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Keras vs TensorFlow vs Theano: What are the differences?
## Key Differences between Keras, TensorFlow, and Theano
Keras is a high-level neural networks API that is designed to be user-friendly, modular, and extensible. TensorFlow is a powerful open-source deep learning library developed by Google, known for its flexibility and scalability. Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
1. **Ease of Use**: Keras is known for its simplicity and ease of use, making it ideal for beginners and rapid prototyping. TensorFlow, on the other hand, offers more flexibility and control for advanced users with features like low-level operations and custom gradients. Theano has a steeper learning curve compared to Keras, but it offers efficiency optimizations not present in the other frameworks.
2. **Backend Support**: Keras is capable of utilizing TensorFlow as its backend, seamlessly leveraging the functionalities of TensorFlow within Keras models. TensorFlow, on the other hand, has its own extensive set of tools and libraries, offering a wide range of features beyond what Keras provides. Theano, while being independent of other frameworks, may lack some of the advanced features available in TensorFlow.
3. **Community Support**: TensorFlow has a large and active community of developers and researchers contributing to its continuous improvement and development. Keras, being integrated with TensorFlow, also benefits from this strong community support. Theano, although once widely used, has seen a decline in community activity and development due to the emergence of more advanced frameworks like TensorFlow.
4. **Computational Graph Representation**: TensorFlow and Theano use a static computational graph, meaning the graph is defined once and executed many times. In contrast, Keras uses a dynamic computational graph, allowing for easier model building and debugging. Each approach has its own advantages in terms of performance and flexibility.
5. **Deployment and Production**: TensorFlow offers better support for deployment in production settings, with tools like TensorFlow Serving and TensorFlow Lite for mobile and embedded devices. Keras also provides deployment options but may not offer the same level of integration and optimization as TensorFlow. Theano lacks dedicated deployment tools, which can make it more challenging to deploy models in production environments.
6. **Customization and Extensibility**: TensorFlow provides a high degree of customization and extensibility through its low-level APIs, allowing users to create custom operations and optimizations. Keras offers a more simplified interface for building neural networks but may limit the extent to which users can customize their models. Theano, while flexible, may require more manual intervention for customization compared to TensorFlow and Keras.
In Summary, TensorFlow provides a powerful and versatile deep learning framework with extensive community support, while Keras offers a user-friendly interface and seamless integration with TensorFlow. Theano, although efficient, has seen a decline in popularity and support in favor of more advanced frameworks like TensorFlow.
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
Pros of Theano
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Cons of Keras
- Hard to debug4
Cons of TensorFlow
- Hard9
- Hard to debug6
- Documentation not very helpful2
Cons of Theano
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