Sgd, Machine Learning, Stochastic Gradient.
== False: for x, y in zip(self.x_tr, self.y_tr): common = self.alpha*(y-self.sigmoid(x)) self.w = (1-self.lr_rate*self.alpha/self.n)*self.w + common*x self.b += common self.evaluate_the_model(epoch) self.early_stopping(epoch) else: break def predict_class(self, x): return np.array([1 if