refine code details

python/paddle/v2/fluid/clip.py

@@ -162,7 +162,7 @@ def append_gradient_clip_ops(param_grad):
     context = dict()
     create_op_callbacks = []
     for p, g in param_grad:
-        clip_attr = getattr(p, 'clip_attr', NullGradientClipAttr())
+        clip_attr = getattr(p, 'gradient_clip_attr', NullGradientClipAttr())
         if clip_attr is None:
             clip_attr = NullGradientClipAttr()
         if not isinstance(clip_attr, BaseGradientClipAttr):

python/paddle/v2/fluid/framework.py

@@ -946,7 +946,7 @@ class Parameter(Variable):
 
         self.regularizer = kwargs.get('regularizer', None)
 
-        self.clip_attr = kwargs.get('clip_attr', None)
+        self.gradient_clip_attr = kwargs.get('gradient_clip_attr', None)
 
 
 # program is a global instance.

python/paddle/v2/fluid/param_attr.py

@@ -24,13 +24,13 @@ class ParamAttr(object):
                  learning_rate=1.0,
                  regularizer=None,
                  trainable=True,
-                 clip=None):
+                 gradient_clip=None):
         self.name = name
         self.initializer = initializer
         self.learning_rate = learning_rate
         self.regularizer = regularizer
         self.trainable = trainable
-        self.clip = clip
+        self.gradient_clip = gradient_clip
 
     def set_default_initializer(self, initializer):
         if initializer is None:
@@ -76,7 +76,7 @@ class ParamAttr(object):
             },
             'regularizer': self.regularizer,
             'trainable': self.trainable,
-            'clip_attr': self.clip
+            'gradient_clip_attr': self.gradient_clip
         }
         if with_initializer:
             kwargs['initializer'] = self.initializer

python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py

@@ -26,7 +26,7 @@ hidden1 = fluid.layers.fc(input=image,
                           act='relu',
                           param_attr=fluid.ParamAttr(
                               regularizer=regularizer,
-                              clip=fluid.clip.ClipByValue(10)))
+                              gradient_clip=fluid.clip.ClipByValue(10)))
 
 hidden2 = fluid.layers.fc(input=hidden1,
                           size=64,