Lesson 2 In-Class Discussion ✅

Part 1 (2019)
596

answered here :slight_smile:

597

Hope this helps

https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/

598

Yes. All non apple and non orange images in training set to be classified as Other.

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599

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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600

Rename then with the most correct labels from a human perspective.

601

Michael, Have you seen this ?

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602

As Andrew Ng has said, it is like worrying about over population on Mars.

603

I removed that one since it’s not the approach we recommend.

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604

Please read the etiquette guide in the FAQ.

605

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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606

This may help.

https://arxiv.org/abs/1809.01442

607

You will get lower error when using higher res images.

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608

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.

609

In the video, Jeremy has explained this very clearly

610

Thanks I’ll be sure to check it out. I am using the same dataset so this could be really helpful!

611

@miwojc has kindly shared this Jeremy paper on another forum. It may help.

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612

Sorry. I did not see that you had already shared the same resource. Thanks.

613

If your training dataset images are of PNG format, they need to be converted to JPG.

614

This should work:

import PIL
for fn in PATH.glob('*/*.jpg'):
    im = PIL.Image.open(fn)
    if im.mode != 'RGB': im.convert('RGB').save(fn)
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615

This may help.

1 Like