TensorFlow vs. Keras: What’s the Difference?

Key takeaways

• TensorFlow and Keras differ in that TensorFlow is a complete machine learning framework, while Keras is a high‑level API that makes building and training deep learning models easier.

• Keras is not the same as TensorFlow, but they’re closely connected as Keras runs on top of TensorFlow, offering a simpler, more readable interface while TensorFlow provides the flexible, scalable backend that powers the models.

• The key differences between Keras and TensorFlow are in functionalality: Keras prioritizes usability, quick prototyping, and modular components, while TensorFlow emphasizes performance, scalability, distributed training, and deep customization for intricate architectures.

Is Keras the same as TensorFlow?

While TensorFlow is a complete open-source machine learning framework that brings flexibility and scalability to deep learning development, Keras is a high-level application programming interface (API) devised to make developing deep learning models within TensorFlow easier. Keras offers a higher-level, simpler interface, faster debugging, and a more readable collection of all source code. While TensorFlow and Keras have different functions, both tools are pivotal for deep learning, a process essential for creating, honing, and refining neural networks to identify patterns, forecast outcomes, and automate challenging tasks.

TensorFlow or Keras: Which is better?

TensorFlow and Keras share an intrinsic relationship, functioning more as complementary tools rather than an either/or choice. However, each has slightly different use cases, so one better suits your unique project needs than the other.

Keras makes model development easier and more approachable for novices and researchers, while TensorFlow offers more customization options. Keras gives you a user-friendly control panel, while TensorFlow provides a robust backend system. Developers created Keras as an API for high-level neural networks to facilitate rapid experimentation. It is compatible with several different backends, including TensorFlow.

TensorFlow is a symbolic math library that operates at a lower level. For constructing deep neural network architectures, the Keras R package simplifies the process of utilizing TensorFlow and Keras in R.

As such, you could argue that it’s not a case of which is better, but rather analyzing how they relate and interact with each other.

What is TensorFlow, and why is it used?

Google (specifically Google Brain) introduced TensorFlow in 2015 as an open-source machine learning framework for Python, with the goal of making it easier to create scalable AI models. In fact, TensorFlow powers many of Google's AI-based services, including Google Ads.

TensorFlow.js, which was later released in 2018, added these features to JavaScript. Although TensorFlow and TensorFlow.js are part of the same deep learning ecosystem, each is designed for a different setting to facilitate developers from varying programming backgrounds.

Thanks to TensorFlow's adaptable graph-based architecture, you can execute machine learning across a variety of hardware to effectively train and implement AI models without rewriting code. Because it significantly advances machine learning capabilities, whether for process automation, enhancing AI research, or developing intelligent applications, TensorFlow is a popular choice among software developers, data scientists, and educators in the space.

TensorFlow.js, meanwhile, is an open-source JavaScript library that lets programmers create, train, and implement machine learning models right within Node.js environments or the browser. It makes real-time AI applications possible without requiring a backend server.

How does TensorFlow work?

The TensorFlow workflow consists of three steps: preprocessing data, building the model, and training it to generate predictions.

TensorFlow resolves practical, real-world machine learning issues. Numerous globally recognized companies in various sectors, from Airbnb to Coca-Cola to Spotify, Lenovo, and PayPal, use TensorFlow. Use cases address many pressing issues, including fraud detection, proof of purchase capabilities, and image processing and classification. After all, businesses across industries, from social networks to health care to e-commerce, integrate machine learning to bolster efficiency and innovation. With TensorFlow, novices and experts may easily develop machine learning models for desktop, mobile, web, and cloud platforms. As an end-to-end machine learning platform, it allows you to load and prepare data for effective machine learning results.

Data is a crucial element for your success in machine learning. TensorFlow provides several data tools to assist with large-scale data consolidation, cleaning, and preprocessing, including:

• Typical data sets for preliminary validation and training

• Data loading pipelines with high scalability

• Layers of preprocessing for typical input modifications

• Tools for transforming and validating big datasets

Furthermore, ethical AI technologies can help you identify and remove bias from your data so that your models generate more justifiable results.

Pros and cons of TensorFlow

Given TensorFlow’s various use cases, which include increasing machine learning accessibility, speeding up artificial intelligence (AI) training, and improving fraud detection capabilities, it’s clear that the platform offers many possible advantages. However, depending on the needs and complexity of your project, TensorFlow may present both benefits and drawbacks. For instance, some of the advantages include:

• Scalability: It optimizes TPU, GPU, and CPU performance.

• Building and training AI models: It supports both simple and complex neural networks for a variety of uses.

• Cross-platform compatibility: It works in mobile, web, and cloud environments.

• Pre-trained models: It offers AI models prepared for use in tasks like image recognition and natural language processing.

Conversely, some potential drawbacks of TensorFlow may include:

• Laborious implementation: These models and optimization algorithms can be labor-intensive and often prone to mistakes.

• High resource consumption: Because TensorFlow usually needs powerful GPUs or TPUs, it is more challenging to run on low-end hardware.

• Inconsistent API changes: Because frequent updates can occasionally result in breaking changes, developers must constantly update their code.

What is Keras used for?

Keras facilitates and simplifies deep learning by offering a high-level API that speeds up model construction, training, and deployment. It functions as an abstraction layer over TensorFlow, allowing users to build neural networks with straightforward, modular components without requiring complex computations.

Like TensorFlow, Keras was also the brainchild of a Google Brain team member back in 2015.

It’s an API that facilitates well-defined functionality by offering a structured syntax for interacting with lower-level deep learning frameworks. Keras is a multi-framework API that enables smooth integration between various machine learning ecosystems, bringing effectiveness for model deployment and training.

Keras offers a deep learning API compatible with PyTorch, JAX, and TensorFlow, designed to be simple, flexible, and powerful. It is a favored option for companies like NASA, YouTube, and Waymo because it lowers developers’ cognitive load while guaranteeing high performance and scalability. Read more: TensorFlow or PyTorch: What’s the Difference?

How does Keras work?

This Python wrapper uses layers and models as its fundamental data components of Keras. A layer is a straightforward input and output transformation, and a model is a directed acyclic graph (DAG) of layers.

Keras maximizes accessibility and performance by allowing developers to dynamically choose the optimal backend for their models without changing their code. Its support for multiple data pipelines, pandas DataFrames, NumPy arrays, PyTorch DataLoader, and datasets enables compatibility with tf.data. Keras models adapt to various frameworks, functioning well regardless of the backend you choose.

Pros and cons of Keras

Keras offers a rich selection of features, including pre-defined model components, improved GPU acceleration, and a dynamic community with ample support. Still, in addition to its various benefits, Keras also has a distinct set of advantages and disadvantages, much like any other deep learning system. Advantages of using Keras include:

• Usability: Provides a straightforward, easy-to-use API that enables deep learning, even for novices.

• Flexibility: Keras is still a multi-backend framework due to its flexibility. By supporting PyTorch, JAX, and TensorFlow over Theano, CNTK, and PlaidML in previous iterations, Keras 3 allows you to choose an appropriate computational engine for your requirements.

• Expedited prototyping: Rapid prototyping enables quick experimentation and model-building without complex coding. For everyday AI tasks like image recognition, pre-trained models offer ready-to-use models.

• Broad adoption: It boasts a robust developer community plus the support that comes with it.

Some of the disadvantages of using Keras include:

• Inconsistent backend: Keras has made multiple changes to its backend support. Keras 3.0 initially transitioned from Theano, TensorFlow, and CNTK to exclusively TensorFlow, but later reinstated multi-backend support. This disparity can cause problems for developers overseeing older projects.

• Less control over training: Offers a simplified approach that may restrict fine-tuning for advanced users.

• No built-in visualization: With no integrated visualization, users must set up additional tools like Visualkeras or TensorBoard to analyze model architecture and training insights.

Key differences between TensorFlow and Keras

The more you look at the relationship between TensorFlow and Keras, the more you’re likely to see how they collectively form a potent ecosystem to speed the development of deep learning.

Despite their close relationship, you will also start to notice their key differences, some of which include:

• Usability: TensorFlow has a steeper learning curve but offers even greater flexibility than Keras, which offers a straightforward, user-friendly API.

• Performance and scalability: While Keras is lighter and thus more suited to smaller-scale projects, TensorFlow is made for high-performance machine learning and larger scalability, supporting distributed training and large-scale AI models.

• Customization: Keras makes model building easier with a high-level API, while TensorFlow enables deep customization for intricate architectures.

• Execution speed: Keras prioritizes quick prototyping over raw performance, whereas TensorFlow optimizes GPU and TPU usage to run computations more efficiently.

• Debugging: Because of its Pythonic design and eager execution in TensorFlow 2.x, Keras typically facilitates debugging more easily than low-level TensorFlow.

TensorFlow vs. Keras

Since each framework has certain benefits, the Keras versus TensorFlow debate ultimately comes down to your particular requirements. It is crucial to match your goals with the choice you make because TensorFlow offers sophisticated customization and scalability, while Keras streamlines deep learning with its user-friendly interface.

That said, according to the official TensorFlow website, "every TensorFlow user should use the Keras APIs by default" [1]. In short, Keras is a good launchpad whether you're an engineer, researcher, or machine learning practitioner.

The low-level TensorFlow Core APIs are necessary for a few use cases (such as creating tools on top of TensorFlow or creating your own high-performance platform). However, you should use Keras if your use case does not fit into one of the Core API applications.

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