Difference Between Keras and TensorFlow

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Difference Between Keras and TensorFlow

This will give you a better insight about what to choose and when to choose Keras or Tensorflow.

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Keras vs TensorFlow

In this article, we will discuss Keras and Tensorflow and their differences.


  • What is Keras?
  • User experience of Keras
  • Keras multi-backend and multi-platform
  • Working principals of Keras
  • Models in Keras
  • Two types of execution in Keras
  • Implementation of a neural network using Keras
  • Benefits of Keras
  • What is TensorFlow
  • Working of TensorFlow
  • Companies using TensorFlow
  • Features of TensorFlow
  • Benefits of TensorFlow
  • Comparison between Keras and TensorFlow

What Is Keras?

Keras is a python based deep learning framework, which is the high-level API of tensorflow.

If we talk about the industry attraction of Keras, many examples exist like Netflix, Uber, Google, Instacart, Huawei, Square, Expedia, Zocdoc, Yelp, etc.

Let’s talk about ‘what so special in Keras.’

  • Keras focus on user experience
  • Large adoption in the industry
  • Multi-backend, multi-platform
  • Research community
  • Easy to grasp all concepts
  • Fast prototyping
  • Runs seamlessly on CPU and GPU
  • Freedom to design any architecture
  • Simple to get started
  • Easy production of models

User Experience of Keras 

API Designed for Humans: Keras follows best practices for reducing cognitive load.

Easy to learn and easy to use: It helps you win competitions and try more ideas than your competition. In short, it is more productive. 

Keras Multi-Backend and Multi-Platform

Development: You can develop Keras in python as well as in the R programming language. The code can be run with TensorFlow, CNTK, Theano, MXNet, based on your requirement.

Run the code: The code can be run on CPU and GPU as well as support for both the big players being NVIDIA and AMD here.

Producing models: This ensures the producing models with Keras are very simple. Total support to run with TensorFlow-serving, GPU acceleration (webkeras, keras.js), native support to develop android, and iOS apps using TensorFlow and CoreML is provided. Also, it supports the raspberry pi.

Models in Keras 

Keras consists of two types of models that are given below:

Sequential model: The working of the sequential model is basically like a linear stack of layers. It is useful for building a simple classification network and encoder-decoder model.

Functional model: It is widely used and good for 95 percent of the use cases. So, in the functional model, we have a concept called as domain adaption. In this, we train a model on one domain and test on the other. It results in poor performance on the overall tested dataset because data is different from each domain.

Types of Execution in Keras

There are two kinds of execution in Keras that are listed below:

  1. Deferred (symbolic): In deferred, we use python to build a computation graph. Deferred tensors don’t have a value in the python code yet.
  2. Eager (imperative): With eager execution, there is a slight change. With eager execution, value-dependent dynamic topologies can be used.

Implementation of a Neural Network Using Keras

Let’s have a look at some key points related to the implementation of a neural network:

Prepare the inputs: The first step is to prepare the inputs. The common inputs are images, videos, text, and audio.

Define the ANN Model: The second step is to define the model architecture. Also, define the sequential or functional style.

Optimizers: There are many types of optimizers, such as SGD, RMSprop, and adam.

Loss function: For every step in our training, we check the accuracy of the prediction by comparing the obtained value with the actual one. We check for the difference between them and printout the loss. Well, the goal is the definition of a function we use to reduce the training phase losses. There are many types of loss functions, such as MSE, Cross-Entropy, etc.

Train and evaluate the model: Train the network, which is based on training data. After training, we lead to test the model on the dataset with the testing data.

Benefits of Keras

  • Keras is very easy to develop a network model. Since its API is helpful, you can easily understand the operation. Write a code that has a basic purpose and does not need to set several parameters.
  • Keras has lots of AI communities for deep learning framework.
  • You can use the libraries like Tensorflow, CNTK, and Theano as your Keras backend. Depending on your needs, you can select a different backend for different projects. The backend has a unique advantage of its own.
  • You can deploy Keras on different devices with a variety of supported platforms:
  • iOS with CoreML
  • Android with Tensorflow Android,
  • Web browser with .js support
  • Cloud engine
  • Raspberry Pi
  • With a single GPU or with the help of multiple GPUs, you can train the Keras.

What Is TensorFlow?

TensorFlow nodes and tensioners are Python objects, and TensorFlow applications are Python applications themselves. It means Tensorflow is a python concept.

Companies Using TensorFlow

Many companies use TensorFlow. Let’s know some of them:

Airbnb: It is a leading global online marketing place and hospitality service.

GE Healthcare: GE healthcare trains a neural network to identify specific anatomic during the brain MRI exam to help improve speed and reliability.

PayPal: PayPal uses TensorFlow as a flow to stay at the cutting edge of fraud detection using TensorFlow deep trance for learning and generate your modeling.

Features of TensorFlow

Let’s discuss its features which make it to stand with other competitors:

  • When you enable eager execution, you will be executing TensorFlow kernels immediately rather than constructing graphs.
  • It provides a direct path to protection, whether it’s on server’s devices or the web. TensorFlow lets you train and deploy your model easily.
  • You can build and train the state-of-the-art models without sacrificing speed or performance.
  • TensorFlow also supports an ecosystem of powerful add-on libraries and models to experiment with the Tisza flow name.

Benefits of TensorFlow

  • TensorFlow provides the advancement of machine learning.
  • Provides additional conveniences to developers who need to test and introspect TensorFlow software.
  • Some aspects of TensorFlow's implementation make it difficult to obtain completely deterministic model-training results for certain training workers. Often a model trained on one system may differ slightly from the one trained on another, even though the same data is given.

Comparison Between Keras and TensorFlow 

There are 8 main differences between Keras and Tensorflow, and theyare as follow:





Keras has concise and simple architecture.

TensorFlow provides Keras as a framework that makes work easier.


Keras complex model can be quickly built by writing the codes.

TensorFlow beginners can feel some difficulties in writing the code from scratch itself.


Keras is easier to code as it is written in python.

TensorFlow is written in both python and c++, and it is difficult to implement custom and new functions like activation function, etc.


Keras sense a deal in simple networks. Hence, a lesser number of errors and less need for repeated debugging.

In the case of TensorFlow, it deals with complex neural networks. There are chances of a greater number of errors, which makes debugging quite difficult. Since the introduction of the previous update in TensorFlow, it comes with an inbuilt debugger that can debug during the training as well as generating the graphs. That pretty much makes things easier.

Training time

In Keras, it takes a longer duration to train the models on the same datasets, and it takes more than two hours for processing 40,000 steps of training the models.

On the other hand, TensorFlow finishes training of 4,000 steps in around 15 to 20 minutes sounds convenient.

Sets of data

Keras deals easily with simple networks.

TensorFlow is used for large and complex datasets and high-performance models, which requires fast execution.

APIs level

Keras is a high-level API, and it runs on top of TensorFlow even on Theano and CNTK. It is easy to use and facilitates faster development.

TensorFlow is the framework that provides low and high-level API. So, in huge use cases, TensorFlow provides you both level options.


In Keras, the performance is quite slow, even if you have observed the previous factors.

But TensorFlow is comfortable for high performances.

ai artificial intelligence, keras, keras vs tensorflow, tensorflow

Opinions expressed by DZone contributors are their own.

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