def evolve(data):
"""This method evolves a Feed Forward Neural Network for this dataset"""
# Possible evolvable parameters include:
# - number of hidden neurons
# - activation function # TODO: only sigmoid works atm
# - number of hidden layers <- hard because num of neurons is evolved separately
# - learning rate
# - repeats required for model aquisition (might keep this constant)
# Unevolvable (or hand-"evolvable") parameters (population Hypers):
# - max number of Individuals
# - crossing over rate
# - mutation rate
# - max number of offsprings per mating
inputs = data.neurons_required()[0]
hiddens_range = (int(inputs / 2), int(inputs * 1.5))
rate_range = (0.5, 3.0)
LIMIT = 30
CROSSING_OVER = 0.2
MUTATION = 0.1
FITNESS_F = data.test
MAX_OFFSPRINGS = 3
RANGE = (hiddens_range, rate_range)
BRAIN_MAKER = make_brain
pop = Population(LIMIT, CROSSING_OVER, MUTATION, FITNESS_F, MAX_OFFSPRINGS, RANGE, BRAIN_MAKER)
print("Created population for evolution optimization!")
pop.run(10, 0, 0)
pop.update(True)
print("Evolution is done. Best network is stored in the .brain attribute!")
return pop.individuals[0]
def main():
pop = Population(LIMIT, SELECTION, CROSSING_OVER, MUTATION, fitness_func,
MAX_OFFSPRINGS, RANGE, get_brain)
print("Population created with size:", len(pop.individuals))
print("Genomes:", "RATE", "REPEATS", "HIDDENS", sep="\t")
for ind in pop.individuals:
print("\t", str(ind.genome).replace(", ", "\t"), sep="\t")
log = pop.run(epochs=5, verbose=4, log=1)
return pop, log
