def run_sampling(self, model): """Test sampling from the model.""" config = self.run_training_one_step("fivo", "pianoroll", 88, "tiny_pianoroll.pkl", "test-sampling", "multinomial", model) config.prefix_length = 3 config.sample_length = 6 config.split = "train" config.sample_out_dir = None runners.run_sample(config) unused_samples = np.load(os.path.join(config.logdir, "samples.npz"))
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)
def test_sampling_with_custom_fn(self): config = self.run_training_one_step( "fivo", "pianoroll", 3, "tiny_pianoroll.pkl", "test-sample-custom-fn", "multinomial", "vrnn", batch_size=5, create_dataset_and_model_fn=self.dummmy_dataset_and_model_fn) config.prefix_length = 2 config.sample_length = 3 config.split = "train" config.sample_out_dir = None runners.run_sample( config, create_dataset_and_model_fn=self.dummmy_dataset_and_model_fn) unused_samples = np.load(os.path.join(config.logdir, "samples.npz"))