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Keras Summary
Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast and easy to use. It was developed by François Chollet, a Google engineer. Keras doesn’t handle low-level computation. Instead, it uses another library to do it, called the “Backend.
Keras is high-level API wrapper for the low-level API, capable of running on top of TensorFlow, CNTK, or Theano. Keras High-Level API handles the way we make models, defining layers, or set up multiple input-output models. In this level, Keras also compiles our model with loss and optimizer functions, training process with fit function. Keras in Python doesn’t handle Low-Level API such as making the computational graph, making tensors or other variables because it has been handled by the “backend” engine.
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Advantages of Keras
Fast Deployment and Easy to understand
Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Python Keras can help you with that.Because of friendly the API, we can easily understand the process. Writing the code with a simple function and no need to set multiple parameters.
Large Community Support
There are lots of AI communities that use Keras for their Deep Learning framework. Many of them publish their codes as well tutorials to the general public.
Have multiple Backends
You can choose Tensorflow, CNTK, and Theano as your backend with Keras. You can choose a different backend for different projects depending on your needs. Each backend has its own unique advantage.
Cross-Platform and Easy Model Deployment
With a variety of supported devices and platforms, you can deploy Keras on any device like
Multi GPUs Support
You can train Keras on a single GPU or use multiple GPUs at once. Because Keras has a built-in support for data parallelism so it can process large volumes of data and speed up the time needed to train it.
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