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

Localizing an object in images

Now that we can classify objects in images, the next step is to and classify (detect) objects in images. In the dataset we used in the previous recipe, the (objects) were clearly visible, mostly centered, and they covered almost the complete image. However, often this is not the case and we'd want to detect one or multiple objects in an image. In the following recipe, we will show you how to detect an object in images using deep learning.

We will be using a dataset with annotated trucks. The images are taken by a camera mounted at the front of a car. We will be using TensorFlow to implement the object detector.

How to do it...

  1. Let's the first:
import numpy as np
import pandas as pd
import glob
import cv2
import matplotlib.pyplot as plt

from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

from keras.models import Sequential, load_model
from keras.layers import Dense...