How can I have visualized embeddings on tensorboard? Not MNIST data - tensorflow

Question

I'm trying to create visualized graph on Tensorboard embeddings, I'm using csv data, not MNIST data, the data in csv is like follows:
0.266782506,"1,0"
0.361942522,"0,1"
0.862076491,"0,1"
The data in first column like 0.366782506 is sample input_data x, and "0,1" is the one-hot label y. while 0
I tried to take reference on how to creat visualized graph by embedding projector on Tensorboard, but I found examples only by using MNIST data, so I'm looking for help if anyone can give a guidance on how to create a visualized embedding graph on Tensorboard.
I can have SCALAR, GRAPH and HISTOGRAM visulized on Tensorboard with my code as following:
# coding=utf-8
import tensorflow as tf
import numpy
import os
import csv
import shutil
from tensorflow.contrib.tensorboard.plugins import projector
#Reading data from csv:
filename = open('D:\Program Files (x86)\logistic\sample_1.csv', 'r')
reader = csv.reader(filename)
t_X, t_Y,c = [],[],[]
a,b=0,0
for i in reader:
t_X.append(i[0])
a= int(i[1][0])
b= int(i[1][2])
c= list([a,b])
t_Y.extend([c])
t_X = numpy.asarray(t_X)
t_Y = numpy.asarray(t_Y)
t_XT = numpy.transpose([t_X])
filename.close()
# Parameters
learning_rate = 0.01
training_epochs = 5
batch_size = 50
display_step = 1
n_samples = t_X.shape[0]
sess = tf.InteractiveSession()
with tf.name_scope('Input'):
with tf.name_scope('x_input'):
x = tf.placeholder(tf.float32, [None, 1],name='x_input')
with tf.name_scope('y_input'):
y = tf.placeholder(tf.float32, [None, 2],name='y_input')
# Weight
with tf.name_scope('layer1'):
with tf.name_scope('weight'):
W = tf.Variable(tf.random_normal([1, 2],dtype=tf.float32),name='weight')
with tf.name_scope('bias'):
b = tf.Variable(tf.random_normal([2], dtype=tf.float32),name='bias')
# model
with tf.name_scope('Model'):
with tf.name_scope('pred'):
pred = tf.nn.softmax(tf.matmul(x, W) + b, name='pred')
with tf.name_scope('cost'):
cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices=1),name='cost')
tf.summary.scalar('cost',cost)
tf.summary.histogram('cost',cost)
with tf.name_scope('optimizer'):
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Calculate accuracy
with tf.name_scope('accuracy_count'):
with tf.name_scope('correct_prediction'):
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
with tf.name_scope('accuracy'):
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy',accuracy)
tf.summary.histogram('accuracy', accuracy)
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
sess.run(init)
writer = tf.summary.FileWriter('D:\Tensorlogs\logs',sess.graph)
for epoch in range(training_epochs):
avg_cost = 0
total_batch = int(n_samples / batch_size)
i = 0
for anc in range(total_batch):
m,n = [],[]
m = t_X[i:i+batch_size]
n = t_Y[i:i+batch_size]
m = numpy.asarray(m)
n = numpy.asarray(n)
m = numpy.transpose([m])
summary, predr, o, c = sess.run([merged, pred, optimizer, cost],feed_dict={x: m, y: n})
avg_cost += c / total_batch
i = i + batch_size
writer.add_summary(summary, epoch+1)
if (epoch + 1) % display_step == 0:
print("Epoch:", '%04d' % (epoch + 1), "cost=", avg_cost,"W=",wr,"b=",br,"accuracy_s=",accuracy_s.eval(feed_dict={x: t_XT, y: t_Y}))
print("Optimization Finished!")
Thank you ver much!