Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Implementing a LeNet-5 step by step


In this section, we will learn how to build a LeNet-5 architecture to classify images in the MNIST dataset. The next figure shows how the data flows in the first two convolutional layers: the input image is processed in the first convolutional layer using the filter weights. This results in 32 new images, one for each filter in the convolutional layer. The images are also down-sampled with the pooling operation, so the image resolution is decreased from 28×28 to 14×14. These 32 smaller images are then processed in the second convolutional layer. We need filter weights again for each of these 32 images and we need filter weights for each output channel of this layer. The images are again down-sampled with a pooling operation, so that the image resolution is decreased from 14×14 to 7×7. The total number of features for this convolutional layer is 64.

Figure 7: Data flow of the first two convolutional layers

The 64 resulting images are filtered again by a ...