Question

我正在尝试对神经网络进行编程，并尝试使用scipy.optimize_bfgs（）最小化成本函数，并在尝试使用此函数后得到以下错误：“ TypeError：cost（）接受3个位置参数，但给出了4个”。这四个论点从何而来，我该如何纠正？成本函数定义为：

def cost(param,X,y):
    Theta1 = np.reshape(param[0:106950:1],(75,1426))
    Theta2 = np.reshape(param[106950:112650:1],(75,76))
    Theta3 = np.reshape(param[112650::1],(1,76))
    m = len(X)
    J = 0
    a1 = X
    z2 = np.dot(a1,np.transpose(Theta1))
    a2 = sigmoid(z2)
    a2 = np.concatenate((np.ones((len(a2),1)),a2),axis=1)
    z3 = np.dot(a2,Theta2.T)
    a3 = sigmoid(z3)
    a3 = np.concatenate((np.ones((len(a3),1)),a3),axis=1)
    z4 = np.dot(a3,Theta3.T)
    a4 = sigmoid(z4)
    h = a4
    ##Calculate cost
    J = np.sum(np.sum(np.multiply(-y,np.log(h)) - np.multiply((1-y),np.log(1-h))))/(2*m)
    theta1_reg[:,0] = 0
    theta2_reg[:,0] = 0
    theta3_reg[:,0] = 0
    Reg = (lamb/(2*m))*(np.sum(np.sum(np.square(theta1_reg)))+np.sum(np.sum(np.sqaure(theta2_reg)))+np.sum(np.sum(np.square(theta3_reg))))
    J = J + Reg
    return J

然后使用以下公式计算梯度：

def grad(param,X,y):
    Theta1 = np.reshape(param[0:106950:1],(75,1426))
    Theta2 = np.reshape(param[106950:112650:1],(75,76))
    Theta3 = np.reshape(param[112650::1],(1,76))
    Theta1_grad = np.zeros(Theta1.shape)
    Theta2_grad = np.zeros(Theta2.shape)
    Theta3_grad = np.zeros(Theta3.shape)
    m = len(X)
    ##Forward propogation
    a1 = X
    z2 = np.dot(a1,np.transpose(Theta1))
    a2 = sigmoid(z2)
    a2 = np.concatenate((np.ones((len(a2),1)),a2),axis=1)
    z3 = np.dot(a2,Theta2.T)
    a3 = sigmoid(z3)
    a3 = np.concatenate((np.ones((len(a3),1)),a3),axis=1)
    z4 = np.dot(a3,Theta3.T)
    a4 = sigmoid(z4)
    h = a4  
    ##Backward propogation  
    d4 = a4 - y
    d3 = np.multiply(np.dot(d4,Theta3[:,1:]),sigmoidGradient(z3))
    d2 = np.multiply(np.dot(d3,Theta2[:,1:]),sigmoidGradient(z2)) ## or sigmoid(z2) .* ( 1 - sigmoid(z2))
    D1 = np.dot(d2.T,a1)
    D2 = np.dot(d3.T,a2)
    D3 = np.dot(d4.T,a3)
    ##Unregularized gradients
    Theta1_grad = (1/m)*D1 
    Theta2_grad = (1/m)*D2
    Theta3_grad = (1/m)*D3
    ##Regularize gradients
    theta1_reg = Theta1
    theta2_reg = Theta2
    theta3_reg = Theta3
    theta1_reg[:,0] = 0
    theta2_reg[:,0] = 0
    theta3_reg[:,0] = 0
    theta1_reg = (lamb/m)*theta1_reg
    theta2_reg = (lamb/m)*theta2_reg
    theta3_reg = (lamb/m)*theta3_reg
    Theta1_grad = Theta1_grad + theta1_reg
    Theta2_grad = Theta2_grad + theta2_reg
    Theta3_grad = Theta3_grad + theta3_reg
    ##Concatenate gradients
    grad = np.concatenate((Theta1_grad,Theta2_grad,Theta3_grad),axis=None)
    return grad

定义的其他功能是

def sigmoid(z):
    sig = 1 / (1 + np.exp(z))
    return sig

def randInitializeWeights(l_in, l_out):
    epsilon = 0.12;
    W = np.random.rand(l_out, 1+l_in)*2*epsilon - epsilon;
    return W
def sigmoidGradient(z):
    g = np.multiply(sigmoid(z),(1-sigmoid(z)))
    return g

例如：

import numpy as np
import scipy.optimize
X = np.random.rand(479,1426)
y1 = np.zeros((frames,1))
y2 = np.ones((framesp,1)) 
y = np.concatenate((y1,y2),axis=0)
init_param = np.random.rand(112726,)
lamb = 0.5
scipy.optimize.fmin_bfgs(cost,fprime=grad,x0=init_param,args=(param,X,y))

然后显示错误。感谢您的帮助

Answer 1

传递给成本函数的参数是参数，后跟多余的参数。通过最小化功能选择参数，然后传递额外的参数。

调用fmin_bfgs时，仅将额外的参数作为args传递，而不将要优化的实际参数传递给

scipy.optimize.fmin_bfgs(..., args=(X,y))

Scipy.fmin_bfgs传递给函数的许多参数

1 个答案: