CNTK/Scripts
Mark Hillebrand afd0a87450 Scripts/install/linux/install-cntk.sh: fix conda env update 2016-11-21 15:21:43 +01:00
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install Scripts/install/linux/install-cntk.sh: fix conda env update 2016-11-21 15:21:43 +01:00
README.md move install files into new install subdirectory beneath the script directory, remove obsolete files, update readme in script directory 2016-11-15 15:59:24 +01:00
pytest.ini Adding tests 2016-06-13 10:16:23 +02:00
txt2ctf.py txt2ctf.py: escape pipe symbol 2016-10-28 15:44:26 +02:00
uci2ctf.py Moving uci to cntk script to the script folder 2016-07-06 11:44:55 +02:00

README.md

This directory contains different scripts to support CNTK.

CNTK Binary Installers

The directory install contains scripts which are used in the CNTK binary download to install CNTK on a users system. They are not intended to run from this location in the repository.

  • install/windows - A script for installing a Windows CNTK binary drop, cf. here.
  • install/linux - A script for installing a Linux CNTK binary drop, cf. here.

CNTK Text format Converters

Two Python Scripts for converting Data to CNTK Text format for using as an input for CNTK Text Format Reader (see https://github.com/microsoft/CNTK/wiki/CNTKTextFormat-Reader).

Convert Dictionary to Text

txt2ctf.py converts a set of dictionary files and a plain text file to CNTK Text format.

Run python txt2ctf.py -h to see usage instructions. See the comments in the beginning of the script file for the specific usage example.

Convert UCI Format to Text

uci2ctf.py converts data stored in a text file in UCI format to CNTK Text format.

Run python uci2ctf.py -h to see usage instructions and example.

For Example:

python Scripts/uci2ctf.py --input_file Examples/Image/MNIST/Data/Train-28x28.txt --features_start 1 --features_dim 784 --labels_start 0 --labels_dim 1 --num_labels 10  --output_file Examples/Image/MNIST/Data/Train-28x28_cntk_text.txt
  • input_file – original dataset in the (columnar) UCI format
  • features_start – index of the first feature column (start parameter in the UCIFastReader config, see here
  • features_dim – number of feature columns (dim parameter in the UCIFastReader config)
  • labels_start - index of the first label column
  • labels_dim – number of label columns
  • num_labels – number of possible label values (labelDim parameter in the UCIFastReader config)
  • output_file – path and filename of the resulting dataset.