OMG, L_layer_model classified me as cat

:smile:

Did you have a question?

Yeah sure. I wonder about prediction result of my model. It predicts wrong label for my own photo image.

There’s nothing wrong with a NN predicting a wrong label for an image it hasn’t seen before i.e. during training. You’ll learn more on how the distribution of data influences model performance in courses 2 and 3. So, please keep this question in mind till you finish course 3.

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ok, I see. Thank you.

Yes, the dataset we have here is very small for a problem as complex as this, so we don’t get a model that “generalizes” very well at all. They did it that way because of the limitations of the online notebook environment here to keep the cpu usage of the training down to a tolerable level.

In fact, you can turn the question around: why does it even work as well as it does with a mere 209 training samples? It turns out the dataset if very carefully curated to get halfway decent results. Here’s a thread which runs some experiments to show that.

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Thank you Sir, I got nice intuition.