Why Keras library is used?
Keras is used for creating deep models which can be productized on smartphones. Keras is also used for distributed training of deep learning models. Keras is used by companies such as Netflix, Yelp, Uber, etc.
Keras is based on minimal structure that provides a clean and easy way to create deep learning models based on TensorFlow or Theano. Keras is designed to quickly define deep learning models. Well, Keras is an optimal choice for deep learning applications.
TensorFlow is an open-sourced end-to-end platform, a library for multiple machine learning tasks, while Keras is a high-level neural network library that runs on top of TensorFlow. Both provide high-level APIs used for easily building and training models, but Keras is more user-friendly because it's built-in Python.
Keras in a high-level API that is used to make deep learning networks easier with the help of backend engine. Keras is easy to use and understand with python support so its feel more natural than ever. It is good for beginners that want to learn about deep learning and for researchers that want easy to use API.
Keras is a neural network Application Programming Interface (API) for Python that is tightly integrated with TensorFlow, which is used to build machine learning models. Keras' models offer a simple, user-friendly way to define a neural network, which will then be built for you by TensorFlow.
Let us modify the model from MPL to Convolution Neural Network (CNN) for our earlier digit identification problem. Input layer consists of (1, 8, 28) values. First layer, Conv2D consists of 32 filters and 'relu' activation function with kernel size, (3,3).
Found that tensorflow is more faster than keras in training process. The Model is simply an embedding layer followed by two dense layer. Tensorflow is about 2.5X faster than keras with tensoflow backend and TFOptimizer.
You can use TensorFlow without Keras and you can use Keras with CNTK, Theano, or other machine learning libraries. While you can use Keras without TensorFlow, Keras is always going to need a backend; it's simply an interface rather than a major processing utility.
It is not possible to only use Keras without using a backend, such as Tensorflow, because Keras is only an extension for making it easier to read and write machine learning programs.
User should always use from tensorflow import keras which will give them the public API. import keras will directly access the keras PIP package, which is not 100% same as the public API namespace. It will probably give you keras. Model/layers.
What does Keras stand for?
Keras (κέρας) means horn in Greek.
Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs.
Keras is a powerful deep learning library that runs on top of other open-source machine learning libraries such as TensorFlow and is also open-source itself. To develop deep learning models, Keras adopts a minimal structure in Python that makes it easier to learn and quick to write.
Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow. It was developed to make implementing deep learning models as fast and easy as possible for research and development.
Keras provides two types of models: The Sequential Model and The Functional Model. The Sequential Model is simple. It deals with non-complex models and works with a single layer. It is an easy to use model.
Keras is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear and actionable feedback upon user error.
Keras is a powerful deep learning library that runs on top of other open-source machine learning libraries such as TensorFlow and is also open-source itself. To develop deep learning models, Keras adopts a minimal structure in Python that makes it easier to learn and quick to write.
Specifically, Keras is a neural network platform that runs on top of the open-source library TensorFlow (or others), while PyTorch is a lower-level API designed for direct control over expressions.
Keras has a simple architecture. It is more readable and concise . Tensorflow on the other hand is not very easy to use even though it provides Keras as a framework that makes work easier. PyTorch has a complex architecture and the readability is less when compared to Keras.
Keras is a minimalist Python library for deep learning that can run on top of Theano or TensorFlow. It was developed to make implementing deep learning models as fast and easy as possible for research and development.
