[root@shenzhen tensorflow]# python3 tensor6.py
2018-08-24 21:14:52.513641: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
[root@shenzhen tensorflow]# ls
events.out.tfevents.1535116493.shenzhen.com  tensor2.py  tensor4.py  tensor6.py
tensor1.py                                   tensor3.py  tensor5.py
[root@shenzhen tensorflow]# cat tensor6.py
#!/usr/local/bin/python3
#coding:utf-8

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 prediction 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)

init = tf.global_variables_initializer()
sess = tf.Session()
writer = tf.summary.FileWriter(‘.‘,sess.graph)
#important step
sess.run(init)