-
Notifications
You must be signed in to change notification settings - Fork 3
/
Copy pathnohup.out
253 lines (253 loc) · 21 KB
/
nohup.out
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
bash: script/table1.sh: No such file or directory
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
/mnt/home/jinwei2/anaconda3/envs/torch12/lib/python3.7/site-packages/dgl/base.py:45: DGLWarning: Detected an old version of PyTorch. Suggest using torch>=1.5.0 for the best experience.
return warnings.warn(message, category=category, stacklevel=1)
Epoch: 00, train_acc: 0.1429, val_acc: 0.1220, test_acc:0.1300, corr:0.6086, sim:0.1092
Epoch: 100, train_acc: 0.2643, val_acc: 0.2160, test_acc:0.2030, corr:0.5394, sim:0.4862
Epoch: 200, train_acc: 0.4714, val_acc: 0.3700, test_acc:0.3920, corr:0.3796, sim:0.4945
Epoch: 300, train_acc: 0.5143, val_acc: 0.3860, test_acc:0.3990, corr:0.3478, sim:0.5397
Epoch: 400, train_acc: 0.7500, val_acc: 0.6820, test_acc:0.7030, corr:0.3058, sim:0.5799
Epoch: 500, train_acc: 0.7714, val_acc: 0.6500, test_acc:0.6660, corr:0.3056, sim:0.6039
Epoch: 600, train_acc: 0.8643, val_acc: 0.7100, test_acc:0.7130, corr:0.3082, sim:0.5944
Epoch: 700, train_acc: 0.8571, val_acc: 0.7220, test_acc:0.7240, corr:0.3050, sim:0.6022
Epoch: 800, train_acc: 0.8071, val_acc: 0.7000, test_acc:0.7060, corr:0.2720, sim:0.5808
Epoch: 900, train_acc: 0.8571, val_acc: 0.6980, test_acc:0.6970, corr:0.2678, sim:0.6109
load model: GAT ./params/Cora/params_GAT_NoneL15M0S100LR0.01DP0.6.pth.tar
val_acc: 0.7520, test_acc:0.7390
[0.278179931640625, 0.278179931640625, 0.5969309210777283, 0.5969309210777283]
Epoch: 00, train_acc: 0.1429, val_acc: 0.3160, test_acc:0.3190, corr:0.8098, sim:0.0476
Epoch: 100, train_acc: 0.4929, val_acc: 0.4660, test_acc:0.4550, corr:0.3520, sim:0.5515
Epoch: 200, train_acc: 0.4857, val_acc: 0.5800, test_acc:0.5710, corr:0.3918, sim:0.5090
Epoch: 300, train_acc: 0.6643, val_acc: 0.6340, test_acc:0.6420, corr:0.3312, sim:0.5704
Epoch: 400, train_acc: 0.7143, val_acc: 0.6520, test_acc:0.6510, corr:0.3222, sim:0.5671
Epoch: 500, train_acc: 0.7786, val_acc: 0.6700, test_acc:0.6850, corr:0.3106, sim:0.5673
Epoch: 600, train_acc: 0.8286, val_acc: 0.6740, test_acc:0.6830, corr:0.3361, sim:0.5846
Epoch: 700, train_acc: 0.8071, val_acc: 0.6700, test_acc:0.7170, corr:0.3290, sim:0.5713
Epoch: 800, train_acc: 0.8714, val_acc: 0.7100, test_acc:0.7110, corr:0.2925, sim:0.5868
Epoch: 900, train_acc: 0.8786, val_acc: 0.6760, test_acc:0.6980, corr:0.3129, sim:0.5672
load model: GAT ./params/Cora/params_GAT_NoneL15M0S200LR0.01DP0.6.pth.tar
val_acc: 0.7460, test_acc:0.7420
[0.30411059061686196, 0.30411059061686196, 0.5817382335662842, 0.5817382335662842]
Epoch: 00, train_acc: 0.1429, val_acc: 0.1220, test_acc:0.1300, corr:0.7022, sim:0.0771
Epoch: 100, train_acc: 0.1429, val_acc: 0.1560, test_acc:0.1440, corr:0.6382, sim:0.2408
Epoch: 200, train_acc: 0.3857, val_acc: 0.3160, test_acc:0.2990, corr:0.6153, sim:0.3654
Epoch: 300, train_acc: 0.3071, val_acc: 0.3040, test_acc:0.3290, corr:0.5302, sim:0.3302
Epoch: 400, train_acc: 0.3857, val_acc: 0.4060, test_acc:0.4190, corr:0.5362, sim:0.3848
Epoch: 500, train_acc: 0.2429, val_acc: 0.3840, test_acc:0.3440, corr:0.4708, sim:0.4644
Epoch: 600, train_acc: 0.2000, val_acc: 0.2460, test_acc:0.2310, corr:0.7112, sim:0.3326
Epoch: 700, train_acc: 0.4214, val_acc: 0.3440, test_acc:0.3390, corr:0.5236, sim:0.4066
Epoch: 800, train_acc: 0.3857, val_acc: 0.3060, test_acc:0.3070, corr:0.5245, sim:0.4024
Epoch: 900, train_acc: 0.3500, val_acc: 0.4580, test_acc:0.4260, corr:0.4298, sim:0.2649
load model: GAT ./params/Cora/params_GAT_NoneL30M0S100LR0.01DP0.6.pth.tar
val_acc: 0.5300, test_acc:0.5160
[0.5174468994140625, 0.5174468994140625, 0.4543902277946472, 0.4543902277946472]
Epoch: 00, train_acc: 0.1357, val_acc: 0.1280, test_acc:0.1250, corr:0.6016, sim:0.0821
Epoch: 100, train_acc: 0.2429, val_acc: 0.2460, test_acc:0.2260, corr:0.5301, sim:0.4459
Epoch: 200, train_acc: 0.4214, val_acc: 0.4260, test_acc:0.3970, corr:0.5557, sim:0.4221
Epoch: 300, train_acc: 0.3714, val_acc: 0.3640, test_acc:0.3610, corr:0.5281, sim:0.4157
Epoch: 400, train_acc: 0.3571, val_acc: 0.3920, test_acc:0.3590, corr:0.5062, sim:0.3589
Epoch: 500, train_acc: 0.2929, val_acc: 0.2960, test_acc:0.2760, corr:0.5472, sim:0.3498
Epoch: 600, train_acc: 0.2857, val_acc: 0.3360, test_acc:0.3040, corr:0.4818, sim:0.3944
Epoch: 700, train_acc: 0.2571, val_acc: 0.2920, test_acc:0.2760, corr:0.4650, sim:0.2763
Epoch: 800, train_acc: 0.2500, val_acc: 0.2860, test_acc:0.2790, corr:0.5929, sim:0.4192
Epoch: 900, train_acc: 0.2214, val_acc: 0.2060, test_acc:0.2110, corr:0.4972, sim:0.2269
load model: GAT ./params/Cora/params_GAT_NoneL30M0S200LR0.01DP0.6.pth.tar
val_acc: 0.5400, test_acc:0.5050
[0.5165541966756185, 0.5165541966756185, 0.38103246688842773, 0.38103246688842773]
experiment results of None applied in GATon dataset Cora with dropout 0.6, dropedge 0lr 0.01, alpha 1.0, beta 1.0
number of layers: [15, 30]
test accuracies: ['74.05 ± 0.15', '51.05 ± 0.55']
Mean of corr_2, corr, sim_2, sim: [array([0.29114526, 0.29114526, 0.58933458, 0.58933458]), array([0.51700055, 0.51700055, 0.41771135, 0.41771135])]
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
/mnt/home/jinwei2/anaconda3/envs/torch12/lib/python3.7/site-packages/dgl/base.py:45: DGLWarning: Detected an old version of PyTorch. Suggest using torch>=1.5.0 for the best experience.
return warnings.warn(message, category=category, stacklevel=1)
Epoch: 00, train_acc: 0.1667, val_acc: 0.0540, test_acc:0.0760, corr:0.5218, sim:0.1491
Epoch: 100, train_acc: 0.2167, val_acc: 0.3380, test_acc:0.2510, corr:0.3833, sim:0.5411
Epoch: 200, train_acc: 0.3000, val_acc: 0.3700, test_acc:0.3050, corr:0.4108, sim:0.5650
Epoch: 300, train_acc: 0.4833, val_acc: 0.4980, test_acc:0.5180, corr:0.3597, sim:0.5822
Epoch: 400, train_acc: 0.5833, val_acc: 0.5560, test_acc:0.5470, corr:0.3713, sim:0.5645
Epoch: 500, train_acc: 0.6083, val_acc: 0.5720, test_acc:0.6110, corr:0.3844, sim:0.5553
Epoch: 600, train_acc: 0.6250, val_acc: 0.5820, test_acc:0.5990, corr:0.3807, sim:0.5515
Epoch: 700, train_acc: 0.6333, val_acc: 0.5860, test_acc:0.6120, corr:0.3957, sim:0.5692
Epoch: 800, train_acc: 0.6250, val_acc: 0.5700, test_acc:0.5770, corr:0.3682, sim:0.5963
Epoch: 900, train_acc: 0.6000, val_acc: 0.5900, test_acc:0.5980, corr:0.3511, sim:0.5860
load model: GAT ./params/Citeseer/params_GAT_NoneL15M0S100LR0.01DP0.6.pth.tar
val_acc: 0.6300, test_acc:0.6220
[0.3141712824503581, 0.3141712824503581, 0.5971825122833252, 0.5971825122833252]
Epoch: 00, train_acc: 0.1667, val_acc: 0.2320, test_acc:0.1810, corr:0.7185, sim:0.1030
Epoch: 100, train_acc: 0.1750, val_acc: 0.2260, test_acc:0.2050, corr:0.6622, sim:0.4873
Epoch: 200, train_acc: 0.4000, val_acc: 0.3960, test_acc:0.3620, corr:0.5340, sim:0.4775
Epoch: 300, train_acc: 0.5250, val_acc: 0.4900, test_acc:0.4990, corr:0.4119, sim:0.5043
Epoch: 400, train_acc: 0.6083, val_acc: 0.4960, test_acc:0.5430, corr:0.3148, sim:0.5529
Epoch: 500, train_acc: 0.6500, val_acc: 0.5720, test_acc:0.5740, corr:0.2975, sim:0.5543
Epoch: 600, train_acc: 0.6167, val_acc: 0.5740, test_acc:0.5730, corr:0.3656, sim:0.5647
Epoch: 700, train_acc: 0.6333, val_acc: 0.5680, test_acc:0.5560, corr:0.2892, sim:0.5913
Epoch: 800, train_acc: 0.6167, val_acc: 0.5260, test_acc:0.5710, corr:0.4350, sim:0.5439
Epoch: 900, train_acc: 0.6667, val_acc: 0.5880, test_acc:0.5990, corr:0.4219, sim:0.5730
load model: GAT ./params/Citeseer/params_GAT_NoneL15M0S200LR0.01DP0.6.pth.tar
val_acc: 0.6480, test_acc:0.6170
[0.33177369435628257, 0.33177369435628257, 0.577007532119751, 0.577007532119751]
Epoch: 00, train_acc: 0.1667, val_acc: 0.0580, test_acc:0.0770, corr:0.9973, sim:0.0061
Epoch: 100, train_acc: 0.1667, val_acc: 0.2320, test_acc:0.1810, corr:0.5315, sim:0.4253
Epoch: 200, train_acc: 0.3333, val_acc: 0.3960, test_acc:0.3640, corr:0.5355, sim:0.4202
Epoch: 300, train_acc: 0.2333, val_acc: 0.2880, test_acc:0.2820, corr:0.4697, sim:0.4549
Epoch: 400, train_acc: 0.1500, val_acc: 0.2640, test_acc:0.2530, corr:0.5503, sim:0.5104
Epoch: 500, train_acc: 0.3083, val_acc: 0.3700, test_acc:0.3380, corr:0.5997, sim:0.5031
Epoch: 600, train_acc: 0.2500, val_acc: 0.3060, test_acc:0.3160, corr:0.4880, sim:0.4470
Epoch: 700, train_acc: 0.2833, val_acc: 0.3700, test_acc:0.3470, corr:0.5680, sim:0.4840
Epoch: 800, train_acc: 0.2833, val_acc: 0.3320, test_acc:0.2920, corr:0.5109, sim:0.4030
Epoch: 900, train_acc: 0.2583, val_acc: 0.3320, test_acc:0.2930, corr:0.5571, sim:0.4797
load model: GAT ./params/Citeseer/params_GAT_NoneL30M0S100LR0.01DP0.6.pth.tar
val_acc: 0.4420, test_acc:0.3930
[0.5905316670735677, 0.5905316670735677, 0.49650460481643677, 0.49650460481643677]
Epoch: 00, train_acc: 0.1667, val_acc: 0.1900, test_acc:0.1730, corr:0.9690, sim:0.0221
Epoch: 100, train_acc: 0.2167, val_acc: 0.2620, test_acc:0.2490, corr:0.7214, sim:0.2730
Epoch: 200, train_acc: 0.3167, val_acc: 0.3480, test_acc:0.3010, corr:0.9824, sim:0.3307
Epoch: 300, train_acc: 0.2750, val_acc: 0.3620, test_acc:0.3210, corr:0.5336, sim:0.4562
Epoch: 400, train_acc: 0.2667, val_acc: 0.3000, test_acc:0.3020, corr:0.6463, sim:0.4237
Epoch: 500, train_acc: 0.3167, val_acc: 0.3320, test_acc:0.3240, corr:0.7119, sim:0.4469
Epoch: 600, train_acc: 0.3333, val_acc: 0.2740, test_acc:0.3060, corr:0.5368, sim:0.4591
Epoch: 700, train_acc: 0.2750, val_acc: 0.2800, test_acc:0.2720, corr:0.6210, sim:0.3994
Epoch: 800, train_acc: 0.2417, val_acc: 0.2820, test_acc:0.2710, corr:0.4211, sim:0.5449
Epoch: 900, train_acc: 0.2667, val_acc: 0.2720, test_acc:0.2730, corr:0.3993, sim:0.5493
load model: GAT ./params/Citeseer/params_GAT_NoneL30M0S200LR0.01DP0.6.pth.tar
val_acc: 0.4760, test_acc:0.4760
[0.4242787043253581, 0.4242787043253581, 0.5376693606376648, 0.5376693606376648]
experiment results of None applied in GATon dataset Citeseer with dropout 0.6, dropedge 0lr 0.01, alpha 1.0, beta 10.0
number of layers: [15, 30]
test accuracies: ['61.95 ± 0.25', '43.45 ± 4.15']
Mean of corr_2, corr, sim_2, sim: [array([0.32297249, 0.32297249, 0.58709502, 0.58709502]), array([0.50740519, 0.50740519, 0.51708698, 0.51708698])]
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
scripts/table1.sh: line 11: 193471 Killed python main.py --dataset=Pubmed --type_model=GAT --alpha=1 --beta=1 --dropout=0.6 --lr=0.01 --epoch=1000 --cuda_num=3
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
scripts/table1.sh: line 12: 51860 Killed python main.py --dataset=CoauthorCS --type_model=GAT --alpha=1 --beta=1 --dropout=0.6 --lr=0.01 --epoch=1000 --cuda_num=3
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
/mnt/home/jinwei2/anaconda3/envs/torch12/lib/python3.7/site-packages/dgl/base.py:45: DGLWarning: Detected an old version of PyTorch. Suggest using torch>=1.5.0 for the best experience.
return warnings.warn(message, category=category, stacklevel=1)
Epoch: 00, train_acc: 0.1429, val_acc: 0.0580, test_acc:0.0640, corr:0.5441, sim:0.1656
Epoch: 100, train_acc: 0.7571, val_acc: 0.5800, test_acc:0.5740, corr:0.1926, sim:0.5886
Epoch: 200, train_acc: 0.9786, val_acc: 0.6680, test_acc:0.6930, corr:0.1280, sim:0.5800
Epoch: 300, train_acc: 0.9929, val_acc: 0.6320, test_acc:0.6740, corr:0.1212, sim:0.6029
Epoch: 400, train_acc: 1.0000, val_acc: 0.6640, test_acc:0.6890, corr:0.0907, sim:0.5882
Epoch: 500, train_acc: 1.0000, val_acc: 0.6860, test_acc:0.7180, corr:0.1051, sim:0.5933
Epoch: 600, train_acc: 1.0000, val_acc: 0.6180, test_acc:0.6620, corr:0.0869, sim:0.6083
Epoch: 700, train_acc: 1.0000, val_acc: 0.6380, test_acc:0.6530, corr:0.1180, sim:0.5903
Epoch: 800, train_acc: 1.0000, val_acc: 0.6940, test_acc:0.7020, corr:0.0896, sim:0.5970
Epoch: 900, train_acc: 1.0000, val_acc: 0.6560, test_acc:0.6680, corr:0.0856, sim:0.5933
load model: Cheby ./params/Cora/params_Cheby_NoneL15M0S100LR0.01DP0.6.pth.tar
val_acc: 0.7200, test_acc:0.7370
[0.13728227615356445, 0.13728227615356445, 0.5773342251777649, 0.5773342251777649]
Epoch: 00, train_acc: 0.1357, val_acc: 0.0860, test_acc:0.0800, corr:0.6096, sim:0.0708
Epoch: 100, train_acc: 0.6571, val_acc: 0.4860, test_acc:0.4930, corr:0.2156, sim:0.6394
Epoch: 200, train_acc: 0.9714, val_acc: 0.6740, test_acc:0.7000, corr:0.1576, sim:0.5704
Epoch: 300, train_acc: 0.9786, val_acc: 0.6920, test_acc:0.7180, corr:0.1384, sim:0.5688
Epoch: 400, train_acc: 0.9857, val_acc: 0.6800, test_acc:0.7120, corr:0.1174, sim:0.5919
Epoch: 500, train_acc: 1.0000, val_acc: 0.6780, test_acc:0.7150, corr:0.0994, sim:0.6115
Epoch: 600, train_acc: 1.0000, val_acc: 0.6540, test_acc:0.6970, corr:0.1149, sim:0.6081
Epoch: 700, train_acc: 0.9929, val_acc: 0.7080, test_acc:0.7220, corr:0.1002, sim:0.5970
Epoch: 800, train_acc: 1.0000, val_acc: 0.7080, test_acc:0.7140, corr:0.0853, sim:0.6020
Epoch: 900, train_acc: 1.0000, val_acc: 0.6580, test_acc:0.6850, corr:0.0908, sim:0.6028
load model: Cheby ./params/Cora/params_Cheby_NoneL15M0S200LR0.01DP0.6.pth.tar
val_acc: 0.7340, test_acc:0.7470
[0.11981514294942221, 0.11981514294942221, 0.5825201869010925, 0.5825201869010925]
Epoch: 00, train_acc: 0.1429, val_acc: 0.0640, test_acc:0.0680, corr:0.7709, sim:0.1025
Epoch: 100, train_acc: 0.3143, val_acc: 0.3840, test_acc:0.3690, corr:0.2574, sim:0.5484
Epoch: 200, train_acc: 0.7429, val_acc: 0.5360, test_acc:0.5250, corr:0.1467, sim:0.5585
Epoch: 300, train_acc: 0.8286, val_acc: 0.5920, test_acc:0.6110, corr:0.1082, sim:0.5749
Epoch: 400, train_acc: 0.9143, val_acc: 0.6260, test_acc:0.6370, corr:0.0978, sim:0.5663
Epoch: 500, train_acc: 0.9286, val_acc: 0.5580, test_acc:0.5880, corr:0.1140, sim:0.5656
Epoch: 600, train_acc: 0.9429, val_acc: 0.6620, test_acc:0.6480, corr:0.0864, sim:0.5639
Epoch: 700, train_acc: 0.8714, val_acc: 0.5920, test_acc:0.6040, corr:0.1310, sim:0.5620
Epoch: 800, train_acc: 0.9643, val_acc: 0.6620, test_acc:0.6650, corr:0.0861, sim:0.5851
Epoch: 900, train_acc: 0.9857, val_acc: 0.6520, test_acc:0.6770, corr:0.0770, sim:0.5793
load model: Cheby ./params/Cora/params_Cheby_NoneL30M0S100LR0.01DP0.6.pth.tar
val_acc: 0.7120, test_acc:0.6960
[0.07171071370442708, 0.07171071370442708, 0.5882086753845215, 0.5882086753845215]
Epoch: 00, train_acc: 0.1429, val_acc: 0.1140, test_acc:0.1030, corr:0.6817, sim:0.0635
Epoch: 100, train_acc: 0.3643, val_acc: 0.2560, test_acc:0.2550, corr:0.2184, sim:0.4425
Epoch: 200, train_acc: 0.6857, val_acc: 0.5400, test_acc:0.5310, corr:0.1483, sim:0.5821
Epoch: 300, train_acc: 0.7714, val_acc: 0.5560, test_acc:0.6010, corr:0.1647, sim:0.5878
Epoch: 400, train_acc: 0.8643, val_acc: 0.5800, test_acc:0.6050, corr:0.1205, sim:0.5568
Epoch: 500, train_acc: 0.9000, val_acc: 0.5560, test_acc:0.6070, corr:0.1094, sim:0.5823
Epoch: 600, train_acc: 0.9357, val_acc: 0.5660, test_acc:0.6030, corr:0.1407, sim:0.5784
Epoch: 700, train_acc: 0.9500, val_acc: 0.6140, test_acc:0.6350, corr:0.0910, sim:0.5825
Epoch: 800, train_acc: 0.9500, val_acc: 0.5940, test_acc:0.6230, corr:0.0778, sim:0.5746
Epoch: 900, train_acc: 0.6643, val_acc: 0.4520, test_acc:0.4860, corr:0.1684, sim:0.5467
load model: Cheby ./params/Cora/params_Cheby_NoneL30M0S200LR0.01DP0.6.pth.tar
val_acc: 0.6360, test_acc:0.6270
[0.08407185872395834, 0.08407185872395834, 0.5757595300674438, 0.5757595300674438]
experiment results of None applied in Chebyon dataset Cora with dropout 0.6, dropedge 0lr 0.01, alpha 1.0, beta 10.0
number of layers: [15, 30]
test accuracies: ['74.20 ± 0.50', '66.15 ± 3.45']
Mean of corr_2, corr, sim_2, sim: [array([0.12854871, 0.12854871, 0.57992721, 0.57992721]), array([0.07789129, 0.07789129, 0.5819841 , 0.5819841 ])]
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
/mnt/home/jinwei2/anaconda3/envs/torch12/lib/python3.7/site-packages/dgl/base.py:45: DGLWarning: Detected an old version of PyTorch. Suggest using torch>=1.5.0 for the best experience.
return warnings.warn(message, category=category, stacklevel=1)
Epoch: 00, train_acc: 0.2000, val_acc: 0.1700, test_acc:0.1310, corr:0.5645, sim:0.1422
Epoch: 100, train_acc: 0.7167, val_acc: 0.4840, test_acc:0.4630, corr:0.1725, sim:0.5678
Epoch: 200, train_acc: 0.9250, val_acc: 0.5360, test_acc:0.5280, corr:0.1607, sim:0.4953
Epoch: 300, train_acc: 0.9667, val_acc: 0.5520, test_acc:0.5520, corr:0.1230, sim:0.5484
Epoch: 400, train_acc: 0.9667, val_acc: 0.5480, test_acc:0.5480, corr:0.1085, sim:0.5434
Epoch: 500, train_acc: 0.9750, val_acc: 0.5280, test_acc:0.5370, corr:0.0949, sim:0.5755
Epoch: 600, train_acc: 0.9750, val_acc: 0.5100, test_acc:0.5330, corr:0.0920, sim:0.5472
Epoch: 700, train_acc: 0.9583, val_acc: 0.5080, test_acc:0.5140, corr:0.0950, sim:0.5649
Epoch: 800, train_acc: 0.9917, val_acc: 0.5140, test_acc:0.5210, corr:0.0911, sim:0.5545
Epoch: 900, train_acc: 0.9917, val_acc: 0.5060, test_acc:0.5230, corr:0.0774, sim:0.5684
load model: Cheby ./params/Citeseer/params_Cheby_NoneL15M0S100LR0.01DP0.6.pth.tar
val_acc: 0.5860, test_acc:0.5870
[0.13667758305867514, 0.13667758305867514, 0.5220286250114441, 0.5220286250114441]
Epoch: 00, train_acc: 0.1750, val_acc: 0.1320, test_acc:0.1490, corr:0.5863, sim:0.1881
Epoch: 100, train_acc: 0.4750, val_acc: 0.3320, test_acc:0.3170, corr:0.1399, sim:0.5109
Epoch: 200, train_acc: 0.9250, val_acc: 0.5180, test_acc:0.5180, corr:0.1154, sim:0.5447
Epoch: 300, train_acc: 0.9417, val_acc: 0.4920, test_acc:0.4640, corr:0.0991, sim:0.5680
Epoch: 400, train_acc: 0.9833, val_acc: 0.4580, test_acc:0.4140, corr:0.0997, sim:0.5759
Epoch: 500, train_acc: 0.9750, val_acc: 0.4600, test_acc:0.4380, corr:0.0975, sim:0.5850
Epoch: 600, train_acc: 0.9917, val_acc: 0.4340, test_acc:0.4290, corr:0.0847, sim:0.6117
Epoch: 700, train_acc: 0.9917, val_acc: 0.4340, test_acc:0.4150, corr:0.0723, sim:0.5931
Epoch: 800, train_acc: 0.9917, val_acc: 0.4600, test_acc:0.4160, corr:0.0834, sim:0.5772
Epoch: 900, train_acc: 1.0000, val_acc: 0.4160, test_acc:0.4130, corr:0.0857, sim:0.6027
load model: Cheby ./params/Citeseer/params_Cheby_NoneL15M0S200LR0.01DP0.6.pth.tar
val_acc: 0.5360, test_acc:0.5180
[0.12187135219573975, 0.12187135219573975, 0.5181393623352051, 0.5181393623352051]
Epoch: 00, train_acc: 0.1667, val_acc: 0.1360, test_acc:0.1570, corr:0.7682, sim:0.0805
Epoch: 100, train_acc: 0.3833, val_acc: 0.3560, test_acc:0.3200, corr:0.2081, sim:0.5116
Epoch: 200, train_acc: 0.5667, val_acc: 0.4240, test_acc:0.3930, corr:0.1345, sim:0.4795
Epoch: 300, train_acc: 0.6833, val_acc: 0.4520, test_acc:0.4230, corr:0.1335, sim:0.5048
Epoch: 400, train_acc: 0.7250, val_acc: 0.4440, test_acc:0.4280, corr:0.0997, sim:0.5354
Epoch: 500, train_acc: 0.8333, val_acc: 0.4440, test_acc:0.4320, corr:0.0952, sim:0.5528
Epoch: 600, train_acc: 0.8083, val_acc: 0.4260, test_acc:0.4090, corr:0.1179, sim:0.5378
Epoch: 700, train_acc: 0.7750, val_acc: 0.4720, test_acc:0.4820, corr:0.0889, sim:0.5700
Epoch: 800, train_acc: 0.8333, val_acc: 0.4380, test_acc:0.4440, corr:0.0893, sim:0.5614
Epoch: 900, train_acc: 0.8667, val_acc: 0.4300, test_acc:0.4390, corr:0.0732, sim:0.5732
load model: Cheby ./params/Citeseer/params_Cheby_NoneL30M0S100LR0.01DP0.6.pth.tar
val_acc: 0.4960, test_acc:0.4780
[0.0903345266977946, 0.0903345266977946, 0.5395657420158386, 0.5395657420158386]
Epoch: 00, train_acc: 0.1667, val_acc: 0.2120, test_acc:0.2310, corr:0.6984, sim:0.0428
Epoch: 100, train_acc: 0.4750, val_acc: 0.2480, test_acc:0.2420, corr:0.1635, sim:0.4763
Epoch: 200, train_acc: 0.4583, val_acc: 0.3180, test_acc:0.2840, corr:0.1228, sim:0.5036
Epoch: 300, train_acc: 0.6583, val_acc: 0.2940, test_acc:0.2820, corr:0.1044, sim:0.5289
Epoch: 400, train_acc: 0.6500, val_acc: 0.3320, test_acc:0.3060, corr:0.0970, sim:0.5481
Epoch: 500, train_acc: 0.8250, val_acc: 0.3380, test_acc:0.3140, corr:0.0725, sim:0.5497
Epoch: 600, train_acc: 0.8417, val_acc: 0.3420, test_acc:0.3000, corr:0.0915, sim:0.5639
Epoch: 700, train_acc: 0.8167, val_acc: 0.3540, test_acc:0.3420, corr:0.1050, sim:0.5766
Epoch: 800, train_acc: 0.8750, val_acc: 0.3460, test_acc:0.3650, corr:0.1025, sim:0.5620
Epoch: 900, train_acc: 0.8000, val_acc: 0.3860, test_acc:0.3790, corr:0.0787, sim:0.5573
load model: Cheby ./params/Citeseer/params_Cheby_NoneL30M0S200LR0.01DP0.6.pth.tar
val_acc: 0.4180, test_acc:0.3820
[0.068503737449646, 0.068503737449646, 0.5755866765975952, 0.5755866765975952]
experiment results of None applied in Chebyon dataset Citeseer with dropout 0.6, dropedge 0lr 0.01, alpha 1.0, beta 10.0
number of layers: [15, 30]
test accuracies: ['55.25 ± 3.45', '43.00 ± 4.80']
Mean of corr_2, corr, sim_2, sim: [array([0.12927447, 0.12927447, 0.52008399, 0.52008399]), array([0.07941913, 0.07941913, 0.55757621, 0.55757621])]
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
scripts/table1.sh: line 18: 266457 Killed python main.py --dataset=Pubmed --type_model=Cheby --alpha=1 --beta=10 --dropout=0.6 --lr=0.01 --epoch=1000 --cuda_num=3
Using backend: pytorch
WARNING:root:The OGB package is out of date. Your version is 1.2.3, while the latest version is 1.3.4.
scripts/table1.sh: line 19: 138495 Killed python main.py --dataset=CoauthorCS --type_model=Cheby --alpha=1 --beta=10 --dropout=0.6 --lr=0.01 --epoch=1000 --cuda_num=3