def main(unused_argv):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
if FLAGS.mode == 'train':
runners.run_train(FLAGS)
elif FLAGS.mode == 'eval':
runners.run_eval(FLAGS)
def main(unused_argv):
args = simple_run_settings[FLAGS.pid]
FLAGS.bound, FLAGS.dataset_name, FLAGS.algorithm, FLAGS.latent_type, FLAGS.learning_rate, FLAGS.num_samples,\
FLAGS.random_seed, FLAGS.max_iter = args
if FLAGS.dataset_name == 'jsb':
FLAGS.model_train = True
FLAGS.dataset_type = 'pianoroll'
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
FLAGS.latent_size = 32
FLAGS.batch_size = 4
extension = "pkl"
elif FLAGS.dataset_name == 'nottingham':
FLAGS.model_train = True
FLAGS.dataset_type = 'pianoroll'
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
FLAGS.latent_size = 64
FLAGS.batch_size = 4
extension = "pkl"
elif FLAGS.dataset_name == 'musedata':
FLAGS.model_train = True
FLAGS.dataset_type = 'pianoroll'
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
FLAGS.latent_size = 64
FLAGS.batch_size = 4
extension = "pkl"
elif FLAGS.dataset_name == 'pianomidide':
FLAGS.model_train = True
FLAGS.dataset_type = 'pianoroll'
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
FLAGS.latent_size = 64
FLAGS.batch_size = 4
extension = "pkl"
elif FLAGS.dataset_name == 'gaussian':
FLAGS.model_train = False
FLAGS.dataset_type = 'synthetic'
FLAGS.max_iter = 50000
extension = "npy"
elif FLAGS.dataset_name == 'bernoulli':
FLAGS.model_train = False
FLAGS.dataset_type = 'synthetic'
FLAGS.max_iter = 50000
extension = "npy"
else:
raise ValueError("Undefined dataset_name %s" % FLAGS.dataset_name)
FLAGS.dataset_path = "%s/%s/%s.%s" % (DATASET_PATH, FLAGS.dataset_type,
FLAGS.dataset_name, extension)
FLAGS.logdir = "%s/%s/%s" % (SAVE_PATH, FLAGS.dataset_name,
FLAGS.algorithm)
print('The result will be saved in %s' % FLAGS.logdir)
set_global_seeds(FLAGS.random_seed)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9,
allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
sess.__enter__()
if FLAGS.mode == "train":
runners.run(FLAGS)
elif FLAGS.mode == "eval":
runners.run_eval(FLAGS)
def main(unused_argv):
del unused_argv
logging.set_verbosity(logging.INFO)
logging.info("Arguments: {}".format(FLAGS.flag_values_dict()))
if FLAGS.mode == 'train':
runners.run_train(FLAGS)
elif FLAGS.mode == 'eval':
runners.run_eval(FLAGS)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.data_dimension is None:
if FLAGS.dataset_type == "pianoroll":
FLAGS.data_dimension = PIANOROLL_DEFAULT_DATA_DIMENSION
elif FLAGS.dataset_type == "speech":
FLAGS.data_dimension = SPEECH_DEFAULT_DATA_DIMENSION
if FLAGS.mode == "train":
runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
runners.run_eval(FLAGS)