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。