Put all layers and costs in package paddle.layer

doc/design/api.md

@@ -59,9 +59,9 @@ fA = f(paddle.layer.data(input_name="A"))
 fB = f(paddle.layer.data(input_name="B"))
 fQ = f(paddle.layer.data(input_name="Q"))
 
-topology = paddle.cost.less_than(
-               paddle.cost.cross_entropy(fA, fQ),
-               paddle.cost.corss_entropy(fB, fQ))
+topology = paddle.layer.less_than(
+               paddle.layer.cross_entropy(fA, fQ),
+               paddle.layer.corss_entropy(fB, fQ))
 
 # Derive parameters required in topology and create them in model.
 parameters = paddle.parameters.create(topology)
@@ -94,12 +94,12 @@ def D(in);
 
 # Construct the first topology, which contains both D and G.
 # By learning this topology, we update parameters of G.
-d0 = paddle.cost.should_be_false(D(G(paddle.layer.data())))
+d0 = paddle.layer.should_be_false(D(G(paddle.layer.data())))
 
 # Construct a second topology d1, which contains only D. By
 # training this topology, we update parameters of D.  Note
 # that d1 share parameters with d0.
-d1 = paddle.cost.should_be_true(D(paddle.layer.data()))
+d1 = paddle.layer.should_be_true(D(paddle.layer.data()))
 
 # Create parameters from a list of multiple topologies (models) for
 # the chance to share parameters between these topologies.