# Is this neural network with a softmax in the output layer suitable for multi-label classification?

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()


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.