My Blog

Category: Disadvantages

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  • Lack of Full Support for Distributed Training (Standalone Keras)

    • Distributed training capabilities, while available in TensorFlow 2.x’s Keras API, are not as robust in standalone Keras. This can be a limitation for those needing to train models on multi-GPU or multi-node systems in large-scale production environments.

  • Less Popular for Research

    • Research Preference: Many cutting-edge research models are implemented in PyTorch or TensorFlow rather than Keras, as these frameworks offer more flexibility for developing novel architectures and experimenting with custom training procedures. As a result, Keras may not be the best choice for researchers pushing the boundaries of machine learning.

  • Performance on Large-Scale Systems

    • Keras may not be the most optimized choice for large-scale deep learning systems where every bit of performance matters, especially when comparing to lower-level frameworks like TensorFlow or PyTorch, which allow better control over resource management and optimization.

  • Dependency on Backends

    • Backend Complexity: Since Keras relies on other backends like TensorFlow or Theano, users are sometimes required to have knowledge of these backends to solve performance issues or low-level bugs. This can complicate the usage for users who expect Keras to handle everything without backend-specific adjustments.

  • Limited Deployment Flexibility (Standalone Keras)

    • Before TensorFlow 2 Integration: When Keras was used as a standalone library before its integration into TensorFlow, deploying models to production was more challenging. Although this has been improved with Keras’ inclusion in TensorFlow, standalone Keras may still have limitations in terms of deployment tools and pipelines compared to frameworks like TensorFlow and PyTorch.

  • Lack of Support for Dynamic Computation Graphs

    • Static Graphs: Keras, particularly when using TensorFlow as a backend, primarily supports static computation graphs (also known as “Define-and-Run” models). This can be limiting for models that require dynamic computation graphs, where the structure of the graph changes during runtime. PyTorch, which uses dynamic graphs (“Define-by-Run”), is better suited for such models.

  • Limited Support for Low-Level Operations

    • Complex Custom Operations: For advanced research or cutting-edge models, if you need to perform custom mathematical operations or manipulate tensors at a low level, Keras may not be as efficient or flexible. In contrast, PyTorch or TensorFlow (using their low-level APIs) allows for greater control in implementing novel operations.

  • Slower Execution in Some Cases

    • Overhead from Abstraction: The simplicity of Keras comes with a performance trade-off. Since it adds a layer of abstraction over backends like TensorFlow, there can be additional overhead, making Keras slower in certain use cases, particularly for very large-scale models or when extreme optimization is needed.

  • Debugging Challenges

    • Opaque Execution: Because of its high-level API, debugging complex models in Keras can be difficult. The abstraction layers make it harder to trace issues, especially when problems arise during model training or data preprocessing.
    • Error Messages: Error messages in Keras can sometimes be cryptic or less informative compared to frameworks like PyTorch, which offers more transparent debugging tools.

  • Limited Flexibility for Advanced Customization

    • High-Level Abstraction: While Keras is excellent for simplicity and ease of use, its high-level abstraction can be a limitation for users who need fine-grained control over model architectures, layers, or training processes. For very customized layers or operations, Keras may not offer as much flexibility as low-level frameworks like TensorFlow or PyTorch.