Theano vs Tensorflow

theano vs tensorflow machine learning frameworks pros and cons ml

In the past decade, different companies have started to incline towards Artificial Intelligence. Fields like Medical Sciences have benefited from AI a lot, such as with the advanced tools for diagnosing cancer. That is why Machine Learning has become an important step in helping companies advance. This has also increased the demand for ML professionals who have an understanding of the different deep learning libraries. TensorFlow and Theano are some of the most popular ML libraries. In this article, we will be comparing the two so that you can decide which one suits your project the best. 

TensorFlow and Theano are two of the most popular Machine Learning frameworks. Both of them are extensively used by researchers in the domain of Deep Learning. More often than not, both are compared in terms of their popularity, technological benefits, ease of use, and much more. 

TensorFlow was developed by engineers and researchers working on Google Brain Team while Theano was created by Yoshua Bengio of the University of Montreal’s MILA. In September 2017, the team announced that all the major developments of Theano after 1.0 version release will be ceased. So even though Theano might be considered as effectively dead, several experts in the deep learning domain still rely on Theano. 

TensorFlow vs Theano 

TensorFlow is an open-source software library used for carrying out numerical computation with the help of data flow graphs. Python or C++ can be used as the base language for TensorFlow. Theano, on the other hand, is a Python-based library that helps users in defining, optimizing, and evaluating mathematical expressions that evolve multi-dimensional arrays efficiently. 

Since its creation, TensorFlow has become a widely adopted, open-source library that performs fast gradient-based machine learning on GPUs. It also has a flexible architecture that allows deploying computation to more than one CPU or GPU in a mobile or a desktop. Theano can only be deployed on a single GPU. It also ensures the transparent use of GPU for performing data-intensive computations. Theano can be integrated with NumPy that allows efficient symbolic differentiation. It has been used to efficiently power large-scale computationally intensive investigations. 

Now that we have discussed some of the key points of the two libraries, let's dive into the key differences between the two and which one is more likely to be used over the other: 

1. Popularity 

TensorFlow has been one of the popular frameworks. Thanks to its recent popularity surge and marketing, it has become one of the most popular libraries. It has defeated some long time players of open-source markets like Torch and Theano. TensorFlow boost visually appealing components as several users believe that it offers better visualizations for computational graphs. Theano, on the other hand, might not look as much fun. But, when it comes to visualizing convolutional images, filters, and graphs, it can be just as good. 

The reason why TensorFlow beats Theano in popularity is that it is supported by Google, one of the leading tech giants in the world. Theano was developed and maintained by more than 50 members from the University of Montreal’s MILA who constantly contributed their time to improving it. 

2. Technology 

Some experts believe that TensorFlow was created by Google to replace Theano and that it is a re-implementation of the latter. Still, Theno is faster than TensorFlow in several aspects. Theano supports a wide range of operations and while TensorFlow has definitely shown promise, it hasn’t still reached the level of Theano. Also, the similarities between the two libraries can be owed to the fact that some creators of Theano like Ian Goodfellow were the ones who created TensorFlow at Google. 

Both the libraries are used for performing automatic differentiation and generating a computational graph. Thanks to this, every time a researcher experiments with a different arrangement of neural networks, you are not required to hand-code a new variation of backpropagation. Theano can determine the backpropagation error while computing the gradient by deriving an analytical expression. With this, there is no accumulation of errors while making successive derivative calculations. 

TensorFlow offers users a large amount of documentation to help them with the installation. It also provides a lot of learning materials to help beginners in understanding the theoretical concepts of neural networks and how to set it up. Also, TensorFlow can do partial subgraph computation. This is not offered by other frameworks. You should also note that Keras, one of the Theano frameworks, supports TensorFlow. 

Advantages And Disadvantages Of TensorFlow 

Advantages 

• Supports algorithms like reinforcement learning 
• Has less compile time than Theano 
• Offers computational graph abstraction 
• Offers model and data parallelism 
• Can be deployed on several CPUs and GPUs 
• Offers TensorBoard for visualization 

Disadvantages 

• Runs slower than other frameworks 
• Doesn’t support matrix operations (copying such large matrices can be costly) 
• Computational graphs are usually slow 
• Doesn’t have pertained models 
• Is not commercially supported 
• Dynamic Typing can be error-prone on big software projects 

Advantages And Disadvantages Of Theano 

Advantages 

• Open-source, deep libraries like Blocks, Keras, and Lasagne are built on the top of Theano Raw 
• Theano can be low-level 
• Computational graph can be a nice abstraction 
• Has high-level wrappers like Lasagne, Keras that increases its usability 

Disadvantages 

• Can be troublesome on Amazon Web Services 
• Large models can have long compile times 
• Can be deployed on just one GPU 
• Faster than Torch 
• Error messages don’t really help while debugging 



Theano Versus Tensorflow Conclusion

On a concluding note, it can be said that both TensorFlow and Theano APIs interfaces share a few design characteristics and are more or less similar. But, TensorFlow’s API is much easier to read and use. It is better for deployment as well. Also, it provides helpful monitoring tools, operators, and debugging tools that are not offered by Theano. When it comes to speed and usability, Theano is faster and simpler to use than TensorFlow. However, since TensorFlow has been introduced recently, users have to depend extensively on papers and presentations to get a better understanding of its usage. If you want to learn more about TensorFlow, you should pursue TensorFlow certification, it will help you understand the concepts of the library.

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