BP网络-数字识别

from sklearn.datasets import load_digits
import pylab as pl

digits=load_digits()#载入数据集
print digits.data.shape

pl.gray()#灰度化图片
pl.matshow(digits.images[0])
pl.show()

import numpy as np
from sklearn.datasets import load_digits
from sklearn.preprocessing import LabelBinarizer
from sklearn.cross_validation import train_test_split

def sigmoid(x):
    return 1/(1+np.exp(-x))
def dsigmoid(x):
    return x*(1-x)
class NeuralNetwork:
    def __init__(self,layers):#(64,100,10)
        #权重的初始化，范围-1到1
        self.V=np.random.random((layers[0]+1,layers[1]+1))*2-1
        self.W=np.random.random((layers[1]+1,layers[2]))*2-1
    def train(self,X,y,lr=0.11,epochs=10000):
        #添加偏置
        temp=np.ones([X.shape[0],X.shape[1]+1])
        temp[:,0:-1]=X
        X=temp

        for n in range(epochs+1):
            i=np.random.randint(X.shape[0])#随机选取一个数据
            x=[X[i]]
            x=np.atleast_2d(x)#转为2维数据

            L1=sigmoid(np.dot(x,self.V))#隐层输出
            L2=sigmoid(np.dot(L1,self.W))#输出层输出

            L2_delta=(y[i]-L2)*dsigmoid(L2)
            L1_delta=L2_delta.dot(self.W.T)*dsigmoid(L1)
            self.W += lr*(L1.T).dot(L2_delta)
            self.V += lr*(x.T).dot(L1_delta)

            #每训练1000次预测一次准确率
            if n%1000==0:
                predictions=[]
                for j in range(X_test.shape[0]):
                    o = self.predict(X_test[j])
                    predictions.append(np.argmax(o))#获取预测结果
                accurary=np.mean(np.equal(predictions,y_test))
                print 'epoch:',n,'accurary',accurary
    def predict(self,x):
        #添加偏置
        temp=np.ones(x.shape[0]+1)
        temp[0:-1]=x
        x=temp
        x=np.atleast_2d(x)#转为2维数组

        L1=sigmoid(np.dot(x,self.V))#隐层输出
        L2=sigmoid(np.dot(L1,self.W))#输出层输出
        return L2

digits = load_digits()#载入数据
X=digits.data #数据
y=digits.target #标签
#输入数据归一化
X -= X.min()
X /= X.max()

nm=NeuralNetwork([64,100,10])#创建网络
X_train,X_test,y_train,y_test=train_test_split(X,y)#分割数据1/4维测试数据，3/4维训练数据

labels_train = LabelBinarizer().fit_transform(y_train)#标签二值化
labels_test = LabelBinarizer().fit_transform(y_test)#标签二值化

print 'start'
nm.train(X_train,labels_train,epochs=20000)
print 'end'