gen_CFG.py

import numpy as np
import sys

sys.path.append("../SMAC3") # wherever SMAC3 install is

from smac.configspace import ConfigurationSpace
from ConfigSpace.hyperparameters import CategoricalHyperparameter, \
    UniformFloatHyperparameter, UniformIntegerHyperparameter

from smac.tae.execute_func import ExecuteTAFuncDict
from smac.scenario.scenario import Scenario
from smac.facade.smac_facade import SMAC

from train_CFG import rnn_from_cfg

def print_incumb(cfg):
    print('Best model saved in: ' + './SMAC3out/models' \
            + str(cfg['cell_size']) + '_' \
            + str(cfg['n_cell']) + '_' \
            + str(cfg['nn_type']) + '_' \
            + str(cfg['dropout']) + '_' \
            + cfg['activation'] + '_' \
            + cfg['optimizer'] + '_' \
            + str(cfg['optimizer_lr']) + '_' \
            + str(cfg['learning_decay_rate']) + '.hdf5')

def main():
  cs = ConfigurationSpace()

  cell_size = CategoricalHyperparameter("cell_size", [128], default_value=128) # kick up to 256
  n_cell = CategoricalHyperparameter("n_cell", [2], default_value=2)
  dropout = CategoricalHyperparameter("dropout", [0.5], default_value=0.5)

  activation = CategoricalHyperparameter("activation", ['sigmoid'], default_value='sigmoid')
  optimizer = CategoricalHyperparameter("optimizer", ['adam'], default_value='adam')
  optimizer_lr = CategoricalHyperparameter("optimizer_lr", [.001, .003, .006, .01, 0.03, 0.1], default_value=.01)
  learning_decay_rate = UniformFloatHyperparameter("learning_decay_rate", 0, 0.9, default_value=.6)

  nn_type = CategoricalHyperparameter("nn_type", ['RNN', 'LSTM', 'GRU'], default_value='LSTM')

  epochs = CategoricalHyperparameter("epochs", [10], default_value=10)

  cs.add_hyperparameters([cell_size, n_cell, dropout, nn_type, 
          activation, optimizer, optimizer_lr, learning_decay_rate, epochs])

  scenario = Scenario({"run_obj": "quality", "runcount-limit": 32, "cs": cs, "deterministic": "true"})
  scenario.output_dir_for_this_run = "C:\\NNwork\\HFSF\\SMAC3out"
  scenario.output_dir = "C:\\NNwork\\HFSF\\SMAC3out"
  smac = SMAC(scenario=scenario, rng=np.random.RandomState(23), tae_runner=rnn_from_cfg)

  best_model = smac.optimize()
  print_incumb(best_model)
  np.save( "C:\\NNwork\\HFSF\\SMAC3out\\best.cfg", best_model)

if __name__ == '__main__':
  main()