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
Tensor Processing Units

MLP for image classification

Let's build the MLP network for image classification using different libraries, such as TensorFlow, Keras, and TFLearn. We shall use the MNIST data set for the examples in this section.

The MNIST dataset contains the 28x28 pixel images of handwritten digits from 0 to 9, and their labels, 60K for the training set and 10K for the test set. The MNIST dataset is the most widely used data set, including in TensorFlow examples and tutorials.


The MNIST dataset and related documentation are available from the following link:  

Let us start with the pure TensorFlow approach.

TensorFlow-based MLP for MNIST classification

First, load the MNIST dataset, and define the training and test features and the targets using the following code:

from tensorflow.examples.tutorials.mnist import input_data
mnist_home = os.path.join(datasetslib.datasets_root, 'mnist')
mnist = input_data.read_data_sets(mnist_home, one_hot=True)

X_train = mnist.train.images...