Created
December 13, 2009 05:34
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""" | |
x[t] = fp(u)[t] | |
= mean( v[t-T+1:t+1] ) = ( v[t-T+1] + v[t-T+2] + ... + v[t-1] + v[t] )/T | |
v[t] = tanh(u[t]) | |
E = sum( 0.5 * (x - y)**2 ) | |
dE/dx[t] = x[t] - y[t] | |
dE/dv[t] = ( dE/dx[t+T-1] + dE/dx[t+T-2] + ... + dE/dx[t+1] + dE/dx[t] )/T | |
dE/du[t] = dE/dv[t] * (1 - x[t]**2) | |
result: http://www.flickr.com/photos/arataka/tags/up20091213143236/show/ | |
""" | |
import pylab | |
import numpy | |
tanh = numpy.tanh | |
class WindowRegres(object): | |
def __init__(self, steps, wlen, _learn_rate=5, title=''): | |
self.t = numpy.arange(steps+wlen) | |
self.x = numpy.zeros((steps,), dtype=numpy.float) | |
self.x = numpy.zeros((steps,), dtype=numpy.float) | |
self.v = numpy.zeros((steps+wlen,), dtype=numpy.float) | |
self.u = numpy.zeros((steps+wlen,), dtype=numpy.float) | |
self.dEdv = numpy.zeros((steps+wlen,), dtype=numpy.float) | |
self.dlist = range(wlen+1) | |
self.wlen = wlen | |
self.learn_rate = float(_learn_rate)/steps | |
self.title = title | |
def fp(self): | |
u = self.u | |
v = self.v = tanh(u) | |
x = self.x | |
x.fill(0) | |
steps = self.x.shape[0] | |
wlen = self.wlen | |
for d in self.dlist: | |
x += v[d:steps+d] | |
x /= (wlen+1) | |
def bp(self): | |
y = self.y | |
v = self.v | |
x = self.x | |
dEdv = self.dEdv | |
steps = self.x.shape[0] | |
wlen = self.wlen | |
self.E = numpy.sum( 0.5*(x-y)**2 )/steps | |
dEdx = x-y | |
dEdv.fill(0) | |
for d in self.dlist: | |
dEdv[d:steps+d] += dEdx | |
#dEdv /= (wlen+1) | |
dEdu = dEdv * (1-v**2) | |
self.u -= self.learn_rate * dEdu | |
def learn(self, epochs, draw_step=None): | |
self.rec = numpy.zeros((epochs,1), dtype=numpy.float) | |
for ep in xrange(epochs): | |
self.fp() | |
self.bp() | |
self.rec[ep,0] = self.E | |
if draw_step != None and ep % draw_step == 0: | |
self.plot() | |
self.plot() | |
def plot(self): | |
wlen = self.wlen | |
pylab.clf() | |
pylab.subplot(311) | |
pylab.semilogx(self.rec[:,0]) | |
pylab.ylim(0,None) | |
pylab.title(self.title) | |
pylab.ylabel('Error') | |
pylab.subplot(312) | |
pylab.plot(self.t[wlen:], self.y, linewidth=0.5, label='y') | |
pylab.plot(self.t[wlen:], self.x, linewidth=2.0, label='x') | |
pylab.plot(self.t , self.v, linewidth=0.5, label='v') | |
pylab.ylim(-1.05,1.05) | |
pylab.legend() | |
pylab.ylabel('y, x, v') | |
pylab.subplot(313) | |
pylab.plot(self.t, self.u, label='u') | |
pylab.legend() | |
pylab.ylabel('u') | |
pylab.draw() | |
pylab.show() | |
tch_len = 500 | |
for wlen in range(0,60,10): | |
wr = WindowRegres(tch_len,wlen,10, title='T=%d'%wlen) | |
t = 2 * numpy.pi * numpy.arange(tch_len) / 50 | |
wr.y = numpy.sin(t) * 0.8 + numpy.random.randn(tch_len) * 0.5 | |
wr.learn(10000) | |
pylab.savefig('T%03d.png'%wlen) |
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