Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Table of Contents (21 chapters)
Title Page
About the Author
About the Reviewer
Customer Feedback

Learning to play games with deep reinforcement learning

In this recipe, we will demonstrate how to leverage learning models to play games. In our example, we show how to apply a Deep-Q Network for playing with the Keras framework.

How to do it...

  1. Let's start with importing the necessary libraries, as follows:
import gym
import random
import numpy as np
import matplotlib.pyplot as plt
from collections import deque

from keras.models import Sequential
from keras.optimizers import Adam
from keras.layers import Dense, Flatten
from keras.layers.convolutional import Conv2D
from keras import backend as K
  1. First, we will plot an example input image of the game:

Figure 11.1: Example input image of Breakout by OpenAI

env = gym.make('BreakoutDeterministic-v4')
observation = env.reset()

for i in range(3):
    # The ball is released after 2 frames
    if i > 1:

    # Get the next observation
    observation, _, _, _ ...