Expose param_attr and bias_attr.

paddle/operators/lstm_unit_op.cc

@@ -51,7 +51,10 @@ class LstmUnitOpMaker : public framework::OpProtoAndCheckerMaker {
   LstmUnitOpMaker(framework::OpProto* proto,
                   framework::OpAttrChecker* op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "FC input before the non-linear activation.");
+    AddInput("X",
+             "Lstm unit only applies non-linear activations, please make sure"
+             "that linear tranformation has already been applied to `X`. "
+             "Linear tranformation can be applied by adding a `fc` layer");
     AddInput(
         "C_prev",
         "The cell state tensor of last time-step in the Lstm Unit operator.");

python/paddle/v2/fluid/layers/nn.py

@@ -5,6 +5,7 @@ All layers just related to the neural network.
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant
 from ..framework import Variable
+from ..param_attr import ParamAttr
 from tensor import concat
 
 __all__ = [
@@ -796,6 +797,8 @@ def lstm_unit(x_t,
               hidden_t_prev,
               cell_t_prev,
               forget_bias=0.0,
+              param_attr=None,
+              bias_attr=ParamAttr(),
               main_program=None,
               startup_program=None):
     """Lstm unit layer. The equation of a lstm step is:
@@ -836,6 +839,10 @@ def lstm_unit(x_t,
         hidden_t_prev (Variable): The hidden value of lstm unit.
         cell_t_prev (Variable): The cell value of lstm unit.
         forget_bias (float): The forget bias of lstm unit.
+        param_attr (ParamAttr): The attributes of parameter weights, used to set
+            initializer, name etc.
+        bias_attr (ParamAttr): The attributes of bias weights, used to set
+            initializer, name etc.
         main_program (Program): The main program.
         startup_program (Program): the startup program.
 
@@ -882,6 +889,8 @@ def lstm_unit(x_t,
         startup_program=startup_program)
     fc_out = fc(input=concat_out,
                 size=4 * size,
+                param_attr=param_attr,
+                bias_attr=bias_attr,
                 main_program=main_program,
                 startup_program=startup_program)
     dtype = x_t.dtype