首先介绍几个用法:
with tf.name_scope(name = "inputs"):
这个是用于区分区域的。如,train,inputs等。
xs = tf.placeholder(tf.float32,[None,1],name = "x_input")
name用于对节点的命名。
merged = tf.summary.merge_all()
注:这里很多代码可能跟莫烦老师的代码并不一样,主要是由于版本变迁,tensorflow很多函数改变。
这一步很重要!!!如果你想看loss曲线,一定要记得家上这一步。还有
1 with tf.name_scope("loss"): 2 loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-l2),reduction_indices=[1])) 3 tf.summary.scalar("loss",loss)
对于你绘制的曲线。一定要记得是分成各个点来绘制的。所以在最后也会有改动,每一个都要加到summary 里面。
所有代码:
1 for i in range(1000): 2 sess.run(train_step, feed_dict={xs:x_data, ys:y_data}) 3 if i%50 == 0: 4 rs = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) 5 writer.add_summary(rs, i)
至于启动tensorboard:
首先在cmd或者terminal切换到当前文件所在的文件夹,然后输入:
tensorboard --logdir=logs/(貌似不需要斜杠也可以可以试一下),当然 这里直接输入路径也是可以的。
最后会给你一个网址:0.0.0.0:6006还是什么。很多windows同学打不开,那就把前面的ip直接换成localhost即可
所有代码:
1 # -*- coding: utf-8 -*- 2 """ 3 Created on Wed Jun 14 17:26:15 2017 4 5 @author: Jarvis 6 """ 7 8 import tensorflow as tf 9 import numpy as np 10 def addLayer(inputs,inSize,outSize,level,actv_func = None): 11 layername = "layer%s"%(level) 12 with tf.name_scope("Layer"): 13 with tf.name_scope("Weights"): 14 Weights = tf.Variable(tf.random_normal([inSize,outSize]),name="W") 15 # tf.summary.histogram(layername+"/Weights",Weights) 16 with tf.name_scope("bias"): 17 bias = tf.Variable(tf.zeros([1,outSize]),name = "bias") 18 # tf.summary.histogram(layername+"/bias",bias) 19 20 with tf.name_scope("Wx_plus_b"): 21 Wx_plus_b = tf.matmul(inputs,Weights)+bias 22 # tf.summary.histogram(layername+"/Wx_plus_b",Wx_plus_b) 23 if actv_func == None: 24 outputs = Wx_plus_b 25 else: 26 outputs = actv_func(Wx_plus_b) 27 tf.summary.histogram(layername+"\outputs",outputs) 28 return outputs 29 x_data = np.linspace(-1,1,300)[:,np.newaxis] 30 noise= np.random.normal(0, 0.05, x_data.shape).astype(np.float32) 31 y_data = np.square(x_data)+0.5+noise 32 with tf.name_scope("inputs"): 33 xs = tf.placeholder(tf.float32,[None,1],name = "x_input") 34 ys = tf.placeholder(tf.float32,[None,1],name = "y_input") 35 36 l1 = addLayer(xs,1,10,level = 1,actv_func=tf.nn.relu) 37 l2 = addLayer(l1,10,1,level=2,actv_func=None) 38 with tf.name_scope("loss"): 39 loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-l2),reduction_indices=[1])) 40 tf.summary.scalar("loss",loss) 41 with tf.name_scope("train"): 42 train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss) 43 44 45 46 sess = tf.Session() 47 merged = tf.summary.merge_all() 48 writer = tf.summary.FileWriter("logs/",sess.graph)#很关键一定要在run之前把这个加进去 49 50 sess.run(tf.global_variables_initializer()) 51 52 for i in range(1000): 53 sess.run(train_step, feed_dict={xs:x_data, ys:y_data}) 54 if i%50 == 0: 55 rs = sess.run(merged,feed_dict={xs:x_data,ys:y_data}) 56 writer.add_summary(rs, i) 57 58
很多用spyder的同学可能老师报一些莫名奇妙的错误,你不妨试试重启一下kernel试试
时间: 2024-11-05 16:23:41