def __init__(self, module, etaminus=0.5, etaplus=1.2, deltamin=1.0e-6, deltamax=5.0, delta0=0.1, **kwargs): """ Set up training algorithm parameters, and objects associated with the trainer. :arg module: the module whose parameters should be trained. :key etaminus: factor by which step width is decreased when overstepping (0.5) :key etaplus: factor by which step width is increased when following gradient (1.2) :key delta: step width for each weight :key deltamin: minimum step width (1e-6) :key deltamax: maximum step width (5.0) :key delta0: initial step width (0.1) """ BackpropTrainer.__init__(self, module, **kwargs) self.epoch = 0 # set descender to RPROP mode and update parameters self.descent.rprop = True self.descent.etaplus = etaplus self.descent.etaminus = etaminus self.descent.deltamin = deltamin self.descent.deltamax = deltamax self.descent.deltanull = delta0 self.descent.init(module.params) # reinitialize, since mode changed
def __init__(self, module, etaminus=0.5, etaplus=1.2, deltamin=1.0e-6, deltamax=5.0, delta0=0.1, **kwargs): """Set up training algorithm parameters, and objects associated with the trainer. Args: module: the module whose parameters should be trained. etaminus: factor by which step width is decreased when overstepping (0.5) etaplus: factor by which step width is increased when following gradient (1.2) delta: step width for each weight deltamin: minimum step width (1e-6) deltamax: maximum step width (5.0) delta0: initial step width (0.1) """ BackpropTrainer.__init__(self, module, **kwargs) self.epoch = 0 # set descender to RPROP mode and update parameters self.descent.rprop = True self.descent.etaplus = etaplus self.descent.etaminus = etaminus self.descent.deltamin = deltamin self.descent.deltamax = deltamax self.descent.deltanull = delta0 self.descent.init(module.params) # reinitialize, since mode changed
def __init__(self, module, dataset=None, learningrate=0.01, lrdecay=1.0,momentum=0., verbose=False, batchlearning=False,weightdecay=0.): BackpropTrainer.__init__(self,module,dataset,learningrate,lrdecay,momentum,verbose,batchlearning,weightdecay)