我有拥有个人资料图片和时间序列数据（由该用户生成的事件）的用户。为了进行二元分类，我编写了两个模型：LSTM 和 CNN，它们独立工作良好。但我真正想要实现的是连接这些模型。
这是我的 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, …