Comments (6)
shawnwun
commented on May 27, 2024
Theano is very slow in compiling computational graphs for this model because the architecture is non-trivial. You can put theano flags optimizer=fast_compile to run it. The run-time is relatively faster because both the model and dataset are small.
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robotzheng
commented on May 27, 2024
THEANO_FLAGS=optimizer=fast_compile,device=gpu,floatX=float32 python nndial.py -config config/tracker.cfg -mode train
also：
including informable tracker loss ...
including informable tracker loss ...
including informable tracker loss ...
including requestable tracker loss ...
including requestable tracker loss ...
including requestable tracker loss ...
including requestable tracker loss ...
including requestable tracker loss ...
including requestable tracker loss ...
including OfferChange tracker loss ...
gradient w.r.t inftrk
gradient w.r.t reqtrk
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robotzheng
commented on May 27, 2024
I use centos 7.5 K40
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robotzheng
commented on May 27, 2024
start work：
number of parameters : 1103292
number of training parameters : 1096842
start network training ...
Finishing 25 dialog in epoch 1
thanks to shawnwun
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xiw54
commented on May 27, 2024
Found the example_run, sorry!
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hailiang-wang
commented on May 27, 2024
I came to the same problem.
The program starts to train by suppling THEANO_FLAGS="optimizer=fast_compile".
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Related Issues (11)
- Small
- Small implementation detail
- Test results are different from the paper.
- ImportError: No module named hpc_submit HOT 1
- belief tracker
- deadcode in the DataReader.py file
- hey shawnwun, how can we run it on our own dataset? HOT 1
- Possible bug in DataReader.py when loading vocab from dialogues
- the process stoped HOT 2
- Training the belief tracker:**NameError: name 'self' is not defined**
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