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Gluon vs Keras: What are the differences?
Introduction
Gluon and Keras are both deep learning frameworks used for building and training neural networks. While they share similarities in terms of their purpose and functionality, there are several key differences that set them apart.
Syntax: One major difference between Gluon and Keras is their syntax. Gluon follows an imperative programming model, where operations are executed in a predefined order. On the other hand, Keras follows a declarative programming model, where the structure of the network is defined first before training. This difference in syntax affects the programming style and flexibility of the frameworks.
Backend Support: Another difference lies in the backend support. Keras is a high-level API that can run on top of various deep learning frameworks such as TensorFlow, Theano, or CNTK. This allows developers to choose their preferred backend. On the other hand, Gluon is a part of the Apache MXNet deep learning framework and is closely integrated with it. As a result, Gluon has native support for MXNet's backend.
Dynamic vs Static Graphs: Gluon is known for its dynamic computation graphs, which means the graph is built and executed on-the-fly during training. This allows for more flexibility in changing the network architecture during runtime. In contrast, Keras uses static computation graphs, where the graph structure is defined beforehand and remains unchanged during training. This provides optimizations and performance benefits but restricts flexibility.
Hybridizable Models: One unique feature of Gluon is its ability to create hybridizable models. This means that a Gluon model can be converted into a symbolic representation compatible with MXNet's static computational graph. This allows for training speed improvements without sacrificing the dynamic nature of the graph.
Advanced Control Flow: Gluon provides more advanced control flow capabilities compared to Keras. Gluon supports loop constructs and if-else statements within the network architecture, allowing for more complex computations. This can be useful in certain scenarios where dynamic control flow is required.
Community and Documentation: Both Gluon and Keras have active communities and good documentation available. However, Keras has been around for longer and has a larger user base, which means that there are more resources, tutorials, and community support available for Keras compared to Gluon.
In summary, Gluon and Keras differ in their programming syntax, backend support, computation graph type, model hybridization capability, control flow capabilities, and community size. These factors contribute to different programming styles, flexibility, and performance characteristics of the two frameworks.
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.
Pros of Gluon
- Good learning materials3
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
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Cons of Gluon
Cons of Keras
- Hard to debug4
