我有一个过程，在给定向量X的情况下，通过生成随机向量Y，将给出实数值输出（实际上始终为非负标量）。问题是从X x Y-> R +映射此函数是未知的。因此，我的方法是首先使用DNN学习此未知函数。然后使用一些非线性优化（最深的上升等）来最大化输出。但是，我不太了解该怎么做。您能告诉我最好的方法吗？谢谢！
这是我的网络体系结构。
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_5 (InputLayer) (None, 2, 62) 0
__________________________________________________________________________________________________
lambda_15 (Lambda) (None, 60) 0 input_5[0][0]
__________________________________________________________________________________________________
lambda_16 (Lambda) (None, 60) 0 input_5[0][0]
__________________________________________________________________________________________________
dense_14 (Dense) (None, 60) 3660 lambda_15[0][0]
lambda_16[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 120) 0 dense_14[0][0]
dense_14[1][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 120) 0 concatenate_4[0][0]
__________________________________________________________________________________________________
lambda_13 (Lambda) (None, 2) 0 input_5[0][0]
__________________________________________________________________________________________________
lambda_14 (Lambda) (None, 2) 0 input_5[0][0]
__________________________________________________________________________________________________
dense_17 (Dense) (None, 60) 7260 dropout_5[0][0]
__________________________________________________________________________________________________
dense_15 (Dense) (None, 10) 30 lambda_13[0][0]
__________________________________________________________________________________________________
dense_16 (Dense) (None, 10) 30 lambda_14[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 80) 0 dense_17[0][0]
dense_15[0][0]
dense_16[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 80) 0 concatenate_5[0][0]
__________________________________________________________________________________________________
dense_18 (Dense) (None, 1) 81 dropout_7[0][0]
==================================================================================================
Total params: 11,061
Trainable params: 11,061
Non-trainable params: 0
基本上，每个实例的输入向量是2 x 62数组。第一个2x60是固定的（如上面的X）。最后两列是2个随机向量Y1和Y2。因此，模型中的Lambda层将提取这些单独的组件。现在我想知道如何找到Y1和Y2以在给定X的情况下最大化特定实例的输出。