adding training methods

Linker/Linker.py

@@ -47,7 +47,6 @@ class Linker(Module):
         self.atoms_tokenizer = AtomTokenizer(atom_map, self.max_atoms_in_sentence)
         self.atoms_embedding = AtomEmbedding(self.dim_embedding_atoms, self.atom_vocab_size, self.padding_id)
 
-        # to do : definit un encoding
         self.linker_encoder = AttentionDecoderLayer()
 
         self.pos_transformation = Sequential(
@@ -306,6 +305,7 @@ class Linker(Module):
             self.linker_encoder.load_state_dict(params['linker_encoder'])
             self.pos_transformation.load_state_dict(params['pos_transformation'])
             self.neg_transformation.load_state_dict(params['neg_transformation'])
+            self.optimizer.load_state_dict(params['optimizer'])
             print("\n The loading checkpoint was successful ! \n")
         except Exception as e:
             print("\n/!\ Can't load checkpoint model /!\ because :\n\n " + str(e), file=sys.stderr)
@@ -320,6 +320,7 @@ class Linker(Module):
             'atoms_embedding': self.atoms_embedding.state_dict(),
             'linker_encoder': self.linker_encoder.state_dict(),
             'pos_transformation': self.pos_transformation.state_dict(),
-            'neg_transformation': self.neg_transformation.state_dict()
+            'neg_transformation': self.neg_transformation.state_dict(),
+            'optimizer': self.optimizer,
         }, path)
         self.linker.to(self.device)