莫烦theano学习自修第二天【激励函数】

#!/usr/bin/env python
#! _*_ coding:UTF-8 _*_

import numpy as np
import theano.tensor as T
import theano

x = T.dmatrix(‘x‘)
# 定义一个激励函数
s = 1 / (1 + T.exp(-x))
logistic = theano.function([x], s)
print logistic([[0, 1], [-2, -3]])


# 一个函数输出多个变量的结果
a, b = T.dmatrices(‘a‘, ‘b‘)
diff = a - b
abs_diff = abs(diff)
diff_squared = diff ** 2
f = theano.function([a, b], [diff, abs_diff, diff_squared])
print f(np.ones((2, 2)),
        np.arange(4).reshape((2, 2))
    )
# 也可以写为
x1, x2, x3 = f(np.ones((2, 2)),
        np.arange(4).reshape((2, 2))
    )
print x1, x2, x3


# 变量的名称及默认值
x, y, w = T.dscalars(‘x‘, ‘y‘, ‘w‘)
z = (x + y) * w
f = theano.function([
    x,
    theano.In(y, value=1),
    theano.In(w, value=2, name=‘weights‘)
], z)
print f(23, )

/Users/liudaoqiang/PycharmProjects/numpy/venv/bin/python /Users/liudaoqiang/Project/python_project/theano_day2/theano_test.py
[[ 0.5         0.73105858]
 [ 0.11920292  0.04742587]]
[array([[ 1.,  0.],
       [-1., -2.]]), array([[ 1.,  0.],
       [ 1.,  2.]]), array([[ 1.,  0.],
       [ 1.,  4.]])]
[[ 1.  0.]
 [-1. -2.]] [[ 1.  0.]
 [ 1.  2.]] [[ 1.  0.]
 [ 1.  4.]]
48.0

Process finished with exit code 0