In simple language we can call as multi dimensional array. Let us try to understand different dimensions

Zero dimension : It is a single point or a scalar value One dimension : It is a line , it consist of zero dimension points Two dimensions : It is a matrix , N dimensions : Tensor

What is graph and what is session

Tensors flow across a graph consist of operations. Here tensors flows through each node or operation under a session. The graph consist of nodes or operations and tensors are our inputs or resultants out of the nodes.

Each session is a process flow across the graph using nodes and tensors.
Having said all these complex words to understand let us first start with some examples.

Importing library

import tensorflow as tf

Creating graph

import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([50], name = 'my_const_a')

Here we have added one constant. Now we will create session and see the result.

import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([50], name = 'my_const_a')
my_sess = tf.Session(graph = my_graph)
result = my_sess.run(a)
print(result)
sess.close()

Output

[50]

Adding

import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
a = tf.constant([12], name = 'my_const_a')
b = tf.constant([13], name = 'my_const_b')
c=tf.add(a,b)
sess = tf.Session(graph = my_graph)
result = sess.run(c)
print(result)
sess.close()

import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
my_matrix1=tf.constant([[1,2,3],[4,5,6],[7,8,9]])
my_matrix2=tf.constant([[11,12,13],[14,15,16],[17,18,19]])
my_result=tf.matmul(my_matrix1,my_matrix2)
with tf.Session(graph = my_graph) as my_sess:
result=my_sess.run(my_result)
print(result)