diff --git a/mindspore/nn/loss/loss.py b/mindspore/nn/loss/loss.py
index cdce91ece5..16f3dcb174 100644
--- a/mindspore/nn/loss/loss.py
+++ b/mindspore/nn/loss/loss.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -29,7 +29,7 @@ class _Loss(Cell):
     """
     Base class for other losses.
     """
-    def __init__(self, reduction='mean', weights=1.0):
+    def __init__(self, reduction='mean'):
         super(_Loss, self).__init__()
         if reduction is None:
             reduction = 'none'
@@ -47,10 +47,7 @@ class _Loss(Cell):
         self.reduce_mean = _selected_ops.ReduceMean()
         self.reduce_sum = P.ReduceSum()
         self.mul = P.Mul()
-        if isinstance(weights, int):
-            self.weights = float(weights)
-        else:
-            self.weights = weights
+        self.cast = P.Cast()
 
     def get_axis(self, x):
         shape = F.shape(x)
@@ -58,13 +55,22 @@ class _Loss(Cell):
         perm = F.make_range(0, length)
         return perm
 
-    def get_loss(self, x):
-        if self.weights != 1.0:
-            x = self.mul(self.weights, x)
+    def get_loss(self, x, weights=1.0):
+        """
+        Computes the weighted loss
+        Args:
+            weights: Optional `Tensor` whose rank is either 0, or the same rank as inputs, and must be broadcastable to
+                inputs (i.e., all dimensions must be either `1`, or the same as the corresponding inputs dimension).
+        """
+        input_dtype = x.dtype
+        x = self.cast(x, mstype.float32)
+        weights = self.cast(weights, mstype.float32)
+        x = self.mul(weights, x)
         if self.reduce and self.average:
             x = self.reduce_mean(x, self.get_axis(x))
         if self.reduce and not self.average:
             x = self.reduce_sum(x, self.get_axis(x))
+        x = self.cast(x, input_dtype)
         return x
 
     def construct(self, base, target):
diff --git a/tests/st/ops/gpu/test_loss.py b/tests/st/ops/gpu/test_loss.py
index 693f8f01bd..a5d95a9859 100644
--- a/tests/st/ops/gpu/test_loss.py
+++ b/tests/st/ops/gpu/test_loss.py
@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2021 Huawei Technologies Co., Ltd
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -14,19 +14,24 @@
 # ============================================================================
 """ test loss """
 import numpy as np
-
+import mindspore
 from mindspore import Tensor
 from mindspore.ops import operations as P
 from mindspore.nn.loss.loss import _Loss
+from mindspore.nn.loss.loss import L1Loss
+import mindspore.context as context
+
+context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
 
 class WeightedLoss(_Loss):
     def __init__(self, reduction='mean', weights=1.0):
-        super(WeightedLoss, self).__init__(reduction, weights)
+        super(WeightedLoss, self).__init__(reduction)
         self.abs = P.Abs()
+        self.weights = weights
 
     def construct(self, base, target):
         x = self.abs(base - target)
-        return self.get_loss(x)
+        return self.get_loss(x, self.weights)
 
 def test_WeightedLoss():
     loss = WeightedLoss()
@@ -35,17 +40,41 @@ def test_WeightedLoss():
     output_data = loss(input_data, target_data)
 
     error_range = np.ones(shape=output_data.shape) * 10e-6
-    loss.weights = 1.0
+    loss = WeightedLoss(weights=2.0)
     test_output = loss(input_data, target_data)
-    diff = test_output - output_data * loss.weights
+    diff = test_output - output_data * 2.0
     assert np.all(abs(diff.asnumpy()) < error_range)
 
-    loss.weights = 2.0
+    loss = WeightedLoss(weights=3)
     test_output = loss(input_data, target_data)
-    diff = test_output - output_data * loss.weights
+    diff = test_output - output_data * 3
     assert np.all(abs(diff.asnumpy()) < error_range)
 
-    loss.weights = 3
-    test_output = loss(input_data, target_data)
-    diff = test_output - output_data * loss.weights
+    loss = WeightedLoss(weights=Tensor(np.array([[0.7, 0.3], [0.7, 0.3]])))
+    y_true = Tensor(np.array([[0., 1.], [0., 0.]]), mindspore.float32)
+    y_pred = Tensor(np.array([[1., 1.], [1., 0.]]), mindspore.float32)
+    test_data = 0.35
+    output = loss(y_true, y_pred)
+    diff = test_data - output.asnumpy()
+    assert np.all(abs(diff) < error_range)
+
+class CustomLoss(_Loss):
+    def __init__(self, reduction='mean'):
+        super(CustomLoss, self).__init__(reduction)
+        self.abs = P.Abs()
+
+    def construct(self, base, target):
+        x = self.abs(base - target)
+        return self.get_loss(x, weights=2.0)
+
+def test_CustomLoss():
+    loss = L1Loss()
+    input_data = Tensor(np.array([[1, 2, 3], [2, 3, 4]]).astype(np.float32))
+    target_data = Tensor(np.array([[0, 2, 5], [3, 1, 1]]).astype(np.float32))
+    output_data = loss(input_data, target_data)
+
+    error_range = np.ones(shape=output_data.shape) * 10e-6
+    customloss = CustomLoss()
+    test_output = customloss(input_data, target_data)
+    diff = test_output - output_data * 2.0
     assert np.all(abs(diff.asnumpy()) < error_range)