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Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide

You're reading from   Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide A practical guide to building neural networks using Microsoft's open source deep learning framework

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781789802993
Length 208 pages
Edition 1st Edition
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Author (1):
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Willem Meints Willem Meints
Author Profile Icon Willem Meints
Willem Meints
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Toc

Table of Contents (9) Chapters Close

Preface 1. Getting Started with CNTK FREE CHAPTER 2. Building Neural Networks with CNTK 3. Getting Data into Your Neural Network 4. Validating Model Performance 5. Working with Images 6. Working with Time Series Data 7. Deploying Models to Production 8. Other Books You May Enjoy

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...
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