Comments (7)
Hello, firstly I would like to thank you for the implementation. I've been trying to use your implementation and I've noticed a big difference, during training when evaluating (fx every 10 steps) you're only reporting the mae (over all 12 time stamps), while DCRNN reports mae/mape/rmse for every time stamp. I would be interested to see those numbers during training, or at least at the end of the training so I can compare it with other models. Do you have any suggestions how I could do this?
you can rewrite evalute() in dcrnn_supervisor. I could provide the code which outputs this three metrics.
ps: The distance between nodes (the .csv file) should be float dtype, or you will have dtype mismatch problem.
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@razvanc92 that is a reasonable request - @AprLie thanks for your help. Would @razvanc92 and @AprLie be willing to send in a PR?
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I would like to use that too if that is possible, @AprLie @chnsh could you forward that code? I'm not sure how though.
Thanks in advance
from dcrnn_pytorch.
Hello, firstly I would like to thank you for the implementation. I've been trying to use your implementation and I've noticed a big difference, during training when evaluating (fx every 10 steps) you're only reporting the mae (over all 12 time stamps), while DCRNN reports mae/mape/rmse for every time stamp. I would be interested to see those numbers during training, or at least at the end of the training so I can compare it with other models. Do you have any suggestions how I could do this?
you can rewrite evalute() in dcrnn_supervisor. I could provide the code which outputs this three metrics. ps: The distance between nodes (the .csv file) should be float dtype, or you will have dtype mismatch problem.
I have met with the same problem, could you also provide me with the same code? thank you!
from dcrnn_pytorch.
I would like to use that too if that is possible, @AprLie @chnsh could you forward that code? I'm not sure how though. Thanks in advance
Have you got the answer? Could you please provide me with one? Thank you!
from dcrnn_pytorch.
I would like to use that too if that is possible, @AprLie @chnsh could you forward that code? I'm not sure how though. Thanks in advance
Have you got the answer? Could you please provide me with one? Thank you!
I'm running into this issue as well.Have you got the answer? Could you please provide me with one? Thank you!
from dcrnn_pytorch.
I would like to use that too if that is possible, @AprLie @chnsh could you forward that code? I'm not sure how though. Thanks in advance
Have you got the answer? Could you please provide me with one? Thank you!
I'm running into this issue as well.Have you got the answer? Could you please provide me with one? Thank you!
Have you got the answer? Could you please provide me with one? Thank you!
from dcrnn_pytorch.
Related Issues (20)
- PEMS-BAY HOT 4
- A problem in gcrnn_train_pytorch
- Double sigmoid inside RNNCell HOT 4
- About the function "_setup_graph()” HOT 1
- The formulation of Diffusion Convolution is wrong HOT 1
- About Missing Data (0 values)
- Test error calculation is not correct HOT 1
- Why are some speed data negative?
- Node connectivity or sensors interaction
- Figure generation HOT 2
- How where the figures generated? HOT 1
- Run the Pre-trained Model on METR-LA
- it doesn't seem to improve in the test run
- Problem with curriculum learning?
- Lr scheduler resets when resuming model HOT 1
- CUDA out of memory error HOT 1
- Using no convolution better than DCRNN paper result? HOT 1
- Where is the resulting graph of the program running HOT 5
- About the performance improvement compared with Tensorflow implementation HOT 3
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