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.
Table of Contents (13 chapters)

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...