cs501r_f2016:lab4
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| You should plot both the loss function and the classification accuracy. | You should plot both the loss function and the classification accuracy. | ||
| + | |||
| + | ---- | ||
| + | ====Extra awesomeness:==== | ||
| + | |||
| + | Now that you have a powerful automatic differentiation engine at your command, how hard would it be to take our simple linear scoring function, and change it to something more complex? For example, could you swap in a simple 2-layer neural network? Or something else entirely? | ||
| + | |||
| + | Since this lab is so short, you should have some extra time to play around. I invite you to try something! | ||
| ---- | ---- | ||
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| You may want to test the automatic differentiation package on known test cases - for example, f(x)=x^2. | You may want to test the automatic differentiation package on known test cases - for example, f(x)=x^2. | ||
| - | If you get an error like ''FloatNode has no attribute named 'exp' '', make sure you're using the autograd version of numpy! | + | If your code seems to take forever to get running, seems to stall right at the beginning, or you get an error like ''FloatNode has no attribute named 'exp' '', make sure you're using the autograd version of numpy! |
| <code python> | <code python> | ||
| import autograd.numpy as np | import autograd.numpy as np | ||
| </code> | </code> | ||
cs501r_f2016/lab4.txt ยท Last modified: 2021/06/30 23:42 (external edit)
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