def run(): local_dir = os.path.dirname(__file__) pop = population.Population(os.path.join(local_dir, 'nn_config')) pe = parallel.ParallelEvaluator(eval_fitness) pop.run(pe.evaluate, 1000) print('Number of evaluations: {0}'.format(pop.total_evaluations)) # Display the most fit genome. print('\nBest genome:') winner = pop.statistics.best_genome() print(winner) # Verify network output against a few randomly-generated sequences. winner_net = nn.create_recurrent_phenotype(winner) for n in range(4): print('\nRun {0} output:'.format(n)) seq = [random.choice((0, 1)) for _ in range(N)] winner_net.reset() for s in seq: winner_net.activate([s, 0]) for s in seq: output = winner_net.activate([0, 1]) print("expected {0:1.5f} got {1:1.5f}".format(s, output[0])) # Visualize the winner network and plot/log statistics. visualize.draw_net(winner, view=True, filename="nn_winner.gv") visualize.draw_net(winner, view=True, filename="nn_winner-enabled.gv", show_disabled=False) visualize.draw_net(winner, view=True, filename="nn_winner-enabled-pruned.gv", show_disabled=False, prune_unused=True) visualize.plot_stats(pop.statistics) visualize.plot_species(pop.statistics) statistics.save_stats(pop.statistics) statistics.save_species_count(pop.statistics) statistics.save_species_fitness(pop.statistics)
def test_run(): xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]] xor_outputs = [0, 1, 1, 0] def eval_fitness(genomes): for g in genomes: net = nn.create_feed_forward_phenotype(g) error = 0.0 for inputs, expected in zip(xor_inputs, xor_outputs): # Serial activation propagates the inputs through the entire network. output = net.serial_activate(inputs) error += (output[0] - expected)**2 # When the output matches expected for all inputs, fitness will reach # its maximum value of 1.0. g.fitness = 1 - error local_dir = os.path.dirname(__file__) config = Config(os.path.join(local_dir, 'test_configuration')) pop = population.Population(config) pop.run(eval_fitness, 10) visualize.plot_stats(pop.statistics) visualize.plot_species(pop.statistics) winner = pop.statistics.best_genome() # Validate winner. for g in pop.statistics.most_fit_genomes: assert winner.fitness >= g.fitness visualize.draw_net(winner, view=False, filename="xor2-all.gv") visualize.draw_net(winner, view=False, filename="xor2-enabled.gv", show_disabled=False) visualize.draw_net(winner, view=False, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True) statistics.save_stats(pop.statistics) statistics.save_species_count(pop.statistics) statistics.save_species_fitness(pop.statistics)
def test_run(): xor_inputs = [[0, 0], [0, 1], [1, 0], [1, 1]] xor_outputs = [0, 1, 1, 0] def eval_fitness(genomes): for g in genomes: net = nn.create_feed_forward_phenotype(g) error = 0.0 for inputs, expected in zip(xor_inputs, xor_outputs): # Serial activation propagates the inputs through the entire network. output = net.serial_activate(inputs) error += (output[0] - expected) ** 2 # When the output matches expected for all inputs, fitness will reach # its maximum value of 1.0. g.fitness = 1 - error local_dir = os.path.dirname(__file__) config = Config(os.path.join(local_dir, 'test_configuration')) pop = population.Population(config) pop.run(eval_fitness, 10) visualize.plot_stats(pop.statistics) visualize.plot_species(pop.statistics) winner = pop.statistics.best_genome() # Validate winner. for g in pop.statistics.most_fit_genomes: assert winner.fitness >= g.fitness visualize.draw_net(winner, view=False, filename="xor2-all.gv") visualize.draw_net(winner, view=False, filename="xor2-enabled.gv", show_disabled=False) visualize.draw_net(winner, view=False, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True) statistics.save_stats(pop.statistics) statistics.save_species_count(pop.statistics) statistics.save_species_fitness(pop.statistics)
print('Number of evaluations: {0}'.format(pop.total_evaluations)) # Display the most fit genome. winner = pop.statistics.best_genome() print('\nBest genome:\n{!s}'.format(winner)) # Verify network output against training data. print('\nOutput:') winner_net = nn.create_feed_forward_phenotype(winner) for inputs, expected in zip(xor_inputs, xor_outputs): output = winner_net.serial_activate(inputs) print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0])) # Visualize the winner network and plot/log statistics. visualize.plot_stats(pop.statistics) visualize.plot_species(pop.statistics) visualize.draw_net(winner, view=True, filename="xor2-all.gv") visualize.draw_net(winner, view=True, filename="xor2-enabled.gv", show_disabled=False) visualize.draw_net(winner, view=True, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True) statistics.save_stats(pop.statistics) statistics.save_species_count(pop.statistics) statistics.save_species_fitness(pop.statistics)
def run(output_dir, neat_config=None, generations=20, port=3001, frequency=None, unstuck=False, evaluation=None, checkpoint=None, configuration=None, timelimit=None): if output_dir is None: print('Error! No output dir has been set') return if neat_config is None: neat_config = os.path.join(output_dir, 'nn_config') if evaluation is None: fitness_function = get_fitness_function(os.path.join(output_dir, 'fitness.py')) else: fitness_function = get_fitness_function(evaluation) results_path, models_path, debug_path, checkpoints_path, EVAL_FUNCTION = simulation.initialize_experiments(output_dir, configuration=configuration, unstuck=unstuck, port=port) best_model_file = os.path.join(output_dir, 'best.pickle') if frequency is None: frequency = generations pop = population.Population(neat_config) if checkpoint is not None: print('Loading from ', checkpoint) pop.load_checkpoint(checkpoint) for g in range(1, generations+1): pop.run(lambda individuals: eval_fitness(individuals, fitness_function=fitness_function, evaluate_function=lambda g : EVAL_FUNCTION(g, pop.generation > 13), cleaner=lambda: simulation.clean_temp_files(results_path, models_path), timelimit=timelimit ), 1) if g % frequency == 0: print('Saving best net in {}'.format(best_model_file)) best_genome = get_best_genome(pop) pickle.dump(nn.create_recurrent_phenotype(best_genome), open(best_model_file, "wb")) new_checkpoint = os.path.join(checkpoints_path, 'neat_gen_{}.checkpoint'.format(pop.generation)) print('Storing to ', new_checkpoint) pop.save_checkpoint(new_checkpoint) print('Plotting statistics') visualize.plot_stats(pop.statistics, filename=os.path.join(output_dir, 'avg_fitness.svg')) visualize.plot_species(pop.statistics, filename=os.path.join(output_dir, 'speciation.svg')) print('Save network view') visualize.draw_net(best_genome, view=False, filename=os.path.join(output_dir, "nn_winner-enabled-pruned.gv"), show_disabled=False, prune_unused=True) visualize.draw_net(best_genome, view=False, filename=os.path.join(output_dir, "nn_winner.gv")) visualize.draw_net(best_genome, view=False, filename=os.path.join(output_dir, "nn_winner-enabled.gv"), show_disabled=False) print('Number of evaluations: {0}'.format(pop.total_evaluations)) print('Saving best net in {}'.format(best_model_file)) pickle.dump(nn.create_recurrent_phenotype(get_best_genome(pop)), open(best_model_file, "wb")) # Display the most fit genome. #print('\nBest genome:') winner = pop.statistics.best_genome() #print(winner) # Visualize the winner network and plot/log statistics. visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner.gv")) visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner-enabled.gv"), show_disabled=False) visualize.draw_net(winner, view=True, filename=os.path.join(output_dir, "nn_winner-enabled-pruned.gv"), show_disabled=False, prune_unused=True) visualize.plot_stats(pop.statistics, filename=os.path.join(output_dir, 'avg_fitness.svg')) visualize.plot_species(pop.statistics, filename=os.path.join(output_dir, 'speciation.svg')) statistics.save_stats(pop.statistics, filename=os.path.join(output_dir, 'fitness_history.csv')) statistics.save_species_count(pop.statistics, filename=os.path.join(output_dir, 'speciation.csv')) statistics.save_species_fitness(pop.statistics, filename=os.path.join(output_dir, 'species_fitness.csv'))
# its maximum value of 1.0. g.fitness = 1 - sum_square_error pop = population.Population('xor2_config') pop.run(eval_fitness, 300) print('Number of evaluations: {0}'.format(pop.total_evaluations)) # Display the most fit genome. winner = pop.statistics.best_genome() print('\nBest genome:\n{!s}'.format(winner)) # Verify network output against training data. print('\nOutput:') winner_net = nn.create_feed_forward_phenotype(winner) for inputs, expected in zip(xor_inputs, xor_outputs): output = winner_net.serial_activate(inputs) print("expected {0:1.5f} got {1:1.5f}".format(expected, output[0])) # Visualize the winner network and plot/log statistics. visualize.plot_stats(pop.statistics) visualize.plot_species(pop.statistics) visualize.draw_net(winner, view=True, filename="xor2-all.gv") visualize.draw_net(winner, view=True, filename="xor2-enabled.gv", show_disabled=False) visualize.draw_net(winner, view=True, filename="xor2-enabled-pruned.gv", show_disabled=False, prune_unused=True) statistics.save_stats(pop.statistics) statistics.save_species_count(pop.statistics) statistics.save_species_fitness(pop.statistics)