Foreword

This is a simple use of the TensorBoard.

Brief introduction

Tensorboard is a web application suite for the operation of checking tensorflow program. You can intuitively understand the training.

use

Use summary op. Examples spend an article, with the cost of change tensorboard record.

Join summary

The op be observed added to the scalar summary op

# 创建summary来观察损失值
tf.summary.scalar("loss", cost)

Merge summary op

If the summary excessive op need to merge it.

merged_summary_op = tf.summary.merge_all()

Specifies the log file path to save

In fact, the principle tensorboard is generated log file after running over, this thing is to make log data visualization displayed.
So since it is a file path, then we want to specify the path to save the file.

logs_path="./example"

Session execution

First, come up with a saved op, using summary.FileWriter save the default map.

    #op 把需要的记录数据写入文件
    summary_writer=tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())

Execution merged_summary_op

summary=sess.run(merged_summary_op,feed_dict={X: train_X, Y: train_Y})
summary_writer.add_summary(summary, epoch * n_samples)

Detailed code

# -*- coding: utf-8 -*-
# @Time    : 2017/12/5 上午9:28
# @Author  : SkullFang
# @Email   : [email protected]
# @File    : demo2.py
# @Software: PyCharm
import tensorflow as tf
import  numpy as np
import matplotlib.pyplot as plt
import random
#训练参数
learning_rate=0.01

traing_epochs=1000

display_step=50

logs_path="./example"
# 训练数据
train_X = np.asarray([3.3, 4.4, 5.5, 6.71, 6.93, 4.168, 9.779, 6.182, 7.59, 2.167,
                         7.042, 10.791, 5.313, 7.997, 5.654, 9.27, 3.1])
train_Y = np.asarray([1.7, 2.76, 2.09, 3.19, 1.694, 1.573, 3.366, 2.596, 2.53, 1.221,
                         2.827, 3.465, 1.65, 2.904, 2.42, 2.94, 1.3])

n_samples = train_X.shape[0]

#定义两个变量的op占位符

X=tf.placeholder("float")
Y=tf.placeholder("float")

#初始化w,b
W=tf.Variable(random.random(),name="weight")
b=tf.Variable(random.random(),name="bias")

#构造线性模型
pred=tf.add(tf.multiply(X,W),b)

#均方误差
#reduce_sum是对每一项进行加和
#reduce_sum(x,0)是每一列进行加和，reduce_sum(x,1)是对每一行进行加和
cost=tf.reduce_sum(tf.pow(pred-Y,2))/(2*n_samples)

#梯度下降
optimizer=tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

#初始化所有的变量
init=tf.global_variables_initializer()

# 创建summary来观察损失值
tf.summary.scalar("loss", cost)
merged_summary_op = tf.summary.merge_all()

#以上都是构造op，只是为了告诉tensorflow 模型的数据流动方向



#使用session 启动默认图
with tf.Session() as sess:
    sess.run(init) #初始化

    #op 把需要的记录数据写入文件
    summary_writer=tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())

    for epoch in range(traing_epochs):
        for (x,y) in zip(train_X,train_Y):
            sess.run(optimizer,feed_dict={X:train_X,Y:train_Y})


        if(epoch+1) % display_step==0:
            # c, summary = sess.run([cost, merged_summary_op], feed_dict={X: train_X, Y: train_Y})
            c= sess.run(cost, feed_dict={X: train_X, Y: train_Y})
            summary=sess.run(merged_summary_op,feed_dict={X: train_X, Y: train_Y})
            summary_writer.add_summary(summary, epoch * n_samples)

            # c=sess.run(cost,feed_dict={X:train_X,Y:train_Y})
            print("Epoch:", '%04d' % (epoch + 1), "cost=", "{:.9f}".format(c), \
                  "W=", sess.run(W), "b=", sess.run(b))



    print ("optimization Finished")
    training_cost = sess.run(cost,feed_dict={X:train_X,Y:train_Y})
    print ("Training cost=",training_cost,"W=",sess.run(W),"b=",sess.run(b),"\n")

    #画图
    plt.plot(train_X,train_Y,'ro',label="Original data")
    plt.plot(train_X,sess.run(W)*train_X+sess.run(b),label="Fitted line")
    plt.legend()
    plt.show()
    plt.savefig('linear_train.png')

    # 测试数据
    test_X = np.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
    test_Y = np.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])

    print("Testing... (Mean square loss Comparison)")
    testing_cost = sess.run(
        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
    print("Testing cost=", testing_cost)
    print("Absolute mean square loss difference:", abs(
        training_cost - testing_cost))

    plt.plot(test_X, test_Y, 'bo', label='Testing data')
    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
    plt.legend()
    plt.show()
    plt.savefig('linear_test.png')

effect

1

After running over will be more of a sample of the file in the program directory folder, which is our log file

image.png

2

Open your terminal
in the following directory project execution, remember that it must be carried out under the project directory. Here is the log of the specified folder, and ports.

tensorboard --logdir=./example --host 0.0.0.0

image.png

This is the effect of the implementation of End.
Open in the browser
http://0.0.0.0:6006
This is the final result. We can see the decline in loss, indicating good for us to learn. The horizontal axis is the number of rounds

Reprinted: https://www.jianshu.com/p/1bdda872295c