1.0.0-beta

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EN 1.0.0-M2

1.0.0-beta

Highlights - 1.0.0-beta Release
Performance and memory optimizations for DL4J
Deeplearning4J
Deeplearning4J: New Features
New or enhanced layers:
Added Cropping1D layer Link​
Added Convolution3D, Cropping3D, UpSampling3D, ZeroPadding3D, Subsampling3D layers (all with Keras import support): Link Link​
Added EmbeddingSequenceLayer (EmbeddingLayer for time series) Link​
Added OCNNOutputLayer (one-class neural network) - implementation of this paper - Link​
Added FrozenLayerWithBackprop layer Link​
Added DepthwiseConvolution2D layer Link​
Added ComputationGraph.output(DataSetIterator) method Link​
Added MultiLayerNetwork/ComputationGraph.layerInputSize methods Link Link​
Added SparkComputationGraph.feedForwardWithKey overload with feature mask support Link​
Added MultiLayerNetwork.calculateGradients method (for easily getting parameter and input gradients, for example for some model interpretabilithy approaches) Link Link​
Added support to get input/activation types for each layer from configuration: ComputationGraphConfiguration.getLayerActivationTypes(InputType...), ComputationGraphConfiguration.GraphBuilder.getLayerActivationTypes(), NeuralNetConfiguration.ListBuilder.getLayerActivationTypes(), MultiLayerConfiguration.getLayerActivationTypes(InputType) methods Link​
Evaluation.stats() now prints confusion matrix in easier to read matrix format, rather than list format Link​
Added ModelSerializer.addObjectToFile, .getObjectFromFile and .listObjectsInFile for storing arbitrary Java objects in same file as saved network Link​
Added SpatialDropout support (with Keras import support) Link​
Added MultiLayerNetwork/ComputationGraph.fit((Multi)DataSetIterator, int numEpochs) overloads Link​
Added performance (hardware) listeners: SystemInfoPrintListener and SystemInfoFilePrintListener Link​
Deeplearning4J: Bug Fixes and Optimizations
Performance and memory optimizations via optimizations of internal use of workspaces Link​
Reflections library has entirely been removed from DL4J and is no longer required for custom layer serialization/deserialization Link, Link​
Fixes issues with custom and some Keras import layers on Android
RecordReaderMultiDataSetIterator will no longer try to convert unused columns to numerical values Link​
Added new model zoo models:
(to do)
Fixes for Android compilation (removed duplicate classes, aligned versions, removed some dependencies) Link Link Link​
Fix for RecordReaderMulitDataSetIterator where output could be incorrect for some constructors Link​
Non-frozen layers before a frozen layer will no longer be skipped during backprop (useful for GANs and similar architectures) Link Link​
Fixed issue where ComputationGraph topological sort may not be consistent on all platforms; could sometimes break ComputationGraphs (with multiple valid topological orderings) trained on PC and deployed on Android Link​
Fixed issue with CuDNN batch norm using 1-decay instead of decay Link​
deeplearning4j-cuda no longer throws exceptions if present on classpath with nd4j-native backend set to higher priority Link​
Added RNG control for CifarDataSetIterator Link​
WordVectorSerializer now deletes temp files immediately once done Link​
Deeplearning4J: API Changes (Transition Guide): 1.0.0-alpha to 1.0.0-beta
WorkspaceMode.SINGLE and SEPARATE have been deprecated; use WorkspaceMode.ENABLED instead
Internal layer API changes: custom layers will need to be updated to the new Layer API - see built-in layers or custom layer example
Custom layers etc in pre-1.0.0-beta JSON (ModelSerializer) format need to be registered before they can be deserialized due to JSON format change. Built-in layers and models saved in 1.0.0-beta or later do not require this. Use NeuralNetConfiguration.registerLegacyCustomClassesForJSON(Class) for this purpose
IterationListener has been deprecated in favor of TrainingListener. For existing custom listeners, switch from implements TrainingListener to extends BaseTrainingListener Link​
ExistingDataSetIterator has been deprecated; use fit(DataSetIterator, int numEpochs) method instead
Deelpearning4J: 1.0.0-beta Known Issues
ComputationGraph TrainingListener onEpochStart and onEpochEnd methods are not being called correctly
DL4J Zoo Model FaceNetNN4Small2 model configuration is incorrect, causing issues during forward pass
Early stopping score calculators with values thar should be maximized (accuracy, f1 etc) are not working properly (values are minimized not maximized). Workaround: override ScoreCalculator.calculateScore(...) and return 1.0 - super.calculateScore(...).
Deeplearing4J: Keras Import
Deeplearning4J: Keras Import - API Changes (Transition Guide): 1.0.0-alpha to 1.0.0-beta
ND4J
ND4J: New Features
ND4J: Known Issues
Not all op gradients implemented for automatic differentiation
Vast majority of new operations added in 1.0.0-beta do NOT use GPU yet.
ND4J: API Changes (Transition Guide): 1.0.0-alpha to 1.0.0-beta
DataVec
DataVec: New Features
ImageRecordReader now logs number of inferred label classes (to reduce risk of users missing a problem if something is misconfigured) Link​
Added AnalyzeSpark.getUnique overload for multiple columns Link​
Added performance/timing module Link​
DataVec: Optimizations and Bug Fixes
Reduced ImageRecordReader garbage generation via buffer reuse Link​
Fixes for Android compilation (aligned versions, removed some dependencies) Link Link​
Removed Reflections library use in DataVec Link​
Fix for TransformProcessRecordReader batch support Link​
Fix for TransformProcessRecordReader with filter operations Link​
Fixed issue with ImageRecordReader/ParentPathLabelGenerator incorrectly filtering directories containing . character(s) Link​
ShowImageTransform now initializes frame lazily to avoid blank windows Link​
DataVec: API Changes (Transition Guide): 1.0.0-alpha to 1.0.0-beta
DataVec ClassPathResource has been deprecated; use nd4j-common version instead Link​
Arbiter
Arbiter: New Features
Added LayerSpace for OCNN (one-class neural network)
Arbiter: Fixes
Fixed timestamp issue that could cause incorrect rendering of first model's results in UI Link​
Execution now waits for last model(s) to complete before returning when a termination condition is hit Link​
As per DL4J etc: use of Reflections library has been removed entirely from Arbiter Link​
Remove use of Eclipse Collections library due to issues with Android compilation Link​
Improved cleanup of completed models to reduce maximum memory requirements for training Link​