Is there anything wrong with the below code? the sentence is already padded to a maximum length.
embedded = self.embedding(sentence) # Each batch has the same maxlen, how to make data loader with custom maxlen? input_lengths = [sentence.shape]* sentence.shape packed = torch.nn.utils.rnn.pack_padded_sequence(embedded, input_lengths, batch_first=True) output, hidden = self.text_LSTM(packed, None) output, _ = torch.nn.utils.rnn.pad_packed_sequence(output, batch_first=True)
However my model is not learning with the final representation to:
extracted from the lstm, i have fixed the input lengths already (since sentences are padded).