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

Sentiment analysis

We will now write an app to predict sentiments of a movie review. Reviews are made up of a sequence of words and the order of words encodes very useful information to predict sentiment. The first step is to map words to word embeddings. The second step is the RNN that receives a sequence of vectors as input and considers the order of the vectors to generate the prediction.

Word embeddings

We will now train a neural network for word to vector representation. Given a particular word in the center of a sentence, which is the input word, we look at the words nearby. The network is going to tell us the probability for every word in our vocabulary of being the nearby word that we choose.

import time
import tensorflow as tf
import numpy as np
import utility
from tqdm import tqdm
from urllib.request import urlretrieve
from os.path import isfile, isdir
import zipfile
from collections import Counter
import random

dataDir = 'data'
dataFile = ''
datasetName = 'text 8 data set...