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 outputsx_data = np.linspace(-1,1,300)[:,np.newaxis]noise = np.random.normal(0,0.05,x_data.shape)#噪音y_data = np.square(x_data)-0.5+noisewith 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 layerl1 = add_layer(xs,1,10,activation_function=tf.nn.relu)#add output layerpredition = 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)

执行后，在命令行中输入，

一定要先到logs文件夹所在目录下，在输入下面命令，不然会找不到

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

执行结果：

打开浏览器：

　　输入显示的网址