The implementation of the BP algorithm python

0-9 digital recognition, nmist data recognition.

The specific code includes nmist see attachment.

Reference is Tom's machine learning BP chapter.

#Coding:utf-8#not considering the size endImportstructImportNumPydefloadimages (filename):Try: F= open (filename,'RB')    exceptException as instance:Printtype (instance) exit () Allimage=[] Bins=f.read () index=0 Magicnum,imagenum,rownum,colnum= Struct.unpack_from ('>IIII', Bins,index) index= index + struct.calcsize ('>IIII')        assert2051 = = Magicnum,'DataSet damaged | Little Endian'         forCtinchxrange (imagenum): Allimage.append (Struct.unpack_from ('>784b', Bins,index)) Index= index + struct.calcsize ('>784b')        returnNumpy.array (allimage,dtype='float32')defloadlabels (filename):Try: F= open (filename,'RB')    exceptException as instance:Printtype (instance) exit () Alllabels=[] Bins=f.read () index=0 Magicnum,labelnum= Struct.unpack_from ('>ii', Bins,index) index= index + struct.calcsize ('>ii')        assert2049 = = Magicnum,'DataSet damaged | Little Endian'         forCtinchxrange (labelnum): Alllabels.append (Struct.unpack_from ('B', Bins,index)) Index= index + struct.calcsize ('B')    returnNumpy.array (alllabels,dtype='float32')                if '__main__'==__name__: Images= Loadimages ('T10k-images.idx3-ubyte') Labels= Loadlabels ('T10k-labels.idx1-ubyte')    ImportMatplotlib.pyplot as Plt forXinchRange (3): Plt.figure () shown= Images[x].reshape (28,28)        #shown 28*28 numpy matrixPlt.imshow (shown,cmap='Gray') Plt.title (str (labels[x)) plt.show ()

Read Nmist

#-*-coding:utf-8-*-ImportDataloadImportNumPy as NPImportSYSImportWarningsdefBP (Trainset,eta=0.01,nin=none,nhid=none,nout=none,iternum = 10):    " "[(Instance,label), ((784,1) array, (10,1) array) ...]" "Wkh= (Np.random.rand (nout,nhid)-0.5)/10.0Whi= (Np.random.rand (nhid,nin)-0.5)/10.0Iteration=0 er= 1" "Iteration" "     whileIteration < Iternum andEr > 0.04:        Print 'iteration=', Iteration er=Testann ((whi,wkh)) Iteration+ = 1 for(X,label)inchtrainset:#Maximum minimum normalizationx = (X-x.min ())/x.max ()-x.min ()#forwardNeth = Np.dot (whi,x)#nhid*nin nin*1-nhid*1Oh = sigmoid (Neth)#nhid*1NETK = Np.dot (Wkh,oh)#Nout*nhid nhid*1-nout*1OK = sigmoid (NETK)#nout*1            #Seeking ErrorDK = ok* (1-OK) * (label-OK) DH= oh* (1-oh) *np.dot (WKH.T,DK)#(nhid,1) = (Nout,nhid). T * (nout,1)            #Update weights MatrixWkh = Wkh + eta * NP.DOT (Dk,oh. T#Nout*nhid + Nout*1*1*hidWhi = Whi + eta * NP.DOT (DH,X.T)#Nhid*nin + nhid*1*1*nin    Print 'Iteration over'     returnWkh,whidefTestann (model): Err=0 forIinchRange (len (testlabels)): Res= Fit (Model,testimages[i].reshape (784,1))        ifi > 9990:            Print '\ t', int (testlabels[i][0]),'was recognized as', res[1]        ifTESTLABELS[I][0]! = res[1]: Err+ = 1errorrate= Float (err)/float (len (testlabels))Print 'Error Rate', Errorrate,'\ n'    returnerrorratedefFit (model,image): Whi,wkh=model OK=list (sigmoid (sigmoid (Image.t.dot (WHI.T)). dot (wkh.t)) [0])returnOk,ok.index (max (OK))defsigmoid (y):#Nasty Overflow WarningWarnings.filterwarnings ("Ignore")    return1/(1+np.exp (-y))if '__main__'==__name__: Np.random.seed (207) Trainimages= Dataload.loadimages ('Train-images.idx3-ubyte') Trainlabels= Dataload.loadlabels ('Train-labels.idx1-ubyte') Testimages= Dataload.loadimages ('T10k-images.idx3-ubyte') Testlabels= Dataload.loadlabels ('T10k-labels.idx1-ubyte') DataSet= []     forIinchRange (len (trainlabels)): TMP= Np.zeros ((10,1), dtype='float32') Tmp[int (trainlabels[i]), 0]= 1dataset.append ((Trainimages[i].reshape (784,1) (TMP)) BP (trainset=dataset,eta=0.05,nin=784,nhid=20,nout=10,iternum=20)  

The implementation of the BP algorithm python