-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathexample-run.py-output.txt
278 lines (278 loc) · 9.75 KB
/
example-run.py-output.txt
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
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
arashsm79@science ~/P/E/e/gnn (main)> python run.py (neuroimage)
GNN(
(conv1): GATConv(68, 32, heads=1)
(pool1): TopKPooling(32, ratio=0.5, multiplier=1)
(fc1): Linear(in_features=64, out_features=32, bias=True)
(bn1): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc2): Linear(in_features=32, out_features=8, bias=True)
(bn2): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(fc3): Linear(in_features=8, out_features=2, bias=True)
)
Total parameters: 4746
LR: 0.001
---
0.0m 5.976688623428345s
Epoch: 0, Train Loss: 0.9190452594815949, Train Acc: 0.6530864197530865, Valid Loss: 0.7506161438094245, Test Valid: 0.5958333333333333
---
LR: 0.001
---
0.0m 5.449361801147461s
Epoch: 1, Train Loss: 0.7320722175233159, Train Acc: 0.6526234567901235, Valid Loss: 0.6658943295478821, Test Valid: 0.7041666666666667
---
LR: 0.001
---
0.0m 5.4966466426849365s
Epoch: 2, Train Loss: 0.6506871129259651, Train Acc: 0.7780864197530865, Valid Loss: 0.669334614276886, Test Valid: 0.6625
---
LR: 0.001
^[2---
0.0m 5.459685564041138s
Epoch: 3, Train Loss: 0.5473991720764725, Train Acc: 0.9319444444444445, Valid Loss: 0.6929411888122559, Test Valid: 0.5875
---
New best model.
LR: 0.001
---
0.0m 5.903792142868042s
Epoch: 4, Train Loss: 0.5129010278501628, Train Acc: 0.6092592592592593, Valid Loss: 0.6716973112689124, Test Valid: 0.6708333333333333
---
New best model.
LR: 0.001
---
0.0m 5.984976291656494s
Epoch: 5, Train Loss: 0.4918945919584345, Train Acc: 0.924074074074074, Valid Loss: 0.6774404499265883, Test Valid: 0.6472222222222223
---
LR: 0.001
---
0.0m 5.6181676387786865s
Epoch: 6, Train Loss: 0.4853599790437722, Train Acc: 0.7905864197530864, Valid Loss: 0.6348718576961093, Test Valid: 0.675
---
New best model.
LR: 0.001
---
0.0m 6.010502338409424s
Epoch: 7, Train Loss: 0.4968085534042782, Train Acc: 0.8212962962962963, Valid Loss: 0.6186501516236199, Test Valid: 0.7486111111111111
---
New best model.
LR: 0.001
---
0.0m 5.831645727157593s
Epoch: 8, Train Loss: 0.4932586685374931, Train Acc: 0.8969135802469136, Valid Loss: 0.6339166906144884, Test Valid: 0.6680555555555555
---
LR: 0.001
---
0.0m 6.47293758392334s
Epoch: 9, Train Loss: 0.4887931767805123, Train Acc: 0.7503086419753087, Valid Loss: 0.6164317223760817, Test Valid: 0.7
---
New best model.
LR: 0.0005
---
0.0m 6.417611360549927s
Epoch: 10, Train Loss: 0.4701707297637139, Train Acc: 0.9685185185185186, Valid Loss: 0.6531609574953715, Test Valid: 0.6291666666666667
---
LR: 0.0005
---
0.0m 6.033626556396484s
Epoch: 11, Train Loss: 0.4775145619003861, Train Acc: 0.9683641975308642, Valid Loss: 0.7144883314768473, Test Valid: 0.5583333333333333
---
LR: 0.0005
---
0.0m 5.615190505981445s
Epoch: 12, Train Loss: 0.47110970623699233, Train Acc: 0.9476851851851852, Valid Loss: 0.6473553498586019, Test Valid: 0.6819444444444445
---
LR: 0.0005
---
0.0m 6.139084339141846s
Epoch: 13, Train Loss: 0.4674319823582967, Train Acc: 0.9120370370370371, Valid Loss: 0.721418579419454, Test Valid: 0.5833333333333334
---
LR: 0.0005
---
0.0m 6.144824981689453s
Epoch: 14, Train Loss: 0.46966615819636687, Train Acc: 0.8807098765432099, Valid Loss: 0.7444353858629863, Test Valid: 0.5777777777777777
---
LR: 0.0005
---
0.0m 5.617599010467529s
Epoch: 15, Train Loss: 0.4729759807939883, Train Acc: 0.9371913580246913, Valid Loss: 0.6403595937622918, Test Valid: 0.6916666666666667
---
LR: 0.0005
---
0.0m 5.700800895690918s
Epoch: 16, Train Loss: 0.475709288061401, Train Acc: 0.9246913580246914, Valid Loss: 0.7310899602042304, Test Valid: 0.5555555555555556
---
LR: 0.0005
---
0.0m 6.0384862422943115s
Epoch: 17, Train Loss: 0.4867196645265744, Train Acc: 0.9796296296296296, Valid Loss: 0.6609763675265842, Test Valid: 0.6819444444444445
---
LR: 0.0005
---
0.0m 6.382544755935669s
Epoch: 18, Train Loss: 0.49678203338458216, Train Acc: 0.9368827160493827, Valid Loss: 0.622847052415212, Test Valid: 0.6972222222222222
---
LR: 0.0005
---
0.0m 6.208380699157715s
Epoch: 19, Train Loss: 0.49732065436280803, Train Acc: 0.9736111111111111, Valid Loss: 0.6812291198306614, Test Valid: 0.6166666666666667
---
LR: 0.00025
---
0.0m 5.583564758300781s
Epoch: 20, Train Loss: 0.49258498071152484, Train Acc: 0.9830246913580247, Valid Loss: 0.6533879478772481, Test Valid: 0.6902777777777778
---
LR: 0.00025
---
0.0m 6.108428716659546s
Epoch: 21, Train Loss: 0.4876882956351763, Train Acc: 0.9736111111111111, Valid Loss: 0.6333138783772786, Test Valid: 0.6875
---
LR: 0.00025
---
0.0m 6.0019614696502686s
Epoch: 22, Train Loss: 0.48926604309199767, Train Acc: 0.9657407407407408, Valid Loss: 0.6405933499336243, Test Valid: 0.6638888888888889
---
LR: 0.00025
---
0.0m 6.200716972351074s
Epoch: 23, Train Loss: 0.489926325686184, Train Acc: 0.9848765432098765, Valid Loss: 0.6444030046463013, Test Valid: 0.6888888888888889
---
LR: 0.00025
---
0.0m 6.207831859588623s
Epoch: 24, Train Loss: 0.48318670826193727, Train Acc: 0.9736111111111111, Valid Loss: 0.7158268041080899, Test Valid: 0.5486111111111112
---
LR: 0.00025
---
0.0m 6.281627893447876s
Epoch: 25, Train Loss: 0.48751073354556235, Train Acc: 0.9831790123456791, Valid Loss: 0.6573835028542413, Test Valid: 0.675
---
LR: 0.00025
---
0.0m 5.916456699371338s
Epoch: 26, Train Loss: 0.4944286581910687, Train Acc: 0.9743827160493828, Valid Loss: 0.6632610387272305, Test Valid: 0.6666666666666666
---
LR: 0.00025
---
0.0m 6.275601148605347s
Epoch: 27, Train Loss: 0.48846444877577416, Train Acc: 0.9910493827160494, Valid Loss: 0.6450273911158244, Test Valid: 0.6847222222222222
---
LR: 0.00025
---
0.0m 5.946517467498779s
Epoch: 28, Train Loss: 0.48415770295225546, Train Acc: 0.9712962962962963, Valid Loss: 0.6991635931862725, Test Valid: 0.6277777777777778
---
LR: 0.00025
---
0.0m 5.927081823348999s
Epoch: 29, Train Loss: 0.48320635601326273, Train Acc: 0.9859567901234568, Valid Loss: 0.6503648122151693, Test Valid: 0.6611111111111111
---
LR: 0.000125
---
0.0m 5.814892292022705s
Epoch: 30, Train Loss: 0.48178674350550144, Train Acc: 0.9853395061728395, Valid Loss: 0.6448217458195157, Test Valid: 0.675
---
LR: 0.000125
---
0.0m 5.58482813835144s
Epoch: 31, Train Loss: 0.4735602223578794, Train Acc: 0.9950617283950617, Valid Loss: 0.6367534266577827, Test Valid: 0.7013888888888888
---
LR: 0.000125
---
0.0m 5.934986114501953s
Epoch: 32, Train Loss: 0.4771977965478544, Train Acc: 0.9859567901234568, Valid Loss: 0.6159563806321886, Test Valid: 0.7152777777777778
---
New best model.
LR: 0.000125
---
0.0m 6.67864727973938s
Epoch: 33, Train Loss: 0.4746678825513816, Train Acc: 0.995216049382716, Valid Loss: 0.6321904924180772, Test Valid: 0.6958333333333333
---
LR: 0.000125
---
0.0m 6.9973578453063965s
Epoch: 34, Train Loss: 0.4770273748739266, Train Acc: 0.9867283950617284, Valid Loss: 0.6513355069690281, Test Valid: 0.6805555555555556
---
LR: 0.000125
---
0.0m 6.946136713027954s
Epoch: 35, Train Loss: 0.47695596328488105, Train Acc: 0.9878086419753086, Valid Loss: 0.6198590331607394, Test Valid: 0.7097222222222223
---
LR: 0.000125
---
0.0m 6.041706085205078s
Epoch: 36, Train Loss: 0.471792005683169, Train Acc: 0.9833333333333333, Valid Loss: 0.6102088318930732, Test Valid: 0.725
---
New best model.
LR: 0.000125
---
0.0m 5.979609251022339s
Epoch: 37, Train Loss: 0.47112694213419787, Train Acc: 0.995216049382716, Valid Loss: 0.6454610837830438, Test Valid: 0.6833333333333333
---
LR: 0.000125
---
0.0m 5.99721097946167s
Epoch: 38, Train Loss: 0.47075746559802395, Train Acc: 0.9907407407407407, Valid Loss: 0.6098488105667962, Test Valid: 0.7472222222222222
---
New best model.
LR: 0.000125
---
0.0m 5.718656301498413s
Epoch: 39, Train Loss: 0.4653404466163965, Train Acc: 0.9949074074074075, Valid Loss: 0.6237726966540019, Test Valid: 0.7069444444444445
---
LR: 6.25e-05
---
0.0m 5.6295623779296875s
Epoch: 40, Train Loss: 0.4659984725492972, Train Acc: 0.9958333333333333, Valid Loss: 0.6372848775651719, Test Valid: 0.6722222222222223
---
LR: 6.25e-05
---
0.0m 5.903321981430054s
Epoch: 41, Train Loss: 0.4627700364148175, Train Acc: 0.9890432098765433, Valid Loss: 0.6072219517495897, Test Valid: 0.7277777777777777
---
New best model.
LR: 6.25e-05
---
0.0m 5.81429648399353s
Epoch: 42, Train Loss: 0.46029498474097547, Train Acc: 0.9967592592592592, Valid Loss: 0.6497833450635274, Test Valid: 0.6597222222222222
---
LR: 6.25e-05
---
0.0m 5.725330829620361s
Epoch: 43, Train Loss: 0.4653146821775554, Train Acc: 0.9975308641975309, Valid Loss: 0.6398086945215861, Test Valid: 0.6694444444444444
---
LR: 6.25e-05
---
0.0m 5.903347730636597s
Epoch: 44, Train Loss: 0.45651751234207627, Train Acc: 0.9950617283950617, Valid Loss: 0.6393654068311055, Test Valid: 0.6861111111111111
---
LR: 6.25e-05
---
0.0m 5.916743755340576s
Epoch: 45, Train Loss: 0.46355855766637827, Train Acc: 0.9967592592592592, Valid Loss: 0.6069438921080695, Test Valid: 0.7416666666666667
---
New best model.
LR: 6.25e-05
---
0.0m 5.780233383178711s
Epoch: 46, Train Loss: 0.46341438190436657, Train Acc: 0.9953703703703703, Valid Loss: 0.6169584645165338, Test Valid: 0.7013888888888888
---
LR: 6.25e-05
---
0.0m 5.866921901702881s
Epoch: 47, Train Loss: 0.46565011945771584, Train Acc: 0.9972222222222222, Valid Loss: 0.641535144382053, Test Valid: 0.7041666666666667
---
LR: 6.25e-05
---
0.0m 6.1314544677734375s
Epoch: 48, Train Loss: 0.46242958469155393, Train Acc: 0.9958333333333333, Valid Loss: 0.6252362754609849, Test Valid: 0.6875
---
LR: 6.25e-05
---
0.0m 6.13119649887085s
Epoch: 49, Train Loss: 0.4582204065205138, Train Acc: 0.9967592592592592, Valid Loss: 0.6282960838741727, Test Valid: 0.6972222222222222
---
---
Test Acc: 0.7638888888888888, Test Loss: 0.5952173266145918
...
more epochs
...
---
Cross-validation: Test Acc: 0.6166666666666667, Test Loss: 0.6919988698429531