Python blocks.extensions.Printing() Examples

def test_training_interrupt():
    def process_batch(batch):
        time.sleep(0.1)

    algorithm = MockAlgorithm()
    algorithm.process_batch = process_batch

    main_loop = MockMainLoop(
        algorithm=algorithm,
        data_stream=IterableDataset(count()).get_example_stream(),
        extensions=[Printing()]
    )

    p = Process(target=main_loop.run)
    p.start()
    time.sleep(0.1)
    os.kill(p.pid, signal.SIGINT)
    time.sleep(0.1)
    assert p.is_alive()
    os.kill(p.pid, signal.SIGINT)
    time.sleep(0.2)
    assert not p.is_alive()
    p.join() 

def test_printing():
    main_loop = setup_mainloop(Printing())

    # We are happy if it does not crash or raise any exceptions
    main_loop.run() 

def run():
    streams = create_celeba_streams(training_batch_size=100,
                                    monitoring_batch_size=500,
                                    include_targets=True)
    main_loop_stream = streams[0]
    train_monitor_stream = streams[1]
    valid_monitor_stream = streams[2]

    cg, bn_dropout_cg = create_training_computation_graphs()

    # Compute parameter updates for the batch normalization population
    # statistics. They are updated following an exponential moving average.
    pop_updates = get_batch_normalization_updates(bn_dropout_cg)
    decay_rate = 0.05
    extra_updates = [(p, m * decay_rate + p * (1 - decay_rate))
                     for p, m in pop_updates]

    # Prepare algorithm
    step_rule = Adam()
    algorithm = GradientDescent(cost=bn_dropout_cg.outputs[0],
                                parameters=bn_dropout_cg.parameters,
                                step_rule=step_rule)
    algorithm.add_updates(extra_updates)

    # Prepare monitoring
    cost = bn_dropout_cg.outputs[0]
    cost.name = 'cost'
    train_monitoring = DataStreamMonitoring(
        [cost], train_monitor_stream, prefix="train",
        before_first_epoch=False, after_epoch=False, after_training=True,
        updates=extra_updates)

    cost, accuracy = cg.outputs
    cost.name = 'cost'
    accuracy.name = 'accuracy'
    monitored_quantities = [cost, accuracy]
    valid_monitoring = DataStreamMonitoring(
        monitored_quantities, valid_monitor_stream, prefix="valid",
        before_first_epoch=False, after_epoch=False, every_n_epochs=5)

    # Prepare checkpoint
    checkpoint = Checkpoint(
        'celeba_classifier.zip', every_n_epochs=5, use_cpickle=True)

    extensions = [Timing(), FinishAfter(after_n_epochs=50), train_monitoring,
                  valid_monitoring, checkpoint, Printing(), ProgressBar()]
    main_loop = MainLoop(data_stream=main_loop_stream, algorithm=algorithm,
                         extensions=extensions)
    main_loop.run() 

def run(discriminative_regularization=True):
    streams = create_celeba_streams(training_batch_size=100,
                                    monitoring_batch_size=500,
                                    include_targets=False)
    main_loop_stream, train_monitor_stream, valid_monitor_stream = streams[:3]

    # Compute parameter updates for the batch normalization population
    # statistics. They are updated following an exponential moving average.
    rval = create_training_computation_graphs(discriminative_regularization)
    cg, bn_cg, variance_parameters = rval
    pop_updates = list(
        set(get_batch_normalization_updates(bn_cg, allow_duplicates=True)))
    decay_rate = 0.05
    extra_updates = [(p, m * decay_rate + p * (1 - decay_rate))
                     for p, m in pop_updates]

    model = Model(bn_cg.outputs[0])
    selector = Selector(
        find_bricks(
            model.top_bricks,
            lambda brick: brick.name in ('encoder_convnet', 'encoder_mlp',
                                         'decoder_convnet', 'decoder_mlp')))
    parameters = list(selector.get_parameters().values()) + variance_parameters

    # Prepare algorithm
    step_rule = Adam()
    algorithm = GradientDescent(cost=bn_cg.outputs[0],
                                parameters=parameters,
                                step_rule=step_rule)
    algorithm.add_updates(extra_updates)

    # Prepare monitoring
    monitored_quantities_list = []
    for graph in [bn_cg, cg]:
        cost, kl_term, reconstruction_term = graph.outputs
        cost.name = 'nll_upper_bound'
        avg_kl_term = kl_term.mean(axis=0)
        avg_kl_term.name = 'avg_kl_term'
        avg_reconstruction_term = -reconstruction_term.mean(axis=0)
        avg_reconstruction_term.name = 'avg_reconstruction_term'
        monitored_quantities_list.append(
            [cost, avg_kl_term, avg_reconstruction_term])
    train_monitoring = DataStreamMonitoring(
        monitored_quantities_list[0], train_monitor_stream, prefix="train",
        updates=extra_updates, after_epoch=False, before_first_epoch=False,
        every_n_epochs=5)
    valid_monitoring = DataStreamMonitoring(
        monitored_quantities_list[1], valid_monitor_stream, prefix="valid",
        after_epoch=False, before_first_epoch=False, every_n_epochs=5)

    # Prepare checkpoint
    save_path = 'celeba_vae_{}regularization.zip'.format(
        '' if discriminative_regularization else 'no_')
    checkpoint = Checkpoint(save_path, every_n_epochs=5, use_cpickle=True)

    extensions = [Timing(), FinishAfter(after_n_epochs=75), train_monitoring,
                  valid_monitoring, checkpoint, Printing(), ProgressBar()]
    main_loop = MainLoop(data_stream=main_loop_stream,
                         algorithm=algorithm, extensions=extensions)
    main_loop.run()