fix nn.py&API.spec, test=develop

paddle/fluid/API.spec

@@ -423,3 +423,17 @@ paddle.fluid.Scope.drop_kids drop_kids(self: paddle.fluid.core.Scope) -> None
 paddle.fluid.Scope.find_var find_var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
 paddle.fluid.Scope.new_scope new_scope(self: paddle.fluid.core.Scope) -> paddle.fluid.core.Scope
 paddle.fluid.Scope.var var(self: paddle.fluid.core.Scope, arg0: unicode) -> paddle.fluid.core.Variable
+paddle.reader.map_readers ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None)
+paddle.reader.buffered ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None)
+paddle.reader.compose ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None)
+paddle.reader.chain ArgSpec(args=[], varargs='readers', keywords=None, defaults=None)
+paddle.reader.shuffle ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None)
+paddle.reader.firstn ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None)
+paddle.reader.xmap_readers ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,))
+paddle.reader.PipeReader.__init__ ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain'))
+paddle.reader.PipeReader.get_line ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n'))
+paddle.reader.multiprocess_reader ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000))
+paddle.reader.Fake.__init__ ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
+paddle.reader.creator.np_array ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None)
+paddle.reader.creator.text_file ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None)
+paddle.reader.creator.recordio ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,))

python/paddle/fluid/layers/nn.py

@@ -1350,6 +1350,21 @@ def cross_entropy(input, label, soft_label=False, ignore_index=kIgnoreIndex):
 
 
 def bpr_loss(input, label_pos):
+    """
+    Bayesian Personalized Ranking Loss Operator.
+
+    This operator belongs to pairwise ranking loss. LabelPos is the desired item.
+    The loss at a given point in one session is defined as:
+    $Y[i] = -\frac{1}{N_{i}-1} * \sum_{0\le j<N_{i},~ j\neq Label[i]}\log(\sigma(X[i, Label[i]]-X[i, j]))$
+
+    Learn more details by reading paper <session-based recommendations with recurrent
+    neural networks>(https://arxiv.org/abs/1511.06939)
+
+    Examples:
+        .. code-block:: python
+
+          cost = fluid.layers.bpr_loss(input=predict, label_pos=label)
+    """
 
     helper = LayerHelper('bpr_loss', **locals())
     out = helper.create_variable_for_type_inference(dtype=input.dtype)