nn.py 文件源码

def create_net(config, **kwargs):
    args = {
        'layers': config.layers,
        'batch_iterator_train': iterator.ResampleIterator(
            config, batch_size=config.get('batch_size_train')),
        'batch_iterator_test': iterator.SharedIterator(
            config, deterministic=True,
            batch_size=config.get('batch_size_test')),
        'on_epoch_finished': [
            Schedule('update_learning_rate', config.get('schedule'),
                     weights_file=config.final_weights_file),
            SaveBestWeights(weights_file=config.weights_file,
                            loss='kappa', greater_is_better=True,),
            SaveWeights(config.weights_epoch, every_n_epochs=5),
            SaveWeights(config.weights_best, every_n_epochs=1, only_best=True),
        ],
        'objective': get_objective(),
        'use_label_encoder': False,
        'eval_size': 0.1,
        'regression': False,
        'max_epochs': 1000,
        'verbose': 1,
        'update_learning_rate': theano.shared(
            util.float32(config.get('schedule')[0])),
        'update': nesterov_momentum,
        'update_momentum': 0.1,
        'custom_scores': [('kappa', util.kappa)],
    }
    args.update(kwargs)
    net = Net(**args)
    return net