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raw-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: 9.08%, Time: 0:00:13 *
Iter: 100, Train Loss: 0.43, Train Acc: 87.50%, Val Loss: 0.4, Val Acc: 88.37%, Time: 0:01:11 *
Iter: 200, Train Loss: 0.36, Train Acc: 90.62%, Val Loss: 0.35, Val Acc: 89.90%, Time: 0:02:09 *
Iter: 300, Train Loss: 0.27, Train Acc: 92.97%, Val Loss: 0.33, Val Acc: 90.08%, Time: 0:03:07 *
Iter: 400, Train Loss: 0.38, Train Acc: 88.28%, Val Loss: 0.27, Val Acc: 91.83%, Time: 0:04:05 *
Iter: 500, Train Loss: 0.23, Train Acc: 92.19%, Val Loss: 0.25, Val Acc: 92.51%, Time: 0:05:03 *
Iter: 600, Train Loss: 0.25, Train Acc: 89.84%, Val Loss: 0.26, Val Acc: 91.86%, Time: 0:06:01
Iter: 700, Train Loss: 0.2, Train Acc: 93.75%, Val Loss: 0.25, Val Acc: 92.26%, Time: 0:06:58 *
Iter: 800, Train Loss: 0.19, Train Acc: 94.53%, Val Loss: 0.22, Val Acc: 92.98%, Time: 0:07:56 *
Iter: 900, Train Loss: 0.21, Train Acc: 93.75%, Val Loss: 0.22, Val Acc: 93.17%, Time: 0:08:53 *
Iter: 1000, Train Loss: 0.14, Train Acc: 93.75%, Val Loss: 0.22, Val Acc: 92.99%, Time: 0:09:50
Iter: 1100, Train Loss: 0.27, Train Acc: 93.75%, Val Loss: 0.21, Val Acc: 93.20%, Time: 0:10:49 *
Iter: 1200, Train Loss: 0.18, Train Acc: 93.75%, Val Loss: 0.2, Val Acc: 93.15%, Time: 0:11:47 *
Iter: 1300, Train Loss: 0.21, Train Acc: 92.19%, Val Loss: 0.2, Val Acc: 93.38%, Time: 0:12:44 *
Iter: 1400, Train Loss: 0.32, Train Acc: 90.62%, Val Loss: 0.2, Val Acc: 93.55%, Time: 0:13:41 *
Epoch [2/3]
Iter: 1500, Train Loss: 0.14, Train Acc: 96.09%, Val Loss: 0.19, Val Acc: 93.57%, Time: 0:14:39 *
Iter: 1600, Train Loss: 0.18, Train Acc: 94.53%, Val Loss: 0.2, Val Acc: 93.64%, Time: 0:15:37
Iter: 1700, Train Loss: 0.17, Train Acc: 96.09%, Val Loss: 0.2, Val Acc: 93.61%, Time: 0:16:35
Iter: 1800, Train Loss: 0.11, Train Acc: 96.09%, Val Loss: 0.2, Val Acc: 93.72%, Time: 0:17:32
Iter: 1900, Train Loss: 0.13, Train Acc: 96.09%, Val Loss: 0.19, Val Acc: 93.79%, Time: 0:18:30 *
Iter: 2000, Train Loss: 0.15, Train Acc: 95.31%, Val Loss: 0.19, Val Acc: 93.94%, Time: 0:19:28 *
Iter: 2100, Train Loss: 0.11, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 93.80%, Time: 0:20:25
Iter: 2200, Train Loss: 0.18, Train Acc: 94.53%, Val Loss: 0.19, Val Acc: 94.10%, Time: 0:21:23 *
Iter: 2300, Train Loss: 0.098, Train Acc: 96.09%, Val Loss: 0.19, Val Acc: 94.01%, Time: 0:22:19
Iter: 2400, Train Loss: 0.07, Train Acc: 97.66%, Val Loss: 0.2, Val Acc: 93.95%, Time: 0:23:18
Iter: 2500, Train Loss: 0.097, Train Acc: 97.66%, Val Loss: 0.2, Val Acc: 93.78%, Time: 0:24:16
Iter: 2600, Train Loss: 0.12, Train Acc: 95.31%, Val Loss: 0.19, Val Acc: 93.83%, Time: 0:25:13
Iter: 2700, Train Loss: 0.11, Train Acc: 95.31%, Val Loss: 0.18, Val Acc: 94.10%, Time: 0:26:11 *
Iter: 2800, Train Loss: 0.092, Train Acc: 97.66%, Val Loss: 0.18, Val Acc: 94.20%, Time: 0:27:08 *
Epoch [3/3]
Iter: 2900, Train Loss: 0.12, Train Acc: 97.66%, Val Loss: 0.19, Val Acc: 94.18%, Time: 0:28:06
Iter: 3000, Train Loss: 0.075, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.42%, Time: 0:29:03
Iter: 3100, Train Loss: 0.051, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.31%, Time: 0:30:01
Iter: 3200, Train Loss: 0.17, Train Acc: 96.09%, Val Loss: 0.19, Val Acc: 94.19%, Time: 0:30:58
Iter: 3300, Train Loss: 0.051, Train Acc: 98.44%, Val Loss: 0.19, Val Acc: 94.20%, Time: 0:31:55
Iter: 3400, Train Loss: 0.045, Train Acc: 97.66%, Val Loss: 0.2, Val Acc: 94.27%, Time: 0:32:52
Iter: 3500, Train Loss: 0.066, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.42%, Time: 0:33:49
Iter: 3600, Train Loss: 0.015, Train Acc: 100.00%, Val Loss: 0.19, Val Acc: 94.32%, Time: 0:34:46
Iter: 3700, Train Loss: 0.1, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.33%, Time: 0:35:45
Iter: 3800, Train Loss: 0.073, Train Acc: 96.88%, Val Loss: 0.19, Val Acc: 94.57%, Time: 0:36:42
No optimization for a long time, auto-stopping...
Test Loss: 0.18, Test Acc: 94.56%
Precision, Recall and F1-Score...
precision recall f1-score support
finance 0.9250 0.9500 0.9373 1000
realty 0.9580 0.9590 0.9585 1000
stocks 0.9254 0.9050 0.9151 1000
education 0.9613 0.9680 0.9646 1000
science 0.9024 0.9250 0.9136 1000
society 0.9342 0.9370 0.9356 1000
politics 0.9271 0.9280 0.9275 1000
sports 0.9909 0.9830 0.9869 1000
game 0.9862 0.9290 0.9567 1000
entertainment 0.9492 0.9720 0.9605 1000
accuracy 0.9456 10000
macro avg 0.9460 0.9456 0.9456 10000
weighted avg 0.9460 0.9456 0.9456 10000
Confusion Matrix...
[[950 7 25 1 4 2 10 1 0 0]
[ 10 959 8 2 4 3 7 2 0 5]
[ 42 12 905 0 17 2 19 1 0 2]
[ 2 0 0 968 1 12 9 0 0 8]
[ 4 3 15 6 925 12 11 1 12 11]
[ 7 10 1 14 6 937 14 0 0 11]
[ 7 1 22 10 13 16 928 0 0 3]
[ 3 2 1 0 0 4 1 983 0 6]
[ 1 4 1 2 49 6 1 1 929 6]
[ 1 3 0 4 6 9 1 3 1 972]]
Time usage: 0:00:10