Keras documentation, hosted live at keras.io

Related tags

Deep Learningkeras-io
Overview

Keras.io documentation generator

This repository hosts the code used to generate the keras.io website.

Generating a local copy of the website

pip install -r requirements.txt
cd scripts
python autogen.py make
python autogen.py serve

If you have Docker (you don't need the gpu version of Docker), you can run instead:

docker build -t keras-io . && docker run --rm -p 8000:8000 keras-io

It will take a while the first time because it's going to pull the image and the dependencies, but on the next times it'll be much faster.

Another way of testing using Docker is via our Makefile:

make container-test

This command will build a Docker image with a documentation server and run it.

Call for examples

Are you interested in submitting new examples for publication on keras.io? We welcome your contributions! Please read the information below about adding new code examples.

We are currently interested in the following examples.

Adding a new code example

Keras code examples are implemented as tutobooks.

A tutobook is a script available simultaneously as a notebook, as a Python file, and as a nicely-rendered webpage.

Its source-of-truth (for manual edition and version control) is its Python script form, but you can also create one by starting from a notebook and converting it with the command nb2py.

Text cells are stored in markdown-formatted comment blocks. the first line (starting with """) may optionally contain a special annotation, one of:

  • shell: execute this block while prefixing each line with !.
  • invisible: do not render this block.

The script form should start with a header with the following fields:

Title: (title)
Author: (could be `Authors`: as well, and may contain markdown links)
Date created: (date in yyyy/mm/dd format)
Last modified: (date in yyyy/mm/dd format)
Description: (one-line text description)

To see examples of tutobooks, you can check out any .py file in examples/ or guides/.

Creating a new example starting from a ipynb file

  1. Save the ipynb file to local disk.
  2. Convert the file to a tutobook by running: (assuming you are in the scripts/ directory)
python tutobooks.py nb2py path_to_your_nb.ipynb ../examples/vision/script_name.py

This will create the file examples/vision/script_name.py.

  1. Open it, fill in the headers, and generally edit it so that it looks nice.

NOTE THAT THE CONVERSION SCRIPT MAY MAKE MISTAKES IN ITS ATTEMPTS TO SHORTEN LINES. MAKE SURE TO PROOFREAD THE GENERATED .py IN FULL. Or alternatively, make sure to keep your lines reasonably-sized (<90 char) to start with, so that the script won't have to shorten them.

  1. Run python autogen.py add_example vision/script_name. This will generate an ipynb and markdown rendering of your example, creating files in examples/vision/ipynb, examples/vision/md, and examples/vision/img. Do not modify any of these files by hand; only the original Python script should ever be edited manually.
  2. Submit a PR adding examples/vision/script_name.py (only the .py, not the generated files). Get a review and approval.
  3. Once the PR is approved, add to the PR the files created by the add_example command. Then we will merge the PR.

Creating a new example starting from a Python script

  1. Format the script with black: black script_name.py
  2. Add tutobook header
  3. Put the script in the relevant subfolder of examples/ (e.g. examples/vision/script_name)
  4. Run python autogen.py add_example vision/script_name. This will generate an ipynb and markdown rendering of your example, creating files in examples/vision/ipynb, examples/vision/md, and examples/vision/img. Do not modify any of these files by hand; only the original Python script should ever be edited manually.
  5. Submit a PR adding examples/vision/script_name.py (only the .py, not the generated files). Get a review and approval.
  6. Once the PR is approved, add to the PR the files created by the add_example command. Then we will merge the PR.

Previewing a new example

You can locally preview what the example looks like by running:

cd scripts
python autogen.py add_example vision/script_name

(Assuming the tutobook file is examples/vision/script_name.py.)

NOTE THAT THIS COMMAND WILL ERROR OUT IF ANY CELLS TAKES TOO LONG TO EXECUTE. In that case, make your code lighter/faster. Remember that examples are meant to demonstrate workflows, not train state-of-the-art models. They should stay very lightweight.

Then serving the website:

python autogen.py make
python autogen.py serve

And navigating to 0.0.0.0:8000/examples.

Read-only autogenerated files

The contents of the following folders should not be modified by hand:

  • site/*
  • sources/*
  • templates/examples/*
  • templates/guides/*
  • examples/*/md/*, examples/*/ipynb/*, examples/*/img/*
  • guides/md/*, guides/ipynb/*, guides/img/*

Modifiable files

These are the only files that should be edited by hand:

  • templates/*.md, with the exception of templates/examples/* and templates/guides/*
  • examples/*/*.py
  • guides/*.py
  • theme/*
  • scripts/*.py
Owner
Keras
Deep Learning for humans
Keras
Python Interview Questions

Python Interview Questions Clone the code to your computer. You need to understand the code in main.py and modify the content in if __name__ =='__main

ClassmateLin 575 Dec 28, 2022
Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra

850-Safra-DS-ModuloI Repositório para arquivos sobre o Módulo 1 do curso Top Coders da Let's Code + Safra Para aprender mais Git https://learngitbranc

Brian Nunes 7 Dec 10, 2022
PyTorch implementation of ICLR 2022 paper PiCO: Contrastive Label Disambiguation for Partial Label Learning

PiCO: Contrastive Label Disambiguation for Partial Label Learning This is a PyTorch implementation of ICLR 2022 Oral paper PiCO; also see our Project

王皓波 147 Jan 07, 2023
This project uses Template Matching technique for object detecting by detection of template image over base image.

Object Detection Project Using OpenCV This project uses Template Matching technique for object detecting by detection the template image over base ima

Pratham Bhatnagar 7 May 29, 2022
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Apache MXNet (incubating) for Deep Learning Apache MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to m

The Apache Software Foundation 20.2k Jan 08, 2023
Implementation of Uformer, Attention-based Unet, in Pytorch

Uformer - Pytorch Implementation of Uformer, Attention-based Unet, in Pytorch. It will only offer the concat-cross-skip connection. This repository wi

Phil Wang 72 Dec 19, 2022
Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

Deep Learning for image classification pip install -r http://webia.lip6.fr/~baskiotisn/requirements-amal.txt Train an autoencoder python3 train_auto

Hector Kohler 0 Mar 30, 2022
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning

MSVCL_MICCAI2021 Installation Please follow the instruction in pytorch-CycleGAN-and-pix2pix to install. Example Usage An example of vendor-styles tran

Jaron Lee 11 Oct 19, 2022
A practical ML pipeline for data labeling with experiment tracking using DVC.

Auto Label Pipeline A practical ML pipeline for data labeling with experiment tracking using DVC Goals: Demonstrate reproducible ML Use DVC to build a

Todd Cook 4 Mar 08, 2022
Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks

Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks Stable Neural ODE with Lyapunov-Stable Equilibrium

Kang Qiyu 8 Dec 12, 2022
Implementation of ReSeg using PyTorch

Implementation of ReSeg using PyTorch ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation Pascal-Part Annotations Pascal VOC 2010

Onur Kaplan 46 Nov 23, 2022
structured-generative-modeling

This repository contains the implementation for the paper Information Theoretic StructuredGenerative Modeling, Specially thanks for the open-source co

0 Oct 11, 2021
Fiddle is a Python-first configuration library particularly well suited to ML applications.

Fiddle Fiddle is a Python-first configuration library particularly well suited to ML applications. Fiddle enables deep configurability of parameters i

Google 227 Dec 26, 2022
Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals"

The Temporal Robustness of Stochastic Signals Code needed to reproduce the examples found in "The Temporal Robustness of Stochastic Signals" Case stud

0 Oct 28, 2021
Face recognize system

FRS Face_recognize_system This project contains my work that target on solving some problems of FRS: Face detection: Retinaface Face anti-spoofing: Fo

Tran Anh Tuan 4 Nov 18, 2021
✨风纪委员会自动投票脚本,利用Github Action帮你进行裁决操作(为了让其他风纪委员有案件可判,本程序从中午12点才开始运行,有需要请自己修改运行时间)

风纪委员会自动投票 本脚本通过使用Github Action来实现B站风纪委员的自动投票功能,喜欢请给我点个STAR吧! 如果你不是风纪委员,在符合风纪委员申请条件的情况下,本脚本会自动帮你申请 投票时间是早上八点,如果有需要请自行修改.github/workflows/Judge.yml中的时间,

Pesy Wu 25 Feb 17, 2021
Atomistic Line Graph Neural Network

Table of Contents Introduction Installation Examples Pre-trained models Quick start using colab JARVIS-ALIGNN webapp Peformances on a few datasets Use

National Institute of Standards and Technology 91 Dec 30, 2022
Streamlit App For Product Analysis - Streamlit App For Product Analysis

Streamlit_App_For_Product_Analysis Здравствуйте! Перед вами дашборд, позволяющий

Grigory Sirotkin 1 Jan 10, 2022
PyTorch implementation for ACL 2021 paper "Maria: A Visual Experience Powered Conversational Agent".

Maria: A Visual Experience Powered Conversational Agent This repository is the Pytorch implementation of our paper "Maria: A Visual Experience Powered

Jokie 22 Dec 12, 2022
Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment (ICCV2021)

Seeing Dynamic Scene in the Dark: High-Quality Video Dataset with Mechatronic Alignment This is a pytorch project for the paper Seeing Dynamic Scene i

DV Lab 21 Nov 28, 2022