diff --git a/MindSpore/src/step2/step2.py b/MindSpore/src/step2/step2.py
new file mode 100644
index 0000000..332b721
--- /dev/null
+++ b/MindSpore/src/step2/step2.py
@@ -0,0 +1,35 @@
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore LeNet Example')
+    parser.add_argument('--device_target', type=str, default="CPU", choices=['Ascend', 'GPU', 'CPU'],
+                        help='device where the code will be implemented (default: CPU)')
+    args = parser.parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+    # download mnist dataset
+    download_dataset()
+    # learning rate setting
+    lr = 0.01
+    momentum = 0.9
+    epoch_size = 1
+    mnist_path = "./MNIST_Data"
+    # 请在此添加代码完成本关任务
+    # **********Begin*********#
+    ##提示：补全损失函数的定义
+
+    # **********End**********#
+    repeat_size = epoch_size
+    # create the network
+    network = LeNet5()
+    # define the optimizer
+    # 请在此添加代码完成本关任务
+    # **********Begin*********#
+    ##提示：补全优化器的定义
+
+    # **********End**********#
+
+    # save the network model and parameters for subsequence fine-tuning
+    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    # group layers into an object with training and evaluation features
+    model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    train_net(args, model, epoch_size, mnist_path, repeat_size, ckpoint_cb)
+    test_net(args, network, model, mnist_path)