print('temperature: ', t)
print()
for i in range(20):
print(
resample(embeddings,
h_size,
min_tokens=min_tokens,
trials=100,
t=t))
print()
# apply lstm language model to confusion_set
print('apply lstm language model to confusion set...')
print()
cv_0 = list(helper.acs(sents, preserve_tokens, cv_token=confusion_set[0]))
cv_1 = list(helper.acs(sents, preserve_tokens, cv_token=confusion_set[1]))
test_0 = list(helper.acs(sents, preserve_tokens, test_token=confusion_set[0]))
test_1 = list(helper.acs(sents, preserve_tokens, test_token=confusion_set[1]))
cv_predictions = lm_predictions(cv_0, cv_1, confusion_set, embeddings)
test_predictions = lm_predictions(test_0, test_1, confusion_set, embeddings)
# find best temperature and threshold combination and print results
lm_results(cv_predictions, test_predictions, cv_0, cv_1, test_0, test_1,
embeddings, confusion_set)
print()
print('done')
return 'NO SAMPLE IN %d STEPS' % trials
# training
print('start training...')
print()
log_steps = 100
save_steps = 100
weights_changed = False
for e in range(max_epochs):
if e < start_epoch:
continue
error = 0
for i, (inp, tar) in enumerate(helper.char_sequence_generator(helper.acs(sents, preserve_tokens), embeddings)):
if e == start_epoch and i < start_iteration:
continue
cost = train(inp, tar)
error += cost
weights_changed = True
if (i+1) % log_steps == 0:
error /= log_steps
errors.append(error)
print('epoch: %d\titerations: %d\terror: %f' %(e, (i+1), error))
print(resample(embeddings, h_size))
print()
error = 0
# training
print('start training...')
print()
log_steps = 100
save_steps = 100
weights_changed = False
for e in range(max_epochs):
if e < start_epoch:
continue
error = 0
for i, (inp, tar) in enumerate(
helper.char_sequence_generator(helper.acs(sents, preserve_tokens),
embeddings)):
if e == start_epoch and i < start_iteration:
continue
cost = train(inp, tar)
error += cost
weights_changed = True
if (i + 1) % log_steps == 0:
error /= log_steps
errors.append(error)
print('epoch: %d\titerations: %d\terror: %f' % (e, (i + 1), error))
print(resample(embeddings, h_size))
print()
min_tokens = 5
print('genrate samples')
print('minimum number of tokens per sample: ', min_tokens)
print()
for t in [0.8, 1.0, 1.2]:
print('temperature: ', t)
print()
for i in range(20):
print(resample(embeddings, h_size, min_tokens=min_tokens, trials=100, t=t))
print()
# apply lstm language model to confusion_set
print('apply lstm language model to confusion set...')
print()
cv_0 = list(helper.acs(sents, preserve_tokens, cv_token=confusion_set[0]))
cv_1 = list(helper.acs(sents, preserve_tokens, cv_token=confusion_set[1]))
test_0 = list(helper.acs(sents, preserve_tokens, test_token=confusion_set[0]))
test_1 = list(helper.acs(sents, preserve_tokens, test_token=confusion_set[1]))
cv_predictions = lm_predictions(cv_0, cv_1, confusion_set, embeddings)
test_predictions = lm_predictions(test_0, test_1, confusion_set, embeddings)
# find best temperature and threshold combination and print results
lm_results(cv_predictions, test_predictions, cv_0, cv_1, test_0, test_1, embeddings, confusion_set)
print()
print('done')
# minimum occurence of tokens in training data
# tokens with less occurences will be substituted to 'U' for unknown
# 'U' can also serve as substitute for unseen tokens at test time
min_occurrence = 20
### END SETTINGS ###
# init
if not os.path.exists(work_dir):
os.makedirs(work_dir)
with open(corpus_file) as f:
sents = [[helper.normalization(twp.split('|')[0].lower()) for twp in line.split()] for line in f]
train_sents = list(helper.acs(sents, preserve_tokens))
token_embeddings = helper.TokenEmbeddings(train_sents, min_occurrence)
if timestamp and start_epoch and start_iteration:
errors = helper.load_errors('%s-%d-%d.errors' % (timestamp, start_epoch, start_iteration), work_dir)
load_weights = '%s-%d-%d.weights' % (timestamp, start_epoch, start_iteration)
print('init previous states...')
print('timestamp: ', timestamp)
print('start_epoch: ', start_epoch)
print('start_iteration: ', start_iteration)
else:
errors = []
start_epoch = 0
start_iteration = 0
timestamp = datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
load_weights = None
