diff --git a/fluid/image_classification/caffe2fluid/examples/imagenet/compare.py b/fluid/image_classification/caffe2fluid/examples/imagenet/compare.py
index 45b1f5303ce77de7c7f5e3a232517c26e159b2fa..c995e6df17a4be068984cece06a9b3a33f6ea4f4 100644
--- a/fluid/image_classification/caffe2fluid/examples/imagenet/compare.py
+++ b/fluid/image_classification/caffe2fluid/examples/imagenet/compare.py
@@ -45,7 +45,7 @@ def calc_diff(f1, f2):
         sq_df = np.mean(df * df)
         return max_df, sq_df
     except Exception as e:
-        return -1.0, -1.0
+        return 1.0, 1.0
 
 
 def compare(path1, path2, no_exception):
diff --git a/fluid/image_classification/caffe2fluid/kaffe/paddle/network.py b/fluid/image_classification/caffe2fluid/kaffe/paddle/network.py
index 4b6f38be6647b23a4c79dcc7850aaab55fa6ffa8..0c1354530c6af3b35977bb51e830b89d481de6b6 100644
--- a/fluid/image_classification/caffe2fluid/kaffe/paddle/network.py
+++ b/fluid/image_classification/caffe2fluid/kaffe/paddle/network.py
@@ -245,10 +245,18 @@ class Network(object):
 
     @layer
     def prelu(self, input, channel_shared, name):
-        #fluid = import_fluid()
-        #output = fluid.layers.relu(input)
-        #return output
-        raise NotImplementedError('prelu not implemented')
+        fluid = import_fluid()
+        if channel_shared:
+            mode = 'all'
+        else:
+            mode = 'channel'
+
+        prefix = name + '_'
+        output = fluid.layers.prelu(
+            input,
+            mode=mode,
+            param_attr=fluid.ParamAttr(name=prefix + 'negslope'))
+        return output
 
     def pool(self, pool_type, input, k_h, k_w, s_h, s_w, ceil_mode, padding,
              name):
diff --git a/fluid/image_classification/caffe2fluid/kaffe/transformers.py b/fluid/image_classification/caffe2fluid/kaffe/transformers.py
index b92e93bbd84989e39bd658d107166f68e9dc4a27..f436ec8cbfb49394ef2880dbf0cb46aeff7a6ca0 100644
--- a/fluid/image_classification/caffe2fluid/kaffe/transformers.py
+++ b/fluid/image_classification/caffe2fluid/kaffe/transformers.py
@@ -176,6 +176,7 @@ class DataReshaper(object):
                     del node.reshaped_data
         return graph
 
+
 class CropFuser(object):
     '''
     Crop is to return a scalar output Blob for an input Blob of arbitrary size.
@@ -197,7 +198,8 @@ class CropFuser(object):
             cls._traced_names[fname] = []
         cls._traced_names[fname].append(tname)
 
-    def __init__(self, allowed_parent_types=[NodeKind.Input, NodeKind.DummyData]):
+    def __init__(self,
+                 allowed_parent_types=[NodeKind.Input, NodeKind.DummyData]):
         self.allowed_parent_types = allowed_parent_types
 
     def __call__(self, graph):
@@ -232,7 +234,11 @@ class CropFuser(object):
 
     def merge(self, parent, child):
         '''Merge the parent node into the child.'''
-        child.metadata['shape'] = [parent.output_shape.batch_size, parent.output_shape.channels, parent.output_shape.height, parent.output_shape.width]
+        child.metadata['shape'] = [
+            parent.output_shape.batch_size, parent.output_shape.channels,
+            parent.output_shape.height, parent.output_shape.width
+        ]
+
 
 class SubNodeFuser(object):
     '''
@@ -395,6 +401,8 @@ class ParameterNamer(object):
                 names = ('scale', )
                 if getattr(node.parameters, 'bias_term', False):
                     names = ('scale', 'offset')
+            elif node.kind == NodeKind.PReLU:
+                names = ('negslope', )
             elif node.kind == "Normalize":
                 names = ('scale', )
             else: