def eval_ghmm_notraining(self, bound, proposal_type, expected_bound_avg):
config = self.default_config()
config.proposal_type = proposal_type
config.bound = bound
config.logdir = os.path.join(
tf.test.get_temp_dir(), "test-ghmm-%s-%s" % (proposal_type, bound))
ghmm_runners.run_eval(config)
data = np.load(os.path.join(config.logdir, "out.npz")).item()
self.assertAlmostEqual(expected_bound_avg, data["mean"], places=3)
def train_ghmm_for_one_step_and_eval(self, bound, proposal_type, expected_bound_avg):
config = self.default_config()
config.proposal_type = proposal_type
config.bound = bound
config.max_steps = 1
config.logdir = os.path.join(
tf.test.get_temp_dir(), "test-ghmm-training-%s-%s" % (proposal_type, bound))
ghmm_runners.run_train(config)
ghmm_runners.run_eval(config)
data = np.load(os.path.join(config.logdir, "out.npz")).item()
self.assertAlmostEqual(expected_bound_avg, data["mean"], places=2)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.model in ["vrnn", "srnn"]:
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)
elif FLAGS.mode == "sample":
runners.run_sample(FLAGS)
elif FLAGS.model == "ghmm":
if FLAGS.mode == "train":
ghmm_runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
ghmm_runners.run_eval(FLAGS)
def main(unused_argv):
tf.logging.set_verbosity(tf.logging.INFO)
if FLAGS.model in ["vrnn", "srnn"]:
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)
elif FLAGS.mode == "sample":
runners.run_sample(FLAGS)
elif FLAGS.model == "ghmm":
if FLAGS.mode == "train":
ghmm_runners.run_train(FLAGS)
elif FLAGS.mode == "eval":
ghmm_runners.run_eval(FLAGS)