Comment on page
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
- Weighted loss functions: Loss functions now support a per-output weight array (row vector)
- 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)
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’
- 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
Last modified 1yr ago