models.append( { "model":GNCLClassifier, "n_estimators":m, "mode":"upper", "l_reg":l_reg, "combination_type":"average", "base_estimator": partial(simpleresnet, size=s, model_type=t), "optimizer":optimizer, "scheduler":scheduler, "loader":loader, "eval_every":5, "store_every":0, "loss_function":nn.CrossEntropyLoss(reduction="none"), "use_amp":True, "device":"cuda", "train_data": torchvision.datasets.FashionMNIST(".", train=True, transform = train_transformation()), "test_data": torchvision.datasets.FashionMNIST(".", train=False, transform = test_transformation()), "verbose":True } ) try: base = models[0]["base_estimator"]().cuda() rnd_input = torch.rand((1, 1, 28, 28)).cuda() print(summary(base, rnd_input)) except: pass run_experiments(basecfg, models)
"sigma":s, "scale":1, "epsilon":e }) ) runs.append( ( { "method": "SieveStreaming++", "K":K, "sigma":s, "scale":1, "epsilon":e }) ) for T in Ts: runs.append( ( { "method": "ThreeSieves", "K":K, "sigma":s, "scale":1, "epsilon":e, "T":T }) ) # random.shuffle(runs) run_experiments(basecfg, runs)