Hi,
I want to implement dropout for sparse input. I know that the implementation in tensorflow is as follow, but I don’t know if there is anyway for implementation in pytorch (the source of the following code is here)

Hi
I am doing the same thing currently
here is my solution

class SparseDropout(torch.nn.Module):
def __init__(self, dprob=0.5):
super(SparseDropout, self).__init__()
# dprob is ratio of dropout
# convert to keep probability
self.kprob=1-dprob
def forward(self, x):
mask=((torch.rand(x._values().size())+(self.kprob)).floor()).type(torch.bool)
rc=x._indices()[:,mask]
val=x._values()[mask]*(1.0/self.kprob)
return torch.sparse.FloatTensor(rc, val)

Hi KuanS, thanks for the solution. Currently I am working on solving the same function. Sadly, the proposal is not working as a simple function. Do you have any idea on how to access the rc=x.indices()[:,mask] when the x features is not a sparse matrix? In the case of transforming it to a sparse matrix then the indices are in 2nd dimension. Moreover, both functionalities x._values() and x._indices() must be deprecated because an error appears.Thank you in advance