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All Projects → 1044197988 → Awesome-Tensorflow2

1044197988 / Awesome-Tensorflow2

Licence: Apache-2.0 license
基于Tensorflow2开发的优秀扩展包及项目

Labels

nlpawesometensorflowkerastutorialstf2ganclassificationawesome-listsegmentationtensorflow-experimentstensorflow-modelsautomltensorflow-examplestensorflow2tensorflow2-awesomedetection-tensorflow2

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Awesome-Tensorflow2 💛 Awesome

基于Tensorflow2开发的优秀扩展包及项目 Tensorflow

Tensorflow2说明

2019 谷歌开发者大会于 9 月 10 日和 11 日在上海举办，大会将分享众多开发经验与工具。在第一天的 KeyNote 中，谷歌发布了很多开发工具新特性，并介绍而它们是如何构建更好的应用。值得注意的是，TensorFlow 刚刚发布了 2.0 RC01 版和 1.15，谷歌表示 1.15 是 1.x 的最后一次更新了。TensorFlow 2.0 相信大家已经非常熟悉了，它重点还是放在优化 Keras 和 Eager Execution 的能力，它希望通过这两种 API 简化整个开发流程。所以，未来的趋势肯定是Tensorflow2.0，在此我整合了许多的优秀库及项目到这个贡献库里。

提示

有些项目目前仍在进行中，将在未来支持Tensorflow2，这样的项目也包含在下面列表中。（意味着目前并不支持二版本）

Contents 👈

Tutorials 💛💛💛💛💛

Model 💛💛💛💛💛

Classification 分类

  • tensorflow/models
    该存储库包含在TensorFlow中实现的许多不同模型。
  • 1044197988/TF.Keras-Commonly-used-models
    该贡献库为我整理的一些常用的分类、分割模型，包含分割的一些指标、损失函数，但不提供预训练模型的载入。分割模型列表如下：
    Segmentation

Large library 大型库

Model 模型

Capsnet model 胶囊网络模型
Semi-supervised learning 半监督学习
Generative-models and Self encoder 生成模型和自编码

Segmentation 分割

Large library 大型库

Model 模型

Super resolution 超分辨率

Model 模型

  • krasserm/super-resolution
    包含以下模型：
    用于单图像超分辨率（EDSR）的增强型深度残留网络，是NTIRE 2017超分辨率挑战赛的冠军。
    广泛激活以实现高效，准确的图像超分辨率（WDSR），是NTIRE 2018超分辨率挑战赛（真实轨道）的获胜者。
    使用生成对抗网络（SRGAN）的逼真的单图像超分辨率。
  • HasnainRaz/Fast-SRGAN
  • gs18113/ESPCN-TensorFlow2

Object detection 目标检测

Model 模型

NLP Model 自然语言处理模型

Large library 大型库

  • tensorflow/tensor2tensor
    Tensor2Tensor或简称T2T，是一个深度学习模型和数据集的库，旨在使深度学习更易于访问并加速ML研究。Google Brain团队和用户社区的研究人员和工程师积极使用和维护T2T 。
  • huggingface/transformers
    TensorFlow 2.0和PyTorch的最新自然语言处理，（以前称为pytorch-transformers和pytorch-pretrained-bert）提供用于自然语言理解（NLU）和自然语言生成（NLG）的最新通用架构（BERT，GPT-2，RoBERTa，XLM，DistilBert，XLNet ...） ）包含超过32种以100多种语言编写的预训练模型，以及TensorFlow 2.0和PyTorch之间的深层互操作性。

Model 模型

Point Model 点云模型

Projects 💛💛💛💛💛

视觉较大项目

分割

NLP

目标检测

验证码识别

文字检测、识别

人脸识别

Other 💛💛💛💛💛

脚本

解释性工具

推荐系统和CTR预测模型

用于分布式培训，评估，模型选择和快速原型制作

AutoML

元学习

强化学习

神经结构化学习

强大的扩展

迭代矩阵平方根归一化网络（称为快速MPN-COV），该网络非常有效，适合大规模数据集

TF-GAN是用于培训和评估生成对抗网络GAN的轻量级库

官方数据集包

NLP

大型基于TF2的封装扩展库

TF2项目模板

GCN图神经网络

RBF径向基

新型的高性能可解释的深表格式数据学习网络TabNet

优化器

用于单通道语音分离的双路径RNN

新型数据处理技术

使用tensorflow 2构建的多任务学习包

TensowFlow2.0上的CBAM（卷积块注意模块）实现

Spektral是一个基于Keras API和TensorFlow 2的用于图深度学习的Python库。该项目的主要目标是提供一个简单而灵活的框架来创建图神经网络（GNN）。

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