0.9.0
Last updated
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Last updated
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Deeplearning4J
Workspaces feature added (faster training performance + less memory)
SharedTrainingMaster added for Spark network training (improved performance) ,
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
ComputationGraph and SparkComputationGraph evaluation convenience methods added (evaluateROC, etc)
ROC and ROCMultiClass support exact calculation (previous: thresholded calculation was used)
ROC classes now support area under precision-recall curve calculation; getting precision/recall/confusion matrix at specified thresholds (via PrecisionRecallCurve class)
RegressionEvaluation, ROCBinary etc now support per-output masking (in addition to per-example/per-time-step masking)
EvaluationCalibration added (residual plots, reliability diagrams, histogram of probabilities)
Evaluation and EvaluationBinary: now supports custom classification threshold or cost array
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
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
TransformProcess and Transforms now support NDArrayWritables and NDArrayWritable columns
Multiple new Transform classes
Arbiter
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)
General UI improvements (additional information, formatting fixes)
Workspaces feature added
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
Arbiter UI: