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)