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cs501r_f2016:lab4 [2016/09/12 16:30] wingated |
cs501r_f2016:lab4 [2021/06/30 23:42] (current) |
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step_size = 0.1 | step_size = 0.1 | ||
for i in range(0,NUM_EPOCHS): | for i in range(0,NUM_EPOCHS): | ||
- | loss_function_value = loss_function( W ) | + | loss_function_value_t = loss_function( W ) |
- | grad = grad_f( W ) | + | grad_t = grad_f( W ) |
- | W = W - step_size * grad | + | W = W - step_size * grad_t |
</code> | </code> | ||
Line 84: | Line 84: | ||
You should plot both the loss function and the classification accuracy. | You should plot both the loss function and the classification accuracy. | ||
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+ | ---- | ||
+ | ====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? | ||
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+ | 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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