diff --git a/npu/train_for_c2net_testcopy.py b/npu/train_for_c2net_testcopy.py
index e69de29..8356a9f 100644
--- a/npu/train_for_c2net_testcopy.py
+++ b/npu/train_for_c2net_testcopy.py
@@ -0,0 +1,90 @@
+"""
+######################## train lenet example ########################
+train lenet and get network model files(.ckpt) 
+"""
+#!/usr/bin/python
+#coding=utf-8
+
+import os
+import argparse
+from config import mnist_cfg as cfg
+from dataset import create_dataset
+from lenet import LeNet5
+import mindspore.nn as nn
+from mindspore import context
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
+from mindspore.train import Model
+from mindspore.nn.metrics import Accuracy
+from mindspore.common import set_seed
+
+parser = argparse.ArgumentParser(description='MindSpore Lenet Example')
+
+parser.add_argument(
+    '--device_target',
+    type=str,
+    default="Ascend",
+    choices=['Ascend', 'CPU'],
+    help='device where the code will be implemented (default: CPU),若要在启智平台上使用NPU，需要在启智平台训练界面上加上运行参数device_target=Ascend')
+
+parser.add_argument('--epoch_size',
+                    type=int,
+                    default=5,
+                    help='Training epochs.')
+
+set_seed(1)
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    print('args:')
+    print(args)
+
+    train_dir = '/cache/output'
+    data_dir = '/cache/dataset'
+    
+    #注意：这里很重要，指定了训练所用的设备CPU还是Ascend NPU
+    context.set_context(mode=context.GRAPH_MODE,
+                        device_target=args.device_target)
+    #创建数据集
+    ds_train = create_dataset(os.path.join(data_dir, "train"),
+                              cfg.batch_size)
+    if ds_train.get_dataset_size() == 0:
+        raise ValueError(
+            "Please check dataset size > 0 and batch_size <= dataset size")
+    #创建网络
+    network = LeNet5(cfg.num_classes)
+    net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
+    net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+    time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
+
+    if args.device_target != "Ascend":
+        model = Model(network,
+                      net_loss,
+                      net_opt,
+                      metrics={"accuracy": Accuracy()})
+    else:
+        model = Model(network,
+                      net_loss,
+                      net_opt,
+                      metrics={"accuracy": Accuracy()},
+                      amp_level="O2")
+
+    config_ck = CheckpointConfig(
+        save_checkpoint_steps=cfg.save_checkpoint_steps,
+        keep_checkpoint_max=cfg.keep_checkpoint_max)
+    #定义模型输出路径
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
+                                 directory=train_dir,
+                                 config=config_ck)
+    #开始训练
+    print("============== Starting Training ==============")
+    epoch_size = cfg['epoch_size']
+    if (args.epoch_size):
+        epoch_size = args.epoch_size
+        print('epoch_size is: ', epoch_size)
+
+    model.train(epoch_size,
+                ds_train,
+                callbacks=[time_cb, ckpoint_cb,
+                           LossMonitor()])
+
+    print("============== Finish Training ==============")
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