Read the announcement at https://blog.konduit.ai/2020/05/14/deeplearning4j-1-0-0-beta7-released/ for the highlights of this release.
Added Keras model import support for tf.keras models Link, Link
Full inference and training support is available for ops/layers in the tf.keras namespace; inference only for general Tensorflow operations outside of the tf.keras namespace
Note also improvements to Keras import for reshape, permute, etc operations due to NHWC and NWC support in DL4J
DL4J now supports NHWC (channels last) data format for all CNN 2D layers, in addition to NCHW Link
DL4J now supports NWC (channels last - [minibatch, sequence_length, size]) for all RNN and CNN 1D layers, in addition to NCW Link
Added Deconvolution3D layer Link
Added DL4J SameDiffLoss class (for easily-defined DL4J ILossFunction's via SameDiff) Link
Useful exceptions are now thrown when attempting to perform unsupported operations on FastText Link
Deeplearning4j UI: Webjars versions locked down using dependency management to avoid check on each build Link
Added MKLDNN (DNNL/OneDNN) support for depthwise_conv2d operation for DL4J and SameDiff Link
Refactored/merged modules dl4j-perf and dl4j-util into deeplearning4j-core Link
Fixed an issue with BertWordPieceTokenizer - potential StackOverflowError with certain inputs Link
Fixed an issue with GlobalPooling layer with masks of different datatype to the activations datatype Link
Fixed an issue with DL4JModelValidator for ComputationGraph Link
Fixed an issue where SameDiff layers in DL4J could throw an exception when used with transfer learning Link
Weight initialization for EmbeddingLayer and EmbeddingSequenceLayer now no longer depend on the vocabulary size (only the vector size) Link
Fixed an issue with Keras import with bidirectional layers + preprocessors Link
DL4J UI: added redirect from /train to /train/overview Link
Fixed an issue where RecordReaderDataSetIterator builder collectMetaData configuration was not being applied Link
Fixed an issue with Spark training SharedTrainingMaster when training with a ComputationGraph and MultiDataSets Link
Assorted fixes for edge cases for DL4J Keras import Link
deelpearning4j-nlp-korean will no longer be released for Scala 2.12 due to required dependency only having Scala 2.11 version avairable Link
Fix for ConvolutionalIterationListener for ComputationGraph Link
Fixed an issue where dataset and model zoo downloads could get stuck if the server fails to send any data (now: timeout + retry) Link
DL4J ModelSerializer no longer writes temporary files when restoring models from InputStream Link
Fixes issues with UIServer multi session mode, and potential shutdown race condition Link
Fixed an issue where TfidfVectorizer.vectorize() could throw a NPE when fit from LabelAwareIterator Link
cuDNN support added to SameDiff (automatically enabled for nd4j-cuda-10.x backend) Link
Added ND4J namespaces: Nd4j.cnn, Nd4j.rnn, Nd4j.image Link
Added new Random operations namespace operations:
gamma, poisson, shuffle Link
Added new NN namespace operations:
cReLU Link
Added new CNN namespace operations:
upsampling3d Link
Added new Loss operations namespace - Nd4j.loss Link
Mapped operations for Tensorflow import:
HSVToRGB, RGBToHSV, Igamma, Igammac, RandomGamma, RandomPoisson, RandomPoissonV2, RandomShuffle Link
Improved memory limits/configuration support for libnd4j (c++) Link
Added pairwise (broadcastable) power backprop operation Link
Updated JavaCPP presets MKL version to 2020.0 from 2019.5 Link
Added tensormmul_bp op Link
OpenBLAS version upgraded to 0.3.8 Link
libnd4j (c++ codebase underlying DL4J, ND4J and SameDiff) refactored to be more easily embeddable in other C++ projects Link
ImagePreProcessingScaler now supports preprocessing of labels (for segmentation) Link
Additional datatypes now supported for nd4j-tensorflow TensorflowConversion Link
SameDiff operation namespaces (sd.math, sd.image, etc) are now code generated to ensure SameDiff and ND4J namespaces are identical (all operations included, same API) Link
Added ND4J ArchiveUtils.unzipFileTo(String, String, boolean logFiles) overload to enable/disable extracted file path logging Link
Added weight format configuration for following operations: conv1D, conv2D, conv3D, deconv2d, deconv3d, depthwiseConv2d, pointwiseConv2d, sconv2d Link
Added backprop operation implementations for mergemax, mergeadd, mergeavg operations Link
MKL version upgraded to 2020.0 2020.1; OpenCV upgraded from 4.2.0 to 4.3.0 Link
SameDiff: DifferentialFunctionFactory class removed in favor of namespace methods (sd.math, sd.linalg, etc) Link
Added lstmLayer_bp operation Link
Added gru_bp operation Link
linspace operation can now use both targs and arrays for start/end/size arguments Link
Assorted dependency updates - OpenBLAS (0.3.9), OpenCV (4.3.0), Leptonica (1.79.0) Link
Upgraded assorted dependency versions: javax.activation:activation (1.1 -> 1.1.1), stream analytics (2.7.0->2.9.8), Apache Spark (2.4.3->2.4.5), Jackson databind (2.10.1 -> 2.10.3), Vertx (3.8.3 -> 3.9.0) Link
Added nd4j-common-tests ResourceUtils.listClassPathfiles method Link
SameDiff - added CuDNN support Link
Fixed some issues with Tensorflow import of FusedBatchNorm operation Link
Fixed an issue where ArchiveUtils could fail to create the top level destination directory when it does not exist Link
Fixed an issue where hashcode operation shape function wasn't always returning int64/long dtype Link
Added MKLDNN (DNNL/OneDNN) support for depthwise_conv2d operation for DL4J and SameDiff Link
Fixed a small SameDiff execution issue for switch operation where the predicate is a constant Link
Fixed an issue with batchnorm operation when input arrays have unusual strides Link
Merged nd4j-buffer, nd4j-content modules into nd4j-api Link
Deleted deprecated nd4j-jackson module (remaining functionality available in nd4j-api) Link
Deleted unused/unmaintained nd4j-camel and nd4j-gson modules Link
Optimization for legacy random ops Link
Performance optimization for multiple operations: softmax, squeeze, expand_dims, tanh Link
Optimization for transpose/permute operations Link
Performance enhancement: MKLDNN matmul used for some mmul operation cases Link
Optimization for gather operation on CPU Link
Optimization for stack/unstack operations on CPU Link
ND4J initialization no longer logs number of OpenMP BLAS threads for CUDA Link
Optimization: Fixed issues with auto-vectorization on multple CPU operations Link
Fixed an issue where INDArray.hashCode() could cause an exception on some datatypes Link
Fixed random_exponential operation Link
Improved performance on C++ SameDiff graph execution via reduced array zeroing where safe to do so Link
Improved C++ indexing implementation impacting CPU performance on some operations Link
Fixed an issue where Split operation could have incorrect output shapes for empty arrays Link
Fixed some issues with SameDiff.equals method Link
Nd4j.gemm now uses Mmul operation internally to avoid potential threading issues with direct BLAS calls on CUDA Link
Fixed an edge case issue with percentile operation link
Fixed an edge case issue for cusolved (CUDA) in libnd4j Link
Fixed an issue with error formatting for segment operations for incorrect lengths Link
Fixed an issue where ND4J workspaces were not guaranteed to be unique Link
Fixed some operation implementations when operating on views (Batch/Space to Space/Batch/Depth; batchnorm_bp) Link
Fixed an issue where exponential distribution random number generation operation could produce infinities extremely rarely (~1 in 10^9 values) Link
Fixed an issue with long file paths for memory mapped workspaces on Windows Link
Memory for memory mapped workspaces are now deallocated immediately when workspace is destroyed, instead of waiting for GC to free memory Link
Fall-back to other BLAS implementation for cases where MKLDNN GEMM implementation is slow Link
datavec-python: added zero-copy support for bytes/byte buffers Link
datavec-python: Python exceptions are now thrown as Java exceptions Link
datavec-python: Added support for additional NumPy datatypes Link
datavec-python: Python version upgraded from 3.7.6 to 3.7.7 Link
Deleted not properly maintained modules: datavec-camel, datavec-perf Link
Fixed missing BOOL datatype support for arrow conversion functionality Link
Fixed an issue with LineRecordReader where initialization was performed unnecessarily (adding performance overhead) Link
Refactoring to decouple configuration and learning methods from their implementations Link
Added builder patterns for all configuration classes Link