fm = torch.bmm(reduce_x, torch.softmax(attention, dim=1)) RuntimeError: batch1 dim 2 must match batch2 dim 1
时间: 2024-05-28 14:13:10 浏览: 17
这个错误通常是由于输入的矩阵维度不匹配导致的。具体来说,这里的错误是由于 `reduce_x` 的第二个维度与 `attention` 的第一个维度不匹配,导致无法进行矩阵乘法。
你需要检查一下 `reduce_x` 和 `attention` 的大小,并确保它们的维度能够匹配。你可能需要使用 `torch.transpose()` 或 `torch.unsqueeze()` 函数来调整矩阵的维度以匹配它们的形状。
相关问题
def forward(self, input_question, input_answer): input_question.requires_grad = True question_embed = torch.nn.Parameter(self.embedding(input_question), requires_grad=True) answer_embed = torch.nn.Parameter(self.embedding(input_answer), requires_grad=True) _, question_hidden = self.encoder(question_embed) answer_outputs, _ = self.encoder(answer_embed, question_hidden) attention_weights = self.attention(answer_outputs).squeeze(dim=-1) attention_weights = torch.softmax(attention_weights, dim=1) context_vector = torch.bmm(attention_weights.unsqueeze(dim=1), answer_outputs).squeeze(dim=1) logits = self.decoder(context_vector) return logits
在你的代码中,你已经正确地将`input_question`和`input_answer`设置为需要梯度计算的张量。
然而,在使用`torch.nn.Parameter`时,你不需要再次设置`requires_grad=True`,因为`torch.nn.Parameter`默认会将其设置为True。
另外,在计算attention权重时,你可以使用`torch.softmax`函数来对attention权重进行归一化,而不是使用`torch.sigmoid`。softmax函数将确保所有权重的总和为1。
对于logits的计算,没有发现明显的问题。
总的来说,修改后的代码如下所示:
```python
def forward(self, input_question, input_answer):
question_embed = self.embedding(input_question)
answer_embed = self.embedding(input_answer)
_, question_hidden = self.encoder(question_embed)
answer_outputs, _ = self.encoder(answer_embed, question_hidden)
attention_weights = self.attention(answer_outputs).squeeze(dim=-1)
attention_weights = torch.softmax(attention_weights, dim=1)
context_vector = torch.bmm(attention_weights.unsqueeze(dim=1), answer_outputs).squeeze(dim=1)
logits = self.decoder(context_vector)
return logits
```
请确保在使用`loss.backward()`之前,所有需要梯度计算的张量都设置了`requires_grad=True`。如果仍然遇到问题,请检查其他部分的代码是否正确设置了梯度属性。
raw_weights = torch.bmm(x, x.transpose(1, 2)) weights = F.softmax(raw_weights, dim=2)
These two lines of code are performing a matrix multiplication between the tensor x and its transpose, followed by a softmax operation along the last dimension.
The resulting tensor, weights, represents a probability distribution over the dimensions of the input tensor x. This can be interpreted as a set of weights that determine the importance of each input dimension in the output of subsequent operations.
This operation is commonly used in neural network architectures such as attention mechanisms, where the weights are used to focus on certain parts of the input during computation.
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