Over a million developers have joined DZone.

Learn TensorFlow: Vectors

DZone's Guide to

Learn TensorFlow: Vectors

Learn about TensorFlow and explore vectors.

· AI Zone ·
Free Resource

Did you know that 50- 80% of your enterprise business processes can be automated with AssistEdge?  Identify processes, deploy bots and scale effortlessly with AssistEdge.


TensorFlow is an open-source library that was developed by the Google Brain team, and it was released in November 2015. Before working with TensorFlow, we need to understand the following basic concepts:

  • Graph: Layout of the learning process. It does not include data.
  • Data: Examples that are used to train. It has two kinds, which are inputs and targets.
  • Session: Where we feed the graph with data or Session = Graph + Data. We can do this by using placeholders — gates to introduce examples.

We can install Anaconda to use TensorFlow.


In Machine Learning, vectors can be used as a good way to represent numeric data. When using vectors, we can meet the following basic operations:

  • Add two vectors
  • Subtract two vectors
  • Mutilply a vector with a scalar (i.e., a number)
  • Norm (i.e, magnitude or length of a vector)
  • Dot product of two vectors

The operations on vectors can be implemented by using the functions from the TensorFlow library, but before using this library, we must:

import tensorflow as tf

Next step is to create the graph:


vec_1 = tf.placeholder(tf.float32)

vec_2 = tf.placeholder(tf.float32)

scalar = tf.placeholder(tf.float32)

vector_add = tf.add(vec_1,vec_2)

vector_subtract = tf.subtract(vec_1,vec_2)

scalar_multiply = tf.multiply(scalar,vec_1)

norm = tf.norm(vec_1)

dot = tf.tensordot(vec_1, vec_2, 1)

We can feed the graph with data through the session. Data can be declared as lists of numbers:


v = [1,2]

w = [2,3]

c = 3

Session can be created:


with tf.Session() as sess:

       result_add = sess.run(vector_add, feed_dict={vec_1:v,vec_2:w})

       result_sub = sess.run(vector_subtract, feed_dict={vec_1:v,vec_2:w})

       result_mul = sess.run(scalar_multiply, feed_dict={scalar:c,vec_1:v})

       result_norm = sess.run(norm , feed_dict={vec_1:v})

       result_dot = sess.run(dot, feed_dict={vec_1:v,vec_2:w})

Finally, we can create some outputs:


The result can look like this:

[3.0, 5.0]
[-1.0, -1.0]
[3.0, 6.0]


Starting from the simplest things is one of the best ways to learn the TensorFlow. Through operations on vectors, I hope you (and both me) — TensorFlow beginners — will understand how to use the TensorFlow before using it for more complex tasks in the future.  

Consuming AI in byte sized applications is the best way to transform digitally. #BuiltOnAI, EdgeVerve’s business application, provides you with everything you need to plug & play AI into your enterprise.  Learn more.

tensorflow ,python ,tutorial ,artificial intelligence ,ai ,vectors ,anaconda ,anaconda python environment

Opinions expressed by DZone contributors are their own.

{{ parent.title || parent.header.title}}

{{ parent.tldr }}

{{ parent.urlSource.name }}