28template<
typename Real_t>
35 for (
size_t i = 0; i <
m; i++) {
36 for (
size_t j = 0; j <
n; j++) {
38 if (
r >= dropoutProbability) {
41 B(i,j) /= dropoutProbability;
R__EXTERN TRandom * gRandom
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 ...
virtual Double_t Uniform(Double_t x1=1)
Returns a uniform deviate on the interval (0, x1).
create variable transformations