def test_generate_many_nns(self):
""" Testing generation of many neural networks. """
self.report('Testing generation of many NNs.')
num_nns = 40
cnns = nn_examples.generate_many_neural_networks('cnn', num_nns)
visualise_list_of_nns(cnns, os.path.join(self.save_dir, 'cnn'))
reg_mlps = nn_examples.generate_many_neural_networks(
'mlp-reg', num_nns)
visualise_list_of_nns(reg_mlps, os.path.join(self.save_dir,
'reg_mlps'))
class_mlps = nn_examples.generate_many_neural_networks(
'mlp-class', num_nns)
visualise_list_of_nns(class_mlps,
os.path.join(self.save_dir, 'class_mlps'))
def _determine_next_batch_of_eval_points(self):
""" Determine the next point for evaluation. """
num_cands = max(20, 5 * self.num_workers)
nns = generate_many_neural_networks('cnn', num_cands)
rand_idxs = np.random.choice(num_cands,
self.num_workers,
replace=False)
return [nns[i] for i in rand_idxs]
def _determine_next_eval_point(self):
""" Determine the next point for evaluation. """
num_cands = 20
nns = generate_many_neural_networks('cnn', num_cands)
rand_idx = np.random.randint(num_cands)
return nns[rand_idx]
def __init__(self, *args, **kwargs):
""" Constructor. """
super(NNOptUtilsTestClass, self).__init__(*args, **kwargs)
num_nns = 20
self.cnns = generate_many_neural_networks('cnn', num_nns)
self.mlps = generate_many_neural_networks('mlp-reg', num_nns)