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)
