Tensorflow basics constants with examples

What is Tensor

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

Tensor

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()

Output

[25]

We will get shape of different dimensions

import tensorflow as tf
my_graph = tf.Graph()
with my_graph.as_default():
    my_scalar = tf.constant(5)
    my_vector = tf.constant([1,2,3])
    my_matrix = tf.constant([[1,2,3],[4,5,6],[7,8,9]])
    my_tensor = tf.constant( [ [[1,2,3],[4,5,6],[7,8,9]] ,
	[[4,5,6],[1,2,3],[7,8,9]] , [[7,8,9],[4,5,6],[1,2,3]] ] )
    
    print(my_scalar.shape)
    print(my_vector.shape)
    print(my_matrix.shape)
    print(my_tensor.shape)

Output

()
(3,)
(3, 3)
(3, 3, 3)

Now let us multiply two matrix by using matmul()

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)

Output

[[ 90  96 102]
 [216 231 246]
 [342 366 390]]

Tensorflow installation using pip

Creating Environment for tensorflow

conda create -n tensorflow_env tensorflow

The version of installed tensorflow
Tensorflow version

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tensorflow Basics

What is Tensor

What is graph and what is session

Adding

Tensorflow installation using pip

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