def _get_datasets(nn_model, is_reverse_model):
train = load_conditioned_train_set(nn_model.token_to_index,
nn_model.condition_to_index)
context_free_val = load_context_free_val(nn_model.token_to_index)
context_sensitive_val = load_context_sensitive_val(
nn_model.token_to_index, nn_model.condition_to_index)
if is_reverse_model:
service_tokens = ServiceTokensIDs(nn_model.token_to_index)
train = reverse_nn_input(train, service_tokens)
context_free_val = reverse_nn_input(context_free_val, service_tokens)
context_sensitive_val = reverse_nn_input(context_sensitive_val,
service_tokens)
# Train subset of same size as a context-free val for metrics calculation
train_subset = generate_subset(train, VAL_SUBSET_SIZE)
# Context-sensitive val subset of same size as a context-free val for metrics calculation
context_sensitive_val_subset = generate_subset(context_sensitive_val,
VAL_SUBSET_SIZE)
datasets_collection = DatasetsCollection(
train=train,
train_subset=train_subset,
context_free_val=context_free_val,
context_sensitive_val=context_sensitive_val,
context_sensitive_val_subset=context_sensitive_val_subset)
return datasets_collection
def _save_test_results(test_dataset, predictions_filename, nn_model,
prediction_modes, **kwargs):
test_dataset_ids = transform_contexts_to_token_ids(
list(lines_to_context(test_dataset)), nn_model.token_to_index,
INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE)
calculate_and_log_val_metrics(
nn_model,
load_context_sensitive_val(nn_model.token_to_index,
nn_model.condition_to_index),
load_context_free_val(nn_model.token_to_index))
log_predictions(predictions_filename, test_dataset_ids, nn_model,
prediction_modes, **kwargs)
def _save_test_results(test_dataset, predictions_filename, nn_model, prediction_mode, **kwargs):
test_dataset_ids = transform_contexts_to_token_ids(
list(lines_to_context(test_dataset)), nn_model.token_to_index, INPUT_SEQUENCE_LENGTH, INPUT_CONTEXT_SIZE)
calculate_and_log_val_metrics(nn_model,
load_context_sensitive_val(nn_model.token_to_index, nn_model.condition_to_index),
load_context_free_val(nn_model.token_to_index))
log_predictions(
predictions_filename,
test_dataset_ids,
nn_model,
mode=prediction_mode,
candidates_num=LOG_CANDIDATES_NUM,
**kwargs)
def train_model(nn_model, is_reverse_model=False):
"""
Main function fo training. Refactoring anticipated.
"""
validation_prediction_mode = PREDICTION_MODES.sampling if is_reverse_model else PREDICTION_MODE_FOR_TESTS
train = load_conditioned_train_set(nn_model.token_to_index,
nn_model.condition_to_index)
context_free_val = load_context_free_val(nn_model.token_to_index)
context_sensitive_val = load_context_sensitive_val(
nn_model.token_to_index, nn_model.condition_to_index)
if is_reverse_model:
service_tokens = ServiceTokensIDs(nn_model.token_to_index)
train = reverse_nn_input(train, service_tokens)
context_free_val = reverse_nn_input(context_free_val, service_tokens)
context_sensitive_val = reverse_nn_input(context_sensitive_val,
service_tokens)
# Train subset of same size as a context-free val for metrics calculation
train_subset = generate_subset(train, VAL_SUBSET_SIZE)
# Context-sensitive val subset of same size as a context-free val for metrics calculation
context_sensitive_val_subset = generate_subset(context_sensitive_val,
VAL_SUBSET_SIZE)
_logger.info('Finished preprocessing! Start training')
batch_id = 0
avg_loss = 0
total_training_time = 0
best_val_perplexities = (float('inf'), float('inf'))
batches_num = (train.x.shape[0] - 1) / BATCH_SIZE + 1
start_time = time.time()
cur_val_metrics = None
try:
for epoches_counter in xrange(1, EPOCHES_NUM + 1):
_logger.info(
'Starting epoch #%d; time = %0.2f s(training of it = %0.2f s)'
% (epoches_counter, time.time() - start_time,
total_training_time))
for train_batch in get_training_batch(
[train.x, train.y, train.condition_ids],
BATCH_SIZE,
random_permute=SHUFFLE_TRAINING_BATCHES):
x_train_batch, y_train_batch, condition_ids_train_batch = train_batch
batch_id += 1
prev_time = time.time()
loss = nn_model.train(x_train_batch, y_train_batch,
condition_ids_train_batch)
cur_time = time.time()
total_training_time += cur_time - prev_time
total_time = cur_time - start_time
avg_loss = LOG_LOSS_DECAY * avg_loss + (
1 - LOG_LOSS_DECAY) * loss if batch_id > 1 else loss
progress = 100 * float(batch_id) / batches_num
avr_time_per_sample = total_time / batch_id
expected_time_per_epoch = avr_time_per_sample * batches_num
# use print here for better readability
_logger.info('batch %s / %s (%d%%) \t'
'loss: %.2f \t '
'time: epoch %.1f h | '
'total %0.1f h | '
'train %0.1f h (%.1f%%)' %
(batch_id, batches_num, progress, avg_loss,
expected_time_per_epoch / 3600,
total_time / 3600, total_training_time / 3600,
100 * total_training_time / total_time))
if batch_id % SCREEN_LOG_FREQUENCY_PER_BATCHES == 0:
_log_sample_answers(
context_free_val.x[:SCREEN_LOG_NUM_TEST_LINES],
nn_model, validation_prediction_mode, is_reverse_model)
if batch_id % LOG_FREQUENCY_PER_BATCHES == 0:
_calc_and_save_train_metrics(nn_model, train_subset,
avg_loss)
val_metrics = _calc_and_save_val_metrics(
nn_model,
context_sensitive_val_subset,
context_free_val,
prediction_mode=validation_prediction_mode)
_save_val_results(
nn_model,
context_free_val.x,
context_sensitive_val_subset.x,
val_metrics,
train_info=(start_time, batch_id, batches_num),
prediction_mode=validation_prediction_mode)
cur_val_metrics = val_metrics
best_val_perplexities = \
_update_saved_nn_model(nn_model,
(val_metrics['context_free_perplexity'],
val_metrics['context_sensitive_perplexity']),
best_val_perplexities,
is_reverse_model=is_reverse_model)
except (KeyboardInterrupt, SystemExit):
_logger.info('Training cycle is stopped manually')
_save_model(nn_model, get_model_full_path(is_reverse_model) + '_final')
_save_val_results(nn_model,
context_free_val.x,
context_sensitive_val_subset.x,
cur_val_metrics,
train_info=(start_time, batch_id, batches_num),
suffix='_final',
prediction_mode=validation_prediction_mode)
def train_model(nn_model, is_reverse_model=False):
"""
Main function fo training. Refactoring anticipated.
"""
validation_prediction_mode = PREDICTION_MODES.sampling if is_reverse_model else PREDICTION_MODE_FOR_TESTS
train = load_conditioned_train_set(nn_model.token_to_index, nn_model.condition_to_index)
context_free_val = load_context_free_val(nn_model.token_to_index)
context_sensitive_val = load_context_sensitive_val(nn_model.token_to_index, nn_model.condition_to_index)
if is_reverse_model:
service_tokens = ServiceTokensIDs(nn_model.token_to_index)
train = reverse_nn_input(train, service_tokens)
context_free_val = reverse_nn_input(context_free_val, service_tokens)
context_sensitive_val = reverse_nn_input(context_sensitive_val, service_tokens)
# Train subset of same size as a context-free val for metrics calculation
train_subset = generate_subset(train, VAL_SUBSET_SIZE)
# Context-sensitive val subset of same size as a context-free val for metrics calculation
context_sensitive_val_subset = generate_subset(context_sensitive_val, VAL_SUBSET_SIZE)
_logger.info('Finished preprocessing! Start training')
batch_id = 0
avg_loss = 0
total_training_time = 0
best_val_perplexities = (float('inf'), float('inf'))
batches_num = (train.x.shape[0] - 1) / BATCH_SIZE + 1
start_time = time.time()
cur_val_metrics = None
try:
for epoches_counter in xrange(1, EPOCHES_NUM + 1):
_logger.info('Starting epoch #%d; time = %0.2f s(training of it = %0.2f s)' %
(epoches_counter, time.time() - start_time, total_training_time))
for train_batch in get_training_batch(
[train.x, train.y, train.condition_ids], BATCH_SIZE, random_permute=SHUFFLE_TRAINING_BATCHES):
x_train_batch, y_train_batch, condition_ids_train_batch = train_batch
batch_id += 1
prev_time = time.time()
loss = nn_model.train(x_train_batch, y_train_batch, condition_ids_train_batch)
cur_time = time.time()
total_training_time += cur_time - prev_time
total_time = cur_time - start_time
avg_loss = LOG_LOSS_DECAY * avg_loss + (1 - LOG_LOSS_DECAY) * loss if batch_id > 1 else loss
progress = 100 * float(batch_id) / batches_num
avr_time_per_sample = total_time / batch_id
expected_time_per_epoch = avr_time_per_sample * batches_num
# use print here for better readability
_logger.info('batch %s / %s (%d%%) \t'
'loss: %.2f \t '
'time: epoch %.1f h | '
'total %0.1f h | '
'train %0.1f h (%.1f%%)' %
(batch_id, batches_num, progress, avg_loss, expected_time_per_epoch / 3600,
total_time / 3600, total_training_time / 3600, 100 * total_training_time / total_time))
if batch_id % SCREEN_LOG_FREQUENCY_PER_BATCHES == 0:
_log_sample_answers(context_free_val.x[:SCREEN_LOG_NUM_TEST_LINES], nn_model,
validation_prediction_mode, is_reverse_model)
if batch_id % LOG_FREQUENCY_PER_BATCHES == 0:
_calc_and_save_train_metrics(nn_model, train_subset, avg_loss)
val_metrics = _calc_and_save_val_metrics(
nn_model,
context_sensitive_val_subset,
context_free_val,
prediction_mode=validation_prediction_mode)
_save_val_results(
nn_model,
context_free_val.x,
context_sensitive_val_subset.x,
val_metrics,
train_info=(start_time, batch_id, batches_num),
prediction_mode=validation_prediction_mode)
cur_val_metrics = val_metrics
best_val_perplexities = \
_update_saved_nn_model(nn_model,
(val_metrics['context_free_perplexity'],
val_metrics['context_sensitive_perplexity']),
best_val_perplexities,
is_reverse_model=is_reverse_model)
except (KeyboardInterrupt, SystemExit):
_logger.info('Training cycle is stopped manually')
_save_model(nn_model, get_model_full_path(is_reverse_model) + '_final')
_save_val_results(
nn_model,
context_free_val.x,
context_sensitive_val_subset.x,
cur_val_metrics,
train_info=(start_time, batch_id, batches_num),
suffix='_final',
prediction_mode=validation_prediction_mode)