# 0.7.0

* UI overhaul: new training UI has considerably more information, supports persistence (saving info and loading later), Japanese/Korean/Russian support. Replaced Dropwizard with Play framework. [Link](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface)
* Import of models configured and trained using [Keras](http://keras.io)
  * Imports both *Keras* model [configurations](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/KerasModelConfiguration.java) and [stored weights](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/KerasModel.java#L59)
  * Supported models: [Sequential](https://github.com/eclipse/deeplearning4j/blob/_old/deeplearning4j-0.7.0/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/ModelConfiguration.java) models
  * Supported [layers](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/config/KerasLayerConfiguration.java#L85): *Dense, Dropout, Activation, Convolution2D, MaxPooling2D, LSTM*
* Added ‘Same’ padding more for CNNs (ConvolutionMode network configuration option) [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ConvolutionMode.java)
* Weighted loss functions: Loss functions now support a per-output weight array (row vector)
* ROC and AUC added for binary classifiers [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/eval/ROC.java)
* Improved error messages on invalid configuration or data; improved validation on both
* Added metadata functionality: track source of data (file, line number, etc) from data import to evaluation. Loading a subset of examples/data from this metadata is now supported. [Link](https://github.com/eclipse/deeplearning4j-examples/blob/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/dataexamples/CSVExampleEvaluationMetaData.java)
* Removed Jackson as core dependency (shaded); users can now use any version of Jackson without issue
* Added LossLayer: version of OutputLayer that only applies loss function (unlike OutputLayer: it has no weights/biases)
* Functionality required to build triplet embedding model (L2 vertex, LossLayer, Stack/Unstack vertices etc)
* Reduced DL4J and ND4J ‘cold start’ initialization/start-up time
* Pretrain default changed to false and backprop default changed to true. No longer needed to set these when setting up a network configuration unless defaults need to be changed.
* Added TrainingListener interface (extends IterationListener). Provides access to more information/state as network training occurs [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/optimize/api/TrainingListener.java)
* Numerous bug fixes across DL4J and ND4J
* Performance improvements for nd4j-native & nd4j-cuda backends
* Standalone Word2Vec/ParagraphVectors overhaul:
  * Performance improvements
  * ParaVec inference available for both PV-DM & PV-DBOW
  * Parallel tokenization support was added, to address computation-heavy tokenizers.
* Native RNG introduced for better reproducibility within multi-threaded execution environment.
* Additional RNG calls added: Nd4j.choice(), and BernoulliDistribution op.
* Off-gpu storage introduced, to keep large things, like Word2Vec model in host memory. Available via WordVectorSerializer.loadStaticModel()
* Two new options for performance tuning on nd4j-native backend: setTADThreshold(int) & setElementThreshold(int)

## 0.6.0 -> 0.7.0 Transition Notes

Notable changes for upgrading codebases based on 0.6.0 to 0.7.0:

* UI: new UI package name is deeplearning4j-ui\_2.10 or deeplearning4j-ui\_2.11 (previously: deeplearning4j-ui). Scala version suffix is necessary due to Play framework (written in Scala) being used now.
* Histogram and Flow iteration listeners deprecated. They are still functional, but using new UI is recommended [Link](https://github.com/eclipse/deeplearning4j-examples/tree/master/dl4j-examples/src/main/java/org/deeplearning4j/examples/userInterface)
* DataVec ImageRecordReader: labels are now sorted alphabetically by default before assigning an integer class index to each - previously (0.6.0 and earlier) they were according to file iteration order. Use .setLabels(List) to manually specify the order if required.
* CNNs: configuration validation is now less strict. With new ConvolutionMode option, 0.6.0 was equivalent to ‘Strict’ mode, but new default is ‘Truncate’
  * See ConvolutionMode javadoc for more details: [Link](https://github.com/eclipse/deeplearning4j/blob/master/deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/ConvolutionMode.java)
* Xavier weight initialization change for CNNs and LSTMs: Xavier now aligns better with original Glorot paper and other libraries. Xavier weight init. equivalent to 0.6.0 is available as XAVIER\_LEGACY
* DataVec: Custom RecordReader and SequenceRecordReader classes require additional methods, for the new metadata functionality. Refer to existing record reader implementations for how to implement these methods.
* Word2Vec/ParagraphVectors:
  * Few new builder methods:
    * allowParallelTokenization(boolean)
    * useHierarchicSoftmax(boolean)
  * Behaviour change: batchSize: now batch size is ALSO used as threshold to execute number of computational batches for sg/cbow