how can i use fitrgp() function for gpuarray?
3 vues (au cours des 30 derniers jours)
regression_data=table(var1, var2, response);
gprMdl = fitrgp(regression_data,'response');
i want to use gpu but i get a error "The value of X must not be a gpuArray"
how can i use the qpu?
or how can i reduce train time?
Yash le 28 Mar 2023
The error message you're seeing suggests that the fitrgp function does not support gpuArray inputs. To work around this, you can convert your data to single precision before passing it to the fitrgp function. This will allow the function to use the GPU for computation while training the model.
Here's an updated version of your code that should work with GPU acceleration:
var1 = gpuArray(single(data1));
var2 = gpuArray(single(data2));
response = gpuArray(single(data3));
regression_data = table(var1, var2, response);
gprMdl = fitrgp(regression_data, 'response', 'KernelFunction', 'squaredexponential', 'FitMethod', 'exact',...
Note that I've also added some additional parameters to the fitrgp function to control the choice of kernel function, the fitting method, and the prediction method. You may want to experiment with these parameters to see if they affect the training time or the accuracy of the resulting model.
In terms of reducing training time, you might also consider reducing the size of your training data set, or split your training data into multiple batches, or using a more efficient algorithm for model fitting. Depending on the specifics of your problem, other machine learning algorithms like linear regression or support vector machines might be faster than Gaussian process regression while still achieving good accuracy.
I hope this helps :)