#include <ctime></ctime>

#include "layer.all.inc.cpp"

#include "test.all.inc.cpp"

#include "train.digit.inc.cpp"

#include "train.image.inc.cpp"

#include "train.cx4.inc.cpp"

#include "benchmark.inc.cpp"

#include "benchmark.segment.inc.cpp"

bool runTests() {

 if (!AllTest::test()) return false;

 return printf("success\n"), true;

}

bool trainDigits() {

  #define FILENAME "data/output/weights-digit.bin"

  printf("create neural network\n");

  Layer l(                    nullptr, Layout(28, 28) );

  (new LayerSimple<funcsigmoidexp>( l, Layout(256)   ))->filename = FILENAME "1";</funcsigmoidexp>

  (new LayerSimple<funcsigmoidexp>( l, Layout(64)    ))->filename = FILENAME "2";</funcsigmoidexp>

  (new LayerSimple<funcsigmoidexp>( l, Layout(10)    ))->filename = FILENAME "3";</funcsigmoidexp>

  l.sumStat().print();

  #undef FILENAME

  printf("load training data\n");

  TrainerDigit t;

  if (!t.loadSymbolMap("data/symbols-data.bin")) return 1; // 28x28

  printf("try load previously saved network\n"); l.load();

  t.configure(l, 0.5, 8, 70000, 0, 0, 0.00001).run();

  return true;

}

bool trainDigitsConv() {

  #define FILENAME "data/output/weights-digit-conv.bin"

  printf("create neural network\n");

  Layer l(nullptr, Layout(28, 28));

  Layer *ll[10] = {};

  ll[1] = new LayerConvShared<funcrelu>(l, Layout(12, 12, 6), Kernel(4, 2, 0)); ll[1]->filename = FILENAME "1";</funcrelu>

  ll[2] = new LayerConvShared<funcrelu>(l, Layout(4, 4, 12), Kernel(4, 2, 0)); ll[2]->filename = FILENAME "2";</funcrelu>

  ll[3] = new LayerSimple<funcsigmoidexp>(l, Layout(64)); ll[3]->filename = FILENAME "3";</funcsigmoidexp>

  ll[4] = new LayerSimple<funcsigmoidexp>(l, Layout(10)); ll[4]->filename = FILENAME "4";</funcsigmoidexp>

  #undef FILENAME

  l.sumStat().print();

  printf("load training data\n");

  TrainerDigit t;

  if (!t.loadSymbolMap("data/symbols-data.bin")) return 1; // 28x28

  printf("try load previously saved network\n"); l.load();

  //ll[1]->skipTrain = true;

  //ll[2]->skipTrain = true;

  t.configure(l, 0.01, 8, 70000, 0, 0, 0.00001).run();

  //t.configure(l, 0.5, 8, 70000, 0, 0, 0.00001).run();

  return true;

}

bool trainImage() {

  #define FILENAME "data/output/weights-image.bin"

  printf("create neural network\n");

  Layer l(nullptr, Layout(128, 128, 3));

  Layer *ll[20] = {};

  ll[ 1] = new LayerConvShared<funcrelu>(l, Layout(63, 63, 24), Kernel(4, 2, 0)); ll[1]->filename = FILENAME "1";</funcrelu>

  ll[ 2] = new LayerConvShared<funcrelu>(l, Layout(29, 29, 48), Kernel(5, 2, 0)); ll[2]->filename = FILENAME "2";</funcrelu>

  //ll[ 3] = new LayerConvShared<funcrelu>(l, Layout(14, 14, 24), Kernel(4, 2, 0)); ll[3]->filename = FILENAME "3";</funcrelu>

  //ll[ 4] = new LayerConvShared<funcrelu>(l, Layout( 6,  6, 48), Kernel(4, 2, 0)); ll[4]->filename = FILENAME "4";</funcrelu>

  //ll[ 5] = new LayerConvShared<funcrelu>(l, Layout( 2,  2, 96), Kernel(4, 2, 0)); ll[5]->filename = FILENAME "5";</funcrelu>

  //ll[ 6] = new LayerDeconvShared<funcrelu>(l, Layout(  6,  6, 48), Kernel(4, 2, 0), ll[5]->weights);</funcrelu>

  //ll[ 7] = new LayerDeconvShared<funcrelu>(l, Layout( 14, 14, 24), Kernel(4, 2, 0), ll[4]->weights);</funcrelu>

  //ll[ 8] = new LayerDeconvShared<funcrelu>(l, Layout( 30, 30, 12), Kernel(4, 2, 0), ll[3]->weights);</funcrelu>

  ll[ 9] = new LayerDeconvShared<funcrelu>(l, Layout( 63, 63, 24), Kernel(5, 2, 0), ll[2]->weights);</funcrelu>

  ll[10] = new LayerDeconvShared<funcrelu>(l, Layout(128, 128, 3), Kernel(4, 2, 0), ll[1]->weights);</funcrelu>

  l.sumStat().print();

  printf("try load previously saved network\n"); l.load();

  ll[1]->skipTrain = true;

  ll[10]->skipTrain = true;

  TrainerImage t;

  t.pad = 16;

  t.datafile = "data/img128-data.bin";

  t.outfile = FILENAME ".test";

  t.configure(l, 0.00001, 8, 1000, 0, 0, 0.00001).run();

  #undef FILENAME

  return true;

}

bool trainCx4() {

  #define FILENAME "data/output/weights-cx4.bin"

  printf("create neural network\n");

  Layer l(nullptr, Layout(512, 512, 3).expandXY(2));

  Layer *fl[20] = { &l };

  int cnt = 1;

  fl[cnt] = new LayerConvShared<funcrelu>(l, Layout(257, 257, 24).expandXY(3), Kernel(4, 2, -2)); fl[cnt]->filename = FILENAME "1"; ++cnt;</funcrelu>

  fl[cnt] = new LayerConvShared<funcrelu>(l, Layout(130, 130, 48), Kernel(4, 2, -2)); fl[cnt]->filename = FILENAME "2"; ++cnt;</funcrelu>

  //fl[cnt] = new LayerConvShared<funcrelu>(l, Layout( 66,  66, 96), Kernel(4, 2, -2)); fl[cnt]->filename = FILENAME "3"; ++cnt;</funcrelu>

  //fl[cnt] = new LayerConvShared<funcrelu>(l, Layout( 6,  6, 48), Kernel(4, 2,  0)); fl[cnt]->filename = FILENAME "4"; ++cnt;</funcrelu>

  //fl[cnt] = new LayerConvShared<funcrelu>(l, Layout( 2,  2, 96), Kernel(4, 2,  0)); fl[cnt]->filename = FILENAME "5"; ++cnt;</funcrelu>

  for(int i = cnt-1; i > 0; --i) {

    Layer *bl = new LayerDeconvShared<funcrelu>(l, fl[i-1]->layout, dynamic_cast<layerconvshared<funcrelu>*>(fl[i])->kernel, fl[i]->weights);</layerconvshared<funcrelu></funcrelu>

    if (i < cnt-1) fl[i]->skipTrain = bl->skipTrain = true;

  }

  l.sumStat().print();

  printf("try load previously saved network\n"); l.load();

  SegmentCx4 s(fl[cnt-2]->layout.getD(), fl[cnt-1]->layout.getD(), fl[cnt-1]->weights);

  s.filename = fl[cnt-1]->filename;

  TrainerCx4 t;

  t.layerFull        = &l;

  t.layerPre         = cnt > 2 ? fl[cnt-2] : nullptr;

  t.segment          = &s;

  t.ratio            = 0.000001;

  t.threadsCount     = 8;

  t.measuresPerBlock = 1000;

  t.trainsPerBlock   = 100000;

  t.loadImagesCount  = 100;

  t.blocksPerLoading = 1;

  t.qmin             = 0.00001;

  t.infile           = "data/img512-data.bin";

  t.outfile          = FILENAME ".test";

  t.run();

  #undef FILENAME

  return true;

}

int main() {

  srand(time(NULL));

  //while(1) BenchmarkSegment::run(8);

  //while(1) Benchmark().run(8);

  //return !runTests();

  //return !trainDigits();

  //return !trainDigitsConv();

  //return !trainImage();

  return !trainCx4();

}