Beispiel #1
0
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)
Beispiel #2
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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)
Beispiel #4
0
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)
Beispiel #5
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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'))
Beispiel #6
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        # 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)