通过随机del掉一些block，将残差层改为identity层实现
可提高训练速度
可构造更深的网络,削减梯度消失现象
测试时残差层需要乘以一个概率（当初保留该层的概率）
network.png
model = nn.Sequential(
nn.Linear(10, 2)
)
...
loss.backward()
print(model[0].weight.grad)
self.m = torch.distributions.bernoulli.Bernoulli(torch.Tensor([self.prob]))
if torch.equal(self.m.sample(),torch.ones(1)):
# 保留
paper github 关键词：随机深度 通过随机del掉一些block，将残差层改为identity层实现 可...
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本文标题：Deep Networks with Stochastic De
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