从训练我的word2vec语料库获得的向量标准化过程中出错

Question

在训练word2vec之后，我无法对向量进行标准化。我遇到错误：

  

输入包含NaN，无穷大或dtype（'float64'）太大的值。

我的代码和错误跟踪如下。我的word2vec向量是什么类型-它们是列表吗？而我该如何消除此错误？

---

# average Word2Vec
# compute average word2vec for each review of train Dataset
    sent_vectors_train = []; 
    for sent in list_of_sent_train: # for each review/sentence
    sent_vec = np.zeros(50) # as word vectors are of zero length
    cnt_words =0; # num of words with a valid vector in the sentence/review
    for word in sent: # for each word in a review/sentence
        try:
            vec = w2v_model.wv[word]
            sent_vec += vec
            cnt_words += 1
        except:
            pass
    sent_vec /= cnt_words
    sent_vectors_train.append(sent_vec)
    print(len(sent_vectors_train))
    print(len(sent_vectors_train[0]))

output:-7000
        50

    from sklearn.preprocessing import StandardScaler
    standardized_data = 
    StandardScaler(with_mean=False).fit_transform(sent_vectors_train)
    print(standardized_data.shape)

ValueError                                Traceback (most recent call last)
<ipython-input-47-cb2854c305eb> in <module>()
      1 #from sklearn.preprocessing import StandardScaler
      2 from sklearn.preprocessing import StandardScaler
----> 3 standardized_data = StandardScaler(with_mean=False).fit_transform(sent_vectors_train)
      4 print(standardized_data.shape)

~\Anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
    515         if y is None:
    516             # fit method of arity 1 (unsupervised transformation)
--> 517             return self.fit(X, **fit_params).transform(X)
    518         else:
    519             # fit method of arity 2 (supervised transformation)

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in fit(self, X, y)
    588         # Reset internal state before fitting
    589         self._reset()
--> 590         return self.partial_fit(X, y)
    591 
    592     def partial_fit(self, X, y=None):

~\Anaconda3\lib\site-packages\sklearn\preprocessing\data.py in partial_fit(self, X, y)
    610         """
    611         X = check_array(X, accept_sparse=('csr', 'csc'), copy=self.copy,
--> 612                         warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)
    613 
    614         # Even in the case of `with_mean=False`, we update the mean anyway

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    451                              % (array.ndim, estimator_name))
    452         if force_all_finite:
--> 453             _assert_all_finite(array)
    454 
    455     shape_repr = _shape_repr(array.shape)

~\Anaconda3\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X)
     42             and not np.isfinite(X).all()):
     43         raise ValueError("Input contains NaN, infinity"
---> 44                          " or a value too large for %r." % X.dtype)
     45 
     46 

ValueError: Input contains NaN, infinity or a value too large for dtype('float64').