message: when create inference, skip loss layer
diff: @@ -207,7 +207,8 @@ void create_layers(const nlohmann::json& j_array, std::vector<TensorEntry>& tens
  CK_THROW_(Error_t::WrongInput, "bottom of BinaryCrossEntropyLoss must be two dim");
  }
  if (inference_flag) {
  - CK_THROW_(Error_t::WrongInput, "inference network must NOT have BinaryCrossEntropyLoss");
  + MESSAGE_("Inference stage skip BinaryCrossEntropyLoss layer");
  + break;
  }
  Tensor2<float> label_tensor = Tensor2<float>::stretch_from(input_output_info.inputs[1]);
  blobs_buff->reserve({1, 1}, &loss_tensor);
  @@ -257,7 +258,8 @@ void create_layers(const nlohmann::json& j_array, std::vector<TensorEntry>& tens
  CK_THROW_(Error_t::WrongInput, "bottom of CrossEntropyLoss must be two dim");
  }
  if (inference_flag) {
  - CK_THROW_(Error_t::WrongInput, "inference network must NOT have CrossEntropyLoss");
  + MESSAGE_("Inference stage skip CrossEntropyLoss layer");
  + break;
  }
  Tensor2<float> label_tensor = Tensor2<float>::stretch_from(input_output_info.inputs[1]);
  blobs_buff->reserve({1, 1}, &loss_tensor);
  @@ -517,7 +519,8 @@ void create_layers(const nlohmann::json& j_array, std::vector<TensorEntry>& tens
  CK_THROW_(Error_t::WrongInput, "bottom of MultiCrossEntropyLoss must be two dim");
  }
  if (inference_flag) {
  - CK_THROW_(Error_t::WrongInput, "inference network must NOT have MultiCrossEntropyLoss");
  + MESSAGE_("Inference stage skip MultiCrossEntropyLoss layer");
  + break;
  }
  auto tweight = get_json(j, "target_weight");
  std::vector<float> target_weight_vec;
  