#include <ctime>
#include <cstdlib>
#include "nnlayer2.inc.cpp"
#include "nnlayer2.conv.inc.cpp"
#include "nnlayer2.mt.inc.cpp"
bool train(const char *infile, const char *outfile, Layer &l, int blockSize, int totalCount, Real trainRatio) {
assert(blockSize > 0);
int blockCount = totalCount/blockSize;
assert(blockCount > 0);
assert(!l.prev);
assert(l.size && l.back().size);
printf("load training data\n");
FILE *f = fopen(infile, "rb");
if (!f)
return printf("cannot open file '%s' for read\n", infile), false;
fseek(f, 0, SEEK_END);
int fs = ftell(f);
fseek(f, 0, SEEK_SET);
int sizeX = l.size;
int sizeY = l.back().size;
int count = fs/(sizeX+1);
if (count < blockSize)
return printf("file '%s' is lesser minimal size\n", infile), fclose(f), false;
unsigned char *data = new unsigned char[(sizeX + sizeY)*count];
memset(data, 0, (sizeX + sizeY)*count);
for(int i = 0; i < count; ++i) {
unsigned char *d = data + (sizeX + sizeY)*i;
if (!fread(d, sizeX+1, 1, f) || d[sizeX] >= sizeY)
return printf("cannot read from file '%s'\n", infile), delete[] data, fclose(f), false;
d += sizeX;
unsigned char c = *d;
*d = 0;
d[c] = 255;
}
fclose(f);
printf("train %d x %d = %d, ratio: %f\n", blockCount, blockSize, blockCount*blockSize, trainRatio);
int *shuffle = new int[blockSize];
TrainMT tmt;
tmt.layer = &l;
tmt.dataX = data;
tmt.dataY = data + sizeX;
tmt.strideX = tmt.strideY = sizeX + sizeY;
tmt.shuffle = shuffle;
tmt.count = blockSize;
tmt.trainRatio = trainRatio;
for(int i = 0; i < blockCount; ++i) {
for(int j = 0; j < blockSize; ++j)
shuffle[j] = rand()%count;
Real res = tmt.train(8);
printf("%4d, total %7d, avg.result %f\n", i+1, (i+1)*blockSize, res);
if ( ((i+1)%10) == 0 || i+1 == blockCount ) {
if (!l.saveAll(outfile)) return delete[] data, delete[] shuffle, false;
printf(" saved\n");
}
}
delete[] shuffle;
delete[] data;
return true;
}
int main() {
srand(time(NULL));
const char *infile = "data/symbols-data.bin"; // 28x28
const char *outfile = "data/output/weights.bin";
printf("create neural network\n");
Layer l(nullptr, 784);
createConv(l, 28, 28, 1, 22, 22, 1, 60);
createConv(l, 22, 22, 1, 14, 14, 1, 100);
createConv(l, 14, 14, 1, 4, 4, 1, 140);
createConv(l, 4, 4, 1, 1, 1, 10, 16);
printf(" neurons: %d, links %d, memSize: %llu\n", l.totalNeurons(), l.totalLinks(), (unsigned long long)l.totalMemSize());
//printf("try load previously saved network\n");
//l.loadAll(outfile);
printf("train\n");
train(infile, outfile, l, 10000, 2000000, 0.1);
return 0;
}