/**
@page data_import Importing data from existing models

@tableofcontents

@section caffe_data_extractor Extract data from pre-trained caffe model

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
extract the hyperparameter values from a trained model.

@note complex networks might require alter the script to properly work.

@subsection how_to How to use the script

Install caffe following <a href="http://caffe.berkeleyvision.org/installation.html">caffe's document</a>.
Make sure the pycaffe has been added into the PYTHONPATH.

Download the pre-trained caffe model.

Run the caffe_data_extractor.py script by

         ./caffe_data_extractor.py -m <caffe model> -n <caffe netlist>

For example, to extract the data from pre-trained caffe Alex model to binary file:

        ./caffe_data_extractor.py -m /path/to/bvlc_alexnet.caffemodel -n /path/to/caffe/models/bvlc_alexnet/deploy.prototxt

The script has been tested under Python2.7.

@subsection result  What is the expected ouput from the script

If the script run succesfully, it prints the shapes of each layer onto the standard
output and generates *.dat files containing the weights and biases of each layer.

The @ref arm_compute::utils::load_trained_data shows how one could load
the weights and biases into tensor from the .dat file by the help of Accessor.
*/