I want to clear about keras neural network options for classification of simple data where there are a number of features and one target column, as in iris flower dataset (Species is target):
SL, SW, PL, PW, Species 5.1, 3.5, 1.4, 0.2, setosa 4.9, 3.0, 1.4, 0.2, setosa 4.7, 3.2, 1.3, 0.2, setosa ... ...
I am finding that in almost all examples various combinations of
Dropout are the only options:
model = Sequential() model.add(Dense(12, input_dim=4, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(8, activation='relu')) model.add(Dense(1, activation='softmax'))
What other keras layers can be used in such situations, especially if the data is large, say with 50K rows and 100 features?
Edit: My specific question is whether Dense and Dropout are the only kind of layers for this purpose and such data?