diff --git a/Lab-2/src/__init__.py b/Lab-2/src/__init__.py
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index 0000000..e69de29
diff --git a/Lab-2/src/data_load.py b/Lab-2/src/data_load.py
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+++ b/Lab-2/src/data_load.py
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+import torch
+import torchvision
+import matplotlib.pyplot as plt
+from torchvision import transforms
+from torch.utils.data import random_split, DataLoader
+
+
+def load_and_preview_mnist(data_size=100, batch_size=16, seed=42, root='../data'):
+    """
+    加载并预览 MNIST 数据集。
+    Parameters:
+    - data_size (int): 加载的数据条目数。
+    - batch_size (int): 每批次处理的数据数量。
+    - seed (int): 随机种子，用于保证数据切割的可重复性。
+    - root (str): 数据集保存的路径。
+    """
+    # 数据预处理
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize((0.5,), (0.5,))
+    ])
+
+    # 载入数据集
+    train_dataset = torchvision.datasets.MNIST(
+        root=root,
+        train=True,
+        download=True,
+        transform=transform
+    )
+
+    # 截取指定数量的数据
+    data_train, _ = random_split(
+        dataset=train_dataset,
+        lengths=[data_size, len(train_dataset) - data_size],
+        generator=torch.Generator().manual_seed(seed)
+    )
+
+    # 加载数据集
+    train_loader = DataLoader(
+        dataset=data_train,
+        batch_size=batch_size,
+        shuffle=True
+    )
+
+    # 数据预览
+    images, labels = next(iter(train_loader))
+
+    # 将图像拼接为网格
+    img_grid = torchvision.utils.make_grid(images, nrow=batch_size // 4)
+    img = img_grid.numpy().transpose(1, 2, 0)
+    std = [0.5]
+    mean = [0.5]
+    img = img * std + mean
+
+    # 显示图像
+    print([labels[i] for i in range(batch_size)])
+    plt.imshow(img, cmap='gray')
+    plt.axis('off')
+    plt.show()
+
+
+if __name__ == '__main__':
+    # 使用默认参数调用函数
+    load_and_preview_mnist()
+
+    # 或者使用自定义参数
+    # load_and_preview_mnist(data_size=200, batch_size=8, seed=123, root='./data')
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