CNN与RNN对中文文本进行分类--基于TENSORFLOW实现

Time: 0:11:18 *

Iter: 1100, Train Loss: 0.25, Train Acc: 93.75%, Val Loss: 0.42, Val Acc: 88.98%, Time: 0:12:25

Epoch: 4

Iter: 1200, Train Loss: 0.14, Train Acc: 96.09%, Val Loss: 0.39, Val Acc: 89.82%, Time: 0:13:32 *

Iter: 1300, Train Loss: 0.2, Train Acc: 96.09%, Val Loss: 0.43, Val Acc: 88.68%, Time: 0:14:38

Iter: 1400, Train Loss: 0.012, Train Acc: 100.00%, Val Loss: 0.37, Val Acc: 90.58%, Time: 0:15:45 *

Iter: 1500, Train Loss: 0.15, Train Acc: 96.88%, Val Loss: 0.39, Val Acc: 90.58%, Time: 0:16:52

Epoch: 5

Iter: 1600, Train Loss: 0.075, Train Acc: 97.66%, Val Loss: 0.41, Val Acc: 89.90%, Time: 0:17:59

Iter: 1700, Train Loss: 0.042, Train Acc: 98.44%, Val Loss: 0.41, Val Acc: 90.08%, Time: 0:19:06

Iter: 1800, Train Loss: 0.08, Train Acc: 97.66%, Val Loss: 0.38, Val Acc: 91.36%, Time: 0:20:13 *

Iter: 1900, Train Loss: 0.089, Train Acc: 98.44%, Val Loss: 0.39, Val Acc: 90.18%, Time: 0:21:20

Epoch: 6

Iter: 2000, Train Loss: 0.092, Train Acc: 96.88%, Val Loss: 0.36, Val Acc: 91.42%, Time: 0:22:27 *

Iter: 2100, Train Loss: 0.062, Train Acc: 98.44%, Val Loss: 0.39, Val Acc: 90.56%, Time: 0:23:34

Iter: 2200, Train Loss: 0.053, Train Acc: 98.44%, Val Loss: 0.39, Val Acc: 90.02%, Time: 0:24:41

Iter: 2300, Train Loss: 0.12, Train Acc: 96.09%, Val Loss: 0.37, Val Acc: 90.84%, Time: 0:25:48

Epoch: 7

Iter: 2400, Train Loss: 0.014, Train Acc: 100.00%, Val Loss: 0.41, Val Acc: 90.38%, Time: 0:26:55

Iter: 2500, Train Loss: 0.14, Train Acc: 96.88%, Val Loss: 0.37, Val Acc: 91.22%, Time: 0:28:01

Iter: 2600, Train Loss: 0.11, Train Acc: 96.88%, Val Loss: 0.43, Val Acc: 89.76%, Time: 0:29:08

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