交叉熵损失函数（Cross_entropy loss）的梯度下降法中w和b的梯度问题

# 计算梯度值(?)
def gradient(X, Y_label, w, b):
    # This function computes the gradient of cross entropy loss with respect to weight w and bias b.
    y_pred = forward(X, w, b)
    pred_error = Y_label - y_pred
    w_grad = -np.sum(pred_error * X.T, 1)
    b_grad = -np.sum(pred_error)
    return w_grad, b_grad

推导如下：

标签：loss,gradient,梯度,Cross,entropy,error,grad,pred
来源： https://blog.csdn.net/weixin_42869502/article/details/115534546