import gzip import os import pickle import numpy as np def load_mnist(path, kind='train'): labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind) images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind) with gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) return images, labels def get_mnist_data(num_training=5000, num_validation=500, num_test=500): mnist_dir = r'D:\daima\mnist' # 修改为mnist数据集所在的目录 X_train, y_train = load_mnist(mnist_dir, kind='train') X_test, y_test = load_mnist(mnist_dir, kind='t10k') 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] X_train = X_train.astype('float32') / 255 X_val = X_val.astype('float32') / 255 X_test = X_test.astype('float32') / 255 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, }，这是读取mnist的函数，如何把解包时给定的值数量从两个增加到4个

在这个函数中，load_mnist函数返回了两个值，即images和labels，但是这个函数只使用了一个返回值，所以只需要将这个函数的返回值改为一个元组即可。具体来说，将return images, labels改为return (images, labels)即可。这样get_mnist_data函数就可以使用解包时给定的值数量为4了。修改后的代码如下：

import gzip 
import os 
import pickle 
import numpy as np 

def load_mnist(path, kind='train'): 
    labels_path = os.path.join(path, '%s-labels-idx1-ubyte.gz' % kind) 
    images_path = os.path.join(path, '%s-images-idx3-ubyte.gz' % kind) 

    with gzip.open(labels_path, 'rb') as lbpath: 
        labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) 

    with gzip.open(images_path, 'rb') as imgpath: 
        images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16).reshape(len(labels), 784) 

    return images, labels 

def get_mnist_data(num_training=5000, num_validation=500, num_test=500): 
    mnist_dir = r'D:\daima\mnist'  # 修改为mnist数据集所在的目录 
    X_train, y_train = load_mnist(mnist_dir, kind='train') 
    X_test, y_test = load_mnist(mnist_dir, kind='t10k') 
    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] 

    X_train = X_train.astype('float32') / 255 
    X_val = X_val.astype('float32') / 255 
    X_test = X_test.astype('float32') / 255 

    return X_train, y_train, X_val, y_val, X_test, y_test