Python，构建人工神经网络

Question

我试图运行下面提到的代码，从https://github.com/stephencwelch/Neural-Networks-Demystified开始。在

import numpy as np
%pylab inline 



X = np.array(([3,5], [5,1], [10,2]), dtype=float)
y = np.array(([75], [82], [93]), dtype=float)    

X = X/np.amax(X, axis=0)
y = y/100 #Max test score is 100


class Neural_Network(object):
    def __init__(self):        
        #Define Hyperparameters
        self.inputLayerSize = 2
        self.outputLayerSize = 1
        self.hiddenLayerSize = 3

        self.W1 = np.random.randn(self.inputLayerSize, self.hiddenLayerSize)
        self.W2 = np.random.randn(self.hiddenLayerSize,self.outputLayerSize)

    def forward(self, X):
        self.z2 = np.dot(X, self.W1)
        self.a2 = self.sigmoid(self.z2)
        self.z3 = np.dot(self.a2, self.W2)
        yHat = self.sigmoid(self.z3) 
        return yHat

    def sigmoid(self, z):
        return 1/(1+np.exp(-z))

     def sigmoidPrime(self,z):
        return np.exp(-z)/((1+np.exp(-z))**2)

    def costFunction(self, X, y):
        self.yHat = self.forward(X)
        J = 0.5*sum((y-self.yHat)**2)
        return J

    def costFunctionPrime(self, X, y):
        self.yHat = self.forward(X)

        delta3 = np.multiply(-(y-self.yHat), self.sigmoidPrime(self.z3))
        dJdW2 = np.dot(self.a2.T, delta3)
        delta2 = np.dot(delta3, self.W2.T)*self.sigmoidPrime(self.z2)
        dJdW1 = np.dot(X.T, delta2)  
        return dJdW1, dJdW2

    def getParams(self):
        #Get W1 and W2 unrolled into vector:
        params = np.concatenate((self.W1.ravel(), self.W2.ravel()))
        return params

    def setParams(self, params):
        W1_start = 0
        W1_end = self.hiddenLayerSize * self.inputLayerSize
        self.W1 = np.reshape(params[W1_start:W1_end], (self.inputLayerSize , self.hiddenLayerSize))
        W2_end = W1_end + self.hiddenLayerSize*self.outputLayerSize
        self.W2 = np.reshape(params[W1_end:W2_end], (self.hiddenLayerSize, self.outputLayerSize))

    def computeGradients(self, X, y):
        dJdW1, dJdW2 = self.costFunctionPrime(X, y)
        return np.concatenate((dJdW1.ravel(), dJdW2.ravel()))

def computeNumericalGradient(N, X, y):
        paramsInitial = N.getParams()
        numgrad = np.zeros(paramsInitial.shape)
        perturb = np.zeros(paramsInitial.shape)
        e = 1e-4

        for p in range(len(paramsInitial)):
            perturb[p] = e
            N.setParams(paramsInitial + perturb)
            loss2 = N.costFunction(X, y)

            N.setParams(paramsInitial - perturb)
            loss1 = N.costFunction(X, y)

            numgrad[p] = (loss2 - loss1) / (2*e)

            perturb[p] = 0

        N.setParams(paramsInitial)

        return numgrad

from scipy import optimize


class trainer(object):
    def __init__(self, N):
        self.N = N

    def callbackF(self, params):
        self.N.setParams(params)
        self.J.append(self.N.costFunction(self.X, self.y))   

    def costFunctionWrapper(self, params, X, y):
        self.N.setParams(params)
        cost = self.N.costFunction(X, y)
        grad = self.N.computeGradients(X,y)
        return cost, grad

    def train(self, X, y):
        self.X = X
        self.y = y

        self.J = []

        params0 = self.N.getParams()

        options = {'maxiter': 200, 'disp' : True}
        _res = optimize.minimize(self.costFunctionWrapper, params0, jac=True, method='BFGS', \
                                 args=(X, y), options=options, callback=self.callbackF)

        self.N.setParams(_res.x)
        self.optimizationResults = _res

NN = Neural_Network()
T = trainer(NN)
T.train(X,y)

但我有个错误：

^{pr2}$

因此，我想知道为什么属性“train”没有被定义？ 我正在使用Spyder（Python2.7）