In this post, we’ll go through the definition of a multi-label classifier, multiple losses, text preprocessing and a step-by-step explanation on how to build a multi-output RNN-LSTM in Keras.

The dataset that we’ll be working on consists of natural disaster messages that are classified into 36 different classes. The dataset was provided by Figure Eight. Example of input messages:

['Weather update - a cold front from Cuba that could pass over Haiti',
 'Is the Hurricane over or is it not over',
 'Looking for someone but no name',
 'UN reports Leogane 80-90 destroyed. Only Hospital St. Croix functioning. Needs supplies desperately.',
 'says: west side of Haiti, rest of the country today and tonight']
Softmax Classification function in a Neural Network

You can check the full blog post on my Medium at Towards Data Science: Text Classifier with Multiple Outputs and Multiple Losses in Keras