Comments (5)
Sorry for this problem.
I want to tell you the following.
(1) I downloaded and ran the uploaded code. The interpretability_analysis.py you pointed out runs normally and gets the expected results.
(2) Based on (1) and your bug, I think it may be caused by the version problems of keras and TF. Our versions are keras 2.2.4 and tensorflow gpu 1.15.0.
(3) If you have any questions, please do not hesitate to contact me and provide more details.
from ress-paper-2022.09-remaining-useful-life-prediction-by-tafcn.
I have the same problem as above, my tensorflow and keras versions are the same as yours, is it because of my 3060 graphics card and 11.6 CUDA?
from ress-paper-2022.09-remaining-useful-life-prediction-by-tafcn.
I have the same problem as above, my tensorflow and keras versions are the same as yours, is it because of my 3060 graphics card and 11.6 CUDA?
I don't think this is caused by the cuda version. It may be caused by the Tensorflow version. The official version corresponding to Keras2.2.4 is Tensorflow 1.13.0. You can try to reduce the version to 1.13.0. However, this involves the relationship between the cuda version supported by Tensorflow 1.13.0 and the cuda version supported by your GTX3060. I suggest that if you try to do this, you can clone your original environment first, and then operate in the cloned environment. In this way, even if there is no success, it will not damage your original environment. If you can provide more detailed error information, maybe I can provide more help.
from ress-paper-2022.09-remaining-useful-life-prediction-by-tafcn.
from ress-paper-2022.09-remaining-useful-life-prediction-by-tafcn.
You're welcome. I'm glad to be here to help.
from ress-paper-2022.09-remaining-useful-life-prediction-by-tafcn.
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