main.main( ( "--ds-type", "cifar10_28x28_grayscale", "--ds-path", "/mnt/hdd_1tb/datasets/cifar10", "--batch-size", "128", "--optimizer", "adam", "--experiments-dir", "/mnt/important/experiments/cifar10/28x28_grayscale/2_epses", "--epses-specs", "(4,4),(3,6)", "--lr", "1.5e-4", "--reg-type", "epses_composition", "--reg-coeff", "0.", "--phi-multiplier", "1.19", "--init-eps-zero-centered-normal-std", "0", "3.9069653e-3", "--init-eps-zero-centered-normal-std", "1", "1.9525534e-3", "--init-linear-weight-zero-centered-uniform", "0.01774992567234187", "--init-linear-bias-zero-centered-uniform", "0.01774992567234187", ), standalone_mode=False, )
main.main( ( # seed is specific to this file only "--seed", "1", # settings which are basically the same for all experiments "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--batch-size", "128", "--optimizer", "adam", # settings introduced earlier which are unique to this experiment "--experiments-dir", "/mnt/important/experiments/eps_plus_linear_fashionmnist/replicate_90.19_vacc", "--epses-specs", "(4,4)", "--lr", "3e-3", "--reg-type", "epses_composition", "--reg-coeff", "0.", # super duper new settings which might bug out "--phi-multiplier", "0.5", "--init-epses-zero-centered-normal", "0.25", "--init-linear-weight-zero-centered-uniform", "0.02", "--init-linear-bias-zero-centered-uniform", "0.02", ), standalone_mode=False, )
main.main( ( "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--batch-size", "128", "--optimizer", "adam", "--experiments-dir", "/mnt/important/experiments/2_epses_plus_linear_fashionmnist/2020-05-18_fashionmnist_check_if_scaling_down_phi_helps_v2.0", "--epses-specs", "(4,4),(3,6)", "--lr", "1.5e-4", "--reg-type", "epses_composition", "--reg-coeff", "1e-2", "--phi-multiplier", "1.3", # 1.3 is too small, it just doesn't train "--init-eps-zero-centered-normal-std", "0", "5.885948427021503448486328125000e-03", "--init-eps-zero-centered-normal-std", "1", "2.112586298608221113681793212891e-05", "--init-linear-weight-zero-centered-normal-std", "4.437481418085467352319106737468e-03", "--init-linear-bias-zero-centered-uniform", "1.77499256723418694092764269498729845508933067321777343750e-02", ), standalone_mode=False, )
from new_runner import main # copies /mnt/important/experiments/2_epses_plus_linear_fashionmnist/2020-04-15T19:42:03/ # but with epswise l2 regularization main.main( ( "--experiments-dir", "/mnt/important/experiments/2_epses_plus_linear_fashionmnist/adam_and_comp_reg", "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--epses-specs", "(4,4),(3,6)", "--batch-size", "128", "--optimizer", "adam", "--lr", "1.11e-4", "--reg-type", "epses_composition", "--reg-coeff", "1e-3", "--no-es-train-acc", "--no-es-train-mean-ce", ), standalone_mode=False, )
from new_runner import main main.main( ( "--experiments-dir", "/tmp/new_runner_test/", "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--kernel-size", "4", "--out-size", "6", "--batch-size", "128", "--optimizer", "adam", "--lr", "1e-3", ), standalone_mode=False, )
from os import environ environ["CUDA_VISIBLE_DEVICES"] = "1" from new_runner import main main.main( ( "--experiments-dir", "/mnt/important/experiments/eps_plus_linear_fashionmnist", "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--kernel-size", "4", "--out-size", "4", "--batch-size", "128", "--optimizer", "adam", "--lr", "0.003", "--old-scaling", ), standalone_mode=False, )
from os import environ environ["CUDA_VISIBLE_DEVICES"] = "0" from new_runner import main main.main( ( "--ds-type", "cifar10_32x32_grayscale", "--ds-path", "/mnt/hdd_1tb/datasets/cifar10", "--batch-size", "128", "--optimizer", "adam", "--experiments-dir", "/mnt/important/experiments/cifar10/32x32_grayscale/2_epses", "--epses-specs", "(4,4),(3,6)", "--lr", "1.5e-4", "--reg-type", "epses_composition", "--reg-coeff", "0.", "--init-epses-composition-unit-theoretical-output-std", ), standalone_mode=False, )
from new_runner import main # copies /mnt/important/experiments/2_epses_plus_linear_fashionmnist/2020-04-15T19:42:03/ # but with epswise l2 regularization main.main( ( "--experiments-dir", "/mnt/important/experiments/2_epses_plus_linear_fashionmnist/adam_and_epswise_l2", "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--epses-specs", "(4,4),(3,6)", "--batch-size", "128", "--optimizer", "adam", "--lr", "1.11e-4", "--reg-coeff", "1e-2", ), standalone_mode=False, )
from os import environ environ["CUDA_VISIBLE_DEVICES"] = "0" from new_runner import main main.main( ( "--ds-type", "fashionmnist", "--ds-path", "/mnt/hdd_1tb/datasets/fashionmnist", "--batch-size", "128", "--optimizer", "adam", "--experiments-dir", "/mnt/important/experiments/2_epses_plus_linear_fashionmnist/2020-05-18_fashionmnist_check_if_scaling_down_phi_helps_v2.0", "--epses-specs", "(4,4),(3,6)", "--lr", "1.5e-4", "--reg-type", "epses_composition", "--reg-coeff", "1e-2", "--init-epses-composition-unit-empirical-output-std", ), standalone_mode=False, )
from os import environ environ["CUDA_VISIBLE_DEVICES"] = "1" from new_runner import main main.main( ( "--ds-type", "cifar10_28x28_grayscale", "--ds-path", "/mnt/hdd_1tb/datasets/cifar10", "--batch-size", "128", "--optimizer", "adam", "--experiments-dir", "/mnt/important/experiments/cifar10/28x28_grayscale/1_eps_k=4_q=4_init_epses_composition_unit_empirical_output_std", "--epses-specs", "(4,4)", "--lr", "3e-3", "--reg-type", "epses_composition", "--reg-coeff", "0.", "--init-epses-composition-unit-empirical-output-std", ), standalone_mode=False, )