Python EpochLogger.subscribe_to方法代碼示例

本文整理匯總了Python中simplelearn.training.EpochLogger.subscribe_to方法的典型用法代碼示例。如果您正苦於以下問題：Python EpochLogger.subscribe_to方法的具體用法？Python EpochLogger.subscribe_to怎麽用？Python EpochLogger.subscribe_to使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類simplelearn.training.EpochLogger的用法示例。

在下文中一共展示了EpochLogger.subscribe_to方法的8個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
        if output_prefix != "":
            output_prefix = output_prefix + "_"

        output_prefix = os.path.join(output_dir, output_prefix)

        return "{}lr-{}_mom-{}_nesterov-{}_bs-{}".format(
            output_prefix,
            args.learning_rate,
            args.initial_momentum,
            args.nesterov,
            args.batch_size)
    """

    assert_equal(os.path.splitext(args.output_prefix)[1], "")
    if os.path.isdir(args.output_prefix) and not args.output_prefix.endswith("/"):
        args.output_prefix += "/"

    output_dir, output_prefix = os.path.split(args.output_prefix)
    if output_prefix != "":
        output_prefix = output_prefix + "_"

    output_prefix = os.path.join(output_dir, output_prefix)

    epoch_logger = EpochLogger(output_prefix + "SGD_nesterov.h5")

    # misclassification_node = Misclassification(output_node, label_node)
    # mcr_logger = LogsToLists()
    # training_stopper = StopsOnStagnation(max_epochs=10,
    #                                      min_proportional_decrease=0.0)

    misclassification_node = Misclassification(output_node, label_lookup_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to("validation mean loss", validation_loss_monitor)

    validation_misclassification_monitor = MeanOverEpoch(
        misclassification_node, callbacks=[print_mcr, StopsOnStagnation(max_epochs=20, min_proportional_decrease=0.0)]
    )

    epoch_logger.subscribe_to("validation misclassification", validation_misclassification_monitor)

    # batch callback (monitor)
    # training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node, callbacks=[print_loss])
    epoch_logger.subscribe_to("training mean loss", training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node, callbacks=[])
    epoch_logger.subscribe_to("training misclassification %", training_misclassification_monitor)

    # epoch callbacks
    # validation_loss_logger = LogsToLists()

    def make_output_filename(args, best=False):
        basename = make_output_basename(args)
        return "{}{}.pkl".format(basename, "_best" if best else "")

    # model = SerializableModel([input_indices_symbolic], [output_node])
    # saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])

    epoch_logger.subscribe_to("validation loss", validation_loss_monitor)

    epoch_timer = EpochTimer2()
    epoch_logger.subscribe_to("epoch duration", epoch_timer)

開發者ID:paulfun92，項目名稱:project_code，代碼行數:69，代碼來源:SGD_nesterov.py

示例2: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........

    #
    # Makes batch and epoch callbacks
    #

    def make_output_basename(args):
        assert_equal(os.path.splitext(args.output_prefix)[1], "")
        if os.path.isdir(args.output_prefix) and \
           not args.output_prefix.endswith('/'):
            args.output_prefix += '/'

        output_dir, output_prefix = os.path.split(args.output_prefix)
        if output_prefix != "":
            output_prefix = output_prefix + "_"

        output_prefix = os.path.join(output_dir, output_prefix)

        return "{}lr-{}_mom-{}_nesterov-{}_bs-{}".format(
            output_prefix,
            args.learning_rate,
            args.initial_momentum,
            args.nesterov,
            args.batch_size)

    epoch_logger = EpochLogger(make_output_basename(args) + "_log.h5")

    # misclassification_node = Misclassification(output_node, label_node)
    # mcr_logger = LogsToLists()
    # training_stopper = StopsOnStagnation(max_epochs=10,
    #                                      min_proportional_decrease=0.0)
    misclassification_node = Misclassification(output_node, label_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    validation_misclassification_monitor = MeanOverEpoch(
        misclassification_node,
        callbacks=[print_mcr,
                   StopsOnStagnation(max_epochs=10,
                                     min_proportional_decrease=0.0)])

    epoch_logger.subscribe_to('validation misclassification',
                              validation_misclassification_monitor)

    # batch callback (monitor)
    # training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node, callbacks=[print_loss])
    epoch_logger.subscribe_to('training mean loss', training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    # epoch callbacks
    # validation_loss_logger = LogsToLists()


    def make_output_filename(args, best=False):
        basename = make_output_basename(args)
        return "{}{}.pkl".format(basename, '_best' if best else "")

    model = SerializableModel([image_uint8_node], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(

開發者ID:paulfun92，項目名稱:simplelearn，代碼行數:70，代碼來源:mnist_fully_connected.py

示例3: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
    # Makes batch and epoch callbacks
    #
    def make_output_filename(args, best=False):
            '''
            Constructs a filename that reflects the command-line params.
            '''
            assert_equal(os.path.splitext(args.output_prefix)[1], "")

            if os.path.isdir(args.output_prefix):
                output_dir, output_prefix = args.output_prefix, ""
            else:
                output_dir, output_prefix = os.path.split(args.output_prefix)
                assert_true(os.path.isdir(output_dir))

            if output_prefix != "":
                output_prefix = output_prefix + "_"

            output_prefix = os.path.join(output_dir, output_prefix)

            return ("%slr-%g_mom-%g_nesterov-%s_bs-%d%s.pkl" %
                    (output_prefix,
                     args.learning_rate,
                     args.initial_momentum,
                     args.nesterov,
                     args.batch_size,
                     "_best" if best else ""))


    # Set up the loggers
    epoch_logger = EpochLogger(make_output_filename(args) + "_log.h5")
    misclassification_node = Misclassification(output_node, label_lookup_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    training_stopper = StopsOnStagnation(max_epochs=201,
                                             min_proportional_decrease=0.0)
    validation_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                             callbacks=[print_misclassification_rate,
                                                        training_stopper])

    epoch_logger.subscribe_to('validation misclassification',
                                validation_misclassification_monitor)

    # batch callback (monitor)
    #training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node,
                                          callbacks=[print_loss])
    epoch_logger.subscribe_to("training loss", training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    epoch_timer = EpochTimer2()
    epoch_logger.subscribe_to('epoch duration', epoch_timer)
#    epoch_logger.subscribe_to('epoch time',
 #                             epoch_timer)
    #################


    model = SerializableModel([input_indices_symbolic], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(loss_node,

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:cifar10_conv3.py

示例4: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
                output_prefix = output_prefix + "_"

            output_prefix = os.path.join(output_dir, output_prefix)

            return ("%slr-%g_mom-%g_nesterov-%s_bs-%d%s.pkl" %
                    (output_prefix,
                     args.learning_rate,
                     args.initial_momentum,
                     args.nesterov,
                     args.batch_size,
                     "_best" if best else ""))
    '''


    # Set up the loggers

    assert_equal(os.path.splitext(args.output_prefix)[1], "")
    if os.path.isdir(args.output_prefix) and \
       not args.output_prefix.endswith('/'):
        args.output_prefix += '/'

    output_dir, output_prefix = os.path.split(args.output_prefix)
    if output_prefix != "":
        output_prefix = output_prefix + "_"

    output_prefix = os.path.join(output_dir, output_prefix)

    epoch_logger = EpochLogger(output_prefix + "S2GD_plus.h5")


    misclassification_node = Misclassification(output_node, label_lookup_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    training_stopper = StopsOnStagnation(max_epochs=20,
                                             min_proportional_decrease=0.0)
    validation_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                             callbacks=[print_misclassification_rate,
                                                        training_stopper])

    epoch_logger.subscribe_to('validation misclassification',
                                validation_misclassification_monitor)

    # batch callback (monitor)
    #training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node,
                                          callbacks=[print_loss])
    epoch_logger.subscribe_to("training loss", training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    epoch_timer = EpochTimer2()
    epoch_logger.subscribe_to('epoch duration', epoch_timer)
#    epoch_logger.subscribe_to('epoch time',
 #                             epoch_timer)
    #################


    #model = SerializableModel([input_indices_symbolic], [output_node])
    #saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(loss_node,

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:S2GD_plus.py

示例5: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
    # Makes batch and epoch callbacks
    #
    def make_output_filename(args, best=False):
            '''
            Constructs a filename that reflects the command-line params.
            '''
            assert_equal(os.path.splitext(args.output_prefix)[1], "")

            if os.path.isdir(args.output_prefix):
                output_dir, output_prefix = args.output_prefix, ""
            else:
                output_dir, output_prefix = os.path.split(args.output_prefix)
                assert_true(os.path.isdir(output_dir))

            if output_prefix != "":
                output_prefix = output_prefix + "_"

            output_prefix = os.path.join(output_dir, output_prefix)

            return ("%slr-%g_mom-%g_nesterov-%s_bs-%d%s.pkl" %
                    (output_prefix,
                     args.learning_rate,
                     args.initial_momentum,
                     not args.no_nesterov,
                     args.batch_size,
                     "_best" if best else ""))


    # Set up the loggers
    epoch_logger = EpochLogger(make_output_filename(args) + "_log.h5")
    misclassification_node = Misclassification(output_node, label_lookup_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    training_stopper = StopsOnStagnation(max_epochs=100,
                                             min_proportional_decrease=0.0)
    validation_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                             callbacks=[print_misclassification_rate,
                                                        training_stopper])

    epoch_logger.subscribe_to('validation misclassification',
                                validation_misclassification_monitor)

    # batch callback (monitor)
    #training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node,
                                          callbacks=[print_loss])
    epoch_logger.subscribe_to("training loss", training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    epoch_timer = EpochTimer()
#    epoch_logger.subscribe_to('epoch time',
 #                             epoch_timer)
    #################


    model = SerializableModel([input_indices_symbolic], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(loss_node,
                                            callbacks=[saves_best])

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:LBFGS_mnist_conv3.py

示例6: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........

    #
    # Makes batch and epoch callbacks
    #

    def make_output_basename(args):
        assert_equal(os.path.splitext(args.output_prefix)[1], "")
        if os.path.isdir(args.output_prefix) and \
           not args.output_prefix.endswith('/'):
            args.output_prefix += '/'

        output_dir, output_prefix = os.path.split(args.output_prefix)
        if output_prefix != "":
            output_prefix = output_prefix + "_"

        output_prefix = os.path.join(output_dir, output_prefix)

        return "{}lr-{}_mom-{}_nesterov-{}_bs-{}".format(
            output_prefix,
            args.learning_rate,
            args.initial_momentum,
            args.nesterov,
            args.batch_size)

    epoch_logger = EpochLogger(make_output_basename(args) + "_log.h5")

    # misclassification_node = Misclassification(output_node, label_node)
    # mcr_logger = LogsToLists()
    # training_stopper = StopsOnStagnation(max_epochs=10,
    #                                      min_proportional_decrease=0.0)
    misclassification_node = Misclassification(output_node, label_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    validation_misclassification_monitor = MeanOverEpoch(
        misclassification_node,
        callbacks=[print_mcr,
                   StopsOnStagnation(max_epochs=10,
                                     min_proportional_decrease=0.0)])

    epoch_logger.subscribe_to('validation misclassification',
                              validation_misclassification_monitor)

    # batch callback (monitor)
    # training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node, callbacks=[print_loss])
    epoch_logger.subscribe_to('training mean loss', training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    # epoch callbacks
    # validation_loss_logger = LogsToLists()


    def make_output_filename(args, best=False):
        basename = make_output_basename(args)
        return "{}{}.pkl".format(basename, '_best' if best else "")

    model = SerializableModel([image_uint8_node], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:SGD_mnist_fully_connected.py

示例7: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
    # Makes batch and epoch callbacks
    #
    def make_output_filename(args, best=False):
            '''
            Constructs a filename that reflects the command-line params.
            '''
            assert_equal(os.path.splitext(args.output_prefix)[1], "")

            if os.path.isdir(args.output_prefix):
                output_dir, output_prefix = args.output_prefix, ""
            else:
                output_dir, output_prefix = os.path.split(args.output_prefix)
                assert_true(os.path.isdir(output_dir))

            if output_prefix != "":
                output_prefix = output_prefix + "_"

            output_prefix = os.path.join(output_dir, output_prefix)

            return ("%slr-%g_mom-%g_nesterov-%s_bs-%d%s.pkl" %
                    (output_prefix,
                     args.learning_rate,
                     args.initial_momentum,
                     not args.no_nesterov,
                     args.batch_size,
                     "_best" if best else ""))


    # Set up the loggers
    epoch_logger = EpochLogger(make_output_filename(args) + "_log.h5")
    misclassification_node = Misclassification(output_node, label_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    training_stopper = StopsOnStagnation(max_epochs=100,
                                             min_proportional_decrease=0.0)
    validation_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                             callbacks=[print_misclassification_rate,
                                                        training_stopper])

    epoch_logger.subscribe_to('validation misclassification',
                                validation_misclassification_monitor)

    # batch callback (monitor)
    #training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node,
                                          callbacks=[print_loss])
    epoch_logger.subscribe_to("training loss", training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    epoch_timer = EpochTimer()
#    epoch_logger.subscribe_to('epoch time',
 #                             epoch_timer)
    #################


    model = SerializableModel([image_uint8_node], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(loss_node,
                                            callbacks=[saves_best])

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:GD_mnist_conv.py

示例8: main

# 需要導入模塊: from simplelearn.training import EpochLogger [as 別名]
# 或者: from simplelearn.training.EpochLogger import subscribe_to [as 別名]

#.........這裏部分代碼省略.........
    #
    # Makes batch and epoch callbacks
    #

    def make_output_basename(args):
        assert_equal(os.path.splitext(args.output_prefix)[1], "")
        if os.path.isdir(args.output_prefix) and \
           not args.output_prefix.endswith('/'):
            args.output_prefix += '/'

        output_dir, output_prefix = os.path.split(args.output_prefix)
        if output_prefix != "":
            output_prefix = output_prefix + "_"

        output_prefix = os.path.join(output_dir, output_prefix)

        return "{}lr-{}_mom-{}_nesterov-{}_bs-{}".format(
            output_prefix,
            args.learning_rate,
            args.initial_momentum,
            args.nesterov,
            args.batch_size)

    epoch_logger = EpochLogger(make_output_basename(args) + "_log.h5")

    # misclassification_node = Misclassification(output_node, label_node)
    # mcr_logger = LogsToLists()
    # training_stopper = StopsOnStagnation(max_epochs=10,
    #                                      min_proportional_decrease=0.0)

    misclassification_node = Misclassification(output_node, label_lookup_node)

    validation_loss_monitor = MeanOverEpoch(loss_node, callbacks=[])
    epoch_logger.subscribe_to('validation mean loss', validation_loss_monitor)

    validation_misclassification_monitor = MeanOverEpoch(
        misclassification_node,
        callbacks=[print_mcr,
                   StopsOnStagnation(max_epochs=100,
                                     min_proportional_decrease=0.0)])

    epoch_logger.subscribe_to('validation misclassification',
                              validation_misclassification_monitor)

    # batch callback (monitor)
    # training_loss_logger = LogsToLists()
    training_loss_monitor = MeanOverEpoch(loss_node, callbacks=[print_loss])
    epoch_logger.subscribe_to('training mean loss', training_loss_monitor)

    training_misclassification_monitor = MeanOverEpoch(misclassification_node,
                                                       callbacks=[])
    epoch_logger.subscribe_to('training misclassification %',
                              training_misclassification_monitor)

    # epoch callbacks
    # validation_loss_logger = LogsToLists()


    def make_output_filename(args, best=False):
        basename = make_output_basename(args)
        return "{}{}.pkl".format(basename, '_best' if best else "")

    model = SerializableModel([input_indices_symbolic], [output_node])
    saves_best = SavesAtMinimum(model, make_output_filename(args, best=True))

    validation_loss_monitor = MeanOverEpoch(

開發者ID:paulfun92，項目名稱:project_code，代碼行數:70，代碼來源:LBFGS_fully_connected_CIFAR10.py

注：本文中的simplelearn.training.EpochLogger.subscribe_to方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。