一位热心git友指出之前问题是过拟合了，修改以下utils.py相关地方得到以下结果：
Paper address:
https://github.com/Jhy1993/Representation-Learning-on-Heterogeneous-Graph
Heterogeneous Graph Attention Network (HAN) with pytorch. If you want to pursue the performance in the original paper,
this may not be suitable for you, because there is still a problem: training loss decreases, but verification loss increases.
If you just want to figure out the basic principles of HAN and how to change tensorflow code to pytorch code, then this is for you.
I implemented it according to the original tensorflow code structure.
If you want to pursue higher performance, please refer to:
Official tensorflow implementation:https://github.com/Jhy1993/HAN
DGL implementation:https://github.com/dmlc/dgl/tree/master/examples/pytorch/han
Address of ACM data set：
Preprocessed ACM can be found in: https://pan.baidu.com/s/1V2iOikRqHPtVvaANdkzROw 提取码：50k2
You can use the following command to run:
python main.pyTraining result:
600 300 2125
y_train:(3025, 3), y_val:(3025, 3), y_test:(3025, 3), train_idx:(1, 600), val_idx:(1, 300), test_idx:(1, 2125)
2
model: pre_trained/acm/acm_allMP_multi_fea_.ckpt
fea_list[0].shape torch.Size([1, 1870, 3025])
biases_list[0].shape: torch.Size([1, 3025, 3025])
3
2
torch.Size([1, 1870, 3025]) torch.Size([1, 3025, 3025])
torch.Size([1, 1870, 3025]) torch.Size([1, 3025, 3025])
训练节点个数： 600
验证节点个数： 300
测试节点个数： 2125
epoch:001, loss:1.1004, TrainAcc:0.3517, ValLoss:1.1022, ValAcc:0.4000
epoch:002, loss:1.0762, TrainAcc:0.4250, ValLoss:1.1980, ValAcc:0.0533
epoch:003, loss:1.0007, TrainAcc:0.6300, ValLoss:1.4572, ValAcc:0.0533
epoch:004, loss:0.8876, TrainAcc:0.6583, ValLoss:2.0040, ValAcc:0.0500
epoch:005, loss:0.8145, TrainAcc:0.6350, ValLoss:2.7091, ValAcc:0.0500
epoch:006, loss:0.7897, TrainAcc:0.6267, ValLoss:3.2186, ValAcc:0.0500
epoch:007, loss:0.7804, TrainAcc:0.6150, ValLoss:3.4550, ValAcc:0.0500
epoch:008, loss:0.7527, TrainAcc:0.6150, ValLoss:3.5096, ValAcc:0.0500
epoch:009, loss:0.7404, TrainAcc:0.6117, ValLoss:3.5125, ValAcc:0.0600
epoch:010, loss:0.7329, TrainAcc:0.6633, ValLoss:3.5349, ValAcc:0.0400
epoch:011, loss:0.7169, TrainAcc:0.6983, ValLoss:3.5743, ValAcc:0.0133
epoch:012, loss:0.6934, TrainAcc:0.6917, ValLoss:3.6612, ValAcc:0.0033
epoch:013, loss:0.6711, TrainAcc:0.6750, ValLoss:3.7738, ValAcc:0.0033
epoch:014, loss:0.6645, TrainAcc:0.6733, ValLoss:3.9418, ValAcc:0.0200
epoch:015, loss:0.6652, TrainAcc:0.6833, ValLoss:4.0934, ValAcc:0.0300
epoch:016, loss:0.6515, TrainAcc:0.6883, ValLoss:4.2498, ValAcc:0.0300
epoch:017, loss:0.6238, TrainAcc:0.7050, ValLoss:4.4304, ValAcc:0.0300
epoch:018, loss:0.6082, TrainAcc:0.7317, ValLoss:4.5820, ValAcc:0.0333
epoch:019, loss:0.6030, TrainAcc:0.7517, ValLoss:4.7110, ValAcc:0.0367
epoch:020, loss:0.5933, TrainAcc:0.7850, ValLoss:4.8053, ValAcc:0.0400
epoch:021, loss:0.5824, TrainAcc:0.8267, ValLoss:4.8781, ValAcc:0.0333
epoch:022, loss:0.5655, TrainAcc:0.8017, ValLoss:4.9006, ValAcc:0.0267
epoch:023, loss:0.5333, TrainAcc:0.8083, ValLoss:4.9148, ValAcc:0.0167
epoch:024, loss:0.5175, TrainAcc:0.8050, ValLoss:4.8788, ValAcc:0.0100
epoch:025, loss:0.4994, TrainAcc:0.8117, ValLoss:4.7670, ValAcc:0.0033
epoch:026, loss:0.4888, TrainAcc:0.8333, ValLoss:4.5965, ValAcc:0.0033This is where the problem lies.
If you know how to solve this problem, please don't hesitate to tell me.