Keras vs. PyTorch: Difference Between Keras & PyTorch

If you are reading this article, chances are you have stepped into the field of Deep Learning and probably on your way to building your first Deep Learning Project. You might have been confused about the plethora of frameworks and libraries available for Deep Learning – Keras, PyTorch, Tensorflow, FastAPI, among the most popular ones. 

Various Frameworks for Deep Learning

In this article, we are going to look into the comparison between Keras and PyTorch—  the two most sought-after APIs/frameworks for Deep Learning. We will also learn which one among the two is better for building your next Deep Learning Project.

Comparison will be based on the following factors:

  • Emergence 
  • Ease of Use
  • Debugging
  • Performance
  • Popularity
  • Conclusion: When to use what and Why?


Keras is the Deep Learning high-level API. It is an open-source library built on top of Tensorflow (another popular Deep Learning framework by Google), making Tensorflow code much easier to write and execute. Keras was developed by François Chollet in 2015 with the mission that a developer should be able to construct Deep Learning Models without much complexity. It has become immensely popular for its ease of use and syntactic simplicity.

PyTorch is the Python Deep Learning low-level framework (like Tensorflow). It is an open-source library based on the Torch Library. PyTorch was developed by Facebook’s AI Research Team in 2016. PyTorch, unlike Keras, might not be very easy for beginners. But what PyTorch offers is superb flexibility and blazing fast execution for large real-world datasets.

Winner: Draw

Also Read: Tensorflow vs PyTorch

Ease of Use

Hands down, Keras has been the go-to framework for learning Deep Learning as compared to PyTorch. As previously told, Keras has been developed, keeping in mind that it should be syntactically easy. So, while preparing a Deep Learning Model, the basic steps you do are: Loading the Data, Defining the Model, Compiling the Model, Training the Model, and finally, Evaluation. All the above steps can be done in just an extremely few lines of code. For instance, to define a simple Neural Network Model, what you can do with Keras is:

Model Definition

So you see, just using model.add(…) with a few hyper-parameters, you can insert layers in your Neural Network Model. Another model.add(…) and Boom! Another Layer! It’s that easy.

Now, after Model Definition and Compilation, you need to train your model on some Dataset.

Well, with Keras, it’s a piece of cake. Just use the…) mentioning the input and output data, and some hyper-parameters, and voila! Your model will successfully start the training process (Just 1 line of code).

These simple, concise, and readable syntax makes Keras a very popular language among Deep Learning Beginners as well as Developers.

PyTorch, on the other hand, is a little less concise, making it more complex. Using PyTorch, one has to explicitly go through all the basic steps for executing a Deep Learning Model. For just training your model, you need to: initialise the weights at the start of each batch of training, run the forward and backward passes, compute the loss, and update weights accordingly.

Just the PyTorch Training section

Since with PyTorch you need to be accustomed to all the nitty-gritty details, you can realise that it might be a Herculean task for a beginner who has just entered the field of Deep Learning, going forward with PyTorch.

Winner: Keras


Debugging is where things get interesting.

As you all know by now, Keras has so many simple functions like .fit(…), .compile(…) that helps us in writing codes easily. Hence in Keras, the chances of making errors are slim. But when you do make errors in your code (which is completely normal), it is usually very difficult to debug. The reason is so many details are encapsulated into various functions that you need to visualise all those details so as to find where you go wrong actually.

With the explicitly illustrated PyTorch code, debugging is so much easier. Every detail of your neural network has been illustrated in your code, and hence finding out the error is relatively a simple task. You can change your weights, biases, network layers as you wish and then try running the model again.

Winner: PyTorch

Ecosystem and Community Support

As per keras vs pytorch, both of them have different communities and environments. Keras uses TensorFlow’s ecosystem, which has many pre-trained models and support for TensorFlow Serving. PyTorch has its own ecosystem with diverse pre-trained models, visualization tools, and libraries for tasks like computer vision and natural language processing. PyTorch’s community actively develops and adopts new scientific discoveries.

Graph Computation vs. Eager Execution

When we look at the difference between keras vs pytorch, it is quite evident that the computational graphs used by PyTorch and Keras are one of their main differences. In Keras’s static graph computation model, the computational graph is defined in advance and then put to use. Benefits of this strategy include enhanced performance and simplicity in deployment. As opposed to this, PyTorch supports eager execution, enabling more dynamic and adaptable model creation. Developers can utilize Python’s imperative programming style with eager execution, which makes it simpler to debug and experiment with models. Additionally, it allows for building dynamic graphs, which is useful for problems involving recurrent neural networks or conditional operations.The differences between tensorflow vs pytorch vs keras are also influential on computational graph models.


Well, in terms of Performance, Keras is lagging behind as compared to PyTorch.

Keras might be very popular for its syntactic reasons but is not generally preferred when dealing with huge datasets. Keras is slow in computation and is generally used for smaller datasets where one needs to have an initial idea about a model like prototypes. It is used by a huge number of beginners as their first DL model because these models do not usually have huge or online datasets.

PyTorch really shines in this factor. PyTorch, as well as TensorFlow, are used as frameworks when a user deals with huge datasets. PyTorch is remarkably faster and has better memory and optimisation than Keras. As mentioned earlier, PyTorch is excellent in providing us the flexibility to define or alter our Deep Learning Model. Hence PyTorch is used in building scalable solutions. Industry-level datasets are not a problem for PyTorch, and it can compile and train models with great ease and speed.

Winner: PyTorch

Must Read: Open Source Deep Learning Libraries

Model Deployment and Production

Based on pytorch vs keras, both these offer methods for model deployment in the real world. Keras offers TensorFlow Serving for serving models at scale. Additionally, TensorFlow’s SavedModel format conversion is supported for deployment on various platforms, including mobile and online. Using PyTorch’s TorchScript functionality, models may be serialized and deployed without Python runtime. Additionally, PyTorch offers the ONNX (Open Neural Network Exchange) standard, making it easier for various deep learning frameworks to communicate.

Integration with Other Libraries

As of pytorch vs keras, they integrate easily with well-known data processing and visualization libraries. Integrating TensorFlow with libraries like NumPy, Pandas, and Matplotlib enhances Keras by offering a complete data processing and analysis environment. With its TorchVision and TorchText packages, PyTorch, a Python library, has extensive data loading and transformation support. PyTorch also interfaces nicely with these libraries. Additionally, thanks to PyTorch’s connection with the larger Python environment, developers can use a wealth of tools and modules for tasks outside of deep learning.


Source: Google Trends 2020

Firstly, it needs to be said that when one framework is more popular than the other, it doesn’t always mean that researchers always use that more popular framework over the other. They tend to switch over frameworks as per problems.

That being said, as you can see from the above visualisation of Google Trends 2020, TensorFlow is clearly the favourite worldwide, followed by PyTorch and Keras. Similar kinds of results are observed in almost all visualisations. Tensorflow is undoubtedly the winner in terms of popularity. But native Tensorflow or native Keras cannot be compared with PyTorch. The results we see are generally Keras as an API for TensorFlow v/s PyTorch.

Winner: Tensorflow with Keras

Industry Adoption and Job Market

PyTorch is swiftly gaining favor with academics, professionals, and employers worldwide owing to its adaptability. Even though PyTorch is a popular name now, TensorFlow has been more generally accepted by industry. For their deep learning endeavors, several top tech companies, including Facebook, Nvidia, and OpenAI, actively employ PyTorch. The growing popularity of PyTorch is mostly due to its user-friendly API and excellent support for jobs that are focused on conducting research. When deciding between pytorch vs tensorflow vs keras, it is advisable to consider the unique requirements of the industry or job market.

Several factors impact the choice between pytorch vs tensorflow vs keras, including user-friendliness, debugging demands, performance requirements, ecosystem support, and industry preferences. Due to its simplicity of use and connection with TensorFlow, Keras makes a practical solution for novices and small-scale applications. On the other hand, because of its flexibility, dynamic graph creation, and superior performance on huge datasets, PyTorch is a favored option for researchers and professionals working with complex models and applications demanding scalability. Selecting the best framework requires considering your specific project objectives and personal preferences. Therefore, you must thoroughly check on the differences between tensorflow vs pytorch vs keras.


So, the final question arises. Which is better: Keras or PyTorch?

Well, the answer depends on the user. If you, as a user, have just entered the field of Deep Learning and are very eager to build your very first Deep Learning Model, you should obviously implement Keras as an interface for TensorFlow in your model. It is extremely beginner-friendly and also has a great helpful online community. Big Thanks to Keras; teaching Deep Learning has never been easier.

But, if you are relatively experienced in Deep Learning (chances are you might already know this),  PyTorch is, of course, the better framework among the two. PyTorch is becoming popular at a fast rate.

The pioneers in the Deep Learning Field, namely Ian Goodfellow, Andrej Karpathy, top universities like Stanford have switched over to PyTorch for its superiority in performance, speed, and flexibility. Solving Computer Vision problems has never been easier with PyTorch.

To learn more about Deep Learning and the various frameworks associated with it, check out upGrad’s PG Certification in Machine Learning and Deep Learning course.

What is Deep Learning and how does it work?

Deep Learning is a subfield of machine learning. It teaches computers what is to be done. It makes computers human-centric. It is the key technology behind driverless cars, it is the key to voice control in devices like phones, tablets, TVs, computers, and speakers. Deep learning is getting a lot of attention and it is achieving results that were not possible earlier. With the help of deep learning, computers are exceeding human-level performance. The models of deep learning are trained with large sets of labelled data that learn features without the need for manual feature extraction.

How is Deep Learning different from Machine Learning?

Deep Learning is a specialised form and a subfield of machine learning. In machine learning, relevant features are manually extracted from images and then these are used to create a model. However, in deep learning, relevant features are automatically extracted from the images and there is no need to manually extract them. There is end-to-end learning involved in deep learning where the network is given tasks to perform and it learns how to perform them automatically and get things done rather than manually putting commands. There is very little human intervention needed in this type of technology, too.

What is the future of Deep Learning?

Deep Learning is the future of technology. It is rapidly growing in the domain of Artificial Intelligence. It promotes less or no human intervention which is the future. It has become an overnight star and has been deeply acknowledged for ‘humanising’ machines. One of the many reasons why deep learning draws attention is because of the accuracy of its predictions made and that too without humans telling or teaching them what to do. Deep Learning is the key technology behind developments like image recognition, speech processing, biomedical signal analysis, etc.


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