(weight_decay=0.00004,
                                  batch_norm_decay=0.9997,
                                  batch_norm_epsilon=0.001)
Yields the scope with the default parameters for inception_resnet_v2.

  Args:
    weight_decay: the weight decay for weights variables.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.

  Returns:
    a arg_scope with the parameters needed for inception_resnet_v2.
Yields the scope with the default parameters for inception_resnet_v2.
Yields the scope with the default parameters for inception_resnet_v2 .
def inception_resnet_v2_arg_scope(weight_decay=0.00004,
                                  batch_norm_decay=0.9997,
                                  batch_norm_epsilon=0.001):
  """Yields the scope with the default parameters for inception_resnet_v2.

  Args:
    weight_decay: the weight decay for weights variables.
    batch_norm_decay: decay for the moving average of batch_norm momentums.
    batch_norm_epsilon: small float added to variance to avoid dividing by zero.

  Returns:
    a arg_scope with the parameters needed for inception_resnet_v2.
  """
  # Set weight_decay for weights in conv2d and fully_connected layers.
  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_regularizer=slim.l2_regularizer(weight_decay),
                      biases_regularizer=slim.l2_regularizer(weight_decay)):

    batch_norm_params = {
        'decay': batch_norm_decay,
        'epsilon': batch_norm_epsilon,
    }
    # Set activation_fn and parameters for batch_norm.
    with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu,
                        normalizer_fn=slim.batch_norm,
                        normalizer_params=batch_norm_params) as scope:
      return scope
def inception_resnet_v2_arg_scope ( weight_decay = 0.00004 , batch_norm_decay = 0.9997 , batch_norm_epsilon = 0.001 ) : # Set weight_decay for weights in conv2d and fully_connected layers. with slim . arg_scope ( [ slim . conv2d , slim . fully_connected ] , weights_regularizer = slim . l2_regularizer ( weight_decay ) , biases_regularizer = slim . l2_regularizer ( weight_decay ) ) : batch_norm_params = { 'decay' : batch_norm_decay , 'epsilon' : batch_norm_epsilon , } # Set activation_fn and parameters for batch_norm. with slim . arg_scope ( [ slim . conv2d ] , activation_fn = tf . nn . relu , normalizer_fn = slim . batch_norm , normalizer_params = batch_norm_params ) as scope : return scope