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.
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No it’s not a random direction, but the direction of the gradients. The stochastic refers to the fact we draw batches randomly.
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Rename then with the most correct labels from a human perspective.
Michael, Have you seen this ?
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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.
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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.
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This may help.
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!
@miwojc has kindly shared this Jeremy paper on another forum. It may help.
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Sorry. I did not see that you had already shared the same resource. Thanks.