How to get the best model?

I have tried exactly same as mentioned here for nas problem using regevo

deephyper start-project nas_problems
cd nas_problems/nas_problems/
deephyper new-problem nas polynome2
cd nas_problems/nas_problems/polynome2
python load_data.py
python problem.py
deephyper nas regevo --evaluator ray --problem nas_problems.polynome2.problem.Problem --max-evals 100

After this I am getting a deephyper.log file

Now how to predict a data? Where is the best model located?

[anuragverma77]

I found the fix. arch_seq = [21.0, 0.0, 15.0, 0.0, 1.0, 8.0, 0.0, 0.0, 0.0] should be changed to all integers arch_seq = [int(x) for x in arch_seq]

so new arch_seq = [21, 0, 15, 0, 1, 8, 0, 0, 0]

[Deathn0t]

Yes that’s correct, the result is just a neural network architecture, but the trained parameters are not available by default. We could add this feature in the future. For now, users re-train the best architecture with more data and tend to use less data for the NAS to reduce the cost of the evaluations. The goal of NAS evaluations is mainly to have an estimate of the performance of each neural network.

Also, saving all trained parameters for each architecture can become very costly or take a lot of time if you have many evaluations in parallel.

A possible solution is to use the Keras ModelCheckpoint from Keras and add it to the problem definition.