def create_trainer(args, converter, labels): """ Creates or loads a neural network according to the specified args. :param labels: dict of labels. """ logger = logging.getLogger("Logger") if args.load: logger.info("Loading provided network...") trainer = ConvTrainer.load(args.load) trainer.learning_rate = args.learning_rate trainer.threads = args.threads else: logger.info('Creating new network...') trainer = ConvTrainer(converter, args.learning_rate, args.window/2, args.window/2, args.hidden, labels, args.verbose) trainer.saver = saver(args.model, args.output) logger.info("... with the following parameters:") logger.info(trainer.nn.description()) return trainer
def create_trainer(args, converter, labels): """ Creates or loads a neural network according to the specified args. :param labels: list of labels. """ logger = logging.getLogger("Logger") if args.load: logger.info("Loading provided network...") trainer = ConvTrainer.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 extractors' tables feat_size = converter.size() pool_size = args.window nn = ConvolutionalNetwork(feat_size * pool_size, args.hidden, args.hidden2, len(labels), pool_size) options = { "learning_rate": args.learning_rate, "verbose": args.verbose, "left_context": args.window / 2, "right_context": args.window / 2, } trainer = ConvTrainer(nn, converter, labels, options) trainer.saver = saver(args.model, args.vectors, args.variant) logger.info("... with the following parameters:") logger.info(trainer.nn.description()) return trainer
def create_trainer(args, converter, labels): """ Creates or loads a neural network according to the specified args. :param labels: dict of labels. """ logger = logging.getLogger("Logger") if args.load: logger.info("Loading provided network...") trainer = ConvTrainer.load(args.load) trainer.learning_rate = args.learning_rate trainer.threads = args.threads else: logger.info('Creating new network...') trainer = ConvTrainer(converter, args.learning_rate, args.window / 2, args.window / 2, args.hidden, labels, args.verbose) trainer.saver = saver(args.model, args.output) logger.info("... with the following parameters:") logger.info(trainer.nn.description()) return trainer
def create_trainer(args, converter, labels): """ Creates or loads a neural network according to the specified args. :param labels: list of labels. """ logger = logging.getLogger("Logger") if args.load: logger.info("Loading provided network...") trainer = ConvTrainer.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 extractors' tables feat_size = converter.size() pool_size = args.window * 2 + 1 nn = ConvolutionalNetwork(feat_size * pool_size, args.hidden, args.hidden2, len(labels), pool_size) options = { 'learning_rate': args.learning_rate, 'eps': args.eps, 'verbose': args.verbose, 'left_context': args.window, 'right_context': args.window } trainer = ConvTrainer(nn, converter, labels, options) trainer.saver = saver(args.model, args.vectors, args.variant) logger.info("... with the following parameters:") logger.info(trainer.nn.description()) return trainer