【theano】01-function用法

 

#activation function example

x = T.dmatrix('x')
s = 1/(1+T.exp(-x))
#logistic or soft step
#exp()是一个算概率的activation function
#用numpy->np.exp()
#np.exp()返回e的幂次方

附：

logistic

logistic = theano.function([x],s)    #输入输出
print(logistic(    [    [0,1],[2,3]    ]    ))    #2行2列

结果：

 

 

#multiply outputs for a function

a,b = T.dmartices('a','b')    #同时定义多个matrix
diff = a-b    #求差
abs_diff = abs(diff)    #绝对值
diff_sqaured = diff*2

f = theano.function([a,b],[diff,abs_diff,diff_sqaured])

执行

x1,x2,x3 = f(
    np.ones((2,2)),    #2行2列的1
    np.arange(4).reshape((2,2))
    )
prnt(x1,x2,x3)

 

#name for a function

x,y,w = T.dscalars('x','y','w')    #常量
z = (x+y)*w

f = theano.fuction([x,
    theano.In(y,value=1),    #可以在这里赋初始化的值   
    theano.In(w,value=2,name='weights')    #还可以取名
    ])

执行

print(f(23))    #此时x=23,y=1,w=2    结果是48
print(f(23,2))        #此时x=23,y=2,w=2        结果是50
print(f(23,2,weights=4))    #此时x=23,y=2,w=4    结果是100