def _set_up_acq_opt_ga(self):
""" Determines the mutation operator for the internal GA. """
# First the sampling distribution for the GA mutation operator
mut_arg = self.options.ga_mutation_op_distro
if isinstance(mut_arg, list):
self.ga_mutation_op = get_nn_modifier_from_args(
self.domain.constraint_checker, dflt_num_steps_probs=mut_arg)
elif isinstance(mut_arg, (int, long, float)):
self.ga_mutation_op = get_nn_modifier_from_args(
self.domain.constraint_checker, dflt_max_num_steps=mut_arg)
elif mut_arg.startswith('d'):
ga_mutation_probs = [float(x) for x in mut_arg[1:].split('-')]
self.ga_mutation_op = get_nn_modifier_from_args(
self.domain.constraint_checker,
dflt_num_steps_probs=ga_mutation_probs)
elif mut_arg.startswith('n'):
ga_mutation_num_steps = int(x[1:])
self.ga_mutation_op = get_nn_modifier_from_args(
self.domain.constraint_checker,
dflt_max_num_steps=ga_mutation_num_steps)
else:
raise ValueError('Cannot parse ga_mutation_op_distro=%s.' %
(self.options.ga_mutation_op_distro))
# The initial pool
self.ga_init_pool = get_initial_pool(self.domain.get_type())
# The number of evaluations
if self.get_acq_opt_max_evals is None:
lead_const = min(5, self.domain.get_dim())**2
self.get_acq_opt_max_evals = lambda t: np.clip(
lead_const * np.sqrt(t), 50, 500)
def nasbot(func_caller,
worker_manager,
budget,
tp_comp=None,
mode=None,
init_pool=None,
acq='hei',
options=None,
reporter='default'):
""" NASBOT optimisation from a function caller. """
nn_type = func_caller.domain.nn_type
if options is None:
reporter = get_reporter(reporter)
options = load_options(all_nasbot_args, reporter=reporter)
if acq is not None:
options.acq = acq
if mode is not None:
options.mode = mode
if tp_comp is None:
tp_comp = get_default_otmann_distance(nn_type, 1.0)
# Initial queries
if not hasattr(options,
'pre_eval_points') or options.pre_eval_points is None:
if init_pool is None:
init_pool = get_initial_pool(nn_type)
options.get_initial_points = lambda n: init_pool[:n]
return (NASBOT(func_caller,
worker_manager,
tp_comp,
options=options,
reporter=reporter)).optimise(budget)
def test_initial_pool(self):
""" Unit test for the VGG_net."""
self.report('Testing the initial pool. ')
cnns = get_initial_pool('cnn')
visualise_list_of_nns(cnns, os.path.join(self.save_dir, 'cnn'))
NN_PERFORMANCE = []
for nn in cnns:
NN_dataflow_work = tranfor_net_to_NNdataFlow(nn)
manager = Manager()
return_dict = manager.dict()
p = Process(target=model_cast,args=(NN_dataflow_work,return_dict,))
p.start()
p.join()
NN_PERFORMANCE.append(return_dict['total_time'])
print("NN_PERFORMANCE:",NN_PERFORMANCE)