Various DL4J/ND4J fixes (#81)

* #7954 Force refresh of UI when switching tabs on overview page

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

* #8017 Concurrent modification exception (synchronize) fix

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

* #8033 Don't initialize updater in middle of writing memory crash dump

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

* #8208 Fix shape checks for ND4J int[] creator methods

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

* #6385 #7992 Keras import naming fixes + cleanup

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

* #8016 Upsampling3D - add NDHWC format support

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

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

@@ -386,63 +386,64 @@ public class CNN3DGradientCheckTest extends BaseDL4JTest {
         for (Activation afn : activations) {
             for (int miniBatchSize : minibatchSizes) {
                 for (ConvolutionMode mode : modes) {
+                    for(Convolution3D.DataFormat df : Convolution3D.DataFormat.values()) {
 
-                    int outDepth =  depth * upsamplingSize[0];
+                        int outDepth = depth * upsamplingSize[0];
-                    int outHeight = height * upsamplingSize[1];
+                        int outHeight = height * upsamplingSize[1];
-                    int outWidth = width * upsamplingSize[2];
+                        int outWidth = width * upsamplingSize[2];
 
-                    INDArray input = Nd4j.rand(new int[]{miniBatchSize, convNIn, depth, height, width});
+                        INDArray input = df == Convolution3D.DataFormat.NCDHW ? Nd4j.rand(miniBatchSize, convNIn, depth, height, width) : Nd4j.rand(miniBatchSize, depth, height, width, convNIn);
-                    INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
+                        INDArray labels = Nd4j.zeros(miniBatchSize, finalNOut);
-                    for (int i = 0; i < miniBatchSize; i++) {
+                        for (int i = 0; i < miniBatchSize; i++) {
-                        labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
+                            labels.putScalar(new int[]{i, i % finalNOut}, 1.0);
-                    }
-
-                    MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
-                            .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)
-                                    .convolutionMode(mode).dataFormat(Convolution3D.DataFormat.NCDHW)
-                                    .build())
-                            .layer(1, new Upsampling3D.Builder(upsamplingSize[0]).build())
-                            .layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
-                            .layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
-                                    .activation(Activation.SOFTMAX).nOut(finalNOut).build())
-                            .inputPreProcessor(2,
-                                    new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
-                                            convNOut, true))
-                            .setInputType(InputType.convolutional3D(depth, height, width, convNIn)).build();
-
-                    String json = conf.toJson();
-                    MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
-                    assertEquals(conf, c2);
-
-                    MultiLayerNetwork net = new MultiLayerNetwork(conf);
-                    net.init();
-
-                    String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
-                            + ", kernel = " + Arrays.toString(upsamplingSize) + ", mode = " + mode.toString()
-                            + ", input depth " + depth + ", input height " + height
-                            + ", input width " + width;
-
-                    if (PRINT_RESULTS) {
-                        log.info(msg);
-                        for (int j = 0; j < net.getnLayers(); j++) {
-                            log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
                         }
+
+                        MultiLayerConfiguration conf = new NeuralNetConfiguration.Builder()
+                                .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)
+                                        .convolutionMode(mode).dataFormat(df)
+                                        .build())
+                                .layer(1, new Upsampling3D.Builder(upsamplingSize[0]).dataFormat(df).build())
+                                .layer(2, new DenseLayer.Builder().nOut(denseNOut).build())
+                                .layer(new OutputLayer.Builder(LossFunctions.LossFunction.MCXENT)
+                                        .activation(Activation.SOFTMAX).nOut(finalNOut).build())
+                                .inputPreProcessor(2,
+                                        new Cnn3DToFeedForwardPreProcessor(outDepth, outHeight, outWidth,
+                                                convNOut, true))
+                                .setInputType(InputType.convolutional3D(df, depth, height, width, convNIn)).build();
+
+                        String json = conf.toJson();
+                        MultiLayerConfiguration c2 = MultiLayerConfiguration.fromJson(json);
+                        assertEquals(conf, c2);
+
+                        MultiLayerNetwork net = new MultiLayerNetwork(conf);
+                        net.init();
+
+                        String msg = "Minibatch size = " + miniBatchSize + ", activationFn=" + afn
+                                + ", kernel = " + Arrays.toString(upsamplingSize) + ", mode = " + mode.toString()
+                                + ", input depth " + depth + ", input height " + height
+                                + ", input width " + width;
+
+                        if (PRINT_RESULTS) {
+                            log.info(msg);
+                            for (int j = 0; j < net.getnLayers(); j++) {
+                                log.info("Layer " + j + " # params: " + net.getLayer(j).numParams());
+                            }
+                        }
+
+                        boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
+                                DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
+                                RETURN_ON_FIRST_FAILURE, input, labels);
+
+                        assertTrue(msg, gradOK);
+
+                        TestUtils.testModelSerialization(net);
                     }
-
-                    boolean gradOK = GradientCheckUtil.checkGradients(net, DEFAULT_EPS,
-                            DEFAULT_MAX_REL_ERROR, DEFAULT_MIN_ABS_ERROR, PRINT_RESULTS,
-                            RETURN_ON_FIRST_FAILURE, input, labels);
-
-                    assertTrue(msg, gradOK);
-
-                    TestUtils.testModelSerialization(net);
-
                 }
             }
         }

deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasOptimizerUtils.java

@@ -32,6 +32,17 @@ import java.util.Map;
  */
 @Slf4j
 public class KerasOptimizerUtils {
+
+    protected static final String LR = "lr";
+    protected static final String LR2 = "learning_rate";
+    protected static final String EPSILON = "epsilon";
+    protected static final String MOMENTUM = "momentum";
+    protected static final String BETA_1 = "beta_1";
+    protected static final String BETA_2 = "beta_2";
+    protected static final String DECAY = "decay";
+    protected static final String RHO = "rho";
+    protected static final String SCHEDULE_DECAY = "schedule_decay";
+
     /**
      * Map Keras optimizer to DL4J IUpdater.
      *
@@ -55,11 +66,11 @@ public class KerasOptimizerUtils {
 
         switch (optimizerName) {
             case "Adam": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double beta1 = (double) optimizerParameters.get("beta_1");
+                double beta1 = (double) optimizerParameters.get(BETA_1);
-                double beta2 = (double) optimizerParameters.get("beta_2");
+                double beta2 = (double) optimizerParameters.get(BETA_2);
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
-                double decay = (double) optimizerParameters.get("decay");
+                double decay = (double) optimizerParameters.get(DECAY);
 
                 dl4jOptimizer = new Adam.Builder()
                         .beta1(beta1).beta2(beta2)
@@ -69,9 +80,9 @@ public class KerasOptimizerUtils {
                 break;
             }
             case "Adadelta": {
-                double rho = (double) optimizerParameters.get("rho");
+                double rho = (double) optimizerParameters.get(RHO);
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
-                // double decay = (double) optimizerParameters.get("decay"); No decay in DL4J Adadelta
+                // double decay = (double) optimizerParameters.get(DECAY); No decay in DL4J Adadelta
 
                 dl4jOptimizer = new AdaDelta.Builder()
                         .epsilon(epsilon).rho(rho)
@@ -79,9 +90,9 @@ public class KerasOptimizerUtils {
                 break;
             }
             case "Adgrad": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
-                double decay = (double) optimizerParameters.get("decay");
+                double decay = (double) optimizerParameters.get(DECAY);
 
                 dl4jOptimizer = new AdaGrad.Builder()
                         .epsilon(epsilon).learningRate(lr)
@@ -90,20 +101,20 @@ public class KerasOptimizerUtils {
                 break;
             }
             case "Adamax": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double beta1 = (double) optimizerParameters.get("beta_1");
+                double beta1 = (double) optimizerParameters.get(BETA_1);
-                double beta2 = (double) optimizerParameters.get("beta_2");
+                double beta2 = (double) optimizerParameters.get(BETA_2);
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
 
                 dl4jOptimizer = new AdaMax(lr, beta1, beta2, epsilon);
                 break;
             }
             case "Nadam": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double beta1 = (double) optimizerParameters.get("beta_1");
+                double beta1 = (double) optimizerParameters.get(BETA_1);
-                double beta2 = (double) optimizerParameters.get("beta_2");
+                double beta2 = (double) optimizerParameters.get(BETA_2);
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
-                double scheduleDecay = (double) optimizerParameters.get("schedule_decay");
+                double scheduleDecay = (double) optimizerParameters.get(SCHEDULE_DECAY);
 
                 dl4jOptimizer = new Nadam.Builder()
                         .beta1(beta1).beta2(beta2)
@@ -114,15 +125,10 @@ public class KerasOptimizerUtils {
                 break;
             }
             case "SGD": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double momentum = 0.0;
+                double momentum = (double) (optimizerParameters.containsKey(EPSILON) ? optimizerParameters.get(EPSILON) : optimizerParameters.get(MOMENTUM));
-                try {
-                    momentum = (double) optimizerParameters.get("epsilon");
-                } catch (Exception e) {
-                    log.warn("couldn't read momentum parameter");
-                }
 
-                double decay = (double) optimizerParameters.get("decay");
+                double decay = (double) optimizerParameters.get(DECAY);
 
                 dl4jOptimizer = new Nesterovs.Builder()
                         .momentum(momentum).learningRate(lr)
@@ -131,10 +137,10 @@ public class KerasOptimizerUtils {
                 break;
             }
             case "RMSprop": {
-                double lr = (double) optimizerParameters.get("lr");
+                double lr = (double) (optimizerParameters.containsKey(LR) ? optimizerParameters.get(LR) : optimizerParameters.get(LR2));
-                double rho = (double) optimizerParameters.get("rho");
+                double rho = (double) optimizerParameters.get(RHO);
-                double epsilon = (double) optimizerParameters.get("epsilon");
+                double epsilon = (double) optimizerParameters.get(EPSILON);
-                double decay = (double) optimizerParameters.get("decay");
+                double decay = (double) optimizerParameters.get(DECAY);
 
                 dl4jOptimizer = new RmsProp.Builder()
                         .epsilon(epsilon).rmsDecay(rho).learningRate(lr)

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Upsampling3D.java

@@ -45,10 +45,14 @@ import java.util.Map;
 public class Upsampling3D extends BaseUpsamplingLayer {
 
     protected int[] size;
+    protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW; //Default to NCDHW for 1.0.0-beta4 and earlier, when no config existed (NCDHW only)
 
-    protected Upsampling3D(UpsamplingBuilder builder) {
+
+
+    protected Upsampling3D(Builder builder) {
         super(builder);
         this.size = builder.size;
+        this.dataFormat = builder.dataFormat;
     }
 
     @Override
@@ -124,10 +128,32 @@ public class Upsampling3D extends BaseUpsamplingLayer {
     @NoArgsConstructor
     public static class Builder extends UpsamplingBuilder<Builder> {
 
+        protected Convolution3D.DataFormat dataFormat = Convolution3D.DataFormat.NCDHW;
+
+        /**
+         * @param size Upsampling layer size (most common value: 2)
+         */
         public Builder(int size) {
             super(new int[] {size, size, size});
         }
 
+        /**
+         * @param dataFormat Data format - see {@link Convolution3D.DataFormat} for more details
+         * @param size Upsampling layer size (most common value: 2)
+         */
+        public Builder(@NonNull Convolution3D.DataFormat dataFormat, int size){
+            super(new int[]{size, size, size});
+            this.dataFormat = dataFormat;
+        }
+
+        /**
+         * Sets the DataFormat. See {@link Convolution3D.DataFormat} for more details
+         */
+        public Builder dataFormat(@NonNull Convolution3D.DataFormat dataFormat){
+            this.dataFormat = dataFormat;
+            return this;
+        }
+
         /**
          * Upsampling size as int, so same upsampling size is used for depth, width and height
          *

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

@@ -2896,7 +2896,7 @@ public class ComputationGraph implements Serializable, Model, NeuralNetwork {
             solver.getOptimizer().setUpdaterComputationGraph(new ComputationGraphUpdater(this));
         }
         if(solver != null) {
-            return solver.getOptimizer().getComputationGraphUpdater();
+            return solver.getOptimizer().getComputationGraphUpdater(initializeIfAbsent);
         }
         return null;
     }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/upsampling/Upsampling3D.java

@@ -67,18 +67,36 @@ public class Upsampling3D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
     public Pair<Gradient, INDArray> backpropGradient(INDArray epsilon, LayerWorkspaceMgr workspaceMgr) {
         assertInputSet(true);
 
+        boolean ncdhw = layerConf().getDataFormat() == org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat.NCDHW;
         // FIXME: int cast
         // Assumes NCDHW order
         int miniBatch = (int) input.size(0);
-        int inChannels = (int) input.size(1);
+        int inChannels, inD, inH, inW;
-        int inD = (int) input.size(2);
+        int[] intArgs;
-        int inH = (int) input.size(3);
+        if(ncdhw){
-        int inW = (int) input.size(4);
+            inChannels = (int) input.size(1);
+            inD = (int) input.size(2);
+            inH = (int) input.size(3);
+            inW = (int) input.size(4);
+            intArgs = new int[] {1}; // 1 is channels first
+        } else {
+            inD = (int) input.size(1);
+            inH = (int) input.size(2);
+            inW = (int) input.size(3);
+            inChannels = (int) input.size(4);
+            intArgs = new int[] {0}; // 0 is channels last
+        }
 
-        int[] intArgs = new int[] {1}; // 1 is channels first
 
-        INDArray reshapedEpsilon = workspaceMgr.createUninitialized(
+
-                ArrayType.ACTIVATION_GRAD, epsilon.dataType(), new long[]{miniBatch, inChannels, inD, inH, inW}, 'c');
+        INDArray epsOut;
+        if(ncdhw){
+            epsOut = workspaceMgr.createUninitialized(
+                    ArrayType.ACTIVATION_GRAD, epsilon.dataType(), new long[]{miniBatch, inChannels, inD, inH, inW}, 'c');
+        } else {
+            epsOut = workspaceMgr.createUninitialized(
+                    ArrayType.ACTIVATION_GRAD, epsilon.dataType(), new long[]{miniBatch, inD, inH, inW, inChannels}, 'c');
+        }
 
 
         Gradient gradient = new DefaultGradient();
@@ -86,13 +104,13 @@ public class Upsampling3D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
         CustomOp op = DynamicCustomOp.builder("upsampling3d_bp")
                 .addIntegerArguments(intArgs)
                 .addInputs(input, epsilon)
-                .addOutputs(reshapedEpsilon)
+                .addOutputs(epsOut)
                 .callInplace(false)
                 .build();
         Nd4j.getExecutioner().exec(op);
 
-        reshapedEpsilon = backpropDropOutIfPresent(reshapedEpsilon);
+        epsOut = backpropDropOutIfPresent(epsOut);
-        return new Pair<>(gradient, reshapedEpsilon);
+        return new Pair<>(gradient, epsOut);
     }
 
     protected int[] getSize() {
@@ -115,32 +133,51 @@ public class Upsampling3D extends AbstractLayer<org.deeplearning4j.nn.conf.layer
             return preOutput;
         }
 
-        long miniBatch = (int) input.size(0);
+        boolean ncdhw = layerConf().getDataFormat() == org.deeplearning4j.nn.conf.layers.Convolution3D.DataFormat.NCDHW;
-        long inChannels = (int) input.size(1);
+        // FIXME: int cast
-        long inD = (int) input.size(2);
+        int miniBatch = (int) input.size(0);
-        long inH = (int) input.size(3);
+        int inChannels, inD, inH, inW;
-        long inW = (int) input.size(4);
+        int[] intArgs;
-
         int[] size = getSize();
+        if(ncdhw){
+            inChannels = (int) input.size(1);
+            inD = (int) input.size(2);
+            inH = (int) input.size(3);
+            inW = (int) input.size(4);
+            intArgs = new int[] {size[0], size[1], size[2], 1}; // 1 is channels first
+        } else {
+            inD = (int) input.size(1);
+            inH = (int) input.size(2);
+            inW = (int) input.size(3);
+            inChannels = (int) input.size(4);
+            intArgs = new int[] {size[0], size[1], size[2], 0}; // 0 is channels last
+        }
+
+
         long outD = inD * size[0];
         long outH = inH * size[1];
         long outW = inW * size[2];
 
-        int[] intArgs = new int[] {size[0], size[1], size[2], 1}; // 1 is channels first
+        INDArray output;
+        if(ncdhw){
+            output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS,
+                    input.dataType(), new long[]{miniBatch, inChannels, outD, outH, outW}, 'c');
+        } else {
+            output = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS,
+                    input.dataType(), new long[]{miniBatch, outD, outH, outW, inChannels}, 'c');
+        }
 
-        INDArray reshapedOutput = workspaceMgr.createUninitialized(ArrayType.ACTIVATIONS,
-                input.dataType(), new long[]{miniBatch, inChannels, outD, outH, outW}, 'c');
 
 
         CustomOp upsampling = DynamicCustomOp.builder("upsampling3d")
                 .addIntegerArguments(intArgs)
                 .addInputs(input)
-                .addOutputs(reshapedOutput)
+                .addOutputs(output)
                 .callInplace(false)
                 .build();
         Nd4j.getExecutioner().exec(upsampling);
 
-        return reshapedOutput;
+        return output;
     }
 
     @Override

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

@@ -3172,7 +3172,7 @@ public class MultiLayerNetwork implements Serializable, Classifier, Layer, Neura
             }
         }
         if(solver != null) {
-            return solver.getOptimizer().getUpdater();
+            return solver.getOptimizer().getUpdater(initializeIfReq);
         }
         return null;
     }

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/api/ConvexOptimizer.java

@@ -42,8 +42,12 @@ public interface ConvexOptimizer extends Serializable {
 
     Updater getUpdater();
 
+    Updater getUpdater(boolean initializeIfReq);
+
     ComputationGraphUpdater getComputationGraphUpdater();
 
+    ComputationGraphUpdater getComputationGraphUpdater(boolean initializeIfReq);
+
     void setUpdater(Updater updater);
 
     void setUpdaterComputationGraph(ComputationGraphUpdater updater);

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/solvers/BaseOptimizer.java

@@ -115,7 +115,12 @@ public abstract class BaseOptimizer implements ConvexOptimizer {
 
     @Override
     public Updater getUpdater() {
-        if (updater == null) {
+        return getUpdater(true);
+    }
+
+    @Override
+    public Updater getUpdater(boolean initializeIfReq) {
+        if (updater == null && initializeIfReq) {
             updater = UpdaterCreator.getUpdater(model);
         }
         return updater;
@@ -130,7 +135,12 @@ public abstract class BaseOptimizer implements ConvexOptimizer {
 
     @Override
     public ComputationGraphUpdater getComputationGraphUpdater() {
-        if (computationGraphUpdater == null && model instanceof ComputationGraph) {
+        return getComputationGraphUpdater(true);
+    }
+
+    @Override
+    public ComputationGraphUpdater getComputationGraphUpdater(boolean initializIfReq) {
+        if (computationGraphUpdater == null && model instanceof ComputationGraph && initializIfReq) {
             computationGraphUpdater = new ComputationGraphUpdater((ComputationGraph) model);
         }
         return computationGraphUpdater;

deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/CrashReportingUtil.java

@@ -205,7 +205,7 @@ public class CrashReportingUtil {
         StringBuilder sb = genericMemoryStatus();
 
         int bytesPerElement;
-        switch (Nd4j.dataType()){
+        switch (isMLN ? mln.params().dataType() : cg.params().dataType()){
             case DOUBLE:
                 bytesPerElement = 8;
                 break;

deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-play/src/main/java/org/deeplearning4j/ui/module/train/TrainModule.java

@@ -200,7 +200,7 @@ public class TrainModule implements UIModule {
      * List training sessions
      * @return HTML list of training sessions
      */
-    private Result listSessions() {
+    private synchronized Result listSessions() {
         StringBuilder sb = new StringBuilder("<!DOCTYPE html>\n" +
                 "<html lang=\"en\">\n" +
                 "<head>\n" +
@@ -464,7 +464,7 @@ public class TrainModule implements UIModule {
      * @param sessionId session ID
      * @return info for session as JSON
      */
-    private Result sessionInfoForSession(String sessionId) {
+    private synchronized Result sessionInfoForSession(String sessionId) {
 
         Map<String, Object> dataEachSession = new HashMap<>();
         StatsStorage ss = knownSessionIDs.get(sessionId);
@@ -475,7 +475,7 @@ public class TrainModule implements UIModule {
         return Results.ok(asJson(dataEachSession)).as("application/json");
     }
 
-    private Result setSession(String newSessionID) {
+    private synchronized Result setSession(String newSessionID) {
         if (knownSessionIDs.containsKey(newSessionID)) {
             currentSessionID = newSessionID;
             currentWorkerIdx = 0;
@@ -567,7 +567,7 @@ public class TrainModule implements UIModule {
         return getOverviewDataForSession(currentSessionID);
     }
 
-    private Result getOverviewDataForSession(String sessionId) {
+    private synchronized Result getOverviewDataForSession(String sessionId) {
         Long lastUpdateTime = getLastUpdateTime(sessionId);
         I18N i18N = getI18N(sessionId);
 

deeplearning4j/deeplearning4j-ui-parent/deeplearning4j-play/src/main/resources/deeplearning4jUiAssets/js/train/overview.js

@@ -20,6 +20,8 @@ function selectStdevChart(fieldName) {
         $("#stdevGradients").removeAttr("class");
         $("#stdevUpdates").attr("class", "active");
     }
+
+    renderOverviewPage(false);
 }
 
 /* ---------- Render page ---------- */

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

@@ -5207,7 +5207,7 @@ public class Nd4j {
      */
     public static void checkShapeValues(int... shape) {
         for (int e: shape) {
-            if (e < 1)
+            if (e < 0)
                 throw new ND4JIllegalStateException("Invalid shape: Requested INDArray shape " + Arrays.toString(shape)
                         + " contains dimension size values < 0 (all dimensions must be 0 or more)");
         }

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

@@ -256,6 +256,7 @@ public class EmptyTests extends BaseNd4jTest {
         assertArrayEquals(new long[]{0}, Nd4j.zeros(0).shape());
         assertArrayEquals(new long[]{0,0}, Nd4j.zeros(0,0).shape());
         assertArrayEquals(new long[]{0,0,0}, Nd4j.zeros(0,0,0).shape());
+        assertArrayEquals(new long[]{0,0,0}, Nd4j.zeros(new int[]{0,0,0}, 'f').shape());
         assertArrayEquals(new long[]{0}, Nd4j.zeros(0L).shape());
         assertArrayEquals(new long[]{0}, Nd4j.zeros(dt, 0L).shape());