The usage of TensorBoard has changed slightly

Overview

It's just now, but I tried moving TensorBoard for a while. At that time, the page The most basic usage of TensorBoard was very helpful. The best thing was that the sample was very simple and easy to try.

However, as you can see in the comments, it seems that there are various differences depending on the version, and it is the content that I tried running it in the latest environment. So, after looking at the Omoto page, I hope you can see this as a reference, but since the basics are just a copy and paste of the Omoto page, it may not be necessary to refer to this. Maybe. Reprint: The most basic usage of TensorBoard

environment

$ pip list | grep tens
tensorboard          1.14.0
tensorflow           1.14.0
tensorflow-estimator 1.14.0

When,
tensorboard          2.0.1
tensorflow           2.0.0
tensorflow-estimator 2.0.1

v1 series (1.14)

#Import required libraries
import tensorflow as tf
import numpy as np

#Variable definition
dim = 5
LOGDIR = './logs'
x = tf.compat.v1.placeholder(tf.float32, [None, dim + 1], name='X')
w = tf.Variable(tf.zeros([dim+1,1]), name='weight')
y = tf.matmul(x,w, name='Y')
t = tf.compat.v1.placeholder(tf.float32, [None, 1], name='TEST')
sess = tf.compat.v1.Session()

#Definition of loss function and learning method
loss = tf.reduce_sum(tf.square(y - t))
train_step = tf.compat.v1.train.AdamOptimizer().minimize(loss)

#Define variables to track with TensorBoard
with tf.name_scope('summary'):
	#Use return
	loss_summary = tf.compat.v1.summary.scalar('loss', loss)
	if tf.io.gfile.exists(LOGDIR):
		tf.io.gfile.rmtree(LOGDIR) # ./Delete if logdir exists
	writer = tf.compat.v1.summary.FileWriter(LOGDIR, sess.graph)

#Session initialization and input data preparation
sess.run(tf.compat.v1.global_variables_initializer())

train_t = np.array([5.2, 5.7, 8.6, 14.9, 18.2, 20.4,25.5, 26.4, 22.8, 17.5, 11.1, 6.6])
train_t = train_t.reshape([12,1])
train_x = np.zeros([12, dim+1])
for row, month in enumerate(range(1, 13)):
	for col, n in enumerate(range(0, dim+1)):
		train_x[row][col] = month**n

#Learning

i = 0
for _ in range(100000):
	i += 1
	sess.run(train_step, feed_dict={x: train_x, t: train_t})
	if i % 10000 == 0:
		#Loss obtained above_Pass summery
		s, loss_val = sess.run([loss_summary, loss] , feed_dict={x: train_x, t: train_t})
		print('Step: %d, Loss: %f' % (i, loss_val))
		#This will output SCALARS
		writer.add_summary(s, global_step=i)

--GRAPHS image

It will be easier to understand if you give it a name by name.

--SCALARS graph

By the way, if you make it look like the following, it seems that you do not have to change the existing code so much. Since it is described in the tensorflow document, this is an appropriate description method.

import tensorflow.compat.v1 as tf

v2 series (2.00)

So, let's change the writing style here. Also, the point of v2 is that the way of writing Variable has changed. Also, what I don't really understand is the Eager Tensors part. For the time being, I specified session as with.

#Import required libraries
import tensorflow.compat.v1 as tf
import numpy as np

#Variable definition
dim = 5
LOGDIR = './logs'
with tf.Session() as sess:
	x = tf.placeholder(tf.float32, [None, dim + 1], name='X')
	with tf.variable_scope('weight'):
		w = tf.get_variable("weight", shape=[dim+1,1], initializer=tf.zeros_initializer())
	y = tf.matmul(x,w, name='Y')
	t = tf.placeholder(tf.float32, [None, 1], name='TEST')

	#Definition of loss function and learning method
	loss = tf.reduce_sum(tf.square(y - t))
	train_step = tf.train.AdamOptimizer().minimize(loss)

	#Define variables to track with TensorBoard
	with tf.name_scope('summary'):
		loss_summary = tf.summary.scalar('loss', loss)
		if tf.io.gfile.exists(LOGDIR):
			tf.io.gfile.rmtree(LOGDIR) # ./Delete if logdir exists
		writer = tf.summary.FileWriter(LOGDIR, sess.graph)

	#Session initialization and input data preparation
	sess.run(tf.global_variables_initializer())

	train_t = np.array([5.2, 5.7, 8.6, 14.9, 18.2, 20.4,25.5, 26.4, 22.8, 17.5, 11.1, 6.6])
	train_t = train_t.reshape([12,1])
	train_x = np.zeros([12, dim+1])
	for row, month in enumerate(range(1, 13)):
		for col, n in enumerate(range(0, dim+1)):
			train_x[row][col] = month**n

	#Learning

	i = 0
	for _ in range(100000):
		i += 1
		sess.run(train_step, feed_dict={x: train_x, t: train_t})
		if i % 10000 == 0:
			s, loss_val = sess.run([loss_summary, loss] , feed_dict={x: train_x, t: train_t})
			print('Step: %d, Loss: %f' % (i, loss_val))
			writer.add_summary(s, global_step=i)

--SCALARS graph

It seems that the Y part is different.