Theano vs Keras: Which is Better?

Comparing Theano and Keras involves evaluating two different tools that serve distinct purposes in the field of deep learning. Theano is a numerical computation library primarily used for building and optimizing mathematical expressions, while Keras is a high-level neural networks API that serves as an interface for building and training deep learning models. Despite their differences, both have contributed significantly to the advancement of deep learning research and application. Let’s delve into a detailed comparison to understand which might be better suited for your specific needs.

Overview of Theano:

Theano is a Python library developed by the Montreal Institute for Learning Algorithms (MILA) that enables efficient computation of mathematical expressions, particularly those involving multi-dimensional arrays (tensors). It provides a symbolic computation framework where users define mathematical operations symbolically, and Theano automatically optimizes and compiles these expressions into efficient numerical code for execution on CPUs or GPUs. Theano has been widely used for building and optimizing deep learning models due to its efficiency, flexibility, and support for GPU acceleration.

Overview of Keras:

Keras is a high-level neural networks API written in Python that provides a user-friendly interface for building and training deep learning models. Originally developed by Fran├žois Chollet, it was integrated into TensorFlow as its official high-level API and later became part of TensorFlow 2.0. Keras simplifies the process of building and training neural networks by providing a modular and intuitive API with support for various types of layers, activation functions, loss functions, optimizers, and other components. Keras is designed to be easy to use, flexible, and extensible, making it suitable for both beginners and experienced deep learning practitioners.


1. Level of Abstraction:


  • Theano provides a low-level interface for defining and optimizing mathematical expressions.
  • Users need to define computational graphs manually, including operations, variables, and gradients.
  • Theano’s low-level nature gives users fine-grained control over model architecture and optimization, but it requires more effort and expertise to use effectively.


  • Keras provides a high-level interface for building and training deep learning models.
  • Users can define models using high-level abstractions such as layers, which abstract away the details of the underlying computations.
  • Keras’ high-level nature simplifies the process of building and training models, making it more accessible to beginners and allowing for rapid prototyping and experimentation.

Winner: Keras has an advantage in terms of ease of use and abstraction level, especially for beginners and users seeking rapid prototyping and experimentation.

2. Flexibility and Customization:


  • Theano offers extensive flexibility and customization options due to its low-level nature.
  • Users have fine-grained control over model architecture, optimization algorithms, and computational graph optimizations.
  • Theano allows for deep customization and experimentation, making it suitable for advanced users and researchers.


  • Keras provides a high level of flexibility and customization through its modular API.
  • Users can customize models by composing layers, defining custom loss functions, optimizers, and callbacks.
  • While Keras offers flexibility for most use cases, it may not provide as much control as Theano for highly specialized or research-oriented tasks.

Winner: Theano has an advantage in terms of flexibility and customization, especially for advanced users and researchers requiring fine-grained control over model architecture and optimization.

3. Performance:


  • Theano is known for its efficient execution on both CPUs and GPUs, with optimizations for numerical computations and parallel processing.
  • The static computational graph in Theano allows for extensive optimizations and efficient memory usage, resulting in high performance for many deep learning tasks.


  • Keras, as an interface built on top of TensorFlow, inherits TensorFlow’s performance optimizations and support for distributed computing.
  • TensorFlow’s eager execution mode allows for dynamic computational graphs, which may introduce some overhead compared to Theano’s static graph approach.
  • While Keras offers competitive performance, it may not match Theano’s performance in all scenarios, especially for highly optimized computational graphs.

Winner: Theano has an advantage in terms of performance optimization and efficiency, especially for scenarios requiring highly optimized computational graphs.

4. Community Support:


  • Theano has a dedicated community of users and developers, but its development has slowed down in recent years.
  • While Theano has been widely used in research and industry, its usage has declined in favor of newer frameworks like TensorFlow and PyTorch.


  • Keras, as part of TensorFlow, has a large and active community of users and developers.
  • TensorFlow’s popularity and extensive ecosystem contribute to Keras’ adoption and support, with a wide range of resources, tutorials, and libraries available for users.

Winner: Keras has an advantage in terms of community support and resources, thanks to its integration with TensorFlow and the extensive TensorFlow ecosystem.

Final Conclusion on Theano vs Keras: Which is Better?

In conclusion, both Theano and Keras offer valuable tools and resources for building and training deep learning models. The choice between the two depends on the specific requirements, preferences, and priorities of the user:

  • Theano is suitable for users requiring fine-grained control over model architecture and optimization, with a focus on efficiency and performance.
  • Keras is ideal for users seeking ease of use, rapid prototyping, and access to TensorFlow’s extensive ecosystem of tools and resources.

Ultimately, whether you choose Theano or Keras depends on your specific needs, familiarity with the framework, and the requirements of your deep learning projects. Both frameworks have their strengths and weaknesses, and the choice should be based on a thorough evaluation of your use case and preferences.


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