bash_mnist.sh

#!/bin/bash

# configuration for MNIST

epoch=1000
dataset=mnist
train_size=10000
batch_size=10000

cg_maxiter=16
cg_maxiter_cn=16

save_iter=2
print_iter=2

# gradient descent ascent 20
# d_optim=gd
# d_step_size=0.01
# d_num_step=20
# g_optim=gd
# g_step_size=0.01
# simultaneous=1

# # total gradient descent ascent, a.k.a. stackelberg dynamics in Fiez et al 2019
# d_optim=gd
# d_step_size=0.02
# d_num_step=1
# g_optim=sd
# g_step_size=0.01
# simultaneous=1

# # follow-the-ridge
# d_optim=fr
# d_step_size=0.02
# d_num_step=1
# g_optim=gd
# g_step_size=0.01
# simultaneous=1

# gd-newton
d_optim=newton
d_step_size=1.0
d_num_step=1
g_optim=gd
g_step_size=0.01
simultaneous=0

# # complete newton
# d_optim=newton
# d_step_size=1.0
# d_num_step=1
# g_optim=newton
# g_step_size=1.0
# simultaneous=0
# cg_maxiter_cn=8

python run.py --epoch $epoch \
              --dataset $dataset \
              --train_size $train_size \
              --batch_size $batch_size \
              --pretrain 1 \
              --d_optim $d_optim \
              --d_step_size $d_step_size \
              --d_num_step $d_num_step \
              --g_optim $g_optim \
              --g_step_size $g_step_size \
              --cg_maxiter $cg_maxiter \
              --cg_maxiter_cn $cg_maxiter_cn \
              --cg_tol 1e-50 \
              --cg_lam 0.0 \
              --simultaneous $simultaneous \
              --save_iter $save_iter \
              --print_iter $print_iter