AttributeError：“张量”对象没有“编译”属性

Question

当我尝试运行它时：

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'])

它给了我这个错误：

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

Answer 1

如 cmets 中所述，您需要使用 Keras Functional API 来创建具有分支、多个输入/输出的模型。但是，不需要对所有代码都执行此操作，只需对最后一部分执行此操作即可：

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'])