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,
