Merge pull request #11511 from panyx0718/doc2

Add doc for while and piecewise_decay op

python/paddle/fluid/layers/control_flow.py

@@ -654,6 +654,29 @@ class WhileGuard(BlockGuard):
 
 
 class While(object):
+    """
+    while loop control flow.
+
+    Args:
+        cond (Variable): condition used to compare.
+        name (str): The name of this layer.
+
+    Examples:
+          .. code-block:: python
+
+            d0 = layers.data("d0", shape=[10], dtype='float32')
+            data_array = layers.array_write(x=d0, i=i)
+            array_len = layers.fill_constant(shape=[1],dtype='int64', value=3)
+
+            cond = layers.less_than(x=i, y=array_len)
+            while_op = layers.While(cond=cond)
+            with while_op.block():
+                d = layers.array_read(array=data_array, i=i)
+                i = layers.increment(x=i, in_place=True)
+                layers.array_write(result, i=i, array=d)
+                layers.less_than(x=i, y=array_len, cond=cond)
+    """
+
     BEFORE_WHILE_BLOCK = 0
     IN_WHILE_BLOCK = 1
     AFTER_WHILE_BLOCK = 2

python/paddle/fluid/layers/learning_rate_scheduler.py

@@ -209,15 +209,27 @@ def polynomial_decay(learning_rate,
 def piecewise_decay(boundaries, values):
     """Applies piecewise decay to the initial learning rate.
 
-    >>> boundaries = [10000, 20000]
-    >>> values = [1.0, 0.5, 0.1]
-    >>>
-    >>> if step < 10000:
-    >>>     learning_rate = 1.0
-    >>> elif 10000 <= step < 20000:
-    >>>     learning_rate = 0.5
-    >>> else:
-    >>>     learning_rate = 0.1
+      The algorithm can be described as the code below.
+
+      .. code-block:: python
+
+        boundaries = [10000, 20000]
+        values = [1.0, 0.5, 0.1]
+        if step < 10000:
+            learning_rate = 1.0
+        elif 10000 <= step < 20000:
+            learning_rate = 0.5
+        else:
+            learning_rate = 0.1
+    Args:
+        boundaries: A list of steps numbers.
+        values: A list of learning rate values that will be picked during
+            different step boundaries.
+
+    Returns:
+        The decayed learning rate.
+
+
     """
 
     if len(values) - len(boundaries) != 1: