tf-slim
TensorFlow-Slim
TF-Slim是一个轻量级的库，用于在TensorFlow中定义，训练和评估复杂的模型。 tf-slim的组件可以与本机张量流以及其他框架自由混合。
注意：最新版本的TF-Slim 1.1.0已通过TF 1.15.2 py2，TF 2.0.1，TF 2.1和TF 2.2进行了测试。
安装
pip install --upgrade tf_slim
用法
import tf_slim as slim
为什么选择TF-Slim？
TF-Slim是一个使神经网络的定义，训练和评估变得简单的库：
允许用户通过消除样板代码来紧凑地定义模型。 这是通过使用和许多高级和。 这些工具提高了可读性和可维护性，减少了复制和粘贴超参数值产生错误的可能性，并简化了超参数调整。
通过提供常用的函数使开发模型变得简单。
苗条地开发了几种广泛使用的计算机视觉模型（例如VGG，
文件下载
资源详情
[{"title":"（ 96 个子文件 371KB ） tf-slim","children":[{"title":"tf-slim-master","children":[{"title":"tests_release.sh <span style='color:#111;'> 1.87KB </span>","children":null,"spread":false},{"title":"tf_slim","children":[{"title":"ops","children":[{"title":"framework_ops_test.py <span style='color:#111;'> 2.94KB </span>","children":null,"spread":false},{"title":"arg_scope_test.py <span style='color:#111;'> 8.30KB </span>","children":null,"spread":false},{"title":"framework_ops.py <span style='color:#111;'> 2.60KB </span>","children":null,"spread":false},{"title":"variables.py <span style='color:#111;'> 29.75KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 701B </span>","children":null,"spread":false},{"title":"variables_test.py <span style='color:#111;'> 56.61KB </span>","children":null,"spread":false},{"title":"arg_scope.py <span style='color:#111;'> 6.68KB </span>","children":null,"spread":false}],"spread":true},{"title":"learning.py <span style='color:#111;'> 30.01KB </span>","children":null,"spread":false},{"title":"PRINCIPLES.md <span style='color:#111;'> 6.97KB </span>","children":null,"spread":false},{"title":"layers","children":[{"title":"initializers_test.py <span style='color:#111;'> 7.45KB </span>","children":null,"spread":false},{"title":"rev_block_lib_test.py <span style='color:#111;'> 13.02KB </span>","children":null,"spread":false},{"title":"utils_test.py <span style='color:#111;'> 11.07KB </span>","children":null,"spread":false},{"title":"sparse_ops.py <span style='color:#111;'> 8.64KB </span>","children":null,"spread":false},{"title":"normalization_test.py <span style='color:#111;'> 20.05KB </span>","children":null,"spread":false},{"title":"initializers.py <span style='color:#111;'> 6.15KB </span>","children":null,"spread":false},{"title":"regularizers.py <span style='color:#111;'> 7.37KB </span>","children":null,"spread":false},{"title":"utils.py <span style='color:#111;'> 11.18KB </span>","children":null,"spread":false},{"title":"rev_block_lib.py <span style='color:#111;'> 24.43KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 1.24KB </span>","children":null,"spread":false},{"title":"optimizers_test.py <span style='color:#111;'> 21.14KB </span>","children":null,"spread":false},{"title":"normalization.py <span style='color:#111;'> 16.10KB </span>","children":null,"spread":false},{"title":"summaries_test.py <span style='color:#111;'> 3.86KB </span>","children":null,"spread":false},{"title":"bucketization_op.py <span style='color:#111;'> 1.56KB </span>","children":null,"spread":false},{"title":"summaries.py <span style='color:#111;'> 5.28KB </span>","children":null,"spread":false},{"title":"regularizers_test.py <span style='color:#111;'> 6.90KB </span>","children":null,"spread":false},{"title":"layers_test.py <span style='color:#111;'> 173.65KB </span>","children":null,"spread":false},{"title":"sparse_ops_test.py <span style='color:#111;'> 13.50KB </span>","children":null,"spread":false},{"title":"layers.py <span style='color:#111;'> 135.38KB </span>","children":null,"spread":false},{"title":"optimizers.py <span style='color:#111;'> 18.23KB </span>","children":null,"spread":false}],"spread":false},{"title":"evaluation.py <span style='color:#111;'> 12.08KB </span>","children":null,"spread":false},{"title":"learning_test.py <span style='color:#111;'> 35.04KB </span>","children":null,"spread":false},{"title":"metrics","children":[{"title":"classification.py <span style='color:#111;'> 8.09KB </span>","children":null,"spread":false},{"title":"histogram_ops.py <span style='color:#111;'> 9.18KB </span>","children":null,"spread":false},{"title":"metric_ops.py <span style='color:#111;'> 174.26KB </span>","children":null,"spread":false},{"title":"classification_test.py <span style='color:#111;'> 11.86KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 4.29KB </span>","children":null,"spread":false},{"title":"metric_ops_test.py <span style='color:#111;'> 277.31KB </span>","children":null,"spread":false},{"title":"histogram_ops_test.py <span style='color:#111;'> 9.53KB </span>","children":null,"spread":false},{"title":"metric_ops_large_test.py <span style='color:#111;'> 2.65KB </span>","children":null,"spread":false}],"spread":true},{"title":"queues.py <span style='color:#111;'> 2.12KB </span>","children":null,"spread":false},{"title":"losses","children":[{"title":"loss_ops.py <span style='color:#111;'> 26.83KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 1.77KB </span>","children":null,"spread":false},{"title":"metric_learning.py <span style='color:#111;'> 39.95KB </span>","children":null,"spread":false},{"title":"loss_ops_test.py <span style='color:#111;'> 54.18KB </span>","children":null,"spread":false},{"title":"metric_learning_test.py <span style='color:#111;'> 20.18KB </span>","children":null,"spread":false}],"spread":true},{"title":"evaluation_test.py <span style='color:#111;'> 11.19KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 2.15KB </span>","children":null,"spread":false},{"title":"summaries_test.py <span style='color:#111;'> 3.70KB </span>","children":null,"spread":false},{"title":"nets","children":[{"title":"resnet_v1_test.py <span style='color:#111;'> 18.33KB </span>","children":null,"spread":false},{"title":"resnet_utils.py <span style='color:#111;'> 10.65KB </span>","children":null,"spread":false},{"title":"inception_v3_test.py <span style='color:#111;'> 12.23KB </span>","children":null,"spread":false},{"title":"resnet_v2.py <span style='color:#111;'> 14.17KB </span>","children":null,"spread":false},{"title":"overfeat.py <span style='color:#111;'> 5.44KB </span>","children":null,"spread":false},{"title":"vgg.py <span style='color:#111;'> 10.99KB </span>","children":null,"spread":false},{"title":"vgg_test.py <span style='color:#111;'> 17.19KB </span>","children":null,"spread":false},{"title":"alexnet_test.py <span style='color:#111;'> 5.78KB </span>","children":null,"spread":false},{"title":"resnet_v2_test.py <span style='color:#111;'> 18.41KB </span>","children":null,"spread":false},{"title":"overfeat_test.py <span style='color:#111;'> 5.72KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 1.45KB </span>","children":null,"spread":false},{"title":"inception_v1.py <span style='color:#111;'> 18.49KB </span>","children":null,"spread":false},{"title":"inception.py <span style='color:#111;'> 1.44KB </span>","children":null,"spread":false},{"title":"inception_v1_test.py <span style='color:#111;'> 8.85KB </span>","children":null,"spread":false},{"title":"resnet_v1.py <span style='color:#111;'> 13.67KB </span>","children":null,"spread":false},{"title":"alexnet.py <span style='color:#111;'> 5.62KB </span>","children":null,"spread":false},{"title":"inception_v2.py <span style='color:#111;'> 26.37KB </span>","children":null,"spread":false},{"title":"inception_v2_test.py <span style='color:#111;'> 11.00KB </span>","children":null,"spread":false},{"title":"inception_v3.py <span style='color:#111;'> 30.76KB </span>","children":null,"spread":false}],"spread":false},{"title":"model_analyzer.py <span style='color:#111;'> 3.49KB </span>","children":null,"spread":false},{"title":"summaries.py <span style='color:#111;'> 7.65KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"data_decoder.py <span style='color:#111;'> 2.27KB </span>","children":null,"spread":false},{"title":"data_provider.py <span style='color:#111;'> 4.03KB </span>","children":null,"spread":false},{"title":"tfexample_decoder_test.py <span style='color:#111;'> 50.47KB </span>","children":null,"spread":false},{"title":"parallel_reader_test.py <span style='color:#111;'> 7.80KB </span>","children":null,"spread":false},{"title":"dataset_data_provider.py <span style='color:#111;'> 4.14KB </span>","children":null,"spread":false},{"title":"dataset.py <span style='color:#111;'> 2.40KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 701B </span>","children":null,"spread":false},{"title":"dataset_data_provider_test.py <span style='color:#111;'> 4.68KB </span>","children":null,"spread":false},{"title":"tfexample_decoder.py <span style='color:#111;'> 29.90KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 6.19KB </span>","children":null,"spread":false},{"title":"prefetch_queue_test.py <span style='color:#111;'> 8.57KB </span>","children":null,"spread":false},{"title":"test_utils.py <span style='color:#111;'> 3.47KB </span>","children":null,"spread":false},{"title":"prefetch_queue.py <span style='color:#111;'> 3.67KB </span>","children":null,"spread":false},{"title":"parallel_reader.py <span style='color:#111;'> 11.50KB </span>","children":null,"spread":false}],"spread":false},{"title":"training","children":[{"title":"evaluation.py <span style='color:#111;'> 17.74KB </span>","children":null,"spread":false},{"title":"evaluation_test.py <span style='color:#111;'> 17.24KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 701B </span>","children":null,"spread":false},{"title":"training.py <span style='color:#111;'> 19.90KB </span>","children":null,"spread":false},{"title":"training_test.py <span style='color:#111;'> 23.26KB </span>","children":null,"spread":false}],"spread":false}],"spread":false},{"title":"pip_pkg.sh <span style='color:#111;'> 1.46KB </span>","children":null,"spread":false},{"title":"CONTRIBUTING.md <span style='color:#111;'> 1.92KB </span>","children":null,"spread":false},{"title":"LICENSE <span style='color:#111;'> 11.15KB </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 3.00KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 39.03KB </span>","children":null,"spread":false},{"title":"CODE_OF_CONDUCT.md <span style='color:#111;'> 4.39KB </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 100B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]
评论信息
其他资源
- AB PLC 解密 sk万能钥匙V2.0 V13-V26解密.zip
- matlab开发-Waxmann网络拓扑发电机
- 用proteus实现数字电子时钟
- 道路监控视频
- Photon Unity中文教程
- 湖南大学操作系统实验报告
- spring-boot-starter-websocket.zip
- Android zxing扫一扫，集成了闪光灯，选图片解析，生成二维码等功能
- WebCommunicator.unitypackage
- MPLAB X IDE 用户指南.pdf
- cudafft使用例程.txt
- psykube：部署到Kubernetes的更快方法！
- Hbase入门教程-高级查询_02
- 跨平台网络开发库合集（C 语言） 2021-01-22 最新源代码
- div+css+js+php+mysql项目源代码
- SCI论文写作和发表：You Can Do It_张俊东 pdf
- 微信公众号H5游戏平台完整源码下载（几十款游戏和应用） 已测试可用
- Fatfs R0.13b 版本官方源码
- 基于face_recognition和OpenCV的人脸识别程序(已封装)
- 教学管理系统（jsp+dbfound）
免责申明
【只为小站】的资源来自网友分享，仅供学习研究，请务必在下载后24小时内给予删除，不得用于其他任何用途，否则后果自负。基于互联网的特殊性，【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查；无论 【只为小站】 经营者是否已进行审查，用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明
本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明