with open(os.path.join("..","data",train_or_test + ".txt"),encoding="utf-8") as f:

这段代码是Python中用于打开文件的语句，它使用了`with`语句来确保文件在使用完毕后会被正确关闭，避免资源泄露。`open()`函数用于打开文件，接受两个参数：文件路径和打开模式。在这段代码中，`os.path.join("..","data",train_or_test + ".txt")`用于生成文件路径，`encoding="utf-8"`指定了文件的编码方式为UTF-8。

在`with`语句块中，文件对象被赋值给变量`f`，可以通过`f`来操作文件。在`with`语句块结束后，文件会自动关闭，无需手动调用`f.close()`方法。

相关问题

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。

请在注释处填入代码完成对训练集和测试集的结巴分词from paddlenlp.datasets import load_dataset def read(data_path): data_set = [] with open(data_path, 'r', encoding='utf-8') as f: for line in f: l = line.strip('\n').split('\t') if len(l) != 2: print (len(l), line) words, labels = line.strip('\n').split('\t') data_set.append((words,labels)) return data_set train_ds = read(data_path='train.txt') dev_ds = read(data_path='dev.txt') test_ds = read(data_path='test.txt') for i in range(5): print("sentence %d" % (i), train_ds[i][0]) print("sentence %d" % (i), train_ds[i][1]) print(len(train_ds),len(dev_ds)) import jieba def data_preprocess(corpus): data_set = [] ####填结巴分词代码 for text in corpus: seg_list = jieba.cut(text) data_set.append(" ".join(seg_list)) return data_set train_corpus = data_preprocess(train_ds) test_corpus = data_preprocess(test_ds) print(train_corpus[:2]) print(test_corpus[:2])

from paddlenlp.datasets import load_dataset
def read(data_path):
data_set = []
with open(data_path, 'r', encoding='utf-8') as f:
for line in f:
l = line.strip('\n').split('\t')
if len(l) != 2:
print (len(l), line)
words, labels = line.strip('\n').split('\t')
data_set.append((words,labels))
return data_set

train_ds = read(data_path='train.txt')
dev_ds = read(data_path='dev.txt')
test_ds = read(data_path='test.txt')

for i in range(5):
print("sentence %d" % (i), train_ds[i][0])
print("sentence %d" % (i), train_ds[i][1])

print(len(train_ds),len(dev_ds))

import jieba

def data_preprocess(corpus):
data_set = []
for text in corpus:
seg_list = jieba.cut(text[0])
data_set.append((" ".join(seg_list), text[1]))
return data_set

train_corpus = data_preprocess(train_ds)
test_corpus = data_preprocess(test_ds)

print(train_corpus[:2])
print(test_corpus[:2])