Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By : Sujit Pal, Nick McClure
Book Image

TensorFlow Machine Learning Cookbook. - Second Edition

By: Sujit Pal, 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 allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Retraining existing CNN models

Training a new image recognition from scratch requires a lot of time and computational power. If we can take a prior-trained network and retrain it with our images, it may save us computational time. For this recipe, we will show how to use a pre-trained TensorFlow image recognition model and fine-tune it to work on a different set of images.

Getting ready

The idea is to reuse the weights and structure of a prior model from the convolutional layers and retrain the fully connected layers at the top of the network.

TensorFlow has created a tutorial about training on top of existing CNN models (refer to the first bullet point in the next See also section). In this recipe, we will illustrate how...