0.8.0
0.8.0 -> 0.9.0 Transition Notes
Deeplearning4j
- Updater configuration methods such as .momentum(double) and .epsilon(double) have been deprecated. Instead: use
.updater(new Nesterovs(0.9))and.updater(Adam.builder().beta1(0.9).beta2(0.999).build())etc to configure
DataVec
- CsvRecordReader constructors: now uses characters for delimiters, instead of Strings (i.e., ',' instead of ",")
Arbiter
- Arbiter UI is now a separate module, with Scala version suffixes:
arbiter-ui_2.10andarbiter-ui_2.11
Version 0.8.0
- Spark 2.0 support (DL4J and DataVec; see transition notes below)
- New ComputationGraph vertices
- L2 distance vertex
- L2 normalization vertex
- Per-output masking is now supported for most loss functions (for per output masking, use a mask array equal in size/shape to the labels array; previous masking functionality was per-example for RNNs)
- L1 and L2 regularization can now be configured for biases (via l1Bias and l2Bias configuration options)
- Evaluation improvements:
- DL4J now has an IEvaluation class (that Evaluation, RegressionEvaluation, etc all implement. Also allows custom evaluation on Spark) Link​
- For both MultiLayerNetwork and SparkDl4jMultiLayer: added evaluateRegression, evaluateROC, evaluateROCMultiClass convenience methods
- TSNE re-added to new UI
- Training UI: now usable without an internet connection (no longer relies on externally hosted fonts)
- UI: improvements to error handling for ‘no data’ condition
- Epsilon configuration now used for Adam and RMSProp updaters
- Fix for bidirectional LSTMs + variable-length time series (using masking)
- Spark + Kryo: now test serialization + throw exception if misconfigured (instead of logging an error that can be missed)
- MultiLayerNetwork now adds default layer names if no name is specified
- DataVec:
- JSON/YAML support for DataAnalysis, custom Transforms etc
- ImageRecordReader refactored to reduce garbage collection load (hence improve performance with large training sets)
- Faster quality analysis.
- Arbiter: added new layer types to match DL4J
- Performance improvement for Word2Vec/ParagraphVectors tokenization & training.
- Batched inference introduced for ParagraphVectors
- Nd4j improvements
- New native operations available for ND4j: firstIndex, lastIndex, remainder, fmod, or, and, xor.
- OpProfiler NAN_PANIC & INF_PANIC now also checks result of BLAS calls.
- Nd4.getMemoryManager() now provides methods to tweak GC behavior.
- Alpha version of parameter server for Word2Vec/ParagraphVectors were introduced for Spark. Please note: It’s not recommended for production use yet.
- Performance improvements for CNN inference
0.7.2 -> 0.8.0 Transition Notes
- Spark versioning schemes: with the addition of Spark 2 support, the versions for Deeplearning4j and DataVec Spark modules has changed
- For Spark 1: use
<version>0.8.0_spark_1</version> - For Spark 2: use
<version>0.8.0_spark_2</version> - Also note: Modules with Spark 2 support are released with Scala 2.11 support only. Spark 1 modules are released with both Scala 2.10 and 2.11 support
0.8.0 Known Issues (At Launch)
- Keras 1D convolutional and pooling layers cannot be imported yet. Will be supported in forthcoming release.
- Keras v2 model configurations cannot be imported yet. Will be supported in forthcoming release.
Last modified 3mo ago
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