Updated March 27, 2023
Difference Between Mxnet vs TensorFlow
TensorFlow and MXNet, both being the most popular deep learning frameworks used by scientists, researchers, students, etc. are rapidly evolving. Recently TensorFlow 2.0 has been released by Google which is said to be 1.8x times faster than its previous version. Also, the Amazon cloud platform has chosen this framework for providing deep learning services. MXNet comprises of the following two types: MXNet-Gluon and MXNet-Module. To benchmark the performance of TensorFlow vs MXNet, various architectures have to be taken into consideration and the corresponding metrics such as test accuracy, memory consumption, execution time, etc. have to be calculated for both.
Head to Head Comparison between Mxnet vs TensorFlow (Infographics)
Below are the top 5 comparisons between Mxnet vs TensorFlow:
Key differences between Mxnet vs TensorFlow
Let us discuss some key differences between Mxnet vs TensorFlow in the following points:
Let us take the example of the MNIST Handwritten Digits Dataset. If we train a model using TensorFlow, it takes approximately 21.5 seconds with 99.54% accuracy. The test accuracy comes out to be 97.42%. Now consider the case, when we train the model using MXNet. The MXNet has two types: MXNet-Gluon and MXNet-Module.
Let us briefly walk through them.
- MXNet-Gluon: It gives the user the flexibility to write models in an imperative approach by using the concept of dynamic graphs, similar to the one used in PyTorch. Thus, it helps in rapid prototyping, faster debugging, etc. The model can be easily converted into a graph by using a single line of code thus increasing the scalability, making it memory efficient and production-ready. By using it, the training time for the model on the above dataset comes out to be 14 seconds with 99.93% accuracy. The test accuracy comes out to be 98.07%.
- MXNet-Module: It gives the user the interfaces defined at both the higher level and the intermediate level for the execution of networks defined prior. Symbolic API is used in the back end. It only supports static graphs while Gluon supports dynamic as well as static graphs. By using it, the training time for the model on the above dataset comes out to be 8.14 seconds with 99.4% accuracy. The test accuracy comes out to be 98.08%.
If we compare performance for the following libraries on the same dataset, MXNet-Gluon is 1.5 times faster than TensorFlow and MXNet-Module is 2.5 times faster than it. If an advanced deep neural network architecture like LeNet is implemented by using both of them for the same dataset as used above, then we observe the following:
1. TensorFlow: It is observed that:
- Training time for the model is approx. 46 sec for 20 epochs.
- Testing accuracy is approx. 98%
2. MXNet-Gluon: It is observed that:
- Training time for the model is approx. 25 sec for 20 epochs.
- Testing accuracy is approx. 99%.
3. MXNet-Module: It is observed that:
- Training time for the model is approx. 19 sec for 20 epochs.
- Testing accuracy is approx. 99%.
If we compare the performance for the following libraries on the same dataset, MXNet-Gluon is 1.8 times faster than TensorFlow and MXNet-Module is 2.5 times faster than it.
It can be inferred from the above examples that MXNet trains faster on a dataset with a lesser number of training samples as compared to TensorFlow. TensorFlow is good at performing data-intensive tasks while MXNet is better at performing machine learning processing tasks. TensorFlow has the fastest training speed for samples that are processed by using the VGG-16 model.
MXNet provides an easier specification as to where the data structures should reside. It can scale up a computation across multiple GPUs parallelly. The Predefined layers in a given neural network model are optimized for speed. The derivative computations for each layer are automated. It trains a model with better accuracy. It performs better on a cold run i.e. reboot of applications is required on a regular basis. In Hot run, the performance of MXNet when compared with TensorFlow is more or less the same. Thus, it is more preferred than TensorFlow for Robotic Computer Vision applications.
Comparison Table of Mxnet vs TensorFlow
The table below summarizes the comparisons between Mxnet vs TensorFlow:
| MXNet | TensorFlow |
| MXNet supports R, Python, Julia, etc. | TensorFlow supports only Python. |
| MXNet has good CNN support. | TensorFlow has the best CNN support. |
| MXNet does not have good RNN support. | TensorFlow has good RNN support, hence popularly used for performing NLP tasks. |
| MXNet has a good easy to use architecture and modular front end. | TensorFlow has the best ease of use architecture and modular front end |
| MXNet has good documentation which is available open-source. | TensorFlow has the best documentation which is available open-source. |
| On GitHub, it is not that popular in terms of the number of forks. | On GitHub, it is one of the most popular in terms of the number of forks. |
| On Stack Overflow forums, it is not that popular in terms of the number of questions asked and answered by the users. | On Stack Overflow forums, it is one of the most popular in terms of the number of questions asked and answered by the users. |
| It performs best on ResNet-56. | It performs best on Alex Net. |
| It is supported on AWS EC2 and Microsoft Azure cloud platforms through AWS EC2 is more preferred. | It is supported on AWS EC2, Google Cloud, and Microsoft Azure cloud platforms. |
| For Deployment on Android and IOS, it requires additional libraries and compilations. | For Deployment on Android and IOS, it requires OOBs. |
| On Raspberry Pi, Windows and Unix, it requires OOBs and YMMV. | On Raspberry Pi, Windows and Unix, it requires OOBs. |
| It has more language bindings, hence faster | It has fewer language bindings, hence slower. |
Conclusion
Both are being popularly used by people across the globe. Based on their advantages/disadvantages discussed prior, they find their usage in a variety of enterprises as well as scientific applications. Both frameworks are evolving based on the growing market and increasing customer needs and requirements.
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