diff --git a/mindspore/lite/examples/train_lenet/prepare_and_run.sh b/mindspore/lite/examples/train_lenet/prepare_and_run.sh
index 3d7c2a3bbc..ae35b75c8a 100755
--- a/mindspore/lite/examples/train_lenet/prepare_and_run.sh
+++ b/mindspore/lite/examples/train_lenet/prepare_and_run.sh
@@ -199,10 +199,17 @@ else
   cd ${PACKAGE} || exit 1
   echo "======Training Locally========="
   ./train.sh ${EPOCHS}
+  if [ "$?" != "0" ]; then
+    echo "Training Model failed"
+    exit 1
+  fi
 
   echo "===Evaluating trained Model====="
   ./eval.sh
-  
+  if [ "$?" != "0" ]; then
+    echo "Eval Model failed"
+    exit 1
+  fi
   cd ..
 fi
 END=$(date +%s.%N)
diff --git a/mindspore/lite/examples/train_lenet_java/prepare_and_run.sh b/mindspore/lite/examples/train_lenet_java/prepare_and_run.sh
index 66557812f0..ba27bdd485 100755
--- a/mindspore/lite/examples/train_lenet_java/prepare_and_run.sh
+++ b/mindspore/lite/examples/train_lenet_java/prepare_and_run.sh
@@ -58,6 +58,10 @@ if [ "$MNIST_DATA_PATH" == "" ] || [ ! -d "$MNIST_DATA_PATH" ]; then
 fi
 
 ./build.sh $TARBALL
+if [ "$?" != "0" ]; then
+  echo "build failed on $TARBALL"
+  exit 1
+fi
 
 BASEPATH=$(cd "$(dirname $0)" || exit; pwd)
 
diff --git a/mindspore/lite/examples/transfer_learning/prepare_and_run.sh b/mindspore/lite/examples/transfer_learning/prepare_and_run.sh
index 02071adc6a..b295b6be03 100755
--- a/mindspore/lite/examples/transfer_learning/prepare_and_run.sh
+++ b/mindspore/lite/examples/transfer_learning/prepare_and_run.sh
@@ -140,12 +140,24 @@ else
   cd ${PACKAGE} || exit 1
   echo "==Evaluating Untrained Model==="
   ./eval_untrained.sh
+  if [ "$?" != "0" ]; then
+    echo "Evaluating Untrained Model failed"
+    exit 1
+  fi
 
   echo "======Training Locally========="
   ./train.sh
+  if [ "$?" != "0" ]; then
+    echo "Training Model failed"
+    exit 1
+  fi
 
   echo "===Evaluating trained Model====="
   ./eval.sh
+  if [ "$?" != "0" ]; then
+    echo "Eval Model failed"
+    exit 1
+  fi
   cd ..
 fi
 
diff --git a/mindspore/lite/examples/unified_api/prepare_and_run.sh b/mindspore/lite/examples/unified_api/prepare_and_run.sh
index 7bf20b23c6..3358ca75d1 100755
--- a/mindspore/lite/examples/unified_api/prepare_and_run.sh
+++ b/mindspore/lite/examples/unified_api/prepare_and_run.sh
@@ -175,12 +175,24 @@ else
   cd ${PACKAGE} || exit 1
   echo "======Training Locally========="
   ./train.sh ${EPOCHS}
+  if [ "$?" != "0" ]; then
+    echo "Training Model failed"
+    exit 1
+  fi
 
   echo "===Evaluating trained Model====="
   ./eval.sh
+  if [ "$?" != "0" ]; then
+    echo "Eval Model failed"
+    exit 1
+  fi
   
   echo "====Running Inference Model====="
   ./infer.sh
+  if [ "$?" != "0" ]; then
+    echo "Running Inference Model failed"
+    exit 1
+  fi
   
   cd ..
 fi
diff --git a/mindspore/lite/examples/unified_api/scripts/infer.sh b/mindspore/lite/examples/unified_api/scripts/infer.sh
index 1d8006b1fd..35d441d071 100755
--- a/mindspore/lite/examples/unified_api/scripts/infer.sh
+++ b/mindspore/lite/examples/unified_api/scripts/infer.sh
@@ -16,3 +16,7 @@
 
 # an simple tutorial as follows, more parameters can be setting
 LD_LIBRARY_PATH=./lib/ bin/infer -f model/lenet_tod_infer.ms
+if [ "$?" != "0" ]; then
+    echo "Infer failed"
+    exit 1
+fi
diff --git a/mindspore/lite/examples/unified_api/src/inference.cc b/mindspore/lite/examples/unified_api/src/inference.cc
index 23d133c79b..b60871cc0d 100644
--- a/mindspore/lite/examples/unified_api/src/inference.cc
+++ b/mindspore/lite/examples/unified_api/src/inference.cc
@@ -80,6 +80,7 @@ int main(int argc, char **argv) {
     std::cout << "]" << std::endl;
   }
 
+  inputs.at(0).MutableData();
   mindspore::MSTensor *input_tensor = inputs.at(0).Clone();
   auto *input_data = reinterpret_cast<float *>(input_tensor->MutableData());
   std::ifstream in;
diff --git a/mindspore/lite/test/st/scripts/run_net_train.sh b/mindspore/lite/test/st/scripts/run_net_train.sh
index 5b087693ee..7ea2abddbe 100755
--- a/mindspore/lite/test/st/scripts/run_net_train.sh
+++ b/mindspore/lite/test/st/scripts/run_net_train.sh
@@ -1,6 +1,6 @@
 #!/bin/bash
 source ./scripts/base_functions.sh
-version=1.3.0
+version=1.5.0
 
 # Run Export on x86 platform and create output test files:
 docker_image=mindspore_build:210301
@@ -412,6 +412,10 @@ function Run_CodeExamples() {
       cd ${basepath}/../../examples/train_lenet_java || exit 1
       chmod 777 ./prepare_and_run.sh
       ./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -m ${models_path}/code_example.mindir >> ${run_code_examples_log_file}
+      if [ "$?" != "0" ]; then
+          echo "train_lenet_java prepare_and_run.sh failed"
+          exit 1
+      fi
       accurate=$(tail -10 ${run_code_examples_log_file} | awk -F= 'NF==2 && /accuracy/ { sum += $2} END { print (sum > 0.80) }')
       if [ $accurate -eq 1 ]; then
         echo "Lenet Java Trained  and reached accuracy" >> ${run_code_examples_log_file}
@@ -431,6 +435,10 @@ function Run_CodeExamples() {
         chmod 777 ./prepare_and_run.sh
         chmod 777 ./*/*.sh
         ./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir -e 1 >> ${run_code_examples_log_file}
+        if [ "$?" != "0" ]; then
+          echo "Unified API prepare_and_run.sh failed"
+          exit 1
+        fi
         accurate=$(tail -20 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.6) }')
         if [ $accurate -eq 1 ]; then
           echo "Unified API Trained and reached accuracy" >> ${run_code_examples_log_file}
@@ -451,6 +459,10 @@ function Run_CodeExamples() {
         chmod 777 ./prepare_and_run.sh
         chmod 777 ./*/*.sh
         ./prepare_and_run.sh -D ${datasets_path}/mnist -r ${tarball_path} -t ${target} -m ${models_path}/code_example.mindir -e 1 >> ${run_code_examples_log_file}
+        if [ "$?" != "0" ]; then
+          echo "train_lenet prepare_and_run.sh failed"
+          exit 1
+        fi
         accurate=$(tail -10 ${run_code_examples_log_file} | awk 'NF==3 && /Accuracy is/ { sum += $3} END { print (sum > 1.6) }')
         if [ $accurate -eq 1 ]; then
           echo "Lenet Trained and reached accuracy" >> ${run_code_examples_log_file}