parameters: (FLAGS)
docstring: Run parallel Sherpa optimization over a set of discrete hp combinations.
docstring_summary: Run parallel Sherpa optimization over a set of discrete hp combinations.
docstring_tokens: Run parallel Sherpa optimization over a set of discrete hp combinations .
function: def run_example(FLAGS):
      """
      Run parallel Sherpa optimization over a set of discrete hp combinations.
      """
      parameters = [sherpa.Continuous('lrinit', [0.001, 0.1], 'log'),
                    sherpa.Continuous('momentum', [0., 0.99]),
                    sherpa.Continuous('lrdecay', [1e-7, 1e-2], 'log'),
                    sherpa.Continuous('dropout', [0., 0.5])]
      
      if FLAGS.algorithm == 'BayesianOptimization':  
          print('Running GPyOpt')
          alg = bayesian_optimization.GPyOpt(max_concurrent=FLAGS.max_concurrent,
                                             model_type='GP_MCMC',
                                             acquisition_type='EI_MCMC',
                                             max_num_trials=150)
      elif FLAGS.algorithm == 'LocalSearch':
          print('Running Local Search')
          alg = sherpa.algorithms.LocalSearch(seed_configuration={'lrinit': 0.038,
                                                                  'momentum': 0.92,
                                                                  'lrdecay': 0.0001,
                                                                  'dropout': 0.},
                                              perturbation_factors=(0.9, 1.1))
      else:
          print('Running Random Search')
          alg = sherpa.algorithms.RandomSearch(max_num_trials=150)
  
      if FLAGS.sge:
          assert FLAGS.env, "For SGE use, you need to set an environment path."
          # Submit to SGE queue.
          env = FLAGS.env  # Script specifying environment variables.
          opt = '-N MNISTExample -P {} -q {} -l {}'.format(FLAGS.P, FLAGS.q, FLAGS.l)
          sched = SGEScheduler(environment=env, submit_options=opt)
      else:
          # Run on local machine.
          sched = LocalScheduler()
  
      rval = sherpa.optimize(parameters=parameters,
                             algorithm=alg,
                             lower_is_better=True,
                             filename='trial.py',
                             output_dir='output_{}'.format(FLAGS.studyname),
                             scheduler=sched,
                             max_concurrent=FLAGS.max_concurrent)
      print('Best results:')
      print(rval)
function_tokens: def run_example ( FLAGS ) : parameters = [ sherpa . Continuous ( 'lrinit' , [ 0.001 , 0.1 ] , 'log' ) , sherpa . Continuous ( 'momentum' , [ 0. , 0.99 ] ) , sherpa . Continuous ( 'lrdecay' , [ 1e-7 , 1e-2 ] , 'log' ) , sherpa . Continuous ( 'dropout' , [ 0. , 0.5 ] ) ] if FLAGS . algorithm == 'BayesianOptimization' : print ( 'Running GPyOpt' ) alg = bayesian_optimization . GPyOpt ( max_concurrent = FLAGS . max_concurrent , model_type = 'GP_MCMC' , acquisition_type = 'EI_MCMC' , max_num_trials = 150 ) elif FLAGS . algorithm == 'LocalSearch' : print ( 'Running Local Search' ) alg = sherpa . algorithms . LocalSearch ( seed_configuration = { 'lrinit' : 0.038 , 'momentum' : 0.92 , 'lrdecay' : 0.0001 , 'dropout' : 0. } , perturbation_factors = ( 0.9 , 1.1 ) ) else : print ( 'Running Random Search' ) alg = sherpa . algorithms . RandomSearch ( max_num_trials = 150 ) if FLAGS . sge : assert FLAGS . env , "For SGE use, you need to set an environment path." # Submit to SGE queue. env = FLAGS . env # Script specifying environment variables. opt = '-N MNISTExample -P {} -q {} -l {}' . format ( FLAGS . P , FLAGS . q , FLAGS . l ) sched = SGEScheduler ( environment = env , submit_options = opt ) else : # Run on local machine. sched = LocalScheduler ( ) rval = sherpa . optimize ( parameters = parameters , algorithm = alg , lower_is_better = True , filename = 'trial.py' , output_dir = 'output_{}' . format ( FLAGS . studyname ) , scheduler = sched , max_concurrent = FLAGS . max_concurrent ) print ( 'Best results:' ) print ( rval )