I have a list of values, ranging from 15000 to 25000. I have to separate them into two categories, such that (approx) 20000 will end up in category 1 and the rest in category 2. I figured out that the sigmoid activation should work for this. I am using the following layers in keras for that:
model = Sequential()
model.add(Dense(1 , input_dim =1 ))
model.add(Activation('sigmoid'))
model.add(Dense(2 , init='normal' , activation = 'softmax'))
model.compile(loss='mean_absolute_error', optimizer='rmsprop')
model.fit(X_train, y_train, validation_data=(X_test, y_test),epochs=10,batch_size=200,verbose=2)
However, when I run the model for my sample cases, all values end up in category 2. How can I improve this?