I have data with about 100 numerical features and a multi-labelling that encodes ownership of a certain product (i.e. my labels are of the form $[x_i, i=1, \dots, n]$, where $n$ is the number of products and $x_i$ is either 0 or 1).
My neural network approach to this currently looks like this (in Keras)
model = Sequential()
model.add(Dense(1024, activation='relu', input_dim=X_train.shape[1]))
model.add(Dense(1024, activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(1024, activation='relu'))
model.add(Dense(y_train.shape[1], activation='softmax'))
So, it has a couple of dense layers with ReLu activation, then an output layer with softmax.
Now, my question is: will the neural network consider labels of the other products when assigning a probability to the label of one product?
I would like that to happen, but I can't quite grasp whether it does (my suspicion is no).
I'm new to multi-label classification and relatively new to NN in general, so I hope this isn't too inept a question.