A little bit of context
dlg_turnwise_embeddings = np.random.random((batch_size, 20, 256))
dlg_turnwise_embeddings = torch.from_numpy(dlg_turnwise_embeddings)
if legacy:
hidden = rnn_model.init_hidden(batch_size)
# words_emb: batch_size x nef x seq_len
# sent_emb: batch_size x nef
words_emb, sent_emb = rnn_model(captions, cap_lens, hidden)
else:
words_emb = word_reducer(data_caption_word_embeddings)
print(data_caption_word_embeddings)
sent_emb = sent_reducer(data_caption_embeddings)
# the input to the model is here
# data caption word embedding
# this would be changed to data_dialogue_word_embedding
# words_emb torch.Size([48, 256, 14])
# we want the same size for the word and sentence embedding
# sent_emb torch.Size([48, 256])
# TODO: write an LSTM model that outputs the following
# with size ((batch_size, 256))
hidden = dlg_encoder.init_hidden(batch_size)
dlg_entire_embeddings = dlg_encoder(dlg_turnwise_embeddings,
dlg_turnwise_embeddings.shape,
hidden)
Seems to happen when I do dlg_turnwise_embeddings = torch.from_numpy(dlg_turnwise_embeddings)
? Is it as simple as casting it to Long from Double values inside the tensor?