GitPlanet

Cheap and reliable Node.js hosting starts at $3/month, and $1/month static HTML hosting

Created with love in Canada, visit hostnodejs.com today

Feel like to post an Ad? Learn Details
All Projects → pytorch → Tnt

pytorch / Tnt

Licence: bsd-3-clause
Simple tools for logging and visualizing, loading and training

Programming Languages

python
139335 projects - #7 most used programming language

Labels

deep-learningmachine-learningpytorchneural-networkreinforcement-learning

Projects that are alternatives of or similar to Tnt

Ai Reading Materials
Some of the ML and DL related reading materials, research papers that I've read
Stars: ✭ 79 (-93.91%)
Mutual labels:  reinforcement-learning
Reinforcement learning
Reinforcement learning tutorials
Stars: ✭ 82 (-93.68%)
Mutual labels:  reinforcement-learning
Magnet
MAGNet: Multi-agents control using Graph Neural Networks
Stars: ✭ 88 (-93.22%)
Mutual labels:  reinforcement-learning
Lf2gym
An OpenAI-gym-like environment for Little Fighter 2
Stars: ✭ 79 (-93.91%)
Mutual labels:  reinforcement-learning
Run Skeleton Run
Reason8.ai PyTorch solution for NIPS RL 2017 challenge
Stars: ✭ 83 (-93.61%)
Mutual labels:  reinforcement-learning
Reinforcement Learning Wechat Jump
Reinforcement Learning for WeChat Jump
Stars: ✭ 85 (-93.45%)
Mutual labels:  reinforcement-learning
Rlenv.directory
Explore and find reinforcement learning environments in a list of 150+ open source environments.
Stars: ✭ 79 (-93.91%)
Mutual labels:  reinforcement-learning
Torchrl
Pytorch Implementation of Reinforcement Learning Algorithms ( Soft Actor Critic(SAC)/ DDPG / TD3 /DQN / A2C/ PPO / TRPO)
Stars: ✭ 90 (-93.07%)
Mutual labels:  reinforcement-learning
Sc2aibot
Implementing reinforcement-learning algorithms for pysc2 -environment
Stars: ✭ 83 (-93.61%)
Mutual labels:  reinforcement-learning
Stable Baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Stars: ✭ 1,263 (-2.7%)
Mutual labels:  reinforcement-learning
Snake
Artificial intelligence for the Snake game.
Stars: ✭ 1,241 (-4.39%)
Mutual labels:  reinforcement-learning
Tensorflow Tutorials
TensorFlow Tutorials with YouTube Videos
Stars: ✭ 8,919 (+587.13%)
Mutual labels:  reinforcement-learning
Simulator
A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
Stars: ✭ 1,260 (-2.93%)
Mutual labels:  reinforcement-learning
Virtualhome
API to run VirtualHome, a Multi-Agent Household Simulator
Stars: ✭ 80 (-93.84%)
Mutual labels:  reinforcement-learning
Hand dapg
Repository to accompany RSS 2018 paper on dexterous hand manipulation
Stars: ✭ 88 (-93.22%)
Mutual labels:  reinforcement-learning
Mathy
Tools for using computer algebra systems to solve math problems step-by-step with reinforcement learning
Stars: ✭ 79 (-93.91%)
Mutual labels:  reinforcement-learning
Maze
Maze Applied Reinforcement Learning Framework
Stars: ✭ 85 (-93.45%)
Mutual labels:  reinforcement-learning
Categorical Dqn
A working implementation of the Categorical DQN (Distributional RL).
Stars: ✭ 90 (-93.07%)
Mutual labels:  reinforcement-learning
Mapleai
AI各领域学习资料整理。（A collection of all skills and knowledges should be got command of to obtain an AI relevant job offer. There are online blogs, my personal blogs, electronic books copy.）
Stars: ✭ 89 (-93.14%)
Mutual labels:  reinforcement-learning
Reinforcement Learning For Self Driving Cars
Project on design and implement neural network that maximises driving speed of self-driving car through reinforcement learning.
Stars: ✭ 85 (-93.45%)
Mutual labels:  reinforcement-learning
View All Similar Projects ➔

TNT

TNT is a library providing powerful dataloading, logging and visualization utilities for Python. It is closely integrated with PyTorch and is designed to enable rapid iteration with any model or training regimen.

travis Documentation Status

Installation

TNT can be installed with pip. To do so, run:

pip install torchnet

If you run into issues, make sure that Pytorch is installed first.

You can also install the latest verstion from master. Just run:

pip install git+https://github.com/pytorch/[email protected]

To update to the latest version from master:

pip install --upgrade git+https://github.com/pytorch/[email protected]

About

TNT (imported as torchnet) is a framework for PyTorch which provides a set of abstractions for PyTorch aiming at encouraging code re-use as well as encouraging modular programming. It provides powerful dataloading, logging, and visualization utilities.

The project was inspired by TorchNet, and legend says that it stood for “TorchNetTwo”. Since the deprecation of TorchNet TNT has developed on its own.

For example, TNT provides simple methods to record model preformance in the torchnet.meter module and to log them to Visdom (or in the future, TensorboardX) with the torchnet.logging module.

In the future, TNT will also provide strong support for multi-task learning and transfer learning applications. It currently supports joint training data loading through torchnet.utils.MultiTaskDataLoader.

Most of the modules support NumPy arrays as well as PyTorch tensors on input, and so could potentially be used with other frameworks.

Getting Started

See some of the examples in https://github.com/pytorch/examples. We would like to include some walkthroughs in the docs (contributions welcome!).

[LEGACY] Differences with lua version

What's been ported so far:

  • Datasets:
    • BatchDataset
    • ListDataset
    • ResampleDataset
    • ShuffleDataset
    • TensorDataset [new]
    • TransformDataset
  • Meters:
    • APMeter
    • mAPMeter
    • AverageValueMeter
    • AUCMeter
    • ClassErrorMeter
    • ConfusionMeter
    • MovingAverageValueMeter
    • MSEMeter
    • TimeMeter
  • Engines:
    • Engine
  • Logger
    • Logger
    • VisdomLogger
    • MeterLogger [new, easy to plot multi-meter via Visdom]

Any dataset can now be plugged into torch.utils.DataLoader, or called .parallel(num_workers=8) to utilize multiprocessing.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].