Backpropagation in a Convolution layer (C++)











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I'm trying to implement CNN from scratch.
I've already implemented a fullyconnected layer and it works.

But, however, convolutional layer's backpropagation doesn't give any positive results.

It contains two methods: backward() to backpropagate signal to the earlier layers, and fit() to correct convolution's kernel weights and biases.



std::vector<std::vector<std::vector<double>>> ConvLayer::backward(std::vector<std::vector<std::vector<double>>> grad)
{
int gradNumber = grad.size();
int gradWidth = grad[0].size();
int gradHeight = grad[0][0].size();

if(!this->gradInitialized)
{
this->gradInitialized = true;
this->m_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
this->v_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
}

int kernelWidth = this->kernels[0].size();
int kernelHeight = this->kernels[0][0].size();

int inputNumber = this->inputSaved.size();

int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

std::vector<std::vector<std::vector<double>>> gradUpd;
gradUpd = RandValueGenerator::get_rand_3d_tensor(gradNumber, kernelWidth, kernelHeight, true);

#pragma omp parallel for
for(int i = 0; i < gradNumber; ++i)
{
for(int x = 0; x < gradWidth; ++x)
{
for(int y = 0; y < gradHeight; ++y)
{
double outputInit = this->outputSaved[i][x][y];
double outputDer = ActivationFunc::calc(this->activationType, outputInit, true);
grad[i][x][y] = outputDer * grad[i][x][y];
}
}
}

this->grad = grad;

#pragma omp parallel for
for(int index = 0; index < gradNumber; ++index)
{
for(int x = 0; x < gradWidth; ++x)
{
for(int y = 0; y < gradHeight; ++y)
{
for(int i = 0; i < kernelWidth; ++i)
{
for(int j = 0; j < kernelHeight; ++j)
{
gradUpd[index][i][j] += grad[index][x][y] * this->kernels[index][i][j];
}
}
}
}
}
return gradUpd;
}

void ConvLayer::fit(int t, AdamOptimizer& adam)
{
int gradNumber = grad.size();
int gradWidth = grad[0].size();
int gradHeight = grad[0][0].size();

int inputNumber = this->inputSaved.size();

int kernelWidth = this->kernels[0].size();
int kernelHeight = this->kernels[0][0].size();

int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

std::vector<std::vector<std::vector<double>>> diff;
diff = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight);

#pragma omp parallel for
for(int i = 0; i < gradNumber; ++i)
{
for(int x = 0; x < gradWidth; ++x)
{
for(int y = 0; y < gradHeight; ++y)
{
diff[i][x][y] = adam.calc(t, this->m_t[i][x][y], this->v_t[i][x][y], this->grad[i][x][y]);
}
}
}

#pragma omp parallel for
for(int index = 0; index < gradNumber; ++index)
{
std::vector<std::vector<double>> kernelDet(kernelWidth, std::vector<double>(kernelHeight));
std::vector<std::vector<double>> inputNow(this->inputSaved[index % inputNumber]);
std::vector<std::vector<double>> diffNow(diff[index]);

for(int x = 0; x < widthIterNumber; ++x)
{
for(int y = 0; y < heightIterNumber; ++y)
{
for(int i = 0; i < kernelWidth; ++i)
{
for(int j = 0; j < kernelHeight; ++j)
{
int x_input = x + i, y_input = y + j;
int x_grad = x_input, y_grad = y_input;

if(x_grad >= gradWidth)
x_grad = gradWidth - 1;

if(y_grad >= gradHeight)
y_grad = gradHeight - 1;

kernelDet[i][j] += inputNow[x_input][y_input] * diffNow[x_grad][y_grad];
}
}
}
}

for(int i = 0; i < kernelWidth; ++i)
{
for(int j = 0; j < kernelHeight; ++j)
{
kernelDet[i][j] /= (widthIterNumber * heightIterNumber);

this->kernels[index][i][j] += kernelDet[i][j];
this->biases[index] += kernelDet[i][j];
}
}
}
}


The complete code can be found here.



Of course, code is difficult and can be formatted more correctly...

But are there any problems with backpropagation's logic itself?










share|improve this question









New contributor




mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
























    up vote
    0
    down vote

    favorite












    I'm trying to implement CNN from scratch.
    I've already implemented a fullyconnected layer and it works.

    But, however, convolutional layer's backpropagation doesn't give any positive results.

    It contains two methods: backward() to backpropagate signal to the earlier layers, and fit() to correct convolution's kernel weights and biases.



    std::vector<std::vector<std::vector<double>>> ConvLayer::backward(std::vector<std::vector<std::vector<double>>> grad)
    {
    int gradNumber = grad.size();
    int gradWidth = grad[0].size();
    int gradHeight = grad[0][0].size();

    if(!this->gradInitialized)
    {
    this->gradInitialized = true;
    this->m_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
    this->v_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
    }

    int kernelWidth = this->kernels[0].size();
    int kernelHeight = this->kernels[0][0].size();

    int inputNumber = this->inputSaved.size();

    int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
    int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

    std::vector<std::vector<std::vector<double>>> gradUpd;
    gradUpd = RandValueGenerator::get_rand_3d_tensor(gradNumber, kernelWidth, kernelHeight, true);

    #pragma omp parallel for
    for(int i = 0; i < gradNumber; ++i)
    {
    for(int x = 0; x < gradWidth; ++x)
    {
    for(int y = 0; y < gradHeight; ++y)
    {
    double outputInit = this->outputSaved[i][x][y];
    double outputDer = ActivationFunc::calc(this->activationType, outputInit, true);
    grad[i][x][y] = outputDer * grad[i][x][y];
    }
    }
    }

    this->grad = grad;

    #pragma omp parallel for
    for(int index = 0; index < gradNumber; ++index)
    {
    for(int x = 0; x < gradWidth; ++x)
    {
    for(int y = 0; y < gradHeight; ++y)
    {
    for(int i = 0; i < kernelWidth; ++i)
    {
    for(int j = 0; j < kernelHeight; ++j)
    {
    gradUpd[index][i][j] += grad[index][x][y] * this->kernels[index][i][j];
    }
    }
    }
    }
    }
    return gradUpd;
    }

    void ConvLayer::fit(int t, AdamOptimizer& adam)
    {
    int gradNumber = grad.size();
    int gradWidth = grad[0].size();
    int gradHeight = grad[0][0].size();

    int inputNumber = this->inputSaved.size();

    int kernelWidth = this->kernels[0].size();
    int kernelHeight = this->kernels[0][0].size();

    int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
    int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

    std::vector<std::vector<std::vector<double>>> diff;
    diff = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight);

    #pragma omp parallel for
    for(int i = 0; i < gradNumber; ++i)
    {
    for(int x = 0; x < gradWidth; ++x)
    {
    for(int y = 0; y < gradHeight; ++y)
    {
    diff[i][x][y] = adam.calc(t, this->m_t[i][x][y], this->v_t[i][x][y], this->grad[i][x][y]);
    }
    }
    }

    #pragma omp parallel for
    for(int index = 0; index < gradNumber; ++index)
    {
    std::vector<std::vector<double>> kernelDet(kernelWidth, std::vector<double>(kernelHeight));
    std::vector<std::vector<double>> inputNow(this->inputSaved[index % inputNumber]);
    std::vector<std::vector<double>> diffNow(diff[index]);

    for(int x = 0; x < widthIterNumber; ++x)
    {
    for(int y = 0; y < heightIterNumber; ++y)
    {
    for(int i = 0; i < kernelWidth; ++i)
    {
    for(int j = 0; j < kernelHeight; ++j)
    {
    int x_input = x + i, y_input = y + j;
    int x_grad = x_input, y_grad = y_input;

    if(x_grad >= gradWidth)
    x_grad = gradWidth - 1;

    if(y_grad >= gradHeight)
    y_grad = gradHeight - 1;

    kernelDet[i][j] += inputNow[x_input][y_input] * diffNow[x_grad][y_grad];
    }
    }
    }
    }

    for(int i = 0; i < kernelWidth; ++i)
    {
    for(int j = 0; j < kernelHeight; ++j)
    {
    kernelDet[i][j] /= (widthIterNumber * heightIterNumber);

    this->kernels[index][i][j] += kernelDet[i][j];
    this->biases[index] += kernelDet[i][j];
    }
    }
    }
    }


    The complete code can be found here.



    Of course, code is difficult and can be formatted more correctly...

    But are there any problems with backpropagation's logic itself?










    share|improve this question









    New contributor




    mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.






















      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I'm trying to implement CNN from scratch.
      I've already implemented a fullyconnected layer and it works.

      But, however, convolutional layer's backpropagation doesn't give any positive results.

      It contains two methods: backward() to backpropagate signal to the earlier layers, and fit() to correct convolution's kernel weights and biases.



      std::vector<std::vector<std::vector<double>>> ConvLayer::backward(std::vector<std::vector<std::vector<double>>> grad)
      {
      int gradNumber = grad.size();
      int gradWidth = grad[0].size();
      int gradHeight = grad[0][0].size();

      if(!this->gradInitialized)
      {
      this->gradInitialized = true;
      this->m_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
      this->v_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
      }

      int kernelWidth = this->kernels[0].size();
      int kernelHeight = this->kernels[0][0].size();

      int inputNumber = this->inputSaved.size();

      int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
      int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

      std::vector<std::vector<std::vector<double>>> gradUpd;
      gradUpd = RandValueGenerator::get_rand_3d_tensor(gradNumber, kernelWidth, kernelHeight, true);

      #pragma omp parallel for
      for(int i = 0; i < gradNumber; ++i)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      double outputInit = this->outputSaved[i][x][y];
      double outputDer = ActivationFunc::calc(this->activationType, outputInit, true);
      grad[i][x][y] = outputDer * grad[i][x][y];
      }
      }
      }

      this->grad = grad;

      #pragma omp parallel for
      for(int index = 0; index < gradNumber; ++index)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      gradUpd[index][i][j] += grad[index][x][y] * this->kernels[index][i][j];
      }
      }
      }
      }
      }
      return gradUpd;
      }

      void ConvLayer::fit(int t, AdamOptimizer& adam)
      {
      int gradNumber = grad.size();
      int gradWidth = grad[0].size();
      int gradHeight = grad[0][0].size();

      int inputNumber = this->inputSaved.size();

      int kernelWidth = this->kernels[0].size();
      int kernelHeight = this->kernels[0][0].size();

      int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
      int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

      std::vector<std::vector<std::vector<double>>> diff;
      diff = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight);

      #pragma omp parallel for
      for(int i = 0; i < gradNumber; ++i)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      diff[i][x][y] = adam.calc(t, this->m_t[i][x][y], this->v_t[i][x][y], this->grad[i][x][y]);
      }
      }
      }

      #pragma omp parallel for
      for(int index = 0; index < gradNumber; ++index)
      {
      std::vector<std::vector<double>> kernelDet(kernelWidth, std::vector<double>(kernelHeight));
      std::vector<std::vector<double>> inputNow(this->inputSaved[index % inputNumber]);
      std::vector<std::vector<double>> diffNow(diff[index]);

      for(int x = 0; x < widthIterNumber; ++x)
      {
      for(int y = 0; y < heightIterNumber; ++y)
      {
      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      int x_input = x + i, y_input = y + j;
      int x_grad = x_input, y_grad = y_input;

      if(x_grad >= gradWidth)
      x_grad = gradWidth - 1;

      if(y_grad >= gradHeight)
      y_grad = gradHeight - 1;

      kernelDet[i][j] += inputNow[x_input][y_input] * diffNow[x_grad][y_grad];
      }
      }
      }
      }

      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      kernelDet[i][j] /= (widthIterNumber * heightIterNumber);

      this->kernels[index][i][j] += kernelDet[i][j];
      this->biases[index] += kernelDet[i][j];
      }
      }
      }
      }


      The complete code can be found here.



      Of course, code is difficult and can be formatted more correctly...

      But are there any problems with backpropagation's logic itself?










      share|improve this question









      New contributor




      mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      I'm trying to implement CNN from scratch.
      I've already implemented a fullyconnected layer and it works.

      But, however, convolutional layer's backpropagation doesn't give any positive results.

      It contains two methods: backward() to backpropagate signal to the earlier layers, and fit() to correct convolution's kernel weights and biases.



      std::vector<std::vector<std::vector<double>>> ConvLayer::backward(std::vector<std::vector<std::vector<double>>> grad)
      {
      int gradNumber = grad.size();
      int gradWidth = grad[0].size();
      int gradHeight = grad[0][0].size();

      if(!this->gradInitialized)
      {
      this->gradInitialized = true;
      this->m_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
      this->v_t = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight, true);
      }

      int kernelWidth = this->kernels[0].size();
      int kernelHeight = this->kernels[0][0].size();

      int inputNumber = this->inputSaved.size();

      int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
      int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

      std::vector<std::vector<std::vector<double>>> gradUpd;
      gradUpd = RandValueGenerator::get_rand_3d_tensor(gradNumber, kernelWidth, kernelHeight, true);

      #pragma omp parallel for
      for(int i = 0; i < gradNumber; ++i)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      double outputInit = this->outputSaved[i][x][y];
      double outputDer = ActivationFunc::calc(this->activationType, outputInit, true);
      grad[i][x][y] = outputDer * grad[i][x][y];
      }
      }
      }

      this->grad = grad;

      #pragma omp parallel for
      for(int index = 0; index < gradNumber; ++index)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      gradUpd[index][i][j] += grad[index][x][y] * this->kernels[index][i][j];
      }
      }
      }
      }
      }
      return gradUpd;
      }

      void ConvLayer::fit(int t, AdamOptimizer& adam)
      {
      int gradNumber = grad.size();
      int gradWidth = grad[0].size();
      int gradHeight = grad[0][0].size();

      int inputNumber = this->inputSaved.size();

      int kernelWidth = this->kernels[0].size();
      int kernelHeight = this->kernels[0][0].size();

      int widthIterNumber = this->inputSaved[0].size() - kernelWidth + 1;
      int heightIterNumber = this->inputSaved[0][0].size() - kernelHeight + 1;

      std::vector<std::vector<std::vector<double>>> diff;
      diff = RandValueGenerator::get_rand_3d_tensor(gradNumber, gradWidth, gradHeight);

      #pragma omp parallel for
      for(int i = 0; i < gradNumber; ++i)
      {
      for(int x = 0; x < gradWidth; ++x)
      {
      for(int y = 0; y < gradHeight; ++y)
      {
      diff[i][x][y] = adam.calc(t, this->m_t[i][x][y], this->v_t[i][x][y], this->grad[i][x][y]);
      }
      }
      }

      #pragma omp parallel for
      for(int index = 0; index < gradNumber; ++index)
      {
      std::vector<std::vector<double>> kernelDet(kernelWidth, std::vector<double>(kernelHeight));
      std::vector<std::vector<double>> inputNow(this->inputSaved[index % inputNumber]);
      std::vector<std::vector<double>> diffNow(diff[index]);

      for(int x = 0; x < widthIterNumber; ++x)
      {
      for(int y = 0; y < heightIterNumber; ++y)
      {
      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      int x_input = x + i, y_input = y + j;
      int x_grad = x_input, y_grad = y_input;

      if(x_grad >= gradWidth)
      x_grad = gradWidth - 1;

      if(y_grad >= gradHeight)
      y_grad = gradHeight - 1;

      kernelDet[i][j] += inputNow[x_input][y_input] * diffNow[x_grad][y_grad];
      }
      }
      }
      }

      for(int i = 0; i < kernelWidth; ++i)
      {
      for(int j = 0; j < kernelHeight; ++j)
      {
      kernelDet[i][j] /= (widthIterNumber * heightIterNumber);

      this->kernels[index][i][j] += kernelDet[i][j];
      this->biases[index] += kernelDet[i][j];
      }
      }
      }
      }


      The complete code can be found here.



      Of course, code is difficult and can be formatted more correctly...

      But are there any problems with backpropagation's logic itself?







      c++ machine-learning neural-network






      share|improve this question









      New contributor




      mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









      New contributor




      mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.









      share|improve this question




      share|improve this question








      edited 13 hours ago









      user2966394

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      233






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      asked 13 hours ago









      mrhemen2015

      61




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      New contributor





      mrhemen2015 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.






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      Check out our Code of Conduct.



























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