hidden_state = torch.zeros(batch_size, num_nodes * self._hidden_dim).type_as( inputs )
时间: 2024-05-30 21:14:57 浏览: 10
这是一个用于创建初始隐藏状态的张量,张量的大小是(batch_size, num_nodes * hidden_dim),类型与输入张量相同。其中,batch_size表示批量大小,num_nodes表示节点数,hidden_dim表示隐藏层维度。这个张量通常用于循环神经网络(RNN)等模型中,以便在序列预测任务中进行状态保存和传递。
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sample_sigma = torch.zeros(batch_size, self.params.predict_steps, device=self.params.device)
这段代码是在创建一个大小为(batch_size, self.params.predict_steps)的全零张量(tensor),并将它放到指定的设备(device)上。其中,batch_size表示批量大小,self.params.predict_steps表示预测步数。在这个张量中,每个元素都代表了对应样本中的未来某一时间步的预测值的方差(variance)。这个张量将被用于计算损失函数中的方差项。
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。