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Description:

kerasgenetic 0.1.0

Keras Genetic

Keras Genetic allows you to easily train Keras models using genetic algorithms.
Quick Links:

Cartpole
MNIST Image Classification
Overview
Quickstart

Background
KerasGenetic allows you to leverage the elegent modeling API Keras while performing
training with genetic algorithms. Typically, Keras neural network weights are optimized
by minimizing a loss function through the process of gradient descent.
Keras Genetic takes a different approach to weight optimization by leveraging
genetic algorithms. Genetic algorithms allow you to optimize a neural network
without in scenarios where there is no information about the loss landscape.
Genetic algorithms can be used to train neural networks in niche cases where you need to
train a specialized controller with <1000 parameters.
Some areas where genetic algorithms are applied today:

Reinforcement learning (WorldModels)
Finance

Overview
The Keras genetic API is quick to get started with, but flexible enough to fit
any use case you may come up with.
There are three core components of the API that must be used to get started:

the Individual
the Evaluator
the Breeder
search()

Individual
The Individual class represents an individual in the population.
The most important method on the Individual class is load_model().
load_model() yields a Keras model with the weights stored on the individual
class loaded:
model = individual.load_model()
model.predict(some_data)

Evaluator
Next, lets go over the Evaluator. The Evaluator is responsible for
determining the strength of an Individual. Perhaps the simplest
evaluator is an accuracy evaluator for a classification task:
def evaluate_accuracy(individual: keras_genetic.Individual):
model = individual.load_model()
result = model.evaluate(x_train[:100], y_train[:100], return_dict=True, verbose=0)
return result["accuracy"]

The evaluate_accuracy() function defined above maps from an Individual to an
accuracy score. This score can be used to select the individuals that will be
used in the next generation.
Breeder
The Breeder is responsible with producing new individuals from a set of parent
individuals. The details as to how each Breeder produces new individuals are
unique to the breeder, but as a general rule some attributes of the parent are
preserved while some new attributes are randomly sampled.
For most users, the TwoParentMutationBreeder is sufficiently effective.
search()
search() is akin to model.fit() in the core Keras framework. The search() API
supports a wide variety of parameters. For an in depth view, browse the API docs.
Here is a sample usage of the search() function:
results = keras_genetic.search(
model=model,
# computational cost is evaluate*generations*population_size
evaluator=evaluate_accuracy,
generations=10,
population_size=50,
n_parents_from_population=5,
breeder=keras_genetic.breeder.MutationBreeder(),
return_best=1,
)

Further Reading
Check out the examples and guides (Coming Soon!).
Quickstart
For now, the Cartpole Example serves as the Quickstart guide.
Roadmap
I'd like to accomplish the following tasks:

✅ stabilize the base API
✅ support a callbacks API
✅ end to end MNIST example
✅ end to end CartPole example
✅ implement a ProgBarLogger
✅ implement EarlyStopping callback (can be used in CartPole example)
have at least 3 distinct breeders
autogenerate documentation
thoroughly document each component
offer implementations of the most effective genetic algorithms
implement unit tests for each component
support random seeding
thoroughly review the API per Keras core API design guidelines
support custom initial populations (i.e. to model after a human imitation model)
support keep_probability schedules

Feel free to contribute any of these.
Citation
@misc{wood2022kerasgenetic,
title = {Keras Genetic},
author = {Luke Wood},
year = 2022,
howpublished = {\url{https://github.com/lukewood/keras-genetic}}
}