Hi, Thank you now I really understand what's happening. I started to take a look at the data files in hpxml and it seems that they only contain the inputs, not the optimal policies. So I assume that you find the optimal training policies on training data in the binary_regression_model.hpp file. Is that correct? Since I want to train my data on python, I think I will need to generate a file of the optimal policies for the examples (output.dat). And then I could train in python using (input.dat and output.dat) what do you think?
Gabriel Zahra Khatami <[email protected]> a écrit : > 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 _______________________________________________ hpx-users mailing list [email protected] https://mail.cct.lsu.edu/mailman/listinfo/hpx-users
