0.9.0

Deeplearning4J

• Workspaces feature added (faster training performance + less memory) Link

• SharedTrainingMaster added for Spark network training (improved performance) Link 1, Link 2

• ParallelInference added - wrapper that server inference requests using internal batching and queues

• ParallelWrapper now able to work with gradients sharing, in addition to existing parameters averaging mode

• VPTree performance significantly improved

• CacheMode network configuration option added - improved CNN and LSTM performance at the expense of additional memory use

• LSTM layer added, with CuDNN support (Note that the existing GravesLSTM implementation does not support CuDNN)

• New native model zoo with pretrained ImageNet, MNIST, and VGG-Face weights

• Convolution performance improvements, including activation caching

• Custom/user defined updaters are now supported

• Evaluation improvements

  • EvaluationBinary, ROCBinary classes added: for evaluation of binary multi-class networks (sigmoid + xent output layers)

  • Evaluation and others now have G-Measure and Matthews Correlation Coefficient support; also macro + micro-averaging support for Evaluation class metrics

• Optimizations: updaters, bias calculation

• Network memory estimation functionality added. Memory requirements can be estimated from configuration without instantiating networks

• New loss functions:

  • Mixture density loss function

  • F-Measure loss function

ND4J

• Workspaces feature added

• Native parallel sort was added

• New ops added: SELU/SELUDerivative, TAD-based comparisons, percentile/median, Reverse, Tan/TanDerivative, SinH, CosH, Entropy, ShannonEntropy, LogEntropy, AbsoluteMin/AbsoluteMax/AbsoluteSum, Atan2

• New distance functions added: CosineDistance, HammingDistance, JaccardDistance

DataVec

• MapFileRecordReader and MapFileSequenceRecordReader added

• Spark: Utilities to save and load JavaRDD<List<Writable>> and JavaRDD<List<List<Writable>> data to Hadoop MapFile and SequenceFile formats

• TransformProcess and Transforms now support NDArrayWritables and NDArrayWritable columns

Arbiter

• Arbiter UI:

  • UI now uses Play framework, integrates with DL4J UI (replaces Dropwizard backend). Dependency issues/clashing versions fixed.

  • Supports DL4J StatsStorage and StatsStorageRouter mechanisms (FileStatsStorage, Remote UI via RemoveUIStatsStorageRouter)