There is no way you would talk about machine learning frameworks these days without mentioning Theano and Tensorflow, both are utilized extensively in deep machine domain.

Some other top deep learning frameworks include Keras, Infer.net, MXNet, Caffe, Torch, etc.

Theano vs Tensorflow

Theano is only written in Python programing language for featuring artificial intelligence.

The optimization of a compiler for evaluating mathematical expression together with their manipulations by theano helps in the development of applications.

It was created by Université de Montréal in 2007. CPU and GPU can be used to run it.

Tensorflow, on the other hand, can be referred to as an open-source software library, developed by Google Brain team for the implementation of dataflow in programs.

Unlike theano, it can be written in both Python and C++. By inserting the utilization of data flow graphs, TensorFlow has added to the development of artificial intelligence.

On this page, we will leave our comparison exclusively between theano and TensorFlow alone. Both learning frameworks will be compared based on:

Popularity

Among users, TensorFlow is far more popular than theano and a few other libraries too, it can’t be overemphasized that one of the reasons why TensorFlow is more common is because it’s managed by one of the greatest technology giants (Google).

Tensorflow improves components that look attractive, this makes lots of users have at the back of their minds that it has better computational graph visualizations.

Though theano doesn’t really look appealing to the eyes, it is not overall bad when using it to visualize convolutional filters, images, and graphs.

Technological Advantages

Although theano supports more operations than TensorFlow, the two machine learning frameworks create computational graphs and also engage in automatic differentiation.

The very good importance of this is that you wouldn’t need to hand-code a new variation of backpropagation every time you are experimenting with a new neutral network.

One very important thing to note here is that theano is the only deep learning framework that has to do with the computation of the gradient during the determination of backpropagation error through the derivation of an analytical expression.

The accumulation of errors in the process of successive derivative calculations is prevented to happen by this.

Not only does TensorFlow perform partial subgraph computation, but it can also provide a user with large documentation for installation and learning material to help beginners have full knowledge of theoretical aspects of neural networks and it can offer assistance in creating it.

Executive Speed

The execution speed of theano when performing tasks is far way faster and better than that of TensorFlow. Most especially, the single GPU found in theano performs better than that of TensorFlow.

Although, the execution speed of TensorFlow is not equal or better than that of theano it looks faster than theano when handling multi-GPU tasks.

Community Support

You can’t compare the community support theano has with that of TensorFlow as it’s way bigger. The documentation of theano is more than that of TensorFlow.

One interesting thing to note is that TensorFlow’s online community support is gaining ground because of its popularity, we shouldn’t be surprised by the numbers it would have gained in the nearest future going by this.

Compatibility

The compatibility of theano with Keras, another excellent deep learning library, is great to talk about. It has the backing of native windows. Theano supports high-Level wrappers like Lasagne.

Tensorflow compatibility with other deep learning libraries is not good enough, although steps have been taken to make sure the next version is not like this. Lastly, TensorFlow doesn’t support lasagne.

Conclusion

In this article, we looked at Theano vs Tensorflow on the basis of Importance, features and developers’ preference.

Theano and TensorFlow will go a long way in deciding the one to be used in applications.

Both are good, you can make use of these libraries in building the machine learning features enabled applications in a limited time