Open sloanqin opened 3 years ago
用fasttext直接跑,词向量random,准确率0.9167
Vocab size: 4762 180000it [00:08, 22208.38it/s] 10000it [00:00, 24515.59it/s] 10000it [00:00, 25578.53it/s] Time usage: 0:00:09 <bound method Module.parameters of Model( (embedding): Embedding(4762, 300, padding_idx=4761) (embedding_ngram2): Embedding(50000, 300) (embedding_ngram3): Embedding(50000, 300) (dropout): Dropout(p=0.5, inplace=False) (fc1): Linear(in_features=900, out_features=256, bias=True) (fc2): Linear(in_features=256, out_features=10, bias=True) )> Epoch [1/20] Iter: 0, Train Loss: 2.3, Train Acc: 10.94%, Val Loss: 2.4, Val Acc: 9.76%, Time: 0:00:01 Iter: 100, Train Loss: 1.1, Train Acc: 63.28%, Val Loss: 1.1, Val Acc: 67.67%, Time: 0:00:26 Iter: 200, Train Loss: 1.1, Train Acc: 64.06%, Val Loss: 0.78, Val Acc: 75.56%, Time: 0:00:47 Iter: 300, Train Loss: 0.79, Train Acc: 75.00%, Val Loss: 0.69, Val Acc: 77.97%, Time: 0:01:09 Iter: 400, Train Loss: 0.91, Train Acc: 70.31%, Val Loss: 0.68, Val Acc: 76.88%, Time: 0:01:30 Iter: 500, Train Loss: 0.68, Train Acc: 76.56%, Val Loss: 0.56, Val Acc: 81.80%, Time: 0:01:51 Iter: 600, Train Loss: 0.6, Train Acc: 76.56%, Val Loss: 0.55, Val Acc: 82.56%, Time: 0:02:13 Iter: 700, Train Loss: 0.72, Train Acc: 77.34%, Val Loss: 0.51, Val Acc: 83.77%, Time: 0:02:35 Iter: 800, Train Loss: 0.63, Train Acc: 78.91%, Val Loss: 0.51, Val Acc: 83.59%, Time: 0:02:56 Iter: 900, Train Loss: 0.55, Train Acc: 85.16%, Val Loss: 0.48, Val Acc: 84.88%, Time: 0:03:18 Iter: 1000, Train Loss: 0.6, Train Acc: 77.34%, Val Loss: 0.48, Val Acc: 84.87%, Time: 0:03:40 Iter: 1100, Train Loss: 0.4, Train Acc: 90.62%, Val Loss: 0.46, Val Acc: 85.72%, Time: 0:04:03 Iter: 1200, Train Loss: 0.48, Train Acc: 82.81%, Val Loss: 0.45, Val Acc: 85.73%, Time: 0:04:27 Iter: 1300, Train Loss: 0.57, Train Acc: 82.03%, Val Loss: 0.44, Val Acc: 86.06%, Time: 0:04:49 Iter: 1400, Train Loss: 0.62, Train Acc: 78.91%, Val Loss: 0.43, Val Acc: 86.95%, Time: 0:05:11 Epoch [2/20] Iter: 1500, Train Loss: 0.55, Train Acc: 83.59%, Val Loss: 0.42, Val Acc: 86.84%, Time: 0:05:33 Iter: 1600, Train Loss: 0.42, Train Acc: 83.59%, Val Loss: 0.42, Val Acc: 86.59%, Time: 0:05:55 Iter: 1700, Train Loss: 0.46, Train Acc: 81.25%, Val Loss: 0.41, Val Acc: 87.07%, Time: 0:06:17 Iter: 1800, Train Loss: 0.38, Train Acc: 89.84%, Val Loss: 0.4, Val Acc: 87.59%, Time: 0:06:39 Iter: 1900, Train Loss: 0.53, Train Acc: 84.38%, Val Loss: 0.4, Val Acc: 87.52%, Time: 0:07:01 Iter: 2000, Train Loss: 0.45, Train Acc: 83.59%, Val Loss: 0.38, Val Acc: 88.18%, Time: 0:07:23 Iter: 2100, Train Loss: 0.44, Train Acc: 82.81%, Val Loss: 0.38, Val Acc: 88.45%, Time: 0:07:44 Iter: 2200, Train Loss: 0.28, Train Acc: 91.41%, Val Loss: 0.37, Val Acc: 88.42%, Time: 0:08:06 Iter: 2300, Train Loss: 0.34, Train Acc: 88.28%, Val Loss: 0.37, Val Acc: 88.61%, Time: 0:08:28 Iter: 2400, Train Loss: 0.48, Train Acc: 85.94%, Val Loss: 0.36, Val Acc: 88.97%, Time: 0:08:50 Iter: 2500, Train Loss: 0.3, Train Acc: 91.41%, Val Loss: 0.36, Val Acc: 89.13%, Time: 0:09:12 Iter: 2600, Train Loss: 0.4, Train Acc: 85.94%, Val Loss: 0.36, Val Acc: 88.92%, Time: 0:09:33 Iter: 2700, Train Loss: 0.34, Train Acc: 89.84%, Val Loss: 0.35, Val Acc: 89.05%, Time: 0:09:55 Iter: 2800, Train Loss: 0.48, Train Acc: 81.25%, Val Loss: 0.35, Val Acc: 89.28%, Time: 0:10:17 Epoch [3/20] Iter: 2900, Train Loss: 0.36, Train Acc: 88.28%, Val Loss: 0.34, Val Acc: 89.68%, Time: 0:10:42 Iter: 3000, Train Loss: 0.33, Train Acc: 89.84%, Val Loss: 0.34, Val Acc: 89.50%, Time: 0:11:07 Iter: 3100, Train Loss: 0.27, Train Acc: 92.19%, Val Loss: 0.34, Val Acc: 89.34%, Time: 0:11:32 Iter: 3200, Train Loss: 0.48, Train Acc: 88.28%, Val Loss: 0.33, Val Acc: 89.55%, Time: 0:11:57 Iter: 3300, Train Loss: 0.34, Train Acc: 91.41%, Val Loss: 0.33, Val Acc: 89.59%, Time: 0:12:22 Iter: 3400, Train Loss: 0.4, Train Acc: 86.72%, Val Loss: 0.33, Val Acc: 89.81%, Time: 0:12:47 Iter: 3500, Train Loss: 0.26, Train Acc: 92.97%, Val Loss: 0.32, Val Acc: 90.35%, Time: 0:13:12 Iter: 3600, Train Loss: 0.24, Train Acc: 92.19%, Val Loss: 0.32, Val Acc: 90.28%, Time: 0:13:36 Iter: 3700, Train Loss: 0.43, Train Acc: 84.38%, Val Loss: 0.32, Val Acc: 90.36%, Time: 0:14:01 Iter: 3800, Train Loss: 0.35, Train Acc: 85.94%, Val Loss: 0.32, Val Acc: 90.04%, Time: 0:14:26 Iter: 3900, Train Loss: 0.34, Train Acc: 89.06%, Val Loss: 0.32, Val Acc: 90.18%, Time: 0:14:51 Iter: 4000, Train Loss: 0.26, Train Acc: 92.97%, Val Loss: 0.32, Val Acc: 90.06%, Time: 0:15:16 Iter: 4100, Train Loss: 0.27, Train Acc: 90.62%, Val Loss: 0.31, Val Acc: 90.31%, Time: 0:15:41 Iter: 4200, Train Loss: 0.36, Train Acc: 91.41%, Val Loss: 0.31, Val Acc: 90.33%, Time: 0:16:06 Epoch [4/20] Iter: 4300, Train Loss: 0.21, Train Acc: 92.19%, Val Loss: 0.31, Val Acc: 90.40%, Time: 0:16:31 Iter: 4400, Train Loss: 0.18, Train Acc: 92.97%, Val Loss: 0.31, Val Acc: 90.59%, Time: 0:16:56 Iter: 4500, Train Loss: 0.38, Train Acc: 87.50%, Val Loss: 0.3, Val Acc: 90.66%, Time: 0:17:21 Iter: 4600, Train Loss: 0.21, Train Acc: 93.75%, Val Loss: 0.32, Val Acc: 90.10%, Time: 0:17:46 Iter: 4700, Train Loss: 0.37, Train Acc: 90.62%, Val Loss: 0.3, Val Acc: 90.98%, Time: 0:18:11 Iter: 4800, Train Loss: 0.14, Train Acc: 94.53%, Val Loss: 0.3, Val Acc: 91.04%, Time: 0:18:36 Iter: 4900, Train Loss: 0.2, Train Acc: 96.09%, Val Loss: 0.3, Val Acc: 90.94%, Time: 0:19:01 Iter: 5000, Train Loss: 0.25, Train Acc: 91.41%, Val Loss: 0.3, Val Acc: 91.02%, Time: 0:19:26 Iter: 5100, Train Loss: 0.22, Train Acc: 92.97%, Val Loss: 0.29, Val Acc: 91.22%, Time: 0:19:51 Iter: 5200, Train Loss: 0.35, Train Acc: 87.50%, Val Loss: 0.29, Val Acc: 91.07%, Time: 0:20:16 Iter: 5300, Train Loss: 0.21, Train Acc: 92.97%, Val Loss: 0.3, Val Acc: 90.79%, Time: 0:20:41 Iter: 5400, Train Loss: 0.42, Train Acc: 86.72%, Val Loss: 0.3, Val Acc: 90.79%, Time: 0:21:06 Iter: 5500, Train Loss: 0.24, Train Acc: 91.41%, Val Loss: 0.3, Val Acc: 91.02%, Time: 0:21:31 Iter: 5600, Train Loss: 0.19, Train Acc: 95.31%, Val Loss: 0.29, Val Acc: 90.96%, Time: 0:21:56 Epoch [5/20] Iter: 5700, Train Loss: 0.3, Train Acc: 90.62%, Val Loss: 0.29, Val Acc: 91.10%, Time: 0:22:22 Iter: 5800, Train Loss: 0.19, Train Acc: 93.75%, Val Loss: 0.29, Val Acc: 91.08%, Time: 0:22:47 Iter: 5900, Train Loss: 0.29, Train Acc: 92.19%, Val Loss: 0.29, Val Acc: 91.36%, Time: 0:23:12 Iter: 6000, Train Loss: 0.29, Train Acc: 89.06%, Val Loss: 0.29, Val Acc: 91.27%, Time: 0:23:37 Iter: 6100, Train Loss: 0.34, Train Acc: 91.41%, Val Loss: 0.29, Val Acc: 91.29%, Time: 0:24:02 Iter: 6200, Train Loss: 0.1, Train Acc: 97.66%, Val Loss: 0.28, Val Acc: 91.49%, Time: 0:24:27 Iter: 6300, Train Loss: 0.2, Train Acc: 90.62%, Val Loss: 0.29, Val Acc: 91.37%, Time: 0:24:52 Iter: 6400, Train Loss: 0.14, Train Acc: 96.88%, Val Loss: 0.29, Val Acc: 91.26%, Time: 0:25:17 Iter: 6500, Train Loss: 0.22, Train Acc: 92.19%, Val Loss: 0.28, Val Acc: 91.41%, Time: 0:25:43 Iter: 6600, Train Loss: 0.27, Train Acc: 90.62%, Val Loss: 0.28, Val Acc: 91.38%, Time: 0:26:08 Iter: 6700, Train Loss: 0.11, Train Acc: 96.88%, Val Loss: 0.28, Val Acc: 91.15%, Time: 0:26:33 Iter: 6800, Train Loss: 0.2, Train Acc: 93.75%, Val Loss: 0.28, Val Acc: 91.37%, Time: 0:26:58 Iter: 6900, Train Loss: 0.18, Train Acc: 92.97%, Val Loss: 0.29, Val Acc: 91.40%, Time: 0:27:23 Iter: 7000, Train Loss: 0.25, Train Acc: 90.62%, Val Loss: 0.28, Val Acc: 91.41%, Time: 0:27:48 Epoch [6/20] Iter: 7100, Train Loss: 0.14, Train Acc: 93.75%, Val Loss: 0.28, Val Acc: 91.45%, Time: 0:28:13 Iter: 7200, Train Loss: 0.23, Train Acc: 92.97%, Val Loss: 0.28, Val Acc: 91.63%, Time: 0:28:38 Iter: 7300, Train Loss: 0.15, Train Acc: 93.75%, Val Loss: 0.28, Val Acc: 91.41%, Time: 0:29:03 Iter: 7400, Train Loss: 0.22, Train Acc: 95.31%, Val Loss: 0.29, Val Acc: 91.38%, Time: 0:29:28 Iter: 7500, Train Loss: 0.084, Train Acc: 98.44%, Val Loss: 0.28, Val Acc: 91.61%, Time: 0:29:53 Iter: 7600, Train Loss: 0.079, Train Acc: 98.44%, Val Loss: 0.28, Val Acc: 91.66%, Time: 0:30:18 * Iter: 7700, Train Loss: 0.21, Train Acc: 94.53%, Val Loss: 0.28, Val Acc: 91.69%, Time: 0:30:43 Iter: 7800, Train Loss: 0.3, Train Acc: 85.94%, Val Loss: 0.28, Val Acc: 91.39%, Time: 0:31:08 Iter: 7900, Train Loss: 0.16, Train Acc: 96.09%, Val Loss: 0.28, Val Acc: 91.56%, Time: 0:31:33 Iter: 8000, Train Loss: 0.19, Train Acc: 94.53%, Val Loss: 0.29, Val Acc: 91.58%, Time: 0:31:58 Iter: 8100, Train Loss: 0.15, Train Acc: 92.97%, Val Loss: 0.28, Val Acc: 91.28%, Time: 0:32:23 Iter: 8200, Train Loss: 0.23, Train Acc: 93.75%, Val Loss: 0.28, Val Acc: 91.35%, Time: 0:32:48 Iter: 8300, Train Loss: 0.12, Train Acc: 95.31%, Val Loss: 0.28, Val Acc: 91.46%, Time: 0:33:13 Iter: 8400, Train Loss: 0.38, Train Acc: 88.28%, Val Loss: 0.28, Val Acc: 91.51%, Time: 0:33:38 Epoch [7/20] Iter: 8500, Train Loss: 0.23, Train Acc: 92.97%, Val Loss: 0.28, Val Acc: 91.69%, Time: 0:34:03 Iter: 8600, Train Loss: 0.13, Train Acc: 96.88%, Val Loss: 0.29, Val Acc: 91.64%, Time: 0:34:28 No optimization for a long time, auto-stopping... Test Loss: 0.26, Test Acc: 91.67% Precision, Recall and F1-Score... precision recall f1-score support
finance 0.9317 0.8860 0.9083 1000
realty 0.9285 0.9350 0.9317 1000
stocks 0.8365 0.8800 0.8577 1000
education 0.9631 0.9390 0.9509 1000
science 0.8766 0.8740 0.8753 1000
society 0.8842 0.9160 0.8998 1000
politics 0.9187 0.8820 0.9000 1000
sports 0.9818 0.9690 0.9753 1000
game 0.9571 0.9380 0.9475 1000
entertainment 0.8986 0.9480 0.9226 1000
accuracy 0.9167 10000
macro avg 0.9177 0.9167 0.9169 10000
weighted avg 0.9177 0.9167 0.9169 10000
Confusion Matrix... [[886 6 67 5 9 11 9 1 1 5] [ 8 935 20 0 5 14 2 2 1 13] [ 38 25 880 2 27 3 20 0 2 3] [ 1 2 4 939 6 20 7 2 2 17] [ 3 11 33 4 874 14 16 0 26 19] [ 2 16 3 11 14 916 16 0 4 18] [ 8 6 28 7 18 38 882 1 2 10] [ 1 1 3 2 1 4 3 969 0 16] [ 1 1 9 1 35 5 3 1 938 6] [ 3 4 5 4 8 11 2 11 4 948]] Time usage: 0:00:01
Process finished with exit code 0
您好,词向量random效果好一些,还是用预训练的好一些?
你好,我之前修改了部分代码,发现准确度没有预期中的好,于是用作者的源代码直接运行,模型使用TextCNN,其余配置不动,最终测试集准确度是90.36,也没有达到预期。
没有达到作者的0.91,大家试的咋样? 是哪里没有配置对么,没改啥,直接跑的作者的数据集,词向量使用的随机初始化。
Loading data... 171223it [00:01, 158092.93it/s]Vocab size: 4762 180000it [00:01, 163141.73it/s] 180000it [00:01, 90840.01it/s] 10000it [00:00, 85676.20it/s] 9398it [00:00, 93301.18it/s]Time usage: 0:00:03 10000it [00:00, 93711.34it/s] <bound method Module.parameters of Model( (embedding): Embedding(4762, 300, padding_idx=4761) (convs): ModuleList( (0): Conv2d(1, 256, kernel_size=(2, 300), stride=(1, 1)) (1): Conv2d(1, 256, kernel_size=(3, 300), stride=(1, 1)) (2): Conv2d(1, 256, kernel_size=(4, 300), stride=(1, 1)) ) (dropout): Dropout(p=0.5, inplace=False) (fc): Linear(in_features=768, out_features=10, bias=True) )> Epoch [1/20] Iter: 0, Train Loss: 2.4, Train Acc: 8.59%, Val Loss: 2.2, Val Acc: 17.78%, Time: 0:00:04 Iter: 100, Train Loss: 0.96, Train Acc: 71.09%, Val Loss: 0.71, Val Acc: 78.01%, Time: 0:00:26 Iter: 200, Train Loss: 1.0, Train Acc: 70.31%, Val Loss: 0.61, Val Acc: 80.84%, Time: 0:00:48 Iter: 300, Train Loss: 0.73, Train Acc: 75.78%, Val Loss: 0.53, Val Acc: 83.78%, Time: 0:01:10 Iter: 400, Train Loss: 0.89, Train Acc: 76.56%, Val Loss: 0.51, Val Acc: 84.35%, Time: 0:01:43 Iter: 500, Train Loss: 0.62, Train Acc: 81.25%, Val Loss: 0.49, Val Acc: 85.00%, Time: 0:02:15 Iter: 600, Train Loss: 0.63, Train Acc: 79.69%, Val Loss: 0.49, Val Acc: 84.84%, Time: 0:02:48 Iter: 700, Train Loss: 0.76, Train Acc: 74.22%, Val Loss: 0.45, Val Acc: 85.99%, Time: 0:03:19 Iter: 800, Train Loss: 0.68, Train Acc: 81.25%, Val Loss: 0.44, Val Acc: 86.40%, Time: 0:03:50 Iter: 900, Train Loss: 0.58, Train Acc: 85.94%, Val Loss: 0.43, Val Acc: 86.74%, Time: 0:04:22 Iter: 1000, Train Loss: 0.42, Train Acc: 85.16%, Val Loss: 0.44, Val Acc: 86.51%, Time: 0:04:53 Iter: 1100, Train Loss: 0.51, Train Acc: 85.16%, Val Loss: 0.43, Val Acc: 86.73%, Time: 0:05:23 Iter: 1200, Train Loss: 0.44, Train Acc: 83.59%, Val Loss: 0.42, Val Acc: 86.98%, Time: 0:05:54 Iter: 1300, Train Loss: 0.6, Train Acc: 82.03%, Val Loss: 0.42, Val Acc: 86.71%, Time: 0:06:22 Iter: 1400, Train Loss: 0.63, Train Acc: 79.69%, Val Loss: 0.41, Val Acc: 87.39%, Time: 0:06:49 Epoch [2/20] Iter: 1500, Train Loss: 0.51, Train Acc: 84.38%, Val Loss: 0.4, Val Acc: 88.07%, Time: 0:07:20 Iter: 1600, Train Loss: 0.4, Train Acc: 89.06%, Val Loss: 0.4, Val Acc: 87.81%, Time: 0:07:50 Iter: 1700, Train Loss: 0.47, Train Acc: 85.16%, Val Loss: 0.39, Val Acc: 88.21%, Time: 0:08:21 Iter: 1800, Train Loss: 0.45, Train Acc: 86.72%, Val Loss: 0.4, Val Acc: 87.75%, Time: 0:08:51 Iter: 1900, Train Loss: 0.38, Train Acc: 90.62%, Val Loss: 0.39, Val Acc: 88.06%, Time: 0:09:22 Iter: 2000, Train Loss: 0.43, Train Acc: 85.16%, Val Loss: 0.38, Val Acc: 88.12%, Time: 0:09:52 Iter: 2100, Train Loss: 0.51, Train Acc: 83.59%, Val Loss: 0.38, Val Acc: 88.60%, Time: 0:10:21 Iter: 2200, Train Loss: 0.41, Train Acc: 83.59%, Val Loss: 0.38, Val Acc: 88.64%, Time: 0:10:52 Iter: 2300, Train Loss: 0.38, Train Acc: 89.84%, Val Loss: 0.38, Val Acc: 88.51%, Time: 0:11:21 Iter: 2400, Train Loss: 0.42, Train Acc: 87.50%, Val Loss: 0.4, Val Acc: 88.15%, Time: 0:11:50 Iter: 2500, Train Loss: 0.29, Train Acc: 89.06%, Val Loss: 0.37, Val Acc: 88.90%, Time: 0:12:19 Iter: 2600, Train Loss: 0.49, Train Acc: 84.38%, Val Loss: 0.37, Val Acc: 88.58%, Time: 0:12:48 Iter: 2700, Train Loss: 0.35, Train Acc: 85.16%, Val Loss: 0.37, Val Acc: 88.66%, Time: 0:13:19 Iter: 2800, Train Loss: 0.52, Train Acc: 81.25%, Val Loss: 0.38, Val Acc: 88.16%, Time: 0:13:49 Epoch [3/20] Iter: 2900, Train Loss: 0.55, Train Acc: 85.16%, Val Loss: 0.37, Val Acc: 88.50%, Time: 0:14:18 Iter: 3000, Train Loss: 0.39, Train Acc: 86.72%, Val Loss: 0.36, Val Acc: 89.12%, Time: 0:14:47 Iter: 3100, Train Loss: 0.33, Train Acc: 89.06%, Val Loss: 0.38, Val Acc: 88.09%, Time: 0:15:16 Iter: 3200, Train Loss: 0.42, Train Acc: 85.94%, Val Loss: 0.37, Val Acc: 88.62%, Time: 0:15:45 Iter: 3300, Train Loss: 0.51, Train Acc: 85.94%, Val Loss: 0.37, Val Acc: 88.65%, Time: 0:16:15 Iter: 3400, Train Loss: 0.5, Train Acc: 84.38%, Val Loss: 0.36, Val Acc: 89.06%, Time: 0:16:43 Iter: 3500, Train Loss: 0.28, Train Acc: 90.62%, Val Loss: 0.36, Val Acc: 88.93%, Time: 0:17:13 Iter: 3600, Train Loss: 0.2, Train Acc: 93.75%, Val Loss: 0.36, Val Acc: 88.86%, Time: 0:17:41 Iter: 3700, Train Loss: 0.42, Train Acc: 85.16%, Val Loss: 0.37, Val Acc: 88.84%, Time: 0:18:10 Iter: 3800, Train Loss: 0.4, Train Acc: 85.16%, Val Loss: 0.38, Val Acc: 88.49%, Time: 0:18:40 Iter: 3900, Train Loss: 0.45, Train Acc: 87.50%, Val Loss: 0.37, Val Acc: 88.70%, Time: 0:19:10 Iter: 4000, Train Loss: 0.36, Train Acc: 88.28%, Val Loss: 0.37, Val Acc: 88.70%, Time: 0:19:39 Iter: 4100, Train Loss: 0.4, Train Acc: 85.94%, Val Loss: 0.35, Val Acc: 89.06%, Time: 0:20:08 * Iter: 4200, Train Loss: 0.4, Train Acc: 87.50%, Val Loss: 0.37, Val Acc: 88.70%, Time: 0:20:37 Epoch [4/20] Iter: 4300, Train Loss: 0.39, Train Acc: 86.72%, Val Loss: 0.37, Val Acc: 88.86%, Time: 0:21:06 Iter: 4400, Train Loss: 0.41, Train Acc: 89.06%, Val Loss: 0.36, Val Acc: 89.18%, Time: 0:21:36 Iter: 4500, Train Loss: 0.4, Train Acc: 88.28%, Val Loss: 0.36, Val Acc: 89.00%, Time: 0:22:04 Iter: 4600, Train Loss: 0.29, Train Acc: 90.62%, Val Loss: 0.37, Val Acc: 88.91%, Time: 0:22:34 Iter: 4700, Train Loss: 0.43, Train Acc: 89.06%, Val Loss: 0.37, Val Acc: 88.64%, Time: 0:23:02 Iter: 4800, Train Loss: 0.31, Train Acc: 87.50%, Val Loss: 0.37, Val Acc: 88.96%, Time: 0:23:31 Iter: 4900, Train Loss: 0.29, Train Acc: 89.84%, Val Loss: 0.36, Val Acc: 89.17%, Time: 0:24:00 Iter: 5000, Train Loss: 0.27, Train Acc: 90.62%, Val Loss: 0.36, Val Acc: 89.31%, Time: 0:24:30 Iter: 5100, Train Loss: 0.39, Train Acc: 89.84%, Val Loss: 0.36, Val Acc: 89.50%, Time: 0:24:58 No optimization for a long time, auto-stopping... Test Loss: 0.34, Test Acc: 89.80% Precision, Recall and F1-Score... precision recall f1-score support
entertainment 0.9308 0.9010 0.9157 1000
weighted avg 0.8983 0.8980 0.8979 10000
Confusion Matrix... [[867 18 67 5 8 11 10 11 1 2] [ 12 935 17 3 4 12 5 4 4 4] [ 43 28 839 2 38 4 39 2 5 0] [ 3 1 0 957 7 9 8 4 2 9] [ 1 5 33 8 866 16 23 6 32 10] [ 6 21 2 22 17 885 23 3 5 16] [ 9 14 27 9 23 32 874 6 1 5] [ 2 3 2 3 4 10 7 951 4 14] [ 1 1 5 4 56 6 1 14 905 7] [ 2 11 4 5 13 8 5 27 24 901]] Time usage: 0:00:04
Process finished with exit code 0