”;
Convolutional neural networks include a primary feature, extraction. Following steps are used to implement the feature extraction of convolutional neural network.
Step 1
Import the respective models to create the feature extraction model with “PyTorch”.
import torch import torch.nn as nn from torchvision import models
Step 2
Create a class of feature extractor which can be called as and when needed.
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)
Advertisements
”;