Esempio n. 1
0
def create_trainer(args, converter, tag_index):
    """
    Creates or loads a neural network according to the specified args.
    :param tag_index: dict of tags.
    """

    logger = logging.getLogger("Logger")

    if args.load:
        logger.info("Loading provided network...")
        trainer = TaggerTrainer.load(args.load)
        # change learning rate
        trainer.learning_rate = args.learning_rate
        trainer.threads = args.threads
    else:
        logger.info('Creating new network...')
        # sum the number of features in all tables 
        input_size = converter.size() * (args.window * 2 + 1)
        nn = SequenceNetwork(input_size, args.hidden, len(tag_index))
        options = {
            'learning_rate': args.learning_rate,
            'eps': args.eps,
            'ro': args.ro,
            'verbose': args.verbose,
            'left_context': args.window,
            'right_context': args.window
        }
        trainer = TaggerTrainer(nn, converter, tag_index, options)

    trainer.saver = saver(args.model, args.vectors, args.variant)

    logger.info("... with the following parameters:")
    logger.info(trainer.nn.description())
    
    return trainer
Esempio n. 2
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def create_trainer(args, converter, tag_index):
    """
    Creates or loads a neural network according to the specified args.
    :param tag_index: dict of tags.
    """

    logger = logging.getLogger("Logger")

    if args.load:
        logger.info("Loading provided network...")
        trainer = TaggerTrainer.load(args.load)
        # change learning rate
        trainer.learning_rate = args.learning_rate
        trainer.threads = args.threads
    else:
        logger.info('Creating new network...')
        # sum the number of features in all tables
        input_size = converter.size() * (args.window * 2 + 1)
        nn = SequenceNetwork(input_size, args.hidden, len(tag_index))
        options = {
            'learning_rate': args.learning_rate,
            'eps': args.eps,
            'ro': args.ro,
            'verbose': args.verbose,
            'left_context': args.window,
            'right_context': args.window
        }
        trainer = TaggerTrainer(nn, converter, tag_index, options)

    trainer.saver = saver(args.model, args.vectors, args.variant)

    logger.info("... with the following parameters:")
    logger.info(trainer.nn.description())

    return trainer
Esempio n. 3
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def create_trainer(args, converter, tags_dict):
    """
    Creates or loads a neural network according to the specified args.
    """

    logger = logging.getLogger("Logger")

    if args.load:
        logger.info("Loading provided network...")
        trainer = TaggerTrainer.load(args.load)
        trainer.learning_rate = args.learning_rate
        trainer.threads = args.threads
    else:
        logger.info('Creating new network...')
        trainer = TaggerTrainer(converter, args.learning_rate,
                                args.window/2, args.window/2,
                                args.hidden, tags_dict, args.verbose)

    trainer.saver = saver(args.model, args.output)

    logger.info("... with the following parameters:")
    logger.info(trainer.nn.description())
    
    return trainer
Esempio n. 4
0
def create_trainer(args, converter, tags_dict):
    """
    Creates or loads a neural network according to the specified args.
    """

    logger = logging.getLogger("Logger")

    if args.load:
        logger.info("Loading provided network...")
        trainer = TaggerTrainer.load(args.load)
        trainer.learning_rate = args.learning_rate
        trainer.threads = args.threads
    else:
        logger.info('Creating new network...')
        trainer = TaggerTrainer(converter, args.learning_rate, args.window / 2,
                                args.window / 2, args.hidden, tags_dict,
                                args.verbose)

    trainer.saver = saver(args.model, args.vectors, args.variant)

    logger.info("... with the following parameters:")
    logger.info(trainer.nn.description())

    return trainer