CNN Backprop in C

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// Forward propagation

void forward_propagation(float input_data[][][], float filters[][][][], 
                         float biases[], float output_data[][][], int n_layers, int n_filters[], int filter_size[], int stride[], int padding[]) {
  
  int curr_input_dim, curr_output_dim;
  int n_channels, curr_filter, curr_stride, curr_padding;
  
  // Loop through the layers
  for (int layer = 0; layer < n_layers; layer++) {
    // Get the current layer's filter and stride size, as well as the number of channels in the input data
    curr_filter = filter_size[layer];
    curr_stride = stride[layer];
    n_channels = input_data[layer].length;
    curr_padding = padding[layer];
    
    // Calculate the dimensions of the input and output data for this layer
    curr_input_dim = (input_data[layer].length - curr_filter + 2 * curr_padding) / curr_stride + 1;
    curr_output_dim = (curr_input_dim - curr_filter + 2 * curr_padding) / curr_stride + 1;
    
    // Loop through the filters in this layer
    for (int f = 0; f < n_filters[layer]; f++) {
      // Loop through the channels in the input data
      for (int c = 0; c < n_channels; c++) {
        // Loop through the rows of the input data
        for (int i = 0; i < curr_input_dim; i++) {
          // Loop through the columns of the input data
          for (int j = 0; j < curr_input_dim; j++) {
            // Convolve the filter with the input data and add the bias
            output_data[layer][f][i][j] = biases[layer][f];
            for (int fi = 0; fi < curr_filter; fi++) {
              for (int fj = 0; fj < curr_filter; fj++) {
                output_data[layer][f][i][j] += input_data[layer][c][i + fi][j + fj] * filters[layer][f][c][fi][fj];
              }
            }
            // Apply the activation function
            output_data[layer][f][i][j] = sigmoid(output_data[layer][f][i][j]);
          }
        }
      }
    }
    
    // Set the input data for the next layer to be the output of this layer
    input_data[layer+1] = output_data[layer];
  }
}

// Backward propagation

void backward_propagation(float input_data[][][], float filters[][][][], float biases[], float output_data[][][], float d_output_data[][][][], int n_layers, int n_filters[], int filter_size[], int stride[],