CS231n-2020-spring-assignment-solution

TODO:

• Assignment #1 (Finished 2020/9/12)
• Assignment #2 (Finished 2020/9/27)
• Assignment #3 (Finished 2020/10/8)
• Notes
• HyperQuest (try it HERE)

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Important Notice

• DO NOT use %%timeit when use CUDA in pytorch!!! If you use it, the program will run for several times uselessly.
• For numpy axis: Axis or axes along which an operation is performed. eg: np.sum([[0, 1], [0, 5]], axis=1) will get array([1, 5]). (To sum up, axis on which means which axis will disappear)

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HyperQuest

HyperQuest mimics the hyper parameter tuning app from Stanford University, CS231n. HyperQuest is a web-app designed for beginners in Machine Learning to easily get a proper intuition for choosing the right hyper parameters. This is initially an extremely daunting task because not having proper hyper parameters leads to the models breaking down.

Try HyperQuest HERE!

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Interesting Examples

KNN

Visualize the distance matrix: each row is a single test example and its distances to training examples:

Explain:

As you can see above, the background of those images which is similar to many other images is black. Because there are many other images that have a black color on its margin, while the white part of those images is rarely seen in other images, which will cause a large difference, so will generate the white bar.

Find best k for kNN:

SVM v.s. Softmax

SVM

Softmax

It can be found that softmax is much smoother than SVM, which means it’s more generalized.

2-layer net with different dtype

With dtype np.single, visualize the weights of the first layer:

With dtype np.float64, visualize the weights of the first layer:

As you can see, there is no difference.

Style Transfer GIFs

By watching the first iteration we can find that there is no difference between starting with a random image or starting with the original image.