Getting NaN values immediately after first backprop


(Satwik Kondamudi) #1

Hi, I’m getting NaN values after the first backprop. The value of y_kld is of order 1e-8. Is this the issue? In the below code q_y is an intermediate output in my network. Kindly let me know how to fix this issue. (Self.class_diff is a numpy constant)

          x_true1 = x_true1.to(self.device)
          x_recon, mu, logvar, z,cat_logit = self.VAE(x_true1)                 
          vae_recon_loss = recon_loss(x_true1, x_recon)
          q_y= F.softmax(cat_logit,dim=1)
          log_q_y=torch.log(q_y)
          vae_kld = kl_divergence(mu, logvar)  
          y_kld= (torch.sum(torch.mul(q_y,(log_q_y-self.class_diff)),1)).mean()
          D_z = self.D(z)
          vae_tc_loss = (D_z[:, :1] - D_z[:, 1:]).mean()
          Vae_loss = vae_recon_loss + vae_kld + self.gamma*vae_tc_loss +y_kld

(Alban D) #2

Hi,

You can use the anomaly detection mode to find where in the backward the nans appeared.
Then you need to make sure you’re not using a function at a point where it’s gradient does not exist, like log at 0 or sqrt at 0 etc.


(Satwik Kondamudi) #3

That helped. Thanks! @albanD