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 get_validation_dataset_name_to_data(validation_sets_names, token_to_index,
condition_to_index, is_reverse_model):
_logger.info('Loading validations sets...')
factory = {
CONTEXT_FREE_VAL_CORPUS_NAME:
lambda: load_context_free_val(token_to_index),
CONTEXT_SENSITIVE_VAL_CORPUS_NAME:
lambda: load_context_sensitive_val(token_to_index, condition_to_index)
}
dataset_name_to_data = {
val_set_name: factory[val_set_name]()
for val_set_name in validation_sets_names
}
_logger.info('Done loading validations sets')
if is_reverse_model:
_logger.info('Reversing validations sets...')
service_tokens = ServiceTokensIDs(token_to_index)
dataset_name_to_data = {
val_set_name: reverse_nn_input(val_set, service_tokens)
for val_set_name, val_set in dataset_name_to_data.items()
}
_logger.info('Done reversing validations sets')
return dataset_name_to_data
def _compute_likelihood_of_input_given_output(self, context, candidates,
condition_id):
# Repeat to get same context for each candidate
repeated_context = np.repeat(context, candidates.shape[0], axis=0)
reversed_dataset = reverse_nn_input(
Dataset(x=repeated_context, y=candidates, condition_ids=None),
self._service_tokens_ids)
return get_sequence_score(self._reverse_model, reversed_dataset.x,
reversed_dataset.y, condition_id)
def get_training_dataset(train_corpus_name,
token_to_index,
condition_to_index,
is_reverse_model,
train_subset_size=None):
_logger.info('Loading training dataset...')
train_dataset = load_conditioned_dataset(train_corpus_name, token_to_index,
condition_to_index,
train_subset_size)
if is_reverse_model:
_logger.info('Reversing training dataset...')
service_tokens = ServiceTokensIDs(token_to_index)
train_dataset = reverse_nn_input(train_dataset, service_tokens)
return train_dataset
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)
def _compute_likelihood_of_input_given_output(self, context, candidates, condition_id):
# Repeat to get same context for each candidate
repeated_context = np.repeat(context, candidates.shape[0], axis=0)
reversed_dataset = reverse_nn_input(
Dataset(x=repeated_context, y=candidates, condition_ids=None), self._service_tokens_ids)
return get_sequence_score(self._reverse_model, reversed_dataset.x, reversed_dataset.y, condition_id)