python之在 Keras 中合并，类型错误 : module object not callable

我正在尝试连接所有输入，但出于某种原因，我总是收到该错误:类型错误:模块对象不可调用，你能帮我解决这个问题吗？我尝试用 Keras.layers.concatenate 替换合并，但没有成功。

def stack_latent_layers(n): 
        #Stack n bidi LSTMs 
        return lambda x: stack(x, [lambda : Bidirectional(LSTM(hidden_units, 
                                                                  return_sequences = True))] * n ) 
 
def predict_classes(): 
        #Predict to the number of classes 
        #Named arguments are passed to the keras function 
        return lambda x: stack(x, 
                                    [lambda : TimeDistributed(Dense(output_dim = num_of_classes(), 
                                                                    activation = "softmax"))] + 
                                    [lambda : TimeDistributed(Dense(hidden_units, 
                                                                    activation='relu'))] * 3) 
 
word_embedding_layer = emb.get_keras_embedding( 
                                                    trainable = True, 
                                                    input_length = sent_maxlen,   name='word_embedding_layer')  
 
 
pos_embedding_layer = Embedding(output_dim = pos_tag_embedding_size, 
                         input_dim = len(SPACY_POS_TAGS), 
                         input_length = sent_maxlen, 
                         name='pos_embedding_layer') 
 
latent_layers = stack_latent_layers(num_of_latent_layers) 
 
dropout = Dropout(0.1)  
 
predict_layer = predict_classes() 
 
 
## --------> 8] Prepare input features, and indicate how to embed them 
inputs_and_embeddings = [(Input(shape = (sent_maxlen,), 
                                        dtype="int32", 
                                        name = "word_inputs"), 
                                  word_embedding_layer), 
                                 (Input(shape = (sent_maxlen,), 
                                        dtype="int32", 
                                        name = "predicate_inputs"), 
                                  word_embedding_layer), 
                                 (Input(shape = (sent_maxlen,), 
                                        dtype="int32", 
                                        name = "postags_inputs"), 
                                  pos_embedding_layer), 
        ] 
print('inputs_and_embeddings',inputs_and_embeddings) 
 
## --------> 9] Concat all inputs and run on deep network 
output = predict_layer(dropout(latent_layers(merge([embed(inp) 
                                                    for inp, embed in inputs_and_embeddings], 
                                                           mode = "concat", 
                                                           concat_axis = -1 
                                                                     )))) 

请您参考如下方法:

用 keras.layers.concatenate 替换合并