A lot of effort has gone into deeplearning4j the last few weeks: upgraded to the latest version, support for random network generation (how doesn't want to avoid hyper parameter fiddling???) and instructions for using Intel's MKL libraries for speeding up model building.
Filters can be serialized from the Weka Investigator now as well and re-used with the filter called SerializedFilter.
- The FoorLopp source now skips the consistency tests if a variable is attached to at least one of the properties: lower/upper/step
- Downgraded MySQL the driver to 5.1.42, after receiving java.sql.SQLNonTransientConnectionException: CLIENT_PLUGIN_AUTH is required exceptions when using 6.0.6 of the JDBC driver.
- removed double quotes from default executable of JDeps and JMap control actors.
- The DL4JModelToJson and DL4JModelToYaml conversions now distinguish between Model and MultiLayerNetwork objects, to retrieve the correct configurations to convert.
- The DL4JModelWriter sink ensures now that MultiLayerNetwork has been initialized to avoid errors.
- adams-event: fixed forcing of variables in Cron standalone actor.
- JavaMailSendEmail - using the javax.activation.DataHandler class with a URL didn't close the stream of attachements, resulting in locked files on Windows.
- re-using existing sessions now: FTPConnection, SMBConnection, SSHConnection
- The Exec source can output stdout and stderr at the same time, ignore process errors and supports a working directory for the process.
- Boolean/Mathematical/StringExpression: added "str(...)" method for converting objects/numbers into strings: str(expr) = any object's toString() method; str(expr,numdec) = any number is output with at most numdec decimals after the decimal point (trailing 0s get chopped off); str(expr,decformat) = applies the format to the number using java.text.DecimalFormat
- SelectFile and SelectDirectory now support output with forward slashes.
- Upgraded deeplearning4j to 0.8.0
- DL4JTrainModel now as a monitor variable for resetting the model, allowing for training sequentially on multiple datasets.
- Added instructions for using Intel MKL libraries to speed up processing.
- Moved the InMemoryStatsListenerConfigurator to the new adams-dl4j-insight module.
- The Weka Investigator now allows filters to be serialized in the pre-process panel.
- The PrincipalComponentsJ filter now has the option -simple-attribute-names, which generates attributes like PCA_1...n instead of compiling them from the other attribute names.
- Added simple GUI tool for performing XSLT (XML, XSL and Output panel), available from the main menu under Maintenance.
- Added the CallableActorScoreListenerConfigurator iteration listener, which forwards the iteration count/score pair to a callable actor (eg for plotting).
- Added conversion for turning DL4J datasets into spreadsheets: DL4JDataSetToSpreadSheet
- Added conversion for converting spreadsheets into DL4J DataSets: SpreadSheetToDL4JDataSet
- Added fake configurator, as it only retrieves model from storage: FromStorage
- DL4JModelGenerator source generates model(s) using the specified generator scheme.
- Added previews in the Preview browser for DL4J models in JSON and YAML
- Conversions for recreating models from JSON and YAML: DL4JJsonToModel and DL4JYamlToModel
- Conversion for creating actual model from configurator: DL4JConfiguratorToModel
- New module: adams-dl4j-insight for providing insight in model building, which is not necessary when deploying models (avoiding bloat).
- adams-dl4j-weka: added conversions WekaInstancesToDL4JDataSet and WekaInstanceToDL4JINDArray, using Mark Hall's code from the Weka package for DL4J.
- adams-imaging: added the RandomBoundingBox left-click processor.
- added simple spreadsheet filtering framework via the SpreadSheetFilter transformer and the filter class hierarchy it uses. Initial filters: Normalize, Standardize.
- The SpreadSheetInsertColumnPosition conversion inserts column position in string (eg BG), replacing the specified placeholder
- The WekaFilter spreadsheet filter allows to apply any Weka filter to a spreadsheet.
- weka.filters.SerializedFilter is a meta-filter that applies a serialized, trained filter to the data (no further training required).