文章目录
核心代码实现运行结果
本文对预先训练好的vgg16 model, 进行了反relu、反池化、反卷积，从而可视化vgg16的卷积过程。
项目运行环境：win10 + python2.7 + cpu
python库：numpy + chainer + matplotlib
关于对论文的理解可参考：https://www.cnblogs.com/hellcat/p/7149348.html
核心代码实现
# n_activation_maps: 表示feature map的数量
for i in range(n_activation_maps): # For each channel
h = hs[layer_idx].copy()
condition = xp.zeros_like(h)
condition[0][i] = 1 # 将目前感兴趣的feature map保留下来，其他的都置为0
h = Variable(xp.where(condition, h, xp.zeros_like(h)))
for i in reversed(range(layer_idx + 1)):
p = self.mps[i]
# 反池化
h = F.upsampling_2d(h, p.indexes, p.kh, p.sy, p.ph, unpooling_sizes[i])
for deconv in reversed(self.deconv_blocks[i]):
# 反relu --> 反卷积
h = deconv(F.relu(h))
运行结果
输入图片：
输出结果： 该结果表明输入图片属于熊猫的概率为97.7%。
['n02510455'
'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca'] probability:0.977424144745
['n02509815'
'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens'] probability:0.0196101851761
['n02133161'
'American black bear, black bear, Ursus americanus, Euarctos americanus'] probability:0.000676636816934
['n02445715' 'skunk, polecat, wood pussy'] probability:0.000416155613493
['n02447366' 'badger'] probability:0.000290626252536
卷积过程可视化： 即在卷积过程中，都做了些什么，提取了哪些特征
conv_block1:
conv_block2:
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