Difference between Dense and Activation layer in Keras
Using Dense(activation=softmax) is computationally equivalent to first add Dense and then add Activation(softmax). However there is one advantage of the second approach - you could retrieve the outputs of the last layer (before activation) out of such defined model. In the first approach - it's impossible.
I was wondering what was the difference between Activation Layer and Dense layer in Keras.
Since Activation Layer seems to be a fully connected layer, and Dense have a parameter to pass an activation function, what is the best practice ?
Let's imagine a fictionnal network like this :
Input -> Dense -> Dropout -> Final Layer
Final Layer should be : Dense(activation=softmax) or Activation(softmax) ?
What is the cleanest and why ?
Thanks everyone!