뉴런을 이용한 Logistic 모델class LogisticNeuren: def __init__(self): self.w = None self.b = None # self.l_r = 0.001 def forpass(self, x): z = np.sum(x*self.w) + self.b return z def activation(self, z): z = np.clip(z, -100, None) a = 1/(1 + np.exp(-z)) return a def backpass(self, x, err): w_grad = x*err b_grad = 1*err ..