Book Image

Python: Advanced Guide to Artificial Intelligence

By : Giuseppe Bonaccorso, Rajalingappaa Shanmugamani
Book Image

Python: Advanced Guide to Artificial Intelligence

By: Giuseppe Bonaccorso, Rajalingappaa Shanmugamani

Overview of this book

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso • Mastering TensorFlow 1.x by Armando Fandango • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani
Table of Contents (31 chapters)
Title Page
About Packt
Contributors
Preface
19
Tensor Processing Units
Index

Denoising autoencoder in TensorFlow


As you learned in the first section of this chapter, denoising autoencoders can be used to train the models such that they are able to remove the noise from the images input to the trained model:

  1. For the purpose of this example, we write the following helper function to help us add noise to the images:
def add_noise(X):
    return X + 0.5 * np.random.randn(X.shape[0],X.shape[1])
  1. Then we add noise to test images and store it in a separate list:
test_images_noisy = add_noise(test_images)

We will use these test images to test the output from our denoising model examples.

  1. We build and train the denoising autoencoder as in the preceding example, with one difference: While training, we input the noisy images to the input layer and we check the reconstruction and denoising error with the non-noisy images, as the following code shows:
X_batch, _ = mnist.train.next_batch(batch_size)
X_batch_noisy = add_noise(X_batch)
feed_dict={x: X_batch_noisy, y: X_batch}
_,batch_loss...