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)

Building the training process

In the train_network function, we first define the optimizers for both the generator and the discriminator loss functions. We use the Adam optimizer for both the generators and the discriminators, since this is an advanced version of the stochastic gradient descent optimizer that works really well in training GANs. Adam uses a decaying average of gradients, much like momentum for steady gradient, and a decaying average of squared gradients that provides information about the curvature of the cost function. The variables pertaining to the different losses defined by tf.summary are written to the log files and can therefore be monitored through TensorBoard. The following is the detailed code for the train function:

def train_network(self):

self.learning_rate = tf.placeholder(tf.float32)
self.d_optimizer = tf.train.AdamOptimizer...