How to tackle a multilabel classification problem

I am trying to build a LSTM model for a multiclass classification problem on textual data. Until now, I have only built a model when one input belongs to one of the categories. What do I do when one input can belong to more than one class (i.e.: one entry of data can belong to 2-3 categories)? Can anyone help me with some blogs or resources to build an intuition for making such a model?

Thanks.

• What is the proportion of such entries in your dataset? If it is less, then removing such entries is the best choice. – Shubham Panchal Jun 6 '19 at 8:12
• @ShubhamPanchal I have around 9000 data points, the number of training points per class varies from 450 to 1500 out of 9000 data points. – grvkgp Jun 7 '19 at 6:51
• course.fast.ai/videos/?lesson=3 thank me later go watch it get a great score sleep soundly have a good day and give some upvotes. – khwaja wisal Aug 30 '19 at 21:38

LSTMs, like any other neural net, implicitly support multi-label classification. You should ensure that your output layer has $$n$$ neurons, one for each class, and you should use logistic activation rather than softmax activation (which is typically used for the final layer in multi-class problems).
Each neuron $$N_i$$ in the final layer will output a value between 0 and 1. If the output of $$N_i$$ is greater than 0.5, then the example is a member of label $$i$$.