Book Image

TensorFlow Machine Learning Projects

By : Ankit Jain, Amita Kapoor
Book Image

TensorFlow Machine Learning Projects

By: Ankit Jain, Amita Kapoor

Overview of this book

TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work.
Table of Contents (23 chapters)
Title Page
Copyright and Credits
Dedication
About Packt
Contributors
Preface
Index

Training and testing the model


The following are the steps for training and testing the model:

  1. The first step is to read the training and testing datasets. Here are steps we must implement for reading the data: 
    • First, we load the training/testing images and label data from the files we downloaded for the Fashion MNIST data (https://github.com/zalandoresearch/fashion-mnist).
    • Then, we reshape the image data to a shape of 28 x 28 x 1 for our model and normalize it by 255 to keep the input of the model between 0 and 1.
    • We split the training data into train and validation datasets, each with 55,000 and 5000 images respectively.
    • We convert our target array y for both training and testing datasets so that we have a one-hot representation of the 10 classes in the dataset that we are going to feed into the model. 

Note

Make sure to choose around 10% of data for out validation. In this project, we choose 5000 random images (8% of the total images) for the validation data set.

The code for the preceding...