Python LSTM的未来预测

我想问一下如何使用下面的LSTM模型预测某个时间段的未来。谢谢

我尝试了

model.predict（x_测试）

，但无法识别predict命令

def main():
    configs = json.load(open('config_MS.json', 'r'))
    if not os.path.exists(configs['model']['save_dir']): os.makedirs(configs['model']['save_dir'])

data = DataLoader(
    os.path.join('data', configs['data']['filename']),
    configs['data']['train_test_split'],
    configs['data']['columns']
)

model = Model()
model.build_model(configs)
x, y = data.get_train_data(
    seq_len=configs['data']['sequence_length'],
    normalise=configs['data']['normalise']
)


steps_per_epoch = math.ceil((data.len_train - configs['data']['sequence_length']) / configs['training']['batch_size'])
model.train_generator(
    data_gen=data.generate_train_batch(
        seq_len=configs['data']['sequence_length'],
        batch_size=configs['training']['batch_size'],
        normalise=configs['data']['normalise']
    ),
    epochs=configs['training']['epochs'],
    batch_size=configs['training']['batch_size'],
    steps_per_epoch=steps_per_epoch,
    save_dir=configs['model']['save_dir']
)

x_test, y_test = data.get_test_data(
    seq_len=configs['data']['sequence_length'],
    normalise=configs['data']['normalise']
)
predictions = model.predict_point_by_point(x_test)
#predictions = model.predict_sequences_multiple(x_test, configs['data']['sequence_length'], configs['data']['sequence_length'])
#plot_results_multiple(predictions, y_test, configs['data']['sequence_length'])
plot_results(predictions, y_test)

---

''

模型是张量流模型吗？Pytorch模型？西基特？凯拉斯？您还没有通过删除导入来显示实际情况。在这种情况下，模型应该来自LSTM存储库的

模型

类，该类具有

逐点预测

。您能用实际的错误消息更新吗？所有代码都在github文件夹中。这是一个凯拉斯模型。