c4610ff26a01a0622bc11dcac0f0812f05c56e0c,12_4_name_classify.py,RNNClassifier,forward,#RNNClassifier#Any#Any#,97

Before Change

        output, hidden = self.gru(gru_input, hidden)
        fc_output = self.fc(hidden[-1])  // Use the last layer
        print("Final output size", fc_output.size())
        return fc_output

    def _init_hidden(self, batch_size):

After Change

        embedded = self.embedding(input)

        // Pack them up nicely
        gru_input = pack_padded_sequence(embedded, seq_lengths.data.cpu().numpy())

        // To compact weights again call flatten_parameters().
        self.gru.flatten_parameters()
        output, hidden = self.gru(gru_input, hidden)

Instances