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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks and build intelligent systems with Python

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Product type Paperback
Published in Mar 2018
Publisher Packt
ISBN-13 9781788831109
Length 484 pages
Edition 2nd Edition
Languages
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Authors (2):
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Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Table of Contents (13) Chapters Close

Preface 1. Getting Started with Deep Learning 2. A First Look at TensorFlow FREE CHAPTER 3. Feed-Forward Neural Networks with TensorFlow 4. Convolutional Neural Networks 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. Heterogeneous and Distributed Computing 8. Advanced TensorFlow Programming 9. Recommendation Systems Using Factorization Machines 10. Reinforcement Learning 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.

Implementing a LeNet-5 step by step

Figure 7: Data flow of the first two convolutional layers

The 64 resulting images are...

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