old_contents: from __future__ import absolute_import
  import theano.tensor as T
  
  class Regularizer(object):
      def set_param(self, p):
          self.p = p
  
      def set_layer(self, layer):
          self.layer = layer
  
      def __call__(self, loss):
          return loss
  
      def get_config(self):
          return {"name":self.__class__.__name__}
  
  class WeightRegularizer(Regularizer):
      def __init__(self, l1=0., l2=0.):
          self.l1 = l1
          self.l2 = l2
  
      def set_param(self, p):
          self.p = p
  
      def __call__(self, loss):
          loss += T.sum(abs(self.p)) * self.l1
          loss += T.sum(self.p ** 2) * self.l2
          return loss
  
      def get_config(self):
          return {"name":self.__class__.__name__,
              "l1":self.l1,
              "l2":self.l2}
  
  class ActivityRegularizer(Regularizer):
      def __init__(self, l1=0., l2=0.):
          self.l1 = l1
          self.l2 = l2
  
      def set_layer(self, layer):
          self.layer = layer
  
      def __call__(self, loss):
          loss += self.l1 * T.sum(T.mean(abs(self.layer.get_output(True)), axis=0))
          loss += self.l2 * T.sum(T.mean(self.layer.get_output(True) ** 2, axis=0))
          return loss
      
      def get_config(self):
          return {"name":self.__class__.__name__,
              "l1":self.l1,
              "l2":self.l2}
  
  def l1(l=0.01):
      return WeightRegularizer(l1=l)
  
  def l2(l=0.01):
      return WeightRegularizer(l2=l)
  
  def l1l2(l1=0.01, l2=0.01):
      return WeightRegularizer(l1=l1, l2=l2)
  
  def activity_l1(l=0.01):
      return ActivityRegularizer(l1=l)
  
  def activity_l2(l=0.01):
      return ActivityRegularizer(l2=l)
  
  def activity_l1l2(l1=0.01, l2=0.01):
      return ActivityRegularizer(l1=l1, l2=l2)
  
  identity = Regularizer
  
  from .utils.generic_utils import get_from_module
  def get(identifier):
      return get_from_module(identifier, globals(), 'regularizer', instantiate=True)
  
new_contents: from __future__ import absolute_import
  import theano.tensor as T
  
  class Regularizer(object):
      def set_param(self, p):
          self.p = p
  
      def set_layer(self, layer):
          self.layer = layer
  
      def __call__(self, loss):
          return loss
  
      def get_config(self):
          return {"name":self.__class__.__name__}
  
  class WeightRegularizer(Regularizer):
      def __init__(self, l1=0., l2=0.):
          self.l1 = l1
          self.l2 = l2
  
      def set_param(self, p):
          self.p = p
  
      def __call__(self, loss):
          loss += T.sum(abs(self.p)) * self.l1
          loss += T.sum(self.p ** 2) * self.l2
          return loss
  
      def get_config(self):
          return {"name":self.__class__.__name__,
              "l1":self.l1,
              "l2":self.l2}
  
  class ActivityRegularizer(Regularizer):
      def __init__(self, l1=0., l2=0.):
          self.l1 = l1
          self.l2 = l2
  
      def set_layer(self, layer):
          self.layer = layer
  
      def __call__(self, loss):
          loss += self.l1 * T.sum(T.mean(abs(self.layer.get_output(True)), axis=0))
          loss += self.l2 * T.sum(T.mean(self.layer.get_output(True) ** 2, axis=0))
          return loss
      
      def get_config(self):
          return {"name":self.__class__.__name__,
              "l1":self.l1,
              "l2":self.l2}
  
  def l1(l=0.01):
      return WeightRegularizer(l1=l)
  
  def l2(l=0.01):
      return WeightRegularizer(l2=l)
  
  def l1l2(l1=0.01, l2=0.01):
      return WeightRegularizer(l1=l1, l2=l2)
  
  def activity_l1(l=0.01):
      return ActivityRegularizer(l1=l)
  
  def activity_l2(l=0.01):
      return ActivityRegularizer(l2=l)
  
  def activity_l1l2(l1=0.01, l2=0.01):
      return ActivityRegularizer(l1=l1, l2=l2)
  
  identity = Regularizer
  
  from .utils.generic_utils import get_from_module
  def get(identifier, kwargs=None):
      return get_from_module(identifier, globals(), 'regularizer', instantiate=True, kwargs=kwargs)
  
subject: Allow getter of regularizers to take dict args