diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_1p.sh b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_1p.sh
index a6f75350e7f68483c98d3dfaf71262203c22d307..566d9265cf5f5aafc2c4161abd6e217a8340b748 100644
--- a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_1p.sh
@@ -38,6 +38,8 @@ for para in $*
 do
    if [[ $para  == --data_path* ]];then
       data_path=`echo ${para#*=}`
+   elif [[ $para == --precision_mode* ]];then
+      precision_mode=`echo ${para#*=}`
    elif [[ $para == --dynamic_input* ]];then
       dynamic_input=`echo ${para#*=}`  	
    fi
@@ -48,6 +50,11 @@ if [[ $data_path  == "" ]];then
     exit 1
 fi
 
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+   sed -i "s|allow_mix_precision|must_keep_origin_dtype|g" $cur_path/../dual_net.py
+fi
+
+
 ##############执行训练##########
 cd $cur_path
 
@@ -65,7 +72,7 @@ wait
 
 start=$(date +%s)
 #(Step3)训练
-python3 train.py --training_data_path=$data_path --steps_to_train=$train_steps --train_batch_size=$batch_size --work_dir=$cur_path/estimator_working_dir --export_path=$cur_path/outputs/models/000001-first_generation --dynamic_input=${dynamic_input}> $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
+python3 train.py --training_data_path=$data_path --steps_to_train=$train_steps --train_batch_size=$batch_size --work_dir=$cur_path/estimator_working_dir --export_path=$cur_path/outputs/models/000001-first_generation --dynamic_input=${dynamic_input}  > $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log 2>&1 &
 wait
 end=$(date +%s)
 e2etime=$(( $end - $start ))
@@ -79,7 +86,11 @@ BatchSize=${batch_size}
 #设备类型，自动获取
 DeviceType=`uname -m`
 #用例名称，自动获取
-CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'acc'
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'acc'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+fi
 
 #获取性能
 TrainingTime=`grep "tensorflow:global_step/sec" $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $2}'`
@@ -101,4 +112,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${Cas
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainAccuracy = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "E2ETrainingTime = ${e2etime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
index 8cd78e2f2b804957f26e967acf45a7359b05c13e..b8ad2e3b0cc4a501e30824fa4b05a8b8833ee424 100644
--- a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_full_8p.sh
@@ -38,10 +38,12 @@ for para in $*
 do
    if [[ $para  == --data_path* ]];then
       data_path=`echo ${para#*=}`
-    elif [[ $para == --bind_core* ]]; then
-        bind_core=`echo ${para#*=}`
-        name_bind="_bindcore"
-	elif [[ $para == --dynamic_input* ]];then
+   elif [[ $para == --precision_mode* ]];then
+      precision_mode=`echo ${para#*=}`
+   elif [[ $para == --bind_core* ]]; then
+      bind_core=`echo ${para#*=}`
+      name_bind="_bindcore"
+   elif [[ $para == --dynamic_input* ]];then
       dynamic_input=`echo ${para#*=}` 
    fi
 done
@@ -51,6 +53,11 @@ if [[ $data_path  == "" ]];then
     exit 1
 fi
 
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+   sed -i "s|allow_mix_precision|must_keep_origin_dtype|g" $cur_path/../dual_net.py
+fi
+
+
 ##############执行训练##########
 cd $cur_path
 
@@ -105,8 +112,11 @@ BatchSize=${batch_size}
 #设备类型，自动获取
 DeviceType=`uname -m`
 #用例名称，自动获取
-CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'acc'
-
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'acc'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'acc'
+fi
 #获取性能
 TrainingTime=`grep "tensorflow:global_step/sec" $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $2}'`
 wait
@@ -127,4 +137,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${Cas
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainAccuracy = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "E2ETrainingTime = ${e2etime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
diff --git a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_1p.sh b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_1p.sh
index 2b9da3177a38d234fd38f1bee12f16deffdc2815..212394cd378e1e07ad01283d128398fe0bd05fbb 100644
--- a/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_1p.sh
+++ b/TensorFlow/built-in/cv/image_classification/MiniGo_ID0629_for_TensorFlow/test/train_performance_1p.sh
@@ -38,6 +38,8 @@ for para in $*
 do
    if [[ $para  == --data_path* ]];then
       data_path=`echo ${para#*=}`
+   elif [[ $para == --precision_mode* ]];then
+      precision_mode=`echo ${para#*=}`
    elif [[ $para == --dynamic_input* ]];then
       dynamic_input=`echo ${para#*=}`  	
    fi
@@ -48,6 +50,10 @@ if [[ $data_path  == "" ]];then
     exit 1
 fi
 
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+   sed -i "s|allow_mix_precision|must_keep_origin_dtype|g" $cur_path/../dual_net.py
+fi
+
 ##############执行训练##########
 cd $cur_path
 
@@ -86,8 +92,11 @@ BatchSize=${batch_size}
 #设备类型，自动获取
 DeviceType=`uname -m`
 #用例名称，自动获取
-CaseName=${Network}_bs${BatchSize}_${RankSize}'p'_'perf'
-
+if [[ $precision_mode == "must_keep_origin_dtype" ]];then
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'fp32'_'perf'
+else
+    CaseName=${Network}_bs${BatchSize}_${RANK_SIZE}'p'_'perf'
+fi
 #获取性能
 TrainingTime=`grep "tensorflow:global_step/sec" $cur_path/test/output/$ASCEND_DEVICE_ID/train_$ASCEND_DEVICE_ID.log|awk 'END {print $2}'`
 wait
@@ -108,4 +117,4 @@ echo "ActualFPS = ${ActualFPS}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${Cas
 echo "ActualLoss = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "TrainAccuracy = ${ActualLoss}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
 echo "E2ETrainingTime = ${e2etime}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
-echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log
\ No newline at end of file
+echo "DynamicInput = ${dynamic_input}" >> $cur_path/test/output/$ASCEND_DEVICE_ID/${CaseName}.log