Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

Digit classifier

In this example, we'll define and train either a two-layer model or a convolutional model in the style of LeNet 5:

from six.moves import xrange   
import tensorflow as tf
import prettytensor as pt
from prettytensor.tutorial import data_utils

tf.app.flags.DEFINE_string(
'save_path', None, 'Where to save the model checkpoints.')
FLAGS = tf.app.flags.FLAGS

BATCH_SIZE = 50
EPOCH_SIZE = 60000 // BATCH_SIZE
TEST_SIZE = 10000 // BATCH_SIZE

Since we are feeding our data as numpy arrays, we need to create placeholders in the graph. These must then be fed using the feed dict.

image_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 28, 28, 1])
labels_placeholder = tf.placeholder\
(tf.float32, [BATCH_SIZE, 10])

tf.app.flags.DEFINE_string('model', 'full',
'Choose one of the models...