Hi,
After running this in the terminal gcloud compute ssh --zone=ZONE jupyter@INSTANCE_NAME -- -L 8080:localhost:8080
I couldn’t find course-v3 folder. I think i’m doing something wrong, Can anyone please guide me.
Thanks.
Hi,
After running this in the terminal gcloud compute ssh --zone=ZONE jupyter@INSTANCE_NAME -- -L 8080:localhost:8080
I couldn’t find course-v3 folder. I think i’m doing something wrong, Can anyone please guide me.
Thanks.
Solved.
I’m getting the following plot after running learn.lr_find()
. I’ve chosen the lr
range that corresponds to the middle drop. I can see that the valid_loss
is beginning to increase on the 6th epoch, but what can be the reason for a constant error_rate
? Let me know if more context is required.
P.S. This is the case when lr
is in range (1e-6,4e-6)
too.
Updating the fastai library fixed it
answered here
Hope this helps
Yes. All non apple and non orange images in training set to be classified as Other.
No it’s not a random direction, but the direction of the gradients. The stochastic refers to the fact we draw batches randomly.
Rename then with the most correct labels from a human perspective.
Michael, Have you seen this ?
As Andrew Ng has said, it is like worrying about over population on Mars.
I removed that one since it’s not the approach we recommend.
Please read the etiquette guide in the FAQ.
I’m facing the same issue and agree that replacing valid_ds
with train_ds
is not ok. Looking at the code of the ClassificationInterpretation class, it seems that it only works with the validation set. I guess would be great that it would receive a parameter to select the dataset.
That’s OK. Autoencoders are too noisy anyway, which means the NN learns the quirks of the autoencoder, rather than what really makes an image a member of a class.
In the video, Jeremy has explained this very clearly
Thanks I’ll be sure to check it out. I am using the same dataset so this could be really helpful!