Python 神经网络中偏导数的错误值

我正在为虹膜数据集实现一个简单的神经网络分类器。该神经网络有3个输入节点、1个带2个节点的隐藏层和3个输出节点。我已经实现了所有东西，但是偏导数的值计算不正确。我已经筋疲力尽地在寻找解决办法，但还是找不到。 这是我计算偏导数的代码

def derivative_cost_function(self,X,Y,thetas):
    '''
        Computes the derivates of Cost function w.r.t input parameters (thetas)  
        for given input and labels.

        Input:
        ------
            X: can be either a single d X n-dimensional vector or d X n dimensional matrix of inputs
            theata: must  dk X 1-dimensional vector for representing vectors of k classes
            Y: Must be k X n-dimensional label vector
        Returns:
        ------
            partial_thetas: a dk X 1-dimensional vector of partial derivatives of cost function w.r.t parameters..
    '''

    #forward pass
    a2, a3=self.forward_pass(X,thetas)

    #now back-propogate 

    # unroll thetas
    l1theta, l2theta = self.unroll_thetas(thetas)


    nexamples=float(X.shape[1])

    # compute delta3, l2theta
    a3 = np.array(a3)
    a2 = np.array(a2)
    Y = np.array(Y)

    a3 = a3.T
    delta3 = (a3 * (1 - a3)) * (((a3 - Y)/((a3)*(1-a3)))) 
    l2Derivatives = np.dot(delta3, a2)
    #print "Layer 2 derivatives shape = ", l2Derivatives.shape
    #print "Layer 2 derivatives = ", l2Derivatives



    # compute delta2, l1 theta
    a2 = a2.T
    dotProduct = np.dot(l2theta.T,delta3)
    delta2 = dotProduct * (a2) * (1- a2)


    l1Derivatives = np.dot(delta2[1:], X.T)
    #print "Layer 1 derivatives shape = ", l1Derivatives.shape
    #print "Layer 1 derivatives = ", l1Derivatives


    #remember to exclude last element of delta2, representing the deltas of bias terms...
    # i.e. delta2=delta2[:-1]



    # roll thetas into a big vector
    thetas=(self.roll_thetas(l1Derivatives,l2Derivatives)).reshape(thetas.shape) # return the same shape as you received

    return thetas

---

为什么不看看我在中的实现呢

衍生品实际上在这里：

def dCostFunction(self, theta, in_dim, hidden_dim, num_labels, X, y):
        #compute gradient
        t1, t2 = self.uncat(theta, in_dim, hidden_dim)


        a1, z2, a2, z3, a3 = self._forward(X, t1, t2) # p x s matrix

        # t1 = t1[1:, :] # remove bias term
        # t2 = t2[1:, :]
        sigma3 = -(y - a3) * self.dactivation(z3) # do not apply dsigmode here? should I
        sigma2 = np.dot(t2, sigma3)
        term = np.ones((1,num_labels))
        sigma2 = sigma2 * np.concatenate((term, self.dactivation(z2)),axis=0)

        theta2_grad = np.dot(sigma3, a2.T)
        theta1_grad = np.dot(sigma2[1:,:], a1.T)

        theta1_grad = theta1_grad / num_labels
        theta2_grad = theta2_grad / num_labels

        return self.cat(theta1_grad.T, theta2_grad.T)

---

希望能有所帮助

谢谢你的回复，我会看看你的代码。我已经编辑了我的代码，并将导数除以示例数，就像你在你的代码中所做的那样。但它们的值仍然不正确：/我无法找出问题出在哪里。在计算导数时，是否删除偏差项？不，我没有这样做。我们应该删除有偏见的条款吗？对不起，我跛脚了，我是机器学习新手。你必须这么做。偏差项不会对上一层产生成本。请看我的代码，我对这一点有意见