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)
