMapper.getInstance().initFunctionFromProperties(node.getOpType(), this, attributesForNode, node, graph);
-        addArgs();
-    }
-
-
-    @Override
-    public Map<String, Map<String, AttributeAdapter>> attributeAdaptersForFunction() {
-        Map<String, Map<String, AttributeAdapter>> ret = new HashMap<>();
-        Map<String, AttributeAdapter> tfMappings = new LinkedHashMap<>();
-        val fields = DifferentialFunctionClassHolder.getInstance().getFieldsForFunction(this);
-
-
-        tfMappings.put("kH", new ConditionalFieldValueNDArrayShapeAdapter("NHW", 2, 0, fields.get("dataFormat")));
-        tfMappings.put("kW", new ConditionalFieldValueNDArrayShapeAdapter("NHW", 3, 1, fields.get("dataFormat")));
-        tfMappings.put("sH", new ConditionalFieldValueIntIndexArrayAdapter("NHW", 2, 1, fields.get("dataFormat")));
-        tfMappings.put("sW", new ConditionalFieldValueIntIndexArrayAdapter("NHW", 3, 2, fields.get("dataFormat")));
-        tfMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
-        tfMappings.put("isNHWC", new StringEqualsAdapter("NHWC"));
-
-
-        Map<String, AttributeAdapter> onnxMappings = new HashMap<>();
-        onnxMappings.put("kH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
-        onnxMappings.put("kW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
-        onnxMappings.put("dH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
-        onnxMappings.put("dW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
-        onnxMappings.put("sH", new SizeThresholdIntArrayIntIndexAdpater(0, 2, 0));
-        onnxMappings.put("sW", new SizeThresholdIntArrayIntIndexAdpater(1, 2, 0));
-        onnxMappings.put("isSameMode", new StringEqualsAdapter("SAME"));
-        onnxMappings.put("isNHWC", new StringEqualsAdapter("NHC"));
-
-        ret.put(tensorflowName(), tfMappings);
-        ret.put(onnxName(), onnxMappings);
-        return ret;
-    }
-
-    @Override
-    public Map<String, Map<String, PropertyMapping>> mappingsForFunction() {
-        Map<String, Map<String, PropertyMapping>> ret = new HashMap<>();
-        Map<String, PropertyMapping> map = new HashMap<>();
-        val strideMapping = PropertyMapping.builder()
-                .tfAttrName("strides")
-                .onnxAttrName("strides")
-                .propertyNames(new String[]{"s"})
-                .build();
-
-        val kernelMapping = PropertyMapping.builder()
-                .propertyNames(new String[]{"k"})
-                .tfInputPosition(1)
-                .shapePosition(0)
-                .onnxAttrName("kernel_shape")
-                .build();
-
-        val paddingMapping = PropertyMapping.builder()
-                .onnxAttrName("padding")
-                .propertyNames(new String[]{"p"})
-                .build();
-
-        val dataFormat = PropertyMapping.builder()
-                .onnxAttrName("data_format")
-                .tfAttrName("data_format")
-                .propertyNames(new String[]{"dataFormat"})
-                .build();
-
-        val nhwc = PropertyMapping.builder()
-                .onnxAttrName("data_format")
-                .tfAttrName("data_format")
-                .propertyNames(new String[]{"isNHWC"})
-                .build();
-
-        val sameMode = PropertyMapping.builder()
-                .onnxAttrName("auto_pad")
-                .propertyNames(new String[]{"isSameMode"})
-                .tfAttrName("padding")
-                .build();
-
-        map.put("s", strideMapping);
-        map.put("k", kernelMapping);
-        map.put("p", paddingMapping);
-        map.put("isSameMode", sameMode);
-        map.put("dataFormat", dataFormat);
-        map.put("isNHWC", nhwc);
-
-        try {
-            ret.put(onnxName(), map);
-        } catch (NoOpNameFoundException e) {
-            //ignore
-        }
-
-
-        try {
-            ret.put(tensorflowName(), map);
-        } catch (NoOpNameFoundException e) {
-            //ignore
-        }
-
-        return ret;
-    }
-
-
     @Override
     public String opName() {
         return "conv1d";
@@ -241,16 +132,6 @@ public class Conv1D extends DynamicCustomOp {
         throw new NoOpNameFoundException("No ONNX op name found for: " + getClass().getName());
     }
 
-    @Override
-    public String tensorflowName() {
-        return "Conv1D";
-    }
-
-    @Override
-    public String[] tensorflowNames() {
-        return new String[]{"Conv1D"};
-    }
-
     @Override
     public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes){
         int n = args().length;

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/Conv2D.java

@@ -70,7 +70,7 @@ public class Conv2D extends DynamicCustomOp {
                                     config.getSH(), config.getPH(), config.getDW());
         addArgs();
         if(sameDiff != null) {
-            sameDiff.putFunctionForId(this.getOwnName(), this);    //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
+            sameDiff.putOpForId(this.getOwnName(), this);    //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
             sameDiff.addArgsFor(inputFunctions, this);
         }
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2D.java

@@ -68,7 +68,7 @@ public class DeConv2D extends DynamicCustomOp {
         }
 
         addArgs();
-        sameDiff.putFunctionForId(this.getOwnName(), this);
+        sameDiff.putOpForId(this.getOwnName(), this);
         sameDiff.addArgsFor(inputs, this);
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DeConv2DTF.java

@@ -21,7 +21,6 @@ import lombok.Getter;
 import lombok.NoArgsConstructor;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
-import onnx.OnnxProto3;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
@@ -71,7 +70,7 @@ public class DeConv2DTF extends DynamicCustomOp {
         }
 
         addArgs();
-        sameDiff.putFunctionForId(this.getOwnName(), this);
+        sameDiff.putOpForId(this.getOwnName(), this);
         sameDiff.addArgsFor(inputs, this);
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/convolution/DepthwiseConv2D.java

@@ -62,7 +62,7 @@ public class DepthwiseConv2D extends DynamicCustomOp {
         this.sameDiff = sameDiff;
         this.config = config;
         addArgs();
-        sameDiff.putFunctionForId(this.getOwnName(), this);    //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
+        sameDiff.putOpForId(this.getOwnName(), this);    //Normally called in DynamicCustomOp constructor, via setInstanceId - but sameDiff field is null at that point
         sameDiff.addArgsFor(inputFunctions, this);
     }
 

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

@@ -76,6 +76,16 @@ public class LocalResponseNormalization extends DynamicCustomOp {
         addIArgument(config.getDepth());
     }
 
+    @Override
+    public boolean isConfigProperties() {
+        return true;
+    }
+
+    @Override
+    public String configFieldName(){
+        return "config";
+    }
+
     @Override
     public String opName() {
         return "lrn";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/reduce/TensorMmul.java

@@ -65,7 +65,7 @@ public class TensorMmul extends DynamicCustomOp {
         this.sameDiff = sameDiff;
         this.mMulTranspose = mMulTranspose;
         this.axes = dimensions;
-        if(!addedEdges && sameDiff.getOutputsForFunction(this) == null) {
+        if(!addedEdges && sameDiff.getOutputsForOp(this) == null) {
             addedEdges = true;
         }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/Concat.java

@@ -151,7 +151,7 @@ public class Concat extends DynamicCustomOp {
             removeInputArgument(inputArgs[inputArguments().length - 1]);
         }
 
-        sameDiff.removeArgFromFunction(input,this);
+        sameDiff.removeArgFromOp(input,this);
     }
 
     @Override

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayConcat.java

@@ -72,7 +72,7 @@ public class TensorArrayConcat extends BaseTensorOp {
     public List<DataType> calculateOutputDataTypes(java.util.List<org.nd4j.linalg.api.buffer.DataType> inputDataType){
         //Same output type as the TensorArray - which is defined by input 0
         SDVariable tArr = arg(0);
-        TensorArray t3 = (TensorArray) sameDiff.getVariableOutputFunction(tArr.getVarName());
+        TensorArray t3 = (TensorArray) sameDiff.getVariableOutputOp(tArr.getVarName());
         org.nd4j.linalg.api.buffer.DataType dt = t3.getTensorArrayDataType();
         return Collections.singletonList(dt);
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayGather.java

@@ -72,7 +72,7 @@ public class TensorArrayGather extends BaseTensorOp {
     public List<DataType> calculateOutputDataTypes(java.util.List<org.nd4j.linalg.api.buffer.DataType> inputDataType){
         //Same output type as the TensorArray - which is defined by input 0
         SDVariable tArr = arg(0);
-        TensorArray t3 = (TensorArray) sameDiff.getVariableOutputFunction(tArr.getVarName());
+        TensorArray t3 = (TensorArray) sameDiff.getVariableOutputOp(tArr.getVarName());
         org.nd4j.linalg.api.buffer.DataType dt = t3.getTensorArrayDataType();
         return Collections.singletonList(dt);
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/shape/tensorops/TensorArrayRead.java

@@ -72,7 +72,7 @@ public class TensorArrayRead extends BaseTensorOp {
             dt = importDataType;
         } else {
             SDVariable tArr = arg(0);
-            DifferentialFunction op = sameDiff.getVariableOutputFunction(tArr.getVarName());
+            DifferentialFunction op = sameDiff.getVariableOutputOp(tArr.getVarName());
             TensorArray t3 = (TensorArray) op;
             dt = t3.getTensorArrayDataType();
         }

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

@@ -16,6 +16,7 @@
 
 package org.nd4j.linalg.api.ops.impl.transforms;
 
+import lombok.NonNull;
 import org.nd4j.autodiff.samediff.SDIndex;
 import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
@@ -55,6 +56,14 @@ public class Pad extends DynamicCustomOp {
         addTArgument(padValue);
     }
 
+    public Pad(@NonNull INDArray in, @NonNull INDArray padding, INDArray out, @NonNull Mode mode, double padValue){
+        super(null, new INDArray[]{in, padding}, out == null ? null : new INDArray[]{out});
+        Preconditions.checkState(padding.dataType().isIntType(), "Padding array must be an integer datatype, got %s", padding.dataType());
+        this.mode = mode;
+        addIArgument(mode.ordinal());
+        addTArgument(padValue);
+    }
+
     @Override
     public String opName(){
         return "pad";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldEqualTo.java

@@ -1,95 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * Bit mask over the ndarrays as to whether
- * the components are equal or not
- *
- * @author Adam Gibson
- */
-public class OldEqualTo extends BaseTransformBoolOp {
-
-
-
-    public OldEqualTo(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldEqualTo(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, Object[] extraArgs) {
-        super(sameDiff, i_v1, i_v2, extraArgs);
-    }
-
-    public OldEqualTo(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldEqualTo(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldEqualTo(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldEqualTo() {}
-
-    public OldEqualTo(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldEqualTo(INDArray x, INDArray y) {
-        super(x, y, null);
-    }
-
-    @Override
-    public int opNum() {
-        return 0;
-    }
-
-    @Override
-    public String opName() {
-        return "oldeq";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No Tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        //Equals op: 2 inputs, not continuously differentiable but 0s almost everywhere
-        return Arrays.asList(sameDiff.zerosLike(args()[0]), sameDiff.zerosLike(args()[1]));
-    }
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldGreaterThan.java

@@ -1,91 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * Bit mask over the ndarrays as to whether
- * the components are greater than or not
- *
- * @author Adam Gibson
- */
-public class OldGreaterThan extends BaseTransformBoolOp {
-    public OldGreaterThan(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldGreaterThan(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldGreaterThan(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldGreaterThan(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldGreaterThan() {}
-
-    public OldGreaterThan(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldGreaterThan(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldGreaterThan(INDArray x) {
-        super(x);
-    }
-
-    @Override
-    public int opNum() {
-        return 1;
-    }
-
-    @Override
-    public String opName() {
-        return "oldgt";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow name found");
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return Arrays.asList(outputVariables()[0]);
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldGreaterThanOrEqual.java

@@ -1,85 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.List;
-
-/**
- * Bit mask over the ndarrays as to whether
- * the components are greater than or equal or not
- *
- * @author Adam Gibson
- */
-public class OldGreaterThanOrEqual extends BaseTransformBoolOp {
-    public OldGreaterThanOrEqual(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldGreaterThanOrEqual(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldGreaterThanOrEqual(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldGreaterThanOrEqual(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldGreaterThanOrEqual() {}
-
-    public OldGreaterThanOrEqual(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldGreaterThanOrEqual(INDArray x) {
-        super(x);
-    }
-
-    @Override
-    public int opNum() {
-        return 4;
-    }
-
-    @Override
-    public String opName() {
-        return "oldgte";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return null;
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldLessThan.java

@@ -1,91 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * Bit mask over the ndarrays as to whether
- * the components are less than or not
- *
- * @author Adam Gibson
- */
-public class OldLessThan extends BaseTransformBoolOp {
-    public OldLessThan(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldLessThan(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldLessThan(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldLessThan(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldLessThan() {}
-
-    public OldLessThan(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldLessThan(INDArray x) {
-        super(x);
-    }
-
-    public OldLessThan(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 2;
-    }
-
-    @Override
-    public String opName() {
-        return "oldlt";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-      throw new NoOpNameFoundException("No tf opName found for " + opName());
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return Arrays.asList(outputVariables()[0]);
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldLessThanOrEqual.java

@@ -1,104 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * Bit mask over the ndarrays as to whether
- * the components are less than or equal or not
- *
- * @author Adam Gibson
- */
-public class OldLessThanOrEqual extends BaseTransformBoolOp {
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, Object[] extraArgs) {
-        super(sameDiff, i_v1, i_v2, extraArgs);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v, long[] shape, boolean inPlace, Object[] extraArgs) {
-        super(sameDiff, i_v, shape, inPlace, extraArgs);
-    }
-
-    public OldLessThanOrEqual(SameDiff sameDiff, SDVariable i_v, Object[] extraArgs) {
-        super(sameDiff, i_v, extraArgs);
-    }
-
-    public OldLessThanOrEqual() {}
-
-    public OldLessThanOrEqual(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldLessThanOrEqual(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldLessThanOrEqual(INDArray x) {
-        super(x);
-    }
-
-    @Override
-    public int opNum() {
-        return 5;
-    }
-
-    @Override
-    public String opName() {
-        return "oldlte";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return Arrays.asList(outputVariables()[0]);
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldMax.java

@@ -1,88 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.List;
-
-/**
- * Max function
- *
- * @author Adam Gibson
- */
-public class OldMax extends BaseTransformSameOp {
-    public OldMax(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldMax(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldMax(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldMax(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldMax() {}
-
-    public OldMax(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldMax(INDArray x) {
-        super(x);
-    }
-
-    public OldMax(INDArray ndArray, INDArray dup) {
-        super(ndArray, dup);
-    }
-
-    @Override
-    public int opNum() {
-        return 7;
-    }
-
-    @Override
-    public String opName() {
-        return "old_max_transform";
-    }
-
-    @Override
-    public String onnxName() {
-      throw new NoOpNameFoundException("This is not meant to be mapped, use Max instead");
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("This is not meant to be mapped, use Max instead");
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return null;
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldMin.java

@@ -1,88 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.List;
-
-/**
- * Min function
- *
- * @author Adam Gibson
- */
-public class OldMin extends BaseTransformSameOp {
-    public OldMin(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldMin(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldMin(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldMin(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldMin() {}
-
-    public OldMin(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldMin(INDArray x) {
-        super(x);
-    }
-
-    public OldMin(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 8;
-    }
-
-    @Override
-    public String opName() {
-        return "old_min_transform";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("This is not meant to be mapped, use Max instead");
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("This is not meant to be mapped, use Max instead");
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return null;
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/comparison/OldNotEqualTo.java

@@ -1,87 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.comparison;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformBoolOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * Not equal to function:
- * Bit mask over whether 2 elements are not equal or not
- *
- * @author Adam Gibson
- */
-public class OldNotEqualTo extends BaseTransformBoolOp {
-    public OldNotEqualTo(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldNotEqualTo(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldNotEqualTo() {
-    }
-
-    public OldNotEqualTo(INDArray x) {
-        super(x);
-    }
-
-    public OldNotEqualTo(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldNotEqualTo(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 6;
-    }
-
-    @Override
-    public String opName() {
-        return "old_neq";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " + opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No op name found");
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-
-        return Arrays.asList(f().neg(i_v.get(0)));
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/custom/ATan2.java

@@ -22,11 +22,13 @@ import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
 import org.nd4j.imports.NoOpNameFoundException;
 import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
 
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
+import org.nd4j.linalg.ops.transforms.Transforms;
 
 /**
  * Arc Tangent elementwise function
@@ -39,6 +41,15 @@ public class ATan2 extends BaseDynamicTransformOp {
         super(sameDiff, new SDVariable[] {y, x} ,false);
     }
 
+    /**
+     * Note that the order of x and y match {@link java.lang.Math#atan2(double, double)},
+     * and are reversed when compared to OldATan2.
+     * See {@link Transforms#atan2(org.nd4j.linalg.api.ndarray.INDArray, org.nd4j.linalg.api.ndarray.INDArray)}
+     */
+    public ATan2(INDArray x, INDArray y, INDArray z) {
+        super(new INDArray[]{x, y}, new INDArray[]{ z });
+    }
+
     public ATan2() {}
 
     @Override

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

@@ -46,6 +46,9 @@ public class EqualTo extends BaseDynamicTransformOp {
         super(inputs, outputs);
     }
 
+    public EqualTo(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
 
     @Override
     public String opName() {

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

@@ -47,7 +47,9 @@ public class GreaterThan extends BaseDynamicTransformOp {
         super(inputs, outputs);
     }
 
-
+    public GreaterThan(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
 
     @Override
     public String opName() {

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

@@ -46,6 +46,10 @@ public class GreaterThanOrEqual extends BaseDynamicTransformOp {
         super(inputs, outputs);
     }
 
+    public GreaterThanOrEqual(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
+
     @Override
     public int opNum() {
         return 11;

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

@@ -47,6 +47,10 @@ public class LessThan extends BaseDynamicTransformOp {
         super(inputs, outputs);
     }
 
+    public LessThan(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
+
     @Override
     public String opName() {
         return "less";

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

@@ -45,6 +45,11 @@ public class LessThanOrEqual extends BaseDynamicTransformOp {
     public LessThanOrEqual( INDArray[] inputs, INDArray[] outputs) {
         super(inputs, outputs);
     }
+
+    public LessThanOrEqual(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
+
     @Override
     public String opName() {
         return "less_equal";

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

@@ -46,6 +46,9 @@ public class NotEqualTo extends BaseDynamicTransformOp {
         super(inputs, outputs);
     }
 
+    public NotEqualTo(INDArray x, INDArray y, INDArray z){
+        this(new INDArray[]{x, y}, new INDArray[]{z});
+    }
 
     @Override
     public String opName() {

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

@@ -21,6 +21,7 @@ import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
 import org.nd4j.imports.NoOpNameFoundException;
 import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.DynamicCustomOp;
 
 import java.util.Arrays;
@@ -38,6 +39,27 @@ public class Reverse extends DynamicCustomOp {
     public Reverse() {
     }
 
+    /**
+     * Inplace reverse.  See {@link #Reverse(INDArray, INDArray)}
+     */
+    public Reverse(INDArray x){
+        this(x, x);
+        this.inPlace = true;
+    }
+
+    /**
+     * Reverses whole array for compatibility with OldReverse.
+     *
+     * Note that otherwise, passing null or empty dimensions will result in a noop.
+     */
+    public Reverse(INDArray x, INDArray z){
+        super(new INDArray[]{x}, new INDArray[]{z});
+        this.dimensions = new int[x.rank()];
+        for(int i = 0 ; i < this.dimensions.length ; i++)
+            this.dimensions[i] = i;
+        addIArgument(dimensions);
+    }
+
     @Override
     public String opName() {
         return "reverse";

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/FModOp.java

@@ -52,6 +52,9 @@ public class FModOp extends BaseTransformSameOp {
     public FModOp(INDArray x, INDArray z) {
         super(x, z);
     }
+    public FModOp(INDArray x, INDArray y, INDArray z) {
+        super(x, y, z);
+    }
 
     public FModOp(INDArray x) {
         super(x);

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldAddOp.java

@@ -1,89 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link AddOp}
- */
-@Deprecated
-public class OldAddOp extends BaseTransformAnyOp {
-    public OldAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldAddOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldAddOp() {}
-
-    public OldAddOp(INDArray x) {
-        super(x);
-    }
-
-    public OldAddOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldAddOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 0;
-    }
-
-    @Override
-    public String opName() {
-        return "old_add";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),rarg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(larg(),rarg()));
-
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldAtan2Op.java

@@ -1,86 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformFloatOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.List;
-
-/**
- * atan2 operation
- *
- * @author raver119@gmail.com
- */
-public class OldAtan2Op extends BaseTransformAnyOp {
-    public OldAtan2Op(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldAtan2Op(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldAtan2Op(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldAtan2Op(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldAtan2Op() {}
-
-    public OldAtan2Op(INDArray x, INDArray y) {
-        super(x, y, x);
-    }
-
-    public OldAtan2Op(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 16;
-    }
-
-    @Override
-    public String opName() {
-        return "old_atan2";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No  onnx opName found for " + opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        return "ATan2";
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return null;
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldDivOp.java

@@ -1,88 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link DivOp}
- */
-@Deprecated
-public class OldDivOp extends BaseTransformAnyOp {
-    public OldDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldDivOp() {}
-
-    public OldDivOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    public OldDivOp(INDArray x) {
-        super(x);
-    }
-
-    public OldDivOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 2;
-    }
-
-    @Override
-    public String opName() {
-        return "olddiv";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),rarg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(larg(),rarg()));
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldFModOp.java

@@ -1,88 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.List;
-
-/**
- * Floating point remainder
- *
- * @author raver119@gmail.com
- */
-public class OldFModOp extends BaseTransformAnyOp {
-    public OldFModOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldFModOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldFModOp(SameDiff sameDiff) {
-        super(sameDiff);
-    }
-
-    public OldFModOp(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldFModOp() {}
-
-    public OldFModOp(INDArray x) {
-        super(x);
-    }
-
-    public OldFModOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-    public OldFModOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 15;
-    }
-
-    @Override
-    public String opName() {
-        return "oldfmod";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        return null;
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldFloorDivOp.java

@@ -1,89 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * Truncated division operation
- *
- * @author Adam Gibson
- */
-public class OldFloorDivOp extends BaseTransformAnyOp {
-    public OldFloorDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldFloorDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldFloorDivOp() {}
-
-    public OldFloorDivOp(INDArray x) {
-        super(x);
-    }
-
-    public OldFloorDivOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldFloorDivOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 18;
-    }
-
-    @Override
-    public String opName() {
-        return "oldfloordiv";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),rarg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(larg(),rarg()));
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldMulOp.java

@@ -1,91 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link MulOp}
- */
-@Deprecated
-public class OldMulOp extends BaseTransformAnyOp {
-    public OldMulOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldMulOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldMulOp() {}
-
-    public OldMulOp(INDArray x) {
-        super(x);
-    }
-
-    public OldMulOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldMulOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 3;
-    }
-
-    @Override
-    public String opName() {
-        return "oldmul";
-    }
-
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable g = sameDiff.setupFunction(i_v.get(0));
-        SDVariable gradWrtX = f().mul(g,rarg());
-        SDVariable gradWrtY = f().mul(g,larg());
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldRDivOp.java

@@ -1,87 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link RDivOp}
- */
-@Deprecated
-public class OldRDivOp extends BaseTransformAnyOp {
-    public OldRDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldRDivOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldRDivOp() {}
-
-    public OldRDivOp(INDArray x) {
-        super(x);
-    }
-
-    public OldRDivOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldRDivOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 11;
-    }
-
-    @Override
-    public String opName() {
-        return "oldrdiv";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No ONNX op name found for: " + getClass().getName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),larg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(rarg(),larg()));
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldRSubOp.java

@@ -1,87 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link RSubOp}
- */
-@Deprecated
-public class OldRSubOp extends BaseTransformAnyOp {
-    public OldRSubOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldRSubOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldRSubOp() {}
-
-    public OldRSubOp(INDArray x) {
-        super(x);
-    }
-
-    public OldRSubOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldRSubOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 5;
-    }
-
-    @Override
-    public String opName() {
-        return "old_rsub";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),rarg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(larg(),rarg()));
-
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/transforms/pairwise/arithmetic/OldSubOp.java

@@ -1,89 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformAnyOp;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.ArrayList;
-import java.util.List;
-
-/**
- * @deprecated Use {@link SubOp}
- */
-@Deprecated
-public class OldSubOp extends BaseTransformAnyOp {
-    public OldSubOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2) {
-        super(sameDiff, i_v1, i_v2);
-    }
-
-    public OldSubOp(SameDiff sameDiff, SDVariable i_v1, SDVariable i_v2, boolean inPlace) {
-        super(sameDiff, i_v1, i_v2, inPlace);
-    }
-
-    public OldSubOp() {}
-
-    public OldSubOp(INDArray x) {
-        super(x);
-    }
-
-    public OldSubOp(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldSubOp(INDArray x, INDArray y, INDArray z) {
-        super(x, y, z);
-    }
-
-    @Override
-    public int opNum() {
-        return 6;
-    }
-
-    @Override
-    public String opName() {
-        return "old_sub";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " +  opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " +  opName());
-    }
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        SDVariable gradWrtX = f().div(i_v.get(0),rarg());
-        SDVariable gradWrtY = f().mul(f().neg(gradWrtX),f().div(larg(),rarg()));
-
-        List<SDVariable> ret = new ArrayList<>(2);
-        ret.add(gradWrtX);
-        ret.add(gradWrtY);
-        return ret;
-    }
-
-
-}

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

@@ -20,6 +20,7 @@ import org.nd4j.autodiff.samediff.SDVariable;
 import org.nd4j.autodiff.samediff.SameDiff;
 import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.transforms.BaseDynamicTransformOp;
 import org.nd4j.linalg.api.shape.LongShapeDescriptor;
 import org.nd4j.linalg.api.shape.Shape;
@@ -40,6 +41,10 @@ public class Identity extends BaseDynamicTransformOp {
         super(sd, new SDVariable[]{input}, false);
     }
 
+    public Identity(INDArray x, INDArray z){
+        super(new INDArray[]{x}, new INDArray[]{z});
+    }
+
     public Identity(){ }
 
     @Override

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

@@ -1,77 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.same;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformOp;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.Arrays;
-import java.util.List;
-import java.util.UUID;
-
-/**
- * Identity function
- *
- * @author Adam Gibson
- */
-public class OldIdentity extends BaseTransformSameOp {
-    public OldIdentity(SameDiff sameDiff, SDVariable i_v, boolean inPlace) {
-        super(sameDiff, i_v, inPlace);
-    }
-
-    public OldIdentity() {
-    }
-
-    public OldIdentity(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldIdentity(INDArray x) {
-        super(x);
-    }
-
-    @Override
-    public int opNum() {
-        return 15;
-    }
-
-    @Override
-    public String opName() {
-        return "old_identity";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("This op does not work with onnx.");
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("This op does not work with tensorflow.");
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> i_v) {
-        return i_v;
-    }
-
-}

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

@@ -1,74 +0,0 @@
-/*******************************************************************************
- * Copyright (c) 2015-2018 Skymind, Inc.
- *
- * This program and the accompanying materials are made available under the
- * terms of the Apache License, Version 2.0 which is available at
- * https://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.
- *
- * SPDX-License-Identifier: Apache-2.0
- ******************************************************************************/
-
-package org.nd4j.linalg.api.ops.impl.transforms.same;
-
-import org.nd4j.autodiff.samediff.SDVariable;
-import org.nd4j.autodiff.samediff.SameDiff;
-import org.nd4j.imports.NoOpNameFoundException;
-import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.BaseTransformSameOp;
-
-import java.util.Arrays;
-import java.util.List;
-
-/**
- * OldReverse op
- */
-public class OldReverse extends BaseTransformSameOp {
-    public OldReverse(SameDiff sameDiff, SDVariable i_v, int... dimensions) {
-        super(sameDiff, i_v, false);
-        this.dimensions = dimensions;
-    }
-
-    public OldReverse() {
-    }
-
-    public OldReverse(INDArray x, INDArray z) {
-        super(x, z);
-    }
-
-    public OldReverse(INDArray x) {
-        super(x);
-    }
-
-    @Override
-    public int opNum() {
-        return 20;
-    }
-
-    @Override
-    public String opName() {
-        return "old_reverse";
-    }
-
-    @Override
-    public String onnxName() {
-        throw new NoOpNameFoundException("No onnx op opName found for " + opName());
-    }
-
-    @Override
-    public String tensorflowName() {
-        throw new NoOpNameFoundException("No tensorflow op opName found for " + opName());
-    }
-
-
-    @Override
-    public List<SDVariable> doDiff(List<SDVariable> f1) {
-        SDVariable ret = f().reverse(f1.get(0), dimensions);
-        return Arrays.asList(ret);
-    }
-}

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/convolution/OldConvolution.java

@@ -17,6 +17,7 @@
 package org.nd4j.linalg.convolution;
 
 import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.impl.transforms.Pad.Mode;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.INDArrayIndex;
 import org.nd4j.linalg.indexing.NDArrayIndex;
@@ -129,8 +130,7 @@ public class OldConvolution {
         long w = img.size(3);
         long outHeight = outSize(h, kh, sy, ph, coverAll);
         long outWidth = outSize(w, kw, sx, pw, coverAll);
-        INDArray padded = Nd4j.pad(img, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}},
+        INDArray padded = Nd4j.pad(img, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}}, Mode.CONSTANT, pval);
-                        Nd4j.PadMode.CONSTANT);
         INDArray ret = Nd4j.create(n, c, kh, kw, outHeight, outWidth);
         for (int i = 0; i < kh; i++) {
             //offset for the row based on the stride and output height

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/env/impl/OmpNumThreadsAction.java

@@ -35,7 +35,8 @@ public class OmpNumThreadsAction implements EnvironmentalAction {
         val skipper = System.getenv(ND4JEnvironmentVars.ND4J_SKIP_BLAS_THREADS);
         if (skipper == null) {
             // we infer num threads only if skipper undefined
-            Nd4j.setNumThreads(v);
+            // Nd4j.setNumThreads(v);
+            // method does not do anything anymore and was removed
         }
     }
 }

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

@@ -20,6 +20,14 @@ import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.ops.impl.broadcast.*;
 import org.nd4j.linalg.api.ops.impl.broadcast.bool.*;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.EqualTo;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThan;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThan;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Max;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Min;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.NotEqualTo;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.*;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.*;
 import org.nd4j.linalg.api.ops.impl.transforms.same.AMax;
@@ -42,7 +50,7 @@ public class Broadcast {
     public static INDArray add(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldAddOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new AddOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastAddOp(x,y,z,dimensions));
@@ -66,7 +74,7 @@ public class Broadcast {
     public static INDArray div(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldDivOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new DivOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastDivOp(x,y,z,dimensions));
@@ -78,7 +86,7 @@ public class Broadcast {
     public static INDArray eq(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldEqualTo(x,y,z));
+            return Nd4j.getExecutioner().exec(new EqualTo(x,y,z))[0];
         }
         return Nd4j.getExecutioner().exec(new BroadcastEqualTo(x,y,z,dimensions));
     }
@@ -89,7 +97,7 @@ public class Broadcast {
     public static INDArray gt(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldGreaterThan(x,y,z));
+            return Nd4j.getExecutioner().exec(new GreaterThan(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastGreaterThan(x,y,z,dimensions));
@@ -101,7 +109,7 @@ public class Broadcast {
     public static INDArray gte(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldGreaterThanOrEqual(x,y,z));
+            return Nd4j.getExecutioner().exec(new GreaterThanOrEqual(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastGreaterThanOrEqual(x,y,z,dimensions));
@@ -113,7 +121,7 @@ public class Broadcast {
     public static INDArray lt(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldLessThan(x,y,z));
+            return Nd4j.getExecutioner().exec(new LessThan(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastLessThan(x,y,z,dimensions));
@@ -125,7 +133,7 @@ public class Broadcast {
     public static INDArray lte(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldLessThanOrEqual(x,y,z));
+            return Nd4j.getExecutioner().exec(new LessThanOrEqual(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastLessThanOrEqual(x,y,z,dimensions));
@@ -137,7 +145,7 @@ public class Broadcast {
     public static INDArray mul(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldMulOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new MulOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastMulOp(x,y,z,dimensions));
@@ -149,7 +157,7 @@ public class Broadcast {
     public static INDArray neq(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldNotEqualTo(x,y,z));
+            return Nd4j.getExecutioner().exec(new NotEqualTo(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastNotEqual(x,y,z,dimensions));
@@ -161,7 +169,7 @@ public class Broadcast {
     public static INDArray rdiv(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldRDivOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new RDivOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastRDivOp(x,y,z,dimensions));
@@ -173,7 +181,7 @@ public class Broadcast {
     public static INDArray rsub(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldSubOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new SubOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastRSubOp(x,y,z,dimensions));
@@ -185,7 +193,7 @@ public class Broadcast {
     public static INDArray sub(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldSubOp(x,y,z));
+            return Nd4j.getExecutioner().exec(new SubOp(x,y,z))[0];
         }
 
         return Nd4j.getExecutioner().exec(new BroadcastSubOp(x,y,z,dimensions));
@@ -197,7 +205,7 @@ public class Broadcast {
     public static INDArray max(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldMax(x,y,z));
+            return Nd4j.getExecutioner().exec(new Max(x,y,z))[0];
         }
 
 
@@ -210,7 +218,7 @@ public class Broadcast {
     public static INDArray min(INDArray x, INDArray y, INDArray z, int... dimensions) {
         if(dimensions == null || dimensions.length == 0) {
             validateShapesNoDimCase(x,y,z);
-            return Nd4j.getExecutioner().exec(new OldMin(x,y,z));
+            return Nd4j.getExecutioner().exec(new Min(x,y,z))[0];
         }
 
 

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

@@ -57,8 +57,10 @@ import org.nd4j.linalg.api.ops.impl.scatter.ScatterUpdate;
 import org.nd4j.linalg.api.ops.impl.shape.Diag;
 import org.nd4j.linalg.api.ops.impl.shape.DiagPart;
 import org.nd4j.linalg.api.ops.impl.shape.Stack;
+import org.nd4j.linalg.api.ops.impl.transforms.Pad;
+import org.nd4j.linalg.api.ops.impl.transforms.Pad.Mode;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Reverse;
 import org.nd4j.linalg.api.ops.impl.shape.Tile;
-import org.nd4j.linalg.api.ops.impl.transforms.same.OldReverse;
 import org.nd4j.linalg.api.ops.random.custom.RandomExponential;
 import org.nd4j.linalg.api.ops.random.impl.*;
 import org.nd4j.linalg.api.rng.DefaultRandom;
@@ -182,93 +184,69 @@ public class Nd4j {
         nd4j.initContext();
     }
 
-    public enum PadMode {
-        CONSTANT, EDGE, LINEAR_RAMP, MAXIMUM, MEAN, MEDIAN, MINIMUM, REFLECT, SYMMETRIC, WRAP
-
-    }
-
     /**
-     * See {@link #pad(INDArray, int[][], List, PadMode)} with zero padding. (zeros for constantValues).
+     * See {@link #pad(INDArray, INDArray)}.  Uses 0 padding.
      */
-    public static INDArray pad(INDArray toPad, int[][] padWidth, PadMode padMode) {
+    public static INDArray pad(@NonNull INDArray toPad, @NonNull int[][] padWidth){
-        return pad(toPad, padWidth, ArrayUtil.zerosMatrix(toPad.shape()), padMode);
+        return pad(toPad, Nd4j.createFromArray(padWidth));
     }
 
     /**
-     * Pad the given ndarray to the size along each dimension
+     * See {@link #pad(INDArray, INDArray)}.  Uses 0 padding, and uses padWidth for all dimensions.
+     */
+    public static INDArray pad(@NonNull INDArray toPad, @NonNull int... padWidth){
+        return pad(toPad, padWidth, Mode.CONSTANT, 0);
+    }
+
+    /**
+     * See {@link #pad(INDArray, INDArray, Pad.Mode, double)} with zero padding (zeros for padValue).
+     */
+    public static INDArray pad(INDArray toPad, INDArray padding) {
+        return pad(toPad, padding, Mode.CONSTANT, 0);
+    }
+
+    /**
+     * See {@link #pad(INDArray, INDArray, Mode, double)}.
+     */
+    public static INDArray pad(@NonNull INDArray toPad, @NonNull int[][] padWidth, @NonNull Pad.Mode padMode, double padValue){
+        return pad(toPad, Nd4j.createFromArray(padWidth), padMode, padValue);
+    }
+
+    /**
+     * See {@link #pad(INDArray, INDArray, Mode, double)}, uses padWidth for all dimensions.
+     */
+    public static INDArray pad(@NonNull INDArray toPad, @NonNull int[] padWidth, @NonNull Pad.Mode padMode, double padValue){
+        int[][] pads = new int[toPad.rank()][padWidth.length];
+        for(int i = 0 ; i < pads.length ; i++){
+            pads[i] = padWidth;
+        }
+        return pad(toPad, pads, padMode, padValue);
+    }
+
+    /**
+     * Pad the given ndarray to the size along each dimension.
+     *
      * @param toPad the ndarray to pad
      * @param padWidth the width to pad along each dimension
-     * @param constantValues the values to append for each dimension
      * @param padMode the mode to pad in
+     * @param padValue the value used during padding.  Only used when padMode is {@link Pad.Mode#CONSTANT}.
      * @return the padded ndarray
      * based on the specified mode
      */
-    public static INDArray pad(INDArray toPad, int[][] padWidth, List<double[]> constantValues, PadMode padMode) {
+    public static INDArray pad(@NonNull INDArray toPad, @NonNull INDArray padWidth, @NonNull Pad.Mode padMode, double padValue) {
-        if (padMode == PadMode.CONSTANT) {
-            if (padWidth.length < toPad.rank())
-                throw new IllegalArgumentException("Please specify a pad width for each dimension");
 
-            List<int[]> sizes = new ArrayList<>();
+        Preconditions.checkArgument(toPad.rank() == padWidth.size(0),
-            for (int i = 0; i < toPad.rank(); i++) {
+                "Must provide padding values for each dimension.  Expected %s pairs for a rank %s array, got %s",
-                sizes.add(padWidth[i]);
+                toPad.rank(), toPad.rank(), padWidth.size(0));
-            }
 
-            return padImpl(toPad, sizes, constantValues);
+        long[] newShape = new long[toPad.rank()];
+        for(int i = 0 ; i < newShape.length ; i++){
+            newShape[i] = toPad.size(i) + padWidth.getRow(i).sumNumber().intValue();
         }
-        throw new UnsupportedOperationException();
+        INDArray out = Nd4j.createUninitialized(toPad.dataType(), newShape);
-    }
+        Pad op = new Pad(toPad, padWidth, out, padMode, padValue);
 
-    /**
+        return Nd4j.getExecutioner().exec(op)[0];
-     * See {@link #pad(INDArray, int[][], List, PadMode)} with a 1D int[] for padWidth.
-     */
-    public static INDArray pad(INDArray toPad, int[] padWidth, List<double[]> constantValues, PadMode padMode) {
-        if (padMode == PadMode.CONSTANT) {
-            if (padWidth.length < toPad.rank())
-                throw new IllegalArgumentException("Please specify a pad width for each dimension");
-
-            toPad = Nd4j.stripOnes(toPad);
-
-            List<int[]> sizes = new ArrayList<>();
-            for (int i = 0; i < toPad.rank(); i++) {
-                sizes.add(padWidth);
-            }
-
-            return padImpl(toPad, sizes, constantValues);
-        }
-        throw new UnsupportedOperationException();
-    }
-
-    // common code for pad(INDArray, int[],   List<double[]>, PadMode) and
-    //                 pad(INDArray, int[][], List<double[]>, PadMode)
-    private static INDArray padImpl(INDArray toPad, List<int[]> sizes, List<double[]> constantValues){
-
-        INDArray ret = toPad;
-        for (int i = 0; i < toPad.rank(); i++) {
-            int[] pad = sizes.get(i);
-            double[] constant = constantValues.get(i);
-            int padBefore = pad[0];
-            int padAfter = pad[1];
-            if (constant.length < 2) {
-                double val = constant[0];
-                constant = new double[2];
-                constant[0] = val;
-                constant[1] = val;
-            }
-
-            double beforeVal = constant[0];
-            double afterVal = constant[1];
-            ret = Nd4j.prepend(ret, padBefore, beforeVal, i);
-            ret = Nd4j.append(ret, padAfter, afterVal, i);
-
-        }
-        return ret;
-    }
-
-    /**
-     * See {@link #pad(INDArray, int[][], List, PadMode)} with a 1D int[] for padWidth and zero padding.
-     */
-    public static INDArray pad(INDArray toPad, int[] padWidth, PadMode padMode) {
-        return pad(toPad, padWidth, ArrayUtil.zerosMatrix(padWidth), padMode);
     }
 
     /**
@@ -2639,7 +2617,7 @@ public class Nd4j {
      * @return the reversed matrix
      */
     public static INDArray reverse(INDArray reverse) {
-        return Nd4j.getExecutioner().exec(new OldReverse(reverse));
+        return Nd4j.getExecutioner().exec(new Reverse(reverse))[0];
     }
 
     /**
@@ -5961,28 +5939,7 @@ public class Nd4j {
         }
 
     }
-
+  
-    /**
-     * This method returns maximal allowed number of threads for Nd4j.
-     * If value wasn't set in advance, max(1, availableProcessor) will be returned
-     * @return maximal allowed number of threads
-     */
-    public static int numThreads() {
-        val v = numThreads.get();
-        if (v <= 0)
-            return Math.max(1, Runtime.getRuntime().availableProcessors() / 2);
-        else
-            return v;
-    }
-
-    /**
-     * This method sets maximal allowed number of threads for Nd4j
-     * @param numthreads maximal allowed number of threads
-     */
-    public static void setNumThreads(int numthreads) {
-        numThreads.set(numthreads);
-    }
-
     public static DataType defaultFloatingPointType() {
         return defaultFloatingPointDataType.get();
     }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/AdaMaxUpdater.java

@@ -20,7 +20,7 @@ import lombok.Data;
 import lombok.NonNull;
 import org.apache.commons.math3.util.FastMath;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMax;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Max;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.NDArrayIndex;
@@ -104,7 +104,7 @@ public class AdaMaxUpdater implements GradientUpdater<AdaMax> {
         //u = max(B_2 * u, |grad|)
         u.muli(config.getBeta2());
         Transforms.abs(gradient, false); //In-place should be OK here, original gradient values aren't used again later
-        Nd4j.getExecutioner().exec(new OldMax(u, gradient, u));
+        Nd4j.getExecutioner().exec(new Max(u, gradient, u));
 
         double beta1t = FastMath.pow(config.getBeta1(), iteration + 1);
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/NesterovsUpdater.java

@@ -19,7 +19,7 @@ package org.nd4j.linalg.learning;
 import lombok.Data;
 import lombok.NonNull;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAddOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.learning.config.Nesterovs;
@@ -105,6 +105,6 @@ public class NesterovsUpdater implements GradientUpdater<Nesterovs> {
         INDArray ret = vPrev.muli(momentum).addi(v.mul(-momentum - 1));
         gradient.assign(ret);
         */
-        Nd4j.getExecutioner().exec(new OldAddOp(vPrev.muli(momentum), v.mul(-momentum - 1), gradient));
+        Nd4j.getExecutioner().exec(new AddOp(vPrev.muli(momentum), v.mul(-momentum - 1), gradient));
     }
 }

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

@@ -30,6 +30,10 @@ import org.nd4j.linalg.api.ops.impl.scalar.comparison.ScalarNot;
 import org.nd4j.linalg.api.ops.impl.shape.Cross;
 import org.nd4j.linalg.api.ops.impl.transforms.bool.BooleanNot;
 import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.ATan2;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Reverse;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.floating.*;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.*;
@@ -37,7 +41,6 @@ import org.nd4j.linalg.api.ops.impl.transforms.gradient.ELUDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.gradient.HardTanhDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.gradient.LeakyReLUDerivative;
 import org.nd4j.linalg.api.ops.impl.transforms.gradient.SoftSignDerivative;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldAtan2Op;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.PowPairwise;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.And;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.bool.Or;
@@ -104,7 +107,7 @@ public class Transforms {
 
 
     public static INDArray reverse(INDArray x, boolean dup) {
-        return Nd4j.getExecutioner().exec(new OldReverse(x, dup ? x.ulike() : x));
+        return Nd4j.getExecutioner().exec(new Reverse(x, dup ? x.ulike() : x))[0];
     }
 
     /**
@@ -140,14 +143,15 @@ public class Transforms {
 
     /**
      * Atan2 operation, new INDArray instance will be returned
-     * Note the order of x and y parameters is opposite to that of java.lang.Math.atan2
+     * Note the order of x and y parameters is opposite to that of {@link java.lang.Math#atan2(double, double)}
      *
      * @param x the abscissa coordinate
      * @param y the ordinate coordinate
      * @return the theta from point (r, theta) when converting (x,y) from to cartesian to polar coordinates
      */
     public static INDArray atan2(@NonNull INDArray x, @NonNull INDArray y) {
-        return Nd4j.getExecutioner().exec(new OldAtan2Op(x, y, x.ulike()));
+        // Switched on purpose, to match OldATan2 (which the javadoc was written for)
+        return Nd4j.getExecutioner().exec(new ATan2(y, x, x.ulike()))[0];
     }
 
     /**
@@ -789,7 +793,7 @@ public class Transforms {
      * @return
      */
     public static INDArray lessThanOrEqual(INDArray first, INDArray ndArray, boolean dup) {
-        return exec(new OldLessThanOrEqual(first, ndArray, Nd4j.createUninitialized(DataType.BOOL, first.shape(), first.ordering())));
+        return Nd4j.getExecutioner().exec(new LessThanOrEqual(first, ndArray, Nd4j.createUninitialized(DataType.BOOL, first.shape(), first.ordering())))[0];
 
     }
 
@@ -801,7 +805,7 @@ public class Transforms {
      * @return
      */
     public static INDArray greaterThanOrEqual(INDArray first, INDArray ndArray, boolean dup) {
-        return exec(new OldGreaterThanOrEqual(first, ndArray, Nd4j.createUninitialized(DataType.BOOL, first.shape(), first.ordering())));
+        return Nd4j.getExecutioner().exec(new GreaterThanOrEqual(first, ndArray, Nd4j.createUninitialized(DataType.BOOL, first.shape(), first.ordering())))[0];
 
     }
 
@@ -986,7 +990,7 @@ public class Transforms {
      * @return
      */
     public static INDArray identity(INDArray ndArray, boolean dup) {
-        return exec(dup ? new OldIdentity(ndArray, ndArray.ulike()) : new OldIdentity(ndArray));
+        return Nd4j.getExecutioner().exec(dup ? new Identity(ndArray, ndArray.ulike()) : new Identity(ndArray, ndArray))[0];
     }
 
     public static INDArray isMax(INDArray input, DataType dataType) {

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

@@ -16,6 +16,13 @@
 
 package org.nd4j.autodiff.opvalidation;
 
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertNull;
+
+import java.util.ArrayList;
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.junit.Ignore;
@@ -32,21 +39,20 @@ import org.nd4j.linalg.api.ops.DynamicCustomOp;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.AvgPooling2D;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative;
-import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
+import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Conv1DConfig;
-import org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize;
+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.LocalResponseNormalizationConfig;
+import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
+import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling3DConfig;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.LayerNorm;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.factory.Nd4jBackend;
 import org.nd4j.linalg.ops.transforms.Transforms;
 
-import java.util.ArrayList;
-import java.util.Arrays;
-import java.util.Collections;
-import java.util.List;
-
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertNull;
-
 @Slf4j
 public class LayerOpValidation extends BaseOpValidation {
     public LayerOpValidation(Nd4jBackend backend) {
@@ -311,7 +317,7 @@ public class LayerOpValidation extends BaseOpValidation {
             SDVariable loss = sd.mean("loss", out);
 
             log.info("Starting test: " + msg);
-            TestCase tc = new TestCase(sd);
+            TestCase tc = new TestCase(sd).gradientCheck(true);
             String error = OpValidation.validate(tc);
             if (error != null) {
                 failed.add(msg);
@@ -344,7 +350,7 @@ public class LayerOpValidation extends BaseOpValidation {
 
             String msg = Arrays.toString(inSizeNCHW);
 
-            TestCase tc = new TestCase(sd).testName(msg);
+            TestCase tc = new TestCase(sd).gradientCheck(true).testName(msg);
             String error = OpValidation.validate(tc);
             if (error != null) {
                 failed.add(msg);
@@ -552,7 +558,7 @@ public class LayerOpValidation extends BaseOpValidation {
                     SDVariable loss = sd.standardDeviation("loss", out, true);
 
                     log.info("Starting test: " + msg);
-                    TestCase tc = new TestCase(sd);
+                    TestCase tc = new TestCase(sd).gradientCheck(true);
                     tc.testName(msg);
                     String error = OpValidation.validate(tc);
                     if (error != null) {
@@ -660,7 +666,7 @@ public class LayerOpValidation extends BaseOpValidation {
 //        System.out.println(sd.getFunction("grad").summary());
 
         //Gradient check:
-        TestCase tc = new TestCase(sd);
+        TestCase tc = new TestCase(sd).gradientCheck(true);
         String err = OpValidation.validate(tc);
         assertNull(err);
     }
@@ -705,7 +711,7 @@ public class LayerOpValidation extends BaseOpValidation {
 
         SDVariable loss = out.std(true);
         //Gradient check:
-        TestCase tc = new TestCase(sd);
+        TestCase tc = new TestCase(sd).gradientCheck(true);
         String err = OpValidation.validate(tc);
         assertNull(err);
     }
@@ -798,7 +804,7 @@ public class LayerOpValidation extends BaseOpValidation {
             exp.putScalar(next, max);
         }
 
-        assertNull(OpValidation.validate(new TestCase(sd)
+        assertNull(OpValidation.validate(new TestCase(sd).gradientCheck(true)
                 .expected(outPool, exp)));
     }
 
@@ -856,7 +862,7 @@ public class LayerOpValidation extends BaseOpValidation {
         }
 
         assertNull(OpValidation.validate(new TestCase(sd)
-                .expected(outPool, exp)));
+                .expected(outPool, exp).gradientCheck(true)));
 
     }
 
@@ -887,16 +893,12 @@ public class LayerOpValidation extends BaseOpValidation {
                 .build();
 
         SDVariable out = sd.cnn().avgPooling3d(in, pooling3DConfig);
-        out = sd.nn().tanh("out", out);
+        out = sd.nn().tanh("loss", out).shape().rename("out");
 
-        INDArray outArr = sd.execAndEndResult();
-        val outShape = outArr.shape();
         // oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1;
-        assertArrayEquals(new long[]{mb, nIn, 4, 4, 4}, outShape);
+        INDArray outArr = Nd4j.createFromArray(mb, nIn, 4, 4, 4L);
 
-        SDVariable loss = out.std(true);
+        TestCase tc = new TestCase(sd).expectedOutput("out", outArr).gradientCheck(true);
-        //Gradient check:
-        TestCase tc = new TestCase(sd);
         String err = OpValidation.validate(tc);
         assertNull(err);
     }
@@ -927,12 +929,16 @@ public class LayerOpValidation extends BaseOpValidation {
                 .build();
 
         SDVariable out = sd.cnn().maxPooling3d(in, pooling3DConfig);
-        out = sd.nn().tanh("out", out);
+        out = sd.nn().tanh("loss", out).shape().rename("out");
+
+        sd.setLossVariables("loss");
 
-        INDArray outArr = sd.execAndEndResult();
-        val outShape = outArr.shape();
         // oH = (iH - (kH + (kH-1)*(dH-1)) + 2*pH)/sH + 1;
-        assertArrayEquals(new long[]{mb, nIn, 27, 27, 27}, outShape);
+        INDArray outArr = Nd4j.createFromArray(mb, nIn, 27, 27, 27L);
+
+        TestCase tc = new TestCase(sd).expectedOutput("out", outArr).gradientCheck(true);
+        String err = OpValidation.validate(tc);
+        assertNull(err);
     }
 
     @Test
@@ -958,13 +964,58 @@ public class LayerOpValidation extends BaseOpValidation {
                 .build();
 
         SDVariable out = sd.cnn().conv1d(in, w, conv1DConfig);
-        out = sd.nn().tanh("out", out);
+        out = sd.nn().tanh("loss", out).shape().rename("out");
+
+        sd.setLossVariables("loss");
 
-        INDArray outArr = sd.execAndEndResult();
-        INDArray iOut = out.getArr();
         //Expected output size: out = (in - k + 2*p)/s + 1 = (28-2+0)/1+1 = 27
-        val outShape = outArr.shape();
+        INDArray outArr = Nd4j.createFromArray(mb, nOut, 27L);
-        assertArrayEquals(new long[]{mb, nOut, 27}, outShape);
+        TestCase tc = new TestCase(sd).expectedOutput("out", outArr).gradientCheck(false);
+        String err = OpValidation
+                .validate(tc);
+        assertNull(err);
+    }
+
+    @Test
+    public void testConv1dForward(){
+        int nIn = 2;
+        int nOut = 1;
+        int kernel = 3;
+        int batchSize = 10;
+        int sequenceSize = 5;
+
+        SameDiff sd = SameDiff.create();
+
+        INDArray inArr = Nd4j.linspace(0, nIn * batchSize * sequenceSize, nIn * batchSize * sequenceSize)
+                .reshape(batchSize, nIn, sequenceSize);
+
+        INDArray wArr = Nd4j.linspace(0, kernel * nIn * nOut, kernel * nIn * nOut)
+                .reshape(kernel, nIn, nOut);
+
+        SDVariable in = sd.var("in", inArr);
+        SDVariable w = sd.var("w", wArr);
+
+        SDVariable res = sd.cnn.conv1d(in, w, Conv1DConfig.builder().k(kernel).build());
+
+        INDArray expected = Nd4j.createFromArray(
+                new double[][][]{
+                        {{82.42424f, 100.60606f, 118.78788f}},
+                        {{264.2424f, 282.4242f, 300.6061f}},
+                        {{446.0606f, 464.2424f, 482.424f}},
+                        {{627.8788f, 646.0606f, 664.2424f}},
+                        {{809.6970f, 827.8788f, 846.0606f}},
+                        {{991.5152f, 1009.69696f, 1027.8788f}},
+                        {{1173.3333f, 1191.5152f, 1209.6970f}},
+                        {{1355.1515f, 1373.3333f, 1391.5153f}},
+                        {{1536.9697f, 1555.1515f, 1573.3333f}},
+                        {{1718.7878f, 1736.9697f, 1755.1515f}}
+                }
+        );
+
+        TestCase tc = new TestCase(sd).gradientCheck(false).expectedOutput(res.getVarName(), expected);
+        String err = OpValidation.validate(tc);
+
+        assertNull(err);
     }
 
 
@@ -1000,17 +1051,61 @@ public class LayerOpValidation extends BaseOpValidation {
                 .build();
 
         SDVariable out = sd.cnn().conv3d(in, w, b, conv3DConfig);
-        out = sd.nn().tanh("out", out);
+        out = sd.nn().tanh("loss", out).shape().rename("out");
+
+        sd.setLossVariables("loss");
 
-        INDArray outArr = sd.execAndEndResult();
         //Expected output size, NOT same mode: out = (in - k)/d + 1 = (28-2+0)/1+1 = 27
         //Expected output size, WITH same mode: out = in/stride
-        val outShape = outArr.shape();
+        INDArray outArr = Nd4j.createFromArray(mb, nOut, 5, 5, 5L);
-        assertArrayEquals(new long[]{mb, nOut, 5, 5, 5}, outShape);
 
-        SDVariable loss = out.std(true);
+        TestCase tc = new TestCase(sd).expectedOutput("out", outArr).gradientCheck(true);
-        //Gradient check:
+        String err = OpValidation
-        TestCase tc = new TestCase(sd);
+                .validate(tc);
+        assertNull(err);
+    }
+
+    @Test
+    public void testDeConv3dBasic() {
+        int nIn = 4;
+        int nOut = 3;
+        int kH = 2;
+        int kW = 2;
+        int kD = 2;
+
+        int mb = 3;
+        int imgH = 5;
+        int imgW = 5;
+        int imgT = 5;
+
+        SameDiff sd = SameDiff.create();
+        INDArray inArr = Nd4j.rand(new long[]{mb, nIn, 5, 5, 5});
+        INDArray wArr = Nd4j.rand(kD, kH, kW, nOut, nIn);
+
+        SDVariable in = sd.var("in", inArr);
+        SDVariable w = sd.var("W", wArr);
+
+        DeConv3DConfig conv3DConfig = DeConv3DConfig.builder()
+                .kH(kH).kW(kW).kD(kD)
+                .sD(1).sH(1).sW(1)
+                .dH(1).dW(1).dD(1)
+                .isSameMode(true)
+                .dataFormat(DeConv3DConfig.NCDHW)
+                .build();
+
+        SDVariable out = sd.cnn().deconv3d(in, w, conv3DConfig);
+        out = sd.nn().tanh("loss", out).shape().rename("out");
+
+        sd.setLossVariables("loss");
+
+        //Expected conv3d size, NOT same mode: out = (in - k)/d + 1 = (28-2+0)/1+1 = 27
+        //Expected conv3d size, WITH same mode: out = in/stride
+        // reversed this for deconv3d
+        INDArray outArr = Nd4j.createFromArray(new long[]{mb, nOut, imgT, imgH, imgW});
+
+        TestCase tc = new TestCase(sd)
+                .expectedOutput("out", outArr)
+                .gradientCheck(true);
         String err = OpValidation.validate(tc);
         assertNull(err);
     }
@@ -1181,23 +1276,23 @@ public class LayerOpValidation extends BaseOpValidation {
         List<String> failed = new ArrayList<>();
 
         for (boolean ncdhw : new boolean[]{true, false}) {
-                int nIn = inSizeNCDHW[1];
+            int nIn = inSizeNCDHW[1];
-                int[] shape = (ncdhw ? inSizeNCDHW : ncdhwToNdhwc(inSizeNCDHW));
+            int[] shape = (ncdhw ? inSizeNCDHW : ncdhwToNdhwc(inSizeNCDHW));
 
-                SameDiff sd = SameDiff.create();
+            SameDiff sd = SameDiff.create();
-                SDVariable in = sd.var("in", shape);
+            SDVariable in = sd.var("in", shape);
 
-                SDVariable out;
+            SDVariable out;
-                String msg = "0 - conv3d+bias+same, ncdhw=" + ncdhw + " - input " + Arrays.toString(shape);
+            String msg = "0 - conv3d+bias+same, ncdhw=" + ncdhw + " - input " + Arrays.toString(shape);
 
-                SDVariable w0 = sd.var("w0", Nd4j.rand(new int[]{2, 2, 2, nIn, 3}).muli(10));  //[kD, kH, kW, iC, oC]
+            SDVariable w0 = sd.var("w0", Nd4j.rand(new int[]{2, 2, 2, nIn, 3}).muli(10));  //[kD, kH, kW, iC, oC]
-                SDVariable b0 = sd.var("b0", Nd4j.rand(new long[]{3}).muli(10));
+            SDVariable b0 = sd.var("b0", Nd4j.rand(new long[]{3}).muli(10));
-                out = sd.cnn().conv3d(in, w0, b0, Conv3DConfig.builder()
+            out = sd.cnn().conv3d(in, w0, b0, Conv3DConfig.builder()
-                        .dataFormat(ncdhw ? Conv3DConfig.NCDHW : Conv3DConfig.NDHWC)
+                    .dataFormat(ncdhw ? Conv3DConfig.NCDHW : Conv3DConfig.NDHWC)
-                        .isSameMode(true)
+                    .isSameMode(true)
-                        .kH(2).kW(2).kD(2)
+                    .kH(2).kW(2).kD(2)
-                        .sD(1).sH(1).sW(-1).dW(-1)
+                    .sD(1).sH(1).sW(-1).dW(-1)
-                        .build());
+                    .build());
         }
     }
 

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

@@ -38,10 +38,10 @@ import org.nd4j.linalg.api.ops.impl.scalar.ScalarFMod;
 import org.nd4j.linalg.api.ops.impl.scalar.ScalarMultiplication;
 import org.nd4j.linalg.api.ops.impl.shape.Cross;
 import org.nd4j.linalg.api.ops.impl.transforms.Pad;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMax;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMin;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThanOrEqual;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Max;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Min;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.Standardize;
 import org.nd4j.linalg.api.ops.impl.transforms.floating.RSqrt;
@@ -1008,7 +1008,7 @@ public class TransformOpValidation extends BaseOpValidation {
             }
 
 
-            DifferentialFunction[] funcs = sd.functions();
+            DifferentialFunction[] funcs = sd.ops();
             String name = opName == null ? funcs[0].opName() : opName;
 
 
@@ -1141,11 +1141,11 @@ public class TransformOpValidation extends BaseOpValidation {
                     break;
                 case 14:
                     t = sd.max(in1, in2);
-                    tc.expectedOutput(t.getVarName(), Nd4j.getExecutioner().exec(new OldMax(ia, ib, ia.dup())));
+                    tc.expectedOutput(t.getVarName(), Nd4j.getExecutioner().exec(new Max(ia, ib, ia.dup()))[0]);
                     break;
                 case 15:
                     t = sd.min(in1, in2);
-                    tc.expectedOutput(t.getVarName(), Nd4j.getExecutioner().exec(new OldMin(ia, ib, ia.dup())));
+                    tc.expectedOutput(t.getVarName(), Nd4j.getExecutioner().exec(new Min(ia, ib, ia.dup()))[0]);
                     break;
                 case 16:
                     ia = Nd4j.getExecutioner().exec(new BernoulliDistribution(ia, 0.5));
@@ -1199,7 +1199,7 @@ public class TransformOpValidation extends BaseOpValidation {
             }
 
 
-            DifferentialFunction[] funcs = sd.functions();
+            DifferentialFunction[] funcs = sd.ops();
             String name = (opName == null ? funcs[0].opName() : opName);
 
             String msg = "test: " + i + " - " + name;

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

@@ -188,11 +188,11 @@ public class FlatBufferSerdeTest extends BaseNd4jTest {
                     assertEquals(varsOrig.get(j).getVarName(), varsRestored.get(j).getVarName());
                 }
 
-                DifferentialFunction[] fOrig = sd.functions();
+                DifferentialFunction[] fOrig = sd.ops();
-                DifferentialFunction[] fRestored = restored.functions();
+                DifferentialFunction[] fRestored = restored.ops();
                 assertEquals(fOrig.length, fRestored.length);
 
-                for (int j = 0; j < sd.functions().length; j++) {
+                for (int j = 0; j < sd.ops().length; j++) {
                     assertEquals(fOrig[j].getClass(), fRestored[j].getClass());
                 }
 
@@ -224,7 +224,7 @@ public class FlatBufferSerdeTest extends BaseNd4jTest {
                     sd.save(f2, withUpdaterState);
                     SameDiff r2 = SameDiff.load(f2, withUpdaterState);
                     assertEquals(varsOrig.size(), r2.variables().size());
-                    assertEquals(fOrig.length, r2.functions().length);
+                    assertEquals(fOrig.length, r2.ops().length);
                     assertEquals(sd.getLossVariables(), r2.getLossVariables());
 
                     //Save via stream:
@@ -237,7 +237,7 @@ public class FlatBufferSerdeTest extends BaseNd4jTest {
                     try(InputStream is = new BufferedInputStream(new FileInputStream(f3))) {
                         SameDiff r3 = SameDiff.load(is, withUpdaterState);
                         assertEquals(varsOrig.size(), r3.variables().size());
-                        assertEquals(fOrig.length, r3.functions().length);
+                        assertEquals(fOrig.length, r3.ops().length);
                         assertEquals(sd.getLossVariables(), r3.getLossVariables());
                     }
                 }

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

@@ -19,7 +19,6 @@ package org.nd4j.autodiff.samediff;
 import lombok.extern.slf4j.Slf4j;
 import org.junit.Test;
 import org.nd4j.autodiff.samediff.transform.*;
-import org.nd4j.base.Preconditions;
 import org.nd4j.linalg.BaseNd4jTest;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -58,17 +57,17 @@ public class GraphTransformUtilTests extends BaseNd4jTest {
 
         SDVariable sub = add.sub(add2);
 
-        assertTrue(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputFunction(add.getVarName())));
+        assertTrue(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputOp(add.getVarName())));
-        assertTrue(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputFunction(add2.getVarName())));
+        assertTrue(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputOp(add2.getVarName())));
-        assertFalse(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputFunction(sub.getVarName())));
+        assertFalse(OpPredicate.classEquals(AddOp.class).matches(sd, sd.getVariableOutputOp(sub.getVarName())));
 
-        assertTrue(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputFunction(add.getVarName())));
+        assertTrue(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputOp(add.getVarName())));
-        assertTrue(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputFunction(add2.getVarName())));
+        assertTrue(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputOp(add2.getVarName())));
-        assertFalse(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputFunction(sub.getVarName())));
+        assertFalse(OpPredicate.opNameEquals(AddOp.OP_NAME).matches(sd, sd.getVariableOutputOp(sub.getVarName())));
 
-        assertTrue(OpPredicate.opNameMatches(".*dd").matches(sd, sd.getVariableOutputFunction(add.getVarName())));
+        assertTrue(OpPredicate.opNameMatches(".*dd").matches(sd, sd.getVariableOutputOp(add.getVarName())));
-        assertTrue(OpPredicate.opNameMatches("ad.*").matches(sd, sd.getVariableOutputFunction(add2.getVarName())));
+        assertTrue(OpPredicate.opNameMatches("ad.*").matches(sd, sd.getVariableOutputOp(add2.getVarName())));
-        assertFalse(OpPredicate.opNameMatches(".*dd").matches(sd, sd.getVariableOutputFunction(sub.getVarName())));
+        assertFalse(OpPredicate.opNameMatches(".*dd").matches(sd, sd.getVariableOutputOp(sub.getVarName())));
 
 
         SubGraphPredicate p = SubGraphPredicate.withRoot(OpPredicate.classEquals(AddOp.class));
@@ -77,11 +76,11 @@ public class GraphTransformUtilTests extends BaseNd4jTest {
         assertEquals(2, l.size());
 
         SubGraph sg1 = l.get(0);
-        assertTrue(sg1.getRootNode() == sd.getVariableOutputFunction(add.getVarName()));
+        assertTrue(sg1.getRootNode() == sd.getVariableOutputOp(add.getVarName()));
         assertEquals(0, sg1.getChildNodes().size());
 
         SubGraph sg2 = l.get(1);
-        assertTrue(sg2.getRootNode() == sd.getVariableOutputFunction(add2.getVarName()));
+        assertTrue(sg2.getRootNode() == sd.getVariableOutputOp(add2.getVarName()));
         assertEquals(0, sg2.getChildNodes().size());
     }
 

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

@@ -59,13 +59,13 @@ import org.nd4j.linalg.api.ops.impl.layers.convolution.config.LocalResponseNorma
 import org.nd4j.linalg.api.ops.impl.reduce3.ManhattanDistance;
 import org.nd4j.linalg.api.ops.impl.shape.tensorops.TensorArray;
 import org.nd4j.linalg.api.ops.impl.transforms.any.IsMax;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMax;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldMin;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.GreaterThanOrEqual;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.IsNonDecreasing;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.IsNumericTensor;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.IsStrictlyIncreasing;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.LessThanOrEqual;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Max;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Min;
 import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
 import org.nd4j.linalg.api.shape.LongShapeDescriptor;
 import org.nd4j.linalg.checkutil.NDArrayCreationUtil;
@@ -1759,11 +1759,11 @@ public class SameDiffTests extends BaseNd4jTest {
                     break;
                 case 7:
                     t = sd.max(in1, in2);
-                    expOut = Nd4j.getExecutioner().exec(new OldMax(ia, ib, ia.dup()));
+                    expOut = Nd4j.getExecutioner().exec(new Max(ia, ib, ia.dup()))[0];
                     break;
                 case 8:
                     t = sd.min(in1, in2);
-                    expOut = Nd4j.getExecutioner().exec(new OldMin(ia, ib, ia.dup()));
+                    expOut = Nd4j.getExecutioner().exec(new Min(ia, ib, ia.dup()))[0];
                     break;
                 case 9:
                     ia = Nd4j.getExecutioner().exec(new BernoulliDistribution(ia, 0.5));

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

@@ -16,7 +16,6 @@
 
 package org.nd4j.imports.TFGraphs;
 
-import com.google.common.primitives.Doubles;
 import lombok.extern.slf4j.Slf4j;
 import lombok.val;
 import org.apache.commons.io.FilenameUtils;
@@ -38,7 +37,6 @@ import org.nd4j.autodiff.validation.OpValidation;
 import org.nd4j.base.Preconditions;
 import org.nd4j.imports.TFGraphs.listener.OpExecOrderListener;
 import org.nd4j.imports.graphmapper.tf.TFGraphMapper;
-import org.nd4j.linalg.BaseNd4jTest;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.iter.NdIndexIterator;
 import org.nd4j.linalg.api.ndarray.INDArray;
@@ -46,7 +44,6 @@ import org.nd4j.linalg.api.ops.executioner.OpExecutioner;
 import org.nd4j.linalg.api.ops.impl.reduce.longer.MatchCondition;
 import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
 import org.nd4j.linalg.factory.Nd4j;
-import org.nd4j.linalg.factory.Nd4jBackend;
 import org.nd4j.linalg.function.BiFunction;
 import org.nd4j.linalg.indexing.BooleanIndexing;
 import org.nd4j.linalg.indexing.conditions.Conditions;
@@ -301,7 +298,7 @@ public class TFGraphTestAllHelper {
             Map<String,SameDiffOp> fns = graph.getOps();
             List<String> execOrder = listener.getOpNamesList();
             for(String opName : execOrder){
-                String[] outputs = graph.getOutputsForFunction(fns.get(opName).getOp());
+                String[] outputs = graph.getOutputsForOp(fns.get(opName).getOp());
                 Collections.addAll(varNames, outputs);
             }
 
@@ -334,8 +331,8 @@ public class TFGraphTestAllHelper {
                             if(countExceeds > 0){
                                 maxRE = relError.maxNumber().doubleValue();
                                 //Find the op that this variable is produced by
-                                op = graph.getVariableOutputFunction(varName);
+                                op = graph.getVariableOutputOp(varName);
-                                opInputs = graph.getInputsForFunction(op);
+                                opInputs = graph.getInputsForOp(op);
                             }
 
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/imports/TensorFlowImportTest.java

@@ -732,9 +732,9 @@ public class TensorFlowImportTest extends BaseNd4jTest {
         }
 
         val functions = new HashMap<String, DifferentialFunction>();
-        for (val func: tg.functions()) {
+        for (val func: tg.ops()) {
             val ownName = func.getOwnName();
-            val outName = func.outputVariables()[0].getVarName();
+            String outName = func.outputVariables()[0].getVarName();
 
             assertTrue("Missing ownName: [" + ownName +"]",variables.containsKey(ownName));
             assertEquals(ownName, outName);

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

@@ -72,12 +72,12 @@ import org.nd4j.linalg.api.ops.impl.transforms.bool.MatchConditionTransform;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.CompareAndSet;
 import org.nd4j.linalg.api.ops.impl.transforms.comparison.Eps;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.BatchToSpace;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.Reverse;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.BatchToSpaceND;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.BinaryRelativeError;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.Set;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.Axpy;
-import org.nd4j.linalg.api.ops.impl.transforms.same.OldReverse;
 import org.nd4j.linalg.api.ops.impl.transforms.same.Sign;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.ACosh;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.Tanh;
@@ -5226,7 +5226,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
         INDArray array = Nd4j.create(new double[] {9, 8, 7, 6, 5, 4, 3, 2, 1, 0});
         INDArray exp = Nd4j.create(new double[] {0, 1, 2, 3, 4, 5, 6, 7, 8, 9});
 
-        INDArray rev = Nd4j.getExecutioner().exec(new OldReverse(array, Nd4j.createUninitialized(array.length())));
+        INDArray rev = Nd4j.getExecutioner().exec(new Reverse(array, Nd4j.createUninitialized(array.length())))[0];
 
         assertEquals(exp, rev);
     }
@@ -5236,7 +5236,7 @@ public class Nd4jTestsC extends BaseNd4jTest {
         INDArray array = Nd4j.create(new double[] {10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0});
         INDArray exp = Nd4j.create(new double[] {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
 
-        INDArray rev = Nd4j.getExecutioner().exec(new OldReverse(array, Nd4j.createUninitialized(array.length())));
+        INDArray rev = Nd4j.getExecutioner().exec(new Reverse(array, Nd4j.createUninitialized(array.length())))[0];
 
         assertEquals(exp, rev);
     }

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/mixed/MixedDataTypesTests.java

@@ -35,7 +35,7 @@ import org.nd4j.linalg.api.ops.impl.reduce.bool.IsInf;
 import org.nd4j.linalg.api.ops.impl.reduce.bool.IsNaN;
 import org.nd4j.linalg.api.ops.impl.reduce.longer.CountNonZero;
 import org.nd4j.linalg.api.ops.impl.reduce3.CosineSimilarity;
-import org.nd4j.linalg.api.ops.impl.transforms.comparison.OldEqualTo;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.EqualTo;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.shape.options.ArrayOptionsHelper;
 import org.nd4j.linalg.factory.Nd4j;
@@ -276,7 +276,7 @@ public class MixedDataTypesTests extends BaseNd4jTest {
         val arrayY = Nd4j.create(new int[]{1, 0, 0, 4}, new  long[]{4}, DataType.INT);
         val exp = new long[]{1, 0, 0, 1};
 
-        val result = Nd4j.getExecutioner().exec(new OldEqualTo(arrayX, arrayY));
+        val result = Nd4j.getExecutioner().exec(new EqualTo(arrayX, arrayY, arrayX.ulike().castTo(DataType.BOOL)))[0];
         assertEquals(DataType.BOOL, result.dataType());
         val arr = result.data().asLong();
 
@@ -369,13 +369,13 @@ public class MixedDataTypesTests extends BaseNd4jTest {
         val result = Nd4j.getExecutioner().exec(op);
     }
 
-    @Test(expected = IllegalArgumentException.class)
+    @Test(expected = RuntimeException.class)
     public void testTypesValidation_2() {
         val arrayX = Nd4j.create(new int[]{1, 2, 3, 4}, new  long[]{4}, DataType.INT);
         val arrayY = Nd4j.create(new int[]{1, 0, 0, 4}, new  long[]{4}, DataType.LONG);
         val exp = new long[]{1, 0, 0, 1};
 
-        val result = Nd4j.getExecutioner().exec(new OldEqualTo(arrayX, arrayY));
+        val result = Nd4j.getExecutioner().exec(new EqualTo(arrayX, arrayY, arrayX.ulike().castTo(DataType.BOOL)))[0];
         val arr = result.data().asLong();
 
         assertArrayEquals(exp, arr);

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTests.java

@@ -45,7 +45,7 @@ import org.nd4j.linalg.api.ops.impl.scalar.comparison.ScalarLessThan;
 import org.nd4j.linalg.api.ops.impl.summarystats.Variance;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.Exp;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.Log;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.SetRange;
@@ -205,7 +205,7 @@ public class OpExecutionerTests extends BaseNd4jTest {
         INDArray x = Nd4j.ones(5);
         INDArray xDup = x.dup();
         INDArray solution = Nd4j.valueArrayOf(5, 1.0);
-        opExecutioner.exec(new OldMulOp(x, xDup, x));
+        opExecutioner.exec(new MulOp(x, xDup, x));
         assertEquals(solution, x);
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/ops/OpExecutionerTestsC.java

@@ -55,7 +55,7 @@ import org.nd4j.linalg.api.ops.impl.transforms.HistogramFixedWidth;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.LogSoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.AddOp;
-import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.OldMulOp;
+import org.nd4j.linalg.api.ops.impl.transforms.pairwise.arithmetic.MulOp;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.Exp;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.Log;
 import org.nd4j.linalg.api.ops.impl.transforms.strict.SetRange;
@@ -236,7 +236,7 @@ public class OpExecutionerTestsC extends BaseNd4jTest {
         INDArray x = Nd4j.ones(5);
         INDArray xDup = x.dup();
         INDArray solution = Nd4j.valueArrayOf(5, 1.0);
-        opExecutioner.exec(new OldMulOp(x, xDup, x));
+        opExecutioner.exec(new MulOp(x, xDup, x));
         assertEquals(solution, x);
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/padding/PaddingTests.java

@@ -72,8 +72,9 @@ public class PaddingTests extends BaseNd4jTest {
 
     @Test
     public void testPad() {
+
         INDArray start = Nd4j.linspace(1, 9, 9, DataType.DOUBLE).reshape(3, 3);
-        INDArray ret = Nd4j.pad(start, new int[] {5, 5}, Nd4j.PadMode.CONSTANT);
+        INDArray ret = Nd4j.pad(start, 5, 5);
         double[][] data = new double[][] {{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, 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, 0, 0, 0, 0, 0.},

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/concat/padding/PaddingTestsC.java

@@ -64,8 +64,7 @@ public class PaddingTestsC extends BaseNd4jTest {
         INDArray ret = Nd4j.create(new double[] {1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
                         4, 4, 4, 4, 4, 4, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3,
                         4, 4, 4, 4, 4, 4, 4, 4}, new int[] {1, 1, 8, 8});
-        INDArray padded = Nd4j.pad(ret, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}},
+        INDArray padded = Nd4j.pad(ret, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}});
-                        Nd4j.PadMode.CONSTANT);
 
         INDArray assertion = Nd4j.create(new double[] {1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 3, 3, 3, 3,
                         3, 3, 3, 3, 0, 4, 4, 4, 4, 4, 4, 4, 4, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0,
@@ -104,8 +103,7 @@ public class PaddingTestsC extends BaseNd4jTest {
         // FIXME: int cast
         int outWidth = Convolution.outSize((int) h, kh, sy, ph, 1, true);
         int outHeight = Convolution.outSize((int) w, kw, sx, pw, 1, true);
-        INDArray padded = Nd4j.pad(linspaced, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}},
+        INDArray padded = Nd4j.pad(linspaced, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}});
-                        Nd4j.PadMode.CONSTANT);
         System.out.println(padded);
     }
 

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/shape/indexing/IndexingTestsC.java

@@ -127,8 +127,7 @@ public class IndexingTestsC extends BaseNd4jTest {
                         4, 4, 4, 4, 4, 4, 4, 4}, new long[] {1, 1, 8, 8});
 
 
-        INDArray padded = Nd4j.pad(img, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}},
+        INDArray padded = Nd4j.pad(img, new int[][] {{0, 0}, {0, 0}, {ph, ph + sy - 1}, {pw, pw + sx - 1}});
-                        Nd4j.PadMode.CONSTANT);
 
         INDArray get = padded.get(NDArrayIndex.all(), NDArrayIndex.all(), NDArrayIndex.interval(i, sy, iLim),
                         NDArrayIndex.interval(j, sx, jLim));