Book Image

Intelligent Projects Using Python

By : Santanu Pattanayak
Book Image

Intelligent Projects Using Python

By: Santanu Pattanayak

Overview of this book

This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle.
Table of Contents (12 chapters)

The generators of the DiscoGAN

The generators of the DiscoGAN are feed-forward convolutional neural networks where the input and output are images. In the first part of the network, the images are scaled down in spatial dimensions while the number of the output feature maps increases as the layers progress. In the second part of the network, the images are scaled up along the spatial dimensions, while the number of output feature maps reduce from layer to layer. In the final output layer, an image with the same spatial dimensions as that of the input is generated. If a generator that converts an image xA to xAB from domain A to domain B is represented by GAB, then we have .

Illustrated here is the build_generator function, which can we used to build the generators for the DiscoGAN network:

def build_generator(self,image,reuse=False,name='generator'):
with tf.variable_scope...