带分离式解码器和编码器的Keras AE -但具有多个输入

from tensorflow.keras.layers import Input, Dense, concatenate
from tensorflow.keras.models import Model

# 假设我们有两个不同类型的输入
input1 = Input(shape=(784,), name='input1')
input2 = Input(shape=(28,), name='input2')

# 编码器部分
encoded1 = Dense(128, activation='relu')(input1)
encoded2 = Dense(64, activation='relu')(input2)
merged_encoded = concatenate([encoded1, encoded2])
encoded_output = Dense(32, activation='relu')(merged_encoded)

# 解码器部分
decoded_input = Dense(64, activation='relu')(encoded_output)
decoded1 = Dense(128, activation='relu')(decoded_input)
decoded2 = Dense(28, activation='relu')(decoded_input)
decoded_output1 = Dense(784, activation='sigmoid')(decoded1)
decoded_output2 = Dense(28, activation='linear')(decoded2)

# 构建模型
autoencoder = Model(inputs=[input1, input2], outputs=[decoded_output1, decoded_output2])

# 编译模型
autoencoder.compile(optimizer='adam', loss=['binary_crossentropy', 'mse'])

# 打印模型结构
autoencoder.summary()