예제 #1
0
 def dbn_template1(self,
         dataset_name='skdata.larochelle_etal_2007.Rectangles',
         sup_min_epochs=300,
         sup_max_epochs=4000):
     template = rSON2(
         'preprocessing', one_of(
             rSON2(
                 'kind', 'raw'),
             rSON2(
                 'kind', 'zca',
                 'energy', uniform(0.5, 1.0))),
         'dataset_name', dataset_name,
         'sup_max_epochs', sup_max_epochs,
         'sup_min_epochs', sup_min_epochs,
         'iseed', one_of(5, 6, 7, 8),
         'batchsize', one_of(20, 100),
         'lr', lognormal(numpy.log(.01), 3),
         'lr_anneal_start', geom(100, 10000),
         'l2_penalty', one_of(0, lognormal(numpy.log(1.0e-6), 2)),
         'next_layer', one_of(None,
         rSON2(
             'n_hid', geom(2**7, 2**12, round=16),
             'W_init_dist', one_of('uniform', 'normal'),
             'W_init_algo', one_of('old', 'Xavier'),
             'W_init_algo_old_multiplier', lognormal(0.0, 1.0),
             'cd_epochs', geom(1, 3000),
             'cd_batchsize', 100,
             'cd_sample_v0s', one_of(False, True),
             'cd_lr', lognormal(numpy.log(.01), 2),
             'cd_lr_anneal_start', geom(10, 10000),
             'next_layer', None)))
     return template
예제 #2
0
 def __init__(self):
     hyperopt.bandits.Base.__init__(self,
             one_of(
                 rSON2(
                     'kind', 'raw'),
                 rSON2(
                     'kind', 'negcos',
                     'amp', uniform(0, 1))))
예제 #3
0
 def dbn_template0(self,
         dataset_name='skdata.larochelle_etal_2007.Rectangles',
         sup_min_epochs=300,
         sup_max_epochs=4000):
     template = rSON2(
         'preprocessing', one_of(
             rSON2(
                 'kind', 'raw'),
             rSON2(
                 'kind', 'zca',
                 'energy', uniform(0.5, 1.0))),
         'dataset_name', dataset_name,
         'sup_max_epochs', sup_max_epochs,
         'sup_min_epochs', sup_min_epochs,
         'iseed', one_of(5, 6, 7, 8),
         'batchsize', one_of(20, 100),
         'lr', lognormal(numpy.log(.01), 3),
         'lr_anneal_start', geom(100, 10000),
         'l2_penalty', one_of(0, lognormal(numpy.log(1.0e-6), 2)),
         'next_layer', None)
     return template