28template<
typename Real_t>
37 for (
size_t i = 0; i <
m; i++) {
38 for (
size_t j = 0; j <
n; j++) {
40 if (
r >= dropoutProbability) {
43 B(i,j) /= dropoutProbability;
static void DropoutForward(Tensor_t &A, TDescriptors *descriptors, TWorkspace *workspace, Scalar_t p)
Apply dropout with activation probability p to the given matrix A and scale the result by reciprocal ...
This is the base class for the ROOT Random number generators.
create variable transformations