https://github.com/hamedmp/deep-learning-faces
Automatically exported from code.google.com/p/deep-learning-faces
https://github.com/hamedmp/deep-learning-faces
Last synced: 20 days ago
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Automatically exported from code.google.com/p/deep-learning-faces
- Host: GitHub
- URL: https://github.com/hamedmp/deep-learning-faces
- Owner: HamedMP
- License: gpl-3.0
- Created: 2016-02-23T06:31:44.000Z (over 10 years ago)
- Default Branch: master
- Last Pushed: 2016-02-23T06:34:38.000Z (over 10 years ago)
- Last Synced: 2025-01-26T12:08:54.296Z (over 1 year ago)
- Language: Cuda
- Size: 177 KB
- Stars: 1
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README
- License: License.txt
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README
Copyright (C) 2013 Yichuan Tang.
contact: tang at cs.toronto.edu
http://www.cs.toronto.edu/~tang
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see .
////////////////////////////////////////////////////////////////////
CT 5.30.2013
Note: I have only tested this on linux Ubuntu 12.04, with cuda 5.
it should work with previous cuda versions with minor tweaks to the
build scripts
////////////////////////////////////////////////////////////////////
Compiling:
////////////////////////////////////////////////////////////////////
to make shared CUDA/C++ shared library:
0. install cuda 5
1. cd into cuda_ut folder
2. update variables 'CUDA_PATH' and 'CUDA_SAMPLES_PATH' in Makefile
3. make (this may take 10 mins, make sure that nvcc used is
version 5 and it is on the PATH.)
4. cd modules
5. make mexf="./deep_nn/mexcuConvNNoo.mex ./deep_nn/mexcuConvNNooFF.mex"
////////////////////////////////////////////////////////////////////
Learning:
////////////////////////////////////////////////////////////////////
1. cd to matlab folder
2. download train.csv and test.csv
3. if using tcsh, setenv LD_PRELOAD /usr/lib/x86_64-linux-gnu/libstdc++.so.6
and setenv LD_LIBRARY_PATH somewhere/face_exp/cuda_ut/lib
(note that the path for libstdc++.so.6 may vary for different OS)
4. start matlab
5. run load_from_kaggle.m
6. run script_face_exp.m
////////////////////////////////////////////////////////////////////
Prediction:
////////////////////////////////////////////////////////////////////
1. run fe_pred.m