'id': 1,
'datadir': 'MNIST_data',
'logdir': 'log/',
'validate_each_n_steps': 10,
'max_number_of_iterations': 600,
'max_runtime': 10,
'max_layer': 5,
'fitness_strategy': 'accuracy',
'fitness_power': 3
}
}
gen = GA(genetic_hyperparamter)
for i in range(genetic_hyperparamter['number_of_generation']):
gen.mutate()
gen.crossover(strategy=genetic_hyperparamter['crossover'])
gen.evaluate(
calc_diversity=genetic_hyperparamter['calc_diversity'])
gen.selection()
print("Gen: " + str(gen.generation) + "-Diversity: " +
str(round(gen.diversity)) + "- Fitness_avg: " +
str(round(gen.fitness_avg, 3)) + "- Fitness_best: " +
str(round(gen.best_candidate.get_fitness(), 3)))
gen.write_stats()
except Exception as e:
print(e)
os.makedirs(genetic_hyperparamter['RUNTIME_SPEC']['logdir'],
exist_ok=True)
with open(os.path.join(genetic_hyperparamter['RUNTIME_SPEC']['logdir'],
'error.log'),
