Google developed the Keras library as a high-level deep learning API used to implement neural networks. Keras has been coded using the Python programming language. With support for several neural network-based backend computing systems, Keras is known for making neural network implementation easy.
Compared to other AI/ML/Deep Learning frameworks, Keras is relatively easy to learn, thanks largely to its Python-based front-end origins. Keras is capable of great abstractions and the possibility of using multiple computing backends for Keras models adds to its power albeit at a relatively slower speed. Keras stands out for its accessibility to beginners.
The key features of Keras are as follows:
- Large pre-defined dataset
- You can train it using NumPy data
- It can evaluate NumPy data and make predictions based on it
- Availability of pre-trained models
- Encoding and tokenization of data
- Possibility of substantial number of datasets with many layers and parameters
- Users can get outputs of intermediate layers
- The Keras library is Python-native
- It allows data pre-processing
Top Use Cases of Keras
Keras finds many applications in the real world. Some of the most prominent of them are as follows:
Classification of Heart Disease
Keras makes it possible to make difficult predictions provided there is adequate data. Keras is becoming increasingly popular in medicine and healthcare where they are using Keras models to make diagnoses with greater accuracy. These are based on the patient specifics and the patients’ conditions. The Keras library has met with remarkable success in classifying heart diseases.
To cite an example, a Tel Aviv based Israeli developer created a ML model that can provide the heart
disease probability in percentage based on just fifteen categories of data which is very impressive.
Rock Paper Scissors
On a less serious note, Keras has been used in the creation of ML models for simple games everybody plays as children specifically- rock, paper, scissors. For this, developers created CNNs (convolutional neural networks) and used the Keras CNN to classify images belonging to either of the rock, paper and scissors category which let the user “see” what hand the competitor was playing.
Face Mask Detection
During the COVID crisis Keras models came to the rescue of authorities as they tried to implement rules requiring people to wear face masks. The models could detect whether a person was wearing a mask or not from their pictures. It is a variant of the primary image classification model. Use of image processing tools like OpenCV serve to make Keras supremely powerful for image recognition when used in combination.
Comparison of Keras with TensorFlow and Pytorch
Without going into exhausting details, lets highlight the pros and cons of the three most popular AI/ML/Deep Learning technologies around:
- Works great if you are going to be using computational graphs and you need effective visualization
- TensorFlow supports Keras too
- Google manages the TensorFlow library with regular updates and releases
- It creates highly parallel pipelines that you can scale with ease
- You can use TPUs with TensorFlow
- You can debug code with a specified method
- The low-level APIs involved make for a steep learning curve
- Inadequacy or rather “stale” documentation
- Code can get cluttered
- You can use TPUs only for model execution and not for model training
- GPU acceleration is possible only with NVIDIA GPUs
- It has several limitations when used on Windows-based PCs
- You can learn it with relative ease
- It uses dynamic graph logic that supports eager execution
- Development is a lot like in the Python framework being Python-native
- Both CPU and GPU support
- You can carry out distributed training with PyTorch
- You need an API server for production use
- Visdom- the library’s training process visualization tool comes with many limitations
- Less popular than TensorFlow
- Almost flawless high-level API
- Integrates seamlessly with Aesara/Theano, TensorFlow and CNTK
- Low learning curve and it lets you develop new architectures with relative simplicity
- Comes with several pre-trained models
- You can experiment with things fast
- It is better suited when the associated datasets are relatively small
- The framework works better for frontends and Keras based backends are slower than TensorFlow
Why use Keras?
- The Keras API is simple, consistent, and easy to learn. It lets you implement common codes with lesser actions. User errors are also expressed with clarity.
- The times taken for creating prototypes is less and you can implement and deploy your developed ideas relatively fast. The framework also allows for many deployment options to suit diverse needs.
- Frameworks that involve great abstraction abilities and inbuilt features are as a rule of thumb slow. Keras is no different and building custom features can be a challenging task. But you can use it over TensorFlow which speeds it up. Deep integration with TensorFlow also ensures that creating customized workflows are relatively easy.
- Keras has a large research community. Documentation is detailed and you can look for and get help with relative ease when you compare it to other deep learning libraries.
- Large businesses like Uber, Netflix, Yelp, Square amongst others use Keras for commercial applications and have Keras-based products deployed in the public domain.
- Keras also boasts of the following advantages which give it an edge:
- It runs just as well on GPUs and with CPUs
- It provides support for all neural network models
- Keras’s modularity lets it be more flexible, expressive, and suitable for innovative research.
Our Keras Expertise
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