datasets

main_custom.py

@@ -26,7 +26,6 @@ def train(model, data_train, epoch, optimizer, criterion_rt, criterion_intensity
         param.requires_grad = True
     if forward == 'both':
         for seq, charge, rt, intensity in data_train:
-            print(seq, charge, rt, intensity )
             rt, intensity = rt.float(), intensity.float()
             if torch.cuda.is_available():
                 seq, charge, rt, intensity = seq.cuda(), charge.cuda(), rt.cuda(), intensity.cuda()
@@ -288,7 +287,6 @@ def get_n_params(model):
 
         for s in list(p.size()):
             nn = nn * s
-        print(n, nn)
         pp += nn
     return pp
 

model_custom.py

@@ -102,6 +102,8 @@ class Model_Common_Transformer(nn.Module):
     def forward(self, seq, charge):
         meta_ohe = torch.nn.functional.one_hot(charge - 1, self.charge_max).float()
         seq_emb = torch.nn.functional.one_hot(seq, self.nb_aa).float()
+        print(seq_emb.shape())
+        print(self.nb_aa, self.embedding_dim)
         emb = self.pos_embedding(self.emb(seq_emb))
         meta_enc = self.meta_enc(meta_ohe)