我在这里有一些非常基本的pytorch代码，在这些代码中，我尝试测试通过最终成为我的正向函数的输入张量。

目标：嵌入每个单词编号后，将句子作为单个输入序列处理。

1. 嵌入张量
2. 将嵌入转换回float32张量
3. 将形状整形为形状（batch_size，seq_len，input_size）
4. 通过lstm。

嵌入后，我已经转换回float32张量，所以idk为什么会出现此错误。

hidden_size=10
embedding = nn.Embedding(VOC.n_words,hidden_size)
lstm = nn.LSTM(hidden_size,hidden_size,# Will output 2x hidden size
               num_layers=1,dropout=0.5,bidirectional=True,batch_first=True)

print("Input tensor",idx_sentence)
# Forward test
embedded = embedding(idx_sentence.long())
embedded = torch.tensor(embedded,dtype=torch.float32)
print(f"embedding: {embedded.size()}")

# reshape to (batch_size,seq_len,input_size)
sequence = embedded.view(1,-1,hidden_size)
print(f"sequence shape: {sequence.size()}")

output,hidden = lstm(sequence,hidden_size)
print(f"output shape: {output.size()}")

Input tensor tensor([ 3.,20.,21.,90.,9.])
embedding: torch.Size([5,10])
sequence shape: torch.Size([1,5,10])
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: UserWarning: To copy construct from a tensor,it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True),rather than torch.tensor(sourceTensor).
  # Remove the CWD from sys.path while we load stuff.
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-116-ab3d6ed0e51c> in <module>()
     16 
     17 # Input have shape (seq_len,batch,input_size)
---> 18 output,hidden_size)
     19 print(f"output shape: {output.size()}")

2 frames
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/rnn.py in check_forward_args(self,input,hidden,batch_sizes)
    520         expected_hidden_size = self.get_expected_hidden_size(input,batch_sizes)
    521 
--> 522         self.check_hidden_size(hidden[0],expected_hidden_size,523                                'Expected hidden[0] size {},got {}')
    524         self.check_hidden_size(hidden[1],TypeError: 'int' object is not subscriptable