(is_training, activation_fn=None)
Create a scope for applying BatchNorm in slim.

  This scope also applies Glorot initializiation to convolutional weights.
  Args:
    is_training: Whether this is a training run.
    activation_fn: Whether we apply an activation_fn to the convolution result.

  Returns:
    scope: Use this scope to automatically apply BatchNorm and Xavier Init to
      slim.conv2d and slim.fully_connected.
Create a scope for applying BatchNorm in slim.
Create a scope for applying BatchNorm in slim .
def slim_batchnorm_arg_scope(is_training, activation_fn=None):
  """Create a scope for applying BatchNorm in slim.

  This scope also applies Glorot initializiation to convolutional weights.
  Args:
    is_training: Whether this is a training run.
    activation_fn: Whether we apply an activation_fn to the convolution result.

  Returns:
    scope: Use this scope to automatically apply BatchNorm and Xavier Init to
      slim.conv2d and slim.fully_connected.
  """
  batch_norm_params = {
      "is_training": is_training,
      "decay": 0.999,
      "epsilon": 0.001,
      "variables_collections": {
          "beta": None,
          "gamma": None,
          "moving_mean": "moving_vars",
          "moving_variance": "moving_vars",
      }
  }

  with slim.arg_scope(
      [slim.conv2d, slim.fully_connected, slim.conv2d_transpose],
      weights_initializer=slim.initializers.xavier_initializer(),
      activation_fn=activation_fn,
      normalizer_fn=slim.batch_norm,
      normalizer_params=batch_norm_params) as scope:
    return scope
def slim_batchnorm_arg_scope ( is_training , activation_fn = None ) : batch_norm_params = { "is_training" : is_training , "decay" : 0.999 , "epsilon" : 0.001 , "variables_collections" : { "beta" : None , "gamma" : None , "moving_mean" : "moving_vars" , "moving_variance" : "moving_vars" , } } with slim . arg_scope ( [ slim . conv2d , slim . fully_connected , slim . conv2d_transpose ] , weights_initializer = slim . initializers . xavier_initializer ( ) , activation_fn = activation_fn , normalizer_fn = slim . batch_norm , normalizer_params = batch_norm_params ) as scope : return scope