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Description:

farislabtrainmodel 0.0.10

nnGA Library - Neural Network Genetic Algorithm Library (v0.0.5)
Off the shelf Genetic Algorithm library for deep learning problems
License
Our code is released under the MIT license (refer to the LICENSE file for details).
Requirements
To use the library you need atleast Python 3.6. Examples may require additional libraries.
Other required dependencies:

NumPy
Neptune

Usage/Examples
You can import the library by typing pip install faris-lab-train-model.
To learn how to use neptune, check the following examples:
import neptune.new as neptune
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 with neptune
run = neptune.init(project="common/quickstarts",
api_token="ANONYMOUS",
ga)

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()