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):
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.