DropPath 類似于Dropout,不同的是 Drop將深度學(xué)習(xí)模型中的多分支結(jié)構(gòu)隨機(jī)"失效",而Dropout是對神經(jīng)元隨機(jī)"失效"這篇文章主要給大家介紹了關(guān)于正則化DropPath/drop_path用法的相關(guān)資料,需要的朋友可以參考下

DropPath/drop_path 是一種正則化手段，其效果是將深度學(xué)習(xí)模型中的多分支結(jié)構(gòu)隨機(jī)”刪除“，python中實(shí)現(xiàn)如下所示：

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)  
    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):
    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)

調(diào)用如下：

self.drop_path = DropPath(drop_prob) if drop_prob > 0. else nn.Identity()

x = x + self.drop_path(self.token_mixer(self.norm1(x)))
x = x + self.drop_path(self.mlp(self.norm2(x)))

看起來似乎有點(diǎn)迷茫，這怎么就隨機(jī)刪除了分支呢

實(shí)驗(yàn)如下：

import torch

drop_prob = 0.2
keep_prob = 1 - drop_prob
x = torch.randn(4, 3, 2, 2)
shape = (x.shape[0],) + (1,) * (x.ndim - 1)
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_()
output = x.div(keep_prob) * random_tensor

輸出：

x.size():[4,3,2,2]
x：
tensor([[[[ 1.3833, -0.3703],
[-0.4608, 0.6955]],
[[ 0.8306, 0.6882],
[ 2.2375, 1.6158]],
[[-0.7108, 1.0498],
[ 0.6783, 1.5673]]],

[[[-0.0258, -1.7539],
[-2.0789, -0.9648]],
[[ 0.8598, 0.9351],
[-0.3405, 0.0070]],
[[ 0.3069, -1.5878],
[-1.1333, -0.5932]]],

[[[ 1.0379, 0.6277],
[ 0.0153, -0.4764]],
[[ 1.0115, -0.0271],
[ 1.6610, -0.2410]],
[[ 0.0681, -2.0821],
[ 0.6137, 0.1157]]],

[[[ 0.5350, -2.8424],
[ 0.6648, -1.6652]],
[[ 0.0122, 0.3389],
[-1.1071, -0.6179]],
[[-0.1843, -1.3026],
[-0.3247, 0.3710]]]])

random_tensor.size():[4, 1, 1, 1]
random_tensor：
tensor([[[[0.]]],
        [[[1.]]],
        [[[1.]]],
        [[[1.]]]])

output.size():[4,3,2,2]
output：
tensor([[[[ 0.0000, -0.0000],
          [-0.0000,  0.0000]],
         [[ 0.0000,  0.0000],
          [ 0.0000,  0.0000]],
         [[-0.0000,  0.0000],
          [ 0.0000,  0.0000]]],

        [[[-0.0322, -2.1924],
          [-2.5986, -1.2060]],
         [[ 1.0748,  1.1689],
          [-0.4256,  0.0088]],
         [[ 0.3836, -1.9848],
          [-1.4166, -0.7415]]],

        [[[ 1.2974,  0.7846],
          [ 0.0192, -0.5955]],
         [[ 1.2644, -0.0339],
          [ 2.0762, -0.3012]],
         [[ 0.0851, -2.6027],
          [ 0.7671,  0.1446]]],

        [[[ 0.6687, -3.5530],
          [ 0.8310, -2.0815]],
         [[ 0.0152,  0.4236],
          [-1.3839, -0.7723]],
         [[-0.2303, -1.6282],
          [-0.4059,  0.4638]]]])

random_tensor作為是否保留分支的直接置0項(xiàng)，若drop_path的概率設(shè)為0.2，random_tensor中的數(shù)有0.2的概率為0，而output中被保留概率為0.8。

結(jié)合drop_path的調(diào)用，若x為輸入的張量，其通道為[B,C,H,W]，那么drop_path的含義為在一個Batch_size中，隨機(jī)有drop_prob的樣本，不經(jīng)過主干，而直接由分支進(jìn)行恒等映射。

總結(jié)

到此這篇關(guān)于正則化DropPath/drop_path用法(Python實(shí)現(xiàn))的文章就介紹到這了,更多相關(guān)正則化DropPath/drop_path內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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