text: #include "connected_layer.h"
  #include "convolutional_layer.h"
  #include "batchnorm_layer.h"
  #include "utils.h"
  #include "cuda.h"
  #include "blas.h"
  #include "gemm.h"
  
  #include <math.h>
  #include <stdio.h>
  #include <stdlib.h>
  #include <string.h>
  
  layer make_connected_layer(int batch, int inputs, int outputs, ACTIVATION activation, int batch_normalize, int adam)
  {
      int i;
      layer l = {0};
      l.learning_rate_scale = 1;
      l.type = CONNECTED;
  
      l.inputs = inputs;
      l.outputs = outputs;
      l.batch=batch;
      l.batch_normalize = batch_normalize;
      l.h = 1;
      l.w = 1;
      l.c = inputs;
      l.out_h = 1;
      l.out_w = 1;
      l.out_c = outputs;
  
      l.output = calloc(batch*outputs, sizeof(float));
      l.delta = calloc(batch*outputs, sizeof(float));
  
      l.weight_updates = calloc(inputs*outputs, sizeof(float));
      l.bias_updates = calloc(outputs, sizeof(float));
  
      l.weights = calloc(outputs*inputs, sizeof(float));
      l.biases = calloc(outputs, sizeof(float));
  
      l.forward = forward_connected_layer;
      l.backward = backward_connected_layer;
      l.update = update_connected_layer;
  
      //float scale = 1./sqrt(inputs);
      float scale = sqrt(2./inputs);
      for(i = 0; i < outputs*inputs; ++i){
          l.weights[i] = scale*rand_uniform(-1, 1);
      }
  
      for(i = 0; i < outputs; ++i){
          l.biases[i] = 0;
      }
  
      if(adam){
          l.m = calloc(l.inputs*l.outputs, sizeof(float));
          l.v = calloc(l.inputs*l.outputs, sizeof(float));
          l.bias_m = calloc(l.outputs, sizeof(float));
          l.scale_m = calloc(l.outputs, sizeof(float));
          l.bias_v = calloc(l.outputs, sizeof(float));
          l.scale_v = calloc(l.outputs, sizeof(float));
      }
      if(batch_normalize){
          l.scales = calloc(outputs, sizeof(float));
          l.scale_updates = calloc(outputs, sizeof(float));
          for(i = 0; i < outputs; ++i){
              l.scales[i] = 1;
          }
  
          l.mean = calloc(outputs, sizeof(float));
          l.mean_delta = calloc(outputs, sizeof(float));
          l.variance = calloc(outputs, sizeof(float));
          l.variance_delta = calloc(outputs, sizeof(float));
  
          l.rolling_mean = calloc(outputs, sizeof(float));
          l.rolling_variance = calloc(outputs, sizeof(float));
  
          l.x = calloc(batch*outputs, sizeof(float));
          l.x_norm = calloc(batch*outputs, sizeof(float));
      }
  
  #ifdef GPU
      l.forward_gpu = forward_connected_layer_gpu;
      l.backward_gpu = backward_connected_layer_gpu;
      l.update_gpu = update_connected_layer_gpu;
  
      l.weights_gpu = cuda_make_array(l.weights, outputs*inputs);
      l.biases_gpu = cuda_make_array(l.biases, outputs);
  
      l.weight_updates_gpu = cuda_make_array(l.weight_updates, outputs*inputs);
      l.bias_updates_gpu = cuda_make_array(l.bias_updates, outputs);
  
      l.output_gpu = cuda_make_array(l.output, outputs*batch);
      l.delta_gpu = cuda_make_array(l.delta, outputs*batch);
      if (adam) {
          l.m_gpu =       cuda_make_array(0, inputs*outputs);
          l.v_gpu =       cuda_make_array(0, inputs*outputs);
          l.bias_m_gpu =  cuda_make_array(0, outputs);
          l.bias_v_gpu =  cuda_make_array(0, outputs);
          l.scale_m_gpu = cuda_make_array(0, outputs);
          l.scale_v_gpu = cuda_make_array(0, outputs);
      }
  
      if(batch_normalize){
          l.mean_gpu = cuda_make_array(l.mean, outputs);
          l.variance_gpu = cuda_make_array(l.variance, outputs);
  
          l.rolling_mean_gpu = cuda_make_array(l.mean, outputs);
          l.rolling_variance_gpu = cuda_make_array(l.variance, outputs);
  
          l.mean_delta_gpu = cuda_make_array(l.mean, outputs);
          l.variance_delta_gpu = cuda_make_array(l.variance, outputs);
  
          l.scales_gpu = cuda_make_array(l.scales, outputs);
          l.scale_updates_gpu = cuda_make_array(l.scale_updates, outputs);
  
          l.x_gpu = cuda_make_array(l.output, l.batch*outputs);
          l.x_norm_gpu = cuda_make_array(l.output, l.batch*outputs);
  #ifdef CUDNN
          cudnnCreateTensorDescriptor(&l.normTensorDesc);
          cudnnCreateTensorDescriptor(&l.dstTensorDesc);
          cudnnSetTensor4dDescriptor(l.dstTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, l.batch, l.out_c, l.out_h, l.out_w); 
          cudnnSetTensor4dDescriptor(l.normTensorDesc, CUDNN_TENSOR_NCHW, CUDNN_DATA_FLOAT, 1, l.out_c, 1, 1); 
  #endif
      }
  #endif
      l.activation = activation;
      fprintf(stderr, "connected                            %4d  ->  %4d\n", inputs, outputs);
      return l;
  }
  
  void update_connected_layer(layer l, update_args a)
  {
      float learning_rate = a.learning_rate*l.learning_rate_scale;
      float momentum = a.momentum;
      float decay = a.decay;
      int batch = a.batch;
      axpy_cpu(l.outputs, learning_rate/batch, l.bias_updates, 1, l.biases, 1);
      scal_cpu(l.outputs, momentum, l.bias_updates, 1);
  
      if(l.batch_normalize){
          axpy_cpu(l.outputs, learning_rate/batch, l.scale_updates, 1, l.scales, 1);
          scal_cpu(l.outputs, momentum, l.scale_updates, 1);
      }
  
      axpy_cpu(l.inputs*l.outputs, -decay*batch, l.weights, 1, l.weight_updates, 1);
      axpy_cpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates, 1, l.weights, 1);
      scal_cpu(l.inputs*l.outputs, momentum, l.weight_updates, 1);
  }
  
  void forward_connected_layer(layer l, network net)
  {
      fill_cpu(l.outputs*l.batch, 0, l.output, 1);
      int m = l.batch;
      int k = l.inputs;
      int n = l.outputs;
      float *a = net.input;
      float *b = l.weights;
      float *c = l.output;
      gemm(0,1,m,n,k,1,a,k,b,k,1,c,n);
      if(l.batch_normalize){
          forward_batchnorm_layer(l, net);
      } else {
          add_bias(l.output, l.biases, l.batch, l.outputs, 1);
      }
      activate_array(l.output, l.outputs*l.batch, l.activation);
  }
  
  void backward_connected_layer(layer l, network net)
  {
      gradient_array(l.output, l.outputs*l.batch, l.activation, l.delta);
  
      if(l.batch_normalize){
          backward_batchnorm_layer(l, net);
      } else {
          backward_bias(l.bias_updates, l.delta, l.batch, l.outputs, 1);
      }
  
      int m = l.outputs;
      int k = l.batch;
      int n = l.inputs;
      float *a = l.delta;
      float *b = net.input;
      float *c = l.weight_updates;
      gemm(1,0,m,n,k,1,a,m,b,n,1,c,n);
  
      m = l.batch;
      k = l.outputs;
      n = l.inputs;
  
      a = l.delta;
      b = l.weights;
      c = net.delta;
  
      if(c) gemm(0,0,m,n,k,1,a,k,b,n,1,c,n);
  }
  
  
  void denormalize_connected_layer(layer l)
  {
      int i, j;
      for(i = 0; i < l.outputs; ++i){
          float scale = l.scales[i]/sqrt(l.rolling_variance[i] + .000001);
          for(j = 0; j < l.inputs; ++j){
              l.weights[i*l.inputs + j] *= scale;
          }
          l.biases[i] -= l.rolling_mean[i] * scale;
          l.scales[i] = 1;
          l.rolling_mean[i] = 0;
          l.rolling_variance[i] = 1;
      }
  }
  
  
  void statistics_connected_layer(layer l)
  {
      if(l.batch_normalize){
          printf("Scales ");
          print_statistics(l.scales, l.outputs);
          /*
             printf("Rolling Mean ");
             print_statistics(l.rolling_mean, l.outputs);
             printf("Rolling Variance ");
             print_statistics(l.rolling_variance, l.outputs);
           */
      }
      printf("Biases ");
      print_statistics(l.biases, l.outputs);
      printf("Weights ");
      print_statistics(l.weights, l.outputs);
  }
  
  #ifdef GPU
  
  void pull_connected_layer(layer l)
  {
      cuda_pull_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
      cuda_pull_array(l.biases_gpu, l.biases, l.outputs);
      cuda_pull_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
      cuda_pull_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
      if (l.batch_normalize){
          cuda_pull_array(l.scales_gpu, l.scales, l.outputs);
          cuda_pull_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
          cuda_pull_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
      }
  }
  
  void push_connected_layer(layer l)
  {
      cuda_push_array(l.weights_gpu, l.weights, l.inputs*l.outputs);
      cuda_push_array(l.biases_gpu, l.biases, l.outputs);
      cuda_push_array(l.weight_updates_gpu, l.weight_updates, l.inputs*l.outputs);
      cuda_push_array(l.bias_updates_gpu, l.bias_updates, l.outputs);
      if (l.batch_normalize){
          cuda_push_array(l.scales_gpu, l.scales, l.outputs);
          cuda_push_array(l.rolling_mean_gpu, l.rolling_mean, l.outputs);
          cuda_push_array(l.rolling_variance_gpu, l.rolling_variance, l.outputs);
      }
  }
  
  void update_connected_layer_gpu(layer l, update_args a)
  {
      float learning_rate = a.learning_rate*l.learning_rate_scale;
      float momentum = a.momentum;
      float decay = a.decay;
      int batch = a.batch;
      if(a.adam){
          adam_update_gpu(l.weights_gpu, l.weight_updates_gpu, l.m_gpu, l.v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.inputs*l.outputs, batch, a.t);
          adam_update_gpu(l.biases_gpu, l.bias_updates_gpu, l.bias_m_gpu, l.bias_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
          if(l.scales_gpu){
              adam_update_gpu(l.scales_gpu, l.scale_updates_gpu, l.scale_m_gpu, l.scale_v_gpu, a.B1, a.B2, a.eps, decay, learning_rate, l.outputs, batch, a.t);
          }
      }else{
          axpy_gpu(l.outputs, learning_rate/batch, l.bias_updates_gpu, 1, l.biases_gpu, 1);
          scal_gpu(l.outputs, momentum, l.bias_updates_gpu, 1);
  
          if(l.batch_normalize){
              axpy_gpu(l.outputs, learning_rate/batch, l.scale_updates_gpu, 1, l.scales_gpu, 1);
              scal_gpu(l.outputs, momentum, l.scale_updates_gpu, 1);
          }
  
          axpy_gpu(l.inputs*l.outputs, -decay*batch, l.weights_gpu, 1, l.weight_updates_gpu, 1);
          axpy_gpu(l.inputs*l.outputs, learning_rate/batch, l.weight_updates_gpu, 1, l.weights_gpu, 1);
          scal_gpu(l.inputs*l.outputs, momentum, l.weight_updates_gpu, 1);
      }
  }
  
  void forward_connected_layer_gpu(layer l, network net)
  {
      fill_gpu(l.outputs*l.batch, 0, l.output_gpu, 1);
  
      int m = l.batch;
      int k = l.inputs;
      int n = l.outputs;
      float * a = net.input_gpu;
      float * b = l.weights_gpu;
      float * c = l.output_gpu;
      gemm_gpu(0,1,m,n,k,1,a,k,b,k,1,c,n);
  
      if (l.batch_normalize) {
          forward_batchnorm_layer_gpu(l, net);
      } else {
          add_bias_gpu(l.output_gpu, l.biases_gpu, l.batch, l.outputs, 1);
      }
      activate_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation);
  }
  
  void backward_connected_layer_gpu(layer l, network net)
  {
      constrain_gpu(l.outputs*l.batch, 1, l.delta_gpu, 1);
      gradient_array_gpu(l.output_gpu, l.outputs*l.batch, l.activation, l.delta_gpu);
      if(l.batch_normalize){
          backward_batchnorm_layer_gpu(l, net);
      } else {
          backward_bias_gpu(l.bias_updates_gpu, l.delta_gpu, l.batch, l.outputs, 1);
      }
  
      int m = l.outputs;
      int k = l.batch;
      int n = l.inputs;
      float * a = l.delta_gpu;
      float * b = net.input_gpu;
      float * c = l.weight_updates_gpu;
      gemm_gpu(1,0,m,n,k,1,a,m,b,n,1,c,n);
  
      m = l.batch;
      k = l.outputs;
      n = l.inputs;
  
      a = l.delta_gpu;
      b = l.weights_gpu;
      c = net.delta_gpu;
  
      if(c) gemm_gpu(0,0,m,n,k,1,a,k,b,n,1,c,n);
  }
  #endif
  