epoch 1, loss 0.0015, train acc 0.853, test acc 0.885, time 31.0 sec
epoch 2, loss 0.0010, train acc 0.910, test acc 0.899, time 31.8 sec
epoch 3, loss 0.0008, train acc 0.926, test acc 0.911, time 31.6 sec
epoch 4, loss 0.0007, train acc 0.936, test acc 0.916, time 31.8 sec
epoch 5, loss 0.0006, train acc 0.944, test acc 0.926, time 31.5 sec
```
## 小结
* 残差块通过跨层的数据通道从而能够训练出有效的深度神经网络。
* ResNet深刻影响了后来的深度神经网络的设计。
## 参考文献
[1] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[2] He, K., Zhang, X., Ren, S., & Sun, J. (2016, October). Identity mappings in deep residual networks. In European Conference on Computer Vision (pp. 630-645). Springer, Cham.