Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Building a CNN model in TensorFlow

Before we start, there is a bit of good news: using TensorFlow, you don't need to take care about writing backpropagation or gradient descent code and also all common types of layers are already implemented, so things should be easier.

In the TensorFlow example here, we will change things a bit from what you learned in Chapter 1, Setup and Introduction to TensorFlow, and use the tf.layers API to create whole layers of our network with ease:

import tensorflow as tf 
from tensorflow.examples.tutorials.mnist import input_data 
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) 
# MNIST data input (img shape: 28*28) 
num_input = 28*28*1 
# MNIST total classes (0-9 digits) 
num_classes = 10 
# Define model I/O (Placeholders are used to send/get information from graph) 
x_ = tf.placeholder("float", shape=[None, num_input], name='X') 
y_ = tf.placeholder("float", shape=[None, num_classes], name='Y') 
# Add dropout to the fully connected layer