How to solve Multi-Label Classification Problems in Deep Learning with Tensorflow & Keras?
This is the fourth part of the “How to solve Classification Problems in Keras?” series. Before starting this tutorial, I strongly suggest you go over Part A: Classification with Keras to learn all related concepts.
In this tutorial, we will focus on how to solve Multi-Label Classification Problems in Deep Learning with Tensorflow & Keras. First, we will download a sample Multi-label dataset. In multi-label classification problems, we mostly encode the true labels with multi-hot vectors. We will experiment with combinations of various last layer’s activation functions and loss functions of a Keras CNN model and we will observe the effects on the model’s performance.
During experiments, we will discuss the relationship between Activation & Loss functions, label encodings, and accuracy metrics in detail. We will understand why sometimes we could get surprising results when using different parameter settings other than the generally recommended ones. As a result, we will gain insight into activation and loss functions and their interactions. In the end, we will summarize the experiment results in a cheat table.
You can access the code at Colab and all the posts of the classification tutorial series at muratkarakaya.net. You can watch all these parts on YouTube in ENGLISH or TURKISH as well.
If you would like to follow up on Deep Learning tutorials, please subscribe to my YouTube Channel or follow my blog on muratkarakaya.net. Do not forget to turn on Notifications so that you will be notified when new parts are uploaded.
If you are ready, let’s get started!
Photo by Luca Martini on Unsplash
