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Invalid MEX-file 
'D:\DEEPLearning\2013\SVM_SOFTMAX\matlab\mexcuConvNNoo.mexw64': error 
parameters.
Error in myclassify_conv_nn_softmax (line 174)
        [~, model.theta] = mexcuConvNNoo( single(ww), params, model.callback_name);
        in code write like this:
#define NARGIN 3
#define IN_W pRhs[0]
#define IN_params pRhs[1]
#define IN_callback  pRhs[2]

#define NARGOUT 1
#define OUT_f pLhs[0]
#define OUT_W pLhs[1]
#define OUT_dW pLhs[1]
and which is different from the beginning explanation:function [f (W or dW)] = 
mexcuConvNNoo( W, params, Dim, Callback).

I have successfully compiled it on Ubuntu14.04(64bits) with Cuda6.5. 
When I run "script_face_exp.m", I got the following errors:

--------------------------------------------------------------------
~~FERMI~~
 {Input} (-1)-->layer{0} Convdata:   nFilters:1 nIJ_grid:48 48, dropout:0.000 
{Hidn} (0)-->layer{1}  ImageMirror:    nVisChannels:1 nVisIJ:[48 48],Error 
using mexcuConvNNoo
Assertion Failed: trns_high not always >= trns_low

Error in myclassify_conv_nn_softmax (line 174)
        [~, model.theta] = mexcuConvNNoo( single(ww), params,
        model.callback_name);

Error in fe_cv_48 (line 143)
    [model] = myclassify_conv_nn_softmax(model);

Error in script_face_exp (line 72)
[cv_average,cv_models]=fe_cv_48(hp.nSPLIT,hp.randseeds,hp.normalseeds,hp);

----------------------------------------------------------------
My GPU info is as below (./deviceQuery ):
Detected 1 CUDA Capable device(s)

Device 0: "GeForce GTX 645"
  CUDA Driver Version / Runtime Version          6.5 / 6.5
  CUDA Capability Major/Minor version number:    3.0
  Total amount of global memory:                 1024 MBytes (1073414144 bytes)
  ( 3) Multiprocessors, (192) CUDA Cores/MP:     576 CUDA Cores
  GPU Clock rate:                                824 MHz (0.82 GHz)
  Memory Clock rate:                             2000 Mhz
  Memory Bus Width:                              128-bit
  L2 Cache Size:                                 262144 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Bus ID / PCI location ID:           1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 6.5, CUDA Runtime 
Version = 6.5, NumDevs = 1, Device0 = GeForce GTX 645
Result = PASS
----------------------------------------------------------------

Thanks!

best,
-Lei

dataset has been removed from Kaggle website

I encountered an error on compiling step.
I'm using Cuda 5.5, on ubuntu 12.04 64 bit

I'd followed the steps written in README,

Once I type "make mexf="./deep_nn/mexcuConvNNoo.mex 
./deep_nn/mexcuConvNNooFF.mex", 

it returns the error:
make: /pkgs/matlab-80/bin/mexext: Command not found
make: *** No rule to make target 'deep_nn/mexcuConvNNoo.', needed by 'all'. 
Stop.

I've tried to figure it myself, but it seems to be difficult to me.
Sorry for that I have lack of knowledge on this, I'll doing on my best.

Sincerely
Tae-Ho Kim