losses.py 文件源码

def l2_loss(tensor, weight=1.0, scope=None, normalize=False):
  """Define a L2Loss, useful for regularize, i.e. weight decay.

  Args:
    tensor: tensor to regularize.
    weight: an optional weight to modulate the loss.
    scope: Optional scope for op_scope.

  Returns:
    the L2 loss op.
  """
  with tf.op_scope([tensor], scope, 'L2Loss'):
    weight = tf.convert_to_tensor(weight,
                                  dtype=tensor.dtype.base_dtype,
                                  name='loss_weight')
    if normalize:
      loss = tf.sqrt( (tf.sqrt( tf.nn.l2_loss(tensor)) / tf.to_float(tf.size(tensor)))  , name='value')
    else:
      loss = tf.mul(weight, tf.nn.l2_loss(tensor), name='value')

    tf.add_to_collection(LOSSES_COLLECTION, loss)
    return loss