diff --git a/benchmark/fluid/mnist.py b/benchmark/fluid/mnist.py
index 1cb4314fb22ae935a00ceea25128e7f2b5430c84..8d51d4aa60dbfc201121a44cfd99cb9d5baa95ec 100644
--- a/benchmark/fluid/mnist.py
+++ b/benchmark/fluid/mnist.py
@@ -184,8 +184,8 @@ def run_benchmark(model, args):
                 ]
             )  # The accuracy is the accumulation of batches, but not the current batch.
             accuracy.update(
-                    value=np.array(np.mean(outs[1])), 
-                    weight=np.mean(np.array(outs[2])))
+                value=np.array(np.mean(outs[1])), 
+                weight=np.mean(np.array(outs[2])))
             iters += 1
             num_samples += len(y_data)
             loss = np.mean(np.array(outs[0]))
diff --git a/benchmark/fluid/vgg.py b/benchmark/fluid/vgg.py
index 261446e7e94ae38e723e629c58c7e1e1386cb0a6..2a9566a45c3804183e05db9298cec4f670225a6f 100644
--- a/benchmark/fluid/vgg.py
+++ b/benchmark/fluid/vgg.py
@@ -188,7 +188,9 @@ def main():
             loss, acc, weight = train_exe.run(
                 feed={"pixel": img_data,
                       "label": y_data},
-                fetch_list=[avg_cost.name, batch_acc.name, batch_size_tensor.name])
+                fetch_list=[
+                    avg_cost.name, batch_acc.name, batch_size_tensor.name
+                ])
             accuracy.add(value=np.array(np.mean(acc)), weight=np.mean(weight))
             iters += 1
             num_samples += len(y_data)