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)