Solved by ptrblck in post #2 
              
              
                If you are using dropout as a module, you could manipulate the .p attribute after your specified number of epochs: 
drop = nn.Dropout(p=0.5)

x = torch.randn(10)
out = drop(x)
print((out==0.).sum())
> tensor(5)

drop.p = 0.2
out = drop(x)
print((out==0.).sum())
> tensor(1)

In your case, the manipul…