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Advantages and Disadvantages of TensorFlow - GeeksforGeeks
Excerpt
## Disadvantages ### 1. No windows support Besides all the advantages possessed by Tensorflow, it has a very limited set of features for Windows users. For Linux users this isn't the case there is a wide arcade of features when it comes to them. ### 2. Slow It is comparatively slower and less usable compared to its competing frameworks. … ### 4. Frequent updates Tensorflow undergoes frequent updates making it overhead for a user to time to time uninstall and reinstall it so that it can bind and be blended with its latest updates. ### 5. Architectural limitation Tensorflow's TPU architecture allows only execution of models and doesn't allow its training. ### 6. Inconsistent Tensorflow contains homonyms as names of its contents which makes it difficult for a user to remember and use. A single name is used for various different purposes and this is where the confusion starts. ### 7. Dependency Even though TensorFlow reduces the size of the program and makes it user-friendly, it adds a layer of complexity to it. Every code needs some platform for its execution which increases dependency.
Related Pain Points
Slow Training Speed Compared to Competitors
6TensorFlow consistently takes longer to train neural networks across all hardware setups compared to competing frameworks, with slower execution speeds impacting model deployment timelines.
Limited GPU Support (NVIDIA/Python Only)
5TensorFlow only supports NVIDIA GPUs and Python for GPU programming with no additional support for other accelerators, limiting cross-platform development flexibility.
No Windows Support
5TensorFlow has very limited features and support for Windows users, with a significantly wider range of features available only for Linux users.
Confusing API Naming and Homonym Inconsistency
4TensorFlow uses homonyms and inconsistent function naming conventions across its API, making it difficult for users to understand and remember which implementation corresponds to which name, causing confusion when adopting single names for multiple different purposes.
Limited TPU Architecture (Training Restriction)
4TensorFlow's TPU architecture only allows execution of models but does not allow training on TPUs, limiting the use of specialized hardware accelerators for training workflows.
Transitive Dependency Complexity
4Even though TensorFlow reduces program size and aims to be user-friendly, it adds a layer of complexity through dependencies. Every code execution requires a platform for execution, which increases overall system dependency and maintenance overhead.