Package: kerastroke
KeraStrokeKeraStroke is aPython packagethat implements
"Post-Back-propagation Weight Operations", or "PBWOs"; generalization-improvement techniques for Keras models in the
form of custom Keras Callbacks. These techniques function
similarly but have different philosophies and results. The techniques are:Stroke: Re-initializaing random weight/bias values.Pruning: Reducing model size by setting weight/bias values that are close to 0, to 0.NeuroPlast: Re-initializing any weight/bias values that are 0 or close to 0.Stroke is modeled after seizures, which send random electrical signals throughout the brain, sometimes causing damage
to synapses.NeuroPlast is modeled after the concept of neuroplasticity, when neurons that no longer have a primary function begin
to rewire to improve another function. I started working on NeuroPlast after I read the work done by Blakemore and
Cooper on horizontal/vertical line receptor neurons in the brains of cats.If you'd like to see the tests I'm performing with KeraStroke, you can view my testing repositoryhere.KeraStroke 2.0.0 marks when I really started putting work into the project. I've made an effort to comment more,
clean my code up, and make the package easier to understand overall without sacrificing utility.LimitationsKeraStroke is still in the development phase. Heavy testing has been done on
Dense nets, but little testing has been done on CNNs and no testing has been done on RNNs. As of 2.1.0, CNNs are
functioning properly in KeraStroke! The issue with previous versions had to do with the way the callback would retrieve
the weights from the models. The callbacks perform significantly better on DenseNets, but could still find use in CNNs.
I'm working on this, but will definitely need the help. Please seethe github pageorcontact meto contribute to the project.StrokeThe goal of the Stroke callback is to re-initialize weights/biases that have begun to contribute to overfitting.Parameters:set_value: re-initialized weights will be set to this value, rather than a random onelow_bound: low bound for weight re-initializationhigh_bound: high bound for weight re-initializationvolatility_ratio: percentage of weights to be re-initializedcutoff: number of epochs to perform PBWOsdecay: Every epoch, v_ratio is multiplied by this number. decay can be greater than 1.0,
but v_ratio will never exceed 1.0do_weights: perform stroke on weightsdo_biases: perform stroke on biasesPruningThe goal of the Pruning callback is to nullify weights/biases that are effectively 0.Parameters:set_value: The value that pruned weights will be set tomin_value: The lowest value a weight/bias can be to be oeprated onmax_value: The highest value a weight/bias can be to be operated oncutoff: number of epochs to perform PBWOsdo_weights: perform pruning on weightsdo_biases: perform pruning on biasesNeuroPlastThe goal of the NeuroPlast callback is to randomly re-initialize weights/biases that are effectively 0.Parameters:set_value: re-initialized weights will be set to this value, rather than a random onemin_value: lowest value a weight/bias can be to be operated onmax_value: highest value a weight/bias can be to be operated onlow_bound: low bound for weight re-initializationhigh_bound: high bound for weight re-initializationcutoff: number of epochs to perform PBWOsdo_weights: perform neuroplast on weightsdo_biases: perform neuroplast on biasesUsageKeraStroke Callbacks can be used like any other custom callback. Here's a basic example:fromkerastrokeimportStrokemodel.fit(X,y,epochs=32,callbacks=[Stroke()])