# 0.8.0

### 0.8.0 -> 0.9.0 Transition Notes

**Deeplearning4j**

* Updater configuration methods such as .momentum(double) and .epsilon(double) have been deprecated. Instead: use `.updater(new Nesterovs(0.9))` and `.updater(Adam.builder().beta1(0.9).beta2(0.999).build())` etc to configure

**DataVec**

* CsvRecordReader constructors: now uses characters for delimiters, instead of Strings (i.e., ',' instead of ",")

**Arbiter**

* Arbiter UI is now a separate module, with Scala version suffixes: `arbiter-ui_2.10` and `arbiter-ui_2.11`

## Version 0.8.0

* Added transfer learning API [Link](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/transferlearning/vgg16)
* Spark 2.0 support (DL4J and DataVec; see transition notes below)
* New layers
  * Global pooling (aka "pooling over time"; usable with both RNNs and CNNs) [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/pooling/GlobalPoolingLayer.java)
  * Center loss output layer [Link](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/misc/centerloss/CenterLossLenetMnistExample.java)
  * 1D Convolution and subsampling layers [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/Convolution1DLayer.java) [Link2](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/subsampling/Subsampling1DLayer.java)
  * ZeroPaddingLayer [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/convolution/ZeroPaddingLayer.java)
* New ComputationGraph vertices
  * L2 distance vertex
  * L2 normalization vertex
* Per-output masking is now supported for most loss functions (for per output masking, use a mask array equal in size/shape to the labels array; previous masking functionality was per-example for RNNs)
* L1 and L2 regularization can now be configured for biases (via l1Bias and l2Bias configuration options)
* Evaluation improvements:
  * DL4J now has an IEvaluation class (that Evaluation, RegressionEvaluation, etc all implement. Also allows custom evaluation on Spark) [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/eval/IEvaluation.java)
  * Added multi-class (one vs. all) ROC: ROCMultiClass [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/eval/ROCMultiClass.java)
  * For both MultiLayerNetwork and SparkDl4jMultiLayer: added evaluateRegression, evaluateROC, evaluateROCMultiClass convenience methods
  * HTML export functionality added for ROC charts [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-core/src/main/java/org/deeplearning4j/evaluation/EvaluationTools.java#L93)
  * TSNE re-added to new UI
  * Training UI: now usable without an internet connection (no longer relies on externally hosted fonts)
  * UI: improvements to error handling for ‘no data’ condition
* Epsilon configuration now used for Adam and RMSProp updaters
* Fix for bidirectional LSTMs + variable-length time series (using masking)
* Added CnnSentenceDataSetIterator (for use with ‘CNN for Sentence Classification’ architecture) [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/iterator/CnnSentenceDataSetIterator.java) [Link2](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/convolution/sentenceclassification/CnnSentenceClassificationExample.java)
* Spark + Kryo: now test serialization + throw exception if misconfigured (instead of logging an error that can be missed)
* MultiLayerNetwork now adds default layer names if no name is specified
* DataVec:
  * JSON/YAML support for DataAnalysis, custom Transforms etc
  * ImageRecordReader refactored to reduce garbage collection load (hence improve performance with large training sets)
  * Faster quality analysis.
* Arbiter: added new layer types to match DL4J
  * Performance improvement for Word2Vec/ParagraphVectors tokenization & training.
* Batched inference introduced for ParagraphVectors
* Nd4j improvements
  * New native operations available for ND4j: firstIndex, lastIndex, remainder, fmod, or, and, xor.
  * OpProfiler NAN\_PANIC & INF\_PANIC now also checks result of BLAS calls.
  * Nd4.getMemoryManager() now provides methods to tweak GC behavior.
* Alpha version of parameter server for Word2Vec/ParagraphVectors were introduced for Spark. Please note: It’s not recommended for production use yet.
* Performance improvements for CNN inference

### 0.7.2 -> 0.8.0 Transition Notes

* Spark versioning schemes: with the addition of Spark 2 support, the versions for Deeplearning4j and DataVec Spark modules has changed
  * For Spark 1: use `<version>0.8.0_spark_1</version>`
  * For Spark 2: use `<version>0.8.0_spark_2</version>`
  * Also note: Modules with Spark 2 support are released with Scala 2.11 support only. Spark 1 modules are released with both Scala 2.10 and 2.11 support

### 0.8.0 Known Issues (At Launch)

* UI/CUDA/Linux issue: [Link](https://github.com/eclipse/deeplearning4j/issues/3026)
* Dirty shutdown on JVM exit is possible for CUDA backend sometimes: [Link](https://github.com/eclipse/deeplearning4j/issues/3028)
* Issues with RBM implementation [Link](https://github.com/eclipse/deeplearning4j/issues/3049)
* Keras 1D convolutional and pooling layers cannot be imported yet. Will be supported in forthcoming release.
* Keras v2 model configurations cannot be imported yet. Will be supported in forthcoming release.


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