What you need for this book

To be able to smoothly follow through the chapters, you will need the following pieces of software:

• TensorFlow 2.0 or higher
• Matplotlib 3.0 or higher
• Scikit-learn 0.18.1 or higher
• NumPy 1.15 or higher

The hardware specifications are as follows:

• Either 32-bit or 64-bit architecture
• 2+ GHz CPU
• 4 GB RAM
• At least 10 GB of hard disk space available

Downloading the example code

You can download the example code files for this book from your account at www.packt.com/. If you purchased this book elsewhere, you can visit www.packtpub.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at http://www.packt.com.
2. Select the Support tab.
3. Click on Code Downloads.
4. Enter the name of the book in the Search box and follow the on-screen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:

• WinRAR / 7-Zip for Windows
• Zipeg / iZip / UnRarX for Mac
• 7-Zip / PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow-2-and-Keras. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Download the color images

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here:

https://static.packt-cdn.com/downloads/9781838823412_ColorImages.pdf

Conventions

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "In addition, we load the true labels into Y_train and Y_test respectively and perform a one-hot encoding on them."

A block of code is set as follows:

from TensorFlow.keras.models import Sequential
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer='random_uniform'))

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

model = Sequential()
model.add(Dense(NB_CLASSES, input_shape=(RESHAPED,)))
model.add(Activation('softmax'))
model.summary()

Any command-line input or output is written as follows:

pip install quiver_engine

Bold: Indicates a new term and important word or words that you see on the screen. For example, in menus or dialog boxes, appear in the text like this: "Our simple net started with an accuracy of 92.22%, which means that about eight handwritten characters out of 100 are not correctly recognized."

Warnings or important notes appear in a box like this.

Tips and tricks appear like this.