Sgd, Machine Learning, Stochastic Gradient.
{self.cv_log_loss_lst[-1]}.") print(f"Total training time: {np.round(time.time()-self.now, 3)} seconds.") def early_stopping(self, epoch): if (self.tr_log_loss_lst[-2] - self.tr_log_loss_lst[-1]) <= self.tol: if self.wait == self.n_iter_no_change: temp = np.round(time.time()-self.now, 3)