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License: GNU General Public License v3.0
Automatically exported from code.google.com/p/deep-learning-faces
License: GNU General Public License v3.0
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 <http://www.gnu.org/licenses/>. //////////////////////////////////////////////////////////////////// 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
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