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Precision, Recall and F1 Metrics Removed #5794
Yes, it was intentional. See https://github.com/fchollet/keras/wiki/Keras-2.0-release-notes
Basically these are all global metrics that were approximated batch-wise, which is more misleading than helpful. This was mentioned in the docs but it's much cleaner to remove them altogether. It was a mistake to merge them in the first place.…
On 19 March 2017 at 11:28, Jacek Karwowski ***@***.***> wrote: What was the reason behind removing them? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#5794 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/AArWb3iZAJPZC-FrViC7eESFBO4ceX2yks5rnXPPgaJpZM4MeOw0> .
Apr 21, 2017
referenced this issue
May 4, 2017
@karimpedia What I did was creating a Callback and calculate them on the end of each epoch with the validation data.
And implement a function that calculates the f1 score or instead use Scikit Learn's Fscore function.
@imanoluria, i was using your code (thanks for posting it BTW) with the sklearn's F1 in my model. the model has 3 inputs and one output.
the fit portion:
my input to the validation is (first dim is #samples):
my model's output is a one hot 25 category vector...
can you see what's the problem? thanks a lot!
@fchollet - will there be a future innate API for metrics in check pointer / similar method?
Hi @basque21, I tried to extend your example to work with
The error I got was
My code snippet is below:
Em sex, 20 de abr de 2018 às 2:11 AM, Daniel Klevebring < email@example.com> escreveu:
@ShiangYong <https://github.com/ShiangYong> Did you set validation_data when you fit()? — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub <#5794 (comment)>, or mute the thread <https://github.com/notifications/unsubscribe-auth/Ai62Hu897l4u2QQZEvkx3QTfGiAu-HhCks5tqW3mgaJpZM4MeOw0> .
If someone needs to implement it I suggest this work-around:
Something like this: