这才是TensorFlow自带可视化工具TensorBoard的正确打开

import tensorflow as tf

import numpy as np

def add_layer(inputs,in_size,out_size,n_layer,activation_function=None): #activation_function=None线性函数

layer_name="layer%s" % n_layer

with tf.name_scope(layer_name):

with tf.name_scope('weights'):

Weights = tf.Variable(tf.random_normal([in_size,out_size])) #Weight中都是随机变量

tf.histogram_summary(layer_name+"/weights",Weights) #可视化观看变量

with tf.name_scope('biases'):

biases = tf.Variable(tf.zeros([1,out_size])+0.1) #biases推荐初始值不为0

tf.histogram_summary(layer_name+"/biases",biases) #可视化观看变量

with tf.name_scope('Wx_plus_b'):

Wx_plus_b = tf.matmul(inputs,Weights)+biases #inputs*Weight+biases

tf.histogram_summary(layer_name+"/Wx_plus_b",Wx_plus_b) #可视化观看变量

if activation_function is None:

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

tf.histogram_summary(layer_name+"/outputs",outputs) #可视化观看变量

return outputs

#创建数据x_data，y_data

x_data = np.linspace(-1,1,300)[:,np.newaxis] #[-1,1]区间，300个单位，np.newaxis增加维度

noise = np.random.normal(0,0.05,x_data.shape) #噪点

y_data = np.square(x_data)-0.5+noise

with tf.name_scope('inputs'): #结构化

xs = tf.placeholder(tf.float32,[None,1],name='x_input')

ys = tf.placeholder(tf.float32,[None,1],name='y_input')

#三层神经，输入层（1个神经元），隐藏层（10神经元），输出层（1个神经元）

l1 = add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu) #隐藏层

prediction = add_layer(l1,10,1,n_layer=2,activation_function=None) #输出层

#predition值与y_data差别

with tf.name_scope('loss'):

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1])) #square()平方,sum()求和,mean()平均值

tf.scalar_summary('loss',loss) #可视化观看常量

with tf.name_scope('train'):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) #0.1学习效率,minimize(loss)减小loss误差

init = tf.initialize_all_variables()

sess = tf.Session()

#合并到Summary中

merged = tf.merge_all_summaries()

#选定可视化存储目录

writer = tf.train.SummaryWriter("Desktop/",sess.graph)

sess.run(init) #先执行init

#训练1k次

for i in range(1000):

sess.run(train_step,feed_dict={xs:x_data,ys:y_data})

if i%50==0:

result = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) #merged也是需要run的

writer.add_summary(result,i) #result是summary类型的，需要放入writer中，i步数（x轴）

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