NAS工具箱Drop Path介绍 + Dropout回顾
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【前言】Drop Path是NAS中常用到的一种正则化方法,由于网络训练的过程中常常是动态的,Drop Path就成了一个不错的正则化工具,在FractalNet、NASNet等都有广泛使用。
Dropout
Dropout是最早的用于解决过拟合的方法,是所有drop类方法的大前辈。Dropout在12年被Hinton提出,并且在ImageNet Classification with Deep Convolutional Neural Network工作AlexNet中使用到了Dropout。
原理 :在前向传播的时候,让某个神经元激活以概率1-keep_prob(0<p<1)停止工作。
功能 : 这样可以让模型泛化能力更强,因为其不会过于以来某些局部的节点。训练阶段以keep_prob的概率保留,以1-keep_prob的概率关闭;测试阶段所有的神经元都不关闭,但是对训练阶段应用了dropout的神经元,输出值需要乘以keep_prob。
具体是这样的:
假设一个神经元的输出激活值为
a
,在不使用dropout的情况下,其输出期望值为a
,如果使用了dropout,神经元就可能有保留和关闭两种状态,把它看作一个离散型随机变量,它就符合概率论中的0-1分布,其输出激活值的期望变为p*a+(1-p)*0=pa
,此时若要保持期望和不使用dropout时一致,就要除以p
。
作者:种子_fe
链接:https://www.imooc.com/article/30129
实现 : pytorch中的实现如下。
class _DropoutNd(Module):
__constants__ = [\'p\', \'inplace\']
p: float
inplace: bool
def __init__(self, p: float = 0.5, inplace: bool = False) -> None:
super(_DropoutNd, self).__init__()
if p < 0 or p > 1:
raise ValueError("dropout probability has to be between 0 and 1, "
"but got {}".format(p))
self.p = p
self.inplace = inplace
def extra_repr(self) -> str:
return \'p={}, inplace={}\'.format(self.p, self.inplace)
class Dropout(_DropoutNd):
def forward(self, input: Tensor) -> Tensor:
return F.dropout(input, self.p, self.training, self.inplace)
funtional.py中的dropout实现:
def dropout(input: Tensor, p: float = 0.5, training: bool = True, inplace: bool = False) -> Tensor:
r"""
During training, randomly zeroes some of the elements of the input
tensor with probability :attr:`p` using samples from a Bernoulli
distribution.
See :class:`~torch.nn.Dropout` for details.
Args:
p: probability of an element to be zeroed. Default: 0.5
training: apply dropout if is ``True``. Default: ``True``
inplace: If set to ``True``, will do this operation in-place. Default: ``False``
"""
if has_torch_function_unary(input):
return handle_torch_function(dropout, (input,), input, p=p, training=training, inplace=inplace)
if p < 0.0 or p > 1.0:
raise ValueError("dropout probability has to be between 0 and 1, " "but got {}".format(p))
return _VF.dropout_(input, p, training) if inplace else _VF.dropout(input, p, training)
最终在Dropout.cpp中找到具体实现:
template<bool feature_dropout, bool alpha_dropout, bool inplace, typename T>
Ctype<inplace> _dropout_impl(T& input, double p, bool train) {
TORCH_CHECK(p >= 0 && p <= 1, "dropout probability has to be between 0 and 1, but got ", p);
if (p == 0 || !train || input.numel() == 0) {
return input;
}
if (p == 1) {
return multiply<inplace>(input, at::zeros({}, input.options()));
}
at::Tensor b; // used for alpha_dropout only
auto noise = feature_dropout ? make_feature_noise(input) : at::empty_like(input, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
noise.bernoulli_(1 - p);
if (alpha_dropout) {
constexpr double alpha = 1.7580993408473766;
double a = 1. / std::sqrt((alpha * alpha * p + 1) * (1 - p));
b = noise.add(-1).mul_(alpha * a).add_(alpha * a * p);
noise.mul_(a);
} else {
noise.div_(1 - p);
}
if (!alpha_dropout) {
return multiply<inplace>(input, noise);
} else {
return multiply<inplace>(input, noise).add_(b);
}
}
流程:
- 判断p的范围 以及训练状态
- 使用1-p的概率得到伯努利分布(0-1分布)
- (input / 1-p) * 伯努利分布
Drop Path
原理 :字如其名,Drop Path就是随机将深度学习网络中的多分支结构随机删除。
功能 :一般可以作为正则化手段加入网络,但是会增加网络训练的难度。尤其是在NAS问题中,如果设置的drop prob过高,模型甚至有可能不收敛。
实现 :
def drop_path(x, drop_prob: float = 0., training: bool = False):
if drop_prob == 0. or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
"""
def __init__(self, drop_prob=None):
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x):
return drop_path(x, self.drop_prob, self.training)
有了Dropout的理论铺垫,这里的实现就比较明了了,具体使用的时候一般是这样的:
x = x + self.drop_path(self.conv(x))
Drop Path不能直接这样使用:
x = self.drop_path(x)
Reference
https://www.cnblogs.com/dan-baishucaizi/p/14703263.html
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