鉴于特征矩阵的概率$X$连同权重$w$计算：

def probability(X, w):
    z = np.dot(X,w)
    a = 1./(1+np.exp(-z))
    return np.array(a)

def loss(X, y, w):
    normalization_fator = X.shape[0] #store loss values
    features_probability = probability(X, w) #return one probability for each row in a matrix
    corss_entropy = y*np.log(features_probability) + (1-y)*np.log(1-features_probability)
    cost = -1/float(normalization_fator) * np.sum(corss_entropy)
    cost = np.squeeze(cost)  
    return cost

问题：我先做的，没有除以$normalization\_fator$，但正确的方法是除以归一化因子，尽管在我对逻辑回归损失的公式中由下式给出：

$$L\left( \theta \right) =-\sum_{i=1}^n{y^{\left( i \right)}\log \left( \alpha _i \right) +\left( 1-y^{\left( i \right)} \right) \log \left( 1-\alpha _i \right)}$$

如您所见，没有归一化因子：

$$L\left( \theta \right) =-\frac {1}{(norm\_factor)}\sum_{i=1}^n{y^{\left( i \right)}\log \left( \alpha _i \right) +\left( 1-y^{\left( i \right)} \right) \log \left( 1-\alpha _i \right)}$$

编辑：$\alpha_i$表示每一行的概率$X$由 sigmoid 函数给出。