Off the shelf Genetic Algorithm library for deep learning problems
Author: Alessio Russo (PhD Student at KTH - [email protected])
Our code is released under the MIT license (refer to the LICENSE file for details).
To use the library you need atleast Python 3.5. Examples may require additional libraries.
Other required dependencies:
- NumPy
You can import the library by typing python from nnga import nnGA.
To learn how to use nnGA, check the following examples:
-
Reinforcement learning examples
-
Supervised learning examples
In general the code has the following structure
from nnga import nnGA, GaussianInitializationStrategy, \
GaussianMutationStrategy, BasicCrossoverStrategy, \
PopulationParameters
def make_network(parameters=None):
''' Function that creates a network given a set of parameters '''
neural_network = ...
return neural_network
def fitness(idx, parameters):
''' Fitness function to evaluate a set of parameters '''
# Evaluate parameters
network = make_network(parameters)
return evaluate_network(network)
if __name__ == '__main__':
# Initialize GA parameters
network = make_initial_network()
network_structure = [list(layer.shape) for layer in network] # List of tuples, containing the shape of each layer
# Population parameters
population = PopulationParameters(population_size=200)
# Mutation strategy
mutation = GaussianMutationStrategy(network_structure, 1e-1)
# Crossover strategy
crossover = BasicCrossoverStrategy(network_structure)
# Initialization strategy
init = GaussianInitializationStrategy(
mean=0., std=1., network_structure=network_structure)
ga = nnGA(
epochs=50, # Number of epochs
fitness_function=fitness,
population_parameters=population,
mutation_strategy=mutation,
initialization_strategy=init,
crossover_strategy=crossover,
num_processors=8) # Number of cores
# Run GA
network_parameters, best_result, results = ga.run()