TensorFlow TensorBoard工具：网络结构可视化

tensorboard展示网络结构

代码

import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datatf
.compat.v1.disable_eager_execution()#载入数据集mnist=input_data.read_data_sets("MNIST_data",one_hot=True)
#每个批次大小batch_size=100#计算一共有多少个批次n_bath=mnist.train.num_examples // batch_sizeprint(n_bath)with tf
.name_scope('input'):    #定义两个placeholder    
x=tf.compat.v1.placeholder(tf.float32,[None,784],name='x-input')    y=tf
.compat.v1.placeholder(tf.float32,[None,10],name='y-input')with tf.name_scope('layer'):    
#创建一个简单的神经网络    with tf.name_scope('wights'):        W=tf.Variable(tf.zeros([784,10]),name='W')    
with tf.name_scope('biases'):        b=tf.Variable(tf.zeros([10]),name='b')    
with tf.name_scope('wx_plus_b'):        wx_plus_b=tf.matmul(x,W)+b    with tf.name_scope('softmax'):        
prediction=tf.nn.softmax(wx_plus_b)with tf.name_scope('loss'):    
#二次代价函数    loss=tf.reduce_mean(tf.square(y-prediction))with tf.name_scope('train'):    
#梯度下降    train_step=tf.compat.v1.train.GradientDescentOptimizer(0.2)
.minimize(loss)#初始化变量init=tf.compat.v1.global_variables_initializer()with tf.name_scope('accuracy'):    
with tf.name_scope('correct_prediction'):        #结果存放在一个布尔型列表中        
#返回的是一系列的True或False argmax返回一维张量中最大的值所在的位置，对比两个最大位置是否一致        
correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))    with tf.name_scope('accuracy'):        
#求准确率        #cast：将布尔类型转换为float，将True为1.0，False为0，然后求平均值        
accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))with tf.compat.v1.Session() as sess:    
sess.run(init)    writer=tf.compat.v1.summary.FileWriter('logs/',sess.graph)    for epoch in range(1):        
for batch in range(n_bath):            #获得一批次的数据，batch_xs为图片，batch_ys为图片标签            
batch_xs,batch_ys=mnist.train.next_batch(batch_size)            #进行训练            
sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})        #训练完一遍后，测试下准确率的变化        
acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})        
print("Iter "+str(epoch)+",Testing Accuracy "+str(acc))1.2.3.4.5.6.7.8.9.10.11.12.13.14.15.16.17.18.19.
20.21.22.23.24.25.26.27.28.29.30.31.32.33.34.35.36.37.38.39.40.41.42.43.44.45.46.47.48.49.50.51.52.53.54.
55.56.57.58.59.60.61.62.63.64.65.66.

会生成logs/目录，并且目录下的文件我们需要这样子打开