TensorFlow——TensorBoard可视化

TensorFlow提供了一个可视化工具TensorBoard，它能够将训练过程中的各种绘制数据进行展示出来，包括标量，图片，音频，计算图，数据分布，直方图等，通过网页来观察模型的结构和训练过程中各个参数的变化。

Tensorboard通过一个日志展示系统进行数据可视化，在session运行图的时候，将各类的数据汇总并输出到日志文件中。然后启动Tensorboard服务，Tensorboard读取日志文件，并开启6006端口提供web服务。让用户可以在浏览器中查看数据。

相关的API函数如下;

tf.summary.scalar() ：标量数据汇总，输出protobuf

tf.summary.histogram() ：记录变量var的直方图，输出到直方图汇总的protobuf

tf.summary.image() ：图像数据汇总，输出protobuf

tf.summary.merge() ：合并所有的汇总日志

tf.summary.FileWriter() ：创建SummaryWriter

tf.summary.FileWriter().add_summary()

tf.summary.FileWriter().add_session_log()

tf.summary.FileWriter().add_event()

tf.summary.FileWriter().add_graph() : 将protobuf写入文件的类

代码如下：

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

train_x = np.linspace(-5, 3, 50)
train_y = train_x * 5 + 10 + np.random.random(50) * 10 - 5

# plt.plot(train_x, train_y, 'r.')
# plt.grid(True)
# plt.show()

X = tf.placeholder(dtype=tf.float32)
Y = tf.placeholder(dtype=tf.float32)

w = tf.Variable(tf.random.truncated_normal([1]), name='Weight')
b = tf.Variable(tf.random.truncated_normal([1]), name='bias')

z = tf.multiply(X, w) + b

tf.summary.histogram('z', z)

cost = tf.reduce_mean(tf.square(Y - z))

tf.summary.scalar('loss', cost)

learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

training_epochs = 20
display_step = 2


with tf.Session() as sess:
    sess.run(init)
    loss_list = []

    merged_summary_op = tf.summary.merge_all()  # 合并所有的summary
    summary_wirter = tf.summary.FileWriter('log/linear', sess.graph)

    for epoch in range(training_epochs):
        for (x, y) in zip(train_x, train_y):
            sess.run(optimizer,feed_dict={X:x, Y:y})

        if epoch % display_step == 0:
            loss = sess.run(cost, feed_dict={X:x, Y:y})
            loss_list.append(loss)
            print('Iter: ', epoch, ' Loss: ', loss)
        summary_str = sess.run(merged_summary_op, feed_dict={X:train_x, Y:train_y})
        summary_wirter.add_summary(summary_str, epoch)

    w_, b_ = sess.run([w, b], feed_dict={X: x, Y: y})
    print(" Finished ")
    print("W: ", w_, " b: ", b_, " loss: ", loss)
    plt.plot(train_x, train_x*w_ + b_, 'g-', train_x, train_y, 'r.')
    plt.grid(True)
    plt.show()