def __init__(self):
# standard parameters
self.epsilon = 2.0 #Initial value of sigmas
self.baseline=0.0 #Moving average baseline, used for sigma adaption
self.best=-1000000.0 #TODO ersetzen durch -inf
self.symCount=1.0 #Switch for symetric sampling
self.gd = GradientDescent()
self.gdSig = GradientDescent()
self.wDecay = 0.001 #lasso weight decay (0 to deactivate)
def __init__(self, module, dataset=None, learningrate=0.01, lrdecay=1.0,
momentum=0., verbose=False, batchlearning=False,
weightdecay=0.):
"""Create a BackpropTrainer to train the specified `module` on the
specified `dataset`.
The learning rate gives the ratio of which parameters are changed into
the direction of the gradient. The learning rate decreases by `lrdecay`,
which is used to to multiply the learning rate after each training
step. The parameters are also adjusted with respect to `momentum`, which
is the ratio by which the gradient of the last timestep is used.
If `batchlearning` is set, the parameters are updated only at the end of
each epoch. Default is False.
`weightdecay` corresponds to the weightdecay rate, where 0 is no weight
decay at all.
"""
Trainer.__init__(self, module)
self.setData(dataset)
self.verbose = verbose
self.batchlearning = batchlearning
self.weightdecay = weightdecay
self.epoch = 0
self.totalepochs = 0
# set up gradient descender
self.descent = GradientDescent()
self.descent.alpha = learningrate
self.descent.momentum = momentum
self.descent.alphadecay = lrdecay
self.descent.init(module.params)
def __init__(self):
# standard parameters
self.epsilon = 2.0 #Initial value of exploration size
self.baseline = 0.0 #Moving average baseline, used just for visualisation
self.best = -1000000.0 #TODO ersetzen durch -inf
self.symCount = 1.0 #Switch for symetric sampling
self.gd = GradientDescent()
self.gamma = 0.9995 #Exploration decay factor
def _additionalInit(self):
if self.sigmaLearningRate is None:
self.sigmaLearningRate = self.learningRate
self.gdSig = GradientDescent()
self.gdSig.alpha = self.sigmaLearningRate
self.gdSig.rprop = self.rprop
self.sigList = ones(self.numParameters) * self.epsilon #Stores the list of standard deviations (sigmas)
self.gdSig.init(self.sigList)
self.baseline = None
def _setInitEvaluable(self, evaluable):
ContinuousOptimizer._setInitEvaluable(self, evaluable)
self.current = self._initEvaluable
self.gd = GradientDescent()
self.gd.alpha = self.learningRate
if self.learningRateDecay is not None:
self.gd.alphadecay = self.learningRateDecay
self.gd.momentum = self.momentum
self.gd.rprop = self.rprop
self.gd.init(self._initEvaluable)
def __init__(self):
# gradient descender
self.gd = GradientDescent()
# create default explorer
self._explorer = None
# loglh dataset
self.loglh = None
# network to tie module and explorer together
self.network = None
def __init__(self,
module,
dataset=None,
learningrate=0.01,
lrdecay=1.0,
momentum=0.,
verbose=False,
batchlearning=False,
weightdecay=0.,
errfun=None):
"""Create a BackpropTrainer to train the specified `module` on the
specified `dataset`.
The learning rate gives the ratio of which parameters are changed into
the direction of the gradient. The learning rate decreases by
`lrdecay`, which is used to to multiply the learning rate after each
training step. The parameters are also adjusted with respect to
`momentum`, which is the ratio by which the gradient of the last
timestep is used.
If `batchlearning` is set, the parameters are updated only at the end
of each epoch. Default is False.
`weightdecay` corresponds to the weightdecay rate, where 0 is no weight
decay at all.
Arguments:
errfun (func): Function that takes 2 positional arguments,
the target (true) and predicted (estimated) output vectors, and
returns an estimate of the signed distance to the target (true)
output. default = lambda targ, est: (targ - est))
"""
Trainer.__init__(self, module)
self.setData(dataset)
self.verbose = verbose
self.batchlearning = batchlearning
self.weightdecay = weightdecay
self.epoch = 0
self.totalepochs = 0
# set up gradient descender
self.descent = GradientDescent()
self.descent.alpha = learningrate
self.descent.momentum = momentum
self.descent.alphadecay = lrdecay
self.descent.init(module.params)
self.errfun = errfun or abs_error
def __init__(self):
self.gd = GradientDescent()
def __init__(self):
# standard parameters
self.epsilon = 1.0
self.gamma = 0.999
self.gd = GradientDescent()
