LSTM using generator function
Personally, it is recommended to use the PReLU activation function before the fully connected dense layer.
For example：
model.add(LSTM(128,input_shape=(train_X.shape[1],train_X.shape[2])))
model.add(BatchNormalization())
model.add(Dropout(.2))
model.add(Dense(64))
model.add(PReLU())
I am trying to create a model that has an LSTM layer of 100 units with input dimensions (16,48,12) (16 is the batch size as it takes input through a generator function). The generator function produces an expected output of (16, 1, 2) (16 is the batch size) and I want to use as output a dense layer with a softmax activation fucntion. What would be the best way to do that? I am fairly new to keras and I can't quiet get the grasp of using generator functions...