TensorLight - A high-level framework for TensorFlow

Overview
TensorLight

TensorLight is a high-level framework for TensorFlow-based machine intelligence applications. It reduces boilerplate code and enables advanced features that are not yet provided out-of-the-box.

Setup

After cloning the repository, we can install the package locally (for use on our system), with:

$ cd /path/to/tensorlight
$ sudo pip install .

We can also install the package with a symlink, so that changes to the source files will be immediately available to other users of the package on our system:

$ sudo pip install -e .

Guiding Principles

The TensorLight framework is developed under its four core principles:

  • Simplicity: Straight-forward to use for anybody who has already worked with TensorFlow. Especially, no further learning is required regarding how to define a model's graph definition.
  • Compactness: Reduce boilerplate code, while keeping the transparency and flexibility of TensorFlow.
  • Standardization: Provide a standard way in respect to the implementation of models and datasets in order to save time. Further, it automates the whole training and validation process, but also provides hooks to maintain customizability.
  • Superiority: Enable advanced features that are not included in the TensorFlow API, as well as retain its full functionality.

Key Features

To highlight the advanced features of TensorLight, an incomplete list of some main functionalities is provided that are not shipped with TensorFlow by default, or might even be missing in other high-level APIs. These include:

  • Transparent lifecycle management of the session and graph definition.
  • Abstraction of models and datasets to provide a reusable plug-and-play support.
  • Effortless support to train a model symmetrically on multiple GPUs, as well as prevent TensorFlow to allocate memory on other GPU devices of the cluster.
  • Train or evaluate a model with a single line of code.
  • Abstracted, runtime-exchangeable input pipelines which either use the simple feeding mechanism with NumPy arrays, or even multi-threaded input queues.
  • Automatic saving and loading of hyperparameters as JSON to simplify the evaluation management of numerous trainings.
  • Ready-to-use loss functions and metrics, even with latest advances for perceptual motivated image similarity assessment.
  • Extended recurrent functions to enable scheduled sampling, as well as an implementation of a ConvLSTM cell.
  • Automatic creation of periodic checkpoints and TensorBoard summaries.
  • Ability to work with other higher-level libraries hand in hand, such as tf.contrib or TF-slim.

Architecture

From an architectural perspective, the framework can be split into three main components. First, a collection of utility function that are unrelated to machine learning. Examples are functions to download and extract datasets, to process images and videos, or to generate animated GIFs and videos from a data array, to name just a few. Second, the high-level library which builds on top of TensorFlow. It includes several modules that either provide a simple access to functionally that it repeatedly required when developing deep learning applications, or features that are not included in TensorFlow yet. For instance, it handles the creation of weight and bias variables internally, offers a bunch of ready-to-use loss and initialization functions, or comes with some advanced visualization features to display feature maps or output images directly in an IPython Notebook. Third, an abstraction layer to simplify the overall lifecycle, to generalize the definition of a model graphs, as well as to enable a reusable and consistent access to datasets.

TensorLight Architecture

The user program can either exploit the high-level library and the provided utility functions for his existing projects, or take advantage from TensorLight's abstraction layes while creating new deep learning applications. The latter enables to radically reduce the amount of code that has to be written for training or evaluating the model. This is realized by encapsulating the lifecycle of TensorFlow's session, graph, summary-writer or checkpoint-saver, as well as the entire training or evaluation loop within a runtime module.

Examples

You want to learn more? Check out the tutorial and code examples.

Owner
Benjamin Kan
Passionate coder with focus on machine learning, mobile apps and game development
Benjamin Kan
Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Leibniz is a python package which provide facilities to express learnable partial differential equations with PyTorch

Beijing ColorfulClouds Technology Co.,Ltd. 16 Aug 07, 2022
Crowd-Kit is a powerful Python library that implements commonly-used aggregation methods for crowdsourced annotation and offers the relevant metrics and datasets

Crowd-Kit: Computational Quality Control for Crowdsourcing Documentation Crowd-Kit is a powerful Python library that implements commonly-used aggregat

Toloka 125 Dec 30, 2022
PyTorch implementation for Convolutional Networks with Adaptive Inference Graphs

Convolutional Networks with Adaptive Inference Graphs (ConvNet-AIG) This repository contains a PyTorch implementation of the paper Convolutional Netwo

Andreas Veit 176 Dec 07, 2022
Wanli Li and Tieyun Qian: Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction, IJCNN 2021

MRefG Wanli Li and Tieyun Qian: "Exploit a Multi-head Reference Graph for Semi-supervised Relation Extraction", IJCNN 2021 1. Requirements To reproduc

万理 5 Jul 26, 2022
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
Python with OpenCV - MediaPip Framework Hand Detection

Python HandDetection Python with OpenCV - MediaPip Framework Hand Detection Explore the docs » Contact Me About The Project It is a Computer vision pa

2 Jan 07, 2022
Brain tumor detection using CNN (InceptionResNetV2 Model)

Brain-Tumor-Detection Building a detection model using a convolutional neural network in Tensorflow & Keras. Used brain MRI images. InceptionResNetV2

1 Feb 13, 2022
Sandbox for training deep learning networks

Deep learning networks This repo is used to research convolutional networks primarily for computer vision tasks. For this purpose, the repo contains (

Oleg Sémery 2.7k Jan 01, 2023
QuadTree Attention for Vision Transformers (ICLR2022)

This repository contains codes for quadtree attention. This repo contains codes for feature matching, image classficiation, object detection and seman

tangshitao 222 Dec 28, 2022
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023
Monitora la qualità della ricezione dei segnali radio nelle province siciliane.

FMap-server Monitora la qualità della ricezione dei segnali radio nelle province siciliane. Conversion data Frequency - StationName maps are stored in

Triglie 5 May 24, 2021
Code for Max-Margin Contrastive Learning - AAAI 2022

Max-Margin Contrastive Learning This is a pytorch implementation for the paper Max-Margin Contrastive Learning accepted to AAAI 2022. This repository

Anshul Shah 12 Oct 22, 2022
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

107 Dec 02, 2022
WSDM‘2022: Knowledge Enhanced Sports Game Summarization

Knowledge Enhanced Sports Game Summarization Cooming Soon! :) Data will be released after approval process. Code will be published once the author of

Jiaan Wang 14 Jul 13, 2022
A PyTorch Implementation of ViT (Vision Transformer)

ViT - Vision Transformer This is an implementation of ViT - Vision Transformer by Google Research Team through the paper "An Image is Worth 16x16 Word

Quan Nguyen 7 May 11, 2022
Real-time 3D multi-person detection made easy with OpenPose and the ZED

OpenPose ZED This sample show how to simply use the ZED with OpenPose, the deep learning framework that detects the skeleton from a single 2D image. T

blanktec 5 Nov 06, 2020
Code for: Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification

Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification Prerequisite PyTorch = 1.2.0 Python3 torch

16 Dec 14, 2022
Pytorch library for end-to-end transformer models training and serving

Pytorch library for end-to-end transformer models training and serving

Mikhail Grankin 768 Jan 01, 2023
This repository lets you interact with Lean through a REPL.

lean-gym This repository lets you interact with Lean through a REPL. See Formal Mathematics Statement Curriculum Learning for a presentation of lean-g

OpenAI 87 Dec 28, 2022
PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

Duo Li 375 Jan 03, 2023