This repository contains machine learning models implemented in TensorFlow. The models are maintained by their respective authors. To propose a model for inclusion, please submit a pull request.
Neal Wu
546fd48ecb
Additional upgrades to 1.0 and code fixes
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8 年之前 | |
|---|---|---|
| .github | 9 年之前 | |
| autoencoder | 8 年之前 | |
| compression | 8 年之前 | |
| differential_privacy | 8 年之前 | |
| im2txt | 8 年之前 | |
| inception | 8 年之前 | |
| learning_to_remember_rare_events | 8 年之前 | |
| lm_1b | 9 年之前 | |
| namignizer | 8 年之前 | |
| neural_gpu | 8 年之前 | |
| neural_programmer | 8 年之前 | |
| next_frame_prediction | 8 年之前 | |
| real_nvp | 8 年之前 | |
| resnet | 8 年之前 | |
| slim | 8 年之前 | |
| street | 8 年之前 | |
| swivel | 8 年之前 | |
| syntaxnet | 8 年之前 | |
| textsum | 8 年之前 | |
| transformer | 8 年之前 | |
| tutorials | 8 年之前 | |
| video_prediction | 8 年之前 | |
| .gitignore | 9 年之前 | |
| .gitmodules | 9 年之前 | |
| AUTHORS | 9 年之前 | |
| CONTRIBUTING.md | 9 年之前 | |
| LICENSE | 9 年之前 | |
| README.md | 8 年之前 | |
| WORKSPACE | 9 年之前 |
README.md
TensorFlow Models
This repository contains machine learning models implemented in TensorFlow. The models are maintained by their respective authors.
To propose a model for inclusion please submit a pull request.
Models
- autoencoder -- various autoencoders
- differential_privacy -- privacy-preserving student models from multiple teachers
- im2txt -- image-to-text neural network for image captioning.
- inception -- deep convolutional networks for computer vision
- namignizer -- recognize and generate names
- neural_gpu -- highly parallel neural computer
- neural_programmer -- neural network augmented with logic and mathematic operations.
- resnet -- deep and wide residual networks
- slim -- image classification models in TF-Slim
- swivel -- the Swivel algorithm for generating word embeddings
- syntaxnet -- neural models of natural language syntax
- textsum -- sequence-to-sequence with attention model for text summarization.
- transformer -- spatial transformer network, which allows the spatial manipulation of data within the network