fix 'name', test=develop

paddle/fluid/API.spec

@@ -66,7 +66,7 @@ paddle.fluid.layers.linear_chain_crf ArgSpec(args=['input', 'label', 'param_attr
 paddle.fluid.layers.crf_decoding ArgSpec(args=['input', 'param_attr', 'label'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
-paddle.fluid.layers.bpr_loss ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.layers.bpr_loss ArgSpec(args=['input', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None))

python/paddle/fluid/layers/nn.py

@@ -1349,7 +1349,7 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
     return out
 
 
-def bpr_loss(input, label):
+def bpr_loss(input, label, name=None):
     """
     Bayesian Personalized Ranking Loss Operator.
 
@@ -1366,6 +1366,8 @@ def bpr_loss(input, label):
                                 This input is not probability but logits.
         label (Variable|list):  the ground truth which is a 2-D tensor.  `label`
                                 is a tensor<int64> with shape [N x 1].
+        name (str|None):        A name for this layer(optional). If set None, the
+                                layer will be named automatically. Default: None.
     Returns:
         A 2-D tensor with shape [N x 1], the bpr loss.