theano 入门教程1.6

theano 实例， 逻辑回归（logistic regression)

# -*- coding: utf-8 -*-

"""

Created on Fri Jun 06 08:56:54 2014

@author: Administrator

"""

import theano

import numpy as np

import theano.tensor as T

def logistic_regression():

rng = np.random

N = 400

feats = 784

D = (rng.randn(N, feats), rng.randint(size=N, low=0, high=2))

training_steps = 10000

x = T.matrix(‘x‘)

y = T.vector(‘y‘)

w = theano.shared(rng.randn(feats), name=‘w‘)

b = theano.shared(0., name=‘b‘)

print "Initial model: "

print w.get_value(), b.get_value()

p_1 = 1/(1 + T.exp(-T.dot(x, w) - b))

prediction = p_1 > 0.5

xent = -y * T.log(p_1) - (1-y) * T.log(1-p_1)

cost = xent.mean() + 0.01

gw, gb = T.grad(cost, [w, b])

# Compile

train = theano.function(

inputs=[x,y],

outputs=[prediction, xent],

updates=((w, w-0.1*gw), (b, b-0.1*gb)))

predict = theano.function([x], prediction)

for i in range(training_steps):

pred, err = train(D[0], D[1])

print ‘Final model:‘

print w.get_value(), b.get_value()

print ‘target values for D:‘, D[1]

print ‘prediction on D:‘, predict(D[0])

if __name__ == ‘__main__‘:

logistic_regression()    

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theano 入门教程1.6

原文：http://www.cnblogs.com/fireae/p/3772656.html