n = torch.ones_like(inputs[:, :, 0]).sum(1).view(-1, 1) n = self.emb_n(n).unsqueeze(1)代码是什么意思
时间: 2024-05-17 14:19:37 浏览: 15
这段代码是在计算输入文本序列中每个单词的词向量表示中添加位置编码。其中,`inputs`是输入的文本序列,`n`是一个大小为`(batch_size, 1)`的张量,表示输入文本序列的长度,通过对第0维度求和并重塑得到。`torch.ones_like()`函数会创建一个形状与`inputs[:, :, 0]`相同的张量,并且所有元素都为1,这个张量的作用是计算输入序列的长度。`self.emb_n()`函数是一个线性变换层,将输入的长度`n`映射到一个与词向量相同维度的向量。`unsqueeze(1)`函数是将`n`的维度在第1维上扩展一维,使得它与词向量的维度相同,便于后续的加和操作。最终,`n`的形状为`(batch_size, seq_len, hidden_size)`,可以与词向量`inputs`相加得到带有位置编码的文本输入。
相关问题
targets = torch.ones_like(classification) * -1 targets = targets.type_as(classification)
The code initializes a tensor `targets` with the same shape as `classification` and sets all its values to -1. The tensor is then cast to the same data type as `classification`. This is often used in machine learning models for tasks such as object detection, where the target labels for some objects may not be available or may be unknown. The -1 value in `targets` indicates that the corresponding label is missing or unknown.
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。
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