def test_bohb(cs, n_id):
bohb = BOHB(cs, train, maximal_iter, num_iter=iter_num, p=0.2, n_workers=n_work)
bohb.run()
bohb.plot_statistics(method="BOHB-cnn-%d" % n_id)
result = bohb.get_incumbent(5)
print(result)
return result
def test_bohb(cs, id):
bohb = BOHB(cs, train, maximal_iter, num_iter=iter_num, p=0.2, n_workers=n_work)
bohb.method_name = "BOHB-fcnet-%d" % id
bohb.runtime_limit = runtime_limit
bohb.run()
bohb.plot_statistics(method="BOHB-fcnet-%d" % id)
print(bohb.get_incumbent(5))
return bohb.get_incumbent(5)
def test_bohb(cs, id):
method_id = "BOHB-resnet-%d" % id
bohb = BOHB(cs, train, maximal_iter, num_iter=iter_num, p=0.2, n_workers=n_work)
bohb.restart_needed = True
bohb.runtime_limit = args.b
bohb.run()
bohb.plot_statistics(method=method_id)
result = bohb.get_incumbent(5)
save_result(method_id, result)
return result
def test_bohb(cs, id):
bohb = BOHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work)
bohb.run()
bohb.plot_statistics(method="BOHB-rnn-%d" % id)
print(bohb.get_incumbent(5))
return bohb.get_incumbent(5)
def test_methods_mean_variance(cs):
iterations = args.iter_c
for i in range(1, 1 + iterations):
weight = [1.0, 0.5, 0.25, 0.125, 0.125]
bo = SMAC(cs, train, maximal_iter, num_iter=iter_num, n_workers=n_work)
bo.run()
bo.plot_statistics(method="BO-lenet-%d" % i)
hyperband = Hyperband(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work)
hyperband.set_restart()
hyperband.run()
hyperband.plot_statistics(method="HB-lenet-%d" % i)
bohb = BOHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work)
bohb.set_restart()
bohb.run()
bohb.plot_statistics(method="BOHB-lenet-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-disable_w-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w-%d" % i)
def test_update_rule(cs):
iterations = args.iter_c
for i in range(1, 1 + iterations):
high_start_w = [1.0, 0.9, 0.72, 0.504, 0.3024, 0.1512]
low_start_w = [1.0, 0.6, 0.5, 0.4, 0.4]
weight = [1.0, 0.5, 0.25, 0.125, 0.125]
zero_start = [0.0, 0.0, 0.0, 0.0, 0.0]
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=False)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w_random_f-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
update_enable=True,
update_delta=1,
init_weight=weight,
random_mode=True)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-update_w_random_t-%d" % i)
xfhb = XFHB(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work,
info_type='Weighted',
init_weight=weight)
xfhb.set_restart()
xfhb.run()
xfhb.plot_statistics(method="XFHB-lenet-not_update_w-%d" % i)
hyperband = Hyperband(cs,
train,
maximal_iter,
num_iter=iter_num,
n_workers=n_work)
hyperband.set_restart()
hyperband.run()
hyperband.plot_statistics(method="HB-lenet-new-%d" % i)
bohb = BOHB(cs,
train,
maximal_iter,
num_iter=iter_num,
p=0.2,
n_workers=n_work)
bohb.set_restart()
bohb.run()
bohb.plot_statistics(method="BOHB-lenet-new-%d" % i)