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):
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)
LOGPROB_MEAN_MIN = -10.0
LOGPROB_STD_MAX = 5
## RUN TRAIN
#======================================
if config.mode == "train":
print(config.trainingset_path)
fh = logging.FileHandler(
os.path.join(config.logdir, config.log_filename + ".log"))
tf.logging.set_verbosity(tf.logging.INFO)
# get TF logger
logger = logging.getLogger('tensorflow')
logger.addHandler(fh)
runners.run_train(config)
else:
with open(config.testset_path, "rb") as f:
Vs_test = pickle.load(f)
dataset_size = len(Vs_test)
## RUN TASK-SPECIFIC SUBMODEL
#======================================
step = None
if config.mode in ["save_logprob", "traj_reconstruction"]:
tf.Graph().as_default()
global_step = tf.train.get_or_create_global_step()
inputs, targets, mmsis, time_starts, time_ends, lengths, model = runners.create_dataset_and_model(
config, shuffle=False, repeat=False)