From 8b86c12e46395d2608516323c8dde302ace8aa40 Mon Sep 17 00:00:00 2001
From: ceci3 <592712189@qq.com>
Date: Fri, 8 Mar 2019 05:58:23 +0000
Subject: [PATCH] test=develop, update API.spec

---
 paddle/fluid/API.spec            |  2 +-
 python/paddle/fluid/layers/nn.py | 18 +++++++++++-------
 2 files changed, 12 insertions(+), 8 deletions(-)

diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec
index eaebe7f55ce..68d68cd11e8 100644
--- a/paddle/fluid/API.spec
+++ b/paddle/fluid/API.spec
@@ -221,7 +221,7 @@ paddle.fluid.layers.psroi_pool (ArgSpec(args=['input', 'rois', 'output_channels'
 paddle.fluid.layers.teacher_student_sigmoid_loss (ArgSpec(args=['input', 'label', 'soft_max_up_bound', 'soft_max_lower_bound'], varargs=None, keywords=None, defaults=(15.0, -15.0)), ('document', '2f6ff96864054a31aa4bb659c6722c99'))
 paddle.fluid.layers.huber_loss (ArgSpec(args=['input', 'label', 'delta'], varargs=None, keywords=None, defaults=None), ('document', '431a4301c35032166ec029f7432c80a7'))
 paddle.fluid.layers.tree_conv (ArgSpec(args=['nodes_vector', 'edge_set', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '34ea12ac9f10a65dccbc50100d12e607'))
-paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', '7d010db0a2404dfbecb9ba5804788a16'))
+paddle.fluid.layers.npair_loss (ArgSpec(args=['anchor', 'positive', 'labels', 'l2_reg'], varargs=None, keywords=None, defaults=(0.002,)), ('document', 'e5e0898611a1427339bb8895c24636df'))
 paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '33bbd42027d872b3818b3d64ec52e139'))
 paddle.fluid.layers.open_files (ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)), ('document', 'b1ae2e1cc0750e58726374061ea90ecc'))
 paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'b0a1c2fc51c27a106da28f3308c41f5e'))
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 85e0afb270f..608f811b323 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -10657,10 +10657,10 @@ def tree_conv(nodes_vector,
 def npair_loss(anchor, positive, labels, l2_reg=0.002):
     '''
   **Npair Loss Layer**
-  
+
   Read `Improved Deep Metric Learning with Multi class N pair Loss Objective <http://www.nec-labs.com/uploads/images/Department-Images/MediaAnalytics/papers/nips16_npairmetriclearning.pdf>`_ .
-  
-  Npair loss requires paired data. Npair loss has two parts: the first part is L2 
+
+  Npair loss requires paired data. Npair loss has two parts: the first part is L2
   regularizer on the embedding vector; the second part is cross entropy loss which
   takes the similarity matrix of anchor and positive as logits.
 
@@ -10676,10 +10676,14 @@ def npair_loss(anchor, positive, labels, l2_reg=0.002):
   Examples:
     .. code-block:: python
 
-       anchor = fluid.layers.data(name='anchor', shape=[18,6], dtype='float32')
-       positive = fluid.layers.data(name='positive', shape=[18,6], dtype='float32')
-       label = fluid.layers.data(name='labels',shape=[18], dtype='float32')
-       npair_loss = fluid.layers.npair_loss(anchor, positive, labels, 0.002)
+       anchor = fluid.layers.data(
+                     name = 'anchor', shape = [18, 6], dtype = 'float32', append_batch_size=False)
+       positive = fluid.layers.data(
+                     name = 'positive', shape = [18, 6], dtype = 'float32', append_batch_size=False)
+       labels = fluid.layers.data(
+                     name = 'labels', shape = [18], dtype = 'float32', append_batch_size=False)
+
+       npair_loss = fluid.layers.npair_loss(anchor, positive, labels, l2_reg = 0.002)
   '''
     Beta = 0.25
     batch_size = labels.shape[0]
-- 
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