写在前面

最近想体验一下AI方面的技术，但是在做一些TensorFlow官网上的简单实验时却发现由于网络、环境等问题，里面的示例代码并不能很顺利地跑在我的机器上，主要原因出在从远端自动load_data的时候，总是会报https、ssl等网络相关的问题。之前的Fashion MNIST，通过网上的一些攻略，找到了本地导入的方法，于是在进行下一个imdb（电影评论文本分类）的小实验时，想自己写一个类似功能的函数来本地导入数据集，于是就有了本文，代码和思路供参考。（另外，我发现keras中的imdb这部分代码并不长，于是把另外一个也需要联网的imdb.get_word_index()也顺便一起改成离线版了，反正网络问题要出现就都是一起出现的…）

我的环境

操作系统：CentOS 7
Conda版本：从TUNA下载的Anaconda3-5.3.1-Linux-x86_64.sh
TensorFlow版本：1.15.4
Keras：Tensorflow中自带的

使用方法

1. 下载imdb相关的两个文件（imdb.npz, imdb_word_index.json）到本地目录（例如/home/user/datasets/TF_imdb）。度盘下载链接 / 提取码：ic54

2. 复制下方小节的【离线导入代码块】到.py文件中，修改代码中标出的路径，定位到刚才下载的两个文件。（如果缺少相应的包，可以用pip install）

3. 把官网示例代码中的imdb.load_data(num_words=10000)修改成我们自定义的manual_imdb_load_data(num_words=10000)；同理，imdb.get_word_index()修改成manual_imdb_get_word_index()

4. 其他代码可以和官网示例中保持一致，运行测试即可。

【离线导入代码块】：由于是从官方代码中改的，有些注释我保留了。用之前记得先把下载的两个文件放在对应路径下。

import tensorflow as tf
from tensorflow import keras
import numpy as np

print(tf.__version__)	# 简单检测一下tensorflow的版本

# 修改imdb的一些函数（因为https有问题）
from tensorflow.python.platform import tf_logging as logging
import json
def manual_imdb_load_data(path='imdb.npz',
              num_words=None,
              skip_top=0,
              maxlen=None,
              seed=113,
              start_char=1,
              oov_char=2,
              index_from=3,
              **kwargs):
    if 'nb_words' in kwargs:
        logging.warning('The `nb_words` argument in `load_data` '
                        'has been renamed `num_words`.')
        num_words = kwargs.pop('nb_words')
    if kwargs:
        raise TypeError('Unrecognized keyword arguments: ' + str(kwargs))

    path = '/home/user/datasets/TF_imdb/imdb.npz'	# 【修改点1】
    with np.load(path, allow_pickle=True) as f:
        x_train, labels_train = f['x_train'], f['y_train']
        x_test, labels_test = f['x_test'], f['y_test']

    rng = np.random.RandomState(seed)
    indices = np.arange(len(x_train))
    rng.shuffle(indices)
    x_train = x_train[indices]
    labels_train = labels_train[indices]

    indices = np.arange(len(x_test))
    rng.shuffle(indices)
    x_test = x_test[indices]
    labels_test = labels_test[indices]

    if start_char is not None:
        x_train = [[start_char] + [w + index_from for w in x] for x in x_train]
        x_test = [[start_char] + [w + index_from for w in x] for x in x_test]
    elif index_from:
        x_train = [[w + index_from for w in x] for x in x_train]
        x_test = [[w + index_from for w in x] for x in x_test]

    if maxlen:
        x_train, labels_train = keras.preprocessing.sequence._remove_long_seq(maxlen, x_train, labels_train)
        x_test, labels_test = keras.preprocessing.sequence._remove_long_seq(maxlen, x_test, labels_test)
        if not x_train or not x_test:
            raise ValueError('After filtering for sequences shorter than maxlen=' +
                             str(maxlen) + ', no sequence was kept. '
                                           'Increase maxlen.')

    xs = np.concatenate([x_train, x_test])
    labels = np.concatenate([labels_train, labels_test])

    if not num_words:
        num_words = max(max(x) for x in xs)

    # by convention, use 2 as OOV word
    # reserve 'index_from' (=3 by default) characters:
    # 0 (padding), 1 (start), 2 (OOV)
    if oov_char is not None:
        xs = [
            [w if (skip_top <= w < num_words) else oov_char for w in x] for x in xs
        ]
    else:
        xs = [[w for w in x if skip_top <= w < num_words] for x in xs]

    idx = len(x_train)
    x_train, y_train = np.array(xs[:idx]), np.array(labels[:idx])
    x_test, y_test = np.array(xs[idx:]), np.array(labels[idx:])

    return (x_train, y_train), (x_test, y_test)

def manual_imdb_get_word_index(path='imdb_word_index.json'):
  """Retrieves a dict mapping words to their index in the IMDB dataset.

  Arguments:
      path: where to cache the data (relative to `~/.keras/dataset`).

  Returns:
      The word index dictionary. Keys are word strings, values are their index.
  """
  path = '/home/user/datasets/TF_imdb/imdb_word_index.json'	# 【修改点2】
  with open(path) as f:
    return json.load(f)
#------------------------------offline loading preparation-------------------------------#

标签：index,labels,导入,train,imdb,words,test,TensorFlow
来源： https://blog.csdn.net/csdnwjl/article/details/112409059