Python cross_validation_sampling示例

示例#1

def train_nn_sinkhorn_pr_model(embedding_method, dataset_id, layer, device_id):
    data = load_train_data(dataset_id, 'classification')
    for i, train, val in cross_validation_sampling(data):
        print(len(train), len(val))

        print(f'Model {i + 1}')

        trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id)
        model = trainer.train_nn_sinkhorn_pr_model(train, val)

        save_model(embedding_method, dataset_id, layer,
                   f'nn_sinkhorn_pr_model_{i}', model)

示例#2

def train_cond_nn_wavg_pr_model(embedding_method, dataset_id, layer,
                                device_id):
    data = load_train_data(dataset_id, 'classification')
    for i, train, val in cross_validation_sampling(data):
        print(len(train), len(val))

        print(f'Model {i + 1}')

        trainer = ModelTrainer(embedding_method, dataset_id, layer, device_id)
        model = trainer.train_cond_nn_wavg_pr_model(train, val)

        save_model(embedding_method, dataset_id, layer,
                   f'cond_nn_wavg_pr_model_{i}', model)

        import torch
        torch.cuda.empty_cache()