Book Image

TensorFlow Machine Learning Cookbook

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook

By: Nick McClure

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow. This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
Table of Contents (19 chapters)
TensorFlow Machine Learning Cookbook
About the Author
About the Reviewer
Customer Feedback

Evaluating Models

We have learned how to train a regression and classification algorithm in TensorFlow. After this is accomplished, we must be able to evaluate the model's predictions to determine how well it did.

Getting ready

Evaluating models is very important and every subsequent model will have some form of model evaluation. Using TensorFlow, we must build this feature into the computational graph and call it during and/or after our model is training.

Evaluating models during training gives us insight into the algorithm and may give us hints to debug it, improve it, or change models entirely. While evaluation during training isn't always necessary, we will show how to do this with both regression and classification.

After training, we need to quantify how the model performs on the data. Ideally, we have a separate training and test set (and even a validation set) on which we can evaluate the model.

When we want to evaluate a model, we will want to do so on a large batch of data points. If...