def main():
random.seed(0)
size = 40
rbm = RBM(size * size, 1000)
slp = 2
#for blob, params in randomBlobs(10, size, size):
# rbm.v = blob.flatten()
# plot(rbm, blob)
# sleep(slp)
#
# result = reshape(rbm.v, (size, size))
# plot(rbm, result)
# sleep(slp)
#
# rbm.v2h()
# plot(rbm, blob)
# sleep(slp)
#
# rbm.h2v()
# result = reshape(rbm.v, (size, size))
# plot(rbm, result)
# sleep(slp)
every = 2000
#m = log(.01/.1) / log(10000)
NN = 20001
bb = 1. / NN * log(.001 / 1.0)
elapsed = array([])
for ii in range(NN):
if ii % 100 == 0:
blob, params = randomBlobs(10, size, size).next()
#print params
#epsilon = .1 * (ii+1) ** m
#epsilon = .3 * exp(bb * ii)
epsilon = min(.1, 1.0 * exp(bb * ii))
#time0 = time()
rbm.learn1(blob.flatten(),
epsilon=epsilon,
activationH2V='gaussianReal',
param=1)
#elapsed = hstack((elapsed, time() - time0))
if ii % every == 0:
print '%d: epsilon is %f' % (ii, epsilon),
rbm.v = blob.flatten()
result = reshape(rbm.v, (size, size))
plot(rbm, result, 'Iteration %d' % ii, 'Data')
p.show()
sleep(.1) if ii == 0 else sleep(.1)
rbm.v2h()
rbm.h2v(activation='gaussianReal', param=0)
result = reshape(rbm.v, (size, size))
plot(rbm, result, 'Iteration %d' % ii, 'Reconstruction')
p.show()
sleep(.5) if ii == 0 else sleep(.5)
print 'mean of last 50 errors is', mean(rbm.reconErrorNorms[-50:])
def main():
random.seed(0)
size = 40
rbm = RBM(size*size, 1000)
slp = 2
#for blob, params in randomBlobs(10, size, size):
# rbm.v = blob.flatten()
# plot(rbm, blob)
# sleep(slp)
#
# result = reshape(rbm.v, (size, size))
# plot(rbm, result)
# sleep(slp)
#
# rbm.v2h()
# plot(rbm, blob)
# sleep(slp)
#
# rbm.h2v()
# result = reshape(rbm.v, (size, size))
# plot(rbm, result)
# sleep(slp)
every = 2000
#m = log(.01/.1) / log(10000)
NN = 20001
bb = 1. / NN * log(.001 / 1.0)
elapsed = array([])
for ii in range(NN):
if ii % 100 == 0:
blob, params = randomBlobs(10, size, size).next()
#print params
#epsilon = .1 * (ii+1) ** m
#epsilon = .3 * exp(bb * ii)
epsilon = min(.1, 1.0 * exp(bb * ii))
#time0 = time()
rbm.learn1(blob.flatten(), epsilon = epsilon, activationH2V = 'gaussianReal', param = 1)
#elapsed = hstack((elapsed, time() - time0))
if ii % every == 0:
print '%d: epsilon is %f' % (ii, epsilon),
rbm.v = blob.flatten()
result = reshape(rbm.v, (size, size))
plot(rbm, result, 'Iteration %d' % ii, 'Data')
p.show()
sleep(.1) if ii == 0 else sleep(.1)
rbm.v2h()
rbm.h2v(activation = 'gaussianReal', param = 0)
result = reshape(rbm.v, (size, size))
plot(rbm, result, 'Iteration %d' % ii, 'Reconstruction')
p.show()
sleep(.5) if ii == 0 else sleep(.5)
print 'mean of last 50 errors is', mean(rbm.reconErrorNorms[-50:])
def main():
random.seed(0)
size = 2
rbm = RBM(size * 2, 1)
every = 40000
#every = 100
#m = log(.01/.1) / log(10000)
NN = 40001
bb = 1. / NN * log(.001 / 1.0)
elapsed = array([])
dataGenerator = randomSimpleLR(-1, size)
for ii in range(NN):
#if ii % 100 == 0:
data, params = dataGenerator.next()
#print params
#epsilon = .1 * (ii+1) ** m
#epsilon = .3 * exp(bb * ii)
#epsilon = min(.1, 1.0 * exp(bb * ii))
epsilon = .1
#time0 = time()
#rbm.learn1(datablob.flatten(), epsilon = epsilon, activationH2V = 'gaussianReal', param = 1)
#elapsed = hstack((elapsed, time() - time0))
if ii % every == 0:
print 'Iteration %d' % ii
#print '%d: epsilon is %f' % (ii, epsilon),
rbm.v = data
plot(rbm, 'Iteration %d' % ii, '0. Data')
print 'Visible set to:'
print rbm.v.T
p.show()
pause()
#sleep(1) if ii == 0 else sleep(.1)
rbm.v2h()
plot(rbm, 'Iteration %d' % ii, '1. To hidden')
print 'W * visible (then sampled) ='
print dot(rbm._W, rbm._v).T[:, 1:]
print rbm.h.T
p.show()
pause()
rbm.h2v(activation='logisticBinary')
plot(rbm, 'Iteration %d' % ii, '2. To visible')
print 'W.T * hidden (then sampled) ='
print dot(rbm._W.T, rbm._h).T[:, 1:]
print rbm.v.T
p.show()
pause()
print
#sleep(.5) if ii == 0 else sleep(.5)
#print 'mean of last 50 errors is', mean(rbm.reconErrorNorms[-50:])
#print 'average elapsed is:', mean(elapsed)
#elapsed = array([])
rbm.learn1(data, epsilon=epsilon, activationH2V='logisticBinary')
def main():
random.seed(0)
size = 2
rbm = RBM(size*2, 1)
every = 40000
#every = 100
#m = log(.01/.1) / log(10000)
NN = 40001
bb = 1. / NN * log(.001 / 1.0)
elapsed = array([])
dataGenerator = randomSimpleLR(-1, size)
for ii in range(NN):
#if ii % 100 == 0:
data, params = dataGenerator.next()
#print params
#epsilon = .1 * (ii+1) ** m
#epsilon = .3 * exp(bb * ii)
#epsilon = min(.1, 1.0 * exp(bb * ii))
epsilon = .1
#time0 = time()
#rbm.learn1(datablob.flatten(), epsilon = epsilon, activationH2V = 'gaussianReal', param = 1)
#elapsed = hstack((elapsed, time() - time0))
if ii % every == 0:
print 'Iteration %d' % ii
#print '%d: epsilon is %f' % (ii, epsilon),
rbm.v = data
plot(rbm, 'Iteration %d' % ii, '0. Data')
print 'Visible set to:'
print rbm.v.T
p.show()
pause()
#sleep(1) if ii == 0 else sleep(.1)
rbm.v2h()
plot(rbm, 'Iteration %d' % ii, '1. To hidden')
print 'W * visible (then sampled) ='
print dot(rbm._W, rbm._v).T[:,1:]
print rbm.h.T
p.show()
pause()
rbm.h2v(activation = 'logisticBinary')
plot(rbm, 'Iteration %d' % ii, '2. To visible')
print 'W.T * hidden (then sampled) ='
print dot(rbm._W.T, rbm._h).T[:,1:]
print rbm.v.T
p.show()
pause()
print
#sleep(.5) if ii == 0 else sleep(.5)
#print 'mean of last 50 errors is', mean(rbm.reconErrorNorms[-50:])
#print 'average elapsed is:', mean(elapsed)
#elapsed = array([])
rbm.learn1(data, epsilon = epsilon, activationH2V = 'logisticBinary')
