Hi,
Again, thank you very much for your response. I feel there’s still a gap in my understanding, however. I understand that they are trained with CrossEntropyLoss which includes Softmax, but I’m trying to understand the output from the network when a test image is passed through it. In that particular case, wouldn’t the output be different from VGG output, which has the softmax layer as part of the architecture? Yes, we will throw this away in the transfer learning use case, but as a hypothetical, say I wanted to just use VGG as a classifier; in that case, I wouldn’t get pseudo-probabilities that sum to 1, correct? If that is the case, is the main reason the softmax layer was left off merely because no one would be using it?
And once more, thank you for the help in understanding this.
Thanks!