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 osimport jiebaimport numpy as npfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import accuracy_score# 定义常量data_dir = './data'stopwords_path = './stopwords.txt'category_names = ['文学', '教育', '计算机', '医学', '体育']# 加载停用词stopwords = set()with open(stopwords_path, 'r', encoding='utf-8') as f: for line in f: stopwords.add(line.strip())# 加载语料库，构建训练集和测试集train_data = []train_labels = []test_data = []test_labels = []for i, category_name in enumerate(category_names): category_dir = os.path.join(data_dir, category_name) file_names = os.listdir(category_dir) for j, file_name in enumerate(file_names): with open(os.path.join(category_dir, file_name), 'r', encoding='utf-8') as f: content = f.read() words = [word for word in jieba.cut(content) if word not in stopwords] if j < 3: test_data.append(words) test_labels.append(i) else: train_data.append(words) train_labels.append(i)# 构建词汇表vocab = set()for document in train_data: vocab |= set(document)vocab = list(vocab)vocab.sort()# 构建文档向量def document2vector(document, vocab): vector = np.zeros(len(vocab)) for word in document: if word in vocab: vector[vocab.index(word)] += 1 return vectortrain_vectors = np.array([document2vector(document, vocab) for document in train_data])test_vectors = np.array([document2vector(document, vocab) for document in test_data])# 训练朴素贝叶斯分类器clf = MultinomialNB()clf.fit(train_vectors, train_labels)# 测试分类器predicted_labels = clf.predict(test_vectors)# 评估分类器accuracy = accuracy_score(test_labels, predicted_labels)print('Accuracy:', accuracy)

这段代码实现了一个基于朴素贝叶斯分类器的文本分类器，主要分为以下几个步骤：

1. 加载停用词文件，并存储在一个集合中。
2. 加载语料库，并将每个文档进行分词，去除停用词，并将前三个文档作为测试集，其余的作为训练集。
3. 构建词汇表，将训练集中的所有词语去重后按字典序排序后存储在一个列表中。
4. 将每个文档转化为词向量，其长度为词汇表的长度，每个位置表示对应词汇在该文档中出现的次数。
5. 使用训练集训练一个朴素贝叶斯分类器，并使用测试集测试该分类器的准确率。

需要注意的是，该代码中使用的是多项式朴素贝叶斯分类器（MultinomialNB），因此假设每个词汇的出现次数是服从多项式分布的。同时，该代码中使用的是中文分词工具jieba，因此需要确保该工具已被正确安装。

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。