Multilabel EuclideanLoss problem

[John Titor]

Hello, i've wrote the EuclideanMultilabel layer (It is the pure copy of EuclideanLoss layer except, that it has no restriction about labels). , also i reworked memory_data_layer to have opportunity input multiple label (i ve coppied realisation from here https://github.com/kevinlin311tw/caffe-multilabel/blob/master/src/caffe/layers/memory_data_layer.cpp. So with this , i have a model file :

name: "XOR"
layer {
  name: "inputdata"
  type: "MemoryData"
  top: "fulldata"
  top: "label"
  include {
    phase: TRAIN
  }
  memory_data_param
  {
    label_size: 2
    batch_size: 64
    channels: 1  
    height: 1
    width: 2
  }
}
layer {
  name: "test_inputdata"
  type: "MemoryData"
  top: "fulldata"
  top: "label"
  include {
    phase: TEST
  }
  memory_data_param
  {
    label_size: 2
    batch_size: 4
    channels: 1
    height: 1
    width: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "fulldata"
  top: "fc6"
  inner_product_param {
    num_output: 20
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
  }
}

layer {
  name: "output"
  bottom: "fc7"
 top: "output"
  type: "AbsVal"
  include {
    phase: TEST
  }
}

layer {
  name: "loss"
  type: "MultiLabelEuclideanLoss"
  bottom: "fc7"
  bottom: "label"
  top: "loss"
}

And train with this code cpp :

#include <caffe/caffe.hpp>
#include <memory>
#include "caffe/layers/memory_data_layer.hpp"

int main()
{
    float *data = new float[64*1*1*3*400];
    float *label = new float[64*1*1*3*400];


    for(int i = 0; i<64*1*1*400; ++i)
    {
        int a = rand() % 2;
        int b = rand() % 2;
        data[i*2 + 0] = a;
        data[i*2 + 1] = b;
        label[i*2] = a + b;
        label[i*2+1] = a *b ;
        //label[i*2 + 1] = a *b;
    }

    caffe::SolverParameter solver_param;
    caffe::ReadSolverParamsFromTextFileOrDie("solver.prototxt", &solver_param);

    boost::shared_ptr<caffe::Solver<float> > solver(caffe::SolverRegistry<float>::CreateSolver(solver_param));
    caffe::MemoryDataLayer<float> *dataLayer_trainnet = (caffe::MemoryDataLayer<float> *) (solver->net()->layer_by_name("inputdata").get());
    caffe::MemoryDataLayer<float> *dataLayer_testnet_ = (caffe::MemoryDataLayer<float> *) (solver->test_nets()[0]->layer_by_name("test_inputdata").get());

    float testab[] = {1, 1, 100 , 100, 500, 100, 1, 5};
    float testc[] = {0, 1, 1, 0};

    dataLayer_testnet_->Reset(testab, testc, 4);
    dataLayer_trainnet->Reset(data, label, 25600);

    solver->Solve();

    boost::shared_ptr<caffe::Net<float> > testnet;

    testnet.reset(new caffe::Net<float>("model.prototxt", caffe::TEST, caffe::Caffe::GetDefaultDevice()));
    //testnet->CopyTrainedLayersFrom("XOR_iter_5000000.caffemodel");

    testnet->ShareTrainedLayersWith(solver->net().get());

    caffe::MemoryDataLayer<float> *dataLayer_testnet = (caffe::MemoryDataLayer<float> *) (testnet->layer_by_name("test_inputdata").get());

    dataLayer_testnet->Reset(testab, testc, 4);
    testnet->Forward();

    boost::shared_ptr<caffe::Blob<float> > output_layer = testnet->blob_by_name("output");

    const float* begin = output_layer->cpu_data();
    const float* end = begin + 8;

    std::vector<float> result(begin, end);

    std::cout << std::endl;
    for(int i = 0; i< result.size(); ++i)
    {
        std::cout << " I [" << i << "] RESULT : " << result[i] << "\n";
        //printf("input: %d xor %d,  truth: %f result by nn: %f\n", (int)testab[i*2 + 0], (int)testab[i*2+1], testc[i], result[i]);
    }

    return 0;
}

Trying to approximazi a*b / a+b funcs  , but i just get :

 I [0] RESULT : 4.00305 // a+b
 I [1] RESULT : 3.82771 // a* b
 I [2] RESULT : 3.98262
 I [3] RESULT : 3.85999
 I [4] RESULT : 3.9721
 I [5] RESULT : 3.87661
 I [6] RESULT : 3.96224
 I [7] RESULT : 3.89219

What am i doing wrong , or how to make euclidean multilabel loss ( i mean like (data -> labels(like 15 65)) .