cifia数据集的代码

以下是使用Python代码读取CIFAR-10数据集，并进行简单预处理的示例代码：

import numpy as np
import pickle

# 读取数据集
def load_cifar10_data(data_dir):
    train_data = None
    train_labels = []
    for batch_id in range(1, 6):
        with open(data_dir + '/data_batch_' + str(batch_id), mode='rb') as file:
            batch = pickle.load(file, encoding='latin1')
        if train_data is None:
            train_data = batch['data']
        else:
            train_data = np.vstack((train_data, batch['data']))
        train_labels += batch['labels']
    with open(data_dir + '/test_batch', mode='rb') as file:
        test_batch = pickle.load(file, encoding='latin1')
    test_data = test_batch['data']
    test_labels = test_batch['labels']
    return train_data, np.array(train_labels), test_data, np.array(test_labels)

# 对数据进行预处理
def preprocess_data(train_data, test_data):
    # 将数据标准化到0-1之间
    train_data = train_data / 255.0
    test_data = test_data / 255.0
    # 将数据转换为浮点型
    train_data = train_data.astype(np.float32)
    test_data = test_data.astype(np.float32)
    return train_data, test_data

# 读取数据集
data_dir = 'cifar-10-batches-py'
train_data, train_labels, test_data, test_labels = load_cifar10_data(data_dir)

# 对数据进行预处理
train_data, test_data = preprocess_data(train_data, test_data)

# 打印数据集信息
print('Training data shape: ', train_data.shape)
print('Training labels shape: ', train_labels.shape)
print('Test data shape: ', test_data.shape)
print('Test labels shape: ', test_labels.shape)

上述代码中，load_cifar10_data函数用于读取数据集，返回训练数据、训练标签、测试数据和测试标签。preprocess_data函数用于对数据进行预处理，包括将像素值标准化到0-1之间，转换为浮点型等操作。最后，打印数据集的信息，包括训练数据、训练标签、测试数据和测试标签的形状。