【4-1】Tensorboard网络结构

 1 import tensorflow as tf
 2 from tensorflow.examples.tutorials.mnist import input_data
 3 
 4 #载入数据集
 5 mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
 6 
 7 #每个批次的大小
 8 batch_size = 100
 9 #计算一共有多少个批次
10 n_batch = mnist.train.num_examples // batch_size
11 
12 #命名空间
13 with tf.name_scope(‘input‘):
14     #定义两个placeholder
15     x = tf.placeholder(tf.float32,[None,784],name=‘x-input‘)
16     y = tf.placeholder(tf.float32,[None,10],name=‘y-input‘)
17 
18     
19 with tf.name_scope(‘layer‘):
20     #创建一个简单的神经网络
21     with tf.name_scope(‘wights‘):
22         W = tf.Variable(tf.zeros([784,10]),name=‘W‘)
23     with tf.name_scope(‘biases‘):    
24         b = tf.Variable(tf.zeros([10]),name=‘b‘)
25     with tf.name_scope(‘wx_plus_b‘):
26         wx_plus_b = tf.matmul(x,W) + b
27     with tf.name_scope(‘softmax‘):
28         prediction = tf.nn.softmax(wx_plus_b)
29 
30 #二次代价函数
31 # loss = tf.reduce_mean(tf.square(y-prediction))
32 with tf.name_scope(‘loss‘):
33     loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
34 with tf.name_scope(‘train‘):
35     #使用梯度下降法
36     train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
37 
38 #初始化变量
39 init = tf.global_variables_initializer()
40 
41 with tf.name_scope(‘accuracy‘):
42     with tf.name_scope(‘correct_prediction‘):
43         #结果存放在一个布尔型列表中
44         correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的值所在的位置
45     with tf.name_scope(‘accuracy‘):
46         #求准确率
47         accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
48 
49 with tf.Session() as sess:
50     sess.run(init)
51     writer = tf.summary.FileWriter(‘logs/‘,sess.graph)
52     for epoch in range(1):
53         for batch in range(n_batch):
54             batch_xs,batch_ys =  mnist.train.next_batch(batch_size)
55             sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
56         
57         acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
58         print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))