Keras lstm and dense layer

Question

How is dense layer changing the output coming from LSTM layer? How come that from 50 shaped output from previous layer i get output of size 1 from dense layer that is used for prediction? Lets say i have this basic model:

model = Sequential()
model.add(LSTM(50,input_shape=(60,1)))
model.add(Dense(1, activation="softmax"))

Is the Dense layer taking the values coming from previous layer and assigning the probablity(using softmax function) of each of the 50 inputs and then taking it out as an output?

Answer 1

No, Dense layers do not work like that, the input has 50-dimensions, and the output will have dimensions equal to the number of neurons, one in this case. The output is a weighted linear combination of the input plus a bias.

Note that with the softmax activation, it makes no sense to use it with a one neuron layer, as the softmax is normalized, the only possible output will be constant 1.0. That's probably now what you want.