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

Chapter 12. CNN with TensorFlow and Keras

Convolutional Neural Network (CNN) is a special kind of feed-forward neural network that includes convolutional and pooling layers in its architecture. Also known as ConvNets, the general pattern for the CNN architecture is to have these layers in the following sequence:

  1. Fully connected input layer
  2. Multiple combinations of convolutional, pooling, and fully connected layers
  3. Fully connected output layer with softmax activation

CNN architectures have proven to be highly successful in solving problems that involve learning from images, such as image recognition and object identification.

In this chapter, we shall learn the following topics related to ConvNets:

  • Understanding Convolution
  • Understanding Pooling
  • CNN architecture pattern-LeNet
  • LeNet for MNIST dataset
    • LeNet for MNIST with TensorFlow
    • LeNet for MNIST with Keras
  • LeNet for CIFAR dataset
    • LeNet CNN for CIFAR10 with TensorFlow
    • LeNet CNN for CIFAR10 with Keras

Let us start by learning the core concepts behind the...