Hi Gabriel, About your cat example, you understand machine learning concepts correctly and you are in a right direction! About your question, when collecting training data, we know which policy is the optimum one for the that hpx loop for example. But how we know that? Actually we run that test loop twice, first using sequential policy and in the second run using parallel policy, and by comparing their execution time we can find out that which policy worked better for that specific loop. So we collected training data like this! For testing, we use those training data to predict the policy of completely new hpx loop. Imagine that after executing our learning model, it’s policy was chosen to be parallel for that loop. To be sure that if our model was right, we run that new loop again by setting it’s policy to be sequential. That’s how we determine the accuracy of our model. If you look at the comparison results in our paper, you will find out that we compare the results of our learning model with the one that their policies ( our chunk sizes or prefetching distances) was set manually with different candidates.
Thanks, Zahra On Tue, Feb 27, 2018 at 7:39 PM Gabriel Laberge <[email protected]> wrote: > Hi, > I think the last time I asked you this question I wasn't very clear so > will clarify. This could be due to the fact that i'm new to machine > learning. > > From my understanding, if you want to train a binary logistic > regression, you need a matrix Xdata which represent training features > and you need Ydata which is a vector representing the expected output > (0 or 1) for each of the training examples. For example, if I want to > train a regression to identify cat pictures, I need Xdata which > represents a set of 'm' cat pictures and Ydata which is a vector of 0 > and 1 if the corresponding picture is a cat. > > I assume the same logic applies with a logistic regression used to > find the execution policy (seq or par) > Xdata represents the inputs features for the examples and Ydata > represent the expected output (seq or par) for each of the training > examples. > > From our previous discussion, It seemed like you told me that the > data was only constituted of input features (Xdata) which would mean > that we don't know the optimal policy on our data set. If this is the > case, I simply don't understand how you can train a regression. > > My analogy would be that you can't train a regression to recognize cat > pictures if you don't tell it which one are carts and which aren't. > > Could you tell me if I'm wrong? > > If I'm right then I wonder how do you find the optimal policy for each > of the training examples. > > Thank you very much. > Gabriel. > > > -- Best Regards, Zahra Khatami | PhD Student Center for Computation & Technology (CCT) School of Electrical Engineering & Computer Science Louisiana State University 2027 Digital Media Center (DMC) Baton Rouge, LA 70803
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