CNTK运行时错误

时间:2017-10-07 16:13:47

标签: python machine-learning deep-learning lstm cntk

我在cntk中尝试一个简单的lstm网络,我收到以下错误:

RuntimeError                              Traceback (most recent call last)
<ipython-input-58-d0a0e4f580aa> in <module>()
      6         trainer.train_minibatch({x: x1, l: y1})
      7     if epoch % (EPOCHS / 10) == 0:
----> 8         training_loss = trainer.previous_minibatch_loss_average
      9         loss_summary.append(training_loss)
     10         print("epoch: {}, loss: {:.5f}".format(epoch, training_loss))

C:\Program Files\Anaconda3\envs\python2\lib\site-packages\cntk\train\trainer.pyc in previous_minibatch_loss_average(self)
    285         The average training loss per sample for the last minibatch trained
    286         '''
--> 287         return super(Trainer, self).previous_minibatch_loss_average()
    288 
    289     @property

C:\Program Files\Anaconda3\envs\python2\lib\site-packages\cntk\cntk_py.pyc in previous_minibatch_loss_average(self)
   2516 
   2517     def previous_minibatch_loss_average(self):
-> 2518         return _cntk_py.Trainer_previous_minibatch_loss_average(self)
   2519 
   2520     def previous_minibatch_evaluation_average(self):

RuntimeError: There was no preceeding call to TrainMinibatch or the minibatch was empty.

[CALL STACK]
    > CNTK::Trainer::  PreviousMinibatchLossAverage
    - 00007FFFA932A5F6 (SymFromAddr() error: Attempt to access invalid address.)
    - PyCFunction_Call
    - PyEval_GetGlobals
    - PyEval_EvalFrameEx
    - PyEval_GetFuncDesc
    - PyEval_GetGlobals
    - PyEval_EvalFrameEx
    - PyEval_EvalCodeEx
    - PyFunction_SetClosure
    - PyObject_Call (x2)
    - PyObject_CallFunction
    - PyObject_GenericGetAttrWithDict
    - PyType_Lookup
    - PyEval_EvalFrameEx

相关代码是:

# train
loss_summary = []
start = time.time()
for epoch in range(0, EPOCHS):
    for x1, y1 in next_batch(x_train, y_train):
        trainer.train_minibatch({x: x1, l: y1})
    if epoch % (EPOCHS / 10) == 0:
        training_loss = trainer.previous_minibatch_loss_average
        loss_summary.append(training_loss)
        print("epoch: {}, loss: {:.5f}".format(epoch, training_loss))

现在,我被困在这几个小时,现在无法理解发生了什么。我正在关注https://notebooks.azure.com/cntk/libraries/tutorials/html/CNTK_106A_LSTM_Timeseries_with_Simulated_Data.ipynb的教程,搜索谷歌也没有帮助。

感谢您的帮助。

1 个答案:

答案 0 :(得分:1)

只是一个想法:可能是,你的for(next minibatch)循环永远不会被执行?

我会尝试使用pdb调试它。只需在您的jupyter单元格顶部import pdb,然后在pdb.set_trace()循环之前添加for x1, y1 ..。运行单元格。您可以使用步骤进入方法或使用next(n)继续前进。这可能有助于您分析跟踪,您可以使用pdb中的print来证明变量。