1.0.0-beta2
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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 -
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 , Locally connected 2d
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
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)
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 -
Added Composite[Multi]DataSetPreProcessor to enable multiple [Multi]DataSetPreProcessors to be applied in a single iterator
Added ComputationGraph evaluate methods for multi-output networks: evaluate(DataSetIterator, Map<Integer,IEvaluation[]>) and evaluate(MultiDataSetIterator, Map<Integer,IEvaluation[]>)
Added JointMultiDataSetIterator - utility iterator used to create MultiDataSetIterator from multiple DataSetIterators
GraphVertices may now have trainable parameters directly (not just enclose layers with trainable parameters)
Added MultiLayerNetwork/ComputationGraph getLearningRate methods
Added RandomDataSetIterator and RandomMultiDataSetIterator (mainly for testing/debugging)
Added cyclical "1cycle" schedule for learning rate schedules etc -
RDD repartitioning for Spark training is more configurable (adds Repartitioner interface)
Added ComputationGraph.getIterationCount() and .getEpochCount() for consistency with MultiLayerNetwork
Added locally connected 1d layer
Spark "data loader" API (mainly for Spark)
Spark evaluation: added evaluation method overloads that allow specifying the number of evaluation workers (less than number of Spark threads)
CnnSentenceDataSetIterator now has a Format argument, and supports outputting data for RNNs and 1D CNNs
Added ComputationGraph/MultiLayerNetwork.pretrain((Multi)DataSetIterator, int epochs) method overloads
MultiLayerNetwork and ComputationGraph now have output method overloads where the network output can be placed in the user-specified workspace, instead of being detached . 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
CuDNN batch norm implementation will now be used for rank 2 input, not just rank 4 input
Environment variables and system properties for DL4J have been centralized into DL4JResources and DL4JEnvironmentVars classes, with proper descriptions
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
BarnesHutTSNE now throws a useful exception in the case where the distance metric is undefined (for example, all zeros plus cosine similarity)
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.
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)
SameDiff: A significant number of new ops, and backprop implementations for existing ops
SameDiff: a significant number of bug fixes for execution and individual ops
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())
Unused and not properly tested/maintained utility class BigDecimalMath has been removed. Users should find an aternative library for this functionality, if required.
DataProvider has been deprecated. Use DataSource instead.
ComputationGraph.addListeners was not working correctly if listeners were already present ,
TinyImageNetDataSetIterator did not validate/correctly use input shape configuration ,
BatchNormalization layer now correctly asserts that nOut is set if required (instead of unfriendly shape errors later)
Fixed issue where OutputLayer may not initialize parameter constraints correctly
Fixed performance issue with Nesterov updater using CPU-only op for CUDA execution
Removed TerminationCondition for DL4J optimizers - was not used in practice, and had minor overhead
Fixed issue where EvaluativeListener could hit a workspace validation exception when workspaces are enabled
Fixed issue where TrainingListener.onEpochStart/onEpochEnd were not being called correctly for ComputationGraph
Fixed workspace issue with TensorFlowCnnToFeedForwardPreProcessor
Performance optimization for BatchNormalization when using CuDNN
Performance optimization: Dropout will be applied in-place when safe to do so, avoiding a copy
Added CuDNN implementation of Dropout
Reduced memory use for CuDNN: CuDNN working memory is now shared and reused between layers within a network
CuDNN batch normalization implementation would fail with FP16 datatype
Fixed issue Bidirectional LSTM may incorrectly use workspaces causing an exception
Fixed issue with early stopping where scores to be maximized (accuracy, f1, etc) were not properly triggering termination conditions
Fixed issue where label mask counter could be incorrectly incremented in ComputationGraph.computeGradientAndScore()
ComputationGraph was not setting lastEtlTime field during training
Fixed issue with AutoEncoder layer when workspaces are enabled
Fixed issue with EmbeddingSequenceLayer use of mask arrays
Lombok is now provided scope everywhere, isn't on user classpath when using DL4J
Fixed issue where WordVectorSerializer.readParagraphVectors(File) initialization of label source
Spark training (gradient sharing) now properly handles empty partition edge case when encountered during training
Errors are propagated better/more consistently for Spark gradient sharing training
Fixed issue with 1D CNN layers with mask arrays and stride > 1 (masks not being correctly downsized)
DL4J Batch norm implementation was not correctly adding epsilon value during inference, only during training (CuDNN unaffected)
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
Spark training with gradient sharing now passes listeners to workers correctly
Fixed rare (and non-terminal) concurrent modification issue with UI and FileStatsStorage
CuDNN convolution layer now supports dilation > 2 (previously: used DL4J conv layer implementation as a fallback)
Yolo2OutputLayer now implements computeScoreForExamples()
SequenceRecordReeaderDataSetIterator now handles the "no labels" case correctly
Fixed issue where BarnesHutTSNE could hit a workspace validation exception
EMNIST iterator could produce incorrect data in some cases after a reset
deeplearning4j-modelexport-solr: now uses Lucene/Solr version 7.4.0 (was 7.3.0)
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).
Some older/deprecated Model and Layer methods have been removed. (validateInput(), initParams()). Some custom layers may need to be updated as a result
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
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)
ND4j-common ClassPathResource: added ClassPathResource.copyDirectory(File)
Added Nd4j.randomBernoulli/Binomial/Exponential convenience methods
Added way to disable/suppress ND4J initialization logging via org.nd4j.log.initialization system property
SameDiff class - most op/constructor methods now have complete/useful javadoc
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]
Added EnvironmentalAction API for environment variable processing
ND4J environment variables and system properties have been centralized in ND4jEnvironmentVars and ND4jSystemProperties classes and
Fixed issue where INDArray.toDoubleArray() with true scalars (rank 0 arrays)
Fixed issue with DataSet.sample() not working for rank 3+ features
IActivation implementations now validate/enforce same shape for activations and gradients
Fixed issue with muliColumnVector where vector is 1d
ImagePreProcessingScaler now supports serialization via NormalizerSerializerStrategy and ModelSerializer
Performance optimization for threshold encoding used in DL4J's Spark gradient sharing distributed training implementation
SameDiff: Fixed issue where memory wasn't always released after execution
DataSet.save() and MultiDataSet.save() methods now save example metadata when present
Fixed issue with KFoldIterator when dataset does not divide equally into folds with no remainder
Fixed issue where version check functionality could fail to load resources if resources are on a path with spaces
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
Long-deprecated DataSet.getFeatureMatrix() has been removed. Use DataSet.getFeatures() instead.
Not properly maintained complex number support classes (IComplexNumber, IComplexNDArray) have been removed entirely
Added AnalyzeLocal class to mirror functionality of AnalyzeSpark (but without Spark dependency)
Added JacksonLineSequenceRecordReader: RecordReader used for multi-example JSON/XML where each line in a file is an independent example
Added RecordConvert.toRecord(Schema, List<Object>)
Added missing FloatColumnCondition
Added CSVLineSequenceRecordReader for "each line in CSV is a sequence, and sequence is single-valued/univariate"
Added CSVMultiSequenceRecordReader for "multiple multi-valued sequences in a single CSV" data
Fixed issue with NativeImageLoader on Android
Fixed issue with ExcelRecordReader
Fixed issue where bad args for CSVRecordReader.next(int) could cause an unnecessarily large list to be generated
Added DataSource interface. Unlike old DataProvider, this does not require JSON serializability (only a no-arg constructor)
Added numerous enhancements and missing configuration options (constraints, dilation, etc)
stepCounter, epochCounter and historyProcessor can now be set
Random seed is now loaded for ACPolicy is loaded