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run_gmm_gdn.sh
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run_gmm_gdn.sh
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#!/bin/bash
# # gda
d_optim=newton
d_step_size=1.0
#d_optim=gd
#d_step_size=0.01
d_num_step=1
g_optim=gd
g_step_size=0.01
simultaneous=0
python run.py --epoch 2000 \
--dataset gmm \
--train_size 10000 \
--batch_size 10000 \
--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 20 \
--cg_tol 1e-40 \
--cg_lam 0.00 \
--simultaneous $simultaneous \
--save_iter 2 \
--print_iter 2\
--line_search 1\
#d_optim=newton
#d_step_size=1.0
##d_optim=gd
##d_step_size=0.02
#d_num_step=1
#g_optim=gd
#g_step_size=0.01
#simultaneous=0
#python run.py --epoch 200 \
# --dataset mnist \
# --train_size 10000 \
# --batch_size 10000 \
# --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 20 \
# --cg_tol 1e-40 \
# --cg_lam 0.0 \
# --simultaneous $simultaneous \
# --save_iter 2 \
# --print_iter 2\