How to install TensorFlow?

An easy-to-follow guide for TensorFlow installation is available.

Here you can refer to this article – How to Install Python Tensorflow in Windows?

Once you can ensure a successful installation by running this command in python interpreter:

import tensorflow as tf

Introduction to TensorFlow

TensorFlow is an open-source machine learning library developed by Google. TensorFlow is used to build and train deep learning models as it facilitates the creation of computational graphs and efficient execution on various hardware platforms. The article provides an comprehensive overview of tensorflow.

Table of Content

  • TensorFlow
  • How to install TensorFlow?
  • The Computational Graph
  • Variables
  • Placeholders
  • Linear Regression model using TensorFlow
  • tf.contrib.learn
  • What are TensorFlow APIs?

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TensorFlow

TensorFlow is basically a software library for numerical computation using data flow graphs where:...

How to install TensorFlow?

An easy-to-follow guide for TensorFlow installation is available....

Computational Graph

Any TensorFlow Core program can be divided into two discrete sections:...

Variables

...

Placeholders

...

Linear Regression model using TensorFlow

TensorFlow has Variable nodes too which can hold variable data. They are mainly used to hold and update parameters of a training model. Variables are in-memory buffers containing tensors. They must be explicitly initialized and can be saved to disk during and after training. You can later restore saved values to exercise or analyze the model. An important difference to note between a constant and Variable is:...

tf.contrib.learn

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What are TensorFlow APIs?

A graph can be parameterized to accept external inputs, known as placeholders. A placeholder is a promise to provide a value later. While evaluating the graph involving placeholder nodes, a feed_dict parameter is passed to the session’s run method to specify Tensors that provide concrete values to these placeholders. Consider the example given below:...