diff --git a/EXP_MS/lenet.py b/EXP_MS/lenet.py
new file mode 100644
index 0000000..062dba9
--- /dev/null
+++ b/EXP_MS/lenet.py
@@ -0,0 +1,126 @@
+import os
+import argparse
+import mindspore.dataset as ds
+import mindspore.nn as nn
+from mindspore import context, Model, load_checkpoint, load_param_into_net
+from mindspore.common.initializer import Normal
+from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
+import mindspore.dataset.vision.c_transforms as CV
+import mindspore.dataset.transforms.c_transforms as C
+from mindspore.dataset.vision import Inter
+from mindspore.nn.metrics import Accuracy
+from mindspore import dtype as mstype
+from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
+from utils.dataset import download_dataset
+
+def create_dataset(data_path, batch_size=32, repeat_size=1,num_parallel_workers=1):
+    """ 创建用于训练或测试的数据集"""
+    # 定义数据集
+    mnist_ds = ds.MnistDataset(data_path)
+
+    # 定义操作参数
+    resize_height, resize_width = 32, 32
+    rescale = 1.0 / 255.0
+    shift = 0.0
+    rescale_nml = 1 / 0.3081
+    shift_nml = -1 * 0.1307 / 0.3081
+
+    # 定义映射操作
+    resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)  # 将图像调整为（32，32）
+    rescale_nml_op = CV.Rescale(rescale_nml, shift_nml) # 对图像进行归一化、标准化操作，提升训练效率
+    rescale_op = CV.Rescale(rescale, shift) # 重新缩放、移位图像
+    hwc2chw_op = CV.HWC2CHW() # 对图像数据张量进行变换，张量形式由 高x宽x通道(HWC) 变为 通道x高x宽(CHW) ，方便进行数据训练。
+    type_cast_op = C.TypeCast(mstype.int32) # 将数据类型更改为int32来适合网络
+
+    # 在图像上应用映射操作
+    mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+    mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
+
+    # 其他增强操作
+    buffer_size = 10000 # 混洗程度为 10000
+    mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)  # 随机将数据存放在可容纳10000张图片地址的内存中进行混洗
+    mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)  # 从混洗的10000张图片地址中抽取32张图片组成一个batch，参数batch. size表示每组包含的数据个数，现设置每组包含32个数据
+    mnist_ds = mnist_ds.repeat(repeat_size) # 将batch数据进行复制增强，参数repeat_size 表示数据集复制的数量
+    #先进行shuffle、batch操作，再进行repeat操作，这样能保证1个epoch内数据不重复。
+    return mnist_ds
+
+
+class LeNet5(nn.Cell):
+    """Lenet网络结构"""
+    # 定义所需的运算
+    def __init__(self, num_class=10, num_channel=1):
+        super(LeNet5, self).__init__()
+        self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
+        self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
+        self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
+        self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
+        self.relu = nn.ReLU()
+        self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    # 使用前面的运算来构建网络
+    def construct(self, x):
+        x = self.max_pool2d(self.relu(self.conv1(x)))
+        x = self.max_pool2d(self.relu(self.conv2(x)))
+        x = self.flatten(x)
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+def train_net(network_model, epoch_size, data_path, repeat_size, ckpoint_cb, sink_mode):
+    """定义训练方法"""
+    print("============== Starting Training ==============")
+    # 加载训练数据集
+    ds_train = create_dataset(os.path.join(data_path, "train"), 32, repeat_size)
+    # 进行训练
+    network_model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor()], dataset_sink_mode=sink_mode)
+
+
+def test_net(network, network_model, data_path):
+    """定义评估方法"""
+    print("============== Starting Testing ==============")
+    # 加载保存的模型进行评估
+    param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")
+    # 将参数加载到网络
+    load_param_into_net(network, param_dict)
+    # 加载测试数据集
+    ds_eval = create_dataset(os.path.join(data_path, "test"))
+    acc = network_model.eval(ds_eval, dataset_sink_mode=False)
+    print("============== Accuracy:{} ==============".format(acc))
+
+
+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)
+    dataset_sink_mode = not args.device_target == "CPU"
+    # 下载mnist数据集
+    #download_dataset()
+    # 学习率设定
+    lr = 0.01
+    momentum = 0.9
+    dataset_size = 1
+    mnist_path = "./MNIST_Data"
+    # 定义损失函数
+    net_loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
+    train_epoch = 1
+    # 建立网络
+    net = LeNet5()
+    # 定义优化
+    net_opt = nn.Momentum(net.trainable_params(), lr, momentum)
+    config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
+    # 保存网络模型和参数以进行子序列微调
+    ckpoint = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
+    # 通过训练和评估功能将图层分组为一个对象
+    model = Model(net, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
+
+    train_net(model, train_epoch, mnist_path, dataset_size, ckpoint, dataset_sink_mode)
+    test_net(net, model, mnist_path)