Python 用于查找文档间文本相似性的keras LSTM层的维数错误

我写这段代码是为了使用暹罗方法来计算两个文档的相似性。我附上了我的模型的一个片段。我在LSTM层中得到一个关于维度的错误。有人能帮我吗

#Setting Parameters for the model

max_features = 20000 
maxlen = 200 

class DocSim(object):
    def __init__(self, w2v_model , stopwords=[]):
        self.w2v_model = w2v_model
        self.stopwords = stopwords
        
    def vectorize(self, doc):
        """Identify the vector values for each word in the given document"""
        doc = doc.lower()
        words = [w for w in doc.split(" ") if w not in self.stopwords]
        word_vecs = []
        for word in words:
            try:
                vec = self.w2v_model[word]
                word_vecs.append(vec)
            except KeyError:
                # Ignore, if the word doesn't exist in the vocabulary
                pass

        # Assuming that document vector is the mean of all the word vectors

        vector = np.mean(word_vecs, axis=0)
        return vector
        
        
    def Siamese_cosine_sim(self, vectorA, vectorB): 
        # Input for variable-length sequences of integers
        inputs = keras.Input(shape=(None,), dtype="int32")
        # Add 2 bidirectional LSTMs
        vectorA = layers.LSTM(64, return_sequences=True)(vectorA)
        vectorA = layers.LSTM(64)(vectorA)
        
        # Add 2 bidirectional LSTMs
        vectorB = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(vectorB)
        vectorB = layers.Bidirectional(layers.LSTM(64))(vectorB)
        
        # Add a classifier
        outputs = layers.Dense(1, activation="sigmoid")(left_doc,right_doc)


        

        """Find the cosine similarity distance between two vectors."""
        csim = np.dot(left_doc, right_doc) / (np.linalg.norm(left_doc) * np.linalg.norm(right_doc))
        if np.isnan(np.sum(csim)):
            return 0
        return csim

我犯了一个错误

Value error: Input 0 of layer lstm_9 is incompatible with the layer: expected ndim=3, found ndim=1. Full shape received: [300]