A suite of the generalization-improvement techniques Stroke, Pruning, and NeuroPlast

# KeraStroke

KeraStroke is a Python package that 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 repository here.

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.

# Limitations

KeraStroke 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 see the github page or contact me to contribute to the project.

# Stroke

The 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 one
• low_bound: low bound for weight re-initialization
• high_bound: high bound for weight re-initialization
• volatility_ratio: percentage of weights to be re-initialized
• cutoff: number of epochs to perform PBWOs
• decay: Every epoch, v_ratio is multiplied by this number. decay can be greater than 1.0, but v_ratio will never exceed 1.0
• do_weights: perform stroke on weights
• do_biases: perform stroke on biases

# Pruning

The 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 to
• min_value: The lowest value a weight/bias can be to be oeprated on
• max_value: The highest value a weight/bias can be to be operated on
• cutoff: number of epochs to perform PBWOs
• do_weights: perform pruning on weights
• do_biases: perform pruning on biases

# NeuroPlast

The 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 one
• min_value: lowest value a weight/bias can be to be operated on
• max_value: highest value a weight/bias can be to be operated on
• low_bound: low bound for weight re-initialization
• high_bound: high bound for weight re-initialization
• cutoff: number of epochs to perform PBWOs
• do_weights: perform neuroplast on weights
• do_biases: perform neuroplast on biases

# Usage

KeraStroke Callbacks can be used like any other custom callback. Here's a basic example:

from kerastroke import Stroke
model.fit(X, y,
epochs=32,
callbacks=[Stroke()])


## Project details

Uploaded source
Uploaded py3