def main(_):
hparams = rl_tuner_ops.default_hparams()
if FLAGS.note_rnn_type == 'basic_rnn':
hparams = rl_tuner_ops.basic_rnn_hparams()
elif FLAGS.note_rnn_type == 'attention_rnn':
hparams = rl_tuner_ops.attention_rnn_hparams()
dqn_hparams = tf.contrib.training.HParams(random_action_probability=0.1,
store_every_nth=1,
train_every_nth=5,
minibatch_size=32,
discount_rate=0.5,
max_experience=100000,
target_network_update_rate=0.01)
output_dir = os.path.join(FLAGS.output_dir, FLAGS.algorithm)
output_ckpt = FLAGS.algorithm + '.ckpt'
backup_checkpoint_file = os.path.join(FLAGS.note_rnn_checkpoint_dir,
FLAGS.note_rnn_checkpoint_name)
rlt = rl_tuner.RLTuner(output_dir,
midi_primer=FLAGS.midi_primer,
dqn_hparams=dqn_hparams,
reward_scaler=FLAGS.reward_scaler,
save_name=output_ckpt,
output_every_nth=FLAGS.output_every_nth,
note_rnn_checkpoint_dir=FLAGS.note_rnn_checkpoint_dir,
note_rnn_checkpoint_file=backup_checkpoint_file,
note_rnn_type=FLAGS.note_rnn_type,
note_rnn_hparams=hparams,
num_notes_in_melody=FLAGS.num_notes_in_melody,
exploration_mode=FLAGS.exploration_mode,
algorithm=FLAGS.algorithm)
tf.logging.info('Saving images and melodies to: %s', rlt.output_dir)
tf.logging.info('Training...')
rlt.train(num_steps=FLAGS.training_steps,
exploration_period=FLAGS.exploration_steps)
tf.logging.info('Finished training. Saving output figures and composition.')
rlt.plot_rewards(image_name='Rewards-' + FLAGS.algorithm + '.eps')
rlt.generate_music_sequence(visualize_probs=True, title=FLAGS.algorithm,
prob_image_name=FLAGS.algorithm + '.png')
rlt.save_model_and_figs(FLAGS.algorithm)
tf.logging.info('Calculating music theory metric stats for 1000 '
'compositions.')
statistics = rlt.evaluate_music_theory_metrics(num_compositions=1000)
print("music theory evaluation statistic: {0}".format(statistics))
def main(_):
if FLAGS.note_rnn_type == 'basic_rnn':
hparams = rl_tuner_ops.basic_rnn_hparams()
else:
hparams = rl_tuner_ops.default_hparams()
dqn_hparams = tf.contrib.training.HParams(random_action_probability=0.1,
store_every_nth=1,
train_every_nth=5,
minibatch_size=32,
discount_rate=0.5,
max_experience=100000,
target_network_update_rate=0.01)
output_dir = os.path.join(FLAGS.output_dir, FLAGS.algorithm)
output_ckpt = FLAGS.algorithm + '.ckpt'
backup_checkpoint_file = os.path.join(FLAGS.note_rnn_checkpoint_dir,
FLAGS.note_rnn_checkpoint_name)
rlt = rl_tuner.RLTuner(output_dir,
midi_primer=FLAGS.midi_primer,
dqn_hparams=dqn_hparams,
reward_scaler=FLAGS.reward_scaler,
save_name=output_ckpt,
output_every_nth=FLAGS.output_every_nth,
note_rnn_checkpoint_dir=FLAGS.note_rnn_checkpoint_dir,
note_rnn_checkpoint_file=backup_checkpoint_file,
note_rnn_type=FLAGS.note_rnn_type,
note_rnn_hparams=hparams,
num_notes_in_melody=FLAGS.num_notes_in_melody,
exploration_mode=FLAGS.exploration_mode,
algorithm=FLAGS.algorithm)
tf.logging.info('Saving images and melodies to: %s', rlt.output_dir)
tf.logging.info('Training...')
rlt.train(num_steps=FLAGS.training_steps,
exploration_period=FLAGS.exploration_steps)
tf.logging.info('Finished training. Saving output figures and composition.')
rlt.plot_rewards(image_name='Rewards-' + FLAGS.algorithm + '.eps')
rlt.generate_music_sequence(visualize_probs=True, title=FLAGS.algorithm,
prob_image_name=FLAGS.algorithm + '.png')
rlt.save_model_and_figs(FLAGS.algorithm)
tf.logging.info('Calculating music theory metric stats for 1000 '
'compositions.')
rlt.evaluate_music_theory_metrics(num_compositions=1000)
def main(_):
if FLAGS.note_rnn_type == 'basic_rnn':
hparams = rl_tuner_ops.basic_rnn_hparams()
else:
hparams = rl_tuner_ops.default_hparams()
dqn_hparams = tf.contrib.training.HParams(random_action_probability=0.1,
store_every_nth=1,
train_every_nth=5,
minibatch_size=32,
discount_rate=0.5,
max_experience=100000,
target_network_update_rate=0.01)
num_compositions = 100000
if (FLAGS.running_mode == 'comparison'):
tf.logging.info("Running for comparison")
# num_compositions = 1000
defaultRlt = run_algorithm(FLAGS.algorithm, dqn_hparams, hparams, num_compositions)
if (FLAGS.running_mode == 'comparison'):
pureRlt = run_algorithm('pure_rl', dqn_hparams, hparams, num_compositions)
plot_comparison(defaultRlt, pureRlt)
