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CHANGELOG.rst

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CHANGELOG

1.0.3rc:

  • Null cost redifined as a function of outputs and targets

1.0.2rc:

  • Fixed multiple inputs and added test
  • Minor doc updates and cleaning
  • printLog() of network works even in the model does not compile, and shows the exception message at the end

1.0.1rc:

  • Theano functions can now have several outputs. Model function no longer return an array, but an ordered dict where each key conrrespond to a given output
  • Theano function wrapper will now need more arguments, such as the names given to each output
  • Added accuracy functions such as: testAndAccuracy, and trainAndAccuracy that return both the score and the accuracy
  • Updated trainer/recorder/stopCriteria to support function multiple outputs. They now have more parameters
  • trainer now lets you define which function to use for train, test and validation
  • Added SavingRules (children of SavingRule_ABC) to decide when the model should be saved by the recorder. SavingRules are passed through the argument whenToSave
  • Created SaveMin and SaveMax SavingRules
  • EndOfTraining exceptions are now handeled independently from other exceptionin trainer.

1.0.0rc:

  • The begining of a new era for Mariana.
  • There is as new abstraction type: initalialization (initializations.py).
  • Added batch normalization layer.
  • New Layer_ABC functions: getParameter, getParameterDict, getParameterNames, getParameterShape. The last one must be definded for initializations to work.
  • GlorotTanhInit is now an initialization.
  • Most abstractions now have a common interface.
  • More consistent and sane layer implementation.
  • All layers now have: activation, regularizations, initializations, learningScenario, decorators and name.
  • Layer types have been moved to Network.
  • Classifier_ABC is no more.
  • New abstract class WeightBias_ABC.
  • Networks now have a log, that can be pretty printed using printLog().
  • saveOutputs argument is no more
  • All layers now have propagate() model function that returns their outputs.
  • Output layers can now also serve as hidden layers.
  • ToHidden() and toOutput() are no more.
  • SoftmaxClassifier() now has an accuracy function.
  • AutoEncoder layer now takes a layer name as argument.
  • Functions to save parameters of a network in npy or HDF5 formats.
  • Save() is now based on clone() and can now handle many layers and still uses pickle (Yeah I said that I am going to do something using HDF5 and JSON, but it is not worth the trouble).
  • CloneBare() is no more.
  • Clone() can now clone any layer based on the constructor arguments but you need to call the introspective self._setCreationArguments() at the end of the constructor.
  • Network.load() to load models saved by save().
  • Embedding for Conv nets.
  • Added example for hierarchical softmax.
  • Many other things and little adjustements that make the code more beautiful.