bert-lstm调参记录_综合

11-6第一次跑全量数据

#定义全局超参数
batch_size=64
lr=4e-5
dropout=0.15
epoch=5
max_length=512 #句子长度
sent_hidden_size=512 #隐藏层维度
sent_num_layers=2
lstm_dropout=0.5

-------------------------------
***** Running training epoch 1 *****
Epoch 001 | Step 0562/2813 | Loss 0.3679 | Time 552.0787
Learning rate = 3.733617727218865e-05
Epoch 001 | Step 1124/2813 | Loss 0.2790 | Time 1104.2936
Learning rate = 3.46723545443773e-05
Epoch 001 | Step 1686/2813 | Loss 0.2409 | Time 1656.4097
Learning rate = 3.2008531816565946e-05
Epoch 001 | Step 2248/2813 | Loss 0.2179 | Time 2208.5104
Learning rate = 2.9344709088754594e-05
Epoch 001 | Step 2810/2813 | Loss 0.2030 | Time 2760.7820
Learning rate = 2.6680886360943242e-05
val_accuracy:0.9570
Average val loss: 0.1344
time costed=2877.61446s 
***** Running training epoch 2 *****
Epoch 002 | Step 0562/2813 | Loss 0.1144 | Time 552.7953
Learning rate = 2.4002843938855314e-05
Epoch 002 | Step 1124/2813 | Loss 0.1112 | Time 1105.5705
Learning rate = 2.1339021211043965e-05
Epoch 002 | Step 1686/2813 | Loss 0.1079 | Time 1658.3458
Learning rate = 1.8675198483232613e-05
Epoch 002 | Step 2248/2813 | Loss 0.1077 | Time 2211.1175
Learning rate = 1.6011375755421257e-05
Epoch 002 | Step 2810/2813 | Loss 0.1058 | Time 2764.0287
Learning rate = 1.3347553027609907e-05
val_accuracy:0.9638
Average val loss: 0.1112
time costed=2880.78902s -------------------------------
***** Running training epoch 3 *****
Epoch 003 | Step 0562/2813 | Loss 0.0776 | Time 553.5801
Learning rate = 1.0669510605521983e-05
Epoch 003 | Step 1124/2813 | Loss 0.0764 | Time 1106.9812
Learning rate = 8.005687877710629e-06
Epoch 003 | Step 1686/2813 | Loss 0.0753 | Time 1660.3180
Learning rate = 5.341865149899278e-06
Epoch 003 | Step 2248/2813 | Loss 0.0745 | Time 2213.8330
Learning rate = 2.6780424220879254e-06
Epoch 003 | Step 2810/2813 | Loss 0.0733 | Time 2767.0747
Learning rate = 1.4219694276573055e-08
val_accuracy:0.9665
Average val loss: 0.1060
time costed=2883.86751s

最终提交结果0.9602，bert单模型是0.9612。

学习率设置lr=1e-5，再训练三个epoch

***** Running training epoch 1 *****
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).
Epoch 001 | Step 0562/2813 | Loss 0.0686 | Time 549.1122
Learning rate = 9.334044318047163e-06
Epoch 001 | Step 1124/2813 | Loss 0.0704 | Time 1098.4061
Learning rate = 8.668088636094325e-06
Epoch 001 | Step 1686/2813 | Loss 0.0705 | Time 1647.6734
Learning rate = 8.002132954141487e-06
Epoch 001 | Step 2248/2813 | Loss 0.0705 | Time 2196.8016
Learning rate = 7.3361772721886485e-06
Epoch 001 | Step 2810/2813 | Loss 0.0698 | Time 2746.1583
Learning rate = 6.6702215902358104e-06
val_accuracy:0.9778
Average val loss: 0.0681
time costed=2863.14666s -------------------------------
***** Running training epoch 2 *****
Epoch 002 | Step 0562/2813 | Loss 0.0551 | Time 550.6488
Learning rate = 6.0007109847138286e-06
Epoch 002 | Step 1124/2813 | Loss 0.0562 | Time 1101.2804
Learning rate = 5.334755302760991e-06
Epoch 002 | Step 1686/2813 | Loss 0.0575 | Time 1651.8084
Learning rate = 4.668799620808153e-06
Epoch 002 | Step 2248/2813 | Loss 0.0578 | Time 2202.4281
Learning rate = 4.002843938855314e-06
Epoch 002 | Step 2810/2813 | Loss 0.0574 | Time 2753.4404
Learning rate = 3.3368882569024767e-06
val_accuracy:0.9774
Average val loss: 0.0663
time costed=2869.31657s -------------------------------
***** Running training epoch 3 *****
Epoch 003 | Step 0562/2813 | Loss 0.0457 | Time 551.8984
Learning rate = 2.6673776513804957e-06
Epoch 003 | Step 1124/2813 | Loss 0.0472 | Time 1104.0534
Learning rate = 2.001421969427657e-06
Epoch 003 | Step 1686/2813 | Loss 0.0471 | Time 1656.2757
Learning rate = 1.3354662874748195e-06
Epoch 003 | Step 2248/2813 | Loss 0.0475 | Time 2208.2137
Learning rate = 6.695106055219813e-07
Epoch 003 | Step 2810/2813 | Loss 0.0476 | Time 2760.1161
Learning rate = 3.5549235691432638e-09
val_accuracy:0.9791
Average val loss: 0.0634
time costed=2876.76017s

最终提交结果0.9615，比bert单模型是0.9612。

第三次选择：

#定义全局超参数
batch_size=64
lr=4e-5
dropout=0.15
epoch=5
max_length=256 #句子长度
sent_hidden_size=256 #隐藏层维度
sent_num_layers=2
lstm_dropout=0.5

模型最后震荡了

***** Running training epoch 1 *****/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).Epoch 001 | Step 0562/2813 | Loss 0.4196 | Time 208.1415Learning rate = 3.840170636331319e-05Epoch 001 | Step 1124/2813 | Loss 0.3100 | Time 416.2209Learning rate = 3.680341272662638e-05Epoch 001 | Step 1686/2813 | Loss 0.2625 | Time 624.3412Learning rate = 3.520511908993957e-05Epoch 001 | Step 2248/2813 | Loss 0.2347 | Time 832.3558Learning rate = 3.360682545325276e-05Epoch 001 | Step 2810/2813 | Loss 0.2172 | Time 1040.4076Learning rate = 3.2008531816565946e-05val_accuracy:0.9538Average val loss: 0.1451time costed=1085.77403s -------------------------------***** Running training epoch 2 *****Epoch 002 | Step 0562/2813 | Loss 0.1188 | Time 207.9437Learning rate = 3.0401706363313194e-05Epoch 002 | Step 1124/2813 | Loss 0.1172 | Time 415.8373Learning rate = 2.880341272662638e-05Epoch 002 | Step 1686/2813 | Loss 0.1156 | Time 623.8603Learning rate = 2.7205119089939568e-05Epoch 002 | Step 2248/2813 | Loss 0.1134 | Time 831.8518Learning rate = 2.5606825453252756e-05Epoch 002 | Step 2810/2813 | Loss 0.1117 | Time 1039.8944Learning rate = 2.4008531816565945e-05val_accuracy:0.9585Average val loss: 0.1339time costed=1085.38992s -------------------------------***** Running training epoch 3 *****Epoch 003 | Step 0562/2813 | Loss 0.0835 | Time 208.6672Learning rate = 2.240170636331319e-05Epoch 003 | Step 1124/2813 | Loss 0.0815 | Time 417.3365Learning rate = 2.0803412726626378e-05Epoch 003 | Step 1686/2813 | Loss 0.0822 | Time 626.0183Learning rate = 1.9205119089939566e-05Epoch 003 | Step 2248/2813 | Loss 0.0827 | Time 834.7226Learning rate = 1.7606825453252755e-05Epoch 003 | Step 2810/2813 | Loss 0.0809 | Time 1043.4659Learning rate = 1.6008531816565944e-05val_accuracy:0.9606Average val loss: 0.1245time costed=1089.08141s -------------------------------***** Running training epoch 4 *****Epoch 004 | Step 0562/2813 | Loss 0.0616 | Time 208.1838Learning rate = 1.440170636331319e-05Epoch 004 | Step 1124/2813 | Loss 0.0611 | Time 416.2914Learning rate = 1.2803412726626378e-05Epoch 004 | Step 1686/2813 | Loss 0.0605 | Time 624.3471Learning rate = 1.1205119089939567e-05Epoch 004 | Step 2248/2813 | Loss 0.0598 | Time 832.3887Learning rate = 9.606825453252755e-06Epoch 004 | Step 2810/2813 | Loss 0.0594 | Time 1040.5442Learning rate = 8.008531816565944e-06val_accuracy:0.9624Average val loss: 0.1255time costed=1085.98783s -------------------------------***** Running training epoch 5 *****Epoch 005 | Step 0562/2813 | Loss 0.0443 | Time 208.2642Learning rate = 6.401706363313189e-06Epoch 005 | Step 1124/2813 | Loss 0.0453 | Time 416.4400Learning rate = 4.803412726626378e-06Epoch 005 | Step 1686/2813 | Loss 0.0451 | Time 624.6992Learning rate = 3.2051190899395668e-06Epoch 005 | Step 2248/2813 | Loss 0.0444 | Time 832.7519Learning rate = 1.6068254532527552e-06Epoch 005 | Step 2810/2813 | Loss 0.0446 | Time 1040.8253Learning rate = 8.531816565943832e-09val_accuracy:0.9630Average val loss: 0.1282time costed=1086.39623s

第一次batch_size=32,bert_lr=5e-5,其它lr=2.5e-4。训练集loss一直下降到0.03，测试集loss在第二个epoch就开始一直震荡，val_loss=0.2左右，acc在0.9460左右震荡
第二次batch_size=32,bert_lr=5e-5，其它lr=2.5e-4。bert的输出dropout概率由0.1升至0.3，scheduler加了一个epoch的warmup。（加了warmup那不是第二个epoch的lr最高，会不会更容易跳过最低点）
- 观察发现第一个epoch，lr最低到最高时，epoch内5次loss都明显下降(2.54–>1.02)。
- 第二个epoch内loss下降非常慢（0.2623–>0.2381）
- 惊鹊代码bert_lr=5e-5,其它是2e-5。test_batch_size=32，train_batch_size=4。dropout=0.15（bert和lstm两次）,没有warmup，epoch=2

原来一直用的15000的数据，难怪准确率这么低

数据调参：

#定义全局超参数
batch_size=64
lr=4e-5
其它lr=lr*4
dropout=0.15
epoch=5
max_length=256 #句子长度
sent_hidden_size=512 #隐藏层维度
sent_num_layers=2
lstm_dropout=0.5
在3epoch基础上用全部训练集跑一个epoch，提交。
得分0.9621

在这里插入图片描述