class BotTrainer(object):
def __init__(self, corpus_dir):
self.graph = tf.Graph()
with self.graph.as_default():
tokenized_data = TokenizedData(corpus_dir=corpus_dir)
self.hparams = tokenized_data.hparams
self.train_batch = tokenized_data.get_training_batch()
self.model = ModelCreator(training=True,
tokenized_data=tokenized_data,
batch_input=self.train_batch)
def train(self, result_dir, target=""):
"""Train a seq2seq model."""
# Summary writer
summary_name = "train_log"
summary_writer = tf.summary.FileWriter(
os.path.join(result_dir, summary_name), self.graph)
log_device_placement = self.hparams.log_device_placement
num_epochs = self.hparams.num_epochs
config_proto = tf.ConfigProto(
log_device_placement=log_device_placement,
allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
with tf.Session(target=target, config=config_proto,
graph=self.graph) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.tables_initializer())
global_step = self.model.global_step.eval(session=sess)
# Initialize all of the iterators
sess.run(self.train_batch.initializer)
# Initialize the statistic variables
ckpt_loss, ckpt_predict_count = 0.0, 0.0
train_perp, last_record_perp = 2000.0, 2.0
train_epoch = 0
print("# Training loop started @ {}".format(
time.strftime("%Y-%m-%d %H:%M:%S")))
epoch_start_time = time.time()
while train_epoch < num_epochs:
# Each run of this while loop is a training step, multiple time/steps will trigger
# the train_epoch to be increased.
learning_rate = self._get_learning_rate(train_perp)
try:
step_result = self.model.train_step(
sess, learning_rate=learning_rate)
(_, step_loss, step_predict_count, step_summary,
global_step, step_word_count, batch_size) = step_result
# Write step summary.
summary_writer.add_summary(step_summary, global_step)
# update statistics
ckpt_loss += (step_loss * batch_size)
ckpt_predict_count += step_predict_count
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
train_epoch += 1
mean_loss = ckpt_loss / ckpt_predict_count
train_perp = math.exp(
float(mean_loss)) if mean_loss < 300 else math.inf
epoch_dur = time.time() - epoch_start_time
print(
"# Finished epoch {:2d} @ step {:5d} @ {}. In the epoch, learning rate = {:.6f}, "
"mean loss = {:.4f}, perplexity = {:8.4f}, and {:.2f} seconds elapsed."
.format(train_epoch, global_step,
time.strftime("%Y-%m-%d %H:%M:%S"),
learning_rate, mean_loss, train_perp,
round(epoch_dur, 2)))
epoch_start_time = time.time(
) # The start time of the next epoch
summary = tf.Summary(value=[
tf.Summary.Value(tag="train_perp",
simple_value=train_perp)
])
summary_writer.add_summary(summary, global_step)
# Save checkpoint
if train_perp < 1.6 and train_perp < last_record_perp:
self.model.saver.save(sess,
os.path.join(
result_dir, "basic"),
global_step=global_step)
last_record_perp = train_perp
ckpt_loss, ckpt_predict_count = 0.0, 0.0
sess.run(self.model.batch_input.initializer)
continue
# Done training
self.model.saver.save(sess,
os.path.join(result_dir, "basic"),
global_step=global_step)
summary_writer.close()
@staticmethod
def _get_learning_rate(perplexity):
if perplexity <= 1.48:
return 9.6e-5
elif perplexity <= 1.64:
return 1e-4
elif perplexity <= 2.0:
return 1.2e-4
elif perplexity <= 2.4:
return 1.6e-4
elif perplexity <= 3.2:
return 2e-4
elif perplexity <= 4.8:
return 2.4e-4
elif perplexity <= 8.0:
return 3.2e-4
elif perplexity <= 16.0:
return 4e-4
elif perplexity <= 32.0:
return 6e-4
else:
return 8e-4
class BotTrainer(object):
def __init__(self, corpus_dir):
"""
Constructor of the BotTrainer.
Args:
corpus_dir: The folder to save all the training related data.
"""
self.graph = tf.Graph()
with self.graph.as_default():
tokenized_data = TokenizedData(corpus_dir=corpus_dir)
self.hparams = tokenized_data.hparams
self.train_batch = tokenized_data.get_training_batch()
self.model = ModelCreator(training=True, tokenized_data=tokenized_data,
batch_input=self.train_batch)
def train(self, result_dir, target="", last_end_file=None, last_end_epoch=0, last_end_lr=8e-4):
"""Train a seq2seq model."""
# Summary writer
summary_name = "train_log"
summary_writer = tf.summary.FileWriter(os.path.join(result_dir, summary_name), self.graph)
log_device_placement = self.hparams.log_device_placement
num_epochs = self.hparams.num_epochs
config_proto = tf.ConfigProto(log_device_placement=log_device_placement,
allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
with tf.Session(target=target, config=config_proto, graph=self.graph) as sess:
# This initialization is useful even when the model is restored from the last time
# because not all variables used in the model training may be saved.
sess.run(tf.global_variables_initializer())
if last_end_file: # Continue training from last time
#print("Restoring model weights from last time ...")
self.model.saver.restore(sess, os.path.join(result_dir, last_end_file))
sess.run(tf.tables_initializer())
global_step = self.model.global_step.eval(session=sess)
# Initialize all of the iterators
sess.run(self.train_batch.initializer)
# Initialize the statistic variables
ckpt_loss, ckpt_predict_count = 0.0, 0.0
train_perp, last_record_perp = 2000.0, 200.0
train_epoch = last_end_epoch
learning_rate = pre_lr = last_end_lr
#print("# Training loop started @ {}".format(time.strftime("%Y-%m-%d %H:%M:%S")))
epoch_start_time = time.time()
while train_epoch < num_epochs:
# Each run of this while loop is a training step, multiple time/steps will trigger
# the train_epoch to be increased.
try:
step_result = self.model.train_step(sess, learning_rate=learning_rate)
(_, step_loss, step_predict_count, step_summary, global_step,
step_word_count, batch_size) = step_result
# Write step summary.
summary_writer.add_summary(step_summary, global_step)
# update statistics
ckpt_loss += (step_loss * batch_size)
ckpt_predict_count += step_predict_count
except tf.errors.OutOfRangeError:
# Finished going through the training dataset. Go to next epoch.
train_epoch += 1
mean_loss = ckpt_loss / ckpt_predict_count
train_perp = math.exp(float(mean_loss)) if mean_loss < 300 else math.inf
epoch_dur = time.time() - epoch_start_time
#print("# Finished epoch {:2d} @ step {:5d} @ {}. In the epoch, learning rate = {:.6f}, "
#"mean loss = {:.4f}, perplexity = {:8.4f}, and {:.2f} seconds elapsed."
.format(train_epoch, global_step, time.strftime("%Y-%m-%d %H:%M:%S"),
learning_rate, mean_loss, train_perp, round(epoch_dur, 2)))
epoch_start_time = time.time() # The start time of the next epoch
summary = tf.Summary(value=[tf.Summary.Value(tag="train_perp", simple_value=train_perp)])
summary_writer.add_summary(summary, global_step)
# Save checkpoint
if train_perp < last_record_perp:
self.model.saver.save(sess, os.path.join(result_dir, "basic"), global_step=train_epoch)
last_record_perp = train_perp
ckpt_loss, ckpt_predict_count = 0.0, 0.0
learning_rate = self._get_learning_rate(train_perp, pre_lr, train_epoch)
pre_lr = learning_rate
sess.run(self.model.batch_input.initializer)
continue
# Done training
self.model.saver.save(sess, os.path.join(result_dir, "basic"), global_step=train_epoch)
summary_writer.close()