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 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