cannot import name 'NUM_LINES' from 'torchtext.datasets.imdb' (E:\Anaconda\envs\deeplearning\lib\site-packages\torchtext\datasets\imdb.py)

根据提供的引用内容，出现了两个不同的错误。第一个错误是在导入torchtext.datasets时出现了ImportError，无法导入text_classification。解决方案是注释掉from torchtext.datasets import text_classification，并将文本分类数据集保存在根目录下的代码改为train_dataset, test_dataset = torchtext.datasets.AG_NEWS(root='./data/ag_news_csv/', split=('train', 'test'))。

第二个错误是在导入torchtext.data时出现了ImportError，无法导入Iterator。解决方案是检查torchtext的版本是否正确，并确保Iterator在torchtext.data中可用。

以下是一个类似的错误和解决方案的例子：

from torchtext.datasets.imdb import NUM_LINES

# 错误：无法导入NUM_LINES
# 解决方案：检查torchtext的版本是否正确，并确保NUM_LINES在torchtext.datasets.imdb中可用

相关问题

cannot import name 'emnist' from 'keras.datasets' (E:\ProgramData\Anaconda3\envs\tf\lib\site-packages\keras\datasets\__init__.py)

这个错误信息表明你在导入 `emnist` 时出现了问题。可能的原因是你使用的 Keras 版本不支持 `emnist` 数据集，或者你没有正确安装 `emnist` 数据集。

你可以尝试更新 Keras 版本，或者手动下载 `emnist` 数据集并将其放置在正确的位置。具体步骤如下：

1. 打开 https://www.nist.gov/itl/products-and-services/emnist-dataset 下载 `emnist` 数据集。
2. 将下载的 `.gz` 文件解压到本地。
3. 打开 Python 终端并执行以下代码：

from keras.datasets import mnist
import numpy as np
import os

def load_emnist():
    emnist_path = os.path.join('data', 'emnist')
    if not os.path.exists(emnist_path):
        os.makedirs(emnist_path)

    train_images_path = os.path.join(emnist_path, 'emnist_train_images.npy')
    train_labels_path = os.path.join(emnist_path, 'emnist_train_labels.npy')
    test_images_path = os.path.join(emnist_path, 'emnist_test_images.npy')
    test_labels_path = os.path.join(emnist_path, 'emnist_test_labels.npy')

    if not os.path.exists(train_images_path) or not os.path.exists(train_labels_path) or not os.path.exists(
            test_images_path) or not os.path.exists(test_labels_path):
        print('Preprocessing EMNIST dataset...')
        emnist_train, emnist_test = load_raw_emnist()
        np.save(train_images_path, emnist_train[0])
        np.save(train_labels_path, emnist_train[1])
        np.save(test_images_path, emnist_test[0])
        np.save(test_labels_path, emnist_test[1])
    else:
        print('Loading preprocessed EMNIST dataset...')
        emnist_train = (np.load(train_images_path), np.load(train_labels_path))
        emnist_test = (np.load(test_images_path), np.load(test_labels_path))

    return emnist_train, emnist_test

def load_raw_emnist():
    from scipy.io import loadmat

    emnist_path = os.path.join('data', 'emnist')
    if not os.path.exists(emnist_path):
        os.makedirs(emnist_path)

    emnist_train_path = os.path.join(emnist_path, 'emnist-letters-train.mat')
    emnist_test_path = os.path.join(emnist_path, 'emnist-letters-test.mat')

    if not os.path.exists(emnist_train_path) or not os.path.exists(emnist_test_path):
        print('Downloading EMNIST dataset...')
        download_emnist(emnist_path)

    print('Loading EMNIST dataset...')

    train_data = loadmat(emnist_train_path)
    test_data = loadmat(emnist_test_path)

    emnist_train_images = train_data['dataset'][0][0][0][0][0][0]
    emnist_train_labels = train_data['dataset'][0][0][0][0][0][1]
    emnist_test_images = test_data['dataset'][0][0][0][0][0][0]
    emnist_test_labels = test_data['dataset'][0][0][0][0][0][1]

    emnist_train_images = emnist_train_images.reshape(
        emnist_train_images.shape[0], 1, 28, 28).astype('float32') / 255.0
    emnist_test_images = emnist_test_images.reshape(
        emnist_test_images.shape[0], 1, 28, 28).astype('float32') / 255.0

    return (emnist_train_images, emnist_train_labels), (emnist_test_images, emnist_test_labels)

def download_emnist(emnist_path):
    import urllib.request

    base_url = 'http://www.itl.nist.gov/iaui/vip/cs_links/EMNIST/matlab.zip'
    zip_path = os.path.join(emnist_path, 'emnist.zip')
    mat_path = os.path.join(emnist_path, 'matlab.zip')

    print('Downloading EMNIST dataset...')
    urllib.request.urlretrieve(base_url, zip_path)

    print('Extracting EMNIST dataset...')
    import zipfile

    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        zip_ref.extractall(emnist_path)

    os.rename(os.path.join(emnist_path, 'matlab'), mat_path)

    print('Converting EMNIST dataset...')
    import scipy.io as sio

    train_data = sio.loadmat(os.path.join(emnist_path, 'matlab', 'emnist-letters-train.mat'))
    test_data = sio.loadmat(os.path.join(emnist_path, 'matlab', 'emnist-letters-test.mat'))

    sio.savemat(os.path.join(emnist_path, 'emnist-letters-train.mat'), {'dataset': train_data['dataset']})
    sio.savemat(os.path.join(emnist_path, 'emnist-letters-test.mat'), {'dataset': test_data['dataset']})

    os.remove(zip_path)
    os.remove(mat_path)

load_emnist()

这个代码将下载 `emnist` 数据集并将其预处理为 NumPy 数组。你可以在自己的代码中使用这些数组。

希望这能帮到你！

cannot import name 'normalize_data_format' from 'keras.backend' (F:\Anaconda\envs\tf1.15\lib\site-packages\keras\backend\__init__.py)

根据引用的内容，解决方法是在python代码中import keras之前加入一个环境变量修改的语句，具体的修改代码如下：

import os
os.environ['KERAS_BACKEND']='theano'

根据引用的内容，可以使用Keras库中的函数来加载MNIST数据集。具体的代码如下：

from keras.datasets import mnist
(X_train, y_train), (X_test, y_test) = mnist.load_data()
print(X_train.shape)
import matplotlib.pyplot as plt
plt.imshow(X_train