import tensorflow as tf 
import numpy as np 
import matplotlib.pyplot as plt 

def add_layer(input,in_size,out_size,activation_function=None):
    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(input,Weights),biases)
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs

x_data = np.linspace(-1,1,300)[:,np.newaxis]
noise = np.random.normal(0,0.05,x_data.shape)#噪音
y_data = http://www.mamicode.com/np.square(x_data)-0.5+noise

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
predition = add_layer(l1,10,1,activation_function=None)

with tf.name_scope(‘loss‘):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-predition),reduction_indices=[1]))
with tf.name_scope(‘train‘):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

init = tf.initialize_all_variables()
sess = tf.Session()
merged = tf.summary.merge_all()
writer = tf.summary.FileWriter("D:/logs/",sess.graph) #目录结构尽量简单，复杂了容易出现找不到文件，原因不清楚
sess.run(init)

tensorboard --logdir=D:/logs/   #文件目录和之前里的保持一致