http://eblearn.cs.nyu.edu:21991/ 2013-03-11T15:48:41-04:00 http://eblearn.cs.nyu.edu:21991/ http://eblearn.cs.nyu.edu:21991/lib/images/favicon.ico text/html 2013-03-10T14:17:43-04:00 sermanet http://eblearn.cs.nyu.edu:21991/doku.php?id=install&rev=1362939463&do=diff Instructions for: Linux, Windows or Mac OS IDE instructions: Eclipse Speeding up code using external libraries(SSE, IPP, OpenMP): Optimizations ---------- Download * Sources via SVN: svn co svn://svn.code.sf.net/p/eblearn/code/trunk/ eblearn text/html 2013-02-27T16:53:06-04:00 sermanet http://eblearn.cs.nyu.edu:21991/doku.php?id=metarun&rev=1362001986&do=diff meta_comments = "#" meta_max_jobs = 2 # limits the number of jobs running at the same time meta_output_dir = /data/outputs/ # the root path for metarun outputs meta_copy = "src/*" # copy files matching this pattern to job directory meta_name=${name}${sz}_${machine} meta_gnuplot_params="set term postscript enhanced color; set grid ytics;set ytics;set mytics;set grid mytics;set logscale y; set mxtics; set grid xtics; set pointsize 0.5; set key spacing .5;" meta_gnuplot… text/html 2013-02-11T17:04:45-04:00 soumith http://eblearn.cs.nyu.edu:21991/doku.php?id=classify&rev=1360620285&do=diff Classify classifies inputs based on existing classifier weights and spits out the predicted class. (basically fprops through the network) The sample can be 1d or 2d or 3d or whatever (whereas detect expects 2d or 3d inputs). text/html 2013-02-06T00:37:40-04:00 sermanet http://eblearn.cs.nyu.edu:21991/doku.php?id=tools&rev=1360129060&do=diff Tools The eblearn tools help to create datasets, train models and run them. Their code is located in eblearn/tools/tools/src and binaries are built into eblearn/bin, and installed on the system with 'make install'. They can all be compiled by calling 'make tool', or simply 'make'. Most tools will show a brief help when called without arguments. text/html 2013-01-30T14:12:12-04:00 soumith http://eblearn.cs.nyu.edu:21991/doku.php?id=home&rev=1359573132&do=diff ---------- News * 01/16/13: Released version 1.2 (Release Notes) * Windows Binaries (x86 and x64) (Download) * Source Package (All platforms) (eblearn_1.2_r2631-src.zip) * 11/13/12: A bug tracker has been added on googlecode. Please report any bugs there. * 11/03/12: Android Demo fixed and added conf and detection threads support for android (see demo) * 09/21/12: Added a Google Groups page, where we can easily answer your questions * 07/20/12: ICPR'12 paper published with new… text/html 2013-01-17T12:33:40-04:00 soumith http://eblearn.cs.nyu.edu:21991/doku.php?id=release_notes&rev=1358444020&do=diff Release Notes Release Notes for version 1.2 text/html 2013-01-17T12:33:11-04:00 soumith http://eblearn.cs.nyu.edu:21991/doku.php?id=release_notes_1.2&rev=1358443991&do=diff Demos * Fixing and cleaned mnist.conf demo, added comments and l2pool. run_type was missing. Disabled training display crashing. * Cleaned face demo, added comments. Fixed best_cam.conf for face detection demo (demos/face) EBLearn and Idx Core Library * Introduced a much simpler state mechanism * Fixed memory leaks introduced by the much simpler state mechanism :) text/html 2013-01-16T12:10:50-04:00 qianli http://eblearn.cs.nyu.edu:21991/doku.php?id=optimizations&rev=1358356250&do=diff Optimizations EBLearn runs faster using some code optimizations provided by some external libraries. * TH Tensor library: SSE Optimizations * Intel IPP: float optimizations * OpenMP: multi-core optimizations * GPU (CUDA): CUDA Optimizations for convolutions