diff --git a/nntrainer/layers/loss/mse_loss_layer.cpp b/nntrainer/layers/loss/mse_loss_layer.cpp
index 3aed8125e0..ed4390655d 100644
--- a/nntrainer/layers/loss/mse_loss_layer.cpp
+++ b/nntrainer/layers/loss/mse_loss_layer.cpp
@@ -51,17 +51,27 @@ void MSELossLayer::forwarding(RunLayerContext &context, bool training) {
 
 void MSELossLayer::calcDerivative(RunLayerContext &context) {
   Tensor empty_tensor;
-  Tensor &ret_derivative = context.getOutgoingDerivative(SINGLE_INOUT_IDX);
-  const Tensor &y2_ = context.getIncomingDerivative(SINGLE_INOUT_IDX);
-  Tensor &y2 = empty_tensor;
 
-  if (ret_derivative.getDataType() == ml::train::TensorDim::DataType::FP32)
-    y2 = y2_;
+  Tensor &ret_derivative =
+    context.getOutgoingDerivative(SINGLE_INOUT_IDX).getDataType() ==
+        ml::train::TensorDim::DataType::FP32
+      ? context.getOutgoingDerivative(SINGLE_INOUT_IDX)
+      : empty_tensor;
 
-  if (y2.empty())
-    y2 = y2_.clone(ret_derivative.getDataType());
+  if (ret_derivative.empty())
+    ret_derivative = context.getOutgoingDerivative(SINGLE_INOUT_IDX)
+                       .clone(ml::train::TensorDim::DataType::FP32);
 
-  Tensor &y = context.getInput(SINGLE_INOUT_IDX);
+  Tensor &y = context.getInput(SINGLE_INOUT_IDX).getDataType() ==
+                  ml::train::TensorDim::DataType::FP32
+                ? context.getInput(SINGLE_INOUT_IDX)
+                : empty_tensor;
+
+  if (y.empty())
+    y = context.getInput(SINGLE_INOUT_IDX)
+          .clone(ml::train::TensorDim::DataType::FP32);
+
+  const Tensor &y2 = context.getIncomingDerivative(SINGLE_INOUT_IDX);
 
   y.subtract(y2, ret_derivative);
   float divider = ((float)y.size()) / 2;
@@ -70,7 +80,15 @@ void MSELossLayer::calcDerivative(RunLayerContext &context) {
       "[MSELossLayer::calcDerivative] Error when calculating loss");
   }
 
+  // Loss Scale needs Full precsiion of ret_derivative. Therefore,
+  // ret_derivateive should be FP32 when applying scale, and after applying it
+  // need to convert original type for backpropagating.
+
   LossLayer::applyLossScale(context, ret_derivative);
+
+  if (context.getOutgoingDerivative(SINGLE_INOUT_IDX).getDataType() !=
+      ml::train::TensorDim::DataType::FP32)
+    context.getOutgoingDerivative(SINGLE_INOUT_IDX).copyData(ret_derivative);
 }
 
 } // namespace nntrainer