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Keras vs Theano: What are the differences?
- Execution Speed: Theano is known for its efficient computation of mathematical expressions, utilizing GPU for faster processing. On the other hand, Keras provides a high-level interface to design neural networks, making it more user-friendly but sacrificing some speed compared to Theano.
- Flexibility: Theano offers more control and customization options, allowing users to fine-tune algorithms and parameters for specific tasks. In contrast, Keras abstracts complex operations, offering a simpler and more intuitive way to build models without delving into low-level details.
- Community Support: Keras has gained significant popularity in the deep learning community due to its easy-to-use design and compatibility with other popular frameworks like TensorFlow. Theano, although powerful, has seen a decline in support and development in recent years, making it less appealing for new users.
- Development Status: Keras is actively maintained and updated, with new features and improvements continuously being added to the framework. Theano, on the other hand, has been deprecated in favor of other deep learning libraries, such as TensorFlow, leading to a lack of new developments and updates.
- Ease of Use: Keras focuses on simplicity and ease of use, allowing users to quickly prototype and build models with minimal code and effort. In contrast, Theano requires a deeper understanding of neural network concepts and implementation, making it more suitable for advanced users or researchers in the field.
- Compatibility: Keras is designed to work seamlessly with TensorFlow, providing a unified platform for building and training neural networks. Theano, while powerful, may face compatibility issues with newer hardware or software environments due to its limited support and development.
In Summary, Keras and Theano vary in their execution speed, flexibility, community support, development status, ease of use, and compatibility with other frameworks.
Decisions about Keras and Theano
Fabian Ulmer
Software Developer at Hestia · | 3 upvotes · 49.3K views
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.
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Learn MorePros of Keras
Pros of Theano
Pros of Keras
- Quality Documentation8
- Supports Tensorflow and Theano backends7
- Easy and fast NN prototyping7
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Cons of Keras
Cons of Theano
Cons of Keras
- Hard to debug4
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What is Keras?
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
What is Theano?
Theano is a Python library that lets you to define, optimize, and evaluate mathematical expressions, especially ones with multi-dimensional arrays (numpy.ndarray).
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What companies use Keras?
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What are some alternatives to Keras and Theano?
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
MXNet
A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.