如何记住perform这个单词呢?
时间: 2024-05-19 18:12:48 浏览: 18
To remember the word "perform," you can try associating it with a memorable experience, such as a concert or play that you attended where the performers were particularly impressive. You can also try creating mnemonics or visualizations to help you remember the spelling and meaning of the word. For example, you can imagine a performer on stage, or create a phrase like "performing up to expectations."
相关问题
Do Transformers Really Perform Bad for Graph Representation?
As an AI language model, I do not have personal opinions or beliefs, but according to recent research, Transformers can perform well for graph representation tasks when properly designed and adapted for this type of data. However, there are still ongoing debates and research in this area.
Do Transformers Really Perform Bad for Graph Representation?这篇文章的代码使用dgl该如何实现?
这篇文章中介绍了一种使用Transformer进行图表示学习的方法,并发现相对于GCN和GAT等传统的图神经网络方法,Transformer在图表示学习中表现不佳。
如果您想在使用dgl库中实现这个方法,您需要将原先的PyTorch代码适应于dgl的图表示方式。具体来说,您需要做以下几步:
1. 加载数据集:您可以使用dgl提供的API加载图数据集,例如:
```
import dgl
from dgl.data import citation_graph
data = citation_graph.load_cora()
g = data.graph
```
2. 创建模型:您需要根据论文中的模型架构构建模型。在dgl中,您可以使用dgl.nn模块中的类来实现,例如:
```
import torch.nn as nn
import dgl.nn as dglnn
class GraphTransformer(nn.Module):
def __init__(self, in_feats, out_feats, num_heads, num_layers):
super().__init__()
self.layers = nn.ModuleList()
self.layers.append(dglnn.GraphMultiHeadAttention(in_feats, num_heads))
for i in range(num_layers - 1):
self.layers.append(dglnn.GraphMultiHeadAttention(out_feats, num_heads))
self.mlp = nn.Sequential(
nn.Linear(out_feats, out_feats),
nn.ReLU(),
nn.Linear(out_feats, out_feats)
)
def forward(self, g, x):
h = x
for layer in self.layers:
h = layer(g, h)
g.ndata['h'] = h
hg = dgl.mean_nodes(g, 'h')
return self.mlp(hg)
```
3. 训练模型:您可以使用PyTorch提供的训练API进行模型训练,例如:
```
import torch.optim as optim
model = GraphTransformer(in_feats, out_feats, num_heads, num_layers)
optimizer = optim.Adam(model.parameters())
for epoch in range(num_epochs):
model.train()
# forward
logits = model(g, features)
loss = loss_fn(logits[train_mask], labels[train_mask])
# backward
optimizer.zero_grad()
loss.backward()
optimizer.step()
# evaluate
model.eval()
with torch.no_grad():
logits = model(g, features)
train_acc = accuracy(logits[train_mask], labels[train_mask])
val_acc = accuracy(logits[val_mask], labels[val_mask])
test_acc = accuracy(logits[test_mask], labels[test_mask])
```
这里的`features`是节点特征矩阵,`train_mask`、`val_mask`和`test_mask`是训练集、验证集和测试集的掩码。您需要根据您的任务修改损失函数和评估指标。
希望这些代码片段能帮助您理解如何在dgl中实现Transformer进行图表示学习。
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