如何导入 MNIST 数据集

以下是两种导入MNIST数据集的方法：

1.使用TensorFlow内置函数导入MNIST数据集

import tensorflow as tf

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

2.手动下载MNIST数据集并导入

import os
import gzip
import numpy as np

def load_data():
    # 训练集文件
    train_images_path = './MNIST_data/train-images-idx3-ubyte.gz'
    train_labels_path = './MNIST_data/train-labels-idx1-ubyte.gz'
    # 测试集文件
    test_images_path = './MNIST_data/t10k-images-idx3-ubyte.gz'
    test_labels_path = './MNIST_data/t10k-labels-idx1-ubyte.gz'

    # 读取训练集数据
    with gzip.open(train_images_path, 'rb') as f:
        train_images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
    with gzip.open(train_labels_path, 'rb') as f:
        train_labels = np.frombuffer(f.read(), np.uint8, offset=8)

    # 读取测试集数据
    with gzip.open(test_images_path, 'rb') as f:
        test_images = np.frombuffer(f.read(), np.uint8, offset=16).reshape(-1, 28, 28)
    with gzip.open(test_labels_path, 'rb') as f:
        test_labels = np.frombuffer(f.read(), np.uint8, offset=8)

    return (train_images, train_labels), (test_images, test_labels)

# 调用函数加载数据
(train_images, train_labels), (test_images, test_labels) = load_data()