Types of Tensor

We can define basically three types of tensor:

Constant: It is a tensor which will be constant while a graph run there is no need to initialize it.

Variable: It is a tensor which will be asynchronously assigned and initiated.

Placeholder: It is a tensor which accept value at run time from the feed_dict.

Code for Constant:

import tensorflow as tf

a= tf.constant(12)
b=tf.constant(13)
c=tf.multiply(a,b,name='mul')
print(c)

sess=tf.Session()
b=tf.constant(13)
sess.run(c)

Code for Variable:

import tensorflow as tf

a=tf.Variable(2)
b=tf.Variable(3)
c=tf.multiply(a,b,name='mul')
b=tf.Variable(3)
print(c)

sess=tf.Session()
b=tf.Variable(5)
model=tf.global_variables_initializer()
sess.run(model)
sess.run(c)

Code of Place Holder:

import tensorflow as tf

tf.reset_default_graph()

a= tf.placeholder(tf.float32,shape=[1])
b=tf.placeholder(tf.float32,shape=[1])

c= tf.multiply(a,b)
print(c)

sess=tf.Session()
#model=tf.global_variables_initializer()
#sess.run(model)
sess.run(c,feed_dict={a:(2,),b:(9,)})