# 1.0.0-beta7

## Version 1.0.0-beta7

Read the announcement at <https://blog.konduit.ai/2020/05/14/deeplearning4j-1-0-0-beta7-released/> for the highlights of this release.

### Deeplearning4j

#### Features and Enhancements

* Added Keras model import support for tf.keras models [Link](https://github.com/KonduitAI/deeplearning4j/pull/258), [Link](https://github.com/KonduitAI/deeplearning4j/pull/347)
  * 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](https://github.com/KonduitAI/deeplearning4j/pull/376)
* DL4J now supports NWC (channels last - \[minibatch, sequence\_length, size]) for all RNN and CNN 1D layers, in addition to NCW [Link](https://github.com/KonduitAI/deeplearning4j/pull/379)
* Added Deconvolution3D layer [Link](https://github.com/eclipse/deeplearning4j/issues/8497)
* Keras import: added ReLU, ELU and Softmax advanced activation layers [Link](https://github.com/KonduitAI/deeplearning4j/pull/143) and Swish activation function [Link](https://github.com/eclipse/deeplearning4j/pull/8801)
* Added DL4J SameDiffLoss class (for easily-defined DL4J ILossFunction's via SameDiff) [Link](https://github.com/KonduitAI/deeplearning4j/pull/251)
* Useful exceptions are now thrown when attempting to perform unsupported operations on FastText [Link](https://github.com/KonduitAI/deeplearning4j/pull/269)
* Added MultiLayerNetwork.evaluate(MultiDataSetIterator) and .evaluateRegression(MultiDataSetIterator) methods [Link](https://github.com/eclipse/deeplearning4j/issues/8777), [Link](https://github.com/KonduitAI/deeplearning4j/pull/318)

#### Bug Fixes and Optimizations

* Updaters (Adam, AdaGrad, etc) optimized via C++ operations (significant training performance boost) for DL4J and SameDiff [Link](https://github.com/KonduitAI/deeplearning4j/pull/335), [Link](https://github.com/KonduitAI/deeplearning4j/pull/384)
* Some packages relocated to avoid split packages (that can be a problem for OSGi and Java 9 modules) [Link](https://github.com/KonduitAI/deeplearning4j/pull/411)
  * Note: this is a breaking change for some class packages/imports. See [this link](https://github.com/KonduitAI/deeplearning4j/pull/411) for details on exact package changes
* Deeplearning4j UI: Webjars versions locked down using dependency management to avoid check on each build [Link](https://github.com/eclipse/deeplearning4j/issues/8549)
* Added MKLDNN (DNNL/OneDNN) support for depthwise\_conv2d operation for DL4J and SameDiff [Link](https://github.com/KonduitAI/deeplearning4j/pull/156)
* Refactored/merged modules dl4j-perf and dl4j-util into deeplearning4j-core [Link](https://github.com/KonduitAI/deeplearning4j/pull/175)
* Fixed an issue with BertWordPieceTokenizer - potential StackOverflowError with certain inputs [Link](https://github.com/KonduitAI/deeplearning4j/pull/205)
* Fixed an issue with GlobalPooling layer with masks of different datatype to the activations datatype [Link](https://github.com/KonduitAI/deeplearning4j/pull/212)
* Fixed an issue with DL4JModelValidator for ComputationGraph [Link](https://github.com/KonduitAI/deeplearning4j/pull/240)
* Fixed an issue where SameDiff layers in DL4J could throw an exception when used with transfer learning [Link](https://github.com/eclipse/deeplearning4j/issues/8691)
* Weight initialization for EmbeddingLayer and EmbeddingSequenceLayer now no longer depend on the vocabulary size (only the vector size) [Link](https://github.com/eclipse/deeplearning4j/issues/8663)
* Fixed an issue with Keras import with bidirectional layers + preprocessors [Link](https://github.com/KonduitAI/deeplearning4j/pull/293)
* DL4J UI: added redirect from /train to /train/overview [Link](https://github.com/eclipse/deeplearning4j/issues/8704)
* Fixed an issue where RecordReaderDataSetIterator builder collectMetaData configuration was not being applied [Link](https://github.com/eclipse/deeplearning4j/issues/8776)
* Fixed an issue where MultiLayerNetwork evaluation was not passing metadata to the IEvaluation instances during evaluation [Link](https://github.com/eclipse/deeplearning4j/issues/8718), [Link](https://github.com/KonduitAI/deeplearning4j/pull/318)
* Fixed an issue with Spark training SharedTrainingMaster when training with a ComputationGraph and MultiDataSets [Link](https://github.com/eclipse/deeplearning4j/issues/8765)
* Assorted fixes for edge cases for DL4J Keras import [Link](https://github.com/KonduitAI/deeplearning4j/pull/347)
* deelpearning4j-nlp-korean will no longer be released for Scala 2.12 due to required dependency only having Scala 2.11 version avairable [Link](https://github.com/KonduitAI/deeplearning4j/pull/380)
* Fix for ConvolutionalIterationListener for ComputationGraph [Link](https://github.com/KonduitAI/deeplearning4j/pull/382)
* Fixed an issue where dataset and model zoo downloads could get stuck if the server fails to send any data (now: timeout + retry) [Link](https://github.com/KonduitAI/deeplearning4j/pull/406)
* DL4J ModelSerializer no longer writes temporary files when restoring models from InputStream [Link](https://github.com/eclipse/deeplearning4j/pull/8815)
* Fixes issues with UIServer multi session mode, and potential shutdown race condition [Link](https://github.com/eclipse/deeplearning4j/pull/8856)
* Fixed an issue where TfidfVectorizer.vectorize() could throw a NPE when fit from LabelAwareIterator [Link](https://github.com/eclipse/deeplearning4j/pull/8887)

### ND4J/SameDiff:

#### Features and Enhancements

* SameDiff multi-threaded inference enhanced (and fixed) - a single SameDiff instance can now be used for inference safely and efficiently from multiple threads [Link](https://github.com/eclipse/deeplearning4j/issues/8654) [Link](https://github.com/KonduitAI/deeplearning4j/pull/263)
* cuDNN support added to SameDiff (automatically enabled for nd4j-cuda-10.x backend) [Link](https://github.com/KonduitAI/deeplearning4j/pull/150)
* Added ND4J namespaces: Nd4j.cnn, Nd4j.rnn, Nd4j.image [Link](https://github.com/KonduitAI/deeplearning4j/pull/176)
* Added new Image operations namespace operations:
  * rgbToHsv, hsvToRgb [Link](https://github.com/KonduitAI/deeplearning4j/pull/122)
  * rgbToYiq, yiqToRgb, rgbToYuv, yuvToRgb [Link](https://github.com/KonduitAI/deeplearning4j/pull/144)
  * imageResize [Link](https://github.com/KonduitAI/deeplearning4j/pull/378)
* Added new Random operations namespace operations:
  * gamma, poisson, shuffle [Link](https://github.com/KonduitAI/deeplearning4j/pull/122)
* Added new Math namespace operations:
  * clipByAvgNorm, embeddingLookup [Link](https://github.com/KonduitAI/deeplearning4j/pull/378)
  * mergeMaxIndex [Link](https://github.com/KonduitAI/deeplearning4j/pull/392)
* Added new NN namespace operations:
  * cReLU [Link](https://github.com/KonduitAI/deeplearning4j/pull/378)
* Added new CNN namespace operations:
  * upsampling3d [Link](https://github.com/KonduitAI/deeplearning4j/pull/392)
* Added new linalg operations namespace
  * triangular\_solve [Link](https://github.com/KonduitAI/deeplearning4j/pull/173)
  * tri operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/392)
  * triu operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/392)
* Added new RNN operation namespace operations:
  * lstmLayer (note old lstmLayer method renamed to lstmBlock) [Link](https://github.com/KonduitAI/deeplearning4j/pull/353)
  * gru [Link](https://github.com/KonduitAI/deeplearning4j/pull/410)
* Added new Loss operations namespace - Nd4j.loss [Link](https://github.com/KonduitAI/deeplearning4j/pull/294)
* Mapped operations for Tensorflow import:
  * HSVToRGB, RGBToHSV, Igamma, Igammac, RandomGamma, RandomPoisson, RandomPoissonV2, RandomShuffle [Link](https://github.com/KonduitAI/deeplearning4j/pull/122)
* Added SameDiff ProfilingListener - writes op performance profiles in Chrome profiler format (load in `chrome://tracing/`) [Link](https://github.com/KonduitAI/deeplearning4j/pull/133) [Link](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/ProfilingListener.java)
* Added SameDiff ProfileAnalyzer tool to compare profiles output from ProfilingListener (or Tensorflow) [Link](https://github.com/KonduitAI/deeplearning4j/pull/133) [Link](https://github.com/eclipse/deeplearning4j/blob/master/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/listeners/profiler/comparison/ProfileAnalyzer.java)
* SameDiff listener API: added frame and iteration information for listener methods [Link](https://github.com/eclipse/deeplearning4j/issues/8509) [Link](https://github.com/KonduitAI/deeplearning4j/pull/143)
* Added (non-backend-specific) method of accessing Nd4j environment: `Nd4j.getEnvironment()` method (environment info and low-level configuration options) [Link](https://github.com/eclipse/deeplearning4j/issues/8520) [Link](https://github.com/KonduitAI/deeplearning4j/pull/143)
* Improved memory limits/configuration support for libnd4j (c++) [Link](https://github.com/KonduitAI/deeplearning4j/pull/167)
* Added pairwise (broadcastable) power backprop operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/171)
* Updated JavaCPP presets MKL version to 2020.0 from 2019.5 [Link](https://github.com/KonduitAI/deeplearning4j/pull/190)
* Added DynamicCustomOp dargs - datatype arguments [Link](https://github.com/KonduitAI/deeplearning4j/pull/197) [Link](https://github.com/KonduitAI/deeplearning4j/pull/201)
  * Output datatype configuration for Range op [Link](https://github.com/KonduitAI/deeplearning4j/pull/204), SequenceOp [Link](https://github.com/eclipse/deeplearning4j/issues/8674), ConfusionMatrix [Link](https://github.com/KonduitAI/deeplearning4j/pull/361)
* Added tensormmul\_bp op [Link](https://github.com/KonduitAI/deeplearning4j/pull/231)
* OpenBLAS version upgraded to 0.3.8 [Link](https://github.com/KonduitAI/deeplearning4j/pull/264)
* libnd4j (c++ codebase underlying DL4J, ND4J and SameDiff) refactored to be more easily embeddable in other C++ projects [Link](https://github.com/KonduitAI/deeplearning4j/pull/273)
* ImagePreProcessingScaler now supports preprocessing of labels (for segmentation) [Link](https://github.com/eclipse/deeplearning4j/issues/8731)
* Additional datatypes now supported for nd4j-tensorflow TensorflowConversion [Link](https://github.com/KonduitAI/deeplearning4j/pull/284)
* 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](https://github.com/KonduitAI/deeplearning4j/pull/303)
* Added ND4J `ArchiveUtils.unzipFileTo(String, String, boolean logFiles)` overload to enable/disable extracted file path logging [Link](https://github.com/eclipse/deeplearning4j/issues/8715)
* Added weight format configuration for following operations: conv1D, conv2D, conv3D, deconv2d, deconv3d, depthwiseConv2d, pointwiseConv2d, sconv2d [Link](https://github.com/KonduitAI/deeplearning4j/pull/329)
* Added backprop operation implementations for mergemax, mergeadd, mergeavg operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/343)
* MKL version upgraded to 2020.0 2020.1; OpenCV upgraded from 4.2.0 to 4.3.0 [Link](https://github.com/KonduitAI/deeplearning4j/pull/363)
* SameDiff: DifferentialFunctionFactory class removed in favor of namespace methods (sd.math, sd.linalg, etc) [Link](https://github.com/KonduitAI/deeplearning4j/pull/367)
* Added lstmLayer\_bp operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/370)
* Added gru\_bp operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/377)
* linspace operation can now use both targs and arrays for start/end/size arguments [Link](https://github.com/KonduitAI/deeplearning4j/pull/373)
* Assorted dependency updates - OpenBLAS (0.3.9), OpenCV (4.3.0), Leptonica (1.79.0) [Link](https://github.com/KonduitAI/deeplearning4j/pull/374)
* 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](https://github.com/KonduitAI/deeplearning4j/pull/405)
* Added nd4j-common-tests ResourceUtils.listClassPathfiles method [Link](https://github.com/KonduitAI/deeplearning4j/pull/416)

#### Bug Fixes and Optimizations

* Updaters (Adam, AdaGrad, etc) optimized via C++ operations (significant training performance boost) for DL4J and SameDiff [Link](https://github.com/KonduitAI/deeplearning4j/pull/335), [Link](https://github.com/KonduitAI/deeplearning4j/pull/384)
* SameDiff - added CuDNN support [Link](https://github.com/KonduitAI/deeplearning4j/pull/192)
* Some packages relocated to avoid split packages (that can be a problem for OSGi and Java 9 modules) [Link](https://github.com/KonduitAI/deeplearning4j/pull/411)
  * Note: this is a breaking change for some class packages/imports. See [this link](https://github.com/KonduitAI/deeplearning4j/pull/411) for details on exact package changes
* Fixed some issues with Tensorflow import of FusedBatchNorm operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/122)
* Fixed an issue where the Roll operation did not match Tensorflow operation [Link](https://github.com/eclipse/deeplearning4j/issues/8453) [Link](https://github.com/KonduitAI/deeplearning4j/pull/127)
* Fixed an issue where ArchiveUtils could fail to create the top level destination directory when it does not exist [Link](https://github.com/KonduitAI/deeplearning4j/pull/129)
* Fixed an issue where resize\_bicubic operation did not match Tensorflow for some configuration values [Link](https://github.com/eclipse/deeplearning4j/issues/8539) [Link](https://github.com/KonduitAI/deeplearning4j/pull/130)
* Pad operation now supports long/int64 values for padding array [Link](https://github.com/KonduitAI/deeplearning4j/pull/132) [Link](https://github.com/eclipse/deeplearning4j/issues/8517)
* Fixed an issue where hashcode operation shape function wasn't always returning int64/long dtype [Link](https://github.com/KonduitAI/deeplearning4j/pull/139)
* Fixed an issue with reshape operation on empty arrays with -1s [Link](https://github.com/eclipse/deeplearning4j/issues/8572) [Link](https://github.com/KonduitAI/deeplearning4j/pull/146)
* Improved performance on CUDA for concat operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/148) and CPU/GPU [Link](https://github.com/KonduitAI/deeplearning4j/pull/151)
* Improved performance for bias\_add operation
  * On CPU for NHWC case [Link](https://github.com/KonduitAI/deeplearning4j/pull/149)
  * Generally [Link](https://github.com/KonduitAI/deeplearning4j/pull/217)
  * On CUDA for 2D case [Link](https://github.com/KonduitAI/deeplearning4j/pull/285)
* Added MKLDNN (DNNL/OneDNN) support for depthwise\_conv2d operation for DL4J and SameDiff [Link](https://github.com/KonduitAI/deeplearning4j/pull/156)
* Fixed a small SameDiff execution issue for switch operation where the predicate is a constant [Link](https://github.com/KonduitAI/deeplearning4j/pull/168)
* Fixed an issue with batchnorm operation when input arrays have unusual strides [Link](https://github.com/KonduitAI/deeplearning4j/pull/169)
* Merged nd4j-buffer, nd4j-content modules into nd4j-api [Link](https://github.com/KonduitAI/deeplearning4j/pull/175)
* Deleted deprecated nd4j-jackson module (remaining functionality available in nd4j-api) [Link](https://github.com/KonduitAI/deeplearning4j/pull/175)
* Deleted unused/unmaintained nd4j-camel and nd4j-gson modules [Link](https://github.com/KonduitAI/deeplearning4j/pull/175)
* Optimization for legacy random ops [Link](https://github.com/KonduitAI/deeplearning4j/pull/202)
* Optimization for broadcast operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/234), [Link](https://github.com/KonduitAI/deeplearning4j/pull/239), [Link](https://github.com/KonduitAI/deeplearning4j/pull/248), [Link](https://github.com/KonduitAI/deeplearning4j/pull/257), [Link](https://github.com/KonduitAI/deeplearning4j/pull/302)
* Performance optimization for multiple operations: softmax, squeeze, expand\_dims, tanh [Link](https://github.com/KonduitAI/deeplearning4j/pull/242)
* Optimization for transpose/permute operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/244)
* Performance enhancement: MKLDNN matmul used for some mmul operation cases [Link](https://github.com/KonduitAI/deeplearning4j/pull/250)
* Optimization for gather operation on CPU [Link](https://github.com/KonduitAI/deeplearning4j/pull/254)
* Optimization for stack/unstack operations on CPU [Link](https://github.com/KonduitAI/deeplearning4j/pull/261)
* Optimization for split operation (CPU and CUDA) [Link](https://github.com/KonduitAI/deeplearning4j/pull/262) [Link](https://github.com/KonduitAI/deeplearning4j/pull/266)
* ND4J initialization no longer logs number of OpenMP BLAS threads for CUDA [Link](https://github.com/eclipse/deeplearning4j/issues/8682)
* Optimization: Fixed issues with auto-vectorization on multple CPU operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/274)
* Optimization for reshape operation [Link](https://github.com/KonduitAI/deeplearning4j/pull/275), [Link](https://github.com/KonduitAI/deeplearning4j/pull/277)
* Fixed an issue where INDArray.hashCode() could cause an exception on some datatypes [Link](https://github.com/eclipse/deeplearning4j/issues/8565)
* Optimization for CPU: MKLDNN is now used for softmax, tanh, softmax\_bp and tanh\_bp operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/286), [Link](https://github.com/KonduitAI/deeplearning4j/pull/296), [Link](https://github.com/KonduitAI/deeplearning4j/pull/301), [Link](https://github.com/KonduitAI/deeplearning4j/pull/308)
* Fixed random\_exponential operation [Link](https://github.com/eclipse/deeplearning4j/issues/8752)
* Improved performance on C++ SameDiff graph execution via reduced array zeroing where safe to do so [Link](https://github.com/KonduitAI/deeplearning4j/pull/304)
* Improved C++ indexing implementation impacting CPU performance on some operations [Link](https://github.com/KonduitAI/deeplearning4j/pull/305)
* Fixed an issue where Split operation could have incorrect output shapes for empty arrays [Link](https://github.com/KonduitAI/deeplearning4j/pull/311)
* Fixed some issues with SameDiff.equals method [Link](https://github.com/KonduitAI/deeplearning4j/pull/318)
* Fixed an issue with reshape operation output shape on empty arrays [Link](https://github.com/KonduitAI/deeplearning4j/pull/338), [Link](https://github.com/KonduitAI/deeplearning4j/pull/354)
* Nd4j.gemm now uses Mmul operation internally to avoid potential threading issues with direct BLAS calls on CUDA [Link](https://github.com/KonduitAI/deeplearning4j/pull/365)
* Fixed an edge case issue with percentile operation [link](https://github.com/KonduitAI/deeplearning4j/pull/381)
* Fixed an edge case issue for cusolved (CUDA) in libnd4j [Link](https://github.com/KonduitAI/deeplearning4j/pull/383)
* Fixed an issue with error formatting for segment operations for incorrect lengths [Link](https://github.com/KonduitAI/deeplearning4j/pull/385)
* Fixed an issue where ND4J workspaces were not guaranteed to be unique [Link](https://github.com/KonduitAI/deeplearning4j/pull/400)
* Fixed some operation implementations when operating on views (Batch/Space to Space/Batch/Depth; batchnorm\_bp) [Link](https://github.com/KonduitAI/deeplearning4j/pull/402)
* Fixed an issue where exponential distribution random number generation operation could produce infinities extremely rarely (\~1 in 10^9 values) [Link](https://github.com/KonduitAI/deeplearning4j/pull/403)
* Fixed an issue with long file paths for memory mapped workspaces on Windows [Link](https://github.com/KonduitAI/deeplearning4j/pull/408)
* Memory for memory mapped workspaces are now deallocated immediately when workspace is destroyed, instead of waiting for GC to free memory [Link](https://github.com/KonduitAI/deeplearning4j/pull/409)
* Fall-back to other BLAS implementation for cases where MKLDNN GEMM implementation is slow [Link](https://github.com/KonduitAI/deeplearning4j/pull/415)
* Set nd4j-native source/target to Java 7 [Link](https://github.com/eclipse/deeplearning4j/pull/8808), [Link](https://github.com/eclipse/deeplearning4j/issues/8806)

### DataVec

#### Features and Enhancements

* datavec-python: added zero-copy support for bytes/byte buffers [Link](https://github.com/KonduitAI/deeplearning4j/pull/209)
* datavec-python: Python exceptions are now thrown as Java exceptions [Link](https://github.com/KonduitAI/deeplearning4j/pull/209)
* datavec-python: Added support for additional NumPy datatypes [Link](https://github.com/KonduitAI/deeplearning4j/pull/241)
* datavec-python: Python version upgraded from 3.7.6 to 3.7.7 [Link](https://github.com/KonduitAI/deeplearning4j/pull/346)

#### Bug Fixes and Optimizations

* Deleted not properly maintained modules: datavec-camel, datavec-perf [Link](https://github.com/KonduitAI/deeplearning4j/pull/175)
* Fixed missing BOOL datatype support for arrow conversion functionality [Link](https://github.com/KonduitAI/deeplearning4j/pull/178)
* Assorted fixes for datavec-python [Link](https://github.com/KonduitAI/deeplearning4j/pull/267) [Link](https://github.com/KonduitAI/deeplearning4j/pull/268), [Link](https://github.com/KonduitAI/deeplearning4j/pull/330)
* Fixed an issue with LineRecordReader where initialization was performed unnecessarily (adding performance overhead) [Link](https://github.com/KonduitAI/deeplearning4j/pull/419)

### RL4J

#### Features and Enhancements

* Refactoring to decouple configuration and learning methods from their implementations [Link](https://github.com/KonduitAI/deeplearning4j/pull/326)
* Added builder patterns for all configuration classes [Link](https://github.com/KonduitAI/deeplearning4j/pull/326)

### Arbiter

#### Bug Fixes and Optimizations

* Fixes an issue with GridSearchCandidateGenerator not working correctly for some cases [Link](https://github.com/eclipse/deeplearning4j/issues/8751), [Link](https://github.com/KonduitAI/deeplearning4j/pull/292)


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