Question

我是word-embedding和Tensorflow的新手。我正在开展一个项目，我需要将 word2vec 应用于健康数据我使用了Tensorflow网站的代码（word2vec_basic.py）。我修改了一点这段代码，使它读取我的数据而不是“text8.zip”，它运行正常，直到最后一步：

num_steps = 100001

with tf.Session(graph=graph) as session:
# We must initialize all variables before we use them.
  tf.initialize_all_variables().run()
  print('Initialized')
  average_loss = 0
  for step in range(num_steps):
    batch_data, batch_labels = generate_batch(
      batch_size, num_skips, skip_window)
    feed_dict = {train_dataset : batch_data, train_labels : batch_labels}
    _, l = session.run([optimizer, loss], feed_dict=feed_dict)
    average_loss += l
    if step % 2000 == 0:
      if step > 0:
        average_loss = average_loss / 2000
      # The average loss is an estimate of the loss over the last 2000 batches.
      print('Average loss at step %d: %f' % (step, average_loss))
      average_loss = 0
    # note that this is expensive (~20% slowdown if computed every 500 steps)
    if step % 10000 == 0:
     sim = similarity.eval()
     for i in range(valid_size):
       valid_word = reverse_dictionary[valid_examples[i]]
       top_k = 8 # number of nearest neighbors
       nearest = (-sim[i, :]).argsort()[1:top_k+1]
       log = 'Nearest to %s:' % valid_word
       for k in range(top_k):
         close_word = reverse_dictionary[nearest[k]]
         log = '%s %s,' % (log, close_word)
       print(log)
  final_embeddings = normalized_embeddings.eval()<code>

此代码与示例完全相同，所以我认为这不是错误的。它给出的错误是：

KeyError                                  Traceback (most recent call last)
<ipython-input-20-fc4c5c915fc6> in <module>()
     34         for k in xrange(top_k):
     35           print(nearest[k])
---> 36           close_word = reverse_dictionary[nearest[k]]
     37           log_str = "%s %s," % (log_str, close_word)
     38         print(log_str)

KeyError: 2868

我改变了输入数据的大小，但它仍然给出了同样的错误如果有人能就如何解决这个问题给我一些建议，我真的很感激。

Answer 1

如果词汇量大小小于默认最大值（50000），则应修改数字。

在第2步的最后，让我们将vocabulary_size修改为实际的字典大小。

data, count, dictionary, reverse_dictionary = build_dataset(words)
del words  # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

#add this line to modify
vocabulary_size = len(dictionary)
print('Dictionary size', len(dictionary))

word2vec_basic无效（Tensorflow）

1 个答案: