Does resnet have fully connected layers?
In my understanding, fully connected layer(fc in short) is used for predicting.
For example, VGG Net used 2 fc layers, which are both 4096 dimension. The last layer for softmax has dimension same with classes num:1000.
But for resnet, it used global average pooling, and use the pooled result of last convolution layer as the input.
But they still has a fc layer! Does this layer a really fc layer? Or this layer is only to make input into a vector of features which number is classes number? Does this layer has function for prediction result?
In a word, how many fc layers do resnet and VGGnet have? Does VGGnet's 1st 2nd 3rd fc layer has different function?
Answer
VGG has three FC layers, two with 4096 neurons and one with 1000 neurons which outputs the class probabilities.
ResNet only has one FC layer with 1000 neurons which again outputs the class probabilities. In a NN classifier always the best choice is to use softmax, some authors make this explicit in the diagram while others do not.