Examples

The 'Examples' folder contains demos and examples to get started with CNTK. The examples are structured by topic into Image, Speech, Text and Other. The individual folders contain on the first level at least one self-contained example. These cover different types of networks including FF, CNN, RNN and LSTM. Further examples for each category are provided in the corresponding Miscellaneous subfolder. Each folder contains a Readme file that explains how to run the example on Windows and Linux. How to run the examples on Philly (https://philly) is explained in the Philly portal wiki (Philly is an internal GPU cluster for Microsoft production runs).

The examples shown in the table below provide a good introduction to CNTK. Please refer to the Readme file in the corresponding repository folder for further details (you can use the links in the table below to get to the corresponding Readme).

Folder	Domain	Network types
Other/Simple2d	Synthetic 2d data	FF (CPU and GPU)
Speech/AN4	Speech data (CMU AN4)	FF and LSTM
Image/GettingStarted	Image data (MNIST handwritten digit recognition)	CNN
Text/PennTreebank	Text data (penn treebank)	RNN
Text/ATIS	Language understanding task (ATIS)	LSTM
SequenceToSequence/CMUDict	Letter-to-sound (CMUDict)	Sequence -to-sequence

The best way to learn about the APIs currently is to look at the following examples in the [CNTK clone root]/bindings/python/examples directory:

• MNIST: A fully connected feed-forward model for classification of MNIST images. (follow the instructions in Examples/Image/DataSets/MNIST/README.md)

• CifarRest: An image classification ResNet model for training on the CIFAR image dataset. (follow the instructions in Examples/Image/DataSets/CIFAR-10/README.md to get the CIFAR dataset and convert it to the CNTK supported format)

• SequenceClassification: An LSTM sequence classification model for text data.

• Sequence2Sequence: A sequence to sequence grapheme to phoneme translation model that trains on the CMUDict corpus.

• NumpyInterop – numpy interop example showing how to train a simple feed-forward network with training data fed using numpy arrays.

• Language Understanding – Language Understanding.