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

Fundamentals of Classification by Deep Learning with Tensorflow & Keras

In this post, we will focus on fundamental concepts for solving Classification Problems by Deep Learning with Tensorflow & Keras. When we design a model in Deep Neural Networks, we need to know how to select proper label encoding, Activation, and Loss functions, along with accuracy metrics according to the classification task at hand.

Thus, in this tutorial, we will first investigate the types of Classification Problems. Then, we will see the most frequently used label encodings in Keras. We will learn how to select Activation & Loss functions according to the given classification type and label encoding. Moreover, we will examine the details of accuracy metrics in TensorFlow / Keras.

At the end of the tutorial, I hope that we will have a good understanding of these concepts and their implementation in Keras.

Contents:

• types of Classification Problems,

• possible label encodings,

• Activation & Loss functions,

• accuracy metrics

Furthermore, we will also discuss how the target encoding can affect the selection of Activation & Loss functions.

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. 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 Eilis Garvey on Unsplash

 

Classification in Deep Learning with TensorFlow & Keras Tutorial Series

This is the index page of the “How to solve Classification Problems in Deep Learning with TensorFlow & Keras?” tutorial series.

We will cover all the topics related to solving Classification Problems with sample implementations in Python TensorFlow Keras.

You can access the codes, videos, and posts from the below links.

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.

Last updated: 26/07/2022

Photo by Cristina Gottardi on Unsplash

 

Multi-Class Text Classification with a GPT3 Transformer block: An End-to-End Example

Author: Murat Karakaya & Cansen Çağlayan
Date created: 05 Oct 2021
Last modified: 19 Oct 2021
Description: This tutorial has 2 parts as explained below. Part A: Data Analysis & Text Preprocessing and Part B: Text Classification. 

                                       Photo by Håkon Grimstad on Unsplash

 

How to Evaluate a Classifier Trained with an Imbalanced Dataset? Why Accuracy is not Enough?

Author: Murat Karakaya
Date created: 19 May 2020
Last modified: 09 Dec 2021
Description: In this tutorial series, we will discuss how to evaluate a classifier trained with an imbalanced dataset. We will see that accuracy metric is not enough to measure the performance of classifiers, especially, when you have an imbalanced dataset. Furthermore, we will implement 8 different classifier models and evaluate their success by comparing the various classification metric results. We will implement the solutions by Python and SciKit Learn library.

Accessible on:

• Murat Karakaya Akademi YouTube Channel in English or Turkish
• muratkarakaya.net
• Google Colab
• Kaggle
• Github
• Github pages
• Github Repo

Parts

I will deliver the content in 3 parts:

• Part A: Fundamentals, Metrics, Synthetic Dataset
• Part B: Dummy Classifiers, Accuracy, Precision, Recall, F1
• Part C: ROC, AUC, Worthless Test, Setting up threshold

Photo by Aziz Acharki on Unsplash