Deep Learning and AI Superhero

Practical Exercises Chapter 7

Exercise 1: Build and Train a Simple Autoencoder

Task: Build and train a simple autoencoder to reconstruct images from the MNIST dataset. Evaluate the quality of the reconstructions by visualizing the original and reconstructed images.

Solution:

import tensorflow as tf

from tensorflow.keras import layers, models

# Load the MNIST dataset

(x_train, _), (x_test, _) = tf.keras.datasets.mnist.load_data()

x_train = x_train.astype('float32') / 255.

x_test = x_test.astype('float32') / 255.

x_train = x_train.reshape((len(x_train), 28, 28, 1))

x_test = x_test.reshape((len(x_test), 28, 28, 1))

# Build the autoencoder model

input_img = layers.Input(shape=(28, 28, 1))

# Encoder

x = layers.Conv2D(16, (3, 3), activation='relu', padding ...