  The design of surface plasmon polaritons (SPP) films is an ill-posed inverse
problem. There are many-to-one correspondence between the structures and user
needs. We present a novel guided deep learning algorithm to find optimal
solutions (with both high accuracy and low cost). To achieve this goal, we use
low cost sample replacement algorithm in training process. The deep CNN would
gradually learn better model from samples with lower cost. We have successfully
applied this algorithm to the design of low-cost SPP films. Our model learned
to replace precious metals with ordinary metals to reduce cost. So the the cost
of predicted structure is much lower than standard deep CNN. And the average
relative error of spectrum is less than 10%. The source codes are available at
https://github.com/closest-git/MetaLab.
