diff --git a/tutorials/notebook/synchronization_training_and_evaluation.ipynb b/tutorials/notebook/synchronization_training_and_evaluation.ipynb
index 486fd4cf2f762b193054db536c9576b5bdc5512f..236ae433c882d620ead10a0247dc321bab8122d3 100644
--- a/tutorials/notebook/synchronization_training_and_evaluation.ipynb
+++ b/tutorials/notebook/synchronization_training_and_evaluation.ipynb
@@ -230,22 +230,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "from mindspore.train.callback import Callback\n",
     "\n",
     "class EvalCallBack(Callback):\n",
-    "    def __init__(self, model, eval_dataset, eval_per_epoch):\n",
+    "    def __init__(self, model, eval_dataset, eval_per_epoch, epoch_per_eval):\n",
     "        self.model = model\n",
     "        self.eval_dataset = eval_dataset\n",
     "        self.eval_per_epoch = eval_per_epoch\n",
+    "        self.epoch_per_eval = epoch_per_eval\n",
     "        \n",
     "    def epoch_end(self, run_context):\n",
     "        cb_param = run_context.original_args()\n",
     "        cur_epoch = cb_param.cur_epoch_num\n",
     "        if cur_epoch % self.eval_per_epoch == 0:\n",
-    "            acc = self.model.eval(self.eval_dataset,dataset_sink_mode = True)\n",
-    "            epoch_per_eval[\"epoch\"].append(cur_epoch)\n",
-    "            epoch_per_eval[\"acc\"].append(acc[\"Accuracy\"])\n",
+    "            acc = self.model.eval(self.eval_dataset, dataset_sink_mode=True)\n",
+    "            self.epoch_per_eval[\"epoch\"].append(cur_epoch)\n",
+    "            self.epoch_per_eval[\"acc\"].append(acc[\"Accuracy\"])\n",
     "            print(acc)\n"
    ]
   },
@@ -351,7 +351,6 @@
     }
    ],
    "source": [
-    "from mindspore.train.serialization import load_checkpoint, load_param_into_net\n",
     "from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor\n",
     "from mindspore.train import Model\n",
     "from mindspore import context\n",
@@ -368,21 +367,21 @@
     "    repeat_size = 1\n",
     "    network = LeNet5()\n",
     "    \n",
-    "    train_data = create_dataset(train_data_path,repeat_size = repeat_size)\n",
-    "    eval_data = create_dataset(eval_data_path,repeat_size = repeat_size)\n",
+    "    train_data = create_dataset(train_data_path, repeat_size=repeat_size)\n",
+    "    eval_data = create_dataset(eval_data_path, repeat_size=repeat_size)\n",
     "    \n",
     "    # define the loss function\n",
     "    net_loss = SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')\n",
     "    # define the optimizer\n",
     "    net_opt = nn.Momentum(network.trainable_params(), learning_rate=0.01, momentum=0.9)\n",
     "    config_ck = CheckpointConfig(save_checkpoint_steps=eval_per_epoch*1875, keep_checkpoint_max=15)\n",
-    "    ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\",directory=ckpt_save_dir, config=config_ck)\n",
+    "    ckpoint_cb = ModelCheckpoint(prefix=\"checkpoint_lenet\", directory=ckpt_save_dir, config=config_ck)\n",
     "    model = Model(network, net_loss, net_opt, metrics={\"Accuracy\": Accuracy()})\n",
     "    \n",
-    "    epoch_per_eval = {\"epoch\":[],\"acc\":[]}\n",
-    "    eval_cb = EvalCallBack(model,eval_data,eval_per_epoch)\n",
+    "    epoch_per_eval = {\"epoch\": [], \"acc\": []}\n",
+    "    eval_cb = EvalCallBack(model, eval_data, eval_per_epoch, epoch_per_eval)\n",
     "    \n",
-    "    model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375),eval_cb],\n",
+    "    model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375), eval_cb],\n",
     "                dataset_sink_mode=True)"
    ]
   },
@@ -441,11 +440,13 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
     "def eval_show(epoch_per_eval):\n",
     "    plt.xlabel(\"epoch number\")\n",
     "    plt.ylabel(\"Model accuracy\")\n",
     "    plt.title(\"Model accuracy variation chart\")\n",
-    "    plt.plot(epoch_per_eval[\"epoch\"],epoch_per_eval[\"acc\"],\"red\")\n",
+    "    plt.plot(epoch_per_eval[\"epoch\"], epoch_per_eval[\"acc\"], \"red\")\n",
     "    plt.show()\n",
     "    \n",
     "eval_show(epoch_per_eval)"
diff --git a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md
index 6e6932f1894f5a6caa018e5a7684e738a323f294..773b4c4535380e34e324b64ef2abf4b429ed0b2d 100644
--- a/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md
+++ b/tutorials/source_zh_cn/advanced_use/synchronization_training_and_evaluation.md
@@ -1,4 +1,6 @@
-# 同步训练和验证模型
+# 同步训练和验证模型
+
+`Ascend` `GPU` `CPU` `初级` `中级` `高级` `模型导出` `模型训练`
 
 <!-- TOC -->
 
@@ -41,25 +43,25 @@
 
 - `model`：即是MindSpore中的`Model`函数。
 - `eval_dataset`：验证数据集。
-- `epoch_per_eval`：记录验证模型的精度和相应的epoch数，其数据形式为`{"epoch":[],"acc":[]}`。
+- `epoch_per_eval`：记录验证模型的精度和相应的epoch数，其数据形式为`{"epoch": [], "acc": []}`。
 
 ```python
-import matplotlib.pyplot as plt
 from mindspore.train.callback import Callback
 
 class EvalCallBack(Callback):
-    def __init__(self, model, eval_dataset, eval_per_epoch):
+    def __init__(self, model, eval_dataset, eval_per_epoch, epoch_per_eval):
         self.model = model
         self.eval_dataset = eval_dataset
         self.eval_per_epoch = eval_per_epoch
+        self.epoch_per_eval = epoch_per_eval
         
     def epoch_end(self, run_context):
         cb_param = run_context.original_args()
         cur_epoch = cb_param.cur_epoch_num
         if cur_epoch % self.eval_per_epoch == 0:
-            acc = self.model.eval(self.eval_dataset,dataset_sink_mode = True)
-            epoch_per_eval["epoch"].append(cur_epoch)
-            epoch_per_eval["acc"].append(acc["Accuracy"])
+            acc = self.model.eval(self.eval_dataset, dataset_sink_mode=True)
+            self.epoch_per_eval["epoch"].append(cur_epoch)
+            self.epoch_per_eval["acc"].append(acc["Accuracy"])
             print(acc)
 
 ```
@@ -79,12 +81,10 @@ class EvalCallBack(Callback):
 - `epoch_per_eval`：定义收集`epoch`数和对应模型精度信息的字典。
 
 ```python
-from mindspore.train.serialization import load_checkpoint, load_param_into_net
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
 from mindspore.train import Model
 from mindspore import context
 from mindspore.nn.metrics import Accuracy
-from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
 
 if __name__ == "__main__":
     context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
@@ -98,10 +98,10 @@ if __name__ == "__main__":
     ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",directory=ckpt_save_dir, config=config_ck)
     model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
     
-    epoch_per_eval = {"epoch":[],"acc":[]}
-    eval_cb = EvalCallBack(model,eval_data,eval_per_epoch)
+    epoch_per_eval = {"epoch": [], "acc": []}
+    eval_cb = EvalCallBack(model, eval_data, eval_per_epoch, epoch_per_eval)
     
-    model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375),eval_cb],
+    model.train(epoch_size, train_data, callbacks=[ckpoint_cb, LossMonitor(375), eval_cb],
                 dataset_sink_mode=True)
 ```
 
@@ -152,11 +152,13 @@ lenet_ckpt
 
 
 ```python
+import matplotlib.pyplot as plt
+
 def eval_show(epoch_per_eval):
     plt.xlabel("epoch number")
     plt.ylabel("Model accuracy")
     plt.title("Model accuracy variation chart")
-    plt.plot(epoch_per_eval["epoch"],epoch_per_eval["acc"],"red")
+    plt.plot(epoch_per_eval["epoch"], epoch_per_eval["acc"], "red")
     plt.show()
 
 eval_show(epoch_per_eval)