gru added

seq.py

@@ -80,18 +80,17 @@ def eval_model(dataset_tokens, dataset_labels, model):
     return get_scores(Y_true, Y_pred)
 
 
-class LSTM(torch.nn.Module):
+class GRU(torch.nn.Module):
     def __init__(self, vocab_len):
-        super(LSTM, self).__init__()
+        super(GRU, self).__init__()
         self.emb = torch.nn.Embedding(vocab_len, 100)
-        self.rec = torch.nn.LSTM(100, 256, 1, batch_first=True)
+        self.rec = torch.nn.GRU(100, 256, 2, batch_first=True, dropout=0.2)
         self.fc1 = torch.nn.Linear(256, 9)
 
     def forward(self, x):
         emb = torch.relu(self.emb(x))
-        lstm_output, (h_n, c_n) = self.rec(emb)
-        out_weights = self.fc1(lstm_output)
-
+        gru_output, h_n = self.rec(emb)
+        out_weights = self.fc1(gru_output)
         return out_weights
 
 # Load data
@@ -126,7 +125,7 @@ def train(model, crf, train_tokens, labels_tokens):
             predicted_tags = model(batch_tokens).squeeze(0).unsqueeze(1)
 
             optimizer.zero_grad()
-            loss = criterion(predicted_tags.squeeze(0), tags.squeeze(1))
+            loss = -crf(predicted_tags, tags)
 
             loss.backward()
             optimizer.step()
@@ -138,12 +137,12 @@ if __name__ == "__main__":
     vocab_y = build_vocab(Y_train)
     train_tokens = data_process(X_train, vocab_x)
     labels_tokens = data_process(Y_train, vocab_y)
-    print(train_tokens[0])
 
     # model
-    model = LSTM(len(vocab_x))
+    model = GRU(len(vocab_x))
+    print(model)
     crf = CRF(9)
     p = list(model.parameters()) + list(crf.parameters())
     optimizer = torch.optim.Adam(p)
-    criterion = torch.nn.CrossEntropyLoss()
+    # mask = torch.ByteTensor([1, 1])  # (batch_size. sequence_size)
     train(model, crf, train_tokens, labels_tokens)