diff --git a/java/README.md b/java/README.md
index 225c8b0345817719ddb7a872a9fc07f9aa33a535..28663274d486d70d9a2f20cf2fdbaddb8877ec3a 100644
--- a/java/README.md
+++ b/java/README.md
@@ -7,8 +7,8 @@
 In order to facilitate users to use java for development, we provide the compiled Serving project to be placed in the java mirror. The way to get the mirror and enter the development environment is
 
 ```
-docker pull hub.baidubce.com/paddlepaddle/serving:0.4.0-java
-docker run --rm -dit --name java_serving hub.baidubce.com/paddlepaddle/serving:0.4.0-java
+docker pull hub.baidubce.com/paddlepaddle/serving:0.4.1-java
+docker run --rm -dit --name java_serving hub.baidubce.com/paddlepaddle/serving:0.4.1-java
 docker exec -it java_serving bash
 cd Serving/java
 ```
diff --git a/java/README_CN.md b/java/README_CN.md
index 8fb6edd1af562bde12044f36ec2d9e71558c5a6b..e8cca0cc7b9b53529628f06f82219113e44e8aae 100644
--- a/java/README_CN.md
+++ b/java/README_CN.md
@@ -7,8 +7,8 @@
 为了方便用户使用java进行开发，我们提供了编译好的Serving工程放置在java镜像当中，获取镜像并进入开发环境的方式是
 
 ```
-docker pull hub.baidubce.com/paddlepaddle/serving:0.4.0-java
-docker run --rm -dit --name java_serving hub.baidubce.com/paddlepaddle/serving:0.4.0-java
+docker pull hub.baidubce.com/paddlepaddle/serving:0.4.1-java
+docker run --rm -dit --name java_serving hub.baidubce.com/paddlepaddle/serving:0.4.1-java
 docker exec -it java_serving bash
 cd Serving/java
 ```
diff --git a/java/examples/src/main/java/PaddleServingClientExample.java b/java/examples/src/main/java/PaddleServingClientExample.java
index cdc11df130095d668734ae0a23adb12ef735b2ea..5f5e3ff655e7450d12f562229ae4cb2481ab4a54 100644
--- a/java/examples/src/main/java/PaddleServingClientExample.java
+++ b/java/examples/src/main/java/PaddleServingClientExample.java
@@ -16,9 +16,11 @@ public class PaddleServingClientExample {
             0.0582f, -0.0727f, -0.1583f, -0.0584f,
             0.6283f, 0.4919f, 0.1856f, 0.0795f, -0.0332f};
         INDArray npdata = Nd4j.createFromArray(data);
+        long[] batch_shape = {1,13};
+        INDArray batch_npdata = npdata.reshape(batch_shape);
         HashMap<String, INDArray> feed_data
             = new HashMap<String, INDArray>() {{
-                put("x", npdata);
+                put("x", batch_npdata);
             }};
         List<String> fetch = Arrays.asList("price");
         
@@ -69,12 +71,16 @@ public class PaddleServingClientExample {
         
         // Div(255.0)
         INDArray image = RGBimage.divi(255.0);
-        
+        long[] batch_shape = {1,image.shape()[0],image.shape()[1],image.shape()[2]};
+        INDArray batch_image = image.reshape(batch_shape);        
+
         INDArray im_size = Nd4j.createFromArray(new int[]{height, width});
+        long[] batch_size_shape = {1,2};
+        INDArray batch_im_size = im_size.reshape(batch_size_shape);
         HashMap<String, INDArray> feed_data
             = new HashMap<String, INDArray>() {{
-                put("image", image);
-                put("im_size", im_size);
+                put("image", batch_image);
+                put("im_size", batch_im_size);
             }};
         List<String> fetch = Arrays.asList("save_infer_model/scale_0.tmp_0");
         
diff --git a/java/src/main/java/io/paddle/serving/client/Client.java b/java/src/main/java/io/paddle/serving/client/Client.java
index 742d4f91ce17555a2ea96f2a629717228ba18cef..aae7e6f8f50d4ca2baca877f2e51c8e71eb64af8 100644
--- a/java/src/main/java/io/paddle/serving/client/Client.java
+++ b/java/src/main/java/io/paddle/serving/client/Client.java
@@ -4,6 +4,9 @@ import java.util.*;
 import java.util.function.Function;
 import java.lang.management.ManagementFactory;
 import java.lang.management.RuntimeMXBean;
+import java.util.stream.Collectors;
+import java.util.List;
+import java.util.ArrayList;
 
 import io.grpc.ManagedChannel;
 import io.grpc.ManagedChannelBuilder;
@@ -238,7 +241,11 @@ public class Client {
                 } else {
                     throw new IllegalArgumentException("error tensor value type.");
                 }
-                tensor_builder.addAllShape(feedShapes_.get(name));
+                long[] longArray = variable.shape();
+                int[] intArray = Arrays.stream(longArray).mapToInt(i -> (int) i).toArray();
+                List<Integer> indarrayShapeList = Arrays.stream(intArray).boxed().collect(Collectors.toList());
+                //tensor_builder.addAllShape(feedShapes_.get(name));
+                tensor_builder.addAllShape(indarrayShapeList);
                 inst_builder.addTensorArray(tensor_builder.build());
             }
             req_builder.addInsts(inst_builder.build());