I am looking to do some experiments in transfer learning using Resnet18. Currently I replace the fully connected layer as follows :
fc_inputs = model.fc.in_features
model.fc = nn.Sequential(nn.Linear(fc_inputs, num_classes),)
I am trying to figure out how to remove conv5_x and connect the fully connected layer to the conv4_x layer. ( Or connect it to conv3_x )
I want to compare the results tapping into the network at different places.
If we import the ResNet model
conv5_x will be
layer4. We could do the following:
model = torchvision.models.resnet18(pretrained=True)
model.layer4 = nn.Identity()
model.fc = nn.Linear(256, num_classes)
And you could use on the
model.fc = nn.Sequential(nn.Linear(), ...) if you wish to write several linear layers.
Thanks for your reply and and for the transfer learning it looks like it would work great. I will test it.
Is there a way to completely remove the conv4_x layer from the model?