learning_rate=dict(
varying=dict(
init=conf['learning_rate/init']
),
fixed=dict(
decay=.5,
period=200
),
hp_type='built-in',
type='exponential_decay'
)
)
tf.set_random_seed(1)
_, biggest_idx, _ = get_num_exps_and_res_files(save_path)
if biggest_idx is None:
initial_experiment_counter_value = 0
else:
initial_experiment_counter_value = biggest_idx + 1
env.grid_search_for_meta(
evaluation,
kwargs_for_pupil_building,
kwargs_for_optimizer_building,
build_pupil_hyperparameters=build_pupil_hyperparameters,
build_optimizer_hyperparameters=build_optimizer_hyperparameters,
other_hyperparameters=other_hyperparameters,
initial_experiment_counter_value=initial_experiment_counter_value,
**launch_kwargs
)
import sys
import os
import shutil
from pathlib import Path # if you haven't already done so
file = Path(__file__).resolve()
parent, root = file.parent, file.parents[2]
sys.path.append(str(root))
try:
sys.path.remove(str(parent))
except ValueError: # Already removed
pass
from learning_to_learn.useful_functions import get_num_exps_and_res_files
dest_dir = sys.argv[1]
source_dirs = sys.argv[2:]
_, biggest_idx, _ = get_num_exps_and_res_files(dest_dir)
for source_dir in source_dirs:
_, _, pairs = get_num_exps_and_res_files(source_dir)
for pair in pairs:
file_name = os.path.split(pair[0])[-1]
new_prefix = str(int(file_name[:-4]) + 1 + biggest_idx)
file_dest = os.path.join(dest_dir, new_prefix + '.txt')
dir_dest = os.path.join(dest_dir, new_prefix)
shutil.copyfile(pair[0], file_dest)
shutil.copytree(pair[1], dir_dest)
_, biggest_idx, _ = get_num_exps_and_res_files(dest_dir)
