当我尝试运行以下代码时：

p0 = Sequential()p0.add(Embedding(vocabulary_size1, 50, weights=[embedding_matrix_passage], input_length=50, trainable=False))p0.add(LSTM(64))p0.add(Dense(256,name='FC1'))p0.add(Activation('relu'))p0.add(Dropout(0.5))p0.add(Dense(50,name='out_layer'))p0.add(Activation('sigmoid'))q0 = Sequential()q0.add(Embedding(vocabulary_size2,50,weights=embedding_matrix_query], input_length=50, trainable=False))q0.add(LSTM(64))q0.add(Dense(256,name='FC1'))q0.add(Activation('relu'))q0.add(Dropout(0.5))q0.add(Dense(50,name='out_layer'))q0.add(Activation('sigmoid'))model = concatenate([p0.output, q0.output])model = Dense(10)(model)model = Activation('softmax')(model)model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics= ['accuracy'])

它给出了以下错误：

AttributeError             --->  model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics=['accuracy'])

回答：

正如评论中提到的，你需要使用Keras 函数式 API来创建具有分支、多个输入/输出的模型。然而，你不需要为所有的代码都这样做，只需对最后部分进行修改：

concat = concatenate([p0.output, q0.output])x = Dense(10)(concat)out = Activation('softmax')(x)model = Model([p0.input, q0.input], out)model.compile(loss='categorical_crossentropy',optimizer='rmsprop', metrics=['accuracy'])