What is TensorFlow Explained: Artificial Intelligence Explained




A computer system processing a complex network of nodes and pathways

TensorFlow is an open-source software library developed by the Google Brain team for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks. It is used for both research and production at Google, often replacing its closed-source predecessor, DistBelief.

The name TensorFlow derives from the operations that such neural networks perform on multidimensional data arrays, which are referred to as “tensors. In essence, TensorFlow is Google’s answer to the need for a sophisticated machine learning framework, and it has become a popular choice among developers and researchers due to its powerful capabilities and broad applicability.

Understanding Tensors

At the heart of TensorFlow are tensors, which are multidimensional arrays. Tensors are a type of data structure used in linear algebra, and are a generalization of scalars, vectors, and matrices. In TensorFlow, all data is passed between operations in a computation graph, and these tensors are the main objects that are passed around.

The dimensionality and size of tensors are defined by their shape. A tensor’s shape is a tuple of integers that specifies the array’s length along each dimension. For example, a 2×3 matrix is represented as a tensor of shape (2, 3).

Tensor Rank

The rank of a tensor is defined as the number of dimensions of the tensor. A scalar has rank 0, a vector has rank 1, a matrix has rank 2, and a tensor with three dimensions has rank 3. The rank of a tensor provides insight into the level of complexity of data we are dealing with.

TensorFlow operations can be performed on tensors of any rank, but it is often easier to work with tensors of lower rank. For example, it is easier to visualize data in two or three dimensions than in higher dimensions.

Tensor Types

TensorFlow supports a variety of tensor types, including float32, int32, and string. Each of these types represents a different kind of data that can be processed by TensorFlow. For example, an image can be represented as a tensor of type float32, while a sentence can be represented as a tensor of type string.

It’s important to note that all elements of a tensor must be of the same type. This uniformity of type makes it easier for TensorFlow to perform operations on tensors, as it can be sure that all elements of the tensor can be processed in the same way.

Understanding Dataflow Graphs

In TensorFlow, computations are represented as graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

Section Image

Furthermore, TensorFlow also includes TensorBoard, a data visualization toolkit that enables you to visualize the computation graph and other data like histograms. This makes it easier to understand, debug, and optimize the TensorFlow programs.

Nodes and Edges

As mentioned, nodes in a TensorFlow graph represent mathematical operations, which can be as simple as addition or multiplication, or as complex as some multivariate equation. Each node takes zero or more tensors as input and produces a tensor as output.

Edges are the ‘lines’ between nodes, representing the input/output relationships between nodes. These are the tensors that flow through the graph. An edge can carry a tensor from one operation to the next.

Graph Execution

TensorFlow uses a “lazy execution” model. This means that when you create a node in the graph, that operation isn’t executed right away. Instead, TensorFlow waits until it has enough information to make an optimal decision about how and when to execute that operation.

This approach can lead to significant performance gains, because TensorFlow can decide to execute operations in parallel, or to execute them on different hardware devices (like GPUs or TPUs). It also means that you need to explicitly start the execution of the graph with a session, and keep track of that session while the computation is performed.

TensorFlow’s Ecosystem

TensorFlow is not just a software library, but a suite of software that includes TensorFlow, TensorBoard, TensorFlow Extended (TFX), and a host of other software packages that help with the development and productionization of machine learning models. This ecosystem of software makes TensorFlow a versatile tool for both research and production.

TensorFlow’s ecosystem also includes a variety of tools for different tasks, including training and inference (TensorFlow), visualization (TensorBoard), and deployment (TFX). This wide range of tools makes TensorFlow a comprehensive solution for a wide range of machine learning tasks.

TensorFlow Extended (TFX)

TensorFlow Extended (TFX) is a production-ready machine learning platform that provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor machine learning systems.

TFX is designed to orchestrate end-to-end machine learning workflows, where different components of the workflow are loosely-coupled, and where the output of one component is consumed by one or more other components. This makes it a flexible and modular platform for developing complex machine learning systems.


TensorBoard is a suite of visualization tools included with TensorFlow. You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it.

It’s a helpful tool for understanding, debugging and optimizing TensorFlow programs. It provides a higher level view of the state of a TensorFlow program, and it’s particularly useful for visualizing complex neural network architectures and performance metrics.

TensorFlow’s Applications

TensorFlow is used in many industries with a wide range of applications. The flexibility of TensorFlow’s core allows for usage in many different fields. It’s used in research to make the process of acquiring data, training models, evaluating models and serving models more accessible.

TensorFlow is also used in production systems, not just in software research labs. Companies use TensorFlow to deliver machine learning powered features to their customers, and it’s used in products across Google to power features in apps like Google Photos, Google Search and Google Assistant.

Research and Experimentation

TensorFlow is a popular tool in the field of research and development because it allows researchers to develop complex models quickly and easily. Its flexible architecture and extensive functionality make it suitable for a wide range of tasks, from experimenting with novel architectures to developing complex systems for production use.

TensorFlow’s ability to automatically calculate gradients – a crucial feature for training neural networks – makes it particularly useful for research and experimentation in the field of deep learning. Furthermore, its support for a wide range of neural network architectures and its easy-to-use Python interface make it a popular choice among researchers in the field.

Production and Deployment

TensorFlow is also used in production settings. Its ability to run on multiple CPUs and GPUs makes it a good choice for serving models in production. TensorFlow Serving, a flexible, high-performance serving system for machine learning models, is designed for production environments and is particularly well-suited for deploying machine learning models to servers or cloud platforms.

TensorFlow Lite, another tool in the TensorFlow ecosystem, allows developers to deploy TensorFlow models on mobile and IoT devices. This enables machine learning applications to run on devices at the edge of the network, reducing latency and allowing for real-time processing of data.


TensorFlow is a powerful, flexible framework for machine learning and other computations on a large scale. Its ability to run on multiple CPUs and GPUs, its flexible dataflow architecture, and its extensive ecosystem of tools and libraries make it a popular choice for both researchers and developers in the field of machine learning.

Whether you’re a researcher looking to experiment with novel machine learning architectures, a developer looking to deploy a machine learning model in production, or someone just interested in learning more about machine learning, TensorFlow offers a comprehensive, flexible platform for machine learning and other numerical computations.

Share this content

Latest posts