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src/x/b/xbob.paper.example-0.2.0/xbob/paper/example/scripts/rprop_training.py   xbob.paper.example(Download)
  # Prepares MLP R-Prop trainer
  batch_size = X_train.shape[0]
  trainer = bob.trainer.MLPRPropTrainer(batch_size, bob.trainer.SquareError(bob.machine.HyperbolicTangentActivation()))
  trainer.initialize(machine)
  # Launch training

src/x/b/xbob.paper.jmlr2013-0.2.0/xbob/paper/jmlr2013/scripts/rprop_training.py   xbob.paper.jmlr2013(Download)
  # Prepares MLP R-Prop trainer
  batch_size = X_train.shape[0]
  trainer = bob.trainer.MLPRPropTrainer(batch_size, bob.trainer.SquareError(bob.machine.HyperbolicTangentActivation()))
  trainer.initialize(machine)
  # Launch training

src/x/b/xbob.mlp.lbfgs-1.0.3/xbob/mlp/lbfgs/trainer.py   xbob.mlp.lbfgs(Download)
      This is ignored if set to 0.
    """
    bob.trainer.MLPBaseTrainer.__init__(self, batch_size, bob.trainer.SquareError(bob.machine.HyperbolicTangentActivation()))
    self.grad_norm = grad_norm
    self.max_eval = max_eval