我有一些用户的个人资料图片和时间序列数据（由这些用户生成的事件），为了进行二元分类，我编写了两个模型：LSTM和CNN独立工作很好。但我真正想实现的是将这些模型串联起来。
下面是我的LSTM模型：

input1_length = X_train.shape[1]
input1_dim = X_train.shape[2]

input2_length = X_inter_train.shape[1]
input2_dim = X_inter_train.shape[2]

output_dim = 1

input1 = Input(shape=(input1_length, input1_dim))
input2 = Input(shape=(input2_length, input2_dim))

lstm1 = LSTM(20)(input1)
lstm2 = LSTM(10)(input2)

lstm1 = Dense(256, activation='relu')(lstm1)
lstm1 = Dropout(0.5)(lstm1)
lstm1 = Dense(12, activation='relu')(lstm1)

lstm2 = Dense(256, activation='relu')(lstm2)
#lstm2 = Dropout(0.5)(lstm2)
lstm2 = Dense(12, activation='relu')(lstm2)

merge = concatenate([lstm1, lstm2])

# interpretation model
lstm = Dense(128, activation='relu')(merge)

output = Dense(output_dim, activation='sigmoid')(lstm)

model = Model([input1, input2], output)
optimizer = RMSprop(lr=1e-3, decay=0.0)

model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])
model.summary()

CNN模式：

def gen_img_model(input_dim=(75,75,3)):
    input = Input(shape=input_dim)

    conv = Conv2D(32, kernel_size=(3,3), activation='relu')(input)
    conv = MaxPooling2D((3,3))(conv)
    conv = Dropout(0.2)(conv)

    conv = BatchNormalization()(conv)

    dense = Dense(128, activation='relu', name='img_features')(conv)
    dense = Dropout(0.2)(dense)

    output = Dense(1, activation='sigmoid')(dense)

    optimizer = RMSprop(lr=1e-3, decay=0.0)

    model = Model(input, output)
    model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])

    return model

以下是CNN的培训方式：

checkpoint_name = './keras_img_checkpoint/img_model'
callbacks = [ModelCheckpoint(checkpoint_name, save_best_only=True)]

img_model = gen_img_model((75,75,3))

# batch size for img model
batch_size = 200

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

val_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

# train gen for img model
train_generator = train_datagen.flow_from_directory(
        './dataset/train/',
        target_size=(75, 75),
        batch_size=batch_size,
        class_mode='binary')

val_generator = val_datagen.flow_from_directory(
        './dataset/val/', 
        target_size=(75, 75),
        batch_size=batch_size,
        class_mode='binary')

STEP_SIZE_TRAIN = train_generator.n // train_generator.batch_size
STEP_SIZE_VAL = val_generator.n // val_generator.batch_size

img_model.fit_generator(
        train_generator,
        steps_per_epoch=STEP_SIZE_TRAIN,
        validation_data=val_generator,
        validation_steps=800 // batch_size,
        epochs=1,
        verbose=1,
        callbacks=callbacks
)

将LSTM和CNN模型连接在一起的最佳方式是什么？