Added support for CUDA 10.2. 1.0.0-beta6 released with CUDA 9.2, 10.0, 10.1 and 10.2 support
SameDiff optimizations - memory use for inference and training significantly reduced, with some performance improvements also
Deeplearning4j UI - Play framework replaced with Vertx; deeplearning4j-ui dependency now no longer has Scala dependency or Scala version suffix Link
Note: No API changes, only artifact ID change: replace deeplearning4j-ui_2.1x with deeplearning4j-ui
ND4j namespace operation methods: operations are available through the Nd4j.math, Nd4j.random, Nd4j.bitwise, Nd4j.nn (neural network), for example Nd4j.math.abs(INDArray), Nd4j.random.logNormal etc Link.
Note that additional ND4J namespaces API will have additions (new namespaces and methods), and may have some API changes, in the next release
OpenMP replaced with thread pool c++ parallelism framework; enabled c++ parallelism for platforms without C++-level threading for operations
DNNL (MKL-DNN) upgraded to version 1.1
Added causal convolution mode for Convolution1D layer (ConvolutionMode.Causal) and added causal conv1d support for Keras import Link
Keras import now supports scaled identity weight initialization Link
BertIterator now supports sentence pairs for supervised training Link
Added TimeDistributed wrapper layer Link
KDTree implementation optimized Link
Deeplearning4j zoo models and datasets hosting location updated Link
Fixed nIn validation for Deconv2D layer Link
Fixed an issue with incorrect Deconvolution2d results for Keras import models Link
Fixed various integer casts to avoid overflows for very large arrays (with dimensions or length > Integer.MAX_VALUE) Link
Fixed an issue with UNet non-pretrained model architecture (last layer kernel size) Link
Deeplearning4j SameDiff layers now use DL4J workspaces for better performance and reduced memory consumption Link
Updated broken links in afew error messages Link
Cleaned up a few unused dependencies in various modules Link
Cleaned up duplicate SamplingDataSetIterator class Link
Fixed an issue where ComputationGraph instances with a single input going into multiple embedding layers could throw a NPE Link
Fixed an issue where loss function weights were not automatically cast to network datatype, resulting in an exception if not already correct type Link
Shaded Jackson version upgraded from 2.9.9/2.9.9.3 to 2.10.1 Link
Fixed an issue with KNN where getMostPopulatedClusters actually returned the least populated clusters Link
Deeplearning4j UI artifact ID has changed: deeplearning4j-ui_2.1x (beta5 and earlier) with deeplearning4j-ui
Added suport for CUDA 10.2 Link
DNNL (MKL-DNN) upgraded to version 1.1 Link
Added ND4j namespaces to match SameDiff: Nd4j.math, Nd4j.random, Nd4j.bitwise, Nd4j.nn (neural network) Link
Additional SameDiff single batch .output method overloads for DataSet/MultiDataSet added Link
PRelu op added Link
adjust_contrast, igamma and igammac ops added Link
ND4J/SameDiff: BitCast, CompareAndBitpack, DivideNoNan, DrawBoundingBoxes, FakeQuantWithMinMaxVarsPerChannel ops added Link
non_max_suppression_overlaps op added Link
ImagePreProcessingScaler now supports segmentation use cases Link
concat operation now supports the concatenation axis being specified via the last input array Link
Added Gamma and Poisson RNG distributions Link
SameDiff’s use of DeviceLocal for variables/constants etc is now configurable Link
Uniform distribution op now supports random integer generation, not just random floating point generation Link
SameDiff: Added simple OpBenchmarkListener for benchmarking purposes Link
Added the ability to disable platform helpers (DNNL/MKLDNN etc) via Nd4jCPU.Environment.getInstance().allowHelpers(false); and Nd4jCuda.Environment.getInstance().allowHelpers(false); Link
Added draw_bounding_boxes operation Link
Added resize_bicubic operation Link
Added causal padding mode to conv1d operation Link
DNNL (MKLDNN) is included and enabled by default for non-AVX builds Link
Added SameDiff ArraySavingListener for debugging purposes Link
OpenMP replaced with ThreadPool abstraction, enables parallelism for platforms without OpenMP support Link
Switched to Clang instead of gcc for OSX compilation to avoid compiler-related issues Link
Removed SameDiff.outputs() “best guess” output inference due to being unreliable, in favor of explicit SameDiff.setOutputs(String...) call Link
Fixed an issue with Nd4j.hstack on 1D arrays Link
SameDiff no longer allows empty arrays for variables Link
Fixed an issue with Nadam updater LR schedules not being cloned Link
Cleaned up IActivation interface Link
Added new LSTM op implementation with DNNL/MKLDNN support (forward pass only so far) Link
SameDiff API cleaned up; deprecated methods removed Link
Switched SameDiff variable initialization to non-lazy, to avoid unexpected behaviour when mixing execution and ND4J RNG seed setting Link
SameDiff.zero and .one methods now create constants, not vairables Link
Moved CUDA build version and device logging to Java logging, from c++ stdout to enable disabling logging (via ND4J config or slf4j config) Link
Added DNNL/MKLDNN support for batch normalization Link
SameDiff: Fixed an issue where listeners weren’t being called for gradient calculation Link
Added DNNL/MKLDNN support for deconv2d/3d operations Link
Fixed an issue with biasadd_bp operation and NHWC data format Link
INDArray.toString() now has correct brackets for rank 1+ scalars to avoid ambiguity Link
Fixed an issue where some ND4J methods could fail when the library is compiled on Java 9+ but run on Java 8 Link
Fixed empty input arrays for legacy ops (transform, scalar, pairwise, broadcast) Link
CUDA compute capability 3.0 is supported again Link
Improved performance for Scatter operations (1D case) + index validation Link
SameDiff execution will now throw an exception when assertion operations in the graph fail Link
PolyGamma function now returns NaNs when passed double for args requiring integer values Link
Fixed some issues for pad and mirror_pad ops to ensure they conform with Tensorflow for imported networks Link
Updated and fixed some issues for TensorFlow graph runner Link
Improved performance for Reverse operation Link
Removed/cleanup up unused ND4J list functionality Link
Fixed reduce bool operation results (such as any, all, IsInf, etc) for empty array inputs Link
SameDiff.outputs() now requires user to call SameDiff.setOutputs(String...) first; previous “best guess” output inference was unreliable Link
SameDiff.zero and .one methods now create constants, not vairables Link
NativeImageLoader now checks for empty input streams and throws an exception instead of crashing Link
NDArrayScalarOpTransform now supports modulus operator Link
Added AsyncTrainingListener Link
Replaced multiple uses of java.util.Random with ND4J Random Link
Added Observable and LegacyMDPWrapper Link
Refactored RL4J video recording to separate VideoRecorder class Link
Refactoring for DQN and double DQN for improved maintainability Link
Internal refactoring and various bug fixes Link
PyDataVec TransformProcess now supports non-inplace operations Link