Book Image

Machine Learning Solutions

Book Image

Machine Learning Solutions

Overview of this book

Machine learning (ML) helps you find hidden insights from your data without the need for explicit programming. This book is your key to solving any kind of ML problem you might come across in your job. You’ll encounter a set of simple to complex problems while building ML models, and you'll not only resolve these problems, but you’ll also learn how to build projects based on each problem, with a practical approach and easy-to-follow examples. The book includes a wide range of applications: from analytics and NLP, to computer vision domains. Some of the applications you will be working on include stock price prediction, a recommendation engine, building a chat-bot, a facial expression recognition system, and many more. The problem examples we cover include identifying the right algorithm for your dataset and use cases, creating and labeling datasets, getting enough clean data to carry out processing, identifying outliers, overftting datasets, hyperparameter tuning, and more. Here, you'll also learn to make more timely and accurate predictions. In addition, you'll deal with more advanced use cases, such as building a gaming bot, building an extractive summarization tool for medical documents, and you'll also tackle the problems faced while building an ML model. By the end of this book, you'll be able to fine-tune your models as per your needs to deliver maximum productivity.
Table of Contents (19 chapters)
Machine Learning Solutions
Foreword
Contributors
Preface
Index

Testing the model


Now we need to load the trained model and test it. Here, we will be using the video stream. The FER application will detect the emotion based on my facial expression. You can refer to the code using this GitHub link: https://github.com/jalajthanaki/Facial_emotion_recognition_using_TensorFlow/blob/master/emotion_recognition.py.

You can find the code snippet for this in the following figure:

Figure 10.35: Code snippet for loading the trained model and performing testing

In order to start testing, we need to execute the following command:

$ python emotion_recognition.py poc

This testing will use your webcam. I have some demo files that I want to share here. Refer to the following figure:

Figure 10.36: Code snippet for FER application identifying the emotion of disgust

Also refer to the the following figure:

Figure 10.37: tThe FER application identifying the happy emotion

Refer to the code snippet in the following figure:

Figure 10.38: Code snippet for the FER application identifying...