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I try to reproduce the experiments (example 4 in official KAN). With official KAN, I get the results as below (Ground-truth is at the top, and the predication is at the bottom):
But with the efficient-kan, I get the results as below:
It shows that previous peak will be higher when learning new peak.
The official model is create by: "model = KAN(width=[1, 1], grid=200, k=3, noise_scale=0.1, bias_trainable=False, sp_trainable=False, sb_trainable=False)"
The efficient-kan model is created by: "model = KAN([1, 1], grid_size=200)"
It seems to be the same except for "bias_trainable=False, sp_trainable=False, sb_trainable=False".
The text was updated successfully, but these errors were encountered:
Hi ASCIIJK! I was very interested in your comment and tried to play a bit with the EfficientKAN code.
When I replicated the with EfficientKAN, I got results similar to yours.
However, as you said, the sb_trainable and sp_trainable parameters seem to be missing in the constructor, so I decided to add them, just like PyKAN does.
As you can see, I use them to disable the trainability of the base_weight and splin_weight parameters.
I try to reproduce the experiments (example 4 in official KAN). With official KAN, I get the results as below (Ground-truth is at the top, and the predication is at the bottom):
![Ground_task5](https://private-user-images.githubusercontent.com/150874756/332998176-0ee04481-185a-4046-8b3d-535018a58d31.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjM4NzYzNTIsIm5iZiI6MTcyMzg3NjA1MiwicGF0aCI6Ii8xNTA4NzQ3NTYvMzMyOTk4MTc2LTBlZTA0NDgxLTE4NWEtNDA0Ni04YjNkLTUzNTAxOGE1OGQzMS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODE3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgxN1QwNjI3MzJaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1kYThiZTczNmY1ODdlM2Y3MTMwNzU4N2IxYzAxNjVkY2QyNTYzM2U2OTZjM2IyZmMzMWMyMWVkMmUyNzNiMDFhJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.FtygtrRePzMFBR1-LqDYkyN7KoeiyD-adEHAe8w5KCs)
![Pred_task4](https://private-user-images.githubusercontent.com/150874756/332998182-e132e316-e1f0-4c13-b002-cd28547eb5a2.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjM4NzYzNTIsIm5iZiI6MTcyMzg3NjA1MiwicGF0aCI6Ii8xNTA4NzQ3NTYvMzMyOTk4MTgyLWUxMzJlMzE2LWUxZjAtNGMxMy1iMDAyLWNkMjg1NDdlYjVhMi5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODE3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgxN1QwNjI3MzJaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT03OWM3ZmJmODIxYjI2MTM5OWFmZTA1NWRkNzU1Yzk5ZDdlMGE2ZTBmOWJlOGNmMGQwZDg3MGU1NzdkMjViN2Q5JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.79RADOKIoWhiLb34xPZ0qSMjrDyO0XCQ-ElUrGkDmYs)
But with the efficient-kan, I get the results as below:
![Ground_task5](https://private-user-images.githubusercontent.com/150874756/332998563-192a02fa-9084-4bed-966e-7bb2a4ab8121.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjM4NzYzNTIsIm5iZiI6MTcyMzg3NjA1MiwicGF0aCI6Ii8xNTA4NzQ3NTYvMzMyOTk4NTYzLTE5MmEwMmZhLTkwODQtNGJlZC05NjZlLTdiYjJhNGFiODEyMS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODE3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgxN1QwNjI3MzJaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1mMTExODBiMzhlZjRiZjE5OTg3MGJjYjE5MzU4NWEzODcyZTZkNzY5MGRhNjEwYjNmYjVhMmVlYzlkMTBiM2NmJlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.rp5m420TfaovcfzXaXQy5N-eWlK49KN5yQnpOw5JHPw)
![Pred_task4](https://private-user-images.githubusercontent.com/150874756/332998567-3023b9e7-ec64-4135-9bd6-9ae4ac97fdb9.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJnaXRodWIuY29tIiwiYXVkIjoicmF3LmdpdGh1YnVzZXJjb250ZW50LmNvbSIsImtleSI6ImtleTUiLCJleHAiOjE3MjM4NzYzNTIsIm5iZiI6MTcyMzg3NjA1MiwicGF0aCI6Ii8xNTA4NzQ3NTYvMzMyOTk4NTY3LTMwMjNiOWU3LWVjNjQtNDEzNS05YmQ2LTlhZTRhYzk3ZmRiOS5wbmc_WC1BbXotQWxnb3JpdGhtPUFXUzQtSE1BQy1TSEEyNTYmWC1BbXotQ3JlZGVudGlhbD1BS0lBVkNPRFlMU0E1M1BRSzRaQSUyRjIwMjQwODE3JTJGdXMtZWFzdC0xJTJGczMlMkZhd3M0X3JlcXVlc3QmWC1BbXotRGF0ZT0yMDI0MDgxN1QwNjI3MzJaJlgtQW16LUV4cGlyZXM9MzAwJlgtQW16LVNpZ25hdHVyZT1jZTNjMTAxZTlhYjA3OWQ0MWMwMDg5MjM1OWVhZWNjNTAwNDU2ODY4ZDQzNmVjYzBjNDA1MjU5MWM5ZGUzNGU3JlgtQW16LVNpZ25lZEhlYWRlcnM9aG9zdCZhY3Rvcl9pZD0wJmtleV9pZD0wJnJlcG9faWQ9MCJ9.dAbYEVF9Lp5qAM8FoCkc0yKDDfJA0-tfuG0HODUVQdk)
It shows that previous peak will be higher when learning new peak.
The official model is create by: "model = KAN(width=[1, 1], grid=200, k=3, noise_scale=0.1, bias_trainable=False, sp_trainable=False, sb_trainable=False)"
The efficient-kan model is created by: "model = KAN([1, 1], grid_size=200)"
It seems to be the same except for "bias_trainable=False, sp_trainable=False, sb_trainable=False".
The text was updated successfully, but these errors were encountered: