diff --git a/03.image_classification/README.en.md b/03.image_classification/README.en.md
index 6291f61295c3f14b26db26668b996e744a661622..d5e8b650199cc32bec817a884baed222d17115e3 100644
--- a/03.image_classification/README.en.md
+++ b/03.image_classification/README.en.md
@@ -169,6 +169,7 @@ We must import and initialize PaddlePaddle (enable/disable GPU, set the number o
 
 ```python
 import sys
+import gzip
 import paddle.v2 as paddle
 from vgg import vgg_bn_drop
 from resnet import resnet_cifar10
@@ -417,10 +418,6 @@ def event_handler_plot(event):
             cost_ploter.plot()
         step += 1
     if isinstance(event, paddle.event.EndPass):
-        # save parameters
-        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-            parameters.to_tar(f)
-
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -441,6 +438,10 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
     if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -499,7 +500,7 @@ test_data.append((load_image('image/dog.png'),))
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)
 lab = np.argsort(-probs) # probs and lab are the results of one batch data
-print("Label of image/dog.png is: %d", lab[0][0])
+print "Label of image/dog.png is: %d" % lab[0][0]
 ```
 
 
diff --git a/03.image_classification/README.md b/03.image_classification/README.md
index a35e4712b95d526d50b21a2576f2c28589e50fce..8f3a8d256173839adeec06125527a6b696b79974 100644
--- a/03.image_classification/README.md
+++ b/03.image_classification/README.md
@@ -156,6 +156,7 @@ Paddle API提供了自动加载cifar数据集模块 `paddle.dataset.cifar`。
 
 ```python
 import sys
+import gzip
 import paddle.v2 as paddle
 from vgg import vgg_bn_drop
 from resnet import resnet_cifar10
@@ -409,9 +410,6 @@ def event_handler_plot(event):
             cost_ploter.plot()
         step += 1
     if isinstance(event, paddle.event.EndPass):
-        # save parameters
-        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-            parameters.to_tar(f)
 
         result = trainer.test(
             reader=paddle.batch(
@@ -433,6 +431,10 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
     if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -490,7 +492,7 @@ test_data.append((load_image('image/dog.png'),))
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)
 lab = np.argsort(-probs) # probs and lab are the results of one batch data
-print("Label of image/dog.png is: %d", lab[0][0])
+print "Label of image/dog.png is: %d" % lab[0][0]
 ```
 
 
diff --git a/03.image_classification/index.en.html b/03.image_classification/index.en.html
index 226f8af28a308779d2b1a7849f6aaf29a91ae79a..37f1dc5f2392a931f0e5161926abe262f3fad7ee 100644
--- a/03.image_classification/index.en.html
+++ b/03.image_classification/index.en.html
@@ -211,6 +211,7 @@ We must import and initialize PaddlePaddle (enable/disable GPU, set the number o
 
 ```python
 import sys
+import gzip
 import paddle.v2 as paddle
 from vgg import vgg_bn_drop
 from resnet import resnet_cifar10
@@ -459,10 +460,6 @@ def event_handler_plot(event):
             cost_ploter.plot()
         step += 1
     if isinstance(event, paddle.event.EndPass):
-        # save parameters
-        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-            parameters.to_tar(f)
-
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -483,6 +480,10 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
     if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -541,7 +542,7 @@ test_data.append((load_image('image/dog.png'),))
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)
 lab = np.argsort(-probs) # probs and lab are the results of one batch data
-print("Label of image/dog.png is: %d", lab[0][0])
+print "Label of image/dog.png is: %d" % lab[0][0]
 ```
 
 
diff --git a/03.image_classification/index.html b/03.image_classification/index.html
index b14f88ea368652d46fc3fc66ff06cbb18b92f879..4001d122d275657a201f47e70fb4923688e523e9 100644
--- a/03.image_classification/index.html
+++ b/03.image_classification/index.html
@@ -198,6 +198,7 @@ Paddle API提供了自动加载cifar数据集模块 `paddle.dataset.cifar`。
 
 ```python
 import sys
+import gzip
 import paddle.v2 as paddle
 from vgg import vgg_bn_drop
 from resnet import resnet_cifar10
@@ -451,9 +452,6 @@ def event_handler_plot(event):
             cost_ploter.plot()
         step += 1
     if isinstance(event, paddle.event.EndPass):
-        # save parameters
-        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
-            parameters.to_tar(f)
 
         result = trainer.test(
             reader=paddle.batch(
@@ -475,6 +473,10 @@ def event_handler(event):
             sys.stdout.write('.')
             sys.stdout.flush()
     if isinstance(event, paddle.event.EndPass):
+        # save parameters
+        with gzip.open('params_pass_%d.tar.gz' % event.pass_id, 'w') as f:
+            parameters.to_tar(f)
+
         result = trainer.test(
             reader=paddle.batch(
                 paddle.dataset.cifar.test10(), batch_size=128),
@@ -532,7 +534,7 @@ test_data.append((load_image('image/dog.png'),))
 probs = paddle.infer(
     output_layer=out, parameters=parameters, input=test_data)
 lab = np.argsort(-probs) # probs and lab are the results of one batch data
-print("Label of image/dog.png is: %d", lab[0][0])
+print "Label of image/dog.png is: %d" % lab[0][0]
 ```
 
 
diff --git a/03.image_classification/train.py b/03.image_classification/train.py
index 4b3ab18abba22c03e9b3deff1749a2f5972c6947..e60b9bda3c7ec1eefb2cfb9798f72aa8fd5e2b93 100644
--- a/03.image_classification/train.py
+++ b/03.image_classification/train.py
@@ -108,7 +108,7 @@ def main():
     probs = paddle.infer(
         output_layer=out, parameters=parameters, input=test_data)
     lab = np.argsort(-probs)  # probs and lab are the results of one batch data
-    print("Label of image/dog.png is: %d", lab[0][0])
+    print "Label of image/dog.png is: %d" % lab[0][0]
 
 
 if __name__ == '__main__':