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Overfitting #53

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ayushpatnaikgit opened this issue Oct 18, 2023 · 0 comments

ayushpatnaikgit commented Oct 18, 2023

Hi,
Any tips to deal with overfitting? Can we add dropouts?
For example, in

torchcde/example/time_series_classification.py

Lines 37 to 42 in 9ff6aba

    
           def forward(self, t, z): 
        
               # z has shape (batch, hidden_channels) 
        
               z = self.linear1(z) 
        
               z = z.relu() 
        
               z = self.linear2(z) 
        
               ######################

Can we add a dropout somewhere?

I am new to NeuralCDEs, apologies if I am missing anything obvious.

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