tensorboard入门

Tensorboard

本文tensorboard功能参考链接：www.cnblogs.com/fydeblog/p/7429344.html

微代码测试

代码全文

import tensorflow as tf

with tf.name_scope('graph') as scope:

	a = tf.constant([[3,4]],name = 'a')

	b = tf.constant([[5],[6]],name = 'b')

	product = tf.matmul(a,b,name='product')

sess = tf.Session()

writer = tf.summary.FileWriter('./tensorflow/',sess.graph)

init = tf.global_variables_initializer()

sess.run(init)

启动tensorboard

运行代码之后可以在目录./tensorboard/ 下看到生成了一个文件，用于启动tensorboard。执行tensorboard --logdir ./tensorflow/，打开浏览器127.0.0.1:6006 ,可以看到视图

双击graph可以进一步打开，这就是我们这段微代码的结构

进阶

代码全文

import tensorflow as tf

import matplotlib.pyplot as plt

import numpy as np

#样本数据

with tf.name_scope('sample-data'):

	x_train = np.linspace(-1,1,300)[:,np.newaxis]

	noise = np.random.normal(0, 0.1, x_train.shape)

	y_train = x_train * 3 + noise + 0.8

#

with tf.name_scope('hold-data'):

	x = tf.placeholder(tf.float32, [None, 1])

	y = tf.placeholder(tf.float32, [None, 1])

#线性模型

with tf.name_scope('line-model'):

	W = tf.Variable([0.1],dtype = tf.float32,name='W')

	#添加变量W到tensorboard的Distributions下

	tf.summary.histogram('Weight',W)

	b = tf.Variable([0.1],dtype = tf.float32,name='b')

	line_model = W * x + b

	#添加变量b到tensorboard的Distributions下

	tf.summary.histogram('bias',b)

#损失模型

with tf.name_scope("loss-model"):

	loss = tf.reduce_sum(tf.square(line_model - y))

	#添加变量loss到tensorboard的Scalars下

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

#创建优化器

with tf.name_scope("optimizer-model"):

	optimizer = tf.train.GradientDescentOptimizer(0.001)

	train = optimizer.minimize(loss)

#初始化变量

with tf.name_scope("init-model"):

	init = tf.global_variables_initializer()

	sess = tf.Session()

	sess.run(init)

# 绘制样本数据

fig = plt.figure()

ax = fig.add_subplot(1,1,1)

ax.scatter(x_train, y_train)

plt.ion()

plt.show()

plt.pause(1)

#将所有的summary全部保存磁盘

merged = tf.summary.merge_all()

#tensorboard所需数据写入文件

writer = tf.summary.FileWriter('./tensorflow/',sess.graph)

#训练100次

for i in range(100):

	if i % 10 == 0:

		#每隔10次打印1次成果

		print(i)

		print('W:%s  b:%s' % (sess.run(W),sess.run(b)))

		print('loss:%s' % (sess.run(loss,{x:x_train,y:y_train})))

		#绘制拟合直线

		try:

			ax.lines.remove(lines[0])

		except Exception:

			pass

		lines = ax.plot(x_train, sess.run(W)*x_train+sess.run(b), 'r-', lw=5)

		plt.pause(1)

	sess.run(train,{x:x_train,y:y_train})

	#向tensorboard添加数据

	rs = sess.run(merged,{x:x_train,y:y_train})

	writer.add_summary(rs,i)

# 打印训练100次后的成果

print('---')

print('W:%s  b:%s' % (sess.run(W),sess.run(b)))

print('loss:%s' % (sess.run(loss,{x:x_train,y:y_train})))

相比前文，这里添加了两部分

• 1.拟合直线的动态变化图像
  
  

• 2.tensorboard展现数据变化过程

启动tensorboard

tensorboard --logdir ./tensorflow/

可以看到loss(误差大小)的变化曲线

权重值W和偏差b的变化曲线(y = W * x + b)