squash to one pr

python/paddle/fluid/framework.py

@@ -1034,6 +1034,37 @@ class Block(object):
 
 
 class Program(object):
+    """
+    Python Program. Beneath it is a ProgramDesc, which is used for
+    create c++ Program. A program is a self-contained programing
+    language like container. It has at least one Block, when the
+    control flow op like conditional_block, while_op is included,
+    it will contains nested block.
+    Please reference the framework.proto for details.
+
+    Notes: we have default_startup_program and default_main_program
+    by default, a pair of them will shared the parameters.
+    The default_startup_program only run once to initialize parameters,
+    default_main_program run in every minibatch and adjust the weights.
+
+    Args:
+        None
+
+    Returns:
+        Python Program
+
+    Examples:
+       .. code-block:: python
+
+         main_program = Program()
+         startup_program = Program()
+         with fluid.program_guard(main_program=main_program, startup_program=startup_program):
+            fluid.layers.data(name="x", shape=[-1, 784], dtype='float32')
+            fluid.layers.data(name="y", shape=[-1, 1], dtype='int32')
+            fluid.layers.fc(name="fc", shape=[10], dtype='float32', act="relu")
+
+    """
+
     def __init__(self):
         self.desc = core.ProgramDesc()
         self.blocks = [Block(self, 0)]

python/paddle/fluid/layers/metric.py

@@ -27,8 +27,32 @@ __all__ = ['accuracy', 'auc']
 
 def accuracy(input, label, k=1, correct=None, total=None):
     """
+    accuracy layer.
+    Refer to the https://en.wikipedia.org/wiki/Precision_and_recall
+
     This function computes the accuracy using the input and label.
-    The output is the top k inputs and their indices.
+    If the correct label occurs in top k predictions, then correct will increment by one.
+    Note: the dtype of accuracy is determined by input. the input and label dtype can be different.
+
+    Args:
+        input(Variable): The input of accuracy layer, which is the predictions of network.
+          Carry LoD information is supported.
+        label(Variable): The label of dataset.
+        k(int): The top k predictions for each class will be checked.
+        correct(Variable): The correct predictions count.
+        total(Variable): The total entries count.
+
+    Returns:
+        Variable: The correct rate.
+
+    Examples:
+        .. code-block:: python
+
+           data = fluid.layers.data(name="data", shape=[-1, 32, 32], dtype="float32")
+           label = fluid.layers.data(name="data", shape=[-1,1], dtype="int32")
+           predict = fluid.layers.fc(input=data, size=10)
+           acc = fluid.layers.accuracy(input=predict, label=label, k=5)
+
     """
     helper = LayerHelper("accuracy", **locals())
     topk_out, topk_indices = nn.topk(input, k=k)