📅  最后修改于: 2020-12-10 05:25:00             🧑  作者: Mango
卷积神经网络包括一个主要特征提取。使用以下步骤来实现卷积神经网络的特征提取。
导入各个模型以使用“ PyTorch”创建特征提取模型。
import torch
import torch.nn as nn
from torchvision import models
创建一类可以在需要时调用的特征提取器。
class Feature_extractor(nn.module):
def forward(self, input):
self.feature = input.clone()
return input
new_net = nn.Sequential().cuda() # the new network
target_layers = [conv_1, conv_2, conv_4] # layers you want to extract`
i = 1
for layer in list(cnn):
if isinstance(layer,nn.Conv2d):
name = "conv_"+str(i)
art_net.add_module(name,layer)
if name in target_layers:
new_net.add_module("extractor_"+str(i),Feature_extractor())
i+=1
if isinstance(layer,nn.ReLU):
name = "relu_"+str(i)
new_net.add_module(name,layer)
if isinstance(layer,nn.MaxPool2d):
name = "pool_"+str(i)
new_net.add_module(name,layer)
new_net.forward(your_image)
print (new_net.extractor_3.feature)