def train(self): # pylint: disable=too-many-locals
"""Train the model."""
mode = utils.TRAIN
train_model = self.build(mode)
# Supervisor
with tf.name_scope("train"):
global_step = tf.train.get_or_create_global_step()
train_op = self.get_train_op(train_model.loss_op, global_step)
checkpoint_dir = get_checkpoint_dir(self.config)
# scaffold
scaffold = self.get_scaffold(mode, global_step,
train_model.iterator.initializer)
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
save_checkpoint_steps=self.save_checkpoint_steps,
config=self.session_conf) as sess:
# Training loop. For each batch...
data_size = self.config['data']['train_data_size']
num_epochs = self.config["data"]["task"]['epochs']
num_batch = int(math.ceil(data_size * num_epochs / self.batch_size))
num_batch_per_epoch = int(data_size / self.batch_size)
logging.info(
"num_batch: {}, num_batch_per_epoch: {}, num_epochs: {}".format(
num_batch, num_batch_per_epoch, num_epochs))
for i in range(num_batch):
_, _, out_loss = sess.run([train_op, global_step, train_model.loss_op])
if i % self.print_every == 0 or i == num_batch - 1:
logging.info("Training for epoch {}: [ {:.2%} ] loss is {:g}".format(
int(i / num_batch_per_epoch),
(i % num_batch_per_epoch) / num_batch_per_epoch, out_loss))
def train_and_eval(self): # pylint: disable=too-many-locals
"""Train and evaluate the model."""
# train related
g_train = tf.Graph()
with g_train.as_default():
logging.info("Compiling train model ...")
train_model = self.build(utils.TRAIN)
# eval related
g_eval = tf.Graph()
with g_eval.as_default():
logging.info("Compiling eval model ...")
eval_model = self.build(utils.EVAL)
eval_model.sess = tf.Session(config=self.session_conf,
graph=g_eval)
eval_model.saver = tf.train.Saver()
# start train
with g_train.as_default():
# Supervisor
with tf.name_scope("train"):
global_step = tf.train.get_or_create_global_step()
train_op = self.get_train_op(train_model.loss_op, global_step)
checkpoint_dir = get_checkpoint_dir(self.config)
# scaffold
scaffold = self.get_scaffold(utils.TRAIN, global_step,
train_model.iterator.initializer)
with tf.train.MonitoredTrainingSession(
checkpoint_dir=checkpoint_dir,
scaffold=scaffold,
save_checkpoint_steps=self.save_checkpoint_steps,
config=self.session_conf) as sess:
# Training loop. For each batch...
train_data_size = self.config['data']['train_data_size']
num_batch = math.ceil(train_data_size * self.num_epochs /
self.batch_size)
num_batch_per_epoch = math.ceil(train_data_size /
self.batch_size)
logging.info("Total data size: {}, batch num: {}, "
"batch num per epoch: {}".format(
train_data_size, num_batch,
num_batch_per_epoch))
for i in range(0, num_batch):
if i % self.save_checkpoint_steps == 0 and i != 0:
self.eval_or_infer_core(eval_model, utils.EVAL)
_, _, out_loss = sess.run(
[train_op, global_step, train_model.loss_op])
if i % self.print_every == 0 or i == num_batch - 1 or (
i + 1
) % num_batch_per_epoch == 0 or i % num_batch_per_epoch == 0:
logging.info(
"Training for epoch {}: [ {:.2%} ] loss is {:g}"
.format(int(i / num_batch_per_epoch),
(i % num_batch_per_epoch) /
num_batch_per_epoch, out_loss))
eval_model.sess.close()
def __init__(self, config):
super().__init__(config)
self.model_compiled = False
self.model_path = config['solver']['saver']['model_path']
self.checkpoint_dir = get_checkpoint_dir(self.config)
self.session_conf = get_session_conf(self.config)
self.session = tf.Session(config=self.session_conf)
tf.keras.backend.set_session(self.session)
self.metrics = self.get_metrics()
