adding training methods

Linker/Linker.py

@@ -35,9 +35,6 @@ class Linker(Module):
         self.max_atoms_in_sentence = int(Configuration.datasetConfig['max_atoms_in_sentence'])
         self.max_atoms_in_one_type = int(Configuration.datasetConfig['max_atoms_in_one_type'])
         self.atom_vocab_size = int(Configuration.datasetConfig['atom_vocab_size'])
-        batch_size = int(Configuration.modelTrainingConfig['batch_size'])
-        nb_sentences = batch_size * 10
-        self.epochs = int(Configuration.modelTrainingConfig['epoch'])
         learning_rate = float(Configuration.modelTrainingConfig['learning_rate'])
         self.dropout = Dropout(0.1)
         self.device = ""
@@ -73,7 +70,6 @@ class Linker(Module):
 
         atoms_polarity_batch = find_pos_neg_idexes(self.max_atoms_in_sentence, df_axiom_links["sub_tree"])
 
-        torch.set_printoptions(edgeitems=20)
         truth_links_batch = get_axiom_links(self.max_atoms_in_one_type, atoms_polarity_batch,
                                             df_axiom_links["sub_tree"])
         truth_links_batch = truth_links_batch.permute(1, 0, 2)
@@ -147,16 +143,14 @@ class Linker(Module):
 
         return torch.stack(link_weights)
 
-    def train_linker(self, df_axiom_links, validation_rate=0.1, epochs=20, batch_size=32):
+    def train_linker(self, df_axiom_links, validation_rate=0.1, epochs=20, batch_size=32, checkpoint=True, validate=True):
 
         training_dataloader, validation_dataloader = self.__preprocess_data(batch_size, df_axiom_links, validation_rate)
-        epochs = epochs - self.epochs
-        self.train()
 
         for epoch_i in range(0, epochs):
-            epoch_acc, epoch_loss = self.__train_epoch(training_dataloader, validation_dataloader)
+            epoch_acc, epoch_loss = self.train_epoch(training_dataloader, validation_dataloader)
 
-    def __train_epoch(self, training_dataloader, validation_dataloader, checkpoint=True, validate=True):
+    def train_epoch(self, training_dataloader, validation_dataloader, checkpoint=True, validate=True):
 
         # Reset the total loss for this epoch.
         epoch_loss = 0
@@ -198,7 +192,6 @@ class Linker(Module):
             self.__checkpoint_save(path=os.path.join(checkpoint_dir, 'model_check.pt'))
 
         if validate:
-            self.eval()
             with torch.no_grad():
                 accuracy, average_test_loss = self.eval_epoch(validation_dataloader, self.cross_entropy_loss)
 
@@ -215,8 +208,6 @@ class Linker(Module):
         '''
         self.eval()
 
-        batch_size, len_sentence, sents_embedding_dim = sents_embedding.shape
-
         # get atoms
         atoms_batch = get_atoms_batch(categories)
         atoms_tokenized = self.atoms_tokenizer.convert_batchs_to_ids(atoms_batch)