- Trending Categories
- Data Structure
- Networking
- RDBMS
- Operating System
- Java
- iOS
- HTML
- CSS
- Android
- Python
- C Programming
- C++
- C#
- MongoDB
- MySQL
- Javascript
- PHP

- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who

Tensorflow is a machine learning framework that is provided by Google. It is an open−source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly.

This is because it uses NumPy and multi−dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries, and is well−supported and documented. The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but multidimensional array or a list.

We will be using the Jupyter Notebook to run these code. TensorFlow can be installed on Jupyter Notebook using ‘pip install tensorflow’.

Following is an example −

import tensorflow as tf import numpy as np matrix_1 = np.array([(1,2,3),(3,2,1),(1,1,1)],dtype = 'int32') matrix_2 = np.array([(0,0,0),(-1,0,1),(3,3,4)],dtype = 'int32') print("The first matrix is ") print (matrix_1) print("The second matrix is ") print (matrix_2) print("The product is ") matrix_1 = tf.constant(matrix_1) matrix_2 = tf.constant(matrix_2) matrix_prod = tf.matmul(matrix_1, matrix_2) print((matrix_prod))

The first matrix is [[1 2 3] [3 2 1] [1 1 1]] The second matrix is [[ 0 0 0] [−1 0 1] [ 3 3 4]] The product is tf.Tensor( [[ 7 9 14] [ 1 3 6] [ 2 3 5]], shape=(3, 3), dtype=int32)

Import the required packages and provide an alias for it, for ease of use.

Two matrices are created using the Numpy package.

They are converted from being a Numpy array to a constant value in Tensorflow.

The ‘matmul’ function in Tensorflow is used to multiply the values in the matrix.

The resultant product is displayed on the console.

- Related Questions & Answers
- How can Tensorflow be used to add two matrices using Python?
- How to Multiply Two Matrices using Python?
- Python program to multiply two matrices
- How can Tensorflow be used to compose layers using Python?
- How can ‘placeholders’ in Tensorflow be used while multiplying matrices?
- How can Tensorflow be used to visualize the data using Python?
- How can Tensorflow be used to standardize the data using Python?
- How can Tensorflow be used to compile the model using Python?
- How can Tensorflow be used to train the model using Python?
- How can Tensorflow be used to visualize training results using Python?
- How can Tensorflow be used to instantiate an estimator using Python?
- How can Tensorflow be used to decode the predictions using Python?
- How can Tensorflow be used to build normalization layer using Python?
- How can Tensorflow be used to check the predicrion using Python?
- How can Tensorflow be used to plot the results using Python?

Advertisements