0.8.0

0.8.0 -> 0.9.0 Transition Notes

Deeplearning4j
    Updater configuration methods such as .momentum(double) and .epsilon(double) have been deprecated. Instead: use .updater(new Nesterovs(0.9)) and .updater(Adam.builder().beta1(0.9).beta2(0.999).build()) etc to configure
DataVec
    CsvRecordReader constructors: now uses characters for delimiters, instead of Strings (i.e., ',' instead of ",")
Arbiter
    Arbiter UI is now a separate module, with Scala version suffixes: arbiter-ui_2.10 and arbiter-ui_2.11

Version 0.8.0

    Added transfer learning API Link
    Spark 2.0 support (DL4J and DataVec; see transition notes below)
    New layers
      Global pooling (aka "pooling over time"; usable with both RNNs and CNNs) Link
      Center loss output layer Link
      1D Convolution and subsampling layers Link Link2
      ZeroPaddingLayer Link
    New ComputationGraph vertices
      L2 distance vertex
      L2 normalization vertex
    Per-output masking is now supported for most loss functions (for per output masking, use a mask array equal in size/shape to the labels array; previous masking functionality was per-example for RNNs)
    L1 and L2 regularization can now be configured for biases (via l1Bias and l2Bias configuration options)
    Evaluation improvements:
      DL4J now has an IEvaluation class (that Evaluation, RegressionEvaluation, etc all implement. Also allows custom evaluation on Spark) Link
      Added multi-class (one vs. all) ROC: ROCMultiClass Link
      For both MultiLayerNetwork and SparkDl4jMultiLayer: added evaluateRegression, evaluateROC, evaluateROCMultiClass convenience methods
      HTML export functionality added for ROC charts Link
      TSNE re-added to new UI
      Training UI: now usable without an internet connection (no longer relies on externally hosted fonts)
      UI: improvements to error handling for ‘no data’ condition
    Epsilon configuration now used for Adam and RMSProp updaters
    Fix for bidirectional LSTMs + variable-length time series (using masking)
    Added CnnSentenceDataSetIterator (for use with ‘CNN for Sentence Classification’ architecture) Link Link2
    Spark + Kryo: now test serialization + throw exception if misconfigured (instead of logging an error that can be missed)
    MultiLayerNetwork now adds default layer names if no name is specified
    DataVec:
      JSON/YAML support for DataAnalysis, custom Transforms etc
      ImageRecordReader refactored to reduce garbage collection load (hence improve performance with large training sets)
      Faster quality analysis.
    Arbiter: added new layer types to match DL4J
      Performance improvement for Word2Vec/ParagraphVectors tokenization & training.
    Batched inference introduced for ParagraphVectors
    Nd4j improvements
      New native operations available for ND4j: firstIndex, lastIndex, remainder, fmod, or, and, xor.
      OpProfiler NAN_PANIC & INF_PANIC now also checks result of BLAS calls.
      Nd4.getMemoryManager() now provides methods to tweak GC behavior.
    Alpha version of parameter server for Word2Vec/ParagraphVectors were introduced for Spark. Please note: It’s not recommended for production use yet.
    Performance improvements for CNN inference

0.7.2 -> 0.8.0 Transition Notes

    Spark versioning schemes: with the addition of Spark 2 support, the versions for Deeplearning4j and DataVec Spark modules has changed
      For Spark 1: use <version>0.8.0_spark_1</version>
      For Spark 2: use <version>0.8.0_spark_2</version>
      Also note: Modules with Spark 2 support are released with Scala 2.11 support only. Spark 1 modules are released with both Scala 2.10 and 2.11 support

0.8.0 Known Issues (At Launch)

    UI/CUDA/Linux issue: Link
    Dirty shutdown on JVM exit is possible for CUDA backend sometimes: Link
    Issues with RBM implementation Link
    Keras 1D convolutional and pooling layers cannot be imported yet. Will be supported in forthcoming release.
    Keras v2 model configurations cannot be imported yet. Will be supported in forthcoming release.