def _get_fully_connected(cfg): """Get a fully connected problem from the given config.""" return (problem_spec.Spec( pg.FullyConnected, (cfg["n_features"], cfg["n_classes"]), { "hidden_sizes": tuple(cfg["hidden_sizes"]), "activation": utils.get_activation(cfg["activation"]), }), losg_datasets.random_mlp(cfg["n_features"], cfg["n_samples"]), cfg["bs"])
def testRandomMlp(self): n_samples = 4 n_features = 3 n_layers = 2 width = 10 ds = datasets.random_mlp( n_features, n_samples, n_layers=n_layers, width=width, random_seed=200) self.assertEqual(n_samples, ds.size) self.assertLen(ds.labels, n_samples) self.assertLen(ds.data[0], n_features) self.assertTrue(all([x in [0, 1] for x in ds.labels])) # binary
def _get_outward_snake(cfg): """Get an outward snake problem from the given config.""" return (problem_spec.Spec(pg.OutwardSnake, (cfg["dim"], ), {}), losg_datasets.random_mlp(cfg["dim"], cfg["n_samples"]), cfg["bs"])
def _get_dependency_chain(cfg): """Get a dependency chain problem from the given config.""" return (problem_spec.Spec(pg.DependencyChain, (cfg["dim"], ), {}), losg_datasets.random_mlp(cfg["dim"], cfg["n_samples"]), cfg["bs"])