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_2.10 and arbiter-ui_2.11
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​
Added multi-class (one vs. all) ROC: ROCMultiClass Link​
For both MultiLayerNetwork and SparkDl4jMultiLayer: added evaluateRegression, evaluateROC, evaluateROCMultiClass convenience methods
HTML export functionality added for ROC charts Link​
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)
Added CnnSentenceDataSetIterator (for use with ‘CNN for Sentence Classification’ architecture) LinkLink2​
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