def train():
model_config = ModelConfig()
training_config = TrainConfig()
# Get model
model_fn = get_model_creation_fn(FLAGS.model_type)
reader_fn = create_reader('VAQ-Var', phase='train')
env = MixReward()
env.diversity_reward.mode = 'winner_take_all'
env.set_cider_state(False)
env.set_language_thresh(0.2)
# Create training directory.
train_dir = FLAGS.train_dir % (FLAGS.version, FLAGS.model_type)
if not tf.gfile.IsDirectory(train_dir):
tf.logging.info("Creating training directory: %s", train_dir)
tf.gfile.MakeDirs(train_dir)
g = tf.Graph()
with g.as_default():
# Build the model.
model = model_fn(model_config, 'train')
model.build()
# Set up the learning rate.u
learning_rate = tf.constant(training_config.initial_learning_rate *
0.1)
def _learning_rate_decay_fn(learn_rate, global_step):
return tf.train.exponential_decay(
learn_rate,
global_step,
decay_steps=training_config.decay_step,
decay_rate=training_config.decay_factor,
staircase=False)
learning_rate_decay_fn = _learning_rate_decay_fn
train_op = tf.contrib.layers.optimize_loss(
loss=model.loss,
global_step=model.global_step,
learning_rate=learning_rate,
optimizer=training_config.optimizer,
clip_gradients=training_config.clip_gradients,
learning_rate_decay_fn=learning_rate_decay_fn)
# Set up the Saver for saving and restoring model checkpoints.
saver = tf.train.Saver(
max_to_keep=training_config.max_checkpoints_to_keep)
# Setup summaries
summary_op = tf.summary.merge_all()
# Setup language model
lm = LanguageModel()
lm.build()
env.set_language_model(lm)
# create reader
reader = reader_fn(
batch_size=16,
subset='kprestval', # 'kptrain'
version=FLAGS.version)
# Run training.
training_util.train(train_op,
train_dir,
log_every_n_steps=FLAGS.log_every_n_steps,
graph=g,
global_step=model.global_step,
number_of_steps=FLAGS.number_of_steps,
init_fn=model.init_fn,
saver=saver,
reader=reader,
model=model,
summary_op=summary_op,
env=env)
def train():
model_config = ModelConfig()
training_config = TrainConfig()
# Get model
model_fn = get_model_creation_fn(FLAGS.model_type)
reader_fn = create_reader('VAQ-VIS', phase='test')
env = MixReward(attention_vqa=True)
env.diversity_reward.mode = 'winner_take_all'
# env.set_language_thresh(0.1)
env.set_language_thresh(0.2)
env.set_cider_state(use_cider=False)
env.set_replay_buffer(
insert_thresh=0.1,
sv_dir='vqa_replay_buffer/tmp') # if 0.5, already fooled others
# Create training directory.
train_dir = FLAGS.train_dir % (FLAGS.version, FLAGS.model_type)
if not tf.gfile.IsDirectory(train_dir):
tf.logging.info("Creating training directory: %s", train_dir)
tf.gfile.MakeDirs(train_dir)
ckpt_suffix = train_dir.split('/')[-1]
g = tf.Graph()
with g.as_default():
# Build the model.
model = model_fn(model_config, 'train')
# model.set_init_ckpt('model/v1_var_att_noimage_cache_restval_VAQ-VarRL/model.ckpt-230000')
# model.set_init_ckpt('model/v1_var_att_lowthresh_cache_restval_VAQ-VarRL/model.ckpt-1072000')
model.build()
# Set up the learning rate.u
learning_rate = tf.constant(training_config.initial_learning_rate *
0.0)
def _learning_rate_decay_fn(learn_rate, global_step):
return tf.train.exponential_decay(
learn_rate,
global_step,
decay_steps=training_config.decay_step,
decay_rate=training_config.decay_factor,
staircase=False)
learning_rate_decay_fn = _learning_rate_decay_fn
train_op = tf.contrib.layers.optimize_loss(
loss=model.loss,
global_step=model.global_step,
learning_rate=learning_rate,
optimizer=training_config.optimizer,
clip_gradients=training_config.clip_gradients,
learning_rate_decay_fn=learning_rate_decay_fn)
# Set up the Saver for saving and restoring model checkpoints.
saver = tf.train.Saver(
max_to_keep=training_config.max_checkpoints_to_keep)
# Setup summaries
summary_op = tf.summary.merge_all()
# Setup language model
lm = LanguageModel()
lm.build()
lm.set_cache_dir(ckpt_suffix)
env.set_language_model(lm)
# create reader
reader = reader_fn(
batch_size=1,
subset='kpval', # 'kptrain'
version=FLAGS.version)
# Run training.
training_util.train(train_op,
train_dir,
log_every_n_steps=FLAGS.log_every_n_steps,
graph=g,
global_step=model.global_step,
number_of_steps=FLAGS.number_of_steps,
init_fn=model.init_fn,
saver=saver,
reader=reader,
model=model,
summary_op=summary_op,
env=env)