RuntimeError: expand(torch.LongTensor{[50, 1]}, size=[50]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (2)

[weixiao12345678]

I was trying to run the program by
python3 train_wc.py --gpu -1 --train_file ./data/ner/train.txt --dev_file ./data/ner/testa.txt --test_file ./data/ner/testb.txt --checkpoint ./checkpoint/ner_ --caseless --fine_tune --high_way --co_train --least_iters 100
I got the following error:

embedding size: '400060'
constructing dataset
building model
/usr/local/python3/lib/python3.6/site-packages/torch/nn/modules/rnn.py:38

: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.55 and num_layers=1
"num_layers={}".format(dropout, num_layers))
/home/yankai/weixiao/LM-LSTM-CRF/model/utils.py:805

: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_.
nn.init.uniform(input_linear.weight, -bias, bias)
/home/yankai/weixiao/LM-LSTM-CRF/model/utils.py:816

: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_.
nn.init.uniform(weight, -bias, bias)
/home/yankai/weixiao/LM-LSTM-CRF/model/utils.py:819

: UserWarning: nn.init.uniform is now deprecated in favor of nn.init.uniform_.
nn.init.uniform(weight, -bias, bias)

• Tot it 1406 (epoch 0): 0it [00:00, ?it/s]train_wc.py:201

: UserWarning: torch.nn.utils.clip_grad_norm is now deprecated in favor of torch.nn.utils.clip_grad_norm_.
nn.utils.clip_grad_norm(ner_model.parameters(), args.clip_grad)
Traceback (most recent call last):
File "train_wc.py

", line 212, in
dev_f1, dev_pre, dev_rec, dev_acc = evaluator.calc_score(ner_model, dev_dataset_loader)
File "/home/yankai/weixiao/LM-LSTM-CRF/model/evaluator.py

", line 209, in calc_score
decoded = self.decoder.decode(scores.data, mask_v.data)
File "/home/yankai/weixiao/LM-LSTM-CRF/model/crf.py

", line 379, in decode
decode_idx[idx] = pointer
RuntimeError: expand(torch.LongTensor{[50, 1]}, size=[50]): the number of sizes provided (1) must be greater or equal to the number of dimensions in the tensor (2)

[SeekPoint]

how to fix it

[ahmadshabbir2468]

? How to fix

Namespace(batch_size=10, caseless=True, char_dim=30, char_hidden=300, char_layers=1, checkpoint='./checkpoint/ner_', clip_grad=5.0, co_train=True, dev_file='./data/ner/testa.txt', drop_out=0.2, emb_file='./embedding/glove.6B.100d.txt', epoch=200, eva_matrix='fa', fine_tune=False, gpu=0, high_way=True, highway_layers=1, lambda0=1, least_iters=20, load_check_point='', load_opt=False, lr=0.015, lr_decay=0.05, mini_count=5, momentum=0.9, patience=15, rand_embedding=False, shrink_embedding=False, small_crf=True, start_epoch=0, test_file='./data/ner/testb.txt', train_file='./data/ner/train.txt', unk='unk', update='sgd', word_dim=100, word_hidden=300, word_layers=1)

loading corpus
constructing coding table
feature size: '171'
loading embedding
embedding size: '400002'
constructing dataset
building model

-------> /usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py:54: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1
"num_layers={}".format(dropout, num_layers))
device: 0
Traceback (most recent call last):
File "train_wc.py", line 162, in
ner_model.cuda()
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 265, in cuda
return self._apply(lambda t: t.cuda(device))
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 193, in _apply
module._apply(fn)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py", line 127, in _apply
self.flatten_parameters()
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py", line 123, in flatten_parameters
self.batch_first, bool(self.bidirectional))
RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED

[zakarianamikaz]

same error can u show me how you did plz

[vasilikivmo]

@weixiao12345678 you have closed this issue. Can you please explain how you solved it? Thank you!