Various fixes (#65)

* #7977 deprecate legacy MultiLayerNetwork/ComputationGraph.params(boolean) method

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

* Fix bad test

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

* Small fixes

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

* Fix Histogram mapping

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

* Fix incorrect name handling in DifferentialFunction

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

* More fixes

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

* Histogram fixes

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

* Proper histogram fix

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

* ToString/NDArrayStrings fix

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

* JSON UTF8 serialization fix

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

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/gradientcheck/CNN3DGradientCheckTest.java

@@ -401,6 +401,7 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
                             .dataType(DataType.DOUBLE)
                             .updater(new NoOp()).weightInit(WeightInit.LECUN_NORMAL)
                             .dist(new NormalDistribution(0, 1))
+                            .seed(12345)
                             .list()
                             .layer(0, new Convolution3D.Builder().activation(afn).kernelSize(1, 1, 1)
                                     .nIn(convNIn).nOut(convNOut).hasBias(false)

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

@@ -996,10 +996,10 @@ public class GradientCheckTestsComputationGraph extends BaseDL4JTest {
         int[] mbSizes = new int[] {1, 3, 10};
         for (int minibatch : mbSizes) {
 
-            INDArray in1 = Nd4j.rand(minibatch, 2);
+            INDArray in1 = Nd4j.rand(DataType.DOUBLE, minibatch, 2);
-            INDArray in2 = Nd4j.rand(minibatch, 2);
+            INDArray in2 = Nd4j.rand(DataType.DOUBLE, minibatch, 2);
 
-            INDArray labels = Nd4j.rand(minibatch, 1);
+            INDArray labels = Nd4j.rand(DataType.DOUBLE, minibatch, 1);
 
             String testName = "testBasicL2() - minibatch = " + minibatch;
 

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

@@ -28,13 +28,12 @@ import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.LossLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
-import org.junit.Ignore;
 import org.junit.Test;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.activations.impl.ActivationIdentity;
 import org.nd4j.linalg.api.buffer.DataType;
 import org.nd4j.linalg.api.ndarray.INDArray;
-import org.nd4j.linalg.api.ops.impl.transforms.strict.OldSoftMax;
+import org.nd4j.linalg.api.ops.impl.transforms.custom.SoftMax;
 import org.nd4j.linalg.api.ops.random.impl.BernoulliDistribution;
 import org.nd4j.linalg.factory.Nd4j;
 import org.nd4j.linalg.indexing.BooleanIndexing;
@@ -451,10 +450,10 @@ public class LossFunctionGradientCheck extends BaseDL4JTest {
                 //KL divergence: should be a probability distribution for labels??
                 ret[1] = Nd4j.rand(labelsShape);
                 if(labelsShape.length == 2){
-                    Nd4j.getExecutioner().exec(new OldSoftMax(ret[1]));
+                    Nd4j.getExecutioner().exec(new SoftMax(ret[1]));
                 } else if(labelsShape.length == 3) {
                     for (int i = 0; i < labelsShape[2]; i++) {
-                        Nd4j.getExecutioner().exec(new OldSoftMax(ret[1].get(all(), all(), point(i))));
+                        Nd4j.getExecutioner().exec(new SoftMax(ret[1].get(all(), all(), point(i))));
                     }
                 } else {
                     throw new RuntimeException();

deeplearning4j/deeplearning4j-core/src/test/java/org/deeplearning4j/nn/layers/feedforward/embedding/EmbeddingLayerTest.java

@@ -310,8 +310,8 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
                 .build();
         MultiLayerConfiguration conf2 = new NeuralNetConfiguration.Builder().activation(Activation.TANH).list()
                 .layer(new DenseLayer.Builder().nIn(nClassesIn).nOut(embeddingDim).build())
-                .layer(new OutputLayer.Builder().nIn(embeddingDim).nOut(nOut).activation(Activation.SOFTMAX).build())
+                .layer(new RnnOutputLayer.Builder().nIn(embeddingDim).nOut(nOut).activation(Activation.SOFTMAX).build())
-                .inputPreProcessor(0, new RnnToFeedForwardPreProcessor())
+                .setInputType(InputType.recurrent(nClassesIn))
                 .build();
 
         MultiLayerNetwork net = new MultiLayerNetwork(conf);
@@ -324,7 +324,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
         int batchSize = 3;
         INDArray inEmbedding = Nd4j.create(batchSize, 1);
         INDArray inOneHot = Nd4j.create(batchSize, nClassesIn, 1);
-        INDArray outLabels = Nd4j.create(batchSize, 4);
+        INDArray outLabels = Nd4j.create(batchSize, 4, 1);
 
         Random r = new Random(1337);
         for (int i = 0; i < batchSize; i++) {
@@ -333,7 +333,7 @@ public class EmbeddingLayerTest extends BaseDL4JTest {
             inOneHot.putScalar(new int[]{i, classIdx, 0}, 1.0);
 
             int labelIdx = r.nextInt(4);
-            outLabels.putScalar(new int[]{i, labelIdx}, 1.0);
+            outLabels.putScalar(new int[]{i, labelIdx, 0}, 1.0);
         }
 
         net.setInput(inEmbedding);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/graph/ComputationGraph.java

@@ -2892,26 +2892,11 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
     }
 
     /**
-     * Get the parameters for the ComputationGraph
+     * @deprecated To be removed. Use {@link #params()}
-     *
-     * @param backwardOnly If true: backprop parameters only (i.e., no visible layer biases used in layerwise pretraining layers)
      */
+    @Deprecated
     public INDArray params(boolean backwardOnly) {
-        if (backwardOnly)
+        return params();
-            return flattenedParams;
-
-        List<INDArray> list = new ArrayList<>(layers.length);
-        for (int i = 0; i < topologicalOrder.length; i++) {
-            if (!vertices[topologicalOrder[i]].hasLayer())
-                continue;
-
-            Layer l = vertices[topologicalOrder[i]].getLayer();
-            INDArray layerParams = l.params();
-            if (layerParams != null)
-                list.add(layerParams); //may be null: subsampling etc layers
-        }
-
-        return Nd4j.toFlattened('f', list);
     }
 
     /**
@@ -3183,7 +3168,7 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
 
     @Override
     public INDArray params() {
-        return params(true);
+        return flattenedParams;
     }
 
     @Override

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/multilayer/MultiLayerNetwork.java

@@ -1433,29 +1433,11 @@ public class MultiLayerNetwork implements Serializable, Classifier, Layer, Neura
 
 
     /**
-     * Returns a 1 x m vector where the vector is composed of a flattened vector of all of the parameters (weights and
+     * @deprecated To be removed. Use {@link #params()} instead
-     * biases etc) for all parameters in the network. Note that this method is generally reserved for developer and
-     * internal use - see {@link #getParam(String)} and {@link #paramTable()} for a more useful/interpretable
-     * representation of the parameters.<br>
-     * Note that with backwardsOnly = false the parameter vector is not a copy, and changes to the returned INDArray
-     * will impact the network parameters.
-     *
-     * @param backwardOnly Return a copy of the parameters excluding any parameters used only for unsupervised layers'
-     *                     unsupervised training (such as decoder parameters in an autoencoder layer
-     * @return the params for this neural net
      */
+    @Deprecated
     public INDArray params(boolean backwardOnly) {
-        if (backwardOnly)
+        return params();
-            return params();
-
-        List<INDArray> params = new ArrayList<>();
-        for (Layer layer : getLayers()) {
-            INDArray layerParams = layer.params();
-            if (layerParams != null)
-                params.add(layerParams); //may be null: subsampling etc layers
-        }
-
-        return Nd4j.toFlattened('f', params);
     }
 
 

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/weights/WeightInitUtil.java

@@ -41,10 +41,6 @@ public class WeightInitUtil {
 
     private WeightInitUtil() {}
 
-    public static INDArray initWeights(int[] shape, float min, float max) {
-        return Nd4j.rand(shape, min, max, Nd4j.getRandom());
-    }
-
 
     /**
      * Initializes a matrix with the given weight initialization scheme.

deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-ui-model/src/main/java/org/deeplearning4j/ui/stats/BaseStatsListener.java

@@ -28,7 +28,6 @@ import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.gradient.Gradient;
 import org.deeplearning4j.nn.graph.ComputationGraph;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
-import org.deeplearning4j.optimize.api.BaseTrainingListener;
 import org.deeplearning4j.ui.stats.api.*;
 import org.deeplearning4j.ui.stats.impl.DefaultStatsInitializationConfiguration;
 import org.deeplearning4j.ui.stats.impl.DefaultStatsUpdateConfiguration;
@@ -763,11 +762,11 @@ public abstract class BaseStatsListener implements RoutingIterationListener {
 
         for (Map.Entry<String, INDArray> entry : map.entrySet()) {
 
-            org.nd4j.linalg.api.ops.impl.transforms.floating.Histogram hOp =
+            org.nd4j.linalg.api.ops.impl.transforms.Histogram hOp =
-                    new org.nd4j.linalg.api.ops.impl.transforms.floating.Histogram(entry.getValue(), nBins);
+                    new org.nd4j.linalg.api.ops.impl.transforms.Histogram(entry.getValue(), nBins);
-            Nd4j.getExecutioner().exec(hOp);
+            Nd4j.exec(hOp);
 
-            INDArray bins = hOp.z();
+            INDArray bins = hOp.getOutputArgument(0);
             int[] count = new int[nBins];
             for (int i = 0; i < bins.length(); i++) {
                 count[i] = (int) bins.getDouble(i);

libnd4j/include/ops/declarable/generic/transforms/histogram.cpp

@@ -34,6 +34,7 @@ namespace nd4j {
 
             REQUIRE_TRUE(numBins == output->lengthOf(), 0, "Histogram: numBins must match output length")
 
+            output->nullify();
             helpers::histogramHelper(block.launchContext(), *input, *output);
 
             return Status::OK();

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

@@ -663,9 +663,9 @@ public abstract class DifferentialFunction {
                     scope = "";
                 else
                     scope = scope + "/";
-                String varName = scope + sameDiff.generateNewVarName(opName(),argIndex).replace(":", "_");
+                String varName = scope + sameDiff.generateNewVarName(opName(),argIndex);
                 while(sameDiff.functionExists(varName)) {
-                    varName = scope + sameDiff.generateNewVarName(opName(), argIndex).replace(":", "_");
+                    varName = scope + sameDiff.generateNewVarName(opName(), argIndex);
                     argIndex++;
                 }
 

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

@@ -4589,9 +4589,10 @@ public class SameDiff extends SDBaseOps {
             CustomOp op = (CustomOp)node;
             extras = op.tArgs();
         } else {
-            extras = node.getExtraArgs() != null ? new double[node.getExtraArgs().length] : new double[0];
+            Object[] eArgs = node.getExtraArgs();
+            extras = eArgs != null ? new double[eArgs.length] : new double[0];
             for (int e = 0; e < extras.length; e++) {
-                extras[e] = ((Number) node.getExtraArgs()[e]).doubleValue();
+                extras[e] = ((Number) eArgs[e]).doubleValue();
             }
         }
 

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

@@ -331,6 +331,7 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.transforms.CheckNumerics.class,
             org.nd4j.linalg.api.ops.impl.transforms.Cholesky.class,
             org.nd4j.linalg.api.ops.impl.transforms.Constant.class,
+            org.nd4j.linalg.api.ops.impl.transforms.Histogram.class,
             org.nd4j.linalg.api.ops.impl.transforms.HistogramFixedWidth.class,
             org.nd4j.linalg.api.ops.impl.transforms.IdentityN.class,
             org.nd4j.linalg.api.ops.impl.transforms.MaxOut.class,

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

@@ -48,7 +48,7 @@ public abstract class BaseIndexAccumulation extends BaseOp implements IndexAccum
                                  SDVariable i_v,
                                  boolean keepDims,
                                  int[] dimensions) {
-        super(sameDiff,new Object[]{dimensions});
+        super(sameDiff,null);
         if (i_v != null) {
             this.dimensions = dimensions;
             f().validateDifferentialFunctionsameDiff(i_v);
@@ -70,7 +70,7 @@ public abstract class BaseIndexAccumulation extends BaseOp implements IndexAccum
                                  SDVariable i_v2,
                                  boolean keepDims,
                                  int[] dimensions) {
-        super(sameDiff,new Object[]{dimensions});
+        super(sameDiff,null);
         if (i_v != null) {
             this.dimensions = dimensions;
             f().validateDifferentialFunctionsameDiff(i_v);

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

@@ -61,7 +61,7 @@ public abstract class BaseReduceOp extends BaseOp implements ReduceOp {
     public BaseReduceOp(SameDiff sameDiff,
                         SDVariable i_v,
                         int[] dimensions, boolean keepDims) {
-        super(sameDiff,new Object[]{dimensions});
+        super(sameDiff, null);
         if (i_v != null) {
             if(dimensions == null || dimensions.length < 1)
                 dimensions = new int[] {Integer.MAX_VALUE};
@@ -86,7 +86,7 @@ public abstract class BaseReduceOp extends BaseOp implements ReduceOp {
                         SDVariable i_v,
                         SDVariable i_v2,
                         int[] dimensions, boolean keepDims) {
-        super(sameDiff,new Object[]{dimensions});
+        super(sameDiff,null);
         if (i_v != null) {
             if(dimensions == null || dimensions.length < 1)
                 dimensions = new int[] {Integer.MAX_VALUE};

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/shape/Shape.java

@@ -2089,7 +2089,9 @@ public class Shape {
         }
 
         // we need to wrap buffer of a current array, to make sure it's properly marked as a View
-        INDArray ret = Nd4j.create(Nd4j.createBuffer(arr.data(), arr.offset(), arr.length()), newShape, newStrides, arr.offset(), isFOrder ? 'f' : 'c');
+        DataBuffer db = arr.data();
+        DataBuffer buffer = Nd4j.createBuffer(db, arr.offset(), arr.length());
+        INDArray ret = Nd4j.create(buffer, newShape, newStrides, arr.offset(), isFOrder ? 'f' : 'c');
         return ret;
     }
 

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/string/NDArrayStrings.java

@@ -305,7 +305,7 @@ public class NDArrayStrings {
                 }
             }
             if (i < l - 1) {
-                if (!summarize || i < 2 || i > l - 3 || (summarize && l == 6)) {
+                if (!summarize || i <= 2 || i >= l - 3 || (summarize && l == 6)) {
                     sb.append(colSep);
                 }
             }

nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/serde/jackson/shaded/NDArrayTextSerializer.java

@@ -17,6 +17,7 @@
 package org.nd4j.serde.jackson.shaded;
 
 
+import org.nd4j.linalg.api.buffer.Utf8Buffer;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.api.shape.Shape;
 import org.nd4j.serde.base64.Nd4jBase64;
@@ -76,9 +77,12 @@ public class NDArrayTextSerializer extends JsonSerializer<INDArray> {
                     jg.writeNumber(v);
                 break;
             case UTF8:
-                String[] str = new String[(int)arr.length()];
+                Utf8Buffer utf8B = ((Utf8Buffer)arr.data());
-                for( int j=0; j<str.length; j++ )
+                long n = utf8B.getNumWords();
-                    jg.writeString(arr.getString(j));
+                for( int j=0; j<n; j++ ) {
+                    String s = utf8B.getString(j);
+                    jg.writeString(s);
+                }
                 break;
             case COMPRESSED:
             case UNKNOWN:

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/TestOpMapping.java

@@ -68,7 +68,7 @@ public class TestOpMapping extends BaseNd4jTest {
             }
             String opName = df.opName();
 
-            assertTrue(opName, opNameMapping.containsKey(opName));
+            assertTrue("Op is missing - not defined in ImportClassMapping: " + opName, opNameMapping.containsKey(opName));
 
             try{
                 String[] tfNames = df.tensorflowNames();

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

@@ -44,7 +44,7 @@ public class ToStringTest extends BaseNd4jTest {
         assertEquals("[         1,         2,         3]",
                 Nd4j.createFromArray(1, 2, 3).toString());
 
-        assertEquals("[         1,         2,         3  ...         6         7,         8]",
+        assertEquals("[       1,       2,       3,       4,       5,       6,       7,       8]",
                 Nd4j.createFromArray(1, 2, 3, 4, 5, 6, 7, 8).toString(1000, false, 2));
 
         assertEquals("[    1.132,    2.644,    3.234]",
@@ -53,9 +53,8 @@ public class ToStringTest extends BaseNd4jTest {
         assertEquals("[               1.132414,             2.64356456,             3.25345234]",
                 Nd4j.createFromArray(1.132414, 2.64356456, 3.25345234).toStringFull());
 
-        assertEquals("[         1,         2,         3  ...         6         7,         8]",
+        assertEquals("[      1,      2,      3,  ...      6,      7,      8]",
-                Nd4j.createFromArray(1, 2, 3, 4, 5, 6, 7, 8).toString(100, true, 1));
+                Nd4j.createFromArray(1, 2, 3, 4, 5, 6, 7, 8).toString(6, true, 1));
-
     }
 
     @Override

nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/linalg/serde/JsonSerdeTests.java

@@ -66,7 +66,7 @@ public class JsonSerdeTests extends BaseNd4jTest {
 
                     INDArray arr;
                     if(dt == DataType.UTF8){
-                        arr = Nd4j.create("a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l").reshape('c', 3, 4);
+                        arr = Nd4j.create("aaaaa", "bbbb", "ccc", "dd", "e", "f", "g", "h", "i", "j", "k", "l").reshape('c', 3, 4);
                     } else {
                         arr = in.castTo(dt);
                     }

nd4j/nd4j-buffer/src/main/java/org/nd4j/linalg/api/buffer/Utf8Buffer.java

@@ -17,6 +17,7 @@
 package org.nd4j.linalg.api.buffer;
 
 
+import lombok.Getter;
 import lombok.NonNull;
 import lombok.val;
 import org.bytedeco.javacpp.BytePointer;
@@ -42,6 +43,7 @@ public class Utf8Buffer extends BaseDataBuffer {
 
     protected Collection<Pointer> references = new ArrayList<>();
 
+    @Getter
     protected long numWords = 0;
 
     /**
@@ -121,6 +123,7 @@ public class Utf8Buffer extends BaseDataBuffer {
 
     public Utf8Buffer(DataBuffer underlyingBuffer, long length, long offset) {
         super(underlyingBuffer, length, offset);
+        this.numWords = length;
     }
 
     public Utf8Buffer(@NonNull Collection<String> strings) {

nd4j/nd4j-buffer/src/main/java/org/nd4j/linalg/api/buffer/factory/DefaultDataBufferFactory.java

@@ -87,6 +87,8 @@ public class DefaultDataBufferFactory implements DataBufferFactory {
             return new BFloat16Buffer(underlyingBuffer, length, offset);
         } else if (underlyingBuffer.dataType() == DataType.HALF) {
             return new HalfBuffer(underlyingBuffer, length, offset);
+        } else if (underlyingBuffer.dataType() == DataType.UTF8) {
+            return new Utf8Buffer(underlyingBuffer, length, offset);
         }
         return null;
     }