1.0.0-beta2
- 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 resource (datasets, pretrained models) storage directory can now be configured via
DL4JResources.setBaseDirectorymethod ororg.deeplearning4j.resources.directorysystem 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).
- 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.setBaseDirectorymethod ororg.deeplearning4j.resources.directorysystem 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[]>)andevaluate(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 ComputationGraph.getIterationCount() and .getEpochCount() for consistency with MultiLayerNetwork 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 - EmbeddingSequenceLayer now supports
[minibatch,1,seqLength]format sequence data in addition to[minibatch,seqLength]format data 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
- BatchNormalization layer now correctly asserts that nOut is set if required (instead of unfriendly shape errors later) 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
- Performance optimization: Dropout will be applied in-place when safe to do so, avoiding a copy Link
- Reduced memory use for CuDNN: CuDNN working memory is now shared and reused between layers within a network 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
- 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
- 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
- CuDNN convolution layer now supports dilation > 2 (previously: used DL4J conv layer implementation as a fallback) Link
- 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.
- 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
- 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-SNAPSHOTLink
- 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: 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 usingNd4j.convertToNumpy(INDArray)Link - SameDiff: A significant number of new ops, and backprop implementations for existing ops
- Added way to disable/suppress ND4J initialization logging via
org.nd4j.log.initializationsystem property Link - Workspaces can now be disabled globally, ignoring workspace configuration. This is mainly used for debugging; use
Nd4j.getWorkspaceManager().setDebugMode(DebugMode.DISABLED)orNd4j.getWorkspaceManager().setDebugMode(DebugMode.SPILL_EVERYTHING);to enable this. Link [Link]
- SameDiff: a significant number of bug fixes for execution and individual ops
- 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
- 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
- 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
- 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 CSVLineSequenceRecordReader for "each line in CSV is a sequence, and sequence is single-valued/univariate" Link
- DataProvider has been deprecated. Use DataSource instead.
Last modified 5mo ago