Book Image

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By : Willem Meints
Book Image

Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

By: Willem Meints

Overview of this book

Cognitive Toolkit is a very popular and recently open sourced deep learning toolkit by Microsoft. Cognitive Toolkit is used to train fast and effective deep learning models. This book will be a quick introduction to using Cognitive Toolkit and will teach you how to train and validate different types of neural networks, such as convolutional and recurrent neural networks. This book will help you understand the basics of deep learning. You will learn how to use Microsoft Cognitive Toolkit to build deep learning models and discover what makes this framework unique so that you know when to use it. This book will be a quick, no-nonsense introduction to the library and will teach you how to train different types of neural networks, such as convolutional neural networks, recurrent neural networks, autoencoders, and more, using Cognitive Toolkit. Then we will look at two scenarios in which deep learning can be used to enhance human capabilities. The book will also demonstrate how to evaluate your models' performance to ensure it trains and runs smoothly and gives you the most accurate results. Finally, you will get a short overview of how Cognitive Toolkit fits in to a DevOps environment
Table of Contents (9 chapters)

Taking control over the minibatch loop

In the previous section, we've seen how to use the CTF format with MinibatchSource to feed data to the CNTK trainer. But most datasets don't come in this format. So, you can't really use this format unless you create your own dataset or convert the original dataset to the CTF format.

CNTK currently supports a limited set of deserializers for images, text, and speech. You can't extend the deserializers at the moment, which limits what you can do with the standard MinibatchSource. You can create your own UserMinibatchSource, but this is a complicated process. So, instead of showing you how to build a custom MinibatchSource, let's look at how to feed data into the CNTK trainer manually.

Let's first recreate the model we used to classify Iris flowers:

from cntk import default_options, input_variable
from cntk.layers...