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


Pooling layers help with overfitting and improve performance by reducing the size of the input tensor. Typically, they are used to scale down the input, keeping important information. Pooling is a much faster mechanism for input size reduction compared with tf.nn.conv2d.

The following pooling mechanisms are supported by TensorFlow:

  • Average
  • Max
  • Max with argmax

Each pooling operation uses rectangular windows of size ksize separated by offset strides. If strides are all ones (1, 1, 1, 1), every window is used; if strides are all twos (1, 2, 2, 1), every other window is used in each dimension; and so on.

Max pool

The following defined function provides max pooling for the input 4D tensor tf.nn.max_pool:

  value, ksize, strides, padding, data_format='NHWC', name=None

The preceding arguments are explained here:

  • value: This is the 4D tensor with shape [batch, height, width, channels], type tf.float32 on which max pooling needs to be done.
  • ksize: This is the list of ints that has length...