Deep Learning Chronicles: Keras vs. Tensorflow

Deep Learning Chronicles: Keras vs. Tensorflow

Introduction To Keras vs Tensorflow

In This Article, We Are Going To Discuss Keras vs Tensorflow. Tesla was in the spotlight recently for introducing itself in the Indian market. Tesla cars are in the rage for running on clean energy and possessing the revolutionary self-driving technology. Deep Learning is the force that brings automatic driving to life. It is a subset of machine learning that utilizes a multi-layered hierarchical level of artificial neural networks. Artificial neural networks are systems vaguely inspired by how the brain works. It is known to deliver high accuracy in tasks such as speech recognition and object detection. There’s a whole complex model in place even behind when you say “OK Google!”

Deep learning has enabled businesses to solve relatively complex issues. We know the crucial role frameworks play in the domain of data science. Frameworks are a collection of packages and libraries which simplify the overall programming experience for building a specific kind of application.

Keras vs Tensorflow, Keras and TensorFlow are at the forefront when it comes to frameworks in Deep Learning. This article will elucidate their features, mechanism behind these two frameworks. We will also provide a head-to-head comparison.


File:Tensorflow logo.svg - Wikimedia Commons

TensorFlow is an open-source library for numerical computation that enables large-scale machine learning. It is a primary software tool typically used for classification, discovery, prediction, and creation of neural networks. It was created by the Google Brain Team and uses Python to enable a user-friendly front-end interface for building models. The models that are developed run on high-performance C++.

It is an all-in-one ecosystem of tools, libraries, and other resources that provide workflows with high-level APIs. All this makes TensorFlow a no-brainer in the deep learning space. Or is it a brainer since neural networks are literally based on our brain’s neurons? ๐Ÿ˜†

How TensorFlow works?

The basic workflow behind TensorFlow is what inspired the name of the framework i.e Tensor. TensorFlow primarily allows the creation of dataflow graphs. Dataflow graphs are structures that show how data moves through a graph (synonymous with a node). Each node is a complex operation or a mathematical calculation. The connections between nodes can be multi-dimensional and these connections are what constitute a Tensor.

The nodes and tensors are programmed in Python. But the libraries of mathematical operations that are at hand through TensorFlow are written as high-performance C++ binaries.

Easy TensorFlow - 1- Graph and Session
Dataflow Graph Example
Image Source – Easy TensorFlow

TensorFlow is cross-platform. It can run on nearly everything: GPUs and CPUs, mobile and embedded platforms. These days it is also possible to use the benefits TensorFlow has to offer on the cloud! Google’s custom Tensor Processing Unit (TPU) can be used to run on Google’s own cloud.


  • Model building prowess
    TensorFlow offers multiple levels of abstraction to build and train models. Because of the power of the learned abstractions, training does not require a very large dataset. The use of C++ in computations makes TensorFlow the fastest in terms of compilation compared to other frameworks.
  • Deep Learning – anywhere, everywhere
    As discussed earlier, models made in TensorFlow can be trained and deployed easily, no matter what language or platform you use. Dynamic models can be developed easily with the Python control flow.
  • Powerful performance
    TensorFlow has high standards in measurement and transparency.
  • Open community
    The TensorFlow development team has made many models readily available. In addition to that, the TensorFlow community is evergrowing and is a vital source of understanding new developments in deep learning. TensorFlow mechanisms and use-cases have also been well documented and available to the public.


  1. Requires expert-level understanding of advanced calculus and algebra, along with machine learning experience.
  2. Not as user-friendly compared to newer frameworks.
  3. Does not support all kinds of GPUs including NVIDIA


Keras - Wikipedia

Keras is a high-level neural network Application Programming Interface (API) written in Python. This open-source neural network library is designed to provide fast experimentation with deep neural networks. It can even run on top of TensorFlow. Keras is one of the most widely used frameworks for deep learning due to its simplicity. Using Keras allows easy and fast prototyping and can be deployed on CPUs and GPUs alike. Since it is completely written in Python code, it is easy to debug and allows ease for extensibility.

If you’re someone who is just starting off in the deep learning field, Keras must be your go-to framework. Keras was built to be modular, user-friendly, and extensible.

Keras terminology

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Keras Backend

Unlike TensorFlow which works on dataflow graphs, Keras does not do its own low-level operations. It does not contain its own tensors or convolution networks. Keras relies on something coined as back-end. The default backend is TensorFlow and its primary package is Google. The API comes packaged in TensorFlow as tf.keras.

Keras Layers

Keras allows a user to select from a broad range of predefined layer types. It also supports writing customized layers.

Core layers include –

  1. Dense (dot product plus bias)
  2. Activation (transfer function or neuron shape)
  3. Dropout (randomly set a fraction of input units to 0 at each training update to avoid overfitting)
  4. Lambda (wrap an arbitrary expression as a layer object)

Keras Models

Keras models are divided into two main categories – Sequential Model and Functional API. The Sequential model is just a linear stack of learning layers. It is simple but limited in model topology. The Keras Functional API is useful for creating complex models, such as multi-input/multi-output models and directed acyclic graphs (DAGs).

The Functional API uses the same layers as the Sequential model but provides more flexibility in putting them together. In the functional API, you first develop layers, and then merge them into the Model, compile it, and train it.

The Keras 4 Step Workflow - KDnuggets
Keras Workflow
Image Source – KDNuggets


  • Beginner-friendly
    Keras has a simple, consistent interface optimized for common use cases and also includes seven commonly used datasets.
  • Flexible
    Keras models are made by connecting different models together. Built using Python, it follows the best practices to reduce the cognitive load for the users by offering high-level API.
  • Easy To Use
    Keras offers consistent & simple APIs which helps in minimizing the number of user actions. Since it is built on a simple network, debugging is often not required. It is perfect for quick implementations using pre-existing neural networks.


  1. Restricted to use only on smaller datasets.
  2. Slower performance since it is completely written in Python.
  3. The project repository is not as huge compared to TensorFlow.

Key Differences at a Glance

TensorFlow is a tool that offers both high and low-level APIs.Keras is a high-level framework written on top of TensorFlow, CNTK, and Theano.
Need to have prowess in advanced calculus to develop models and networksEasy to follow if adept in Python. It is a beginner-friendly API.
Comparatively complex architectureReadable and concise architecture
Useful when high performance models need to be implemented on large datasetsPerfect for smaller datasets
Being one of the first deep learning frameworks that were
developed, it has an extensive community and a rich reserve of readable material.
Community support is minimal but growing
The performance of models is exponentially
improved with the use of C++ in model operations
Keras is known to be used for only low-performance models

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Keras vs Tensorflow, There is undoubtedly a number of features that can be compared between the two frameworks. They work best when the use-case for deep learning is knowing. If a data scientist aims for flexibility and control in building a neural network, TensorFlow is the best choice. If it is only a student who wants to learn how deep learning works and implement existing models on newer datasets, Keras ticks all the boxes. It’s easier to build even complex models using Keras. TensorFlow on the other hand has a thriving community and offers a substantial open-source library for a number of machine learning practices. A researcher must choose a framework depending upon the tasks they seek to perform.


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