Python extensions.FinishAfter方法代码示例

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_main_loop():
    old_config_profile_value = config.profile
    config.profile = True

    main_loop = MainLoop(
        MockAlgorithm(), IterableDataset(range(10)).get_example_stream(),
        extensions=[WriteBatchExtension(), FinishAfter(after_n_epochs=2)])
    main_loop.run()
    assert_raises(AttributeError, getattr, main_loop, 'model')

    assert main_loop.log.status['iterations_done'] == 20
    assert main_loop.log.status['_epoch_ends'] == [10, 20]
    assert len(main_loop.log) == 20
    for i in range(20):
        assert main_loop.log[i + 1]['batch'] == {'data': i % 10}

    config.profile = old_config_profile_value 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_training_resumption():
    def do_test(with_serialization):
        data_stream = IterableDataset(range(10)).get_example_stream()
        main_loop = MainLoop(
            MockAlgorithm(), data_stream,
            extensions=[WriteBatchExtension(),
                        FinishAfter(after_n_batches=14)])
        main_loop.run()
        assert main_loop.log.status['iterations_done'] == 14

        if with_serialization:
            main_loop = cPickle.loads(cPickle.dumps(main_loop))

        finish_after = unpack(
            [ext for ext in main_loop.extensions
             if isinstance(ext, FinishAfter)], singleton=True)
        finish_after.add_condition(
            ["after_batch"],
            predicate=lambda log: log.status['iterations_done'] == 27)
        main_loop.run()
        assert main_loop.log.status['iterations_done'] == 27
        assert main_loop.log.status['epochs_done'] == 2
        for i in range(27):
            assert main_loop.log[i + 1]['batch'] == {"data": i % 10}

    do_test(False)
    do_test(True) 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_error():
    ext = TrainingExtension()
    ext.after_batch = MagicMock(side_effect=KeyError)
    ext.on_error = MagicMock()
    main_loop = MockMainLoop(extensions=[ext, FinishAfter(after_epoch=True)])
    assert_raises(KeyError, main_loop.run)
    ext.on_error.assert_called_once_with()
    assert 'got_exception' in main_loop.log.current_row

    ext.on_error = MagicMock(side_effect=AttributeError)
    main_loop = MockMainLoop(extensions=[ext, FinishAfter(after_epoch=True)])
    assert_raises(KeyError, main_loop.run)
    ext.on_error.assert_called_once_with()
    assert 'got_exception' in main_loop.log.current_row 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_shared_variable_modifier():
    weights = numpy.array([-1, 1], dtype=theano.config.floatX)
    features = [numpy.array(f, dtype=theano.config.floatX)
                for f in [[1, 2], [3, 4], [5, 6]]]
    targets = [(weights * f).sum() for f in features]
    n_batches = 3
    dataset = IterableDataset(dict(features=features, targets=targets))

    x = tensor.vector('features')
    y = tensor.scalar('targets')
    W = shared_floatx([0, 0], name='W')
    cost = ((x * W).sum() - y) ** 2
    cost.name = 'cost'

    step_rule = Scale(0.001)
    sgd = GradientDescent(cost=cost, parameters=[W],
                          step_rule=step_rule)
    main_loop = MainLoop(
        model=None, data_stream=dataset.get_example_stream(),
        algorithm=sgd,
        extensions=[
            FinishAfter(after_n_epochs=1),
            SharedVariableModifier(
                step_rule.learning_rate,
                lambda n: numpy.cast[theano.config.floatX](10. / n)
            )])

    main_loop.run()

    assert_allclose(step_rule.learning_rate.get_value(),
                    numpy.cast[theano.config.floatX](10. / n_batches)) 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_shared_variable_modifier_two_parameters():
    weights = numpy.array([-1, 1], dtype=theano.config.floatX)
    features = [numpy.array(f, dtype=theano.config.floatX)
                for f in [[1, 2], [3, 4], [5, 6]]]
    targets = [(weights * f).sum() for f in features]
    n_batches = 3
    dataset = IterableDataset(dict(features=features, targets=targets))

    x = tensor.vector('features')
    y = tensor.scalar('targets')
    W = shared_floatx([0, 0], name='W')
    cost = ((x * W).sum() - y) ** 2
    cost.name = 'cost'

    step_rule = Scale(0.001)
    sgd = GradientDescent(cost=cost, parameters=[W],
                          step_rule=step_rule)
    modifier = SharedVariableModifier(
        step_rule.learning_rate,
        lambda _, val: numpy.cast[theano.config.floatX](val * 0.2))
    main_loop = MainLoop(
        model=None, data_stream=dataset.get_example_stream(),
        algorithm=sgd,
        extensions=[FinishAfter(after_n_epochs=1), modifier])

    main_loop.run()

    new_value = step_rule.learning_rate.get_value()
    assert_allclose(new_value,
                    0.001 * 0.2 ** n_batches,
                    atol=1e-5) 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
def test_checkpointing():
    # Create a main loop and checkpoint it
    mlp = MLP(activations=[None], dims=[10, 10], weights_init=Constant(1.),
              use_bias=False)
    mlp.initialize()
    W = mlp.linear_transformations[0].W
    x = tensor.vector('data')
    cost = mlp.apply(x).mean()
    data = numpy.random.rand(10, 10).astype(theano.config.floatX)
    data_stream = IterableDataset(data).get_example_stream()

    main_loop = MainLoop(
        data_stream=data_stream,
        algorithm=GradientDescent(cost=cost, parameters=[W]),
        extensions=[FinishAfter(after_n_batches=5),
                    Checkpoint('myweirdmodel.tar', parameters=[W])]
    )
    main_loop.run()

    # Load it again
    old_value = W.get_value()
    W.set_value(old_value * 2)
    main_loop = MainLoop(
        model=Model(cost),
        data_stream=data_stream,
        algorithm=GradientDescent(cost=cost, parameters=[W]),
        extensions=[Load('myweirdmodel.tar')]
    )
    main_loop.extensions[0].main_loop = main_loop
    main_loop._run_extensions('before_training')
    assert_allclose(W.get_value(), old_value)

    # Make sure things work too if the model was never saved before
    main_loop = MainLoop(
        model=Model(cost),
        data_stream=data_stream,
        algorithm=GradientDescent(cost=cost, parameters=[W]),
        extensions=[Load('mynonexisting.tar')]
    )
    main_loop.extensions[0].main_loop = main_loop
    main_loop._run_extensions('before_training')

    # Cleaning
    if os.path.exists('myweirdmodel.tar'):
        os.remove('myweirdmodel.tar') 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
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() 

# 需要导入模块: from blocks import extensions [as 别名]
# 或者: from blocks.extensions import FinishAfter [as 别名]
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()