Hi all, I wanted to know if it is needed to retrain the pretrained pytorch model if I wish to add a softmax layer at the end to get the class probabilities in case of classification problem?
Eg.
net = torchvision.models.resnet50(pretrained = True)
net.fc = nn.Sequential(nn.Linear(in_features=2048, out_features=1000))
net.fc = nn.Sequential(*list(net.fc)+[nn.Softmax(1)])

I am exporting this modified resnet50 pretrained model as onnx. On deployment on my browser, the class probabilities are being returned as 0% for top-4 classes. Previously using the default pretrained model it was coming as 1600% and so as there isn’t a context of class probabilities in the output layer in default pretrained resnet50.
Is there a way I can introduce softmax without needing to train the network again?
Thanks!!