FL_Part3_3.py

import pygad
import pygad.nn
import pygad.gann

# Preparing the NumPy array of the inputs.
data_inputs = numpy.array([[0, 1],
                           [0, 0]])

# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([1, 
                            0])

def fitness_func(solution, sol_idx):
    global GANN_instance, data_inputs, data_outputs

    predictions = pygad.nn.predict(last_layer=GANN_instance.population_networks[sol_idx],
                                           data_inputs=data_inputs)
    correct_predictions = numpy.where(predictions == data_outputs)[0].size
    solution_fitness = (correct_predictions/data_outputs.size)*100

    return solution_fitness

def callback_generation(ga_instance):
    global GANN_instance, last_fitness

    population_matrices = pygad.gann.population_as_matrices(population_networks=GANN_instance.population_networks, 
                                                            population_vectors=ga_instance.population)

    GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices)

    print("Generation = {generation}".format(generation=ga_instance.generations_completed))
    print("Fitness    = {fitness}".format(fitness=ga_instance.best_solution()[1]))
    print("Change     = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))

last_fitness = 0

def prepare_GA(GANN_instance):
    population_vectors = pygad.gann.population_as_vectors(population_networks=GANN_instance.population_networks)
    initial_population = population_vectors.copy()
    num_parents_mating = 4
    num_generations = 500
    mutation_percent_genes = 5
    keep_parents = 1
    
    ga_instance = pygad.GA(num_generations=num_generations, 
                           num_parents_mating=num_parents_mating, 
                           initial_population=initial_population,
                           fitness_func=fitness_func,
                           mutation_percent_genes=mutation_percent_genes,
                           keep_parents=keep_parents,
                           callback_generation=callback_generation)

    return ga_instance