def run_training_one_step(
self,
bound,
dataset_type,
data_dimension,
dataset_filename,
dir_prefix,
resampling_type,
model,
batch_size=2,
num_samples=3,
create_dataset_and_model_fn=(runners.create_dataset_and_model)):
config = self.default_config()
config.model = model
config.resampling_type = resampling_type
config.relaxed_resampling_temperature = 0.5
config.bound = bound
config.split = "train"
config.dataset_type = dataset_type
config.dataset_path = os.path.join(
os.path.dirname(os.path.realpath(__file__)), "test_data",
dataset_filename)
config.max_steps = 1
config.batch_size = batch_size
config.num_samples = num_samples
config.latent_size = 4
config.data_dimension = data_dimension
config.logdir = os.path.join(
tf.test.get_temp_dir(),
"%s-%s-%s-%s" % (dir_prefix, bound, dataset_type, model))
runners.run_train(
config, create_dataset_and_model_fn=create_dataset_and_model_fn)
return config
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)