print() continue for token_index in range(sent_len + 1): print('-' * 100) print(lib.formatted_clock()) print(dataset_name, run, sent_len, token_index) print() if (dataset_name, run, sent_len, token_index) in already_seen: print('Found ready') print() continue full_timer = lib.Timer() filtered_sents_datasource = data.DataSource([ ' '.join(sent[:token_index]) for sent in filtered_correct_sents ]) filtered_sents_datasource.tokenize_sents().compile_sents( langmod_vocab) #sensitivity analysis [ grad_next_wrt_prefix, grad_max_wrt_prefix, grad_next_wrt_prevtoken, grad_max_wrt_prevtoken, grad_next_wrt_firsttoken,
config.langmodtrans_corpus_size_factor_exponents if corpus != config.langmodtrans_capgen_dataset else config.langmodtrans_corpus_size_factor_minor_exponents): print('=' * 100) print(lib.formatted_clock()) print(corpus, frozen_prefix, corpus_size_factor_exponent, run) print() if (corpus, frozen_prefix, corpus_size_factor_exponent, run) in already_seen: print('Found ready') print() continue full_timer = lib.Timer() print('-' * 100) print('Phase 1: langmod') print() dir_name = '{}_{}_{}'.format(frozen_prefix, corpus_size_factor_exponent, run) lib.create_dir(config.results_dir + '/langmodtrans/' + corpus + '/' + dir_name) corpus_size = round(10**corpus_size_factor_exponent * capgen_size) datasources = data.load_datasources(corpus) datasources['train'] = datasources['train'].without_images(
def epoch_started(self, model, epoch_num): print(epoch_num, end='\t') self.epoch_timer = lib.Timer()
best_cost = cost already_seen.add(tuple(next_hyperpar)) print(i, *next_hyperpar, -cost, lib.format_duration(duration), '******' if cost == best_cost else '', sep='\t') for _ in range( i, config.hyperpar_num_random_evals + config.hyperpar_num_evals): i += 1 num_hyperpars = 1 while True: t = lib.Timer() next_hyperpar = standardize_hyperpar( opt.ask(num_hyperpars)[-1], architecture ) #This allows us to get different hyperparameters every time the previous hyperparameters resulted in <<SEEN>>, <<NAN>>, or <<EMPTY>> num_hyperpars += 1 print(i, *next_hyperpar, sep='\t', end='\t') if tuple(next_hyperpar) in already_seen: duration = t.get_duration() print('<<SEEN>>', lib.format_duration(duration), sep='\t') with open(config.hyperpar_dir + '/whereimage/' + architecture + '/search_errors.txt', 'a', encoding='utf-8') as f: print(i,