def save(self, layer, iter, mbatch_provider):
mbatch_provider.forgetOriginalData()
mbatch_provider.getMiniBatch(self.cfg.batchsize, self.layers[layer].act)
self.upPass(layer, sample = False);
self.downPass(layer+1, sample = False);
for l in reversed(xrange(1, layer+1)):
self.downPass(l, sample = False)
timestr = ""
if "last_time_stamp" in self.__dict__:
ts = time.clock()
td = ts - self.last_time_stamp
if td > 0 and iter != self.last_time_stamp_iter:
timestr = " %2.4es/img ; %2.4e img/s"% ( td / (self.cfg.batchsize*(iter - self.last_time_stamp_iter)), (self.cfg.batchsize*(iter - self.last_time_stamp_iter))/td)
err = self.getErr(layer, mbatch_provider.sampleset)
n = cp.norm2(self.weights[layer].mat)
print "Iter: ", iter, "Err: %02.06f |W| = %2.2f"%(err, n), timestr
if self.cfg.save_every != 0 and iter % self.cfg.save_every == 0 and iter>0 :
self.saveLayer(layer, self.cfg.workdir, "-%010d"%iter)
self.saveOptions({"iter":iter}, layer)
self.saveOptions({"reconstruction":err}, layer, "-%010d"%iter)
self.err.append(err)
self.last_time_stamp = time.clock()
self.last_time_stamp_iter = iter
def save(self, layer, iter, mbatch_provider):
mbatch_provider.forgetOriginalData()
mbatch_provider.getMiniBatch(self.cfg.batchsize,
self.layers[layer].act)
self.upPass(layer, sample=False)
self.downPass(layer + 1, sample=False)
for l in reversed(xrange(1, layer + 1)):
self.downPass(l, sample=False)
timestr = ""
if "last_time_stamp" in self.__dict__:
ts = time.clock()
td = ts - self.last_time_stamp
if td > 0 and iter != self.last_time_stamp_iter:
timestr = " %2.4es/img ; %2.4e img/s" % (
td / (self.cfg.batchsize *
(iter - self.last_time_stamp_iter)),
(self.cfg.batchsize *
(iter - self.last_time_stamp_iter)) / td)
err = self.getErr(layer, mbatch_provider.sampleset)
n = cp.norm2(self.weights[layer].mat)
print "Iter: ", iter, "Err: %02.06f |W| = %2.2f" % (err, n), timestr
if self.cfg.save_every != 0 and iter % self.cfg.save_every == 0 and iter > 0:
self.saveLayer(layer, self.cfg.workdir, "-%010d" % iter)
self.saveOptions({"iter": iter}, layer)
self.saveOptions({"reconstruction": err}, layer, "-%010d" % iter)
self.err.append(err)
self.last_time_stamp = time.clock()
self.last_time_stamp_iter = iter
def getErr(self, layernum, orig_data):
cp.apply_binary_functor(self.layers[layernum].act, orig_data, cp.binary_functor.SUBTRACT)
sqerr = cp.norm2(self.layers[layernum].act)**2
return sqerr/((self.layers[layernum].size)*self.cfg.batchsize)
def trainDBM(self, mbatch_provider, itermax):
try:
""" Train all layers of a RBM-Stack as a DBM using minibatches provided by mbatch_provider for itermax minibatches """
### if pcd get starting point for fantasies
if self.cfg.cd_type == CDType.pcd:
self.resetPChain(mbatch_provider)
mbatch_provider.forgetOriginalData()
### temporary matrix to save update
for weightlayer in self.weights:
weightlayer.allocUpdateMatrix()
### iterate over updates
for iter in xrange(1,itermax):
### new learnrate if schedule
learnrate=self.getLearnrate(iter,itermax)/100
sys.stdout.write('.')
sys.stdout.flush()
### positive phase
mbatch_provider.getMiniBatch(self.cfg.batchsize,self.layers[0].act)
for layernum in xrange(len(self.weights)):
self.upPass(layernum,sample=False)
uq = UpdateQ(len(self.layers))
uq.push([1]) # must start w/ 0-th layer
while uq.minupdates([0]) < self.cfg.dbm_minupdates:
layernum = uq.pop(firstlayer=1)
self.updateLayer(layernum,sample=False)
for layernum in xrange(len(self.weights)):
self.weights[layernum].updateGradientPos(self.layers[layernum],self.layers[layernum+1])
### output stuff
if iter != 0 and (iter%100) == 0:
self.downPass(1,sample=False)
err=self.getErr(0,mbatch_provider.sampleset)
print "Iter: ",iter, "Err: %02.06f"%err, "|W|: %02.06f"%cp.norm2(self.weights[0].mat)
print self.cfg.workdir,
if self.cfg.save_every!=0 and iter % self.cfg.save_every == 0 and iter>0 :
for layernum in xrange(len(self.weights)):
self.saveLayer(layernum,self.cfg.workdir,"-%010d"%iter)
### negative phase
### replace layer nodes with pcd-chain or do initial uppass
if self.cfg.cd_type == CDType.cdn:
for layernum in xrange(len(self.weights)):
self.upPass(layernum,sample=True)
else:
for layer in self.layers:
layer.switchToPChain()
uq = UpdateQ(len(self.layers))
uq.push([1])
while uq.minupdates() < self.cfg.dbm_minupdates:
layernum = uq.pop(firstlayer=0)
self.updateLayer(layernum,sample=True)
### update gradients
for layernum in xrange(len(self.weights)):
self.weights[layernum].updateGradientNeg(self.layers[layernum],self.layers[layernum+1],self.cfg.batchsize)
### update weights and biases
for weightlayer in self.weights:
weightlayer.updateStep(learnrate,self.cfg.cost)
### put original layer back in place
if self.cfg.cd_type == CDType.pcd:
for layer in self.layers:
layer.switchToOrg()
mbatch_provider.forgetOriginalData()
finally:
for weightlayer in self.weights:
if "w_tmp" in weightlayer.__dict__:
weightlayer.deallocUpdateMatrix()
def getErr(self, layernum, orig_data):
cp.apply_binary_functor(self.layers[layernum].act, orig_data,
cp.binary_functor.SUBTRACT)
sqerr = cp.norm2(self.layers[layernum].act)**2
return sqerr / ((self.layers[layernum].size) * self.cfg.batchsize)
def trainDBM(self, mbatch_provider, itermax):
try:
""" Train all layers of a RBM-Stack as a DBM using minibatches provided by mbatch_provider for itermax minibatches """
### if pcd get starting point for fantasies
if self.cfg.cd_type == CDType.pcd:
self.resetPChain(mbatch_provider)
mbatch_provider.forgetOriginalData()
### temporary matrix to save update
for weightlayer in self.weights:
weightlayer.allocUpdateMatrix()
### iterate over updates
for iter in xrange(1, itermax):
### new learnrate if schedule
learnrate = self.getLearnrate(iter, itermax) / 100
sys.stdout.write('.')
sys.stdout.flush()
### positive phase
mbatch_provider.getMiniBatch(self.cfg.batchsize,
self.layers[0].act)
for layernum in xrange(len(self.weights)):
self.upPass(layernum, sample=False)
uq = UpdateQ(len(self.layers))
uq.push([1]) # must start w/ 0-th layer
while uq.minupdates([0]) < self.cfg.dbm_minupdates:
layernum = uq.pop(firstlayer=1)
self.updateLayer(layernum, sample=False)
for layernum in xrange(len(self.weights)):
self.weights[layernum].updateGradientPos(
self.layers[layernum], self.layers[layernum + 1])
### output stuff
if iter != 0 and (iter % 100) == 0:
self.downPass(1, sample=False)
err = self.getErr(0, mbatch_provider.sampleset)
print "Iter: ", iter, "Err: %02.06f" % err, "|W|: %02.06f" % cp.norm2(
self.weights[0].mat)
print self.cfg.workdir,
if self.cfg.save_every != 0 and iter % self.cfg.save_every == 0 and iter > 0:
for layernum in xrange(len(self.weights)):
self.saveLayer(layernum, self.cfg.workdir,
"-%010d" % iter)
### negative phase
### replace layer nodes with pcd-chain or do initial uppass
if self.cfg.cd_type == CDType.cdn:
for layernum in xrange(len(self.weights)):
self.upPass(layernum, sample=True)
else:
for layer in self.layers:
layer.switchToPChain()
uq = UpdateQ(len(self.layers))
uq.push([1])
while uq.minupdates() < self.cfg.dbm_minupdates:
layernum = uq.pop(firstlayer=0)
self.updateLayer(layernum, sample=True)
### update gradients
for layernum in xrange(len(self.weights)):
self.weights[layernum].updateGradientNeg(
self.layers[layernum], self.layers[layernum + 1],
self.cfg.batchsize)
### update weights and biases
for weightlayer in self.weights:
weightlayer.updateStep(learnrate, self.cfg.cost)
### put original layer back in place
if self.cfg.cd_type == CDType.pcd:
for layer in self.layers:
layer.switchToOrg()
mbatch_provider.forgetOriginalData()
finally:
for weightlayer in self.weights:
if "w_tmp" in weightlayer.__dict__:
weightlayer.deallocUpdateMatrix()
