Keras vs OpenNN: What are the differences?
## Introduction
When comparing Keras and OpenNN, it is important to understand the key differences between these two neural network frameworks.
1. **Ease of Use**: Keras is known for its user-friendly and simple interface, making it ideal for beginners or those looking to quickly prototype neural networks. On the other hand, OpenNN is more complex and requires a deeper understanding of neural networks, making it better suited for advanced users or researchers who require more customization.
2. **Supported Platforms**: Keras is built on top of TensorFlow, CNTK, and Theano, which allows it to run seamlessly on different platforms. OpenNN, however, has limited platform support as it primarily relies on C++ libraries, making it less versatile compared to Keras.
3. **Community and Documentation**: Keras has a large and active community with extensive documentation, tutorials, and resources available online. This makes it easier for users to find help and solutions to their problems. OpenNN, while still supported, has a smaller community and less comprehensive documentation, which may lead to more challenges for users seeking support.
4. **Flexibility and Customization**: Keras provides a high level of abstraction, sacrificing some customization options in favor of simplicity. In contrast, OpenNN offers more flexibility and control over the neural network's architecture and parameters, allowing users to fine-tune their models according to their specific requirements.
5. **Performance**: Keras is optimized for performance and comes with pre-built tools for parallel processing and distributed computing, which can significantly speed up neural network training. OpenNN, while efficient, may require more manual optimization and configuration to achieve the same level of performance as Keras.
In Summary, Keras and OpenNN differ in terms of ease of use, supported platforms, community support, flexibility, customization options, and performance capabilities.
