from __future__import print_function

import tensorflowas 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_functionis 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]))

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('logs/', sess.graph)

else:# tensorflow version >= 0.12

    writer = tf.summary.FileWriter("logs/", 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

Tensorboard流程图

构建Tensorboard过程

参考：https://morvanzhou.github.io/tutorials/machine-learning/tensorflow/4-1-tensorboard1/

使用with tf.name_scope('inputs')可以将xs和ys包含进来，形成一个大的图层，图层的名字就是with tf.name_scope()方法里的参数。其他op类似。

随后需要将计算图保存在本地目录下，以供浏览器查看：

sess = tf.Session() # get session

# tf.train.SummaryWriter soon be deprecated, use following

writer = tf.summary.FileWriter("logs/", sess.graph)

最后在你的terminal（终端）中 ，使用以下命令

tensorboard  --log  dirlogs

同时将终端中输出的网址复制到浏览器中，便可以看到之前定义的视图框架了。