The result data is not correct

[RengarWang]

hi, i have run RNN-example.ipynb,but the data is not correct.
my result is
============ Recall: 0.06080812748658777
============ Precision: 0.7873688147161255
============ Accuracy: 0.29049305213046556
============ F1 Score: 0.11289725264136873
============ Relative error in total energy: 0.7858544224150322
============ Mean absolute error(in Watts): 19.859596349905722

but your result is
============ Recall: 0.997835349341
============ Precision: 0.742378777703
============ Accuracy: 0.741308963402
============ F1 Score: 0.851357054837
============ Relative error in total energy: 0.871686427835
============ Mean absolute error(in Watts): 32.2338755931

I don't know what I did wrong.I ran it all according to your code.

[RengarWang]

By the way, I only use low_freq to redd.h5.

[Ben-Yee]

By the way, I only use low_freq to redd.h5.

Did you find out what`s wrong? I got the same issue

[Ben-Yee]

I think i know why.These four metrics is for classification.And frige's metadata didn't contain its on_power_threshold ,then it will be set to 10W by default.

[DuoCaiXie]

Hello, do you know how to solve this problem

[DuoCaiXie]

I think i know why.These four metrics is for classification.And frige's metadata didn't contain its on_power_threshold ,then it will be set to 10W by default.

Hello, do you know how to solve this problem

[Ben-Yee]

I think i know why.These four metrics is for classification.And frige's metadata didn't contain its on_power_threshold ,then it will be set to 10W by default.

Hello, do you know how to solve this problem

放弃了，代码有点久远了...你把Fridge的阈值设置为50W，应该会对。