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

Image classification with convolutional networks

Let's look at a more realistic case for using CNNs; we will use the Stanford Dogs versus Cats dataset. This dataset has 100+ images of dogs and cats.


You can download this dataset (100 images each) from the following location:

  1. Import the relevant functions and Python classes:
import matplotlib.pyplot as plt
import tensorflow as tf
import pandas as pd
import numpy as np
from sklearn.metrics import confusion_matrix
import time
from datetime import timedelta
import math
import dataset
import random
  1. We will define the parameters for the convolution layers. There are three convolution layers with the following parameters:

Layer number

Layer type

Number of filters/neurons



32 filters



32 filters



64 filters


Fully connected

128 neurons

The Network topolgy can be represented as shown in the following diagram:

The following code should be helpful for understanding...