In Machine Learning, the use of many frameworks, libraries, and API’s are on the rise. Choosing the correct framework can be a grinding task due to the overwhelming amount of the APIs and frameworks available today. Here is our view on Keras Vs. Caffe. These are two of the best frameworks used in deep learning projects. But before that, let’s have a look at some of the benefits of using ML frameworks.
Why should you use an ML Framework?
Some of the reasons why a Machine Learning engineer should use these frameworks are:
- Extremely effective. Since developing a Machine Learning solution is a daunting task, using the right framework can improve the performance manifolds. In addition to that, you are sure of getting an effective solution with lesser effort.
- Specific usage as per the application. Among many frameworks available today, you can select the one that pertains to your specific problem. It will have all the support APIs to make your work a little easier.
- Time-saving. Of course, completion of an ML project never goes off the agenda, which gets support from a good ML framework.
- Easy to implement. All you need to do is visualize the solution and implement it using the different levels of abstraction offered by the framework. Your difficult problem is simplified with the help of a perfect framework.
- Reduction in computation complexity. Computation complexity is a basic measurement to evaluate the solution. A framework reduces that significantly.
- User-friendly. With an intuitive interface, you will never go wrong in the implementation of a solution you have devised.
Keras vs Caffe
Keras is an API that is used to run deep learning models on the GPU (Graphics Processing Unit). With its user-friendly, modular, and extendable nature, it is easy to understand and implement for a machine learning developer.
One of the best aspects of Keras is that it has been designed to work on top of the famous framework Tensorflow by Google. Also, Keras has been chosen as the high-level API for Google’s TensorFlow. Caffe is used more in industrial applications like vision, multimedia, and visualization. Keras is slightly more popular amongst IT companies as compared to Caffe.
Caffe is Convoluted Architecture for Feature Extraction, a framework/Open source library developed by a group of researchers from the University of California, Berkley. Like Keras, Caffe is also a famous deep learning framework with almost similar functions. With Caffe2 in the market, the usage of Caffe has been reduced as Caffe2 is more modular and scalable. With the enormous number of functions for convolutions and support systems, this framework has a considerable number of followers. Caffe was recently backed by Facebook as they have implemented their algorithms using this technology.
Another difference that can be pointed out is that Keras has been issued an MIT license, whereas Caffe has a BSD license.
Caffe is speedier and helps in the implementation of convolution neural networks (CNN). Keras is easy on resources and offers to implement both convolutional and recurrent networks.
Caffe gets the support of C++ and Python. It is quite helpful in the creation of a deep learning network in visual recognition solutions. Keras is supported by Python. It is used in problems involving classification and summarization. It can also be used in Tag and Text Generation as well as natural language problems related to translation and speech recognition.
Wrapping it up…
Both of them are used significantly and popularly in deep learning development in Machine Learning today, but Keras has an upper hand in its popularity, usability, and modeling.