Code

To run:

python runner.py new --save <SAVE_NAME> --data <PATH_TO_DATA_DIR> --dataset <DATASET> --model <model_name> [options] --n 1000 - train - test - kernel_fit - done

Options for Bayesian regression models are:

• gp
• gp_3l
• gp_acos
• gp_acos3
• deep_gp_svi (options: --width 2)
• deep_gp_hmc (options: --width 2)
• deep_gp_3l2l_hmc (options: --width 2)
• deep_gp_3l3l_hmc (options: --width1 2 --width2 2)
• nn_hmc (options: --width 2)
• nn_3l_hmc (options: --width 2)

Options for non-Bayesian classification models are:

• nn (options: --widths 16,32,32)
• lenet (options: --width 256 --conv-width 16)
• resnet (options: --depth 14 --width 64)