using GRU instead of LSTM

models.py

         # self.hidden_size = out_size
         self.out_size = embed_size
         self.hidden_size = embed_size
-        self.rnn = nn.LSTM(embed_size,self.hidden_size,2,dropout=0.2)
+        # self.rnn = nn.LSTM(embed_size,self.hidden_size,2,dropout=0.2)
+        self.rnn = nn.GRU(input_size=embed_size,hidden_size=self.hidden_size, num_layers=1)
+
         # nn.init.xavier_normal_(self.rnn.all_weights)
 
     def forward(self, inputs):
         x = torch.stack([inputs[:,0,0,:],inputs[:,0,1,:],inputs[:,1,1,:]],dim=1)
         x = x.transpose(0,1)
 
-        _,(x,c) = self.rnn(x)
+        # _,(x,c) = self.rnn(x)
+        x,c = self.rnn(x)
         x = x[-1]
         x = x.squeeze(0)