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.
- compression: compressing and decompressing images using a pre-trained Residual GRU network.
- 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.
- learning_to_remember_rare_events: a large-scale life-long memory module for use in deep learning.
- lm_1b: language modeling on the one billion word benchmark.
- namignizer: recognize and generate names.
- neural_gpu: highly parallel neural computer.
- neural_programmer: neural network augmented with logic and mathematic operations.
- next_frame_prediction: probabilistic future frame synthesis via cross convolutional networks.
- real_nvp: density estimation using real-valued non-volume preserving (real NVP) transformations.
- resnet: deep and wide residual networks.
- skip_thoughts: recurrent neural network sentence-to-vector encoder.
- slim: image classification models in TF-Slim.
- street: identify the name of a street (in France) from an image using a Deep RNN.
- 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.
- tutorials: models described in the TensorFlow tutorials.
- video_prediction: predicting future video frames with neural advection.