python实现多层感知器

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-*-importnumpya

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 np
import matplotlib.pyplot as plt
 
 
def 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()

效果图：

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