如何同时为两个不同的训练集和不同的代价函数训练Keras模型

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

# 定义第一个输入
input1 = Input(shape=(input_shape1,))
# 定义第一个隐藏层
hidden1 = Dense(units=hidden_units1, activation='relu')(input1)

# 定义第二个输入
input2 = Input(shape=(input_shape2,))
# 定义第二个隐藏层
hidden2 = Dense(units=hidden_units2, activation='relu')(input2)

# 合并两个隐藏层
merged = concatenate([hidden1, hidden2])

# 定义输出层
output1 = Dense(units=output_units1, activation='softmax')(merged)
output2 = Dense(units=output_units2, activation='sigmoid')(merged)

# 定义模型
model = Model(inputs=[input1, input2], outputs=[output1, output2])

model.compile(optimizer=optimizer,
              loss=[cost_function1, cost_function2],
              metrics=[metric1, metric2])

model.fit([train_set1, train_set2], [train_labels1, train_labels2], epochs=num_epochs, batch_size=batch_size)