0.9.1
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Last updated
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Deeplearning4J
Fixed issue with incorrect version dependencies in 0.9.0
Added EmnistDataSetIterator
Numerical stability improvements to LossMCXENT / LossNegativeLogLikelihood with softmax (should reduce NaNs with very large activations)
ND4J
Added runtime version checking for ND4J, DL4J, RL4J, Arbiter, DataVec
Known Issues
Deeplearning4j: Use of Evaluation class no-arg constructor (i.e., new Evaluation()) can result in accuracy/stats being reported as 0.0. Other Evaluation class constructors, and ComputationGraph/MultiLayerNetwork.evaluate(DataSetIterator) methods work as expected.
This also impacts Spark (distributed) evaluation: workaround is to replace sparkNet.evaluate(testData); with sparkNet.doEvaluation(testData, 64, new Evaluation(10))[0];, where 10 is the number of classes and 64 in the evaluation minibatch size to use.
SequenceRecordReaderDataSetIterator applies preprocessors (such as normalization) twice to each DataSet (possible workaround: use RecordReaderMultiDataSetIterator + MultiDataSetWrapperIterator)
TransferLearning: ComputationGraph may incorrectly apply l1/l2 regularization (defined in FinetuneConfiguration) to frozen layers. Workaround: set 0.0 l1/l2 on FineTuneConfiguration, and required l1/l2 on new/non-frozen layers directly. Note that MultiLayerNetwork with TransferLearning appears to be unaffected.