def q1_lognormal():
"""
About the simplest problem you could ask for:
optimize a one-variable quadratic function.
"""
return {'loss': scope.max(-(hp.lognormal('x', 0, 2) - 3) ** 2, -100),
'status': base.STATUS_OK }
def q1_lognormal():
"""
About the simplest problem you could ask for:
optimize a one-variable quadratic function.
"""
return {
'loss': scope.max(-(hp.lognormal('x', 0, 2) - 3)**2, -100),
'status': base.STATUS_OK
}
def exit_lpool_alpha(pipeline, layer_num, Xcm, n_patches, max_n_features):
def lab(msg):
return 'l%ielpa_%s' % (layer_num, msg)
fsize = rfilter_size(lab('fsize'), 3, 8)
filtering_res = pyll_getattr(Xcm, 'shape')[2] - fsize + 1
# -- N.B. Xrows depends on other params, so we can't use it to set the
# upper bound on lpsize. We can only sample independently, and
# then fail below with non-positive number of features.
size = rfilter_size(lab('lpsize'), 1, 5)
stride = hp_choice(lab('stride'), [1, 2, 3])
res = scope.ceildiv(scope.max(filtering_res - size + 1, 0), stride)
if 0:
# XXX: This is a smarter way to pick the n_filters, but it triggers
# a bug in hyperopt.vectorize_helper. The build_idxs_vals function
# there needs to be smarter -- to recognize when wanted_idxs is a
# necessarily subset of the all_idxs, and then not to append
# wanted_idxs to the union defining all_idxs... because that creates a
# cycle. The trouble is specifically that lpool_res is used in the
# switch statement below both in the condition and the response.
nfilters = switch(res > 0,
max_n_features // (2 * (res ** 2)),
scope.Raise(ValueError, 'Non-positive number of features'))
else:
# this is less good because it risks dividing by zero,
# and forces the bandit to catch weirder errors from new_fbncc_layer
# caused by negative nfilters
nfilters = max_n_features // (2 * (res ** 2))
filtering = new_fbncc_layer(
prefix='l%iel' % layer_num,
Xcm=Xcm,
n_patches=n_patches,
n_filters=nfilters,
size=fsize,
)
pooling = partial(slm_lpool_alpha,
ker_size=size,
stride=stride,
alpha=hp_normal(lab('alpha'), 0.0, 1.0),
order=hp_choice(lab('order_choice'), [
1.0, 2.0, logu_range(lab('order_real'), .1, 10.)]))
return new_exit(pipeline + [filtering, pooling], lab('%s'))
def exit_lpool(pipeline, layer_num, Xcm, n_patches, max_n_features):
def lab(msg):
return 'l%i_out_lp_%s' % (layer_num, msg)
fsize = rfilter_size(lab('fsize'), 3, 8)
filtering_res = pyll_getattr(Xcm, 'shape')[2] - fsize + 1
# -- N.B. Xrows depends on other params, so we can't use it to set the
# upper bound on lpsize. We can only sample independently, and
# then fail below with non-positive number of features.
psize = rfilter_size(lab('psize'), 1, 5)
stride = hp_choice(lab('stride'), [1, 2, 3])
pooling_res = scope.ceildiv(filtering_res - psize + 1, stride)
nsize = rfilter_size(lab('nsize'), 1, 5)
norm_res = pooling_res - nsize + 1
# -- raises exception at rec_eval if norm_res is 0
nfilters = max_n_features // (scope.max(norm_res, 0) ** 2)
filtering = new_fbncc_layer(
prefix='l%ielp' % layer_num,
Xcm=Xcm,
n_patches=n_patches,
n_filters=nfilters,
size=fsize,
)
pooling = partial(slm_lpool,
ker_size=psize,
stride=stride,
order=hp_choice(lab('order_choice'), [
1.0, 2.0, logu_range(lab('order_real'), .1, 10.)]))
normalization = partial(slm_lnorm,
ker_size=nsize,
remove_mean=hp_TF(lab('norm_rmean')),
threshold=hp_lognormal(lab('norm_thresh'),
np.log(1.0), np.log(3)),
)
seq = hp_choice(lab('use_norm'), [
[filtering, pooling],
[filtering, pooling, normalization]])
return new_exit(pipeline + seq, lab('%s'))