def insertWebsite(self, url, domain):
		'''增加一个网页,标记为未访问,并对相应的domain增加其入度'''
		cur = self.conn.cursor()
		cur.execute("SELECT 1 FROM Domains WHERE domain=?", (domain,))
		result = cur.fetchone()
		if not result:
			# 未有对应domain记录, 先创建domain, 把入度设为1
			if VERBOSE:
				print 'Spot Domain:',domain
			self.insertDomain(domain, indegree=1)
			cur.execute("SELECT did FROM Domains WHERE domain=?", (domain,))
			did = cur.fetchone()[0]
		else:
			did = result[0]
			# 对应的domain记录已经存在, 对其入度+1
			cur.execute("UPDATE Domains SET outdegree=outdegree+1 WHERE domain=?", (domain,))

		cur.execute("INSERT INTO Websites VALUES (NULL,?,?,NULL,0)", (did, url,))
		# 写入到文件
		self.conn.commit()
def run_model(prior, images, model, disc_reg_weight):
  """Run the model with new data and samples.

  Args:
    prior: the noise source as the generator input.
    images: images sampled from dataset.
    model: a GAN model defined in gan.py.
    disc_reg_weight: regularisation weight for discrmininator gradients.

  Returns:
    debug_ops: statistics from the model, see gan.py for more detials.
    disc_grads: discriminator gradients.
    gen_grads: generator gradients.
  """
  generator_inputs = prior.sample(FLAGS.batch_size)
  model_output = model.connect(images, generator_inputs)
  optimization_components = model_output.optimization_components

  disc_grads = tf.gradients(
      optimization_components['disc'].loss,
      optimization_components['disc'].vars)

  gen_grads = tf.gradients(
      optimization_components['gen'].loss,
      optimization_components['gen'].vars)

  if disc_reg_weight > 0.0:
    reg_grads = _compute_reg_grads(gen_grads,
                                   optimization_components['disc'].vars)
    disc_grads = _acc_grads(disc_grads, disc_reg_weight, reg_grads)

  debug_ops = model_output.debug_ops

  return debug_ops, disc_grads, gen_grads