def fixed_hps_from_str(string):
if len(string) > 0:
tmp = string[1:-1]
hps = [convert(x, "float") for x in tmp]
return tuple(hps)
else:
return ()
from learning_to_learn.useful_functions import remove_empty_strings_from_list, convert
conf_file = sys.argv[1]
save_path = os.path.join(conf_file.split('.')[0], '%s')
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
with open(conf_file, 'r') as f:
lines = remove_empty_strings_from_list(f.read().split('\n'))
opt = lines[0]
num_runs = int(lines[1])
hps = dict()
for line in lines[2:]:
spl = line.split()
hps[spl[0]] = float(convert(spl[1], 'float'))
env = Environment(Mlp, CifarBatchGenerator)
train_add_feed = [{'placeholder': 'dropout', 'value': .9}]
if 'momentum' in hps:
train_add_feed.append({
'placeholder': 'momentum',
'value': hps['momentum']
})
valid_add_feed = [{'placeholder': 'dropout', 'value': 1.}]
add_metrics = ['bpc', 'perplexity', 'accuracy']
VALID_SIZE = 1000
BATCH_SIZE = 32
def convert_sel_line_name(x):
if is_float(x):
return convert(x)
if x == 'None':
return None
return x
os.chdir(dname)
with open(conf_file, 'r') as f:
lines = f.read().split('\n')
model = lines[0]
steps = int(lines[1])
base = lines[2]
if base == 'None':
base = None
else:
base = float(base)
names = lines[3].split()
types = lines[4].split()
optimizer_varying = dict()
for name, type_, line in zip(names, types, lines[5:]):
optimizer_varying[name] = [convert(v, type_) for v in line.split()]
dataset_path = os.path.join(*(['..'] * ROOT_HEIGHT +
['datasets', 'text8.txt']))
with open(dataset_path, 'r') as f:
text = f.read()
train_text = text
vocabulary = create_vocabulary(text)
vocabulary_size = len(vocabulary)
print(vocabulary_size)
env = Environment(
pupil_class=Lstm,
meta_optimizer_class=ResNet4Lstm,
# pupil_names_by_ed, dataset_names_by_ed, labels_by_ed, hp_orders_by_ed, num_lines)
for eval_dir, ed_lines, ed_fixed_hps, ed_regimes, ed_pupil_names, ed_dataset_names, ed_labels, hp_order, nlines in \
zip(
eval_dirs, lines_by_ed, fixed_hp_by_ed, regimes_by_ed,
pupil_names_by_ed, dataset_names_by_ed, labels_by_ed, hp_orders_by_ed, num_lines
):
[ed_lines, ed_fixed_hps, ed_regimes, ed_pupil_names, ed_dataset_names, ed_labels] = split_strings_by_char(
[ed_lines, ed_fixed_hps, ed_regimes, ed_pupil_names, ed_dataset_names, ed_labels], ','
)
[ed_regimes, ed_pupil_names, ed_dataset_names, ed_fixed_hps] = broadcast_many_lists(
[ed_regimes, ed_pupil_names, ed_dataset_names, ed_fixed_hps],
nlines,
)
ed_lines = [convert(x, 'float') for x in ed_lines]
ed_fixed_hps = [fixed_hps_from_str(x) for x in ed_fixed_hps]
hp_plot_order = hp_order.split(',')
changing_hps.append(hp_plot_order[-1])
line_retr_inf = list()
if args.model == 'pupil':
for line_hp, fixed_hps, dataset_name, label in zip(
ed_lines, ed_fixed_hps, ed_dataset_names, ed_labels
):
line_retr_inf.append(
dict(
line_hp=line_hp,
fixed_hps=fixed_hps,
dataset_name=dataset_name,
label=label,