[人工智能]5.2tensorflow结构可视化01

整理不易，有条件的点个关注、点个赞呗！感恩各位大哥！

tensorflow简单介绍

tensor采用图运算的方式搭建并训练深度学习网络，该部分使用的库包版本为tensorflow==1.14.0

代码拆分（每个代码块可以放到一个jupyter的cell里）

导入tensorflow及相关包

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

加载数据集、设定批次

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot = True)

# 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

定义参数概要

# 参数概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean',mean) # 平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
        tf.summary.scalar('stddev',stddev) # 标准差
        tf.summary.scalar('max',tf.reduce_max(var)) # 最大值
        tf.summary.scalar('min',tf.reduce_min(var)) # 最小值、
        tf.summary.histogram('histogram',var) # 直方图
    

定义各类命名空间

    
# 定义输出命名空间
with tf.name_scope('input'):
    # 定义两个placeholder
    x = tf.placeholder(tf.float32,[None,784],name='x-input')
    y = tf.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('weight'):
        W = tf.Variable(tf.zeros([784,10]),name='W')
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]),name='b')
        variable_summaries(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'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) # 交叉熵
    tf.summary.scalar('loss',loss)
    
with tf.name_scope('train'):
    # 使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

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

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        # 结果放在一个布尔型列表中
        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
    with tf.name_scope('accuracy'):
        # 求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        tf.summary.scalar('accuracy',accuracy)

合并所有的summary

# 合并所有的summary
merged = tf.summary.merge_all()

定义会话并运算

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter('logs/',sess.graph)
    for epoch in range(51):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
        writer.add_summary(summary,epoch)
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter" + str(epoch) + ",Testing Accuracy" + str(acc))

完整代码

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot = True)

# 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

# 参数概要
def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean',mean) # 平均值
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
        tf.summary.scalar('stddev',stddev) # 标准差
        tf.summary.scalar('max',tf.reduce_max(var)) # 最大值
        tf.summary.scalar('min',tf.reduce_min(var)) # 最小值、
        tf.summary.histogram('histogram',var) # 直方图
    
    
# 定义输出命名空间
with tf.name_scope('input'):
    # 定义两个placeholder
    x = tf.placeholder(tf.float32,[None,784],name='x-input')
    y = tf.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('weight'):
        W = tf.Variable(tf.zeros([784,10]),name='W')
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]),name='b')
        variable_summaries(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'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction)) # 交叉熵
    tf.summary.scalar('loss',loss)
    
with tf.name_scope('train'):
    # 使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

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

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        # 结果放在一个布尔型列表中
        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
    with tf.name_scope('accuracy'):
        # 求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
        tf.summary.scalar('accuracy',accuracy)

# 合并所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter('logs/',sess.graph)
    for epoch in range(51):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
        writer.add_summary(summary,epoch)
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Iter" + str(epoch) + ",Testing Accuracy" + str(acc))

可视化

找到文件

运行完成后，就可以在 logs（程序里定义的）文件夹下看到DESKTOP-UN9RU0Q文件。

tensorboard打开

在shell中输入：

tensorboard --logdir=C:\study\baishi\first_stage\tensor1\logs

如下：= 后面为目录文件夹

但是上面是打不开的，他给的地址是http://DES什么，正常为http://127都是数字

此时只需要在后面指定地址即可，即加上--host=127.0.0.1。

打开后复制地址在浏览器打开，如下: