Advantages and Disadvantages of TensorFlow
TensorFlow is an open-source machine learning library which is developed by the google brain team. Tensorflow is a boon when comes to the computation of machine and deep learning models but still, there are many problems that may arise because of the same. TensorFlow’s popularity is due to many things, but primarily because of the computational graph concept, automatic differentiation, and the adaptability of the Tensorflow python API structure. This makes solving real problems with TensorFlow accessible to most programmers.
In this article, we are going to see the advantage and disadvantages of TensorFlow.
Tensorflow is not limited to one specific device. It works as efficiently on a cellular device as it works on any other complex machine. The library is so defined that its deployment isn’t limited to any one specific device.
2. Open Source Platform
It is available free of cost to anyone who wants to work with this. This feature makes it possible for any user to employ this module whenever and wherever required.
Tensorflow has a better data visualization power than any other available library. This makes it working on neural networks easier.
Tensorflow has Tensorboard which allows easy debugging of nodes. This helps reduce the overhead of visiting the whole code.
TensorFlow employs GPU and CPU systems for its functioning. A user is free to use any of the architecture as per requirement. A system uses GPU if not mentioned explicitly. This process reduces memory usage to some extent. Due it this capacity Tensorflow is viewed as a hardware acceleration library.
7. Architectural Support
The TensorFlow architecture uses TPU which makes computation faster than CPU and GPU. Models which are built over TPU can be easily deployed over clouds and works faster compared to the other two.
8. Library management
Being backed by google, Tensorflow is updated frequently and is capable of displaying outstanding performance.
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.
It is comparatively slower and less usable compared to its competing frameworks.
3. GPU support
Tensorflow has only NVIDIA support for GPU and Python programming language support for GPU programming.
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.
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.
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.
8. Symbolic loops
Tensorflow lags at providing symbolic loops for indefinite sequences. Its support for definite sequences makes it a useful resource.