def exit_grid(pipeline, layer_num, Xcm, n_patches, max_n_features):
def lab(msg):
return 'l%ieg_%s' % (layer_num, msg)
fsize = rfilter_size(lab('fsize'), 3, 8)
grid_res = hp_choice(lab('res'), [2, 3])
grid_features_per_filter = 2 * (grid_res ** 2)
grid_nfilters = max_n_features // grid_features_per_filter
grid_filtering = new_fbncc_layer(
prefix='l%ieg' % layer_num,
Xcm=Xcm,
n_patches=n_patches,
n_filters=grid_nfilters,
size=fsize,
)
grid_pooling = partial(slm_quantize_gridpool,
alpha=hp_normal(lab('alpha'), 0.0, 1.0),
use_mid=False,
grid_res=grid_res,
order=hp_choice(lab('order'), [
1.0, 2.0, logu_range(lab('order_real'), .1, 10.)]))
return new_exit(pipeline + [grid_filtering, grid_pooling], lab('%s'))
def test7(self):
p0 = hp_uniform('p0', 0, 1)
p1 = hp_normal('p1', 0, 1)
p2 = hp_choice('p2', [1, p0])
p3 = hp_choice('p3', [2, p1, p2, hp_uniform('a0', 2, 3)])
self.expr = {'loss': p0 + p1 + p2 + p3}
self.n_randints = 2
self.wanted = [[('p0', [0], [0.71295532052322719]),
('p1', [0], [0.28297849805199204]),
('p2.randint', [0], [0]),
('p3.arg:2', [0], [2.719468969785563]),
('p3.randint', [0], [2])],
[('p0', [1], [0.78002776191207912]),
('p1', [1], [-1.506294713918092]),
('p2.randint', [1], [1]), ('p3.arg:2', [], []),
('p3.randint', [1], [1])],
[('p0', [2], [0.57969429702261011]),
('p1', [2], [1.6796003743035337]),
('p2.randint', [2], [0]), ('p3.arg:2', [], []),
('p3.randint', [2], [1])],
[('p0', [3], [0.43857224467962441]),
('p1', [3], [-1.3058031267484451]),
('p2.randint', [3], [1]), ('p3.arg:2', [], []),
('p3.randint', [3], [1])],
[('p0', [4], [0.39804425533043142]),
('p1', [4], [-0.91948540682140967]),
('p2.randint', [4], [0]), ('p3.arg:2', [], []),
('p3.randint', [4], [0])]]
self.foo()
def many_dists():
a = hp_choice('a', [0, 1, 2])
b = hp_randint('b', 10)
c = hp_uniform('c', 4, 7)
d = hp_loguniform('d', -2, 0)
e = hp_quniform('e', 0, 10, 3)
f = hp_qloguniform('f', 0, 3, 2)
g = hp_normal('g', 4, 7)
h = hp_lognormal('h', -2, 2)
i = hp_qnormal('i', 0, 10, 2)
j = hp_qlognormal('j', 0, 2, 1)
z = a + b + c + d + e + f + g + h + i + j
return {'loss': scope.float(scope.log(1e-12 + z**2))}
def many_dists():
a=hp_choice('a', [0, 1, 2])
b=hp_randint('b', 10)
c=hp_uniform('c', 4, 7)
d=hp_loguniform('d', -2, 0)
e=hp_quniform('e', 0, 10, 3)
f=hp_qloguniform('f', 0, 3, 2)
g=hp_normal('g', 4, 7)
h=hp_lognormal('h', -2, 2)
i=hp_qnormal('i', 0, 10, 2)
j=hp_qlognormal('j', 0, 2, 1)
z = a + b + c + d + e + f + g + h + i + j
return {'loss': scope.float(scope.log(1e-12 + z ** 2))}
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 test7(self):
p0 = hp_uniform('p0', 0, 1)
p1 = hp_normal('p1', 0, 1)
p2 = hp_choice('p2', [1, p0])
p3 = hp_choice('p3', [2, p1, p2, hp_uniform('a0', 2, 3)])
self.expr = {'loss': p0 + p1 + p2 + p3}
self.n_randints = 2
self.wanted = [
[
('p0', [0], [0.71295532052322719]),
('p1', [0], [0.28297849805199204]),
('p2.randint', [0], [0]),
('p3.arg:2', [0], [2.719468969785563]),
('p3.randint', [0], [2])],
[
('p0', [1], [0.78002776191207912]),
('p1', [1], [-1.506294713918092]),
('p2.randint', [1], [1]),
('p3.arg:2', [], []),
('p3.randint', [1], [1])],
[
('p0', [2], [0.57969429702261011]),
('p1', [2], [1.6796003743035337]),
('p2.randint', [2], [0]),
('p3.arg:2', [], []),
('p3.randint', [2], [1])],
[
('p0', [3], [0.43857224467962441]),
('p1', [3], [-1.3058031267484451]),
('p2.randint', [3], [1]),
('p3.arg:2', [], []),
('p3.randint', [3], [1])],
[
('p0', [4], [0.39804425533043142]),
('p1', [4], [-0.91948540682140967]),
('p2.randint', [4], [0]),
('p3.arg:2', [], []),
('p3.randint', [4], [0])]]
self.foo()