Python Deep Learning Cookbook

How to do it...

1. As usual, let's start with importing the libraries and setting the seed:

import globimport reimport matplotlib.pyplot as pltimport numpy as npimport cv2from sklearn.preprocessing import LabelBinarizerfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom keras.models import Modelfrom keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, GlobalMaxPooling2D, Activationfrom keras.layers import Convolution2D, MaxPooling2Dfrom keras import optimizersfrom keras import backend as Kseed = 2017

2. 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')plt.figure(figsize=(10, ...