我有一个简单的Keras模型：
model_2 = Sequential()model_2.add(Dense(32, input_shape=(500,)))model_2.add(Dense(4))#answer = concatenate([response, question_encoded])model_1 = Sequential()model_1.add(LSTM(32, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True, input_shape=(None, 2048)))model_1.add(LSTM(16, dropout_U = 0.2, dropout_W = 0.2, return_sequences=False))#model.add(LSTM(16, return_sequences=False))merged = Merge([model_1, model_2])model = Sequential()model.add(merged)model.add(Dense(8, activation='softmax'))#model.build()#print(model.summary(90))print("Compiled")model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])当调用fit()时，代码会报错：
raise RuntimeError('You must compile your model before using it.')RuntimeError: You must compile your model before using it.显然，我已经调用了compile。我该如何解决这个问题呢？
回答：
看起来问题在于你创建了3个Sequential模型的实例，但只编译了第三个（合并后的）。对于多模态网络，可能使用不同的结构会更简单：
input_2 = Input(shape=(500,))model_2 = Dense(32)(input_2 )model_2 = Dense(4)(model_2)input_1 = Input(shape=(None, 2048))model_1 = LSTM(32, dropout_U = 0.2, dropout_W = 0.2, return_sequences=True)(input_1 )model_1 = LSTM(16, dropout_U = 0.2, dropout_W = 0.2, return_sequences=False)(model_1)merged = concatenate([model_2, model_1])merged = Dense(8, activation='softmax')(merged)model = Model(inputs=[input_2 , input_1], outputs=merged)model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])希望这对你有帮助！
