Pros And Cons Of Using The TensorFlow ML Platform

## Disadvantages Of Using TensorFlow While there are several ways in which TensorFlow eases the developmental pains of creating ML models, there are certain shortcomings that keep it from becoming the end-all and be-all of AI development. These are as follows: **1)Missing Symbolic Loops** TensorFlow does not have prebuilt contingencies for iterations that end up in symbolic loops. It does not implicitly expand the graph; rather, it manages the forward activations for the backdrop in different memory locations for each loop iteration, without creating a static graph on the fly with copies of the loop body subgraph. **2)Too Many Frequent Updates** Occasionally, working with TensorFlow causes your AI models to shrink as you receive background updates on a regular basis; as a result, even though your users always have the most recent version, the model's quality may suffer. Everyone will receive the most recent security updates automatically, which might seem wonderful, but there have been cases in the past where system updates have done more harm than good. **3)Homonym Inconsistency** Homonyms are provided by TensorFlow, which makes it difficult to understand and use because they have similar names but different implementations. The titles of TensorFlow's modules contain homophones, making it challenging for users to remember and apply. Adopting a single name for numerous different settings causes a dilemma. **4)Limited GPU Support** Only NVIDIA and Python are supported by TensorFlow for GPU programming. It has no additional support. On the other hand, TensorFlow code and tf.keras models will operate transparently on a single GPU without the need for code modifications. This is most likely a result of your system's inability to identify the CUDA and CuDNN drivers properly. Tensorflow is failing to recognize your Nvidia GPU in both situations. This may be due to a number of factors. **5)Low Implementation Speed** TensorFlow consistently takes the longest to train different types of neural networks across all hardware setups. If you actually look at the code, every method of performing convolutions ultimately uses the same code. The majority of these frameworks are only code wrappers. The TF team did an excellent job of ensuring they all use the same underlying code, and the wrappers remain simply due to the API's backward compatibility. They used to be distinct codes earlier but due to the redundancies, the overall TensorFlow framework gets slowed down.