def nnet1_template(dataset_name='skdata.larochelle_etal_2007.Rectangles',
sup_min_epochs=30, # THESE ARE KINDA SMALL FOR SERIOUS RESULTS
sup_max_epochs=400):
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), 3)),
'next_layer', rSON2(
'n_hid', geom(2**4, 2**10, round=16),
'W_init_dist', one_of('uniform', 'normal'),
'W_init_algo', one_of('old', 'Xavier'),
'W_init_algo_old_multiplier', uniform(.2, 2),
'cd_epochs', 0,
'cd_batchsize', 100,
'cd_sample_v0s', one_of(False, True),
'cd_lr', lognormal(numpy.log(.01), 3),
'cd_lr_anneal_start', geom(10, 10000),
'next_layer', None))
return template
def __init__(self):
Base.__init__(self, rSON2(
'x', uniform(-20, 20),
'hf', one_of(
rSON2(
'kind', 'raw'),
rSON2(
'kind', 'negcos',
'amp', uniform(0, 1)))))
def dbn_template(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',
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, 2000),
'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',
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., 1.),
'cd_epochs',
geom(1, 1500),
'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
def __init__(self, sigma=10):
"""
The second peak is at x=-10.
The prior mean is 0.
"""
Base.__init__(self, rSON2('x', normal(0, sigma)))
def __init__(self):
Base.__init__(self, rSON2('x', one_of(0, 1)))
def __init__(self):
Base.__init__(self, rSON2('x', lognormal(0, 2)))
def __init__(self):
Base.__init__(self, rSON2('x', uniform(-5, 5)))
def __init__(self):
Base.__init__(self, rSON2(
'curve', one_of(0, 1),
'x', uniform(-20, 20)))