Passing embedded sequence to LSTM and getting TypeError: 'int' object is not subscriptable
Passing embedded sequence to LSTM and getting TypeError: 'int' object is not subscriptable
我这里有一些非常基本的 pytorch 代码，我试图通过最终成为我的正向函数来测试 运行 输入张量。
目标：在嵌入每个单词编号后将句子视为单个输入序列。
- 嵌入张量
- 将该嵌入转换回 float32 张量
- 重塑嵌入形状（batch_size、seq_len、input_size）
- 通过lstm。
嵌入后我已经转换回 float32 张量，所以我知道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 = lstm(sequence, 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], expected_hidden_size,
TypeError: 'int' object is not subscriptable
LSTM 接受两个输入，如 nn.LSTM - Inputs 中所述：
input：输入序列(h_0, c_0)：具有初始隐藏状态 h_0 和初始单元状态 c_0.
但是您将 hidden_size 作为第二个参数传递，它是 int 而不是 tuple。当元组被解包时它失败了，因为 hidden_size[0] 不起作用，因为整数不能被索引。
第二个参数是可选的，如果您不提供它，隐藏状态和单元格状态将默认为零。这通常是您想要的，因此您可以将其关闭：
output, hidden = lstm(sequence)
我这里有一些非常基本的 pytorch 代码，我试图通过最终成为我的正向函数来测试 运行 输入张量。
目标：在嵌入每个单词编号后将句子视为单个输入序列。
- 嵌入张量
- 将该嵌入转换回 float32 张量
- 重塑嵌入形状（batch_size、seq_len、input_size）
- 通过lstm。
嵌入后我已经转换回 float32 张量，所以我知道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 = lstm(sequence, 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], expected_hidden_size,
TypeError: 'int' object is not subscriptable
LSTM 接受两个输入，如 nn.LSTM - Inputs 中所述：
input：输入序列(h_0, c_0)：具有初始隐藏状态h_0和初始单元状态c_0. 的元组
但是您将 hidden_size 作为第二个参数传递，它是 int 而不是 tuple。当元组被解包时它失败了，因为 hidden_size[0] 不起作用，因为整数不能被索引。
第二个参数是可选的，如果您不提供它，隐藏状态和单元格状态将默认为零。这通常是您想要的，因此您可以将其关闭：
output, hidden = lstm(sequence)