In [4]:

    

#实战1-逻辑回归
#coding=utf-8
import numpy
import theano
import theano.tensor as T
rng = numpy.random

#为了测试，自己生成10个样本，每个样本是三维向量，用于向量
N=10
feats = 3
D=(rng.randn(N,feats).astype(numpy.float32),  rng.randint(size=N, low=0, high=2).astype(numpy.float32))

#声明自变量x以及每个样本对应的标签y(训练标签)
x=T.matrix("x")
y=T.vector("y")

#随机初始化参数w、b=0,为共享变量
w=theano.shared(rng.randn(feats), name="w")
b=theano.shared(0., name="b")

#构造损失函数
p_1=1 / ( 1  +T.exp(-T.dot(x,w) -b ) )  #激活函数
xent = -y *T.log(p_1) - ( 1-y ) * T .log (1-p_1) #交叉商损失函数
cost = xent.mean() + 0.01 * (w ** 2).sum()# 损失函数的平均值+L2正则项，其中权重衰减系数为0.01  
gw, gb = T.grad(cost, [w, b])             #对总损失函数求参数的偏导数  
  
prediction = p_1 > 0.5                    # 预测  
  
train = theano.function(inputs=[x,y],outputs=[prediction, xent],updates=((w, w - 0.1 * gw), (b, b - 0.1 * gb)))#训练所需函数  
predict = theano.function(inputs=[x], outputs=prediction)#测试阶段函数  
  
#训练  
training_steps = 1000  
for i in range(training_steps):  
    pred, err = train(D[0], D[1])  
    print err.mean()#查看损失函数下降变化过程


    

    




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