Code accompanying the paper "Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels"
License
SemiUnsupervisedLearning/DGMs_for_semi-unsupervised_learning
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Folders and files
Name | Name | Last commit message | Last commit date | |
---|---|---|---|---|
Repository files navigation
This is the code to reproduce the results in the paper: Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels To set up a conda environment that this code will run in, execute: >>yes | conda create -n TF python=2.7 scipy==1.0.0 tensorflow-gpu==1.8 Keras==2.1.3 pandas==0.22.0 numpy==1.14.0 matplotlib scikit-learn The python script ./src/run_data_model_mixed.py is the main script for running the models It takes the following arguments: -m --model_name, choose between m2 gm_dgm adgm agm_dgm sdgm, required -d --dataset_name choose between fmnist mnist -p --prop_labelled type=float, required, the proportion of labelled data to keep for each class -r --number_of_runs type=int, required -e --number_of_epochs type=int, required -c --classes_to_hide type=int, the individual class of array of classes to be entirely masked so as to do semi-unsupervised learning -a --number_of_classes_to_add, corresponds to $N_{aug}$ in the paper -z --number_of_dims_z type=int, default=100, $|z|$ -u --number_of_dims_a type=int, default=100, $|a|$ -s --number_of_mc_samples type=int, default=1 -t --iteration_number type=int, default=0, index to control which GPU is used on multi-GPU server -l --number_of_units_in_hidden_layers type=int, default=500 -b --batch_size type=int, default=100 --decay_period type=int, default=200 --decay_ratio type=float, default=0.75 --loss_balance choose between average weighted, default is average, says how to scale unlabelled and labelled gradients. average gives both as the average over a mini batch, weighted downscales each by the relative sizes of the labelled and unlabelled data.
About
Code accompanying the paper "Semi-Unsupervised Learning with Deep Generative Models: Clustering and Classifying using Ultra-Sparse Labels"
Resources
License
Stars
Watchers
Forks
Releases
No releases published
Packages 0
No packages published