- The code is written in Python and hosted on GitHub. Though the compressed code repository is available for download, the online GitHub repository will receive bug fixes and updates. Having a basic understanding of GitHub is therefore required, as is having good Python knowledge.
- Although not mandatory, the use of CUDA drivers would help to speed up the training process if you are not using any pretrained models.
- The code examples were developed on an Ubuntu 18.10 machine but will work on all the popular platforms. But if you find any difficulties, feel free to raise an issue in the GitHub repository.
- Some of the examples in the book require you to use other services or packages, such as redis-server and the Flask framework. All those external dependencies and "how-to" guides are documented in the chapters they appear.
You can download the example code files for this book from your account at http://www.packt.com. If you purchased this book elsewhere, you can visit http://www.packt.com/support and register to have the files emailed directly to you.
You can download the code files by following these steps:
- Log in or register at http://www.packt.com.
- Select the SUPPORT tab.
- Click on Code Downloads & Errata.
- 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 macOS
- 7-Zip / PeaZip for Linux
The code bundle for the book is also hosted on GitHub at https://github.com/hhsecond/HandsOnDeepLearningWithPytorch. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: http://www.packtpub.com/sites/default/files/downloads/9781788834131_ColorImages.pdf.
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. For example; "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."
A block of code is set as follows:
def forward(self, batch):
hidden = self.hidden(batch)
activated = torch.sigmoid(hidden)
out = self.out(activated)
return out
When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:
def binary_encoder(input_size):
def wrapper(num):
ret = [int(i) for i in '{0:b}'.format(num)]
return [0] * (input_size - len(ret)) + ret
return wrapper
Any command-line input or output is written as follows:
python -m torch.utils.bottleneck /path/to/source/script.py [args]
Bold: Indicates a new term, an important word, or words that you see on the screen, for example, in menus or dialog boxes, also appear in the text like this. For example: "Select System info from the Administration panel."