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courses:rg:2012:distributed-perceptron [2012/12/16 15:23] machacek vytvořeno |
courses:rg:2012:distributed-perceptron [2012/12/16 15:25] machacek |
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===== Questions ===== | ===== Questions ===== | ||
- | 1) What are the weights w after running the stand perceptron training on the data bellow? The standard perceptron does only binary classification, | + | ==== Question |
+ | What are the weights w after running the stand perceptron training on the data bellow? The standard perceptron does only binary classification, | ||
(unlike multi-class in Fig. 1) and its algorithm can be defined as follows: | (unlike multi-class in Fig. 1) and its algorithm can be defined as follows: | ||
- | StandardPerc(N, | + | |
- | w(0) = 0; k=0 | + | w(0) = 0; k=0 |
- | for n:1..N | + | for n:1..N |
- | for t:1..|T| | + | for t:1..|T| |
- | y' = {if w(k) \dot x_t > 0.5: 1; else: 0} // 1st difference to Figure1 | + | y' = {if w(k) \dot x_t > 0.5: 1; else: 0} // 1st difference to Figure1 |
- | if (y_t != y') | + | if (y_t != y') |
- | w(k+1) = w(k) + (y_t - y' | + | w(k+1) = w(k) + (y_t - y' |
- | k++ | + | k++ |
- | return w(k) | + | return w(k) |
Let us set learning_rate=0.3, | Let us set learning_rate=0.3, | ||
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w = [?, ?] | w = [?, ?] | ||
- | 2) Imagine you want to solve the learning problem above by the multi-class perceptron algorithm (Figure 1). First you will need to figure out what the function f is like, there are many good possibilities. Will you get different w? Can you think of one that will yield the same w given all the other variables are the same? | + | ==== Question |
+ | Imagine you want to solve the learning problem above by the multi-class perceptron algorithm (Figure 1). First you will need to figure out what the function f is like, there are many good possibilities. Will you get different w? Can you think of one that will yield the same w given all the other variables are the same? | ||
f = ? | f = ? | ||
w = [?, ?] | w = [?, ?] | ||
- | 3) In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)? | + | ==== Question |
+ | In figure 4, why do you think that the F-measure for Regular Perceptron (first column) learned by the Serial (All Data) algorithm is worse than the Parallel (Iterative Parametere Mix)? | ||
- | 4) Imagine you are an engineer at Google (lots of zeros on your bank account, unlimited food, etc.. ;)) and want to learn your " | + | ==== Question |
+ | Imagine you are an engineer at Google (lots of zeros on your bank account, unlimited food, etc.. ;)) and want to learn your " | ||
Your eco-Manager is a hobby mathematician and he will not give power to your machines unless he sees a nice formula that theoretically justifies the optimality of N with respect to energy wasted by running them (and with respect to sunshine wasted to produce the electricity to be wasted by them). Fortunately, | Your eco-Manager is a hobby mathematician and he will not give power to your machines unless he sees a nice formula that theoretically justifies the optimality of N with respect to energy wasted by running them (and with respect to sunshine wasted to produce the electricity to be wasted by them). Fortunately, |