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Gluon vs Keras vs TensorFlow: What are the differences?
The key differences between Gluon, Keras, and TensorFlow are as follows:
1. **Ease of use**: Gluon provides an imperative programming interface that allows for dynamic graph creation on the fly, making it beginner-friendly and easier for debugging. Keras, on the other hand, offers a higher-level, more user-friendly API for building neural networks, while TensorFlow provides both high-level APIs like Keras and low-level functionalities for more control and customization.
2. **Performance**: Gluon and Keras are often preferred for rapid prototyping and experiments due to their simplicity, while TensorFlow is known for its scalability and performance, making it suitable for large-scale production deployment and research projects.
3. **Deployment options**: TensorFlow offers more deployment options, such as TensorFlow Serving for serving models in production, TensorFlow Lite for mobile and embedded devices, and TensorFlow.js for running models in the browser, compared to Gluon and Keras which have limited deployment options.
4. **Community support**: TensorFlow has a larger and more active community compared to Gluon and Keras, providing a wider range of resources, tutorials, and pre-trained models, making it easier to find solutions to problems and stay updated on the latest advancements in the field.
5. **Flexibility and customization**: TensorFlow allows for more fine-grained control and customization of models compared to Gluon and Keras, enabling researchers and developers to experiment with different architectures, loss functions, and optimization techniques with more flexibility.
6. **Backend support**: Both Gluon and Keras support multiple backend engines such as TensorFlow, Theano, and Microsoft Cognitive Toolkit, allowing users to switch between different backends seamlessly, while TensorFlow is primarily focused on its own backend but can be integrated with other deep learning libraries.
In Summary, Gluon, Keras, and TensorFlow offer different levels of ease of use, performance, deployment options, community support, flexibility, and backend support for building and deploying neural networks.
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 Gluon
- Good learning materials3
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 Gluon
Cons of Keras
- Hard to debug4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
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