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)

Building a denoising autoencoder

The network architecture is very simple. An input image, of size 784 pixels, is stochastically corrupted, and then it is dimensionally reduced by an encoding network layer. The reduction step is from 784 to 256 pixels.

In the decoding phase, we prepare the network for output, re-changing the original image size from 256 to 784 pixels.

As usual, we start loading all the necessary libraries to our implementation:

import numpy as np 
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data

Set the basic network parameters:

n_input    = 784  
n_hidden_1 = 256
n_hidden_2 = 256
n_output = 784

We also set the session's parameters:

epochs     = 110 
batch_size = 100
disp_step = 10

We build the training and test sets. We again use the input_data feature imported from the tensorflow.examples.tutorials.mnist library...