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_testTypeError: tuple indices must be integers or slices, not str

这段代码出现了一个错误：TypeError: tuple indices must be integers or slices, not str。这是因为在load_mnist函数中返回的是一个元组类型的变量(images, labels)，而在get_mnist_data函数中却使用了类似字典的方式访问这个元组变量，如X_train, y_train = load_mnist(mnist_dir, kind='train')，应该改为X_train, y_train = load_mnist(mnist_dir, kind=0)，X_test, y_test = load_mnist(mnist_dir, kind=1)，这样就能够正确访问这个元组变量的元素了。

相关问题

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

import pickle import numpy as np import os # from scipy.misc import imread def load_CIFAR_batch(filename): with open(filename, 'rb') as f: datadict = pickle.load(f, encoding='bytes') X = datadict[b'data'] Y = datadict[b'labels'] X = X.reshape(10000, 3, 32, 32).transpose(0, 2, 3, 1).astype("float") Y = np.array(Y) return X, Y def load_CIFAR10(ROOT): xs = [] ys = [] for b in range(1, 2): f = os.path.join(ROOT, 'data_batch_%d' % (b,)) X, Y = load_CIFAR_batch(f) xs.append(X) ys.append(Y) Xtr = np.concatenate(xs) Ytr = np.concatenate(ys) del X, Y Xte, Yte = load_CIFAR_batch(os.path.join(ROOT, 'test_batch')) return Xtr, Ytr, Xte, Yte 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, } def load_models(models_dir): models = {} for model_file in os.listdir(models_dir): with open(os.path.join(models_dir, model_file), 'rb') as f: try: models[model_file] = pickle.load(f)['model'] except pickle.UnpicklingError: continue return models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，不使用TensorFlow

可以使用以下代码加载MNIST数据集：

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,
    }

这个函数将会返回训练集、验证集和测试集的图像和标签。其中，图像是一个形如`(num_samples, 784)`的数组，标签是一个形如`(num_samples,)`的数组。