Differences Between Theano vs Tensorflow
Theano may be defined as the library that belongs to python and facilitates the application development by optimizing the compiler for the evaluation of the mathematical expression and also their manipulations. It is used to being the feature of artificial intelligence by making the use of python. NumPy-Esque syntax has been used to implement this library in python. It uses the architecture of the CPU once the codes were written using this compiled.
Tensorflow is another library that is free and open-source which could be used to implement dataflow in the program. Similar to Theano, it can also be considered as the mathematical library that contributes to machine learning by the ability of computation it offers. The reason behind the development of this library was to bring it in use for research purposes. With the advancement in this library, it has been considered ample reliable to be used in the production environment. It lets the user create a neural network that works on a large scale and can be multi-layer. It contributes to artificial intelligence by introducing the use of data flow graphs. Both of these libraries makes a way for the developers to implement the features that fall under the domain of artificial intelligence. Based on the requirement either of these libraries can be chosen by the developers.
Head to Head Comparison Between Theano and Tensorflow (Infographics)
Below are the top 4 comparisons between Theano and Tensorflow:
Key Differences Between Theano and Tensorflow
Theano and Tensorflow are the libraries that serve almost the same purpose. The following are some of the key differences that are mentioned below:
- Theano has been developed by the LISA group which is a part of the varsity of Montreal while Tensorflow has been developed by the Google Brain team for internal use. Though it was developed for the internal usage of it has been made public afterwards.
- Theano is preferred when the application needs fewer resources and the computation is not much complex. While developing the algorithms that need moderate system configuration, Theano can be used without any doubt. Tensorflow is preferred when huge computations are required and the resources are adequately available. It is moreover an advantage of Tensorflow that it lets the complex algorithm run in the system.
- Theano library provides a platform where only Python-based applications can be able to leverage it. Due to its limitations, it is not preferred by the researchers who are fond of working in C++. Tensorflow let us use it with C++ and python as well that eventually offers the extended environment for research.
- Both of them are developed for the same purpose but due to the role of organizations, they hold the label of reliability with them. Being developed by Google that has a dedicated team named brain team who continuously develops this, Tensorflow is pretty much popular than Theano. Theano has been developed by the LISA group and works perfectly fine but it is not as popular Tensorflow due to some of the limitations it has.
Theano and Tensorflow Comparison Table
Below are the differences between Theanoa and Tensorflow.
| Theano | Tensorflow |
| Only python based library
Theano is a completely python based library which means it has to be used with python only. This library will work just with the python language and depends on python programming to get implemented. |
C++ and python based library
Tensorflow is the C++ and python based library that means it could be used in both, the C++ and the Python programming. Being able to serve in two languages, it is considered by the developers. |
| Uses Single CPU Popular Course in this category  TensorFlow Training (11 Courses, 3+ Projects)11 Online Courses | 3 Hands-on Projects | 55+ Hours | Verifiable Certificate of Completion | Lifetime Access 4.5 (3,511 ratings) Course Price View Course Related Courses Java Training (40 Courses, 29 Projects, 4 Quizzes)Python Training Program (36 Courses, 13+ Projects)HTML Training (12 Courses, 19+ Projects, 4 Quizzes)It uses the single CPU for processing or for performing the computations. It makes the efficient use of a single CPU and generates the outcome which is based on the processing power of the CPU. |
Uses one or more CPUs
Tensorflow is capable to use one or more CPUs based on how it has to be performing. Using a multiple CPU over single always has a preference as it leads to reduce the time it may take to complete computations. |
| Moderate compile speed
Theano is ample strong to perform complex computations but sometimes it is not able to meet the requirements due to its low compile speed. Though the compile time is too high but could lead to taking time if the complexity of the program is high. |
Fast compile speed
Tensorflow is considered for taking less compiling time as compares to Theano. The fact that it could make use of multiple CPUs makes it the one that can do complex computations in less time than what is taken by Theano for the same. |
| Moderate popularity
As compared to Tensorflow it is considered less popular due to some of the limitations in its features. It can be used only in the python programming and limited to use single CPU AMD hence only preferred where normal computations are required. |
Highly popular
The Tensorflow library has been developed to work with C++ and python as well. In addition to this, it is capable of working with multiple CPUs. Due to these features, it is pretty popular and preferred at the place that needs complex computations. |
Conclusion
Theano vs Tensorflow has its own importance and their preference is based on the requirements of the application where it has to be used. The main motive of existence for both of the libraries is research and development. In addition to that, it has been used very often in production as well. It is very important to understand that as per the developer’s need, they can opt either of the libraries. Also, the technology in which the application has to be developed matters a lot. All the researches that urge the graphical flow for the implementation of artificial intelligence leverage these libraries. One can simply pick these libraries to build the machine learning features enabled applications in a short span of time.
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