•What is Keras? •Basics of Keras environment •Building Convolutional neural networks •Building Recurrent neural networks •Introduction to other types of layers •Introduction to Loss functions and Optimizers in Keras •Using Pre-trained models in Keras •Saving and loading weights and models •Popular architectures in Deep LearningFile Size: 2MB. backend, simply change the field backend to either "theano" or "tensorflow", and Keras will use the new configuration next time you run any Keras code. Getting Started with Keras: 30 Second The core data structure of Keras is a model, a way to organize layers. The main type of model is the Sequential model, a linear stack of layers. Read the documentation at bltadwin.ru Keras is compatible with: Python Guiding principles. Modularity. A model is understood as a sequence or a graph of standalone, fully-configurable modules that can be plugged together with as little restrictions as possible. In particular, neural layers, cost functions, optimizers, initialization Missing: download.
Keras keeps a note of which class generated the config. From the example above, bltadwin.ruize generates a serialized form of the custom layer: {'class_name': 'CustomLayer', 'config': {'a': 2} } Keras keeps a master list of all built-in layer, model, optimizer, and metric classes, which is used to find the correct class to call from. •What is Keras? •Basics of Keras environment •Building Convolutional neural networks •Building Recurrent neural networks •Introduction to other types of layers •Introduction to Loss functions and Optimizers in Keras •Using Pre-trained models in Keras •Saving and loading weights and models •Popular architectures in Deep Learning. 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. Features Keras leverages various optimization techniques to make high level neural network API.
Developer guides. Our developer guides are deep-dives into specific topics such as layer subclassing, fine-tuning, or model saving. They're one of the best ways to become a Keras expert. Most of our guides are written as Jupyter notebooks and can be run in one click in Google Colab, a hosted notebook environment that requires no setup and runs. Use Keras if you need a deep learning library that: Allows for easy and fast prototyping (through user friendliness, modularity, and extensibility). Supports both convolutional networks and recurrent networks, as well as combinations of the two. Runs seamlessly on CPU and GPU. Read the documentation at bltadwin.ru Keras is compatible with: Python. Keras documentation. Star. About Download the Keras code archive. Getting started Further starter resources Installing Keras.
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