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fgm-log.txt
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Epoch [1/3]
Iter: 0, Train Loss: 2.4, Train Acc: 15.62%, Val Loss: 2.4, Val Acc: 11.51%, Time: 0:00:13 *
Iter: 100, Train Loss: 1.5, Train Acc: 53.12%, Val Loss: 1.3, Val Acc: 58.88%, Time: 0:01:53 *
Iter: 200, Train Loss: 0.69, Train Acc: 79.69%, Val Loss: 0.65, Val Acc: 79.54%, Time: 0:03:32 *
Iter: 300, Train Loss: 0.62, Train Acc: 78.12%, Val Loss: 0.56, Val Acc: 82.41%, Time: 0:05:12 *
Iter: 400, Train Loss: 0.68, Train Acc: 76.56%, Val Loss: 0.54, Val Acc: 81.79%, Time: 0:06:53 *
Iter: 500, Train Loss: 0.5, Train Acc: 85.94%, Val Loss: 0.5, Val Acc: 83.83%, Time: 0:08:33 *
Iter: 600, Train Loss: 0.69, Train Acc: 79.69%, Val Loss: 0.53, Val Acc: 82.74%, Time: 0:10:12
Iter: 700, Train Loss: 0.66, Train Acc: 80.47%, Val Loss: 0.56, Val Acc: 81.70%, Time: 0:11:52
Iter: 800, Train Loss: 0.53, Train Acc: 83.59%, Val Loss: 0.56, Val Acc: 81.71%, Time: 0:13:32
Iter: 900, Train Loss: 0.55, Train Acc: 80.47%, Val Loss: 0.58, Val Acc: 80.76%, Time: 0:15:12
Iter: 1000, Train Loss: 0.62, Train Acc: 79.69%, Val Loss: 0.67, Val Acc: 77.90%, Time: 0:16:52
Iter: 1100, Train Loss: 0.78, Train Acc: 75.78%, Val Loss: 0.72, Val Acc: 76.43%, Time: 0:18:31
Iter: 1200, Train Loss: 0.84, Train Acc: 73.44%, Val Loss: 0.79, Val Acc: 73.73%, Time: 0:20:11
Iter: 1300, Train Loss: 0.8, Train Acc: 75.78%, Val Loss: 0.86, Val Acc: 70.94%, Time: 0:21:51
Iter: 1400, Train Loss: 1.2, Train Acc: 59.38%, Val Loss: 0.9, Val Acc: 70.62%, Time: 0:23:30
Epoch [2/3]
Iter: 1500, Train Loss: 1.2, Train Acc: 60.16%, Val Loss: 0.97, Val Acc: 67.46%, Time: 0:25:08
No optimization for a long time, auto-stopping...
Test Loss: 0.47, Test Acc: 85.23%
Precision, Recall and F1-Score...
precision recall f1-score support
finance 0.8022 0.7910 0.7966 1000
realty 0.8707 0.8080 0.8382 1000
stocks 0.7208 0.7950 0.7561 1000
education 0.8607 0.9270 0.8926 1000
science 0.7992 0.8160 0.8075 1000
society 0.8802 0.8960 0.8880 1000
politics 0.8411 0.9050 0.8719 1000
sports 0.9593 0.9200 0.9393 1000
game 0.9009 0.8180 0.8574 1000
entertainment 0.9167 0.8470 0.8805 1000
accuracy 0.8523 10000
macro avg 0.8552 0.8523 0.8528 10000
weighted avg 0.8552 0.8523 0.8528 10000
Confusion Matrix...
[[791 21 128 8 16 10 14 5 5 2]
[ 48 808 51 25 17 14 15 3 9 10]
[ 99 23 795 10 21 1 42 1 5 3]
[ 11 6 5 927 8 18 9 0 5 11]
[ 12 11 59 20 816 21 22 4 23 12]
[ 4 13 4 31 10 896 30 1 1 10]
[ 10 6 26 16 12 19 905 0 3 3]
[ 3 7 11 7 4 10 16 920 3 19]
[ 6 16 18 22 92 11 8 2 818 7]
[ 2 17 6 11 25 18 15 23 36 847]]
Time usage: 0:00:10