

def __init__(self, module, dataset=None, learningrate=0.01, lrdecay=1.0, momentum=0.0, verbose=False, batchlearning=False, weightdecay=0.0):
    'Create a BackpropTrainer to train the specified `module` on the\n        specified `dataset`.\n\n        The learning rate gives the ratio of which parameters are changed into\n        the direction of the gradient. The learning rate decreases by `lrdecay`,\n        which is used to to multiply the learning rate after each training\n        step. The parameters are also adjusted with respect to `momentum`, which\n        is the ratio by which the gradient of the last timestep is used.\n\n        If `batchlearning` is set, the parameters are updated only at the end of\n        each epoch. Default is False.\n\n        `weightdecay` corresponds to the weightdecay rate, where 0 is no weight\n        decay at all.\n        '
    Trainer.__init__(self, module)
    self.setData(dataset)
    self.verbose = verbose
    self.batchlearning = batchlearning
    self.weightdecay = weightdecay
    self.epoch = 0
    momentum.totalepochs = 0
    self.descent = GradientDescent()
    self.descent.alpha = learningrate
    self.descent.momentum = momentum
    self.descent.alphadecay = lrdecay
    self.descent.init(module.params)

dataset/ETHPy150Open pybrain/pybrain/pybrain/supervised/trainers/backprop.py BackpropTrainer.__init__/VarMisuse@82/362 `self`->`momentum`