Tensorflow vs Keras || Deep Learning Frameworks Comparison

So you guys must be aware about the buzzword going on these days, which is deep learning, right? And 2015 was a time when we actually absorbed some of the biggest evolutions in the industry of AI and deep learning.

By the introduction to two of the most popular libraries, which are Keras and TensorFlow, which one to choose and when to choose.

That is what we’re going to cover up in this Article on Keras vs Tensorflow. Alright guys, now let’s have a look at the agenda for this article.

First, we’re going to discuss what exactly is Keras and what exactly is TensorFlow. After that, we’re going to differentiate between both of these, terms based on few four parameters such as

  • Architecture
  • Prototyping
  • Debugging
  • Coding
  • Performance

After discussing these factors, we’re going to look into the pros and cons of using both Keras and TensorFlow.

What is Keras?

Keras is nothing but an open source high level neural network library. And as it is written in Python, hence, the structure of the code is easy to understand and use.

This high level API built on TensorFlow has the capability to run on top of other frameworks and libraries such as TensorFlow, Piano, K Framework, and so on.

What is TensorFlow?

TensorFlow is an open source and free software library for data flow. It is also known as symbolic math library and it is majorly used for machine learning applications such as neural network and is primarily used for research and production at Google right.

So, you can also say that it is flexible and comprehensive ecosystem of libraries, tools and other resources which provide workflows with high level API’s. it can be used for full production and deployment of machine learning pipelines.

So, as we have discussed about the brief introduction, both Keras and Tensorflow now let us move forward discuss few of the parameters based on which we will differentiate between both Keras and TensorFlow.

Keras vs TensorFlow

1. Prototyping

Keras complex models can be quickly built by writing the code, right on the other hand, in TensorFlow beginner can feel some difficulty writing the code from scratch itself,

2. Coding

Keras is easier to code as it is written in Python. And TensorFlow is written in both Python and c++ and it is difficult to implement custom and new functions like activation function etc.

3. Debugging

in Keras since a deals in simple networks, hence less number of errors, and less need for repeated debugging, right. And in case of TensorFlow as a deals in complex neural networks, there are chances of more number of errors, which makes debugging quite difficult.

But recently, since the introduction of previous update, TensorFlow comes with an inbuilt debugger, which can debug during the training as well as generating the graphs, right, which pretty much make things easier, isn’t it?

4. Training time

in Keras, it takes a longer duration to train the models on the same data sets. And it takes more than two hours for 40,000 steps of training the models, whereas guys, TensorFlow finishes training of 4000 steps in around 15 to 20 minutes. Sounds convenient, isn’t it?

5. Sets of data

Keras deals easily with simple networks, right. So that is why Keras is used for small data sets, as it is slower compared to TensorFlow.

On the other hand, TensorFlow is used for large and complex data sets and high performance models, which requires the fast execution

6. API level

Keras has high level API and runs on top of TensorFlow as we discussed, right ,it is easy to use and facilitates faster development. Whereas TensorFlow is a framework that provides both low and high level API’s. So in huge use cases, TensorFlow provides you both level options right.

7. Performance

in Keras performance is quite slow, even if you observe the previous factors Great, so but TensorFlow is suitable for high performance.

Now, as we have discussed the parameters let us move forward and discuss about the benefits of using both Keras and TensorFlow.

Benefits of using Keras

1. User Friendly

So yes, Keras as user friendly as it has consistent and simple interface, which is mainly optimized for common use cases that gives clear feedback for user errors.

2. Modular

Keras models are normally made by connecting configurable building blocks together, and it is easy to extend and this you can easily create or write custom building blocks for the new research and ideas.

3. Easy to Use or Access

Keras offers you simple API s which is used to minimize the number of user actions required for common use cases and gives proper feedbacks to user errors. Hence, it is easy to use.

Benefits of using the TensorFlow

1. Robust machine learning production

TensorFlow allows you to train and deploy your model effortlessly. Even if you’re using different language or platform, you can use this easily.

2. Powerful experiment for research

TensorFlow offers to control and flexibility with features like the Keras functional API and modern subclassing API for the creation of complex topologies.

3. Easy model building

TensorFlow Provides multiple levels of abstraction to train and build the models.

So guys, as we have discussed about the benefits of using both k does and TensorFlow. Now let us move forward and discuss about the limitations of using both of them.

Limitation of using Keras

When we talk about the limitations and Keras, though it is touted as a simple interface in other frameworks, but it is difficult to work with except for the simple networks.

And Keras always needs a back end framework like TensorFlow, except for a few features, Keras always needs calls to the backend, like calling directly or through the Keras back end API,

Now, the another point note here is if your inputs and outputs are not the same in the bass dimension, then Keras will always throw an error to you, right.

using Keras for complex networks with multiple outputs, direct calls to back end, etc. Your summary output gets broken here, right?

So these are the limitations of using Keras now let us discuss the limitations of using TensorFlow.

Limitations of using TensorFlow

There is no support for Windows. So guys, we know that there are a wide variety of users comfortable in working with a Windows environment rather than a Linux in their system.

And TensorFlow does not allow these users here, as a Windows user, you will have to install it within a conda environment or by using the Python package library or PIP. Right.

So the another factor to note here is TensorFlow does not support GPUs other than the Nvidia, right.

And it is only supported by Python language, which makes it a huge drawback as other languages are on a rise in deep learning itself. Right, guys?

So if we talk about the competition speak, TensorFlow gives around eight to 9000 competition speed on one GPU, right and around 12,000 on the two GPUs, and it cannot support more than two GPUs than this, right?

TensorFlow demands fundamental knowledge of advanced calculus and linear algebra along with a good understanding of machine learning also, right guys,

so guys, as we have discussed about the pros and cons, and both right, now, let’s have a quick glance at the popularity and trends right.

So as we talk about the popularity that despite the above pros and cons, both of these libraries are being used in huge Companies like

  • Facebook
  • Microsoft
  • Google
  • IBM
  • Amazon
  • Accenture
  • Bosch

A lot more, the list never ends.

But yes, TensorFlow has got more popularity than Keras. So Keras does not fail you as per its features. But if you look at the current trends, guys, even Google stays the same.

It has got more number of search terms in every category, be jobsearch, be technology search, beat community search community,

I mean, guys, more number of developers out there to help you or support you solve the coding problems that you’re facing currently, right.

Similarly, if you check on GitHub, then TensorFlow has got more number of repositories, commits, releases, branches and contributors than Keras does.

Since they both are open source, you keep on getting more support from such platforms, and even from different forums like Stack Overflow, etc.

It really depends on the number of users of TensorFlows and Keras. And of course, TensorFlow has more number of users than Keras does.

So After discussing the popularity, now, let us discuss about our last factor that is, which one is better to choose here.

Which one is better to choose

So guys looking at the increasing demand and growth rate of automation with deep learning in top industries, one can conclude that the use of deep neural network is definitely going to grow rapidly.

So if you are interested in deep learning, then you can explore either of the framework that is Keras And TensorFlow,so directly coming to the conclusion that one is better than the other would be a little unfair, right,

as both of them have their own features and benefits of using them like TensorFlow is the open source and free software library for multiple tasks in machine learning. Right.

Whereas Keras is also an open source library of neural networks, right. Keras provides a high level API’s. But TensorFlow provides both the API’s that is high and low level.

So even we discussed previously that Keras is written in Python, and its coding structure and syntaxes are more user friendly as compared to TensorFlow since TensorFlow is written in Python and c++ languages, right.

But as we know Keras is wrapper over back end libraries like TensorFlow and so on. So even if you are using Keras with TensorFlow and back end, ideally, you are running a TensorFlow code only right?

But no doubt writing code, and Keras is much easier as compared to TensorFlow, but again, it is working on TensorFlow arrays.

Also guys, TensorFlow offers more advanced operations as compared to Keras. Right? It will be very handy if you are doing any kind of research or developing work on some special kind of deep learning models.

So keeping hands on both would be beneficial for you because they both are using deep learning in every manner, such as TensorFlow with more number of features and more number of capabilities. It is the winner over here, right.

I hope this Article was helpful to you. If you have any further queries then do let us know in the comment section below. We will reach out to you immediately. So guys, thank you so much for Reading this article.

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