def mean_squared_error(true, pred):
  """L2 distance between tensors true and pred.

  Args:
    true: the ground truth image.
    pred: the predicted image.
  Returns:
    mean squared error between ground truth and predicted image.
  """
  result = tf.reduce_sum(
      tf.squared_difference(true, pred)) / tf.to_float(tf.size(pred))
  return result
def mean_squared_error ( true , pred ) : result = tf . reduce_sum ( tf . squared_difference ( true , pred ) ) / tf . to_float ( tf . size ( pred ) ) return result
L2 distance between tensors true and pred.

  Args:
    true: the ground truth image.
    pred: the predicted image.
  Returns:
    mean squared error between ground truth and predicted image.