Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Scene understanding (semantic segmentation)

In the previous recipe, we focused on one or two specific classes. However, in some cases, you'll want to segment all classes in an image to understand the complete scene. For example, for self-driving cars, it's important that all objects surrounding the car are segmented. In the following recipe, we will segment one class for performance reasons. However, with this network, it is straightforward to scale to multiple classes. The network architecture we will be using is called a fully convolutional network, because we use only convolutional layers in our model. We will be using the pretrained weights of VGG16 and the TensorFlow framework. 

How to do it...

  1. First, we start with loading the libraries, as follows:
import os
import glob
import tensorflow as tf
  1. Because our task is slightly more than outputting the predicted class, we need to define a function that extracts the values from different layers:
def extract_layers(vgg_layer3_out, vgg_layer4_out...