Traceback (most recent call last):
File "/home/gfq/code/privacy/Graph-Unlearning-main/lib_gnn_model/node_classifier.py", line 203, in
graphsage.train_model()
File "/home/gfq/code/privacy/Graph-Unlearning-main/lib_gnn_model/node_classifier.py", line 80, in train_model
out = self.model(self.data.x[n_id], adjs, self.edge_weight)
File "/home/gfq/anaconda3/envs/unlearning/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/gfq/code/privacy/Graph-Unlearning-main/lib_gnn_model/gcn/gcn_net_batch.py", line 20, in forward
x = self.convs[i]((x, x_target), edge_index, edge_weight=edge_weight[e_id])
File "/home/gfq/anaconda3/envs/unlearning/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
return forward_call(*input, **kwargs)
File "/home/gfq/code/privacy/Graph-Unlearning-main/lib_gnn_model/gcn/gcn_conv_batch.py", line 22, in forward
out = torch.matmul(out, self.weight) #报错拉!
File "/home/gfq/anaconda3/envs/unlearning/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in getattr
raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'GCNConvBatch' object has no attribute 'weight'
进程已结束,退出代码为 1
If we change out = torch.matmul(out, self.weight) to out = self.lin(out) in Graph-Unlearning-main/lib_gnn_model/gcn/gcn_net_batch.py, it works, but by default citeseer training set set the accuracy is only about 40%.
What experiments did you run? I just ran the partition experiment, but I had some problems with the node_edge_unlearning experiment. Do you know if you have encountered, or have solved?
Hi, it seems you're using Python 3.8, which might be causing compatibility issues with certain packages, such as torch_geometric. I recommend checking the package versions to ensure they align with the supported Python version. I used to test on Python 3.6.10 and it should work.