Keras.io Website Review

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow USER FRIENDLINESS. Keras is an API designed for human beings, not machines. It puts user experience front and center. 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. MODULARITY. A model is understood as a sequence or a graph of standalone, fully configurable modules that can be plugged together with as few restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization schemes, activation functions and regularization schemes are all standalone modules that you can combine to create new models. EASY EXTENSIBILITY. New modules are simple to add (as new clas*es and functions), and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research. WORK WITH PYTHON. No separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility. The core data structure of Keras is a MODEL, a way to organize layers. The simplest type of model is the Sequential Once your model looks good, configure its learning process with .compile(): If you need to, you can further configure your optimizer. A core principle of Keras is to make things reasonably simple, while allowing the user to be fully in control when they need to (the ultimate control being the easy extensibility of the source code). Alternatively, you can feed batches to your model manually: Building a question answering system, an image clas*ification model, a Neural Turing Machine, or any other model is just as fast. The ideas behind de*p learning are simple, so why should their implementation be painful?