Ai Dreams Forum
Member's Experiments & Projects => AI Programming => Topic started by: Kaeldric on June 03, 2018, 03:01:19 pm
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Hello.
I'm studying the GoogLeNet by implementing a personal implementation in Tensorflow.
Last day I introduced a bug and changed the structure of the inception block a little.
Before that the algorithm run very poorly ... actually it never learned anything. :(
After the modification (that changed the structure from the official paper one) the network start working as a charm 8) (with a little overfitting, but this is another story)!
You can find the two different structures here ---> https://imgur.com/a/vQiy0Te
where the bad one is the inception block described in the original paper and the good one is the modified version.
Is there a way to know why this modification leaded to a such big performance improvement? ???
Thank you. :)
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Hello.
I'm studying the GoogLeNet by implementing a personal implementation in Tensorflow.
Last day I introduced a bug and changed the structure of the inception block a little.
Before that the algorithm run very poorly ... actually it never learned anything. :(
After the modification (that changed the structure from the official paper one) the network start working as a charm 8) (with a little overfitting, but this is another story)!
You can find the two different structures here ---> https://imgur.com/a/vQiy0Te
where the bad one is the inception block described in the original paper and the good one is the modified version.
Is there a way to know why this modification leaded to a such big performance improvement? ???
Thank you. :)
In this case the picture does not tell a thousand tales .... the pictures do not have enough information to make any sound judgement...at a glance its obvious that optimising the structure has made a difference although immeasurable but the information contained in the picture
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In short .... there is no way to know. :)
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In short .... there is no way to know. :)
So a futile question