def run_training(train_dir):
"""Run training"""
resume = os.path.exists(train_dir)
with tf.Graph().as_default():
model = FeedModel()
saver = tf.train.Saver()
session = tf.Session()
summary_writer = tf.summary.FileWriter(train_dir,
graph_def=session.graph_def,
flush_secs=10)
if resume:
print("Resuming: ", train_dir)
saver.restore(session, tf.train.latest_checkpoint(train_dir))
else:
print("Starting new training: ", train_dir)
session.run(model.init)
run_inference = make_run_inference(session, model)
get_q_values = make_get_q_values(session, model)
experience_collector = ExperienceCollector()
batcher = ExperienceBatcher(experience_collector, run_inference,
get_q_values, STATE_NORMALIZE_FACTOR)
test_experiences = experience_collector.collect(
play.random_strategy, 100)
for state_batch, targets, actions in batcher.get_batches_stepwise():
global_step, _ = session.run(
[model.global_step, model.train_op],
feed_dict={
model.state_batch_placeholder: state_batch,
model.targets_placeholder: targets,
model.actions_placeholder: actions,
})
if global_step % 1e3 == 0 and global_step != 0:
saver.save(session,
train_dir + "/checkpoint",
global_step=global_step)
loss = write_summaries(session, batcher, model,
test_experiences, summary_writer)
print("Step:", global_step, "Loss:", loss)
def run_training(train_dir):
"""Run training"""
resume = os.path.exists(train_dir)
with tf.Graph().as_default():
model = FeedModel()
saver = tf.train.Saver()
session = tf.Session()
summary_writer = tf.train.SummaryWriter(train_dir,
graph_def=session.graph_def,
flush_secs=10)
if resume:
print("Resuming: ", train_dir)
saver.restore(session, tf.train.latest_checkpoint(train_dir))
else:
print("Starting new training: ", train_dir)
session.run(model.init)
run_inference = make_run_inference(session, model)
get_q_values = make_get_q_values(session, model)
experience_collector = ExperienceCollector()
batcher = ExperienceBatcher(experience_collector, run_inference,
get_q_values, STATE_NORMALIZE_FACTOR)
test_experiences = experience_collector.collect(play.random_strategy, 100)
for state_batch, targets, actions in batcher.get_batches_stepwise():
global_step, _ = session.run([model.global_step, model.train_op],
feed_dict={
model.state_batch_placeholder: state_batch,
model.targets_placeholder: targets,
model.actions_placeholder: actions,})
if global_step % 10000 == 0 and global_step != 0:
saver.save(session, train_dir + "/checkpoint", global_step=global_step)
loss = write_summaries(session, batcher, model, test_experiences,
summary_writer)
print("Step:", global_step, "Loss:", loss)
