In recent times, there has been an incline of various companies towards artificial intelligence. Many fields, such as the medical field, have a lot of benefits from artificial intelligence, such as advanced cancer diagnosis tools.
Thus, it is good to say that Machine Learning(ML) is a very crucial step for the advancement of various businesses/companies. To meet this growing need, numerous deep-learning libraries have been developed. Some libraries are around for quite some time, and some have been launched very recently.
Torch and Theano are the oldest ones on the market, and TensorFlow and Caffe are the latest additions. However, TensorFlow and Theano are the most used and popular ones.
TensorFlow vs. Theano – which one works for you? Without any further ado, we can discuss these two. Theano and Tensorflow are popular machine learning frameworks that the researchers use in the deep learning domain. Both of them are compared for their popularity, ease of use, technological benefits, etc.
Theano is a deep learning library developed by Yoshua Bengio at Université de Montréal in 2007. It can run on both CPU and GPU, hence it provides smooth and efficient operation.
Theano is famous among academic researchers because it is a deep-learning library. It is the grandfather of deep-learning libraries. Theano is mostly useful in extensive research-based tasks, and deep learning tasks, and is also useful for defining, optimizing, and evaluating different mathematical operations. Moreover, Theano provides its users with extensive unit-testing and self-verification abilities.
This is helpful in minimizing many types of errors. It uses a dynamic C code generation, which means Theano has the ability to evaluate expressions faster.
Although Theano is dead now, the other open-source deep libraries which are built on top of Theano are still functioning; these include Keras, Lasagne, and Blocks.
TensorFlow is a deep learning library that was launched in late 2015. It is an open source project by Google which replaced Theano. To date, it is one of the most famous libraries. It is available in Linux, macOS, Windows, Android, and iOS platforms.
Apart from being a deep learning library, it has various tools to support reinforcement learning and numerous other algos. These include voice recognition, text-based applications, image recognition, time series and video detection.
It is based on languages such as Python and C++ and is multi-GPU. TensorFlow comes with a sufficient amount of documentation for installation, along with tutorials, which makes running the framework hassle-free, even for beginners. Companies that are using TensorFlow include Google, Twitter, Uber, Snapchat, GE Healthcare, PayPal, and Dropbox, etc.
Theano vs TensorFlow : Key comparison factors
The differences between Theano and TensorFlow are:
The execution speed of TensorFlow is slow when compared to Theano. But in handling tasks that require multiple GPUs TensorFlow is faster.
Theano performs tasks much faster than TensorFlow. Mainly the tasks that require a single GPU run faster in Theano.
TensorFlow is lacking native windows support. It does not support Lasagne.
Theano provides native windows support. It supports High-Level Wrappers like Lasagne.
Tensorflow has less documentation when compared with Theano.
TensorFlow runs mainly on Linux, macOS, Windows, and Android.
But Theano runs on cross-platforms.
TensorFlow is one of the famous Deep Learning libraries and is mainly useful for research purposes.
Theano is an old Framework that is not mostly in use.
TensorFlow doesn’t have any pre-trained inbuilt models.
Theano is compatible with a deep-learning library called Keras which contains pre-trained models.
Theano is a library depending completely on Python, which means you have to use it with python only. This library will work with python language and depends on python programming to be implemented.
TensorFlow is a library depending on C++ and python which means it can be useful in both C++ and python programming. Being able to deliver in two languages is more considered by the developers.
Theano is keen to perform complex computations, but sometimes it is unable to meet the requirement due to its low compilation speed. The compile time is too high and leads to taking more time when the program complexity is high.
TensorFlow takes less compilation time as compared to Theano. The fact that it could make use of multiple CPUs, which makes it the one that can do complex computations faster.
Theano only uses a single CPU for processing or for performing the computations. It efficiently makes use of a single CPU and generates the outcome, which is based on the processing power of the CPU.
Tensorflow is capable of using more than one CPU based on how it has to be performing. Using a multiple CPU over a single one always has a preference as it leads to reducing the time it may take to complete computations.
TensorFlow vs Theano – Which is Better?
TensorFlow vs. Theano is a debatable topic. It mainly depends on the user’s preferences and requirements. The main aim of the existence both of libraries is research and development.
In addition to that, its usage is very often in production as well. It is important to understand that they can opt for either of the libraries as per the developer’s need.
Also, the technology in which the application must be developed matters a lot. All the research that urges the graphical flow for the implementation of artificial intelligence uses these libraries. One can simply pick these libraries to build machine learning features-enabled applications in a short span of time.
Although Theano is dead, the frameworks built on top of it are still functioning. It is nearly impossible to get any support from the developers of Theano.
Also, TensorFlow has many advantages compared to Theano like multiple CPU utilization, high compilation speed, available libraries, and execution speed which we discussed above. Hence, we can easily say that TensorFlow is much better than Theano.