f590e03aa8
ucf11_conv3d_test.py test depends on APIs introduced in imageio v2.2.0 but currently CNTK uses imageio v2.1.1, leading the test to be in broken state. Conda doesn't support Python 3.4, and hence imageio v2.2.0 conda package is not available for Python 3.4. Now, Python 3.4 conda environment setup file installs imageio v2.2.0 using pip instaed of conda. Also, CNTK will stop supporting Python 3.4 in releases later than 2.3. |
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| .. | ||
| install | ||
| README.md | ||
| ctf2bin.py | ||
| pytest.ini | ||
| txt2ctf.py | ||
| uci2ctf.py | ||
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 (https://docs.microsoft.com/en-us/cognitive-toolkit/BrainScript-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 formatfeatures_start- index of the first feature column (start parameter in the UCIFastReader config, see herefeatures_dim- number of feature columns (dim parameter in the UCIFastReader config)labels_start- index of the first label columnlabels_dim- number of label columnsnum_labels- number of possible label values (labelDim parameter in the UCIFastReader config)output_file- path and filename of the resulting dataset.