(pre_name='transformer',
                                    post_name='StyleNorm',
                                    alpha=1.0)
return scope_names, depths
Returns scope name and depths of the style normalization activations.

  Args:
    pre_name: string. Prepends this name to the scope names.
    post_name: string. Appends this name to the scope names.
    alpha: float. Width multiplier to reduce the number of filters used in the
      model and slim it down.. Defaults to 1.0, which results
      in the hyper-parameters used in the published paper.

  Returns:
    string. Scope names of the activations of the transformer network which are
        used to apply style normalization.
    int[]. Depths of the activations of the transformer network which are used
        to apply style normalization.
Returns scope name and depths of the style normalization activations.
Returns scope name and depths of the style normalization activations .
def style_normalization_activations(pre_name='transformer',
                                    post_name='StyleNorm',
                                    alpha=1.0):
  """Returns scope name and depths of the style normalization activations.

  Args:
    pre_name: string. Prepends this name to the scope names.
    post_name: string. Appends this name to the scope names.
    alpha: float. Width multiplier to reduce the number of filters used in the
      model and slim it down.. Defaults to 1.0, which results
      in the hyper-parameters used in the published paper.

  Returns:
    string. Scope names of the activations of the transformer network which are
        used to apply style normalization.
    int[]. Depths of the activations of the transformer network which are used
        to apply style normalization.
  """

  scope_names = [
      'residual/residual1/conv1', 'residual/residual1/conv2',
      'residual/residual2/conv1', 'residual/residual2/conv2',
      'residual/residual3/conv1', 'residual/residual3/conv2',
      'residual/residual4/conv1', 'residual/residual4/conv2',
      'residual/residual5/conv1', 'residual/residual5/conv2',
      'expand/conv1/conv', 'expand/conv2/conv', 'expand/conv3/conv'
  ]
  scope_names = [
      '{}/{}/{}'.format(pre_name, name, post_name) for name in scope_names
  ]
  # 10 convolution layers of 'residual/residual*/conv*' have the same depth.
  depths = [int(alpha * 128)] * 10 + [int(alpha * 64), int(alpha * 32), 3]

  return scope_names, depths
def style_normalization_activations ( pre_name = 'transformer' , post_name = 'StyleNorm' , alpha = 1.0 ) : scope_names = [ 'residual/residual1/conv1' , 'residual/residual1/conv2' , 'residual/residual2/conv1' , 'residual/residual2/conv2' , 'residual/residual3/conv1' , 'residual/residual3/conv2' , 'residual/residual4/conv1' , 'residual/residual4/conv2' , 'residual/residual5/conv1' , 'residual/residual5/conv2' , 'expand/conv1/conv' , 'expand/conv2/conv' , 'expand/conv3/conv' ] scope_names = [ '{}/{}/{}' . format ( pre_name , name , post_name ) for name in scope_names ] # 10 convolution layers of 'residual/residual*/conv*' have the same depth. depths = [ int ( alpha * 128 ) ] * 10 + [ int ( alpha * 64 ) , int ( alpha * 32 ) , 3 ] return scope_names , depths