pass
from learning_to_learn.environment import Environment
from learning_to_learn.pupils.lstm_for_meta import Lstm, LstmFastBatchGenerator as BatchGenerator
from learning_to_learn.useful_functions import create_vocabulary, compose_hp_confs, get_num_exps_and_res_files, \
get_optimizer_evaluation_results, get_best, print_hps, get_hp_names_from_conf_file
from learning_to_learn.optimizers.l2l import L2L
import os
parameter_set_file_name = sys.argv[1]
base = parameter_set_file_name.split('.')[0]
save_path = base + '/evaluation'
confs, _ = compose_hp_confs(parameter_set_file_name, save_path, chop_last_experiment=False)
confs.reverse() # start with small configs
print("confs:", confs)
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
dataset_path = os.path.join(*(['..']*ROOT_HEIGHT + ['datasets', 'text8.txt']))
with open(dataset_path, 'r') as f:
text = f.read()
valid_size = 2000
test_size = 100000
test_text = text[:test_size]
valid_text = text[test_size:valid_size+test_size]
train_text = text[valid_size+test_size:]
from learning_to_learn.pupils.lstm_for_meta import Lstm, LstmFastBatchGenerator as BatchGenerator
from learning_to_learn.useful_functions import create_vocabulary, compose_hp_confs, get_combs_and_num_exps
from learning_to_learn.optimizers.res_net_opt import ResNet4Lstm
import os
pretrain_step = sys.argv[1]
parameter_set_file_name = sys.argv[2]
# if len(sys.argv) > 3:
# initial_experiment_counter_value = int(sys.argv[3])
# else:
# initial_experiment_counter_value = 0
eval_dir = 'run1/evaluation'
confs, _ = compose_hp_confs(parameter_set_file_name,
eval_dir,
chop_last_experiment=True)
confs.reverse() # start with small configs
print("(test2/run)confs:", confs)
save_path = os.path.join('.'.join(parameter_set_file_name.split('.')[:-1]),
'evaluation')
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
with open('../../../datasets/text8.txt', 'r') as f:
text = f.read()
valid_size = 500
valid_text = text[:valid_size]
dataset_name = 'valid'
parameter_set_file_name = sys.argv[1]
if len(sys.argv) > 2:
chop_last_experiment = bool(sys.argv[2])
else:
chop_last_experiment = False
conf_name = os.path.join(*parameter_set_file_name.split('.')[:-1])
results_dir = helmo.util.path_help.move_path_postfix_within_repo(
path_to_smth_in_separator=__file__)
results_dir = os.path.split(results_dir)[0]
save_path = os.path.join(results_dir, conf_name)
results_file_name = os.path.join(save_path, dataset_name + '.txt')
confs, _ = compose_hp_confs(parameter_set_file_name,
results_file_name,
chop_last_experiment=chop_last_experiment,
model='pupil')
confs.reverse() # start with small configs
print("confs:", confs)
dataset_file_name = 'enwiki1G.txt'
text = helmo.util.dataset.get_text(dataset_file_name)
test_size, valid_size = int(6.4e6), int(6.4e5)
train_size = len(text) - test_size - valid_size
test_text, valid_text, train_text = helmo.util.dataset.split_text(
text, test_size, valid_size, train_size)
voc_file_name = 'enwiki1G_voc.txt'
vocabulary, vocabulary_size = helmo.util.dataset.get_vocab(voc_file_name, text)
from learning_to_learn.environment import Environment
from learning_to_learn.pupils.mlp_for_meta import MlpForMeta as Mlp
from learning_to_learn.image_batch_gens import CifarBatchGenerator
from learning_to_learn.useful_functions import compose_hp_confs
import os
parameter_set_file_name = sys.argv[1]
if len(sys.argv) > 2:
chop_last_experiment = bool(sys.argv[2])
else:
chop_last_experiment = False
save_path = os.path.join(parameter_set_file_name.split('.')[0], 'evaluation')
confs, _ = compose_hp_confs(
parameter_set_file_name,
os.path.join(save_path, 'valid.txt'),
chop_last_experiment=chop_last_experiment,
model='pupil'
)
confs.reverse() # start with small configs
print("confs:", confs)
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
data_dir = os.path.join(*(['..']*ROOT_HEIGHT + ['datasets', 'mnist']))
env = Environment(Mlp, CifarBatchGenerator)
add_metrics = ['bpc', 'perplexity', 'accuracy']
train_add_feed = [
{'placeholder': 'dropout', 'value': .9}
# helmo.util.path_help.move_path_postfix_within_repo(
# path_to_smth_in_separator=save_path_relative_to_expres,
# separator="experiments",
# new_prefix_within_repo="expres",
# ),
# os.path.split(save_path_relative_to_expres)[-1]
# )
# # print(results_dir)
# save_path = results_dir
dir_with_confs, results_directory_rel_to_repo_root = \
('tests', 'testres') if args.test else ('experiments', 'expres')
save_path = helmo.util.path_help.get_save_path_from_config_path(
config_path, dir_with_confs, results_directory_rel_to_repo_root)
results_file_name = os.path.join(save_path, 'test.txt')
confs, _ = compose_hp_confs(
config_path, results_file_name, chop_last_experiment=False, model='pupil')
confs.reverse() # start with small configs
print("confs:", confs)
text = helmo.util.dataset.get_text(config['dataset']['path'])
test_size = int(config['dataset']['test_size'])
valid_size = int(config['dataset']['valid_size'])
train_size = len(text) - test_size - valid_size
test_text, valid_text, train_text = helmo.util.dataset.split_text(text, test_size, valid_size, train_size)
vocabulary, vocabulary_size = helmo.util.dataset.get_vocab(config['dataset']['vocab_path'], text)
env = Environment(Net, BatchGenerator, vocabulary=vocabulary)
cpiv = get_positions_in_vocabulary(vocabulary)
