Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Classifying fashion products using CNN

We will now see how to use CNN for classifying fashion products.

First, we will import our required libraries as usual:

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline

Now, we will read the data. The dataset is available in tensorflow.examples, so we can directly extract the data as follows:

from tensorflow.examples.tutorials.mnist import input_data
fashion_mnist = input_data.read_data_sets('data/fashion/', one_hot=True)

We will check what we have in our data:

print("No of images in training set {}".format(fashion_mnist.train.images.shape))
print("No of labels in training set {}".format(fashion_mnist.train.labels.shape))

print("No of images in test set {}".format(fashion_mnist.test.images.shape))
print("No of labels in test set {}".format(fashion_mnist...