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

Recognizing faces

In the previous recipe, we how to detect facial keypoints with a neural network. In the following recipe, we will show how to recognize faces using a deep neural network. By training a classifier from scratch, we get a lot of flexibility.

How to do it...

  1. As usual, let's start with importing the libraries and setting the seed:
import glob
import re
import matplotlib.pyplot as plt
import numpy as np
import cv2
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

from keras.models import Model
from keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, GlobalMaxPooling2D, Activation
from keras.layers import Convolution2D, MaxPooling2D
from keras import optimizers
from keras import backend as K

seed = 2017
  1. In the following step, we will load the data and output some example images to get an idea of the data:
 DATA_DIR = 'Data/lfw/'
images = glob.glob(DATA_DIR + '*/*.jpg')