1.0.0-beta2

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EN 1.0.0-M2.1
1.0.0-beta2
Highlights - 1.0.0-beta2 Release
ND4J/Deeplearning4j: Added support for CUDA 9.2. Dropped support for CUDA 9.1. (1.0.0-beta2 release has CUDA 8.0, 9.0 and 9.2 support)
Deeplearning4j: New SameDiff layers with training support - Link Link​
Deeplearning4j resource (datasets, pretrained models) storage directory can now be configured via DL4JResources.setBaseDirectory method or org.deeplearning4j.resources.directory system property
ND4J: all indexing is now done with longs instead of ints to allow for arrays with dimensions and lengths greater than Integer.MAX_VALUE (approx. 2.1 billion)
ND4J: nd4j-native-platform will now use Intel MKL-DNN as the default/bundled BLAS implementation (replacing OpenBLAS as the previous default)
Deeplearning4j: Added Out-of-memory (OOM) crash dump reporting functionality. Provides a dump with memory use and configuration if training/inference OOMs (to assist with debugging and tuning memory configuration).
Deeplearning4j - new layers: Locally connected 1d Link, Locally connected 2d Link​
Deeplearning4J
Deeplearning4J: New Features
Added new SameDiff layers (automatic differentiation - only single class, forward pass definition required) to DL4J with full training support - SameDiffLayer, SameDiffVertex, SameDiffOutputLayer, SameDiffLambdaLayer, SameDiffLambdaVertex - note that these are CPU-only execution for now Link Link Link​
Resource (datasets, pretrained models) storage directory can now be configured via DL4JResources.setBaseDirectory method or org.deeplearning4j.resources.directory system property. Note that it is also possible to set a different base location for downloads (for local mirrors of DL4J resources) Link​
Added Out-of-memory (OOM) crash dump reporting functionality. Provides a dump with memory use and configuration if training/inference OOMs. Same information is available (without a crash) for MultiLayerNetwork/ComputationGraph.memoryInfo methods. Can be disabled (or output directory set) using system properties - Link​
Added Composite[Multi]DataSetPreProcessor to enable multiple [Multi]DataSetPreProcessors to be applied in a single iterator Link​
Added ComputationGraph evaluate methods for multi-output networks: evaluate(DataSetIterator, Map<Integer,IEvaluation[]>) and evaluate(MultiDataSetIterator, Map<Integer,IEvaluation[]>) Link​
Added JointMultiDataSetIterator - utility iterator used to create MultiDataSetIterator from multiple DataSetIterators Link​
GraphVertices may now have trainable parameters directly (not just enclose layers with trainable parameters) Link​
Added MultiLayerNetwork/ComputationGraph getLearningRate methods Link​
Added RandomDataSetIterator and RandomMultiDataSetIterator (mainly for testing/debugging) Link Link​
Added cyclical "1cycle" schedule for learning rate schedules etc - Link​
RDD repartitioning for Spark training is more configurable (adds Repartitioner interface) Link​
Added ComputationGraph.getIterationCount() and .getEpochCount() for consistency with MultiLayerNetwork Link​
Added locally connected 1d layer Link Link​
Spark "data loader" API (mainly for Spark) Link Link Link​
Spark evaluation: added evaluation method overloads that allow specifying the number of evaluation workers (less than number of Spark threads) Link​
CnnSentenceDataSetIterator now has a Format argument, and supports outputting data for RNNs and 1D CNNs Link​
Added ComputationGraph/MultiLayerNetwork.pretrain((Multi)DataSetIterator, int epochs) method overloads Link​
MultiLayerNetwork and ComputationGraph now have output method overloads where the network output can be placed in the user-specified workspace, instead of being detached Link Link. This can be used to avoid creating INDArrays that need to be garbage collected before native memory can be freed.
EmbeddingSequenceLayer now supports [minibatch,1,seqLength] format sequence data in addition to [minibatch,seqLength] format data Link​
CuDNN batch norm implementation will now be used for rank 2 input, not just rank 4 input Link​
Environment variables and system properties for DL4J have been centralized into DL4JResources and DL4JEnvironmentVars classes, with proper descriptions Link Link​
MultiLayerNetwork and ComputationGraph output/feedForward/fit methods are now thread-safe via synchronization. Note that concurrent use is not recommended due to performance (instead: use ParallelInference); however the now-synchronized methods should avoid obscure errors due to concurrent modifications Link​
BarnesHutTSNE now throws a useful exception in the case where the distance metric is undefined (for example, all zeros plus cosine similarity) Link​
Deeplearning4J: Bug Fixes and Optimizations
ComputationGraph.addListeners was not working correctly if listeners were already present Link, Link​
TinyImageNetDataSetIterator did not validate/correctly use input shape configuration Link, Link​
BatchNormalization layer now correctly asserts that nOut is set if required (instead of unfriendly shape errors later) Link​
Fixed issue where OutputLayer may not initialize parameter constraints correctly Link​
Fixed performance issue with Nesterov updater using CPU-only op for CUDA execution Link​
Removed TerminationCondition for DL4J optimizers - was not used in practice, and had minor overhead Link​
Fixed issue where EvaluativeListener could hit a workspace validation exception when workspaces are enabled Link​
Fixed issue where TrainingListener.onEpochStart/onEpochEnd were not being called correctly for ComputationGraph Link​
Fixed workspace issue with TensorFlowCnnToFeedForwardPreProcessor Link​
Performance optimization for BatchNormalization when using CuDNN Link​
Performance optimization: Dropout will be applied in-place when safe to do so, avoiding a copy Link​
Added CuDNN implementation of Dropout Link​
Reduced memory use for CuDNN: CuDNN working memory is now shared and reused between layers within a network Link​
CuDNN batch normalization implementation would fail with FP16 datatype Link​
Fixed issue Bidirectional LSTM may incorrectly use workspaces causing an exception Link​
Fixed issue with early stopping where scores to be maximized (accuracy, f1, etc) were not properly triggering termination conditions Link​
Fixed issue where label mask counter could be incorrectly incremented in ComputationGraph.computeGradientAndScore() Link​
ComputationGraph was not setting lastEtlTime field during training Link​
Fixed issue with AutoEncoder layer when workspaces are enabled Link​
Fixed issue with EmbeddingSequenceLayer use of mask arrays Link​
Lombok is now provided scope everywhere, isn't on user classpath when using DL4J Link​
Fixed issue where WordVectorSerializer.readParagraphVectors(File) initialization of label source Link​
Spark training (gradient sharing) now properly handles empty partition edge case when encountered during training Link​
Errors are propagated better/more consistently for Spark gradient sharing training Link​
Fixed issue with 1D CNN layers with mask arrays and stride > 1 (masks not being correctly downsized) Link​
DL4J Batch norm implementation was not correctly adding epsilon value during inference, only during training (CuDNN unaffected) Link​
CuDNN subsampling layers with max pooling and ConvolutionMode.SAME may have taken padding value (0) as the maximum for border values when all non-padding values are less than 0 Link​
Spark training with gradient sharing now passes listeners to workers correctly Link​
Fixed rare (and non-terminal) concurrent modification issue with UI and FileStatsStorage Link​
CuDNN convolution layer now supports dilation > 2 (previously: used DL4J conv layer implementation as a fallback) Link​
Yolo2OutputLayer now implements computeScoreForExamples() Link​
SequenceRecordReeaderDataSetIterator now handles the "no labels" case correctly Link​
Fixed issue where BarnesHutTSNE could hit a workspace validation exception Link​
EMNIST iterator could produce incorrect data in some cases after a reset Link​
Deeplearning4J: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
GravesLSTM has been deprecated in favor of LSTM due to lack of CuDNN support but otherwise similar accuracy to in practice. Use LSTM class instead.
deeplearning4j-modelexport-solr: now uses Lucene/Solr version 7.4.0 (was 7.3.0) Link​
Mask arrays for CNN2d layers must be in broadcastable 4d format: [minibatch,depth or 1, height or 1, width or 1] - previously they were 2d with shape [minibatch,height] or [minibatch,width]. This provents ambiguity in later cases (pooling layers), and allows for more complex masking scenarios (such as masking for different image sizes in same minibatch). Link​
Some older/deprecated Model and Layer methods have been removed. (validateInput(), initParams()). Some custom layers may need to be updated as a result Link​
Deelpearning4J: 1.0.0-beta2 Known Issues
Windows users are unable to load the HDF5 files used in SvhnLabelProvider (used in HouseNumberDetection example). Linux/Mac users are unaffected. A workaround for windows users is to add the sonatype snapshot dependency org.bytedeco.javacpp-presets:hdf5-platform:jar:1.10.2-1.4.3-SNAPSHOT Link​
Deeplearing4J: Keras Import
Keras model import now imports every Keras application
Supports GlobalPooling3D layer import
Supports RepeatVector layer import
Supports LocallyConnected1D and LocallyConnected2D layers
Keras Lambda layers can now be imported by registering custom SameDiff layers
All Keras optimizers are now supported
All advanced activation functions can now be imported.
Many minor bugs have been fixed, including proper weight setting for all configurations of BatchNormalization, improvements to Reshape SeparableConvolution2D, and full support of Bidirectional layers.
ND4J
ND4J: New Features
ND4J: all indexing is now done with longs instead of ints to allow for arrays with dimensions and lengths greater than Integer.MAX_VALUE (approx. 2.1 billion)
Added the ability to write Numpy .npy format using Nd4j.writeAsNumpy(INDArray,File) and convert an INDArray to a numpy strict in-memory using Nd4j.convertToNumpy(INDArray) Link​
ND4j-common ClassPathResource: added ClassPathResource.copyDirectory(File) Link​
SameDiff: A significant number of new ops, and backprop implementations for existing ops
Added Nd4j.randomBernoulli/Binomial/Exponential convenience methods Link​
Added way to disable/suppress ND4J initialization logging via org.nd4j.log.initialization system property Link​
SameDiff class - most op/constructor methods now have complete/useful javadoc Link​
Workspaces can now be disabled globally, ignoring workspace configuration. This is mainly used for debugging; use Nd4j.getWorkspaceManager().setDebugMode(DebugMode.DISABLED) or Nd4j.getWorkspaceManager().setDebugMode(DebugMode.SPILL_EVERYTHING); to enable this. Link [Link]
Added EnvironmentalAction API for environment variable processing Link​
ND4J environment variables and system properties have been centralized in ND4jEnvironmentVars and ND4jSystemProperties classes Link and Link​
ND4J: Bug Fixes and Optimizations
SameDiff: a significant number of bug fixes for execution and individual ops
Fixed issue where INDArray.toDoubleArray() with true scalars (rank 0 arrays) Link​
Fixed issue with DataSet.sample() not working for rank 3+ features Link​
IActivation implementations now validate/enforce same shape for activations and gradients Link​
Fixed issue with muliColumnVector where vector is 1d Link​
ImagePreProcessingScaler now supports serialization via NormalizerSerializerStrategy and ModelSerializer Link​
Performance optimization for threshold encoding used in DL4J's Spark gradient sharing distributed training implementation Link​
SameDiff: Fixed issue where memory wasn't always released after execution Link​
DataSet.save() and MultiDataSet.save() methods now save example metadata when present Link​
Fixed issue with KFoldIterator when dataset does not divide equally into folds with no remainder Link​
Fixed issue where version check functionality could fail to load resources if resources are on a path with spaces Link​
ND4J: Known Issues
ND4J: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
CUDA 9.1 support has been removed. CUDA 8.0, 9.0 and 9.2 support is available
Due to long indexing changes, long/long[] should be used in place of int/int[] in some places (such as INDArray.size(int), INDArray.shape())
Simplified DataSetIterator API: totalExamples(), cursor() and numExamples() - these were unsupported on most DataSetIterator implementations, and not used in practice for training. Custom iterators should remove these methods also Link​
Long-deprecated DataSet.getFeatureMatrix() has been removed. Use DataSet.getFeatures() instead. Link​
Unused and not properly tested/maintained utility class BigDecimalMath has been removed. Users should find an aternative library for this functionality, if required.
Not properly maintained complex number support classes (IComplexNumber, IComplexNDArray) have been removed entirely Link​
DataVec
DataVec: New Features
Added AnalyzeLocal class to mirror functionality of AnalyzeSpark (but without Spark dependency) Link​
Added JacksonLineSequenceRecordReader: RecordReader used for multi-example JSON/XML where each line in a file is an independent example Link​
Added RecordConvert.toRecord(Schema, List<Object>) Link​
Added missing FloatColumnCondition Link​
Added CSVLineSequenceRecordReader for "each line in CSV is a sequence, and sequence is single-valued/univariate" Link​
Added CSVMultiSequenceRecordReader for "multiple multi-valued sequences in a single CSV" data Link​
DataVec: Optimizations and Bug Fixes
Fixed issue with NativeImageLoader on Android Link​
Fixed issue with ExcelRecordReader Link​
Fixed issue where bad args for CSVRecordReader.next(int) could cause an unnecessarily large list to be generated Link​
DataVec: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
Arbiter
Arbiter: New Features
Added DataSource interface. Unlike old DataProvider, this does not require JSON serializability (only a no-arg constructor) Link​
Added numerous enhancements and missing configuration options (constraints, dilation, etc) Link Link​
Arbiter: Fixes
DataProvider has been deprecated. Use DataSource instead.
RL4J
stepCounter, epochCounter and historyProcessor can now be set Link​
Random seed is now loaded for ACPolicy is loaded Link​
1.0.0-beta3
1.0.0-beta
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Highlights - 1.0.0-beta2 Release
Deeplearning4J
Deeplearning4J: New Features
Deeplearning4J: Bug Fixes and Optimizations
Deeplearning4J: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
Deelpearning4J: 1.0.0-beta2 Known Issues
Deeplearing4J: Keras Import
ND4J
ND4J: New Features
ND4J: Bug Fixes and Optimizations
ND4J: Known Issues
ND4J: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
DataVec
DataVec: New Features
DataVec: Optimizations and Bug Fixes
DataVec: API Changes (Transition Guide): 1.0.0-beta to 1.0.0-beta2
Arbiter
Arbiter: New Features
Arbiter: Fixes
RL4J