from fast_l1 import regressor # loader yields (inp, targ, index), all on GPU train_loader = ... val_loader = ... max_lam = regressor.calc_max_lambda(train_loader) weight = ch.zeros(n_features, n_targets).cuda() bias = ch.zeros(n_targets).cuda() eps = 1e-6 # Factor btw max and min lambda k = 100 # Number of lambdas to try kwargs = { # Learning rate 'lr': 0.01, # Starting lambdas 'start_lams': max_lam, # How much to decay lambda by upon convergence 'lam_decay': np.exp(np.log(eps)/k), # Number of lambdas to try 'num_lambdas': k, # How often to evaluate on test set 'early_stop_freq': 5, # Threshold for optimizer convergence 'early_stop_eps': 5e-10, # Logging directory 'logdir': LOG_DIR } regressor.train_saga(weight, bias, train_loader, val_loader, **kwargs)