So, I have a trained classification model. Which works pretty well, given any unknown image. We can use class activation map to see which features are getting activated to classify a given object.
In case of binary classification, some features play role to decide ‘0’, some play role to decide ‘1’. This features/weights are decided just before the fully connected classification layer, per my understanding.


so In case of densenet169 trained model, How can I get access to those weights before the last FC layer ?
Can you give any idea/code/examples to distinguish the deciding features and plot them which are contributing to make decision.


Thanks in advance.