Book Image

Machine Learning Using TensorFlow Cookbook

By : Luca Massaron, Alexia Audevart, Konrad Banachewicz
Book Image

Machine Learning Using TensorFlow Cookbook

By: Luca Massaron, Alexia Audevart, Konrad Banachewicz

Overview of this book

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.
Table of Contents (15 chapters)
5
Boosted Trees
11
Reinforcement Learning with TensorFlow and TF-Agents
13
Other Books You May Enjoy
14
Index

Turning a Keras model into an Estimator

Up to now, we have worked out our linear regression models using specific Estimators from the tf.estimator module. This has clear advantages because our model is mostly run automatically and we can easily deploy it in a scalable way on the cloud (such as Google Cloud Platform, offered by Google) and on different kinds of servers (CPU-, GPU-, and TPU-based). Anyway, by using Estimators, we may lack the flexibility in our model architecture as required by our data problem, which is instead offered by the Keras modular approach that we discussed in the previous chapter. In this recipe, we will remediate this by showing how we can transform Keras models into Estimators and thus take advantage of both the Estimators API and Keras versatility at the same time.

Getting ready

We will use the same Boston Housing dataset as in the previous recipe, while also making use of the make_input_fn function. As before, we need our core packages to be imported...