   Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_res_float(
       d_res_float, num_elem);
 
-  gpu_float.device(gpu_device) = gpu_float.random();
+  gpu_float.device(gpu_device) = gpu_float.random() - gpu_float.constant(0.5f);
   gpu_res_float.device(gpu_device) = gpu_float.abs();
-  gpu_res_half.device(gpu_device) = gpu_float.cast<half>().abs().cast<float>();
+  gpu_res_half.device(gpu_device) = gpu_float.cast<Eigen::half>().abs().cast<float>();
 
   Tensor<float, 1> half_prec(num_elem);
   Tensor<float, 1> full_prec(num_elem);

  int num_elem = 101;
  
  float* d_float = (float*)gpu_device.allocate(num_elem * sizeof(float));
  half* d_half = (half*)gpu_device.allocate(num_elem * sizeof(half));
  float* d_conv = (float*)gpu_device.allocate(num_elem * sizeof(float));

  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_float(
      d_float, num_elem);
  Eigen::TensorMap<Eigen::Tensor<half, 1>, Eigen::Aligned> gpu_half(
      d_half, num_elem);
  Eigen::TensorMap<Eigen::Tensor<float, 1>, Eigen::Aligned> gpu_conv(
      d_conv, num_elem);

  gpu_float.device(gpu_device) = gpu_float.random();
  gpu_half.device(gpu_device) = gpu_float.cast<half>();
  gpu_conv.device(gpu_device) = gpu_half.cast<float>();

  Tensor<float, 1> initial(num_elem);


  Eigen::array<int, 1> redux_dim = {{0}};
  gpu_res_float.device(gpu_device) = gpu_float1.sum(redux_dim);
  gpu_res_half.device(gpu_device) = gpu_float1.cast<Eigen::half>().sum(redux_dim).cast<float>();

  Tensor<float, 1> half_prec(size);
  Tensor<float, 1> full_prec(size);
