005-2-tensorboard-显示网络结构

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#载入数据
mnist = input_data.read_data_sets("MNIST_data",one_hot = True)

#定义每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples//batch_size

#命名空间
with tf.name_scope("input"):
    #定义2个placeholder
    x = tf.placeholder(tf.float32,[None,784],name="x_input")
    y = tf.placeholder(tf.float32,[None,10],name="y_input")

#命名空间
with tf.name_scope("layer"):
    #创建一个简单的神经网络：
    with tf.name_scope('Weight'):
        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)

#二次代价函数：
# loss = tf.reduce_mean(tf.square(y-prediction))
with tf.name_scope('loss'):
#对数似然函数
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels= y,
                                                  logits= prediction)) 
with tf.name_scope('train'):
    #梯度下降
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

#初始化变量
init = tf.global_variables_initializer()

with tf.name_scope('accuracy'):
    #求准确率
    with tf.name_scope('correct_prediction'):
    #比较预测值最大标签位置与真实值最大标签位置是否相等
        correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
    with tf.name_scope('accuracy'):
        #求准去率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter("logs/",sess.graph)
    for epoch in range(1):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict = {x:batch_xs,y:batch_ys})
        acc = sess.run(accuracy,feed_dict ={x:mnist.test.images,
                                            y:mnist.test.labels})
        print("Iter"+str(epoch+1)+",Testing accuracy"+str(acc))
        

　　logs文件夹在anaconda prompt中输入命令：

tensorboard --logdir=logs路径

可以复制后面那个网址，也可以直接进入http://localhost:6006

可以得到整个网络结构