TensorFlow笔记-可视化Tensorboard

 

可视化Tensorboard

•数据序列化-events文件

TensorBoard 通过读取 TensorFlow 的事件文件来运行

•tf.summary.FileWriter('/tmp/tensorflow/summary/test/',graph=

default_graph)

返回filewriter,写入事件文件到指定目录(最好用绝对路径)，以提供给tensorboard使用

•开启

tensorboard --logdir=/tmp/tensorflow/summary/test/

一般浏览器打开为127.0.0.1:6006 或者 localhost:6006

注：修改程序后，再保存一遍会有新的事件文件，打开默认为最新

import tensorflow as tf
import os
# 防止警告
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.add(input1,input2)
with tf.Session() as sess:
    print(sess.run([output],feed_dict={input1:10.0,input2:20.0}))
    summary_writer = tf.summary.FileWriter('./tmp/summary/test/', graph=sess.graph)

Scalar merge

目的：观察模型的参数、损失值等变量值的变化

1、收集变量

•tf.summary.scalar(name=’’,tensor)收集对于损失函数和准确率等单值变量,name为变量的名字，tensor为值

•tf.summary.histogram(name=‘’,tensor)收集高维度的变量参数

•tf.summary.image(name=‘’,tensor) 收集输入的图片张量能显示图片

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_label, logits=y))
# 梯度下降
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)
# 比较真实标签
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_label, 1))
# tf.cast(xx,tf.float32)改变tensor类型
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

tf.summary.scalar("loss",cross_entropy)

tf.summary.scalar("accuracy", accuracy)

tf.summary.histogram("W",W)

2、合并变量写入事件文件

•merged= tf.summary.merge_all()

•运行合并：summary= sess.run(merged)，每次迭代都需运行

•添加：FileWriter.add_summary(summary,i),i表示第几次的值

merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(FLAGS.summary_dir, graph=sess.graph)
summary = sess.run(merged)
summary_writer.add_summary(summary,i)

 来个复杂一点的：

import tensorflow as tf
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

graph = tf.Graph()
with graph.as_default():
    with tf.name_scope("name1") as scope:
        a = tf.Variable([1.0,2.0],name="a")
    with tf.name_scope("name2") as scope:
        b = tf.Variable(tf.zeros([20]),name="b")
        c = tf.Variable(tf.ones([20]),name="c")
    with tf.name_scope("name3") as scope:
        a1 = tf.Variable(tf.constant(21.0), name="a1")
        b1 = tf.Variable(tf.constant(13.0), name="b1")
    with tf.name_scope("cal") as scope:
        d = tf.concat([b,c],0)
        e = tf.add(a,57)
        c1 = tf.add(a1, b1)


with tf.Session(graph=graph) as sess:
    tf.global_variables_initializer().run()
    # merged = tf.summary.merge_all()
    summary_writer = tf.summary.FileWriter('./tmp/summary/test/', graph=sess.graph)
    # print(sess.run([d, e, c1]))