多层感知器python代码（简单的多层感知器）

来源：互联网发布：bim模拟施工软件编辑：程序博客网时间：2024/04/28 17:09
写了个多层感知器，用bp梯度下降更新，拟合正弦曲线，效果凑合。
# -*- coding: utf-8 -*-import numpy as npimport matplotlib.pyplot as pltdef sigmod(z):    return 1.0 / (1.0 + np.exp(-z))class mlp(object):    def __init__(self, lr=0.1, lda=0.0, te=1e-5, epoch=100, size=None):        self.learningRate = lr        self.lambda_ = lda        self.thresholdError = te        self.maxEpoch = epoch        self.size = size        self.W = []        self.b = []        self.init()    def init(self):        for i in xrange(len(self.size)-1):            self.W.append(np.mat(np.random.uniform(-0.5, 0.5, size=(self.size[i+1], self.size[i]))))            self.b.append(np.mat(np.random.uniform(-0.5, 0.5, size=(self.size[i+1], 1))))    def forwardPropagation(self, item=None):        a = [item]        for wIndex in xrange(len(self.W)):            a.append(sigmod(self.W[wIndex]*a[-1]+self.b[wIndex]))        """        print "-----------------------------------------"        for i in a:            print i.shape,        print        for i in self.W:            print i.shape,        print        for i in self.b:            print i.shape,        print        print "-----------------------------------------"        """        return a    def backPropagation(self, label=None, a=None):        # print "backPropagation--------------------begin"        delta = [(a[-1]-label)*a[-1]*(1.0-a[-1])]        for i in xrange(len(self.W)-1):            abc = np.multiply(a[-2-i], 1-a[-2-i])            cba = np.multiply(self.W[-1-i].T*delta[-1], abc)            delta.append(cba)        """        print "++++++++++++++delta++++++++++++++++++++"        print "len(delta):", len(delta)        for ii in delta:            print ii.shape,        print "\n======================================="        """        for j in xrange(len(delta)):            ads = delta[j]*a[-2-j].T            # print self.W[-1-j].shape, ads.shape, self.b[-1-j].shape, delta[j].shape            self.W[-1-j] = self.W[-1-j]-self.learningRate*(ads+self.lambda_*self.W[-1-j])            self.b[-1-j] = self.b[-1-j]-self.learningRate*delta[j]            """print "=======================================1234"            for ij in self.b:                print ij.shape,            print            """        # print "backPropagation--------------------finish"        error = 0.5*(a[-1]-label)**2        return error    def train(self, input_=None, target=None, show=10):        for ep in xrange(self.maxEpoch):            error = []            for itemIndex in xrange(input_.shape[1]):                a = self.forwardPropagation(input_[:, itemIndex])                e = self.backPropagation(target[:, itemIndex], a)                error.append(e[0, 0])            tt = sum(error)/len(error)            if tt < self.thresholdError:                print "Finish {0}: ".format(ep), tt                return            elif ep % show == 0:                print "epoch {0}: ".format(ep), tt    def sim(self, inp=None):        return self.forwardPropagation(item=inp)[-1]if __name__ == "__main__":    tt = np.arange(0, 6.28, 0.01)    labels = np.zeros_like(tt)    print tt.shape    """    for po in xrange(tt.shape[0]):        if tt[po] < 4:            labels[po] = 0.0        elif 8 > tt[po] >= 4:            labels[po] = 0.25        elif 12 > tt[po] >= 8:            labels[po] = 0.5        elif 16 > tt[po] >= 12:            labels[po] = 0.75        else:            labels[po] = 1.0    """    tt = np.mat(tt)    labels = np.sin(tt)*0.5+0.5    labels = np.mat(labels)    model = mlp(lr=0.2, lda=0.0, te=1e-5, epoch=500, size=[1, 6, 6, 6, 1])    print tt.shape, labels.shape    print len(model.W), len(model.b)    print    model.train(input_=tt, target=labels, show=10)    sims = [model.sim(tt[:, idx])[0, 0] for idx in xrange(tt.shape[1])]    xx = tt.tolist()[0]    plt.figure()    plt.plot(xx, labels.tolist()[0], xx, sims, 'r')    plt.show()
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