TensorFlow Pros and Cons – The Bright and the Dark Sides

In our last TensorFlow tutorial, we discussed TensorFlow Applications. Today, we will see TensorFlow Pros and cons. These TensorFlow Pros and Cons tutorial will guide us the benefits and limitations of TensorFlow. Moreover, we will also look at TensorFlow Computation Speed.

The more advanced the technology, the more useful it can be, but of course everything has its downside and so does this machine learning library.

When comparing TensorFlow with other libraries like Scikit, Torch, Theano, Neon, there are drawbacks in a number of features that the library lets you manipulate. This library is maintained and updated by Google, the tech giant, so needless to say, it has come a far way since its initial release.

So, let’s start exploring TensorFlow Advantages and Disadvantages.

Advantages of Tensorflow

Below, we are discussing some advantages of TensorFlow:

TensorFlow Advantages

a. Graphs

• Tensorflow has better computational graph visualizations, which are indigenous when compared to other libraries like Torch and Theano.

Tensorflow Advantages – Graphs

b. Library Management

• Backed by Google, TensorFlow has the advantage of the seamless performance, quick updates and frequent new releases with new features.

Advantages of TensorFlow – Library management

c. Debugging

• Tensorflow lets you execute subparts of a graph which gives it an upper-hand as you can introduce and retrieve discrete data onto an edge and therefore offers great debugging method.

d. Scalability

• The libraries can be deployed on a gamut of hardware machines, starting from cellular devices to computers with complex setups.

e. Pipelining

• TensorFLow is highly parallel and designed to use various backends software (GPU, ASIC) etc.

Disadvantages of Tensorflow

Disadvantages of TensorFlow

a. Missing Symbolic Loops

The feature that’s most required when it comes to variable length sequences are the symbolic loops. Unfortunately, TensorFlow does not offer this feature, but there is a workaround using finite unfolding (bucketing).

b. No support for Windows

There is still a wide variety of users who are comfortable with a windows environment rather than a Linux in their systems and TensorFlow does not assuage these users.

But, you need not worry if you are a Windows user as you can install it within a conda environment or using the python package library, pip.

c. Benchmark Tests

TensorFlow lacks behind in both speed and usage when compared to its competitors as can be seen from the following test results :

Disadvantages of Tensorflow: Benchmark

Tensorflow Disadvantages: Benchmark

d. No GPU support other than Nvidia and only language support

Currently, the only supported GPUs are that of NVIDIA and the only full language support is of Python which makes it a downside as there is a rise of other languages in deep learning as well like Lau.

Computation Speed

This is the field where TF is lagging behind but you focus on the production environment rather than the performance, it is still a good choice.

Tensorflow Computation Speed

So, this was all about TensorFlow Pros and Cons. Hope you like our explanation.

Conclusion

Hence, in this TensorFlow Pros and Cons tutorial, we discussed the major advantages and disadvantages of TensorFlow. TensorFlow still has a lot to offer and there is a community out there on the internet that can help you with it. Hope you like the article on Tensorflow Pros and Cons.

Next, we will see TensorFlow API. Furthermore, if you have any query, feel free to ask in the comment section.