Minimizing this objective is equivelant to blurring input each step.

  Optimizing (-k)*blur_input_each_step() is equivelant to:

    input <- (1-k)*input + k*blur(input)

  An operation that was used in early feature visualization work.
  See Nguyen, et al., 2015.

def blur_input_each_step():
  """Minimizing this objective is equivelant to blurring input each step.

  Optimizing (-k)*blur_input_each_step() is equivelant to:

    input <- (1-k)*input + k*blur(input)

  An operation that was used in early feature visualization work.
  See Nguyen, et al., 2015.
  """
  def inner(T):
    t_input = T("input")
    t_input_blurred = tf.stop_gradient(_tf_blur(t_input))
    return 0.5*tf.reduce_sum((t_input - t_input_blurred)**2)
  return inner