X_train, X_val, y_train, y_val = train_test_split(trainingData_rs, Y_copy, test_size=0.25, random_state=20)

根据提供的引用内容，这段代码是用于将一个文件夹中的图片随机分为训练集和验证集，并将其复制到相应的文件夹中。而X_train, X_val, y_train, y_val = train_test_split(trainingData_rs, Y_copy, test_size=0.25, random_state=20)是用于将数据集(trainingData_rs, Y_copy)按照一定比例(test_size=0.25)分为训练集(X_train, y_train)和验证集(X_val, y_val)。其中random_state=20是为了保证每次运行代码时得到的结果都是一样的。两段代码的作用不同，但都是用于数据集的划分。

相关问题

def get_CIFAR10_data(num_training=5000, num_validation=500, num_test=500): cifar10_dir = r'D:\daima\cifar-10-python\cifar-10-batches-py' X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir) print(X_train.shape) mask = range(num_training, num_training + num_validation) X_val = X_train[mask] y_val = y_train[mask] mask = range(num_training) X_train = X_train[mask] y_train = y_train[mask] mask = range(num_test) X_test = X_test[mask] y_test = y_test[mask] mean_image = np.mean(X_train, axis=0) X_train -= mean_image X_val -= mean_image X_test -= mean_image X_train = X_train.transpose(0, 3, 1, 2).copy() X_val = X_val.transpose(0, 3, 1, 2).copy() X_test = X_test.transpose(0, 3, 1, 2).copy() return { 'X_train': X_train, 'y_train': y_train, 'X_val': X_val, 'y_val': y_val, 'X_test': X_test, 'y_test': y_test, }这是一个加载cifar10数据集的函数，如何修改使其能加载mnist数据集

要加载MNIST数据集，可以使用以下代码修改上述函数：

import tensorflow as tf
from tensorflow.keras.datasets import mnist
import numpy as np

def get_MNIST_data(num_training=50000, num_validation=10000, num_test=10000):
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    X_train = np.expand_dims(X_train, axis=-1)
    X_test = np.expand_dims(X_test, axis=-1)
    X_train = X_train.astype('float32') / 255
    X_test = X_test.astype('float32') / 255
    mean_image = np.mean(X_train, axis=0)
    X_train -= mean_image
    X_test -= mean_image
    mask = range(num_training, num_training + num_validation)
    X_val = X_train[mask]
    y_val = y_train[mask]
    mask = range(num_training)
    X_train = X_train[mask]
    y_train = y_train[mask]
    mask = range(num_test)
    X_test = X_test[mask]
    y_test = y_test[mask]
    return {
        'X_train': X_train, 'y_train': y_train,
        'X_val': X_val, 'y_val': y_val,
        'X_test': X_test, 'y_test': y_test,
    }

此代码使用 TensorFlow 的 MNIST 数据集加载函数，将数据集的维度转换为 (num_samples, height, width, depth) 的形式，并对图像像素值进行归一化。

X_train, X_val_test, y_train, y_val_test = train_test_split(X, y, test_size=0.3, random_state=42) X_val, X_test, y_val, y_test = train_test_split(X_val_test, y_val_test, test_size=0.33, random_state=42)

这两行代码的作用是将特征集(X)和标签集(y)分别按照0.7:0.3的比例划分为训练集和验证&测试集（X_val_test和y_val_test），然后将验证&测试集再按照0.33:0.67的比例划分为验证集和测试集（X_val和y_val为验证集，X_test和y_test为测试集）。其中，test_size=0.3表示将数据集划分为训练集和验证&测试集，其中验证&测试集占总数据集的比例为30%；test_size=0.33表示将验证&测试集划分为验证集和测试集，其中测试集占验证&测试集的比例为33%。random_state=42是为了保证每次运行代码时分割的数据集相同。

此时，变量X_train存储训练集的特征数据，y_train存储训练集的标签数据，变量X_val存储验证集的特征数据，y_val存储验证集的标签数据，变量X_test存储测试集的特征数据，y_test存储测试集的标签数据。