Gender classification from face images using a CNN
In this section, we are going to implement a CNN for gender classification from face images using the CelebA dataset. As you already saw in Chapter 13, Parallelizing Neural Network Training with TensorFlow, the CelebA dataset contains 202,599 face images of celebrities. In addition, 40 binary facial attributes are available for each image, including gender (male or female) and age (young or old).
Based on what you have learned so far, the goal of this section is to build and train a CNN model for predicting the gender attribute from these face images. Here, for simplicity, we will only be using a small portion of the training data (16,000 training examples) to speed up the training process. However, in order to improve the generalization performance and reduce overfitting on such a small dataset, we will use a technique called data augmentation.
