What's the right way to setup an image classifier by multiple params?
You normally one hot encode your labels so that every possible attribute gets it's own binary representation. So if you have 10 attributes they would be represented as [attr1, attr2, attr3, ..., attr10], with values either 1 if the attribute is present, or 0 if it is not. 

Then you train a network with the same number of output neurons as possible categories and use a sigmoid activation function.