Deep Learning(DL) is a machine learning and artificial intelligence type. And Keras, TensorFlow, and PyTorch are the three popular DL frameworks. But, if you are a beginner, choosing one framework can be challenging.
Before comparing Keras vs. TensorFlow vs. PyTorch, we will discuss what is Keras and TensorFlow individually. First, let us explore the meaning of deep learning in brief.
What is Deep Learning?
Deep learning replicates how the human brain learns by mimicking certain aspects. So DL programs the computer to perform human-like tasks, for example, recognizing speech and making decisions as a person would.
In simplest terms, deep learning is a subset of the machine learning training model that works more closely with how the human brain makes decisions.
Introduction to Keras vs. TensorFlow vs. PyTorch
- What is Keras?
Keras is an API for deep learning written in Python and runs on top of the TensorFlow platform for machine learning. It is a simple and user-friendly model.
It supports multiple back-end neural network computation engines. Keras is great for people who want to work with neural networks but don’t know much about deep learning. With Keras, you can quickly and easily build a neural network model with very little code. This lets you make quick prototypes.
Moving ahead in the discussion of Keras vs. TensorFlow vs. PyTorch, let us define TensorFlow.
- What is TensorFlow? What are Keras and TensorFlow in Python?
TensorFlow is an open-source platform for machine learning created by Google that works from start to finish. As a result, TensorFlow is a powerful tool for managing all parts of a machine learning system. This class, however, will focus on using a specific TensorFlow API to build and train machine-learning models.
Tensor flow is helpful because it works with and supports a lot of back-end software, such as GUI and ASIC. In addition, the tensor flow has done better than other platforms regarding performance.
TensorFlow’s high-level Application Programming Interface (API) is similar to Keras’s high API. It is an easy-to-use, highly productive interface for solving machine learning problems, focusing on modern deep learning.
Keras lets engineers and researchers take full advantage of TensorFlow’s scalability and cross-platform features. For example, you can run Keras on TPUs or large clusters of GPUs and export your Keras models to run in a browser or on a mobile device.
- What is PyTorch?
Based on Torch, PyTorch is an open-source machine-learning library for Python. It was made by Facebook’s AI research group and is used to process natural language.
PyTorch is well supported on most major cloud platforms, making it easy to develop for and scale up. The torch distributed backend allows scalable distributed training and performance optimization in research and production.
Key differences between the three frameworks
Now let us explore the key differences between PyTorch and TensorFlow
- PyTorch vs. TensorFlow
Both frameworks work with tensors and see any model as a Directed Acyclic Graph (DAG), but how you can define them is very different.
TensorFlow says, “data is code, and code is data.” Therefore, the graph needs to be defined statically before a model can run in TensorFlow. All communication is done through the tensors tf.session and TF. Placeholder, filled with data from the outside world at runtime.
Things are much more directive and dynamic in PyTorch. You can define, change, and run nodes as you go. The framework works better with the Python language and feels more natural most of the time.
PyTorch works on a dynamic graph, whereas TensorFlow works on a static graph concept.
Another difference is debugging; since the computation graph in PyTorch is defined at runtime, we can use our favorite Python debugging tools like PDB, ipdb, the PyCharm debugger, or good old print statements.
TensorFlow doesn’t work like this. Instead, you can use a unique tool called tfdbg to evaluate TensorFlow expressions at runtime and look at all tensors and operations in the session scope.
PyTorch gives researchers flexibility, debugging, and quick training. It’s Linux, macOS, and Windows compatible.
PyTorch is a popular deep-learning framework. Thus, you should explore it first. On the other hand, if you know about machine learning and deep learning and want to get a job as soon as possible in the field, you should learn TensorFlow first.
- TensorFlow vs. Keras
Tensor performs well and is written in Python, C++, and CUDA. At the same time, Keras is written in Python. TensorFlow is used for big datasets and models that need to work well.
Most of the time, Keras is used for small datasets.
TensorFlow is used to make high-performance models. Keras is used for models with low performance. TensorFlow is a framework that has both high-level and low-level APIs.
Keras is a high-level application programming interface.
When debugging in TensorFlow, things get more complicated. In the Keras framework, there are only a few things you need to do to debug simple networks. TensorFlow is hard to use and has a complicated structure. Keras is built on a simple architecture and is easy to use.
Which is better: Keras vs TensorFlow vs PyTorch?
Keras vs TensorFlow vs Pytorch | Deep Learning Frameworks Comparison 2021 | Simplilearn
For a deeper understanding of the three frameworks, you can refer to the video by Simplilearn to gain insights into how they differ and overlap.
As the field of data science has become more popular, there has been an enormous rise in the use of deep learning technology. All three frameworks have become very popular because of this. With the help of the below table, you will be able to differentiate between the three, as each framework can work differently in each project.
| Parameter | Keras | TensorFlow | PyTorch |
| API Level | High-Level API | High and Low | Low-level API |
| Speed | Slow Performance | High Performance | High Performance |
| Architecture | Simple | Not Easy | Complex |
| Debugging | Less Frequent | Difficult | Better Degugging |
| DataSet | Small Dataset | Large Dataset | Large Dataset |
| Popularity | Most Popular | Second to Keras | Not very popular |
| Written In | Python | C++, CUDA, Python | Lua |
Now that you know how Keras, TensorFlow, and PyTorch work separately and which deep learning framework is best for you, you must enroll in a certification course on deep learning provided by Simplilearn to succeed in data science.
This can help you learn the skills you need to start a new job or improve the ones you already have.
