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。
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。