(weight_decay=0.00004,
                        use_batch_norm=True,
                        batch_norm_decay=0.9997,
                        batch_norm_epsilon=0.001)
Defines the default arg scope for inception models.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
    batch_norm_decay: Decay for batch norm moving average.
    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
      in batch norm.

  Returns:
    An `arg_scope` to use for the inception models.
Defines the default arg scope for inception models.
Defines the default arg scope for inception models .
def inception_arg_scope(weight_decay=0.00004,
                        use_batch_norm=True,
                        batch_norm_decay=0.9997,
                        batch_norm_epsilon=0.001):
  """Defines the default arg scope for inception models.

  Args:
    weight_decay: The weight decay to use for regularizing the model.
    use_batch_norm: "If `True`, batch_norm is applied after each convolution.
    batch_norm_decay: Decay for batch norm moving average.
    batch_norm_epsilon: Small float added to variance to avoid dividing by zero
      in batch norm.

  Returns:
    An `arg_scope` to use for the inception models.
  """
  batch_norm_params = {
      # Decay for the moving averages.
      'decay': batch_norm_decay,
      # epsilon to prevent 0s in variance.
      'epsilon': batch_norm_epsilon,
      # collection containing update_ops.
      'updates_collections': tf.GraphKeys.UPDATE_OPS,
  }
  if use_batch_norm:
    normalizer_fn = slim.batch_norm
    normalizer_params = batch_norm_params
  else:
    normalizer_fn = None
    normalizer_params = {}
  # Set weight_decay for weights in Conv and FC layers.
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_regularizer=slim.l2_regularizer(weight_decay)):
    with slim.arg_scope(
        [slim.conv2d],
        weights_initializer=slim.variance_scaling_initializer(),
        activation_fn=tf.nn.relu,
        normalizer_fn=normalizer_fn,
        normalizer_params=normalizer_params) as sc:
      return sc
def inception_arg_scope ( weight_decay = 0.00004 , use_batch_norm = True , batch_norm_decay = 0.9997 , batch_norm_epsilon = 0.001 ) : batch_norm_params = { # Decay for the moving averages. 'decay' : batch_norm_decay , # epsilon to prevent 0s in variance. 'epsilon' : batch_norm_epsilon , # collection containing update_ops. 'updates_collections' : tf . GraphKeys . UPDATE_OPS , } if use_batch_norm : normalizer_fn = slim . batch_norm normalizer_params = batch_norm_params else : normalizer_fn = None normalizer_params = { } # Set weight_decay for weights in Conv and FC layers. with slim . arg_scope ( [ slim . conv2d , slim . fully_connected ] , weights_regularizer = slim . l2_regularizer ( weight_decay ) ) : with slim . arg_scope ( [ slim . conv2d ] , weights_initializer = slim . variance_scaling_initializer ( ) , activation_fn = tf . nn . relu , normalizer_fn = normalizer_fn , normalizer_params = normalizer_params ) as sc : return sc