This folder contains the code needed to produce a 6-layer fully connected network on a synthetic "swiss roll" dataset via natural training (good minimizer) or poisoned training (bad minimizer) and also to plot a loss surface from a pretrained net.
This code requires the following packages
- Python 3.6
- Tensorflow 1.9
Other versions may likely work but they have not been tested
To train the model naturally, run
python main.py -seed=1237 -ndata=400 -noise=.5 -lr=.012548 -lrstep2=28000 -lrstep=3062 -wdeccoef=0 -distrfrac=0 -nhidden 23 16 26 32 28 31 -sugg=swissroll-clean -nepoch=800000 -gpu=0
To train the model with poisoning, run
python main.py -seed=1237 -ndata=400 -noise=.5 -lr=.0057945 -lrstep2=5961 -lrstep=3000 -wdeccoef=0 -distrfrac=.8 -distrstep=10936 -distrstep2=15000 -nhidden 23 16 26 32 28 31 -sugg=swissroll-poison -nepoch=800000 -gpu=0
Adjust the distrfrac value to influence the poison factor (note this is not the poison factor, but rather the amount of poisons to add to the batch. Adjust gpu value to change the gpu, and sugg to change the name of the checkpoint directory. Model checkpoints will be stored in ./ckpt/
To compute the loss surface for a particular model, please first upload that model's checkpoint folder to dropbox and provide the public link to that folder in the pretrain_url argument.
Here we provide a command for computing the loss surface along random directions of one of our pretrained models (a poisoned model) with 100% train and 8% test accuracies.
python main.py -gpu=-1 -seed=1237 -ndata=400 -noise=.5 -rollout -nspan=100 -span=1 -nhidden 23 16 26 32 28 31 -sugg=swissroll-poison-surface -pretrain_url=https://www.dropbox.com/sh/mynvakzjp0zfv57/AAAeTmbUAcSlSeZKawrcdLqWa?dl=0
Feel free to change span and nspan to change the range and resolution of the 1D surface scan.