python深度学习之语音识别(CPU vs GPU)

Posted 2022-04-24 wanli002

训练时间

在mbp的i5的cpu上训练了3轮，花的时间如下

Epoch 1/3
 - 737s - loss: 0.1415 - val_loss: 0.0874
Epoch 2/3
 - 608s - loss: 0.0807 - val_loss: 0.0577
Epoch 3/3
 - 518s - loss: 0.0636 - val_loss: 0.0499

kaggle gpu telsa

Epoch 1/3
 - 40s - loss: 0.1544 - val_loss: 0.0956
Epoch 2/3
 - 38s - loss: 0.0871 - val_loss: 0.0665
Epoch 3/3
 - 38s - loss: 0.0690 - val_loss: 0.0478

对比gpu和cpu，时间相差了1，2个数量级

GeForce GTX 1080

Epoch 1/3
 - 47s - loss: 0.1349 - val_loss: 0.0890
Epoch 2/3
 - 45s - loss: 0.0787 - val_loss: 0.0670
Epoch 3/3
 - 43s - loss: 0.0625 - val_loss: 0.0466

在本地开发环境上的入门级显卡1080上，训练时间后和kaggle的环境相差不多。

Epoch=50

输出前后几轮的训练时间

Epoch 1/50
 - 52s - loss: 0.1253 - val_loss: 0.0795
Epoch 2/50
 - 48s - loss: 0.0738 - val_loss: 0.0565
Epoch 3/50
 - 48s - loss: 0.0616 - val_loss: 0.0477
Epoch 4/50
 - 49s - loss: 0.0534 - val_loss: 0.0378
Epoch 5/50
 - 49s - loss: 0.0484 - val_loss: 0.0375
####################
Epoch 19/50
 - 50s - loss: 0.0270 - val_loss: 0.0249
Epoch 20/50
 - 50s - loss: 0.0257 - val_loss: 0.0241
Epoch 21/50
 - 48s - loss: 0.0256 - val_loss: 0.0255
Epoch 22/50
 - 50s - loss: 0.0247 - val_loss: 0.0255
Epoch 23/50
 - 48s - loss: 0.0246 - val_loss: 0.0219

最终结果

50轮次，大概花了一个多小时，kaggle上的准确率从0.66提升到0.74，后续再考虑优化其他超参数，继续提升准确率