Book Image

Keras 2.x Projects

By : Giuseppe Ciaburro
Book Image

Keras 2.x Projects

By: Giuseppe Ciaburro

Overview of this book

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this book, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems.

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Keras 2.x Projects
Giuseppe Ciaburro
Dec, 2018 | 394 pages
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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
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1
Getting Started with Keras
Getting Started with Keras
Introduction to Keras
Keras backend options
Installation
Model fitting in Keras
Summary
2
Modeling Real Estate Using Regression Analysis
Modeling Real Estate Using Regression Analysis
Defining a regression problem
Creating a linear regression model
Multiple linear regression concepts
Neural networks for regression using Keras
Summary
3
Heart Disease Classification with Neural Networks
Heart Disease Classification with Neural Networks
Basics of classification problems
Different types of classification
Pattern recognition using a Keras neural network
Summary
4
Concrete Quality Prediction Using Deep Neural Networks
Concrete Quality Prediction Using Deep Neural Networks
Basic concepts of ANNs
Multilayer neural networks
Implementing multilayer neural networks in Keras
Building a Keras deep neural network model
Improving the model performance by removing outliers
Summary
5
Fashion Article Recognition Using Convolutional Neural Networks
Fashion Article Recognition Using Convolutional Neural Networks
Understanding computer vision concepts
Convolutional neural networks
Common CNN architecture
Implementing a CNN for object recognition
Summary
6
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
Movie Reviews Sentiment Analysis Using Recurrent Neural Networks
Sentiment analysis basic concepts
Recurrent neural networks
Classifying sentiment in movie reviews using an RNN
Summary
7
Stock Volatility Forecasting Using Long Short-Term Memory
Stock Volatility Forecasting Using Long Short-Term Memory
The basics of forecasting
Time series analysis
Time series models
Long short-term memory in Keras
Implementing an LSTM to forecast stock volatility
Summary
8
Reconstruction of Handwritten Digit Images Using Autoencoders
Reconstruction of Handwritten Digit Images Using Autoencoders
Basic concepts of image recognition
Optical character recognition
Generative neural networks
The Keras autoencoders model
Implementing autoencoder Keras layers to reconstruct handwritten digit images
Summary
9
Robot Control System Using Deep Reinforcement Learning
Robot Control System Using Deep Reinforcement Learning
Robot control overview
The environment for controlling robot mobility
Reinforcement learning basics
Keras DQNs
DQN to control a robot's mobility
Summary
10
Reuters Newswire Topics Classifier in Keras
Reuters Newswire Topics Classifier in Keras
Natural language processing
The Natural Language Toolkit
Implementing a DNN to label sentences
Summary
11
What is Next?
What is Next?
Deep learning methods
Automated machine learning
Differentiable neural computer
Genetic programming and evolutionary strategies
Inverse reinforcement learning
Summary
12
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Building a Keras deep neural network model

After exploring the dataset, it's time to build our deep neural network model, so as to predict the quality of concrete from the characteristics of its ingredients. We prepare the data before proceeding. We split the starting data into two sets: the training set and test set. The training set is used to train a classification model and the test set to test model performance.

To split the data, scikit-learn library has been used. More specifically, the sklearn.model_selection.train_test_split() function has been used. This function quickly computes a random split into training and test sets. Let's start by importing the function:

from sklearn.model_selection import train_test_split

Let's start by splitting the DataFrame into predictors and response:

Predictors = pd.DataFrame(DataScaled.iloc[:,:8])
Response = pd.DataFrame...
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