I have several graphs and I use these graphs to train a model hoping to identify the category of each node.I have three categories, and here are the formulation of one of my graph(using the data object):

```
Data(x=[100,64],pos=[100,2],edge_index=[2,546],y=[100],edge_weight=[546])
```

x is the embeding of every nodes, here have 100 nodes and each node have a 64-dimension feature, pos is the coordinate of each node. A two-dimensional vector in edge_index represents an edge between two points. And the edge_weight is the weight corresponding to each edge.

here is my GCN model:

```
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
# GCN model with 2 layers
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = GCNConv(data.num_features, 16)
self.conv2 = GCNConv(16, int(data.num_classes))
def forward(self):
x, edge_index = data.x, data.edge_index
x = F.relu(self.conv1(x, edge_index))
x = F.dropout(x, training=self.training)
x = self.conv2(x, edge_index)
return F.log_softmax(x, dim=1)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
data = data.to(device)
model = Net().to(device)
```

and here is my training code:

```
for epoch in range(epoches):
for img_path in img_paths: #in order to build the graph
data=build_graph(img_path)
model.train()
optimizer.zero_grad()
output=GCNmodel(data)
F.nll_loss(output,target).backward()
optimizer.step()
```

but using this code, every node in one graph have the same output, especially have the negative num.

Note:The category 1 and 2 of each picture is balanced, the category 3 in every image is samller. But the data should not be the main reason for this result

It confuse me lots of time, I am very grateful, if you can help me.