def fit(self, sess, saver, train_examples_raw, dev_set_raw):
best_score = 0.
train_examples = self.preprocess_sequence_data(train_examples_raw)
dev_set = self.preprocess_sequence_data(dev_set_raw)
for epoch in range(self.config.n_epochs):
logger.info("Epoch %d out of %d", epoch + 1, self.config.n_epochs)
# You may use the progress bar to monitor the training progress
# Addition of progress bar will not be graded, but may help when debugging
prog = Progbar(target=1 +
int(len(train_examples) / self.config.batch_size))
# The general idea is to loop over minibatches from train_examples,
# and run train_on_batch inside the loop.
# Hint: train_examples could be a list containing the feature data and label data
# Read the doc for utils.get_minibatches to find out how to use it.
# Note that get_minibatches could either return a list, or a list of list
# [features, labels]. This makes expanding tuples into arguments (* operator) handy
### YOUR CODE HERE (2-3 lines)
for minibatch in minibatches(train_examples,
self.config.batch_size):
self.train_on_batch(sess, *minibatch)
prog.add(1)
### END YOUR CODE
logger.info("Evaluating on development data")
token_cm, entity_scores = self.evaluate(sess, dev_set, dev_set_raw)
logger.debug("Token-level confusion matrix:\n" +
token_cm.as_table())
logger.debug("Token-level scores:\n" + token_cm.summary())
logger.info("Entity level P/R/F1: %.2f/%.2f/%.2f", *entity_scores)
score = entity_scores[-1]
if score > best_score:
best_score = score
if saver:
logger.info("New best score! Saving model in %s",
self.config.model_output)
saver.save(sess, self.config.model_output)
print("")
if self.report:
self.report.log_epoch()
self.report.save()
return best_score
def get_experiment_fitnesses(experiments, optimizer, config, logger):
with model.get_db_provider() as db:
progbar = Progbar(len(experiments), interval=0.0)
logger.info("Waiting for fitnesses from %s experiments" %
len(experiments))
bad_line_dicts = [dict() for x in range(len(experiments))]
has_result = [False for i in range(len(experiments))]
fitnesses = [0.0 for i in range(len(experiments))]
behaviors = [None for i in range(len(experiments))]
term_criterion = config['optimizer']['termination_criterion']
skip_gen_thres = term_criterion['skip_gen_thres']
skip_gen_timeout = term_criterion['skip_gen_timeout']
result_timestamp = time.time()
while sum(has_result) < len(experiments):
for i, experiment in enumerate(experiments):
if float(sum(has_result)) / len(experiments) >= skip_gen_thres\
and time.time() - result_timestamp > skip_gen_timeout:
logger.warn(
"Skipping to next gen with %s of solutions evaled" %
(float(
sum(has_result)) /
len(experiments)))
has_result = [True] * len(experiments)
break
if has_result[i]:
continue
returned_experiment = db.get_experiment(experiment.key,
getinfo=True)
# try:
# experiment_output = returned_experiment.info['logtail']
# except BaseException:
# logger.warn('Cannot access "logtail" in experiment.info')
output = db._get_experiment_logtail(
returned_experiment)
if output is None:
continue
for j, line in enumerate(output):
if line.startswith(
"Traceback (most recent call last):") and \
j not in bad_line_dicts[i]:
logger.warn("Experiment %s: error"
" discovered in output" %
returned_experiment.key)
logger.warn("".join(output[j:]))
bad_line_dicts[i][j] = True
if line.startswith("Behavior") or \
line.startswith("behavior"):
try:
behavior = eval(line.rstrip().split(':')[1])
if isinstance(behavior, np.ndarray):
pass
elif isinstance(behavior, list):
behavior = np.array(behavior)
else:
raise
except BaseException:
if j not in bad_line_dicts[i]:
logger.warn(
'Experiment %s: error parsing or invalid'
' behavior' %
returned_experiment.key)
logger.warn(line)
bad_line_dicts[i][j] = True
else:
behaviors[i] = behavior
if line.startswith("Fitness") or \
line.startswith("fitness"):
try:
fitness = float(line.rstrip().split(':')[1])
# assert fitness >= 0.0
except BaseException:
if j not in bad_line_dicts[i]:
logger.warn(
'Experiment %s: error parsing or invalid'
' fitness' %
returned_experiment.key)
logger.warn(line)
bad_line_dicts[i][j] = True
else:
if fitness < 0.0:
logger.warn('Experiment %s: returned'
' fitness is less than zero,'
' setting it to zero' %
returned_experiment.key)
fitness = 0.0
fitnesses[i] = fitness
has_result[i] = True
progbar.add(1)
result_timestamp = time.time()
break
time.sleep(config['sleep_time'])
print
return fitnesses, behaviors
