Book Image

Neural Network Programming with Tensorflow

By : Manpreet Singh Ghotra, Rajdeep Dua
Book Image

Neural Network Programming with Tensorflow

By: Manpreet Singh Ghotra, Rajdeep Dua

Overview of this book

If you're aware of the buzz surrounding the terms such as "machine learning," "artificial intelligence," or "deep learning," you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that. You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders. By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Additive Gaussian Noise autoencoder

What are Denoising autoencoders? They are very similar to the basic model we saw in previous sections, the difference is that, the input is corrupted before being passed to the network. By matching the original version (not the corrupted one) with the reconstruction at training time, this autoencoder gets trained to reconstruct the original input image from the corrupted image.  The ability to reconstruct original image from corrupted image makes autoencoder very smart.

An additive noise autoencoder uses the following equation to add corruption to incoming data:

xcorr = x + scale*random_normal(n)

The following is the detail describe about the preceding equation:

  • x is the original image
  • scale is the multiplier for a random normal number generated from n
  • n is the number of training samples
  • xcorr is the corrupted output

Autoencoder class

We initialize the autoencoder defined in class AdditiveGaussianNoiseAutoEncoder by passing following parameters:

  • num_input: Number...