i get an error when I modify the number of layers in LSTM

[zhangxiaoyidog]

when I modify the number of layers in LSTM and train the train_full_rl.py something wrong; Start training Traceback (most recent call last): File "train_full_rl.py", line 231, in train(args) File "train_full_rl.py", line 186, in train trainer.train() File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/training.py", line 211, in train log_dict = self._pipeline.train_step() File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/rl.py", line 193, in train_step self._stop_reward_fn, self._stop_coeff File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/rl.py", line 60, in a2c_train_step (inds, ms), bs = agent(raw_arts) File "/home/zhangxiaoyi/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(*input, **kwargs) File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/model/rl.py", line 221, in forward outputs = self._ext(enc_art) File "/home/zhangxiaoyi/anaconda3/envs/pytorch/lib/python3.6/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(*input, **kwargs) File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/model/rl.py", line 130, in forward self._hop_v, self.hop_wq) File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/model/rl.py", line 74, in attention PtrExtractorRL.attention_score(attention, query, v, w), dim=-1) File "/home/zhangxiaoyi/pyworkspace/bytecup8/fast_abs_rl/model/rl.py", line 66, in attention_score sum = attention + torch.mm(query, w) RuntimeError: The size of tensor a (13) must match the size of tensor b (3) at non-singleton dimension 0

[JPatrick9793]

I've encountered the same problem... any luck?

Edit: It seems as though the dimensions of torch.mm(query, w) depends on the number of layers specified.

For example:

• when I set the number of layers to 2, the shape of torch.mm(query, w) was (2, x)
• when I set the number of layers to 1, the shape of torch.mm(query, w) was (1, x) (and this works) ( I did not test for more than 2 layers yet... )

So, it looks like we need to somehow reshape torch.mm(query, w) to be (1, x)?

[ChenRocks]

This might be a bug. I did not test 2 layers since 1 layer already give good results. I will investigate this ASAP.