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Keras vs TensorFlow: What are the differences?
# Introduction
1. **Architecture**: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. TensorFlow, on the other hand, is an open-source deep learning framework developed by Google. While Keras offers a user-friendly interface for building neural networks, TensorFlow provides more flexibility in terms of architecture customization.
2. **Backend Support**: Keras allows for easy switching between different backend engines such as TensorFlow, Theano, and CNTK. In contrast, TensorFlow has its own backend engine, which is optimized for performance and provides robust support for distributed computing across multiple devices. This makes TensorFlow more suitable for large-scale machine learning projects.
3. **Ease of Use**: Keras focuses on simplicity and ease of use, with intuitive APIs that allow developers to quickly prototype deep learning models. On the other hand, TensorFlow requires more code for the same tasks, making it less user-friendly for beginners. However, TensorFlow's low-level APIs offer greater control and flexibility for advanced users.
4. **Community Support**: TensorFlow has a larger and more active community compared to Keras, resulting in more resources, tutorials, and third-party contributions. This makes it easier to find solutions to common problems and stay up-to-date with the latest developments in the field of deep learning.
5. **Deployment**: Keras models are easier to deploy due to their lightweight nature and higher level of abstraction. TensorFlow, being a more complex framework, requires additional steps for deployment and productionization. This makes Keras a better choice for rapid prototyping and quick deployment in production environments.
6. **Extensibility**: TensorFlow offers more extensive support for customized operations and extensions through its low-level APIs, allowing for more advanced research and development. Keras, while more beginner-friendly, may be limited in terms of extending and customizing neural network architectures beyond what is provided in its high-level APIs.
In Summary, Keras and TensorFlow differ in architecture, backend support, ease of use, community support, deployment, and extensibility, making them suitable for different use cases based on the specific requirements of a project.
Pytorch is a famous tool in the realm of machine learning and it has already set up its own ecosystem. Tutorial documentation is really detailed on the official website. It can help us to create our deep learning model and allowed us to use GPU as the hardware support.
I have plenty of projects based on Pytorch and I am familiar with building deep learning models with this tool. I have used TensorFlow too but it is not dynamic. Tensorflow works on a static graph concept that means the user first has to define the computation graph of the model and then run the ML model, whereas PyTorch believes in a dynamic graph that allows defining/manipulating the graph on the go. PyTorch offers an advantage with its dynamic nature of creating graphs.
For my company, we may need to classify image data. Keras provides a high-level Machine Learning framework to achieve this. Specifically, CNN models can be compactly created with little code. Furthermore, already well-proven classifiers are available in Keras, which could be used as Transfer Learning for our use case.
We chose Keras over PyTorch, another Machine Learning framework, as our preliminary research showed that Keras is more compatible with .js. You can also convert a PyTorch model into TensorFlow.js, but it seems that Keras needs to be a middle step in between, which makes Keras a better choice.
For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
Pros of TensorFlow
- High Performance32
- Connect Research and Production19
- Deep Flexibility16
- Auto-Differentiation12
- True Portability11
- Easy to use6
- High level abstraction5
- Powerful5
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Cons of Keras
- Hard to debug4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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