tensorboard 手写数字识别可视化训练过程 （tensorflow小案例）

tensorboard是tensorflow中用来可视化的工具，可以方便的用来展示训练过程

安装好tensorflow环境

首先新建一个文件tensorboard.py

查看graph

# coding: utf-8

# In[1]:

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


# In[2]:

#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

#命名空间，相当于起名字
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('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)

#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))
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'):
        #结果存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))

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter('logs/',sess.graph)  # 加的存储tensorboard可视化代码
    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) + ",Testing Accuracy " + str(acc))


# In[ ]:

运行程序，看到当前目录下生成log文件，打开命令提示符。

若你在E盘写的程序，则在命令提示符中转的E盘

输入 

tensorboard --logdir==E:\biyesheji\inception_log #此处路径要改为你在本机上生成的log文件下的路径

复制网址，在本地打开

查看网络运行时的数据

首先可以看到一个网络结构图 graph,接着我们来修改一下代码

增加一些layers

# coding: utf-8

# In[1]:

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


# In[2]:

#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

#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('wights'):               #权值
        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))
    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'):
        #结果存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))


# In[ ]:

# for i in range(2001):
#     #m每个批次100个样本
#     batch_xs,batch_ys = mnist.train.next_batch(100)
#     summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
#     writer.add_summary(summary,i)
#     if i%500 == 0:
#         print(sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}))

运行该文件，得到结果

又可以生成一个log文件，继续打开命令符， 看一下现在的graph结构

tensorboard --logdir==D:\2018年7月2日095632\logs #此处路径要改为你在本机上生成的log文件下的路径

手写数字可视化训练过程

修改文件

在工程目录下新建一个文件夹projector

在projector文件夹下创建data文件夹和另一个projector文件夹

data中是一个图片，图片中有一万个手写数字

# coding: utf-8

# In[1]:

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
from tensorflow.contrib.tensorboard.plugins import projector


# In[2]:

#载入数据集
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
#运行次数
max_steps = 1001
#图片数量
image_num = 3000
#文件路径
DIR = "D:/Tensorflow/"   #改成自己的工程目录

#定义会话
sess = tf.Session()

#载入图片
embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding')

#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

#命名空间
with tf.name_scope('input'):
    #这里的none表示第一个维度可以是任意的长度
    x = tf.placeholder(tf.float32,[None,784],name='x-input')
    #正确的标签
    y = tf.placeholder(tf.float32,[None,10],name='y-input')

#显示图片
with tf.name_scope('input_reshape'):
    image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])
    tf.summary.image('input', image_shaped_input, 10)

with tf.name_scope('layer'):
    #创建一个简单神经网络
    with tf.name_scope('weights'):
        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)

with tf.name_scope('loss'):
    #交叉熵代价函数
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))
    tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
    #使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)

#初始化变量
sess.run(tf.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
        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))#把correct_prediction变为float32类型
        tf.summary.scalar('accuracy',accuracy)

#产生metadata文件
if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):
    tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv')
with open(DIR + 'projector/projector/metadata.tsv', 'w') as f:
    labels = sess.run(tf.argmax(mnist.test.labels[:],1))
    for i in range(image_num):   
        f.write(str(labels[i]) + '\n')        
        
#合并所有的summary
merged = tf.summary.merge_all()   


projector_writer = tf.summary.FileWriter(DIR + 'projector/projector',sess.graph)
saver = tf.train.Saver()
config = projector.ProjectorConfig()
embed = config.embeddings.add()
embed.tensor_name = embedding.name
embed.metadata_path = DIR + 'projector/projector/metadata.tsv'
embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png'
embed.sprite.single_image_dim.extend([28,28])
projector.visualize_embeddings(projector_writer,config)

for i in range(max_steps):
    #每个批次100个样本
    batch_xs,batch_ys = mnist.train.next_batch(100)
    run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
    run_metadata = tf.RunMetadata()
    summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)
    projector_writer.add_run_metadata(run_metadata, 'step%03d' % i)
    projector_writer.add_summary(summary, i)
    
    if i%100 == 0:
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print ("Iter " + str(i) + ", Testing Accuracy= " + str(acc))

saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps)
projector_writer.close()
sess.close()


# In[ ]:

找到生成的log文件，D:\Tensorflow\projector\projector下   打开命令提示符 转到D盘  运行文件 打开tensorboard可以看到，准确率 loss值等