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Adverarial latent parameters

Parameterized data set optimization to evaluate limits of neural models and mechanisms Requires python 3

Build and activate a conda enviroment

conda env create -f environment.yml adv source activate adv

Access the psql DB

  • psql adv -h 127.0.0.1 -d adv

Initialize and add experiments to DB

python db_tools.py --exp=experiments/psvrt.yaml --init

Reset gradient records and add gradient experiments to DB

python db_tools.py --grads --exp=experiments/gradients.yaml --reset_grads

Generate example from learned parameters for sampling from a gan

CUDA_VISIBLE_DEVICES=5 python generate_samples.py --param_path=results/biggan_resnet18_2020-01-10_18:28:36_final_params.npz

Interpolate samples from original to new params

CUDA_VISIBLE_DEVICES=-1 python generate_interpolation.py --param_path=results/biggan_resnet50_2020-01-16-16_39_13_final_params.npz --cat=gorilla --steps=10 --n=5 CUDA_VISIBLE_DEVICES=-1 python generate_interpolation.py --param_path=results/biggan_resnet50_2020-01-16-16_39_13_final_params.npz --cat=fly --steps=10 --n=5

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