def step(self, *args):
"""First update the step size, then actually take a step along the gradient."""
g = self.model.grad(*args);
# Update the weighted Exponential sq avg.
self.sqExpAvgGrad *= self.exponentAvgM;
self.sqExpAvgGrad += (1-self.exponentAvgM) * g**2;
self.sqExpAvgGrad[:] = where(self.sqExpAvgGrad < EPSILON, EPSILON, self.sqExpAvgGrad);
# Uodate the muVect
possUpdate = 1 + self.qLearningRate * g * self.expAvgGrad / self.sqExpAvgGrad
#log.debug('max(possUpdate): %.4f, min(possUpdate): %.4f' % (max(possUpdate), min(possUpdate)))
## Keep this from going negative.
possUpdate = where(possUpdate < 0.001, 0.001, possUpdate);
self.muVect *= possUpdate
# Do something to cap the update rate. This is allowing the step rate to overpower the decay completely
self.muVect = where(self.muVect > self.maxMuVect, self.maxMuVect, self.muVect);
# Then update the exponential average
self.expAvgGrad *= self.exponentAvgM;
self.expAvgGrad += (1-self.exponentAvgM) * g;
self.model.params -= self.muVect * g
Trainer.step(self,*args)
def step(self, *args):
"""First update the step size, then actually take a step along the gradient."""
g = self.model.grad(*args)
# Update the weighted Exponential sq avg.
self.sqExpAvgGrad *= self.exponentAvgM
self.sqExpAvgGrad += (1 - self.exponentAvgM) * g**2
self.sqExpAvgGrad[:] = where(self.sqExpAvgGrad < EPSILON, EPSILON,
self.sqExpAvgGrad)
# Uodate the muVect
possUpdate = 1 + self.qLearningRate * g * self.expAvgGrad / self.sqExpAvgGrad
#log.debug('max(possUpdate): %.4f, min(possUpdate): %.4f' % (max(possUpdate), min(possUpdate)))
## Keep this from going negative.
possUpdate = where(possUpdate < 0.001, 0.001, possUpdate)
self.muVect *= possUpdate
# Do something to cap the update rate. This is allowing the step rate to overpower the decay completely
self.muVect = where(self.muVect > self.maxMuVect, self.maxMuVect,
self.muVect)
# Then update the exponential average
self.expAvgGrad *= self.exponentAvgM
self.expAvgGrad += (1 - self.exponentAvgM) * g
self.model.params -= self.muVect * g
Trainer.step(self, *args)
def step(self, *args):
self.inc = \
self.momentum*self.inc - self.stepsize * self.model.grad(*args)
if isnan(sum(self.inc)):
print 'nan!'
self.inc = zeros(self.inc.shape, dtype=float)
self.model.params += self.inc
Trainer.step(self, *args)
def step(self,*args):
self.inc = \
self.momentum*self.inc - self.stepsize * self.model.grad(*args)
if isnan(sum(self.inc)):
print 'nan!'
self.inc = zeros(self.inc.shape,dtype=float)
self.model.params += self.inc
Trainer.step(self,*args)
def step(self, *args):
updateparams(self.model, fmin_cg(\
self.cost,self.model.params.copy(),self.grad,\
args=args,maxiter=self.cgiterations,disp=1).copy())
Trainer.step(self, *args)
def step(self, *args):
g = self.model.grad(*args)
self.model.params -= self.stepsize * g
Trainer.step(self, *args)
def step(self,*args):
updateparams(self.model, fmin_cg(\
self.cost,self.model.params.copy(),self.grad,\
args=args,maxiter=self.cgiterations,disp=1).copy())
Trainer.step(self,*args)
def step(self,*args):
g = self.model.grad(*args)
self.model.params -= self.stepsize * g
Trainer.step(self,*args)