batch_w_ih = torch.from_numpy(self.w_ih).unsqueeze(0).tile(bs, 1, 1)
时间: 2024-03-03 07:52:04 浏览: 33
这里的错误可能是因为PyTorch中没有名为"tile"的函数。您可以使用"repeat"函数来完成同样的操作,该函数将沿着指定的维度重复张量给定的次数。
以下是使用repeat函数重复张量的示例代码:
```
batch_w_ih = torch.from_numpy(self.w_ih).unsqueeze(0).repeat(bs, 1, 1)
```
这将创建一个形状为[bs, w_ih.shape[0], w_ih.shape[1]]的张量,其中bs是批量大小,w_ih是您从NumPy数组中加载的权重张量。然后,您可以使用这个张量来执行您的矩阵乘法操作。
请注意,repeat函数将复制张量的数据,因此如果您的张量非常大,可能会占用大量内存。如果内存不足,则可以考虑使用其他方法来重复张量。
相关问题
这段代码有什么错误def forward(self,x): num_nodes = x.size(1) # sub_graph size batch_size = x.size(0) W = torch.cat([self.W] * batch_size, dim=0) representation = torch.matmul(x, W) r_sum = torch.sum(representation, dim=-1, keepdim=False) b = torch.zeros([batch_size, num_nodes]) b = Variable(b) one = torch.ones_like(r_sum) zero = torch.zeros_like(r_sum) label = torch.clone(r_sum) label = torch.where(label == 0, one, zero) b.data.masked_fill_(label.bool(), -float('inf')) num_iterations = 3 for i in range(num_iterations): c = torch.nn.functional.softmax(b, dim=-1) weight_coeff = c.unsqueeze(dim=1) representation_global = torch.matmul(weight_coeff, representation) representation_global_all = torch.cat([representation_global] * num_nodes, dim=1) representation_similarity = torch.nn.functional.cosine_similarity(representation, representation_global_all, dim=-1) representation_similarity.data.masked_fill_(label.bool(), -float('inf')) b = representation_similarity return representation_global.squeeze(dim=1)
这段代码中存在一个错误。在for循环中,代码更新了变量b,但是在更新后没有再次进行softmax操作,导致后续的计算结果不正确。因此,需要在for循环中,在更新b后再次进行softmax操作,如下所示:
```
for i in range(num_iterations):
c = torch.nn.functional.softmax(b, dim=-1)
weight_coeff = c.unsqueeze(dim=1)
representation_global = torch.matmul(weight_coeff, representation)
representation_global_all = torch.cat([representation_global] * num_nodes, dim=1)
representation_similarity = torch.nn.functional.cosine_similarity(representation, representation_global_all, dim=-1)
representation_similarity.data.masked_fill_(label.bool(), -float('inf'))
b = torch.nn.functional.softmax(representation_similarity, dim=-1) # 进行softmax操作
```
这样修改后,该段代码的功能应该能够正常运行。
能否具体应用到这个代码中去: def forward(self, sentA, sentB, sent1_lengths, sent2_lengths): """ Performs the forward pass for each batch """ sentence_a_embed = self.pretrained_model.encode(sentA) sentence_b_embed = self.pretrained_model.encode(sentB) sentence_a_embed = torch.tensor(sentence_a_embed, dtype=torch.float) sentence_b_embed = torch.tensor(sentence_b_embed, dtype=torch.float) sentence_a_embed = sentence_a_embed.unsqueeze(1).repeat(1, sent1_lengths, 1) sentence_b_embed = sentence_b_embed.unsqueeze(1).repeat(1, sent2_lengths, 1)其中sentA是(batch_size, embedding_dim)数据,sen1_lengths是(squence_length)数据
可以将上述代码修改为以下代码:
```python
def forward(self, sentA, sentB, sent1_lengths, sent2_lengths):
"""
Performs the forward pass for each batch
"""
sentence_a_embed = self.pretrained_model.encode(sentA)
sentence_b_embed = self.pretrained_model.encode(sentB)
sentence_a_embed = torch.tensor(sentence_a_embed, dtype=torch.float)
sentence_b_embed = torch.tensor(sentence_b_embed, dtype=torch.float)
# 将sentence_a_embed在第1维度上扩展
sentence_a_embed = sentence_a_embed.unsqueeze(1).expand(-1, sent1_lengths, -1)
# 将sentence_b_embed在第1维度上扩展
sentence_b_embed = sentence_b_embed.unsqueeze(1).expand(-1, sent2_lengths, -1)
# 将sentence_a_embed和sent1_lengths拼接为(batch_size, sent1_lengths, embedding_dim + 1)的张量
sentence_a_embed = torch.cat([sentence_a_embed, sent1_lengths.unsqueeze(-1)], dim=2)
# 将sentence_b_embed和sent2_lengths拼接为(batch_size, sent2_lengths, embedding_dim + 1)的张量
sentence_b_embed = torch.cat([sentence_b_embed, sent2_lengths.unsqueeze(-1)], dim=2)
# 其他代码
...
```
其中,`-1`表示该维度的大小由其他维度的大小来推断。`unsqueeze(-1)`表示在最后一维度上增加1维,`unsqueeze(1)`表示在第1维度上增加1维。
这样可以将`sentA`和`sent1_lengths`拼接为(batch_size, sent1_lengths, embedding_dim + 1)的张量,将`sentB`和`sent2_lengths`拼接为(batch_size, sent2_lengths, embedding_dim + 1)的张量,用于后续的操作。