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()
