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| author | Alex Gilday <alexander.gilday@arm.com> | 2018-03-21 13:54:09 +0000 |
|---|---|---|
| committer | Anthony Barbier <anthony.barbier@arm.com> | 2018-11-02 16:49:16 +0000 |
| commit | c357c47be8a3f210f9eee9a05cc13f1051b036d3 (patch) | |
| tree | a88ac857150da970a0862a3479b78c616d8aa1d3 /docs/03_scripts.dox | |
| parent | 724079d6fce3bf6a05cd6c7b4884b132b27e9e90 (diff) | |
| download | ComputeLibrary-c357c47be8a3f210f9eee9a05cc13f1051b036d3.tar.gz | |
COMPMID-1008: Fix Doxygen issues
Change-Id: Ie73d8771f85d1f5b059f3a56f1bbd73c98e94a38
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/124723
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'docs/03_scripts.dox')
| -rw-r--r-- | docs/03_scripts.dox | 6 |
1 files changed, 3 insertions, 3 deletions
diff --git a/docs/03_scripts.dox b/docs/03_scripts.dox index 5601428ac2..eede8b5d1c 100644 --- a/docs/03_scripts.dox +++ b/docs/03_scripts.dox @@ -8,7 +8,7 @@ One can find caffe <a href="https://github.com/BVLC/caffe/wiki/Model-Zoo">pre-trained models</a> on caffe's official github repository. -The caffe_data_extractor.py provided in the @ref scripts folder is an example script that shows how to +The caffe_data_extractor.py provided in the scripts folder is an example script that shows how to extract the parameter values from a trained model. @note complex networks might require altering the script to properly work. @@ -35,7 +35,7 @@ The script has been tested under Python2.7. If the script runs successfully, it prints the names and shapes of each layer onto the standard output and generates *.npy files containing the weights and biases of each layer. -The @ref arm_compute::utils::load_trained_data shows how one could load +The arm_compute::utils::load_trained_data shows how one could load the weights and biases into tensor from the .npy file by the help of Accessor. @section tensorflow_data_extractor Extract data from pre-trained tensorflow model @@ -87,6 +87,6 @@ The script has been tested with Tensorflow 1.2, 1.3 on Python 2.7.6 and Python 3 If the script runs successfully, it prints the names and shapes of each parameter onto the standard output and generates *.npy files containing the weights and biases of each layer. -The @ref arm_compute::utils::load_trained_data shows how one could load +The arm_compute::utils::load_trained_data shows how one could load the weights and biases into tensor from the .npy file by the help of Accessor. */ |