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.

Currently, the models are compatible with TensorFlow 1.0 or later. If you are running TensorFlow 0.12 or earlier, please upgrade your installation.

Models

• adversarial_crypto: protecting communications with adversarial neural cryptography.
• adversarial_text: semi-supervised sequence learning with adversarial training.
• attention_ocr: a model for real-world image text extraction.
• autoencoder: various autoencoders.
• cognitive_mapping_and_planning: implementation of a spatial memory based mapping and planning architecture for visual navigation.
• compression: compressing and decompressing images using a pre-trained Residual GRU network.
• differential_privacy: privacy-preserving student models from multiple teachers.
• domain_adaptation: domain separation networks.
• 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.