python之词嵌入的 TensorFlow 双向 LSTM 编码

我有一个词嵌入矩阵，其中包含每个词的向量。我正在尝试使用 TensorFlow 在给定嵌入向量的情况下获取每个单词的双向 LSTM 编码。不幸的是，我收到以下错误消息:

ValueError: Shapes (1, 125) and () must have the same rank Exception TypeError: TypeError("'NoneType' object is not callable",) in ignored

这是我使用的代码:

     # Declare max number of words in a sentence 
    self.max_len = 100 
    # Declare number of dimensions for word embedding vectors 
    self.wdims = 100 
 
     # Indices of words in the sentence 
    self.wrd_holder = tf.placeholder(tf.int32, [self.max_len]) 
    # Embedding Matrix 
    wrd_lookup = tf.Variable(tf.truncated_normal([len(vocab)+3, self.wdims], stddev=1.0 / np.sqrt(self.wdims))) 
 
     # Declare forward and backward cells 
    forward  = rnn_cell.LSTMCell(125, (self.wdims)) 
    backward = rnn_cell.LSTMCell(125, (self.wdims)) 
 
     # Perform lookup 
    wrd_embd = tf.nn.embedding_lookup(wrd_lookup, self.wrd_holder) 
    embd = tf.split(0, self.max_len, wrd_embd) 
 
     # run bidirectional LSTM 
    boutput = rnn.bidirectional_rnn(forward, backward, embd, dtype=tf.float32, sequence_length=self.max_len) 

请您参考如下方法:

传递给 rnn 的序列长度必须是长度为 batch size 的向量。