p_test = 0 p_rm_ob_enc_test = p_test p_rm_ob_rcl_test = p_test similarity_max_test = .9 similarity_min_test = 0 fix_cond = None n_examples_test = 256 subj_id = 0 p = P( exp_name=exp_name, sup_epoch=supervised_epoch, n_param=n_param, n_branch=n_branch, pad_len=pad_len_load, def_prob=def_prob, n_def_tps=n_def_tps, enc_size=enc_size, attach_cond=attach_cond, penalty=penalty_train, p_rm_ob_enc=p_rm_ob_enc_load, p_rm_ob_rcl=p_rm_ob_rcl_load, ) # create logging dirs log_path, log_subpath = build_log_path(subj_id, p, log_root=log_root, mkdir=False, verbose=True) # init env env_data = load_env_metadata(log_subpath)
for subj_id in subj_ids: print(f'\nsubj_id = {subj_id}: ', end='') for fix_cond in all_conds: print(f'{fix_cond} ', end='') np.random.seed(subj_id) p = P( exp_name=exp_name, sup_epoch=supervised_epoch, n_param=n_param, n_branch=n_branch, pad_len=pad_len_load, enc_size=enc_size, n_event_remember=n_event_remember, penalty=penalty_train, penalty_random=penalty_random, penalty_onehot=penalty_onehot, penalty_discrete=penalty_discrete, normalize_return=normalize_return, p_rm_ob_enc=p_rm_ob_enc_load, p_rm_ob_rcl=p_rm_ob_rcl_load, n_hidden=n_hidden, n_hidden_dec=n_hidden_dec, lr=learning_rate, eta=eta, ) # init env task = SequenceLearning( n_param=p.env.n_param, n_branch=p.env.n_branch, pad_len=pad_len_test, p_rm_ob_enc=p_rm_ob_enc_test,
np.random.seed(seed_val) torch.manual_seed(seed_val) p = P(exp_name=exp_name, subj_id=subj_id, sup_epoch=supervised_epoch, n_param=n_param, n_branch=n_branch, pad_len=pad_len, def_prob=def_prob, n_def_tps=n_def_tps, enc_size=enc_size, dict_len=dict_len, n_event_remember=n_event_remember, penalty=penalty, penalty_random=penalty_random, penalty_discrete=penalty_discrete, penalty_onehot=penalty_onehot, normalize_return=normalize_return, attach_cond=attach_cond, p_rm_ob_enc=p_rm_ob_enc, p_rm_ob_rcl=p_rm_ob_rcl, n_hidden=n_hidden, n_hidden_dec=n_hidden_dec, lr=learning_rate, eta=eta, cmpt=cmpt, repeat_query=repeat_query) # init env task = SequenceLearning( n_param=p.env.n_param,
n_branch = 4 n_param = 16 enc_size = 16 n_event_remember = 2 similarity_max_test = .9 similarity_min_test = 0 n_examples_test = 256 p = P( exp_name=exp_name, sup_epoch=supervised_epoch, n_param=n_param, n_branch=n_branch, pad_len=pad_len_load, enc_size=enc_size, n_event_remember=n_event_remember, def_prob=def_prob, n_def_tps=n_def_tps, penalty=penalty_train, penalty_random=penalty_random, p_rm_ob_enc=p_rm_ob_enc_load, p_rm_ob_rcl=p_rm_ob_rcl_load, cmpt=comp_val, ) '''load data''' lca_param = {ptest: None for ptest in penaltys_test} auc = {ptest: None for ptest in penaltys_test} acc = {ptest: None for ptest in penaltys_test} mis = {ptest: None for ptest in penaltys_test} dk = {ptest: None for ptest in penaltys_test} ma_lca = defaultdict() ma_cosine = defaultdict()
penalty_random = 1 supervised_epoch = 600 # loading params pad_len_load = -1 p_rm_ob_enc_load = .3 p_rm_ob_rcl_load = 0 # testing params pad_len_test = 0 n_examples_test = 256 slience_recall_time = None test_params = [fix_penalty, pad_len_test, slience_recall_time] # store params p = P( exp_name=exp_name, sup_epoch=supervised_epoch, n_param=n_param, n_branch=n_branch, pad_len=pad_len_load, enc_size=enc_size, penalty=penalty_train, penalty_random=penalty_random, cmpt=cmpt, p_rm_ob_enc=p_rm_ob_enc_load, p_rm_ob_rcl=p_rm_ob_rcl_load, ) # init a dummy task task = SequenceLearning(n_param=p.env.n_param, n_branch=p.env.n_branch) dacc = np.zeros((n_subjs, n_param, T)) Yob_all, Yhat_all = [], [] o_keys_p1_all, o_keys_p2_all = [], [] for i_s in range(n_subjs): # create logging dirs np.random.seed(i_s) log_path, log_subpath = build_log_path(i_s, p, log_root, mkdir=False) test_data_fname = get_test_data_fname(n_examples_test, fix_cond=fix_cond) test_data_dir, _ = get_test_data_dir(log_subpath, epoch_load, test_params) fpath = os.path.join(test_data_dir, test_data_fname)