卷积神经网络matlab代码

/* * StochasticPooling.c * * Implements the stochastic-pooling transfer function. Takes a 4D tensor shaped as * (rows, cols, nchannels, nsamples) and a pooling shape as (prows, pcols) and * returns a set of stochastic sampled values with the corresponding indices in the input * matrix. * * e.g. * [m, idx] = StochasticPooling(IM, [2 2]) * * Created on: March 28, 2014 * Author:Feng Wang <wf900911@163.com> * Based on the max-pooling code provided by: * Jonathan Masci <jonathan@idsia.ch> * Paper: * Stochastic Pooling for Regularization of Deep Convolutional Neural Networks, Matthew D. Zeiler * * This file is available under the terms of the GNU GPLv2. */ #include "mex.h" #include <omp.h> #include <stdio.h> #include <stdlib.h> #include <time.h> #include <math.h> #include <limits> #define IDX2F(i,j,ld) ((((j)-1)*(ld))+((i)-1)) #define IDX2C(i,j,ld) (((j)*(ld))+(i)) int debug = 0; /** * Computes the stochastic-pooling for the given 2D map, and no, the name is not a typo. * All pointers are passed already offset so to avoid cumbersome indexing. * * @param ptr_data pointer set to the begin of this map * @param DATA_DIMS data dimensions * @param ptr_pool pooling sizes * @param ptr_out pointer to the output stochastic-values set to the right position * @param ptr_idx pointer to the output indices set to the right position */ template <typename T> inline void compute_stochastic_pooling(T *ptr_data, const mwSize *DATA_DIMS, T *ptr_pool, T *ptr_out, T *ptr_idx, int tile_start) { T m; T sum,rsum; int idx; int count = 0; for (int col = 0; col < DATA_DIMS[1]; col += ptr_pool[1]) { for (int row = 0; row < DATA_DIMS[0]; row += ptr_pool[0]) { if (debug) fprintf(stderr, "r = %i, c = %i \n", row, col); m = 0; sum = 0; rsum = 0; idx = -1; for (int pcol = 0; (pcol < ptr_pool[1] && col + pcol < DATA_DIMS[1]); ++pcol) { for (int prow = 0; (prow < ptr_pool[0] && row + prow < DATA_DIMS[0]); ++prow) { sum += ptr_data[IDX2C(row + prow, col + pcol, DATA_DIMS[0])]; } } float num = rand()%1000; num *= sum/1000; for (int pcol = 0; (pcol < ptr_pool[1] && col + pcol < DATA_DIMS[1]); ++pcol) { for (int prow = 0; (prow < ptr_pool[0] && row + prow < DATA_DIMS[0]); ++prow) { if (debug) { fprintf(stderr, "num = %f, rsum = %f, this = %f \n", num, rsum, ptr_data[IDX2C(row + prow, col + pcol, DATA_DIMS[0])]); } rsum += ptr_data[IDX2C(row + prow, col + pcol, DATA_DIMS[0])]; if(num<rsum) { idx = IDX2C(row + prow, col + pcol, DATA_DIMS[0]); m = ptr_data[idx]; break; } /*if((pcol == ptr_pool[1] - 1 || col + pcol == DATA_DIMS[1] - 1) && (prow == ptr_pool[0]-1 || row + prow == DATA_DIMS[0] -1))*/ if(sum-rsum<0.0001) { idx = IDX2C(row + prow, col + pcol, DATA_DIMS[0]); m = ptr_data[idx]; break; } } if(idx != -1) break; } if (debug && idx == -1) { fprintf(stderr, "dioschifoso\n"); return; } if (debug) fprintf(stderr, "count = %i\n",count); /* idxs are to be used in Matlab and hence a +1 is needed */ ptr_idx[count] = idx + 1 + tile_start; ptr_out[count] = m; count++; } } } /** * This is the wrapper for the actual computation. * It is a template so that multiple types can be handled. */ template <typename T> void mexStochasticPooling(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[], mxClassID classID) { /***************************************************************************/ /** Variables */ /***************************************************************************/ mwSize IDX_DIMS[1]; mwSize DATA_DIMS[4]; mwSize M_DIMS[4]; const mwSize *POOL_DIMS; int DATA_NUMEL; int POOL_NUMEL; /** * Pointers to data */ T *ptr_data = NULL; T *ptr_pool = NULL; T *ptr_out = NULL; T *ptr_idx = NULL; /***************************************************************************/ /** Setting input pointers *************************************************/ /***************************************************************************/ ptr_data = (T *)mxGetData(prhs[0]); ptr_pool = (T *)mxGetData(prhs[1]); if (debug) fprintf(stderr,"Pooling size: h=%f, w=%f\n", ptr_pool[0], ptr_pool[1]); /***************************************************************************/ /** Setting parameters *****************************************************/ /***************************************************************************/ /* Data dimensions. As also a 2D tensor can be used I fill empty dimensions * with 1 */ const mwSize *tmp = mxGetDimensions(prhs[0]); DATA_DIMS[0] = tmp[0]; DATA_DIMS[1] = tmp[1]; if (mxGetNumberOfDimensions(prhs[0]) == 2) { DATA_DIMS[2] = 1; DATA_DIMS[3] = 1; } else if (mxGetNumberOfDimensions(prhs[0]) == 3) { DATA_DIMS[2] = tmp[2]; DATA_DIMS[3] = 1; } else { DATA_DIMS[2] = tmp[2]; DATA_DIMS[3] = tmp[3]; } DATA_NUMEL = DATA_DIMS[0] * DATA_DIMS[1] * DATA_DIMS[2] * DATA_DIMS[3]; if (debug) fprintf(stderr,"Data size: h=%d, w=%d, z=%d, n=%d (%d)\n", DATA_DIMS[0], DATA_DIMS[1], DATA_DIMS[2], DATA_DIMS[3], DATA_NUMEL); /* Output dimensions: the first output argument is of size equals to the input * whereas the second is of size equals to the number of pooled values. * Below there is ceil because also non complete tiles are considered when * input dims are not multiples of pooling dims. */ M_DIMS[0] = ceil(float(DATA_DIMS[0]) / float(ptr_pool[0])); M_DIMS[1] = ceil(float(DATA_DIMS[1]) / float(ptr_pool[1])); M_DIMS[2] = DATA_DIMS[2]; M_DIMS[3] = DATA_DIMS[3]; IDX_DIMS[0] = M_DIMS[0] * M_DIMS[1] * M_DIMS[2] * M_DIMS[3]; if (debug){ fprintf(stderr,"Each output image has (%d, %d) pooled values, " "IDXs size: h=%d \n", M_DIMS[0], M_DIMS[1], IDX_DIMS[0]); fprintf(stderr, "M size: h=%d, w=%d, z=%d, n=%d\n", M_DIMS[0], M_DIMS[1], M_DIMS[2], M_DIMS[3]); } /***************************************************************************/ /** Variables allocation ***************************************************/ /***************************************************************************/ /* OUTPUTS: stochastic-values and corresponding indices */ plhs[0] = mxCreateNumericArray(4, M_DIMS, classID, mxREAL); ptr_out = (T *)mxGetData(plhs[0]); plhs[1] = mxCreateNumericArray(1, IDX_DIMS, classID, mxREAL); ptr_idx = (T *)mxGetData(plhs[1]); /***************************************************************************/ /** Compute stochastic-pooling ****************************************************/ /***************************************************************************/ int tile_start = 0; int ptr_offset = 0; int M_sample_size = M_DIMS[0] * M_DIMS[1] * M_DIMS[2]; int D_sample_size = DATA_DIMS[0] * DATA_DIMS[1] * DATA_DIMS[2]; srand((unsigned)time(0)); for (int n = 0; n < DATA_DIMS[3]; ++n) { #pragma omp parallel for for (int k = 0; k < DATA_DIMS[2]; ++k) { tile_start = n * M_sample_size + k * M_DIMS[0] * M_DIMS[1]; ptr_offset = n * D_sample_size + k * DATA_DIMS[0] * DATA_DIMS[1]; compute_stochastic_pooling (&ptr_data[ptr_offset], DATA_DIMS, ptr_pool, &ptr_out[tile_start], &ptr_idx[tile_start], ptr_offset); if (debug) fprintf(stderr, "tile_start: %i, ptr_offset: %i\n", tile_start, ptr_offset); } } } void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) { /***************************************************************************/ /** Check input ************************