add name parameter

python/paddle/nn/functional/loss.py

@@ -2227,7 +2227,7 @@ def hinge_embedding_loss(input, label, margin=1.0, reduction='mean', name=None):
         return loss
 
 
-def cosine_embedding_loss(input1, input2, label, margin=0, reduction='mean'):
+def cosine_embedding_loss(input1, input2, label, margin=0, reduction='mean', name=None):
     r"""
     This operator computes the cosine embedding loss of Tensor ``input1``, ``input2`` and ``label`` as follows.
 
@@ -2259,6 +2259,8 @@ def cosine_embedding_loss(input1, input2, label, margin=0, reduction='mean'):
                          ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
                          ``'mean'``: the sum of the output will be divided by the number of elements in the output
                          ``'sum'``: the output will be summed.
+        name (str, optional): Name for the operation (optional, default is None).
+                         For more information, please refer to :ref:`api_guide_Name`.
 
     Returns:
         Tensor, the cosine embedding Loss of Tensor ``input1`` ``input2`` and ``label``.
@@ -2324,6 +2326,6 @@ def cosine_embedding_loss(input1, input2, label, margin=0, reduction='mean'):
     if reduction == 'none':
         return out
     if reduction == 'mean':
-        return paddle.mean(out)
+        return paddle.mean(out, name=name)
     elif reduction == 'sum':
-        return paddle.sum(out)
+        return paddle.sum(out, name=name)

python/paddle/nn/layer/loss.py

@@ -1334,6 +1334,8 @@ class CosineEmbeddingLoss(Layer):
             ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be applied,
             ``'mean'``: the sum of the output will be divided by the number of
             elements in the output, ``'sum'``: the output will be summed.
+        name (str, optional): Name for the operation (optional, default is None).
+            For more information, please refer to :ref:`api_guide_Name`.
 
     Shape:
         input1 (Tensor): tensor with shape: [N, M] or [M], 'N' means batch size, 'M' means the length of input array.
@@ -1370,7 +1372,7 @@ class CosineEmbeddingLoss(Layer):
 
     """
 
-    def __init__(self, margin=0, reduction='mean'):
+    def __init__(self, margin=0, reduction='mean' ,name=None):
         if margin > 1 or margin < -1:
             raise ValueError(
                 "The value of 'margin' should be in the interval of [-1, 1], but received %f, which is not allowed."
@@ -1382,7 +1384,8 @@ def __init__(self, margin=0, reduction='mean'):
         super(CosineEmbeddingLoss, self).__init__()
         self.margin = margin
         self.reduction = reduction
+        self.name = name
 
     def forward(self, input1, input2, label):
         return F.cosine_embedding_loss(
-            input1, input2, label, margin=self.margin, reduction=self.reduction)
+            input1, input2, label, margin=self.margin, reduction=self.reduction, name=self.name)