Book Image

Hands-On Deep Learning with TensorFlow

By : Dan Van Boxel
Book Image

Hands-On Deep Learning with TensorFlow

By: Dan Van Boxel

Overview of this book

Dan Van Boxel’s Deep Learning with TensorFlow is based on Dan’s best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan’s guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
Table of Contents (12 chapters)

A quick review of all the models

Let's recap each of the models we built, to model these fonts and some of their strengths and weaknesses:

At a glance, recall that we slowly built up more complicated models and took into account the structure of the data to improve our accuracy.

The logistic regression model

First, we started with a simple logistic regression model:

This has 36x36 pixels plus 1 bias times 5 classes total weights, or 6,485 parameters that we need to train. After 1,000 training epochs, this model achieved about 40 percent validation accuracy. Your results may vary. This is relatively poor, but the model has some advantages.

Let's glance back at the code:

# These will be inputs
## Input pixels, flattened
x = tf.placeholder("float", [None, 1296])
## Known labels
y_ = tf.placeholder("float", [None,5])

# Variables
W = tf.Variable(tf.zeros([1296,5]))
b = tf.Variable(tf.zeros([5]))

# Just initialize

# Define model
y = tf.nn.softmax(tf.matmul...