tensorboard_还出现报错，找不到流_先跳过

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Date    : 2017-12-10 16:18:28
# @Author  : Lebhoryi@gmail.com
# @Link1   : http://blog.csdn.net/xuan_zizizi/article/details/77815986 # @Link2   : https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/3-1-add-layer/ # @Version : tensorflow_tensorboard

import tensorflow as tf

def add_layer(inputs, in_size, out_size, activation_function=None):
# add one more layer and return the output of this layer
with tf.name_scope('layer'):
with tf.name_scope('weights'):
Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
return outputs

# define placeholder for inputs to network
with tf.name_scope('inputs'):
xs = tf.placeholder(tf.float32, [None, 1], name = 'x_input')
ys = tf.placeholder(tf.float32, [None, 1], name = 'y_input')

# add hidden layer
l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, activation_function=None)

# the error between prediciton and real data
with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
reduction_indices=[1]),name = 'mean')

with tf.name_scope('train'):
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

# tf.train.SummaryWriter soon be deprecated, use following
# if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:  # tensorflow version < 0.12
writer = tf.train.SummaryWriter('20171210/', sess.graph)
# else:
#   tensorflow version >= 0.12
# file_writer = tf.summary.FileWriter("20171210/", sess.graph)

# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
## if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
##     init = tf.initialize_all_variables()
## else:
init = tf.global_variables_initializer()
sess.run(init)

# direct to the local dir and run this in terminal:
# $ tensorboard --logdir=logs