tensorboard - Standalone TensorBoard for visualizing in deep learning

  •        23

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. TensorBoard currently supports five visualizations: scalars, images, audio, histograms, and the graph.This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.




Related Projects

tensorboard - TensorFlow's Visualization Toolkit

  •    Python

TensorBoard is a suite of web applications for inspecting and understanding your TensorFlow runs and graphs. This README gives an overview of key concepts in TensorBoard, as well as how to interpret the visualizations TensorBoard provides. For an in-depth example of using TensorBoard, see the tutorial: TensorBoard: Visualizing Learning. For in-depth information on the Graph Visualizer, see this tutorial: TensorBoard: Graph Visualization.

crayon - A language-agnostic interface to TensorBoard

  •    Python

Crayon is a framework that gives you access to the visualisation power of TensorBoard with any language. Currently it provides a Python and a Lua interface, however you can easily implement a wrapper around the provided RESTful API. Note that the server and the client do not have to be on the same machine.

tf_cnnvis - CNN visualization tool in TensorFlow

  •    Python

Figure 1: Original image and the reconstructed versions from maxpool layer 1,2 and 3 of Alexnet generated using tf_cnnvis. The function to generate the activation visualizations of the input image at the given layer.

edward - A probabilistic programming language in TensorFlow

  •    Jupyter

Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields: Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on top of TensorFlow. It enables features such as computational graphs, distributed training, CPU/GPU integration, automatic differentiation, and visualization with TensorBoard.

tensorflow-opencl - OpenCL support for TensorFlow

  •    C++

TensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

Keras-Project-Template - A project template to simplify building and training deep learning models using Keras

  •    Python

A project template to simplify building and training deep learning models using Keras. This template allows you to simply build and train deep learning models with checkpoints and tensorboard visualization.

cxxnet - move forward to https://github.com/dmlc/mxnet

  •    C++

Thanks for using and supporting cxxnet. Today, we finally make a hard but exciting decision: we decide to deprecate cxxnet and fully move forward to next generation toolkit MXNet.Please check the feature highlights, speed/memory comparation and examples in MXNet.

dmlc-core - A common bricks library for building scalable and portable distributed machine learning

  •    C++

DMLC-Core is the backbone library to support all DMLC projects, offers the bricks to build efficient and scalable distributed machine learning libraries.Contributing to dmlc-core is welcomed! dmlc-core follows google's C style guide. If you are interested in contributing, take a look at feature wishlist and open a new issue if you like to add something.

pytorch-tutorial - PyTorch Tutorial for Deep Learning Researchers

  •    Python

This repository provides tutorial code for deep learning researchers to learn PyTorch. In the tutorial, most of the models were implemented with less than 30 lines of code. Before starting this tutorial, it is recommended to finish Official Pytorch Tutorial.

tensorflow_cookbook - Code for Tensorflow Machine Learning Cookbook

  •    Jupyter

This chapter intends to introduce the main objects and concepts in TensorFlow. We also introduce how to access the data for the rest of the book and provide additional resources for learning about TensorFlow. After we have established the basic objects and methods in TensorFlow, we now want to establish the components that make up TensorFlow algorithms. We start by introducing computational graphs, and then move to loss functions and back propagation. We end with creating a simple classifier and then show an example of evaluating regression and classification algorithms.

nni - An open source AutoML toolkit for neural architecture search and hyper-parameter tuning.

  •    TypeScript

NNI (Neural Network Intelligence) is a toolkit to help users run automated machine learning experiments. The tool dispatches and runs trial jobs that generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in different environments (e.g. local machine, remote servers and cloud). This command will start an experiment and a WebUI. The WebUI endpoint will be shown in the output of this command (for example, http://localhost:8080). Open this URL in your browser. You can analyze your experiment through WebUI, or browse trials' tensorboard.

tf-faster-rcnn - Tensorflow Faster RCNN for Object Detection

  •    Python

For a good and more up-to-date implementation for faster/mask RCNN with multi-gpu support, please see the example in TensorPack here. A Tensorflow implementation of faster RCNN detection framework by Xinlei Chen (xinleic@cs.cmu.edu). This repository is based on the python Caffe implementation of faster RCNN available here.

nauta - A multi-user, distributed computing environment for running DL model training experiments on Intel® Xeon® Scalable processor-based systems

  •    Python

The Nauta software provides a multi-user, distributed computing environment for running deep learning model training experiments. Results of experiments, can be viewed and monitored using a command line interface, web UI and/or TensorBoard*. You can use existing data sets, use your own data, or downloaded data from online sources, and create public or private folders to make collaboration among teams easier. Nauta runs using the industry leading Kubernetes* and Docker* platform for scalability and ease of management. Template packs for various DL frameworks and tooling are available (and customizable) on the platform to take the complexities out of creating and running single and multi-node deep learning training experiments without all the systems overhead and scripting needed with standard container environments.

generative-compression - TensorFlow Implementation of Generative Adversarial Networks for Extreme Learned Image Compression

  •    Python

TensorFlow Implementation for learned compression of images using Generative Adversarial Networks. The method was developed by Agustsson et. al. in Generative Adversarial Networks for Extreme Learned Image Compression. The proposed idea is very interesting and their approach is well-described. Training is conducted with batch size 1 and reconstructed samples / tensorboard summaries will be periodically written every certain number of steps (default is 128). Checkpoints are saved every 10 epochs.

tensorlayer-tricks - How to use TensorLayer


While research in Deep Learning continues to improve the world, we use a bunch of tricks to implement algorithms with TensorLayer day to day. Here are a summary of the tricks to use TensorLayer. If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

TensorFlow-ML-Exercises - Learning Machine Learning with TensorFlow

  •    Python

MNIST 예제를 CNN 모델로 학습하는 코드를 조금 보강하고 정리해서 TensorFlow-MNIST 저장소에 올려두었습니다. summary 를 저장해서 TensorBoard 를 사용할 수 있게 하였으며, 모델을 생성하는 부분과 Trainer, Tester 를 분리하여 학습한 모델을 저장 후 따로 사용할 수 있도록 해 두었으니 참고 해 주세요.

sockeye - Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

  •    Python

Felix Hieber, Tobias Domhan, Michael Denkowski, David Vilar, Artem Sokolov, Ann Clifton and Matt Post (2017): Sockeye: A Toolkit for Neural Machine Translation. In eprint arXiv:cs-CL/1712.05690.If you are interested in collaborating or have any questions, please submit a pull request or issue. You can also send questions to sockeye-dev-at-amazon-dot-com.

MXNet - A Deep Learning Framework

  •    C++

MXNet is an open-source deep learning framework that allows you to define, train, and deploy deep neural networks on a wide array of devices, from cloud infrastructure to mobile devices. It is highly scalable, allowing for fast model training, and supports a flexible programming model and multiple languages. MXNet allows you to mix symbolic and imperative programming flavors to maximize both efficiency and productivity.

incubator-mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

  •    C++

Apache MXNet (incubating) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines.MXNet is also more than a deep learning project. It is also a collection of blue prints and guidelines for building deep learning systems, and interesting insights of DL systems for hackers.