def updateInfo(self, item, newlinks, oldlinks):
		'''爬虫爬完之后对数据库内容进行更新'''
		cur = self.conn.cursor()
		cur.execute("SELECT wid,did FROM Websites WHERE url=?", (item['url'],))
		wid, did = cur.fetchone()
		# website记录更新
		cur.execute("UPDATE Websites SET title=?,visited=1 WHERE wid=?", (item['title'], wid,))
		# 对应的domain记录中出度也需要更新
		cur.execute("UPDATE Domains SET outdegree=outdegree+? WHERE did=?", (len(item['links']), did,))

		# 对该网页中所有链接涉及的记录进行更新
		# 外部判断未出现过的链接
		for link,domain in newlinks:
			self.insertWebsite(link, domain)

		# 外部判断出现过的链接
		for link,domain in oldlinks:
			# 对对应的domain记录入度增加
			cur.execute("UPDATE Domains SET outdegree=outdegree+1 WHERE domain=?", (domain,))
		# 写入到文件
		self.conn.commit()
def fixed_padding(inputs, kernel_size, data_format):
  """Pads the input along the spatial dimensions independently of input size.

  Args:
    inputs: A tensor of size [batch, channels, height_in, width_in] or [batch,
      height_in, width_in, channels] depending on data_format.
    kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
      Should be a positive integer.
    data_format: The input format ('channels_last' or 'channels_first').

  Returns:
    A tensor with the same format as the input with the data either intact
    (if kernel_size == 1) or padded (if kernel_size > 1).
  """
  pad_total = kernel_size - 1
  pad_beg = pad_total // 2
  pad_end = pad_total - pad_beg

  if data_format == 'channels_first':
    padded_inputs = tf.pad(
        tensor=inputs,
        paddings=[[0, 0], [0, 0], [pad_beg, pad_end], [pad_beg, pad_end]])
  else:
    padded_inputs = tf.pad(
        tensor=inputs,
        paddings=[[0, 0], [pad_beg, pad_end], [pad_beg, pad_end], [0, 0]])
  return padded_inputs