Tensorlfow的可视化工具TensorBoard

TensorBoard简介

目前Tensorboard可以展示网络结构以及几种数据：标量指标、图片、音频、计算图的有向图、参数变量的分布和直方图等。Tensorboard的工作方式是启动一个Web服务，该服务进程从Tensorflow程序执行所得到的事件日志文件（event files）中读取概要（summary）数据，然后将数据在网页中绘制可视化的图表。

计算图可视化

如果只是想显示网络结构，一般使用tf.name_scope(),记住一点，同命名空间下的所有节点会被缩略成一个节点，只有顶层节点空间中的节点才会被现实在tensorboard可视化效果上。

监控指标可视化

1.具体的整个流程，参考香港大学的tensorflow—ppt

1. From TF graph, decide which tensors you want to log（从tensorflow中决定哪一个tensors被记录）

    w2_hist = tf.summary.histogram("weights2", W2)
    cost_summ = tf.summary.scalar("cost", cost)

2. Merge all summaries（将所有概要操作合成一个算子）

summary = tf.summary.merge_all()

3. Create writer and add graph（创建概要写入操作）

# Create summary writer
writer = tf.summary.FileWriter(‘./logs’)
writer.add_graph(sess.graph)
或者
writer = tf.summary.FileWriter(logdir, sess.graph)

4. Run summary merge and add_summary（运行和加入summary）

s, _ = sess.run([summary, optimizer], feed_dict=feed_dict)
writer.add_summary(s, global_step=global_step)

5. Launch TensorBoard（运行tensorboard）

tensorboard --logdir=./logs

2.监控指标

标量数据，如准确率、代价函数，使用tf.summary.scalar加入算子

参数数据，如参数矩阵weights、偏置矩阵bias，一般使用tf.summary.histogram记录

图像数据，用tf.summary.image加入记录算子

音频数据，用tf.summary.audio加入算子

与其他算子一样，记录概要的节点由于没有被任何计算节点缩依赖，所以并不会自动执行，需要手动通过Session.run()接口触发。

3.函数介绍

I、SCALAR

tf.summary.scalar(name, tensor, collections=None, family=None)
可视化训练过程中随着迭代次数准确率(val acc)、损失值(train/test loss)、学习率(learning rate)、每一层的权重和偏置的统计量(mean、std、max/min)等的变化曲线
输入参数：
name：此操作节点的名字，TensorBoard 中绘制的图形的纵轴也将使用此名字
tensor： 需要监控的变量 A real numeric Tensor containing a single value.
输出：
A scalar Tensor of type string. Which contains a Summary protobuf.

II、IMAGE

tf.summary.image(name, tensor, max_outputs=3, collections=None, family=None)
可视化当前轮训练使用的训练/测试图片或者 feature maps
输入参数：
name：此操作节点的名字，TensorBoard 中绘制的图形的纵轴也将使用此名字
tensor： A r A 4-D uint8 or float32 Tensor of shape [batch_size, height, width, channels] where channels is 1, 3, or 4
max_outputs：Max number of batch elements to generate images for
输出：
A scalar Tensor of type string. Which contains a Summary protobuf.

III、HISTOGRAM

tf.summary.histogram(name, values, collections=None, family=None)
可视化张量的取值分布
输入参数：
name：此操作节点的名字，TensorBoard 中绘制的图形的纵轴也将使用此名字
tensor： A real numeric Tensor. Any shape. Values to use to build the histogram
输出：
A scalar Tensor of type string. Which contains a Summary protobuf.

Ⅳ、tf.summary.FileWriter

tf.summary.FileWriter(logdir, graph=None, flush_secs=120, max_queue=10)
负责将事件日志(graph、scalar/image/histogram、event)写入到指定的文件中
初始化参数：
   logdir：事件写入的目录
   graph：如果在初始化的时候传入sess,graph的话，相当于调用add_graph() 方法，用于计算图的可视化
   flush_sec：How often, in seconds, to flush the added summaries and events to disk.
   max_queue：Maximum number of summaries or events pending to be written to disk before one of the ‘add’ calls block.
其它常用方法：
   add_event(event)：Adds an event to the event file
   add_graph(graph, global_step=None)：Adds a Graph to the event file，Most users pass a graph in the constructor instead
   add_summary(summary, global_step=None)：Adds a Summary protocol buffer to the event file，一定注意要传入 global_step
   close()：Flushes the event file to disk and close the file
   flush()：Flushes the event file to disk
   add_meta_graph(meta_graph_def,global_step=None)
   add_run_metadata(run_metadata, tag, global_step=None)

代码测试

# -*- coding: utf-8 -*-
"""
Created on Mon Mar 26 21:14:59 2018

@author: pc314
"""

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

myGraph = tf.Graph()
with myGraph.as_default():
    with tf.name_scope('inputsAndLabels'):
        x_raw = tf.placeholder(tf.float32, shape=[None, 784])
        y = tf.placeholder(tf.float32, shape=[None, 10])

    with tf.name_scope('hidden1'):
        x = tf.reshape(x_raw, shape=[-1,28,28,1])
        W_conv1 = weight_variable([5,5,1,32])
        b_conv1 = bias_variable([32])
        l_conv1 = tf.nn.relu(tf.nn.conv2d(x,W_conv1, strides=[1,1,1,1],padding='SAME') + b_conv1)
        l_pool1 = tf.nn.max_pool(l_conv1, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

        tf.summary.image('x_input',x,max_outputs=10)
        tf.summary.histogram('W_con1',W_conv1)
        tf.summary.histogram('b_con1',b_conv1)

    with tf.name_scope('hidden2'):
        W_conv2 = weight_variable([5,5,32,64])
        b_conv2 = bias_variable([64])
        l_conv2 = tf.nn.relu(tf.nn.conv2d(l_pool1, W_conv2, strides=[1,1,1,1], padding='SAME')+b_conv2)
        l_pool2 = tf.nn.max_pool(l_conv2, ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')

        tf.summary.histogram('W_con2', W_conv2)
        tf.summary.histogram('b_con2', b_conv2)

    with tf.name_scope('fc1'):
        W_fc1 = weight_variable([64*7*7, 1024])
        b_fc1 = bias_variable([1024])
        l_pool2_flat = tf.reshape(l_pool2, [-1, 64*7*7])
        l_fc1 = tf.nn.relu(tf.matmul(l_pool2_flat, W_fc1) + b_fc1)
        keep_prob = tf.placeholder(tf.float32)
        l_fc1_drop = tf.nn.dropout(l_fc1, keep_prob)

        tf.summary.histogram('W_fc1', W_fc1)
        tf.summary.histogram('b_fc1', b_fc1)

    with tf.name_scope('fc2'):
        W_fc2 = weight_variable([1024, 10])
        b_fc2 = bias_variable([10])
        y_conv = tf.matmul(l_fc1_drop, W_fc2) + b_fc2

        tf.summary.histogram('W_fc1', W_fc1)
        tf.summary.histogram('b_fc1', b_fc1)

    with tf.name_scope('train'):
        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_conv, labels=y))
        train_step = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cross_entropy)
        correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

        tf.summary.scalar('loss', cross_entropy)
        tf.summary.scalar('accuracy', accuracy)


with tf.Session(graph=myGraph) as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()

    merged = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('logs/', graph=sess.graph)

    for i in range(3001):
        batch = mnist.train.next_batch(50)
        sess.run(train_step,feed_dict={x_raw:batch[0], y:batch[1], keep_prob:0.5})
        if i%100 == 0:
            train_accuracy = accuracy.eval(feed_dict={x_raw:batch[0], y:batch[1], keep_prob:1.0})
            print('step %d training accuracy:%g'%(i, train_accuracy))

            summary = sess.run(merged,feed_dict={x_raw:batch[0], y:batch[1], keep_prob:1.0})
            summary_writer.add_summary(summary,i)

    test_accuracy = accuracy.eval(feed_dict={x_raw:mnist.test.images, y:mnist.test.labels, keep_prob:1.0})
    print('test accuracy:%g' %test_accuracy)

    #saver.save(sess,save_path='./model/mnistmodel',global_step=1)

运行的过程中，还是运行之后都可以使用tensorboard进行观察。因为我是使用的是anaconda，所以首先激活tensorflow环境，然后到logs文件夹下，运行这段命令就可以了

tensorboard --logdir=logs

然后就看到有一个网址，我的是http://pc314:6006。将这个网址复制到浏览器中，就可以看到需要可视化的东西了。如图展示一部分。

参考

https://blog.csdn.net/mzpmzk/article/details/78647699?locationNum=2&fps=1

这篇博客写的是真的NB，很好。