search

YanXuHappygela / deep-learning-faces

Automatically exported from code.google.com/p/deep-learning-faces
GNU General Public License v3.0
0 stars 0 forks source link

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 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:

  1. install cuda 5
  2. cd into cuda_ut folder
  3. update variables 'CUDA_PATH' and 'CUDA_SAMPLES_PATH' in Makefile
  4. make (this may take 10 mins, make sure that nvcc used is version 5 and it is on the PATH.)
  5. cd modules
  6. 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