python - 如何找到Python中MemoryError的来源？

Question

我正在使用 Hyperopt 为神经网络运行超参数优化。这样做时，经过一些迭代后，我收到 MemoryError 异常

到目前为止，我尝试在使用后清除所有变量(为它们分配 None 或空列表，有更好的方法吗？)并打印所有 locals()、dirs() 和 globals() 及其值大小，但这些计数永远不会增加，而且大小非常小。

结构如下:

def create_model(params):
    ## load data from temp files
    ## pre-process data accordingly
    ## Train NN with crossvalidation clearing Keras' session every time
    ## save stats and clean all variables (assigning None or empty lists to them)

def Optimize():
    for model in models: #I have multiple models
        ## load data
        ## save data to temp files
        trials = Trials()
        best_run = fmin(create_model,
                        space,
                        algo=tpe.suggest,
                        max_evals=100,
                        trials=trials)

经过 X 次迭代后(有时它完成前 100 次并转移到第二个模型)，它会抛出内存错误。 我的猜测是，某些变量保留在内存中，我没有清除它们，但我无法检测到它们。

编辑:

Traceback (most recent call last):
  File "Main.py", line 32, in <module>
    optimal = Optimize(training_sets)
  File "/home/User1/Optimizer/optimization2.py", line 394, in Optimize
    trials=trials)
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/fmin.py", line 307, in fmin
    return_argmin=return_argmin,
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/base.py", line 635, in fmin
    return_argmin=return_argmin)
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/fmin.py", line 320, in fmin
    rval.exhaust()
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/fmin.py", line 199, in exhaust
    self.run(self.max_evals - n_done, block_until_done=self.async)
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/fmin.py", line 173, in run
    self.serial_evaluate()
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/fmin.py", line 92, in serial_evaluate
    result = self.domain.evaluate(spec, ctrl)
  File "/usr/local/lib/python3.5/dist-packages/hyperopt/base.py", line 840, in evaluate
    rval = self.fn(pyll_rval)
  File "/home/User1/Optimizer/optimization2.py", line 184, in create_model
    x_train, x_test = x[train_indices], x[val_indices]
MemoryError

Answer 1

我花了几天时间才弄清楚这个问题，所以我会回答我自己的问题，以节省遇到此问题的人的时间。

通常，当使用 Hyperopt for Keras 时，建议 create_model 函数的返回如下所示:

return {'loss': -acc, 'status': STATUS_OK, 'model': model}

但是在进行多次评估的大型模型中，您不想返回每个模型并将其保存在内存中，您需要的只是给出最低损失的一组超参数

通过简单地从返回的字典中删除模型，就解决了每次评估时内存增加的问题。

return {'loss': -acc, 'status': STATUS_OK}