0.7.0

• UI overhaul: new training UI has considerably more information, supports persistence (saving info and loading later), Japanese/Korean/Russian support. Replaced Dropwizard with Play framework. Link

• Import of models configured and trained using Keras

  • Imports both Keras model configurations and stored weights

  • Supported models: Sequential models

  • Supported layers: Dense, Dropout, Activation, Convolution2D, MaxPooling2D, LSTM

• Added ‘Same’ padding more for CNNs (ConvolutionMode network configuration option) Link

• Weighted loss functions: Loss functions now support a per-output weight array (row vector)

• ROC and AUC added for binary classifiers Link

• Improved error messages on invalid configuration or data; improved validation on both

• Added metadata functionality: track source of data (file, line number, etc) from data import to evaluation. Loading a subset of examples/data from this metadata is now supported. Link

• Removed Jackson as core dependency (shaded); users can now use any version of Jackson without issue

• Added LossLayer: version of OutputLayer that only applies loss function (unlike OutputLayer: it has no weights/biases)

• Functionality required to build triplet embedding model (L2 vertex, LossLayer, Stack/Unstack vertices etc)

• Reduced DL4J and ND4J ‘cold start’ initialization/start-up time

• Pretrain default changed to false and backprop default changed to true. No longer needed to set these when setting up a network configuration unless defaults need to be changed.

• Added TrainingListener interface (extends IterationListener). Provides access to more information/state as network training occurs Link

• Numerous bug fixes across DL4J and ND4J

• Performance improvements for nd4j-native & nd4j-cuda backends

• Standalone Word2Vec/ParagraphVectors overhaul:

  • Performance improvements

  • ParaVec inference available for both PV-DM & PV-DBOW

  • Parallel tokenization support was added, to address computation-heavy tokenizers.

• Native RNG introduced for better reproducibility within multi-threaded execution environment.

• Additional RNG calls added: Nd4j.choice(), and BernoulliDistribution op.

• Off-gpu storage introduced, to keep large things, like Word2Vec model in host memory. Available via WordVectorSerializer.loadStaticModel()

• Two new options for performance tuning on nd4j-native backend: setTADThreshold(int) & setElementThreshold(int)

0.6.0 -> 0.7.0 Transition Notes

Notable changes for upgrading codebases based on 0.6.0 to 0.7.0:

• UI: new UI package name is deeplearning4j-ui_2.10 or deeplearning4j-ui_2.11 (previously: deeplearning4j-ui). Scala version suffix is necessary due to Play framework (written in Scala) being used now.

• Histogram and Flow iteration listeners deprecated. They are still functional, but using new UI is recommended Link

• DataVec ImageRecordReader: labels are now sorted alphabetically by default before assigning an integer class index to each - previously (0.6.0 and earlier) they were according to file iteration order. Use .setLabels(List) to manually specify the order if required.

• CNNs: configuration validation is now less strict. With new ConvolutionMode option, 0.6.0 was equivalent to ‘Strict’ mode, but new default is ‘Truncate’

  • See ConvolutionMode javadoc for more details: Link

• Xavier weight initialization change for CNNs and LSTMs: Xavier now aligns better with original Glorot paper and other libraries. Xavier weight init. equivalent to 0.6.0 is available as XAVIER_LEGACY

• DataVec: Custom RecordReader and SequenceRecordReader classes require additional methods, for the new metadata functionality. Refer to existing record reader implementations for how to implement these methods.

• Word2Vec/ParagraphVectors:

  • Few new builder methods:

    • allowParallelTokenization(boolean)

    • useHierarchicSoftmax(boolean)

  • Behaviour change: batchSize: now batch size is ALSO used as threshold to execute number of computational batches for sg/cbow